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atmosphere | 0000 | 10.1016/j.envpol.2018.04.071 | table | Table 1 Summary of the mean concentrations of $\mathrm{PM}_{2.5}$ , OC, and EC $\left(\upmu\mathrm{g}\:\mathsf{m}^{-3}\right)$ in Shenzhen during the controlled and uncontrolled periods at two sampling sites, along with the concentrations of SOA for isoprene, $\mathfrak{x}_{}$ -pinene, $\upbeta.$ -caryophyllene and toluene $(\mathrm{ng~m}^{-3}$ ). P-values correspond to t-test comparisons of the controlled and uncontrolled periods at each site, with $\mathsf{p}<0.05$ considered to be statistically significant. |
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atmosphere | 0000 | 10.1016/j.envpol.2018.04.071 | image | Fig. 1. Wind rose plots (a) showing the frequency of wind directions in Shenzhen during the controlled period and uncontrolled periods, along with time series of the daily ambient concentrations of $\mathrm{PM}_{2.5}$ at Longgang and Peking University and visibility (b); time series of carbonaceous aerosol (OC, EC, and OC:EC) for Longgang (c) and Peking University (d). |
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atmosphere | 0000 | 10.1016/j.envpol.2018.04.071 | image | Fig. 2. Source apportionment of $\mathrm{PM}_{2.5}\ \mathrm{OC}$ in Shenzhen at Longgang (LG) and Peking University (PU), reported as the average relative source contributions to OC $(\%)$ during controlled and uncontrolled periods. |
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atmosphere | 0000 | 10.1016/j.envpol.2018.04.071 | image | Fig. 3. Ambient concentrations of secondary organic tracers: A) sum of three isoprene SOA tracers, B) the sum of four $\mathfrak{x}$ -pinene SOA tracers, and C) one toluene SOA tracer at LG and PU during the controlled and uncontrolled periods. |
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atmosphere | 0000 | 10.1016/j.envpol.2018.04.071 | image | Fig. 4. Comparison of the fossil and contemporary sources of total carbon (equivalent to the sum of organic and elemental carbon) estimated by a) radiocarbon $(^{14}\mathrm{C})$ analysis, and b) chemical mass balance (CMB) modeling. |
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atmosphere | 0000 | 10.1016/j.envpol.2018.04.071 | text | Not supported with pagination yet | Source apportionment of fine particulate matter organic carbon in Shenzhen, China by chemical mass balance and radiocarbon methods\*
Ibrahim M. Al-Naiema a, Subin Yoon b, Yu-Qin Wang c, d, Yuan-Xun Zhang c, e, Rebecca J. Sheesley b, \*, Elizabeth A. Stone a, f, \*\*
a Department of Chemistry, University of Iowa, Iowa City, IA 52242, USA
b Department of Environmental Science, Baylor University, Waco, TX 76798, USA
c College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
d School of Environmental Science and Engineering, Shaanxi University of Science & Technology, Xi'an, Shaanxi 710021, China
e Huairou Eco-Environmental Observatory, Chinese Academy of Sciences, Beijing 101408, China
f Chemical and Biochemical Engineering, University of Iowa, Iowa City, IA 52242, USA
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 15 December 2017
Received in revised form
28 March 2018
Accepted 16 April 2018
Keywords:
Air quality
Aerosol
Emission control
Secondary organic aerosol
Universiade
Chemical mass balance (CMB) modeling and radiocarbon measurements were combined to evaluate the sources of carbonaceous fine particulate matter $(\mathsf{P M}_{2.5})$ in Shenzhen, China during and after the 2011 summer Universiade games when air pollution control measurements were implemented to achieve air quality targets. Ambient $\mathsf{P M}_{2.5}$ filter samples were collected daily at two sampling sites (Peking University Shenzhen campus and Longgang) over 24 consecutive days, covering the controlled and uncontrolled periods. During the controlled period, the average $\mathrm{PM}_{2.5}$ concentration was less than half of what it was after the controls were lifted. Organic carbon (OC), organic molecular markers (e.g., levoglucosan, hopanes, polycyclic aromatic hydrocarbons), and secondary organic carbon (SOC) tracers were all significantly lower during the controlled period. After pollution controls ended, at Peking University, OC source contributions included gasoline and diesel engines $(24\%)$ , coal combustion $(6\%)$ , biomass burning $(12.2\%)$ , vegetative detritus $(2\%)$ , biogenic SOC (from isoprene, $\mathfrak{x}$ -pinene, and $\upbeta$ -caryophyllene; $7.1\%)$ , aromatic SOC $(23\%)$ , and other sources not included in the model $(25\%)$ . At Longgang after the controls ended, similar source contributions were observed: gasoline and diesel engines $(23\%)$ coal combustion $(7\%)$ , biomass burning $(17.7\%)$ , vegetative detritus $(1\%),$ biogenic SOC (from isoprene, $\pmb{\mathrm{\Omega}}$ - pinene, and $\upbeta{}_{\cdot}$ -caryophyllene; $5.3\%$ ), aromatic SOC $(13\%)$ , and other sources $(33\%)$ . The contributions of the following sources were smaller during the pollution controls: biogenic SOC (by a factor of 10e16), aromatic SOC (4e12), coal combustion (1.5e6.8), and biomass burning (2.3e4.9). CMB model results and radiocarbon measurements both indicated that fossil carbon dominated over modern carbon, regardless of pollution controls. However, the CMB model needs further improvement to apportion contemporary carbon (i.e. biomass burning, biogenic SOC) in this region. This work defines the major contributors to carbonaceous $\mathrm{PM}_{2.5}$ in Shenzhen and demonstrates that control measures for primary emissions could significantly reduce secondary organic aerosol (SOA) formation.
$\copyright$ 2018 Elsevier Ltd. All rights reserved.
1. Introduction
Shenzhen is a rapidly developing and heavily urbanized coastal city in the Pearl River Delta (PRD) region of China. The economic growth of PRD cities has been accompanied by increased emission of air pollutants. Elevated levels of particulate matter (PM) have been attributed to primary emissions from industry and transportation and secondary aerosol formation (He et al., 2011; Huang et al., 2014; Yuan et al., 2006). High concentrations of PM pose risk to public health (Huang et al., 2012) and negatively affect visibility (Tan et al., 2009). Assessment of the chemical composition of PM and its contributing emission sources is therefore crucial to the implementation of effective air quality regulations.
To estimate source contributions to ambient PM in the PRD region, receptor models including positive matrix factorization (Dai et al., 2013; Kuang et al., 2015) and chemical mass balance (CMB) modeling (Wang et al., 2016; Zheng et al., 2011) have both been used. CMB has been widely used to apportion PM to its primary emission sources when source profiles are available (Kong et al., 2010). In Guangzhou, for instance, secondary organic carbon (SOC), coal combustion, and cooking sources were found to have contributed more than $20\%$ , $14\%$ , and $11\%$ of fine particle $(\mathsf{P M}_{2.5})$ organic carbon (OC), respectively (Wang et al., 2016). These results underscore the importance of combustion and secondary sources to $\mathsf{P M}_{2.5}$ OC in the PRD.
To distinguish carbonaceous PM derived from fossil fuels from that devrived from contemporary sources, radiocarbon $(^{14}{\mathsf{C}},\;t_{1/}$ $_{2}=5730$ years) measurements have been used (Gelencser et al., 2007; Gustafsson et al., 2009). $^{14}\!C$ is conserved with respect to emission conditions, atmospheric transport and chemical transformations (Szidat et al., 2004), making it a reliable tracer of modern carbonaceous matter. Using a combination of $^{14}\!C$ measurements and organic tracers in an industrial city in the PRD, Liu et al. (2014) demonstrated that fossil sources contributed $71\%$ of the elemental carbon (EC) and $38\%$ of OC detected. In a Chinese regional background site on Hainan Island ( $550\,\mathrm{km}$ from Shenzhen), radiocarbon analysis demonstrated that the contribution of fossil sources to EC was $51\%$ and OC was $30\%$ (Zhang et al., 2014), with fossil fuel emissions transported from regional industrial cities. These findings demonstrate strong influences from both modern and fossil fuel emissions on carbonaceous $\mathsf{P M}_{2.5}$ in the PRD.
Secondary organic aerosols (SOA) that are important contributors to PM mass are produced in the atmosphere through the photooxidation of VOCs from biogenic and anthropogenic precursors (Kroll and Seinfeld, 2008). The contributions of some precursors to SOA can be estimated using a SOA tracer approach, developed by Kleindienst et al. (2007), in which SOA is estimated based on the ambient concentration of SOA tracers by way of SOA tracer-to-SOA (or tracer-to-SOC) mass ratios determined in smog chambers. In this way, aromatic precursors (i.e. toluene) have been shown to contribute two-thirds of the total estimated SOC in the PRD region (Ding et al., 2012). This dominance reflects the significance of anthropogenic activities on SOA production in the PRD, with a minor contribution from biogenic VOCs like isoprene and monoterpenes.
The effects of short-term pollution control on the concentration and composition of atmospheric PM have been the focus of prior field studies in China. Vehicular and industrial emission controls were enforced during the 2008 Beijing Olympic Games, reducing $\mathsf{P M}_{10}$ , nitrogen oxides $\left(\mathsf{N O}_{\mathtt{X}}\right)$ , sulfur dioxide $\left({\mathrm{SO}_{2}}\right)$ , and non-methane VOC by $55\%$ , $47\%$ , $41\%$ , and $57\%$ , respectively (Wang et al., 2010). Simultaneously, black carbon $(45\%)$ , OC $(31\%)$ , and polycyclic aromatic hydrocarbons (PAH) decreased (Wang et al., 2011). Benzene, toluene, ethylbenzene, and xylenes (BTEX) decreased $\mathrm{by}\geq47\%$ (Liu et al., 2009). In addition to emission controls, Gao et al. (2011) suggested that wind direction and precipitation also contributed to air pollutant reductions during this period. In another effort at reducing air pollution during sporting events, during the 16th Asian Games in 2010 in Guangzhou, emissions from power plants, industry, mobile sources, and construction activities were restricted and $\mathsf{P M}_{2.5}$ decreased by $26\%$ , while both $S0_{2}$ and $\Nu0_{\tt x}$ dropped by ${>}40\%$ (Liu et al., 2013). These studies, focused predominantly on primary air pollutants, underscore the importance of controlling emission sources for improving air quality. However, the effect of pollution controls on the relative abundances of SOC from biogenic and anthropogenic origins has not previously been evaluated.
In 2011, Shenzhen hosted the 26th summer Universiade, an international sporting event, during which strict controls on emission sources were implemented to improve air quality, including reduction of: (i) emission of $\Nu0_{\mathtt{x}}$ from power plants, commercial and industrial boilers, and motor vehicles; (ii) $S0_{2}$ emission by controlling fuel sulfur content, the flue gas from desulphurization units, and coal-fired power plants (iii) VOC emissions from industries including printing, adhesives, and furniture; (iv) PM and other air pollutants from construction sites, open biomass burning, and on-road vehicles (Dewan et al., 2016; Wang et al., 2014). In addition to the controls on emission sources in Shenzhen, industrial activities in neighboring cities were minimized. These conditions provided a unique opportunity to examine the effect of anthropogenic activities on the absolute and relative levels of primary and secondary $\mathsf{P M}_{2.5}$ sources in Shenzhen.
In this study, we assess $\mathsf{P M}_{2.5}$ concentrations, composition, and sources, both under strict emission controls (the “controlled period”), and after Universiade, when the controls were lifted (the “uncontrolled period”). Organic molecular markers and SOA tracers were measured in $\mathsf{P M}_{2.5}$ collected over 24 days. $\mathsf{P M}_{2.5}$ OC was apportioned by a molecular marker-driven CMB model (Schauer et al., 1996) and the SOC tracer method (Kleindienst et al., 2007). Tracers included levoglucosan for biomass burning (Simoneit et al., 1999), PAH and hopanes for fossil fuels including coal combustion (Zhang et al., 2008) and vehicle emissions (Schauer et al., 2002), odd-numbered $\mathbf{n}$ -alkanes for vegetative detritus (Rogge et al., 1993), and SOA products identified in chamber studies for biogenic-and aromatic-VOC derived SOA (Kleindienst et al., 2007). The resulting contributions of fossil and modern sources to OC and elemental carbon (EC) were compared to radiocarbon measurements of fossil and modern carbon over the same time period. This study examines differences in $\mathsf{P M}_{2.5}$ and its sources during and after Universiade in 2011, providing new insight to the effect of primary emission controls on SOC.
2. Methods
2.1. Sampling
$\mathsf{P M}_{2.5}$ samples were simultaneously collected from two sampling locations in Shenzhen, China from 12 August to 4 September 2011. Teflon and quartz filters ( $47\,\mathrm{mm}$ , Whatman) were used to collect $\mathsf{P M}_{2.5}$ samples for mass and organic speciation, respectively. The Longgang (LG) site is located in the Longgang district of Shenzhen $\langle22.70^{\circ}\mathrm{N}$ , $114.21^{\circ}\mathrm{E}$ , $161\;\mathrm{m}$ ) on top of a 31-floor residential building at a height of $90\,\mathrm{m}$ from the ground level, about $500\,\mathrm{m}$ north of the main Universiade stadium. The Peking University (PU) site is located at Nanshan district of Shenzhen $.22.60^{\circ}\mathsf{N},$ $113.97^{\circ}\mathrm{E}$ , $50\,\mathrm{m}$ , 45 hm north of the LG site) atop of a graduate building at a height of $16\,\mathrm{m}$ . The samplers' heights provided well-mixed air masses at the point of sample collection. Detailed descriptions for both sampling site and sampling techniques are provided elsewhere (Dewan et al., 2016). Wind and visibility data during this study were obtained from the weather forecast at Shenzhen Bao'an International Airport (SGSZ), approximately 18 and $48\,\mathrm{km}$ east of PU and LG sampling sites, respectively. The difference in the altitudes of the two sites could have affected PM collected, so throughout this report we emphasize differences between controlled and uncontrolled periods at each site, rather than comparisons across the two sites.
2.2. Chemical analysis of organic species
Filter extraction and gas chromatography-mass spectrometry (GC-MS) followed established methods (Al-Naiema et al., 2015; Stone et al., 2012) and are summarized in the supplementary information (SI).
2.3. Chemical mass balance source apportionment modeling
Chemical mass balance (CMB v8.2) modeling (EPA, 2004) was used to estimate source contributions to organic carbon in $\operatorname{PM}_{2.5},$ using effective variance weighted least squares (Watson et al., 1984). The CMB model relies on prior knowledge of emission profiles and assumes that those profiles are representative of the investigated samples. Source profiles were selected to represent emission sources and conditions in China when possible, i.e. for biomass burning (Zhang et al., 2007) and coal combustion (Zhang et al., 2008). When such profiles were not available, profiles developed elsewhere, but applied previously in source apportionment in China (Guo et al., 2013; Zheng et al., 2005), were used. Input source profiles and chemical species for the model are reported in the SI. The model fit was considered acceptable when the correlation coefficient $(\mathbb{R}^{2})$ was greater than 0.8, and chi-squared $(\chi^{2})$ was less than 7.
2.4. Radiocarbon $(^{I4}C)$ measurements
Four composites and one lab blank were prepared for $^{14}\!C$ analysis. The composites included equal mass fractions of filter samples collected during the controlled and uncontrolled periods for both LG and PU sites. This compositing scheme ensured that composites impartially represented source contributions from each sample regardless of varying daily mass concentration.
Filter punches for each composite and blank were collected in baked petri dishes, acid-fumigated in a desiccator over $^{1\,\mathrm{N}}$ hydrochloric acid for $12\,\mathrm{h}$ , and then dried at $60\,^{\circ}\mathrm{C}$ for $1\,\mathrm{h}$ . Each petri dish was then wrapped in baked aluminum foil, bagged in individual Ziploc bags, and shipped on ice to the National Ocean Science Accelerator Mass Spectrometry (NOSAMS) facility for $^{14}\!C$ analysis. At NOSAMS, samples were analyzed using an accelerator mass spectrometry (AMS) to determine the fraction of modern $\left(\mathrm{F_{m}}\right)$ carbon. $\mathsf{F}_{\mathrm{m}}$ is the deviation of the $^{14}\mathrm{C}/^{12}\mathrm{C}$ ratio in a sample from $95\%$ of the reference “Modern”, NBS Oxalic Acid I, which is normalized to $\mathtt{\delta}\delta13\mathsf{C}_{\mathrm{VPDB}}=-19\%_{0}$ (Olsson, 1970). Apportionment for contemporary (or non-fossil) to fossil fuel sources can be calculated using a mixing model ratio for $\Delta^{14}C_{\mathrm{TOC}}$ :
$$
\Delta^{14}C_{T O C}=(\Delta^{14}C_{c o n t e m p o r a r y})\:(f_{M})\:+(\Delta^{14}C_{f o s s i l})\:(1\!-\!f_{M})
$$
In the calculation, known end-member $\Delta^{14}C$ values were included for radiocarbon-dead fossil fuel $(-1000\%_{00})$ (Gustafsson et al., 2009) and contemporary $(+67.5\%)$ . The end-member value for contemporary sources was an average of wood burning $(+107.5\%)$ (Zotter et al., 2014) and fresh biogenic $(+28.1\%_{0})$ (Widory, 2006) sources. The $f_{M}$ corrected for knownend-members is multiplied by ambient concentration to calculate fossil and contemporary carbon concentrations.
2.5. Statistical analysis
Statistical analyses were used to evaluate significant differences in $\mathsf{P M}_{2.5}$ composition and sources during the controlled and uncontrolled periods. A nonparametric t-test (Wilcoxon) was used to assess the statistical differences at the $95\%$ confidence interval using Statistical Package for the Social Sciences (SPSS) software.
3. Results and discussion
3.1. $P M_{2.5}$ mass concentrations
$\mathsf{P M}_{2.5}$ concentrations measured at Longgang (LG) and Peking University (PU) sites in Shenzhen were significantly lower during Universiade games when strict emission controls were implemented (12e23 August) than during the uncontrolled period (24 August e 4 September; Table 1). At LG, the average $\mathsf{P M}_{2.5}$ concentration during the controlled period was $24.9\pm5.0\ensuremath{\,\upmu\mathrm{g\,m}^{-3}}$ versus $53.8\pm6.1~\upmu\mathrm{g}\,\mathrm{\bar{m}}^{-3}$ during the uncontrolled period. At PU, the average $\mathsf{P M}_{2.5}$ concentrations were $12.8\pm3.5\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ during the controlled period and $48.0\pm8.1\;\upmu\mathrm{g}\,\mathrm{m}^{-3}$ during the uncontrolled period.
Pollution controls were not the only influence on $\mathsf{P M}_{2.5}$ concentrations over these time periods: wind directions shifted between the controlled and uncontrolled periods. Southerly winds that bring relatively clean ocean air to Shenzhen were predominant during the controlled period, while northwesterly winds transporting relatively polluted air from continental areas were more prevalent during the uncontrolled period (Fig. 1a). Thus, changes in wind direction added to the effects of emissions control to lower $\mathsf{P M}_{2.5}$ by $54\%$ at LG and $73\%$ for PU, on average, during the controlled period. Lower $\mathsf{P M}_{2.5}$ concentrations corresponded to doubling of visibility (Fig. 1b).
The average $\mathsf{P M}_{2.5}$ concentrations during the uncontrolled period are comparable to those reported in summer time in Shenzhen (Dai et al., 2013; Niu et al., 2006) and are approximately half of $\mathsf{P M}_{2.5}$ levels reported in winter $(99.0\pm17.6)\$ (Niu et al., 2006). Thus, the uncontrolled period is considered to be representative of typical summertime concentrations in the absence of emission controls.
On all of the study days, the measured $\mathsf{P M}_{2.5}$ concentrations in Shenzhen were below the $24\,\mathrm{h}$ average $\mathsf{P M}_{2.5}$ Class-II standard for urban and industrial cities of $75\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ (Chinese Ambient Air Quality Standard, GB 3095e2012) (GB3095, 2012).
3.2. Elemental and organic carbon
EC and OC were significant contributors to $\mathsf{P M}_{2.5}$ mass, with average contributions of $24.2\pm4.6\%$ and $9.5\pm4.2\%$ at LG and $31.4\pm6.7\%$ and $15.5\pm9.2\%$ at PU, respectively. OC concentrations at both sites were significantly increased after the controlled period (Table 1; Fig. 1). Meanwhile, EC increased significantly at the PU site, but only slightly (not significantly) at the LG site (Table 1; Fig. 1). Further discussion of OC and EC levels are provided elsewhere (Wang et al., 2014). OC:EC ratios across both sites increased from an average of 1.7 during the controlled period to 3.6 during the uncontrolled period (Fig. 1), indicating a shift in the sources of carbonaceous aerosol between the controlled and uncontrolled periods. OC and EC sources are discussed in section 3.3.
The OC and EC concentrations during the uncontrolled period were comparable to prior studies in the PRD during late summer. Prior studies have reported OC in Shenzhen in the summer of 2004 ranging $4.0{-}20.6\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ and EC ranging $1.7{-}3.7\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ , with a mean OC:EC ratio of 3.4 (Niu et al., 2006). Slightly lower concentrations were reported for Shenzhen in 2002, with mean OC levels of $7.6\pm4.9\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ and EC levels of $4.2\pm3.1\ \upmu\mathrm{g}\,\mathrm{m}^{-3}$ (Cao et al., 2004). In nearby Guangzhou in the summer of 2008, OC ranged from 1.92 to $13.7\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ and EC ranged $0.69{-}5.07\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ with a mean OC:EC of 3.41 (Ding et al., 2012). These comparisons indicate that the uncontrolled OC and EC levels in Shenzhen found in this study are typical for this region.
3.3. Source apportionment by CMB modeling
The CMB model apportioned $\mathsf{P M}_{2.5}$ OC to five primary sources (vegetative detritus, gasoline vehicles, diesel engines, coal combustion, and biomass burning) and four secondary sources (SOC from isoprene, a-pinene, b-caryophyllene, and aromatic precursors). The observed concentrations of molecular markers used in source apportionment are summarized in Table 1 and Table S1. CMB results are summarized in Fig. 2, Fig. S1, and Table S2 for controlled and uncontrolled conditions at each site. CMB results were not reported for several days that had an unacceptable model fit (August 12, 22, 26 and September 1 for LG, and August 24, 29, and 31 for PU), as indicated by $\mathrm{R}^{2}<0.8$ and/or $\chi^{2}$ values $>7$ (section 2.3), indicating that the selected profiles poorly fit the ambient data. The difference between the concentrations of apportioned sources and total OC mass is represented as “other OC”. On average, during controlled and uncontrolled periods, respectively the model apportioned $90\%$ and $67\%$ of OC in LG, and $88\%$ and $75\%$ of OC in PU.
During the controlled period at LG, the average OC mass of $5.9\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ was apportioned $80\%$ to primary sources and $10\%$ to secondary sources; $10\%$ was not apportioned. The major primary OC sources at LG, on average, were gasoline vehicles $(38\%)$ , diesel engines $(20\%)$ , and coal combustion $(12\%)$ .
At PU during the controlled period, the average OC mass of $4.3\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ was apportioned $80\%$ to primary sources and $8\%$ to secondary sources; $12\%$ was not apportioned. The major primary OC sources were gasoline vehicles $(30\%)$ , diesel engines $(26\%)$ , and biomass burning $(21\%)$ (Table S2). With regard to the modelresolved secondary OC, aromatic precursors were more significant contributors than biogenic precursors by a factor of 4.5 at LG and 2.5 at PU.
After the controls were lifted, OC at LG was apportioned $48\%$ to primary sources and $19\%$ to secondary sources; $33\%$ was not apportioned. At PU, OC was apportioned $44\%$ to primary sources and $31\%$ to secondary sources; $25\%$ was not apportioned. The major primary OC sources at both sites after the controls were lifted were gasoline vehicles, diesel engines, and biomass burning (Table S2). In regard to secondary OC, precursors were determined to be aromatic, $\mathfrak{a}$ -pinene, and isoprene, and SOC was dominated by aromatics for both sites.
The OC fraction not attributed to primary and secondary sources is expected to derive from sources that were not included in the model. Based on prior studies in the PRD (Li et al., 2012; Zheng et al., 2011), these are expected to include cigarette smoke, cooking emissions, and road dust. In addition, SOA is likely to be underestimated (Zheng et al., 2002), as SOC formed from VOC emitted by biomass burning, semi-VOCs like long-chain alkanes, and other precursors were not included in this model (Huang et al., 2011).
3.3.1. Gasoline and diesel engines
The combined vehicular emissions from gasoline and diesel engines are the largest source of OC in Shenzhen. Together, gasoline and diesel engines contributed $3.33\ensuremath{~\upmu\mathrm{g}\mathrm{c}\ \mathrm{m}^{-3}}$ and $3.03\ensuremath{\,\upmu\mathrm{g}\mathrm{C}\,\:\mathrm{m}^{-3}}$ during the controlled and uncontrolled periods at LG, while they contributed $2.41\;\upmu\mathrm{g}\mathrm{C}\;\mathrm{m}^{-3}$ and $3.34\,\upmu\mathrm{gC}\,\textrm{m}^{-3}$ at PU, respectively. These results indicate a slight decrease in vehicle-derived OC at LG after the controls were lifted, which may be attributed to the drop in post-Universiade transportation demands near the main stadium that was closer to this site. Alternatively, the control on motor vehicles (alternating odd-even license plate vehicle operation) did not likely influence the change in OC at LG. Meanwhile, there was a substantially larger increase in vehicle-derived OC at PU after the controls were lifted.
The stability of the CMB model, with respect to its estimate of vehicular contributions to OC, was evaluated in a sensitivity test in which the non-catalyzed gasoline profile used in the “base-case” model results was replaced with a catalyzed profile (Lough et al., 2007), as described in section 2.3 (Lough and Schauer, 2007). The summed gasoline and diesel engine contributions to OC for the base-case scenario, relative to the sensitivity test, are shown in Fig. S2 for each site. The results show a good agreement $(\mathbb{R}^{2}\geq0.994)$ between the base case and sensitivity test. The slopes of these regressions indicate a minor underestimation ( $.11\substack{-13\%}$ of the vehicle contribution) of the base case relative to the sensitivity test, which is within the standard error of the estimate. Thus, the selection of the non-gasoline engine profile has only a minor influence on the estimated vehicular contribution to OC, indicating that this is a robust estimate.
3.3.2. Coal combustion
Coal combustion contributions were lower at both sites during the controlled period, though more significantly reduced at PU.
During the controlled period, the contribution from coal combustion was slightly reduced to $0.65\pm0.08\;\upmu\mathrm{g}\mathrm{C}\;\mathrm{m}^{-3}$ in LG and significantly reduced to $0.13\pm0.03\mathrm{\,\mugC\;m}^{-3}$ in PU (Table S2). During the uncontrolled period, coal contributed $0.93\pm0.13\mathrm{\,\upmugC\,m^{-3}}$ at LG and $0.88\pm0.14\,\upmu\mathrm{g}\mathrm{{C}\,m}^{-3}$ at PU, which are comparable to annual average contributions of coal combustion sources in Shenzhen of $1.10\pm0.12\:\upmu\mathrm{gC}\:\:\mathrm{m}^{-3}$ , reported elsewhere (Zheng et al., 2011). The more significant change observed at PU is likely due to the fact that there were more coal-operated power plants near PU compared to LG (Dewan et al., 2016).
3.3.3. Biomass burning
OC from biomass burning was lower at both sites during the controlled period. The average biomass burning contribution to $\mathsf{P M}_{2.5}$ OC during the controlled period was $0.47\pm0.12\,\upmu\mathrm{g}\mathrm{C}\ \mathrm{m}^{-3}$ at LG and $0.77\pm0.06\,\upmu\mathrm{g}\mathrm{C}\,\mathrm{m}^{-3}$ at PU. After the controls were lifted, the average contribution of biomass burning to OC (in $\upmu\mathrm{g}(\mathrm{~m}^{-3})$ increased by a factor of 4.9 at LG and by 2.3 at PU. The increase shows the influence of the restriction on emissions from woodfired industrial boilers and biomass burning. These results indicate that reducing biomass combustion in the PRD can improve air quality in Shenzhen through reductions in the associated $\mathrm{PM}_{2.5}\,0C.$
It is potentially an important policy measure, as previous studies have found that biomass burning contributed $20.8\%$ to $\mathsf{P M}_{2.5}$ OC in Shenzhen during October, when households may have used biofuels for heating (Zheng et al., 2011).
3.3.4. Vegetative detritus
Vegetative detritus made a minor contribution to OC in Shenzhen, with its average contribution less than $2.5\%$ of OC at both sites (Table S2). Apportioning vegetative detritus using the CMB model is largely dependent on the concentrations of $\mathbf{n}$ -alkanes, which can have either biogenic or anthropogenic origins. The carbon preference index (CPI) is a diagnostic parameter that provides a means of identifying the sources of n-alkanes, where a CPI greater than three is an indication of biological origins, while a CPI of approximately one suggests anthropogenic origin (Simoneit, 1989). Average CPI value for $\mathbf{n}$ -alkane $\left(\mathsf{C}_{25}\mathrm{-}\mathsf{C}_{34}\right)$ in this study was 1.5 in LG and 1.7 in PU, with no significant change in CPI values for controlled versus uncontrolled periods. The CPI result indicates very little odd-carbon preference, as n-alkanes are primarily from evaporation and combustion of fossil fuels. Consistent with this is the small contribution from vegetative detritus to OC in Shenzhen.
3.3.5. Secondary organic aerosol
SOC in Shenzhen was largely dominated by aromatic precursors, which contributed more than $70\%$ of the apportioned SOC. The high level of SOC from aromatic relative to biogenic precursors had been previously observed in the PRD region (Ding et al., 2012), and attributed to the elevated levels of aromatic VOCs such as toluene in this industrial region (Barletta et al., 2008), compared to those quantified in other cities (Mohamed et al., 2002; von Schneidemesser et al., 2010). The contribution of SOC from aromatic, isoprene, and $\mathfrak{a}$ -pinene precursors to OC is discussed in detail in section 3.4.
3.3.6. Source apportionment of EC
Elemental carbon (EC) was simultaneously apportioned with OC by the CMB model. During the controlled period at PU, EC was primarily attributed to diesel engines $(96\pm2\%$ ; $\pm$ standard deviation), with minor contributions from non-catalyzed gasoline vehicles $(1\pm1\%)$ , coal combustion $(1\pm1\%)$ , and biomass burning $(2\pm1\%)$ . During the uncontrolled period at PU absolute concentrations of EC were higher, as were the absolute contributions from diesel engines, coal combustion, and biomass burning, with $97\%$ of EC attributed to fossil sources.
At LG during the controlled period, EC was attributed largely to diesel engines $(92\pm6\%)$ , with minor contributions from coal combustion $(6\pm4\%)$ , non-catalyzed gasoline engines $(1\pm1\%)$ and biomass burning $(1\pm1\%)$ . During the uncontrolled period, absolute concentrations of EC at LG increased, as did absolute contributions to EC from diesel engines, coal combustion, and biomass burning; the EC contribution from fossil sources was estimated to be $96\%$ .
CMB results indicated that diesel engines were largely responsible for the observed increase in EC from the controlled to uncontrolled periods at LG (from 3.3 to $2.8\,\upmu\mathrm{g}\mathrm{C}\,\mathrm{m}^{-3}$ ) and PU $(3.8{-}2.8\ensuremath{\,\upmu\mathrm{g}}\ensuremath{\mathrm{C}}\ensuremath{\,\mathrm{m}}^{-3})$ and that other fossil fuel and biomass emissions were minor contributors to EC throughout this study. However, previous studies in the PRD that conducted radiocarbon source apportionment of EC reported a greater biomass burning influence than the current study does. In Guangzhou, PRD, Zhang et al. (2015) reported EC was $57\pm5\%$ fossil during a less-polluted event and $80\pm2\%$ fossil during a heavily polluted event in 2013, while Liu et al. (2014) reported that EC was $60{-}91\%$ fossil EC for eight samples spanning 2012e13. Although these studies represent a different location in the PRD, these studies suggest a relatively larger contribution from contemporary carbon to EC than was found in this study. Potential reasons for this are discussed in section 3.6.
3.4. Secondary organic aerosol
3.4.1. Isoprene-derived SOC
Three isoprene tracers, 2-methylglyceric acid (MGA) and 2- methyltetrols (2-methylthreitol and 2-methylerythritol; MTLs) were observed in Shenzhen (Table 1, Fig. 3a). During the controlled period, the estimated contribution of isoprene SOC to OC was significantly lower $\langle{\mathsf{p}}<0.05\rangle$ , by a factor of 5 in LG and 4.5 in PU. The overall isoprene SOC contributed less than $2\%$ to the OC mass in both sampling sites, indicating that isoprene SOC is not a major source of OC in Shenzhen.
On average, the concentrations of the sum of the three tracers increased significantly during the uncontrolled period from $5.6\pm4.5\,\mathrm{ng\,m}^{-3}$ to $30.9\pm27.9\,\mathrm{ng\,m}^{-3}$ in LG, and from $2.7\pm2.0\:\mathrm{ng}\:\mathrm{m}^{-3}$ to $46.6\pm29.9\,\mathrm{ng\,m}^{-3}$ at PU, respectively (Table 1). The MGA/MTL ratio remained steady throughout: the average $\pm$ standard deviation) MGA/MTL ratio was 0.6 $\left(\pm0.6\right)$ in LG, and 0.5 $(\pm0.4)$ in PU. Since MGA forms through the high- $\cdot\mathrm{NO}_{\tt x}$ isoprene SOA formation pathway (Surratt et al., 2010), this result suggests that there was no substantive shift in the effect of $\mathsf{N O}_{\mathtt{X}}$ on forming isoprene SOA.
It is likely that controlled period reductions in emissions of anthropogenic pollutants (e.g., sulfur dioxide that contribute to aerosol acidity when oxidized to sulfuric acid), as indicated by the lower sulfate levels (Dewan et al., 2016), decreased the extent of SOA formation at both sites. In the Southeastern United States, sulfate and isoprene SOA positively correlate $\mathrm{\Deltaxu}$ et al., 2015), indicating acid-enhanced isoprene SOA formation. Similarly, in this dataset, there is a statistically significant positive correlation between sulfate and isoprene tracer concentrations $\mathrm{\bfr}\!>\!0.58$ , $\mathsf{p}<0.004;$ , with trends shown in Fig. S3. Thus, reductions in biogenic SOA may be accessible by decreasing anthropogenic sulfate levels.
3.4.2. $\alpha$ -Pinene-derived SOC
$\mathfrak{a}$ -Pinene SOC contributed up to $3.5\%$ of OC in Shenzhen (Table S2), and that contribution was significantly increased $(\mathsf{p}<0.05)$ by an average factor of 2.6 in LG and 8.8 in PU during the uncontrolled period. Four $\pmb{\alpha}$ -pinene tracers (3-hydroxyglutaric acid, pinic acid, 2-hydroxy-4,4-dimethylglutaric acid, and 3-acetyl hexanedioic acid) were consistently detected in the samples collected from both sampling sites (Fig. 3b, Table 1). In general, the concentrations of these tracers in PU are higher than those in LG, likely because the PU site is surrounded by forest, unlike LG which is more urbanized with fewer green spaces. Like isoprene tracers, the sum of $\pmb{\alpha}$ -pinene tracers was significantly higher during the uncontrolled period $(38.5.\pm40.2\mathrm{\,ng\,m^{-3}})$ than in the controlled period $(6.8\pm8.7\,\mathrm{ng\,m}^{-3})$ ) at LG and PU $(103.0\pm76.7\,\mathrm{ng\,m}^{-3}$ and $2.5\pm2.5\:\mathrm{ng}\:\mathrm{m}^{-3}$ , respectively).
$\pmb{\alpha}$ -Pinene SOA tracer levels showed consistent temporal trends to isoprene SOA tracers (Fig. 3). It has been previously reported that $\pmb{\alpha}$ -pinene SOA tracer concentrations correlate with gas phase concentrations of $\Nu0_{\mathtt{x}}$ and $S0_{2}$ $\mathrm{{Xu}}$ et al., 2015). Monoterpene SOA reduction during the controlled period, then, is likely due to emission controls’ reduction of the ambient concentrations of these species. Although there was no significant correlation between $\pmb{\alpha}$ pinene SOA tracers and nitrate ions, there is a significant moderate correlation between the sum of $\mathfrak{a}$ -pinene SOA tracers and sulfate at PU $\operatorname{\mathrm{\ddot{r}}}\operatorname{=}0.56$ , ${\tt p}=0.005)$ and a significant strong correlation at LG $\mathrm{\bf~r}\!=\!0.79$ , ${\tt p}<0.001$ , Fig. S3). The ratios of biogenic SOA tracers-tosulfate were consistently greater during the uncontrolled period, suggesting that SOA formation was enhanced during the uncontrolled period. Thus, $\pmb{\alpha}.$ -pinene SOA may also be reduced by controlling primary emissions.
Shenzhen, with an average contribution of less than $1.8\%$ (Table S2). Upon emission control, the concentrations of $\upbeta{\mathrm{.}}$ -caryophyllene SOC decreased significantly at both sites (Table 1).
3.4.4. Aromatic VOC-derived SOC
The SOC from aromatic VOCs was estimated based on the ambient concentrations of 2,3-dihydroxy-4-oxopentanoic acid (DHOPA). The average ambient concentrations of DHOPA rose from $5.1\pm4.7\mathrm{\,ng\,m^{-3}}$ in LG and $2.0\pm1.4\,\mathrm{ng\,m}^{-3}$ in PU to $12.3\pm9.5\mathrm{\,ng\,m}^{-3}$ and $26.8\pm9.2\mathrm{\,ng\,m}^{-3}$ , respectively, after the pollution controls were lifted. The increase in DHOPA is statistically significant at both sites: LG $\left(\mathbf{p}=0.06\right)$ and PU $\left\langle\mathbf{p}=0.002\right\rangle$ . SOC from aromatic VOCs was the most abundant contributor to OC among the quantified sources and its contribution to OC during the uncontrolled period was as high as $23\%$ at the PU site.
Aromatic SOC contributed much more to $\mathsf{P M}_{2.5}$ OC than did biogenic SOC from isoprene and $\pmb{\alpha}$ -pinene. These observations can be explained by prior observations that the rates of aromatic VOC emissions, such as toluene and xylenes, are higher than emission rates of biogenic VOC in industrial megacities in the PRD region (Wang et al., 2013). Several studies of megacities such as Mexico City (Stone et al., 2010) and the PRD (Ding et al., 2012; Wang et al., 2013), have also reported higher contributions from anthropogenic precursors to SOC than biogenic precursors. This result illustrates that anthropogenic SOC contributes more than biogenic sources to the OC fraction of $\mathsf{P M}_{2.5}$ in megacities, despite global SOA budgets largely dominated by biogenic sources (Henze et al., 2008).
3.5. Isotope analysis
3.5.1. Radiocarbon $(^{I4}C)$
For the radiocarbon results, the total carbon was determined to be predominantly fossil for the entire period; these total carbon results would include both OC and EC contributions. By following ambient levels and percent contributions of both fossil and contemporary carbon, it is possible to determine whether any relative or absolute change in source contributions occurred over the study period. Ambient concentrations indicated that fossil and contemporary carbon increased at both sites during the uncontrolled period, but the concentration of contemporary carbon increased to a greater degree. PU had more fossil signature than LG, in both controlled and uncontrolled periods.
3.5.2. Stable isotopes: carbon $(^{13}C)$
Stable carbon and nitrogen $\hat{\textrm{\textcent}}^{13}\hat{\textrm{C}}$ and $\S~^{15}\mathrm{{N}}^{\cdot}$ ) for both sampling locations are shown in Fig. S4. At LG, the average $\eth\,^{13}\!C$ during the controlled period was $-27.2\pm0.5\%_{0}$ and increased significantly to $-26.6\pm0.2\%_{0}$ after the emission control $\mathbf{\nabla}_{\cdot}\mathbf{p}=0.003)$ . At PU the average $\eth\ ^{13}C$ was constant in both periods $(-26.5)$ . The stable carbon fraction of total carbon can be affected by the end members, or isotope signature of the primary emission sources; however, it can also be affected by kinetic isotope effects during reaction of gas or particle phase species. More investigation would be needed to determine what is driving the small change in the $\eth\nobreakspace\nobreakspace13_{C}$ at LG. However, since the difference is only apparent at LG during the controlled period, this does further stress the difference between the sites during the controlled period. The sites were significantly different in $\mathfrak{d}^{\ 1\bar{3}}\mathfrak{C}$ during the controlled period $\mathbf{\nabla}.\mathbf{p}<0.001^{\prime}$ ); during the uncontrolled period, the $^{13}C$ data from the two sites were not significantly different $\mathbf{\check{p}}=0.440$ using student two population (two-tailed) t-tests). There was no difference in the $^{15}\mathrm{N}$ by site or by the presence or absence of controls.
3.6. Comparison of CMB and radiocarbon source apportionment results
Prior to comparing CMB and $^{14}\!C$ results, the CMB-estimated source contributions to EC were summed with those for OC, so that fossil and contemporary contributions to total carbon could be compared across the two methods, as seen in Fig. 4. CMB results were excluded from source apportionment results for days with unacceptable model fit, as described in section 2.3, whereas $^{14}\!C$ measurements were performed as a composite and included all sampling days. Because CMB and $^{14}\!C$ are distinct ways of quantifying the average difference between controlled and uncontrolled periods, $^{14}\!C$ can indicate the overall bias in the CMB results and potential bias introduced by excluding non-fit days.
For the uncontrolled period, the radiocarbon measurements and CMB estimates of fossil carbon agree within $2\%$ of their total carbon contribution (Table S3) for both sampling locations. Thus, the unapportioned OC unresolved by the CMB model $.25.6\%$ at LG and $20.1\%$ at PU) is contemporary in origin. For the controlled period, the radiocarbon measurements indicated a smaller fossil carbon fraction than was estimated by the CMB model, by an average of $19\%$ of total C at LG and $8\%$ at PU (Table S3). However, the radiocarbon measurements of fossil and contemporary carbon are either within or near to one standard deviation of the mean CMB estimates. While the radiocarbon was composited by control status, the tracer-based CMB apportionment was daily and demonstrated large day-to-day variability in source contributions. The overestimate of fossil C in the CMB model may result from an underestimation of biomass burning contributions to EC, because the cereal straw burning profile (Zhang et al., 2007) has a low EC-to-OC ratio. As discussed in section 3.3, EC was attributed almost entirely to fossil sources during the controlled period, which does not match previous measurements of EC radiocarbon in the PRD. In addition, if levoglucosan the primary biomass burning tracer degrades during transport, CMB would under-estimate the contribution of biomass burning to OC (Lai et al., 2014). Similar to the uncontrolled period, we conclude that the OC unapportioned by the CMB model is derived from contemporary sources (Table S3). The underestimate of contemporary OC by CMB modeling relative to $^{14}\!C$ measurements was also observed in Bakersfield, California (Sheesley et al., 2017).
In sum, while the CMB model provides source specificity in the apportionment of $\operatorname{PM}_{2.5}\!\operatorname{C}$ to its sources, the $^{14}\!C$ measurements provide constraint in interpreting the unapportioned OC fraction. Here, the unapportioned OC is shown to be contemporary in origin; biomass burning and/or biogenic SOC are the likely origins. This suggests that improved CMB model representation is needed for a more complete apportionment of OC by this approach. This could include improved source characterization and improved handling of degradation or atmospheric lifetime of tracers in the model.
4. Conclusions
Key findings from this study include:
$\mathsf{P M}_{2.5}$ concentrations in Shenzhen were significantly lower during the period of emission controls by $54\%$ at LG and $73\%$ at PU. However, because the wind blew from different directions during these two periods, it is not evident what extent of these reductions were due to stricter emission controls versus changes in wind direction.
OC was significantly lower during the controlled period, and CMB source apportionment modeling indicated significant reductions in OC contributions from coal combustion, biomass burning, and SOC from isoprene, $\pmb{\alpha}.$ -pinene, $\upbeta$ -caryophyllene, and aromatic VOCs.
The correlation of biogenic SOA tracers with sulfate suggested that anthropogenic emissions via acidic PM enhanced SOA formation during the uncontrolled period.
Aromatic SOC contributed up to $8\%$ of OC during the controlled period and up to $23\%$ during the uncontrolled period, indicating that anthropogenic VOC strongly influence SOA formation.
Measurements of $^{14}\!C$ content indicated the importance of fossil and contemporary sources of OC and EC, with both decreasing in their ambient concentrations during the controlled period.
Radiocarbon estimates for the fossil contribution to carbon agree with CMB source apportionment within the uncertainty of the CMB estimates. Together, these data indicate that the unapportioned OC fraction in CMB is mainly from contemporary sources (i.e. biomass burning and biogenic SOA).
Acknowledgments
I.M.A. and E.A.S. were supported by the University of Iowa. R.J.S. and S.Y. were supported by Baylor University. We thank James J. Schauer from the University of Wisconsin-Madison for leadership in coordinating this research study.
Appendix A. Supplementary data
Supplementary data related to this article can be found at https://doi.org/10.1016/j.envpol.2018.04.071.
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atmosphere | 0001 | 10.1080/10962247.2012.701193 | image | Figure 1. $\mathrm{PM}_{2.5}$ samples were taken in seven southern China cities: Chongqing (CQ), Guangzhou (GZ), Hong Kong (HK), Hangzhou (HZ), Shanghai (SH), Wuhan (WH), and Xiamen (XM); and seven northern China cities: Beijing (BJ), Changchun (CC), Jinchang (JC), Qingdao (QD), Tianjin (TJ), Xi’an (XA), and Yulin (YL). Filter samples were obtained from 0900 to 0900 LST the next morning over 2-week periods during winter (January 6–20) and summer (June $3-$ July 30) of 2003. Cities are classified as representing northern and southern China since: (1) precipitation events are more frequent and intense in southern China, and (2) northern China cities have lower wintertime temperatures, resulting in a greater amount of domestic heating, often using coal, along with shallower and more prolonged surface inversions at night and early morning. |
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atmosphere | 0001 | 10.1080/10962247.2012.701193 | image | Figure 2. Average (square), median (central horizontal bar), 25th and 75th percentiles (lower and upper bars), 1st and 99th percentiles (lower and upper x), and minimum and maximum $(-)$ concentrations for each chemical component across all cities and seasons. Average chemical components are ordered by abundance, with OC $(24.5~\upmu\mathrm{g}\,\textrm{m}^{-3})$ ), $\mathrm{SO}_{4}^{\ 2-}$ $(19.9\ \upmu\mathrm{g}\ \mathrm{m}^{-3})$ ), $\mathrm{NO}_{3}^{\mathrm{~-~}}$ $(9.9~\upmu\mathrm{g}$ $\mathfrak{m}^{-3}$ , $\mathrm{NH_{4}}^{+}$ $(9.2~\upmu\mathrm{g}\textrm{m}^{-3})$ ), EC $(6.5~\upmu\mathrm{g}\textrm{m}^{-3})$ , $\mathrm{Cl^{-}}$ $(3.1~\upmu\mathrm{g}\textrm{m}^{-3})$ ), $\mathrm{K}^{+}\,(1.\,9~\upmu\mathrm{g}$ $\mathfrak{m}^{-3},$ , and $\mathrm{Na}^{+}\,(1.5~\upmu\mathrm{g}\,\mathrm{m}^{-3})$ all being at important levels. |
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atmosphere | 0001 | 10.1080/10962247.2012.701193 | table | Table 1. Arithmetic averages standard deviations (mg m3) for PM2.5 mass and chemical components by city and season. See Figure 1 for city codes. Each average contains 14 values |
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atmosphere | 0001 | 10.1080/10962247.2012.701193 | table | Table 2. Comparison of $\mathrm{PM}_{2.5}$ chemical component ratios for the 14 Chinese cities with ratios from selected cities in Europe, Canada, Mexico, and the United States |
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atmosphere | 0001 | 10.1080/10962247.2012.701193 | image | Figure 3. Relationships between $\mathrm{PM}_{2.5}$ As, $\mathrm{Pb}$ , and $\mathrm{SO}_{4}{}^{2-}$ concentrations from the 14 cities during winter and summer, 2003. |
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atmosphere | 0001 | 10.1080/10962247.2012.701193 | image | Figure 4. Wintertime material balance of $\mathrm{PM}_{2.5}$ for the 14 Chinese cities. Organic matter (OM) is estimated as $1.6\times\mathrm{OC}$ (Chen and Yu, 2007; El-Zanan et al., 2005; ElZanan et al., 2009) to account for unmeasured hydrogen and oxygen. Geological material is estimated as $25\times\mathrm{Fe}$ (Cao et al., 2008; Wu et al., 2011) to account for unmeasured oxygen and non-iron minerals. “Others” is the remaining unaccounted-for mass after subtracting the sum of measured components from the $\mathrm{PM}_{2.5}$ mass. Unaccounted-for mass can be potentially composed of unmeasured geological material (e.g., calcium carbonate), a higher fraction of oxygen in OM, and liquid water associated with $\mathrm{NH_{4}}^{+}$ , $\mathrm{NO}_{3}^{\mathrm{~-~}}$ , and $\mathrm{SO}_{4}^{\ 2-}$ at the $35\%$ to $45\%$ relative humidity filter weighing conditions. |
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atmosphere | 0001 | 10.1080/10962247.2012.701193 | image | Figure 5. Summertime material balance of $\mathrm{PM}_{2.5}$ for the 14 Chinese cities. Organic matter, geological material, and others are explained in the Figure 4 caption. |
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atmosphere | 0001 | 10.1080/10962247.2012.701193 | table | Table 3. Comparison of PM2.5 and major chemical concentrations (mg m3) from this study with measurements from other PM2.5 studies in Beijing (BJ), Xi’an (XA), Shanghai (SH), and Guangzhou (GZ) |
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atmosphere | 0001 | 10.1080/10962247.2012.701193 | text | Not supported with pagination yet | Journal of the Air & Waste Management Association
Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/uawm20
Winter and Summer $\mathbf{PM}_{2.5}$ Chemical Compositions in Fourteen Chinese Cities
Jun-Ji Cao a , Zhen-Xing Shen b , Judith C. Chow a c , John G. Watson a c , Shun-Cheng Lee d Xue-Xi Tie a e , Kin-Fai Ho a , Ge-Hui Wang a & Yong-Ming Han a
a Key Lab of Aerosol, SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences , Xi'an , China
b Department of Environmental Sciences and Engineering , Xi'an Jiaotong University , Xi'an , China
c Division of Atmospheric Sciences , Desert Research Institute , Reno , Nevada , USA d The Hong Kong Polytechnic University , Hong Kong
e National Center for Atmospheric Research , Boulder , Colorado , USA
Accepted author version posted online: 24 Jul 2012.Published online: 24 Sep 2012.
To cite this article: Jun-Ji Cao , Zhen-Xing Shen , Judith C. Chow , John G. Watson , Shun-Cheng Lee , Xue-Xi Tie , KinFai Ho , Ge-Hui Wang & Yong-Ming Han (2012) Winter and Summer $\mathrm{PM}_{2.5}$ Chemical Compositions in Fourteen Chinese Cities, Journal of the Air & Waste Management Association, 62:10, 1214-1226, DOI: 10.1080/10962247.2012.701193
To link to this article: http://dx.doi.org/10.1080/10962247.2012.701193
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TECHNICAL PAPER
Winter and Summer PM2.5 Chemical Compositions in Fourteen Chinese Cities
Jun-Ji Cao,1,⁄ Zhen-Xing Shen,2 Judith C. Chow,1,3 John G. Watson,1,3 Shun-Cheng Lee,4
Xue-Xi Tie,1,5 Kin-Fai Ho,1 Ge-Hui Wang,1 and Yong-Ming Han1
1Key Lab of Aerosol, SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an, China
2Department of Environmental Sciences and Engineering, Xi’an Jiaotong University, Xi’an, China
3Division of Atmospheric Sciences, Desert Research Institute, Reno, Nevada, USA
4The Hong Kong Polytechnic University, Hong Kong
5National Center for Atmospheric Research, Boulder, Colorado, USA
⁄Please address correspondence to: Jun-Ji Cao, Institute of Earth Environment, Chinese Academy of Sciences (CAS), No. 10 Fenghui South Road,
High-Tech Zone, Xi’an 710075, China; e-mail: [email protected]
$P M_{2.5}$ in 14 of China’s large cities achieves high concentrations in both winter and summer with averages $>\!I O O\;\mu g\;m^{-3}$ being common occurrences. A grand average of $I l5\,\mu g\,m^{-3}$ was found for all cities, with a minimum of $27\,\mu g\,m^{-3}$ measured at Qingdao during summerand a maximum of $\!\!\!\!\!\operatorname{\mathrm{356}}\mu g\,m^{-3}$ at $X i$ ’an during winter. Both primary and secondary $P M_{2.5}$ are important contributors at all of the cities and during both winter and summer. While ammonium sulfate is a large contributor during both seasons, ammonium nitrate contributions are much larger during winter. Lead levels are still high in several cities, reaching an average of $I.68\;\mu g\;m^{-3}$ in $X i$ ’an. High correlations of lead with arsenic and sulfate concentrations indicate that much of it derives from coal combustion, rather than leaded fuels, which were phased out by calendar year 2000. Although limited fugitive dust markers were available, scaling of iron by its ratios in source profiles shows ${\sim}20\%$ of $P M_{2.5}$ deriving from fugitive dust in most of the cities. Multipollutant control strategies will be needed that address incomplete combustion of coal and biomass, engine exhaust, and fugitive dust, as well as sulfur dioxide, oxides of nitrogen, and ammonia gaseous precursors for ammonium sulfate and ammonium nitrate.
Implications: $\mathrm{PM}_{2.5}$ mass and chemical composition show large contributions from carbon, sulfate, nitrate, ammonium, and fugitive dust during winter and summer and across fourteen large cities. Multipollutant control strategies will be needed that address both primary $\mathrm{PM}_{2.5}$ emissions and gaseous precursors to attain China’s recently adopted $\mathrm{PM}_{2.5}$ national air quality standards.
Introduction
gases to secondary sulfate $(\mathrm{SO}_{4}^{\ 2-})$ , nitrate $\left(\mathrm{NO}_{3}^{\mathrm{~-}}\right)$ , ammonium $\mathrm{(NH_{4}}^{+})$ , and organic carbon (OC).
Suspended particulate matter (PM) is the major pollutant in many Chinese cities (Chan and Yao, 2008; Tie and Cao, 2009). Coal combustion to generate electricity and for domestic cooking and heating constitutes $\sim\!70\%$ of the national energy budget (NAE et al., 2008). Total biomass burning in China, which includes domestic cooking and residential heating, field burning of crop residue, forest fires, and grassland fires, is estimated at $511.3~\mathrm{{Tg}~\mathrm{{yr}^{-1}}}$ (Yan et al., 2006). Improved engines and tighter emission standards are being offset by rapid growth in the motor vehicle fleet (Han and Hayashi, 2008). Paved and unpaved roads, construction, agricultural operations, and wind-blown soil eject geological material into the atmosphere (Du et al., 2008; Xuan et al., 2004). These and other emitters are contributing to high PM levels in Chinese cities, both through direct PM emissions and through conversion of sulfur dioxide $(\mathrm{SO}_{2})$ , nitrogen oxides $\mathrm{(NO_{x})}$ , ammonia $\left(\mathrm{NH}_{3}\right)$ , and volatile organic compound (VOC)
The Chinese government issued a national $\mathrm{PM}_{2.5}$ standard on February 29, 2012, that requires cities to have concentrations below $\dot{3}5~\upmu\mathrm{g}~\mathrm{m}^{-3}$ annual average and $<\!75\ \upmu\mathrm{g}\ \mathrm{m}^{-3}$ for $24\ \mathrm{hr}.$ , beginning in 2016 (http://cleanairinitiative.org/portal/node/ 8163). These standards were adopted owing to recognized adverse effects of $\mathrm{PM}_{2.5}$ chemical components on human health, visibility, and materials (Hu et al., 2009; Mauderly and Chow, 2008; Pope and Dockery, 2006; Watson, 2002). Elements, ions, and carbon fractions are often measured in $\mathrm{PM}_{2.5}$ to better evaluate the adverse effects and to indicate contributing sources. Several studies have reported these measurements in China (Cao et al., 2011; Chow et al., 2006; Deng et al., 2011; Duan et al., 2006; Gu et al., 2011; Guinot et al., 2007; He et al., 2001; Ho et al., 2006; Hu et al., 2010; Louie et al., 2005; Louie et al., 2005; Shen et al., 2007; So et al., 2007; Song et al., 2007; Sun et al., 2004; Wang et al., 2006; Wang et al., 2007; Wu et al., 2003; Xu et al., 2004; Yang et al., 2011; Zhang et al., 2010; Zhang and Friedlander, 2000; Zhao et al., 2010), but the areas studied, sampling site zones of representation, sampling periods, variables measured, and analysis methods are of insufficient consistency to evaluate similarities and differences. Reported here are consistently characterized simultaneous winter and summer $\mathrm{PM}_{2.5}$ mass and chemical concentrations obtained during 2003 at receptors with neighborhood and urban scale (Chow et al., 2002) in 14 of China’s major cities. These measurements are used to compare and contrast the situation across a broad range of emissions and meteorology, examine seasonal changes, and assess contributions from coal combustion using elemental concentration ratios. These measurements from nearly a decade ago provide a baseline against which to evaluate future speciated $\mathrm{PM}_{2.5}$ measurements that will be needed to create and evaluate the multipollutant (Chow and Watson, 2011) control strategies required to attain the national standards.
Materials and Methods
As shown in Figure 1, measurement sites were located in 14 economically developed and developing cities across China. The neighborhood- and urban-scale sites were located on the campuses of schools and research institutes, as previously described (Cao et al., 2007; Cao et al., 2011; Han et al., 2009; Ho et al., 2007; Wang et al., 2006). Filter samplers were located on rooftops at 6 to $^{20\textrm{m}}$ above ground level for around 2 weeks of sampling during winter (January 6–20) and summer (June $3-$ July 30) of 2003.
$\mathrm{PM}_{2.5}$ samples were obtained on prefired $(900^{\circ}\mathrm{C},3\,\mathrm{h})$ ) 47-mm Whatman QM-A quartz-fiber filters by mini-volume air samplers (Airmetrics, Eugene, OR) at $5\ \mathrm{L\min}^{-1}$ flow rates. The exposed filters were stored at ${\sim}4^{\circ}\mathrm{C}$ after sampling, including shipping to the Xi’an laboratory, to minimize evaporation of volatile components. Filters were weighed before and after sampling with a $\pm1-\upmu\mathrm{g}$ sensitivity Sartorius MC5 electronic microbalance (Sartorius, Göttingen, Germany) after 24-hr equilibration at 20 to $23{}^{\circ}\mathbf{C}$ and 35 to $45\%$ relative humidity (RH). Each filter was weighed at least three times before and after sampling. The maximum differences among the three repeated weights were less than $10~\upmu\mathrm{g}$ for blank filters and less than $20~\upmu\mathrm{g}$ for exposed filters. The collected PM was the difference between the average of exposed weights and the average of unexposed weights. Field blanks were also collected at each sampling site every seventh day by exposing filters in the sampler without drawing air through them; these were used to account for passive deposition or artefacts introduced between sample changing.
Elemental concentrations of Fe, Ti, Mn, Zn, As, Br, and Pb in filter deposits were determined by energy-dispersive x-ray fluorescence (ED-XRF) spectrometry (PANalytical Epsilon 5, Almelo, The Netherlands) (Chow and Watson, 2012; Watson et al., 2012). Other elements, such as Si, Ca, Al, and Mg, were not quantified owing to high and variable blank values on quartzfiber filters and potential biases caused by absorption of lowenergy x-rays from particles penetrating into the filter. XRF measurements on nine collocated Teflon-membrane and quartzfiber filters from Xi’an were comparable for these elements, with correlations $(r)$ ranging from 0.982 for Fe and $Z\mathfrak{n}$ (with slopes of 1.054 and 1.062, respectively) to 0.915 for As (with slope of 1.204). Measurement precision was determined as the standard deviation of several analyses of the same samples, yielding $\pm7.6\%$ for Fe, $\pm8.6\%$ for Ti, $\pm12.5\%$ for Mn, $\pm7.6\%$ for $Z\mathfrak{n}$ , $\pm23.5\%$ for As, $\pm33.3\%$ for Br, and $\pm7.9\%$ for $\mathrm{Pb}$ at typical concentration levels. Instrumental detection limits are $24.0~\mathrm{ng}$ $\mathfrak{m}^{-3}$ for Fe, $14.0\,\mathrm{ng}\,\mathrm{m}^{-3}$ for Ti, $25.0\,\mathrm{ng}\,\mathrm{m}^{-3}$ for Mn, $24.0\,\mathrm{ng}\,\mathrm{m}^{-3}$ for Zn, $26.0\,\mathrm{ng}\,\mathrm{m}^{\bar{-}3}$ for As, $9.0\,\mathrm{ng~m}^{-3}$ for Br, and $21.0\,\mathrm{n}\bar{\mathrm{g}}\,\mathrm{m}^{-3}$ for $\mathrm{Pb}$ based on the uncertainties of blank filter counts. Replicate measurements were taken for every eight samples, and no differences were found that exceeded the precision intervals.
Following XRF analysis, the filter was sectioned with a precision cutter and one-fourth was extracted in $10\;\mathrm{mL}$ of distilled deionized water; the extract was submitted to ion chromatographic (IC) analysis (Shen et al., 2008; Shen et al., 2009) for cations $\mathrm{Na}^{+}$ , $\mathrm{NH_{4}}^{+}$ , and $\ K^{+}$ and anions $\mathrm{SO}_{4}^{\ 2-}$ , $\mathrm{NO}_{3}^{\mathrm{~-~}}$ , and $\mathrm{Cl}^{-}$ . Detection limits were $4.6\,\upmu\mathrm{g}\,\mathrm{L}^{-1}$ for $\mathrm{Na}^{+}$ , $4.0\,\upmu\mathrm{g}\,\mathrm{L}^{-1}$ for $\mathrm{NH_{4}}^{+}$ , $10.0~\upmu\mathrm{g}\,\mathrm{L}^{-1}$ for $\ K^{+}$ , $0.5~{\upmu\mathrm{g}}~\mathrm{L}^{-1}$ for $\mathrm{Cl}^{-}$ , $15\ \upmu\mathrm{g}\,\mathrm{L}^{-1}$ for $\mathrm{NO}{_3}^{-}$ , and $\overline{{20}}\ \upmu\mathrm{g}\ \mathrm{L}^{-1}$ for $\mathrm{SO}_{4}^{\bar{2}-}$ . Reference materials (National Research Center for Certified Reference Materials, China) agreed with analyses values within $\pm~4\%$ . One in 10 extracts was reanalyzed and none of the differences between these replicates exceeded precision intervals. Blank values were also subtracted from sample concentrations.
Organic carbon (OC) and elemental carbon (EC) were determined on a $0.5{\mathrm{-cm}}^{2}$ punch from each filter by a DRI model 2001 carbon analyzer (Atmoslytic, Inc., Calabasas, CA) following the IMPROVE thermal/optical reflectance (TOR) protocol (Cao et al., 2003; Chow et al., 1993; Chow et al., 2007; Chow et al., 2011). This produced four OC fractions (OC1, OC2, OC3, and OC4 at 120, 250, 450, and $550^{\circ}\mathrm{C}$ , respectively, in a helium [He] atmosphere); OP (a pyrolyzed carbon fraction determined when reflected laser light attained its original intensity after oxygen $[\mathrm{O}_{2}]$ was added to the analysis atmosphere); and three EC fractions (EC1, EC2, and EC3 at 550, 700, and $800^{\circ}\mathrm{C}$ , respectively, in a $2\%\,\mathrm{O}_{2}/98\%$ He atmosphere). OC is defined as ${\mathrm{OC}}1+{\mathrm{OC}}2+{\mathrm{OC}}3+{\mathrm{OC}}4+{\mathrm{OP}}_{}$ and EC is defined as $\mathrm{EC}1+$ $\mathrm{EC}2+\mathrm{EC}3-\mathrm{OP}$ .
Results and Discussion
$\mathrm{PM}_{2.5}$ mass concentrations
Figure 2 shows the wide distribution of concentrations observed across seasons and cities. The grand average of 115 $\upmu\mathrm{g}\,\mathrm{m}^{-3}$ is more than 3 times the annual standard, and the highest 24-hr value of $543.9~\upmu\mathrm{g}\mathrm{~m}^{-3}$ , found in Xi’an during winter, is more than 7 times the 24-hr standard. OC, $\mathrm{SO}_{4}^{\,\,2-}\,\mathrm{N}\bar{\mathrm{O}_{3}}^{-}$ , $\mathrm{NH_{4}}^{+}$ , and EC are the most abundant species, all with averages exceeding $5~{\upmu\mathrm{g}}~\mathrm{m}^{-3}$ . Elemental averages are less than the averages for carbon and ions, with Fe having the highest average of $2.4~\upmu\mathrm{g}$ $\mathfrak{m}^{-3}$ at Chongqing during winter. Concentrations ranged over several orders of magnitude, with the range increasing as the average concentration decreased. This variability indicates large spatial and temporal differences across the network.
Table 1 summarizes winter and summer $\mathrm{PM}_{2.5}$ averages for each city. Standard deviations are typically $25\%$ to $50\%$ of the averages, indicating that these averages are not highly influenced by extreme events. Standard errors (standard deviation divided by the square root of the number of samples) of the averages are in the range of $6\%$ to $13\%$ .
In every city except Beijing and Xiamen (no summer data), wintertime $\mathrm{PM}_{2.5}$ exceeded those of summertime, in many cases by a factor of 2 or more. Seasonal averages for $\mathrm{PM}_{2.5}$ mass were similar in Beijing, with a winter/summer ratio of 0.88, in contrast to the highest ratio of 4.9 at Qingdao, a coastal city in northern China. The lack of difference in the mass ratio for Beijing is partially due to the lack of change in the OC concentrations, which were $23.9\pm12.4\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ in winter, only $20\%$ higher than summer. The winter/summer mass ratios for other cities are reflected in the major chemical component averages, which are 2 to 3 for OC, EC, and $\mathrm{SO}_{4}{}^{2-}$ in most cities, with $\mathrm{NO}{_3}^{-}$ and $\mathrm{NH_{4}}^{+}$ showing even higher winter/summer differences.
Average wintertime $\mathrm{PM}_{2.5}$ was lowest in Xiamen $(74.2~\upmu\mathrm{g}$ $\mathfrak{m}^{-3}.$ ) and highest in Xi’an $(356.3\;\upmu\mathrm{g}\,\mathrm{m}^{-3})$ ). $\mathrm{PM}_{2.5}$ was higher at inland cities (e.g., Xi’an, Wuhan, and Chongqing), and lower at the coastal (e.g., Xiamen and Hong Kong) and desert (i.e., Jinchang) cities. For the summer samples, average $\mathrm{PM}_{2.5}$ was lowest in Qingdao $(27.3\;\;\upmu\mathrm{g}\;\;\mathrm{m}^{-3})$ , and highest in Beijing $\left(131.6~\upmu\mathrm{g}~\mathrm{m}^{-3}\right)$ ).
$\mathrm{PM}_{2.5}$ composition
OC and EC exhibited winter maxima and summer minima. OC was the most abundant wintertime $\mathrm{PM}_{2.5}$ constituent in all cities except Hangzhou and Hong Kong, ranging from 13.3 (Hong Kong) to $\bar{9}5.8~\upmu\mathrm{g}~\mathrm{m}^{-3}$ (Xi’an). Wintertime EC levels vary with OC concentrations, which ranged from 4.6 (Jinchang) to $\dot{2}1.5~\upmu\mathrm{g}\;\mathrm{m}^{-3}$ (Xi’an). This co-occurrence is expected, as OC and EC typically result from incomplete combustion of solid and liquid fuels (Lighty et al., 2000). OC and EC concentrations were highest in the inland cities, such as Changchun, Xi’an, Wuhan, and Chongqing, and lower in the coastal cities, such as Qingdao, Xiamen, and Hong Kong.
Wintertime $\mathrm{SO}_{4}^{-2-}$ was the second most abundant component of $\mathrm{PM}_{2.5}$ for all the cities except Hong Kong, varying from 11.5 $\upmu\mathrm{g}\textrm{m}^{-3}$ in Jinchang to $60.9~\mathrm{\dot{\mu}g~m}^{-\overline{{3}}}$ in Chongqing. This was followed by $\mathrm{NO}_{3}^{\mathrm{~-~}}$ , ranging from $2.1~\upmu\mathrm{g}\mathrm{~m}^{-3}$ (Jinchang) to 29 $\upmu\mathrm{g}\:\mathrm{m}^{-3}$ (Xi’an), and $\mathrm{NH_{4}}^{+}$ ranged from $6.6\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ (Jinchang) to $29.8~\,\upmu\mathrm{g}\,\,\mathrm{m}^{-3}$ (Xi’an). These high secondary ammonium sulfate $((\mathrm{NH}_{4})_{2}\mathrm{SO}_{4})$ and ammonium nitrate $\left(\mathrm{NH}_{4}\mathrm{NO}_{3}\right)$ levels imply the need for precursor gas, as well as primary PM, emission reductions to reduce $\mathrm{PM}_{2.5}$ mass. The higher $\mathrm{NH}_{4}\mathrm{NO}_{3}$ values in winter than summer are consistent with a shift in equilibrium from the gas to particle phase with lower temperatures and higher RH (Stelson et al., 1979).
$\ K^{+}$ is considered a marker for biomass burning (Andreae, 1983; Duan et al., 2004), although it is also a component of certain soils and sea spray (Pytkowicz and Kester, 1971). Wintertime $\ K^{+}$ levels exceeded $3~\upmu\mathrm{g}\,\textrm{m}^{-3}$ at Xi’an, Wuhan, Chongqing, and Hangzhou. The inland cities experience cold temperatures during winter and have abundant biomass available for residential heating.
Fe is a marker for fugitive dust, although it also originates from heavy industry. The wintertime Fe concentration was highest at $2.4\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ in Chongqing, followed by Xi’an $(1.8~\upmu\mathrm{g}\,\mathrm{m}^{-\overline{{3}}})$ , with the lowest wintertime average of $0.6\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ at Xiamen. The two arid-region cities had low Fe concentrations, $1.2\ \upmu\mathrm{g}\ \mathrm{m}^{-3}$ at Jinchang and $0.7~\upmu\mathrm{g}\textrm{m}^{-3}$ at Yulin. The wintertime Fe averages did not correlate well with other soil components such as Ti and Mn across the sites, which may indicate additional Fe sources or variability in the fugitive dust compositions.
The highest wintertime As $(0.11\;\upmu\mathrm{g}\,\mathrm{m}^{-3})$ ) and $\mathrm{Pb}\,(1.7\,\upmu\mathrm{g}\,\mathrm{m}^{-3})$ ) concentrations were found at Xi’an. As and $\mathrm{Pb}$ are found in Chinese coal (Tian et al., 2011; Want et al., 2006), while $\mathrm{Pb}$ gasoline additives were discontinued in 2000 (Xu et al., 2012). The highest Br average was found at a coastal city, Qingdao $(0.17\ \upmu\mathrm{g}\:\textrm{m}^{-3})$ , consistent with a potential marine aerosol contribution.
Summertime averages were lower than those for winter for nearly all chemical components. In most cases, this can be attributed to warmer weather that improved dispersion and shifted the $\mathrm{NH}_{4}\mathrm{NO}_{3}$ from the particle to gas phase. Lower OC and EC averages are probably less related to domestic biomass and coal combustion, which is consistent with lower $\Chi^{+}$ and As averages. Engine exhaust and agricultural burning emissions are expected to contribute larger portions of OC and EC during summer.
$\mathrm{NO}_{3}^{\mathrm{~-~}}$ and $\mathrm{NH_{4}}^{+}$ show the biggest contrast between winter and summer, consistent with the change in equilibrium. $\mathrm{SO}_{4}^{\ 2-}$ levels were also much lower during summer than winter. This would be consistent with more nearby $\mathrm{SO}_{2}$ to $\mathrm{SO}_{4}{}^{2-}$ conversion during winter, possibly in conjunction with reactive fogs and clouds (Pandis et al., 1992) and with local accumulation under stagnant conditions. The summer values could be more influenced by standard photochemical mechanisms occurring during long-range transport (Qian et al., 2001).
The Fe and Ti fugitive dust markers do not show a clear winter/summer pattern, being higher in some cities during summer and lower in others. The sampling periods did not include the April/May Asian dust storms (Gong and Zhang, 2008; Li et al., 2008) that are causes of high $\mathrm{PM}_{2.5}$ during these periods. The other elements do not show major or consistent differences between winter and summer, except that the summertime averages are generally lower. The summer $\mathrm{Pb}$ average in Xi’an decreased by more than a factor of two $(0.75~\upmu\mathrm{g}\;\mathrm{m}^{-\frac{\tt3}{\tt4}})$ ).
Chemical ratios as source indicators
Several potential sources of different chemical components were mentioned earlier. These can be better understood by examining some of the elemental ratios available from the data set that might correspond to similar ratios in the source profiles. OC/EC ratios across the 14 cities are compared in Cao et al. (2007). Given the large role of domestic and industrial coal use, the $\mathrm{SO}_{4}^{\;\;2-}/\mathrm{OC}$ , $\mathrm{SO}_{4}^{\frac{\gamma}{2}-}/\mathrm{EC}$ , $\mathrm{NO}_{3}^{\mathrm{~-}}/\mathrm{SO}_{4}^{\mathrm{~2-}}$ , $\mathrm{As/Fe}_{\mathrm{,}}$ , and $\mathrm{Pb/Fe}$ ratios are compared with ratios from other cities in Table 2. The 2003 $\mathrm{SO}_{4}^{\;\;\hat{2}-}/\mathrm{OC}$ ratio found in this study $(0.90\pm0.43)$ is much higher than that for the other cities, as is the $\mathrm{SO}_{4}{}^{2-}/\mathrm{EC}$ ratio $(3.42\pm2.06)\$ . Only Toronto had a higher $\mathrm{SO}_{4}^{~2-}/\mathrm{EC}$ ratio (i.e., 4.93), mostly due to low EC levels. As noted earlier, there are spatial and seasonal variations in these ratios that reflect local and regional contributions.
$\mathrm{NO}_{3}^{\bar{-}}/\mathrm{SO}_{4}^{\;2-}$ ratios have been used to evaluate relative contributions from coal-burning emissions, which abound in $\mathrm{NO_{x}}$ and $\mathrm{SO}_{2}$ , and engine exhaust, which is a major $\mathrm{NO_{x}}$ emitter but contains little $\mathrm{SO}_{2}$ (Hu etal.,2002; Wang etal.,2005; Yaoetal.,2002). Average $\mathrm{NO}_{3}^{\scriptscriptstyle-}/\mathrm{SO}_{4}^{\;2-}$ ratios were 0.61 in winter and 0.30 in summer. The $\mathrm{NO}_{3}^{\mathrm{~-}}/\mathrm{SO}_{4}^{\mathrm{~2-}}$ ratio for Toronto (0.81) was ${>}75\%$ higher than the value found in this study (i.e., $0.46\,\pm\,0.27)$ , while the ratios in Seattle, WA (0.43), and Mexico City (0.45) were comparable. $\mathrm{As/Fe}$ and $\mathrm{Pb/Fe}$ ratios were $0.04\pm\:0.03$ and $0.39\pm\:0.32$ , respectively, much higher than those for the other cities and indicative of the ash in uncontrolled coal combustion. Figure 3 shows a reasonably good association of $\mathrm{Pb}$ and $\bar{\mathrm{SO}_{4}}^{2-}$ concentrations with the As marker for coal ash. The scatter (e.g., Figure 3d) is typical of different ash composition and $\mathrm{SO}_{2}$ to $\bar{\mathrm{SO}_{4}}^{2-}$ transformation rates. The $\mathrm{Pb/As}$ correlation indicates that the Pb more probably derives from the coal ash than from the remnants from leaded gasoline, as also indicated by differences in abundances for $\mathrm{Pb}$ isotopic ratios (Xu et al., 2012; Widory et al., 2010; Zheng et al., 2004).
Material balance
Material balances estimating organic matter and soil from their marker species are shown in Figures 4 and 5 for the winter and summer seasons. Consistent with the previous discussion, organic material (OM), $\mathrm{SO}_{4}^{\ 2-}$ , $\mathrm{NO}_{3}^{\mathrm{~-~}}$ , and $\bar{\mathrm{NH}_{4}}^{+}$ are large components. OC takes on an even larger rolewhen its unmeasured hydrogen and oxygen components are taken into account as OM. The role of geological material is also enhanced when the Fe marker is leveraged by reasonable assumptions about its abundance in Chinese soils. Approximately 0 to $15\%$ of the measured mass is not quantified by the chemical analysis, which is potentially due to unmeasured species, underestimations for weighting factors for OM and geological material, and uncertainties in filter equilibration and gravimetric analysis (Malm et al., 2011; Kajino et al., 2006).
For winter samples, contributions in order of importance were $\mathrm{OM}>$ geological material $>$ sulfate $>$ nitrate> ammonium $>$ elemental carbon at major cities such as Beijing, Tianjin, Wuhan, Chongqing, Hangzhou, and Xiamen. Compositions differed for Yulin, Xi’an, and Hong Kong, where the wintertime $\mathrm{SO}_{4}^{\ 2-}$ contribution exceeded that from geological material. At arid Jinchang, the geological material contribution exceeded the $\mathrm{SO}_{4}^{\ 2-}$ and OM contributions.
During summer, most cities follow the general trend of $\mathrm{OM}>$ geological material $>$ sulfate $>$ nitrate, with elemental carbon contributions higher than ammonium contributions at all cities but Beijing and Tianjin. The contribution from $\mathrm{SO}_{4}^{\ 2-}$ in Hong Kong differed between winter $(25\%)$ and summer $(14\%)$ . At Shanghai and Hangzhou, $\mathrm{SO}_{4}{}^{2-}$ contributions exceeded those of geological material.
Comparison with other $\mathrm{PM}_{2.5}$ speciation studies in Chinese cities
Table 3 compares city-specific results from this study with chemical concentrations from other major cities (i.e., Beijing, Xi’an, Shanghai, and Guangzhou). Although there are differences in magnitude owing to the differences in measurement periods, zones of representation, and measurement methods, the major components are similar in magnitude and order of importance for nearly all of the studies. There is no evidence of major upward or downward trends in mass and chemical composition from 1999 to 2006, but this is expected, given the short durations of the measurement programs and the large variability in emissions and meteorology expected over this time period. Trends in the United States have only been associated with emission reductions over long periods of a decade or more using chemically speciated measurements that are specific to those emissions.
$\mathrm{PM}_{2.5}$ in Chinese cities versus non-Chinese cities
Table 4 compares the major components from Chinese cities with $\mathrm{PM}_{2.5}$ compositions in other countries. The geological material contribution is on the order of $10\%$ in $\mathrm{PM}_{2.5}$ from the non-Chinese cities, about half of that estimated from this study $(19.5\%)$ . Roadside sites in St. Louis, MO, and Barcelona, Spain, showed more comparable geological material contributions (15.4 and $15.2\%)$ ). The OM fractions in the Chinese cities are similar to most of the other cities, although the absolute OM concentrations are much higher in China. $\mathrm{SO}_{4}^{\ 2-}$ , $\mathrm{NO}_{3}^{\mathrm{~-~}}$ , and $\mathrm{NH_{4}}^{+}$ are important in $\mathrm{PM}_{2.5}$ in all of the cities, but their fractions are more variable and their absolute values are generally lower than those found in the 14 Chinese cities.
Average $\mathrm{PM}_{2.5}$ concentrations for this study ranged from 3 to 9 times higher than the values in Seoul, Yokohama, St. Louis, Indianapolis, Toronto, Mexico City, Barcelona, and Milan, with corresponding 2 to 10 times higher levels of OM. The average Chinese secondary aerosol concentrations for $\mathrm{SO}_{4}^{\ 2-}$ , $\mathrm{NO}_{3}^{\mathrm{~-~}}$ , and $\mathrm{NH_{4}}^{+}$ were 2–5, 1–10, and 2–7 times higher, respectively, than those in other cities in the world. As and $\mathrm{Pb}$ were 10 times and average geological material was 5–43 times those found in other cities.
Conclusions
$\mathrm{PM}_{2.5}$ in 14 of China’s large cities achieved high concentrations in both winter and summer of 2003 with averages ${>}100~\upmu\mathrm{g}\,\mathsf{m}^{-3}$ being common occurrences. A grand average of $115\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ was found for all cities, with a minimum of $27.3\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ measured at coastal Qingdao during summer and a maximum of $356.3\ensuremath{~\upmu\mathrm{g}\,\mathrm{m}^{-3}}$ at inland Xi’an during winter. Both primary and secondary $\mathrm{PM}_{2.5}$ are important contributors at all of the cities during both winter and summer. While ammonium sulfate is a large contributor during both seasons, ammonium nitrate contributions are much larger during winter. Lead levels are still high in several cities, reaching an average of $1.68\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ in Xi’an during winter. High correlations of lead with arsenic and sulfate concentrations indicate that much of it derives from coal combustion rather than leaded fuels that were phased out by calendar year 2000. Although limited fugitive dust markers were available, scaling of iron by its ratios in source profiles shows ${\sim}20\%$ of $\mathrm{PM}_{2.5}$ deriving from fugitive dust in most of the cities. Multipollutant control strategies will be needed that address incomplete combustion of coal and biomass, engine exhaust, and fugitive dust, as well as sulfur dioxide, oxide of nitrogen, and ammonia gaseous precursors for ammonium sulfate and ammonium nitrate.
Acknowledgments
This work was supported by the Natural Science Foundation of China (NSFC40925009), projects from Chinese Academy of Sciences (KZCX2-YW-BR-10, O929011018, and KZCX2-YW148). The authors thank Jo Gerrard of the Desert Research Institute for her assistance in assembling and editing the paper.
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About the Authors
Jun-Ji Cao, Kin-Fai Ho, Ge-Hui Wang, and Yong-Ming Han are professors in the Division of Aerosol & Environment, Institute of Earth Environment, Chinese Academy of Sciences.
Xue-Xi Tie is a scientist at National Center for Atmospheric Research, USA.
Zhen-Xing Shen is an associate professor at Xi’an Jiaotong University, China.
Judith C. Chow and John G. Watson are research professors in Desert Research Institute, USA.
Shun-Cheng Lee is a professor in the Hong Kong Polytechnic University, Hong Kong.
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | image | Fig. 1. Location of the sampling site at Xi’an, China. |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | table | Table 1. Average of OC and EC concentrations during September 2003 to February 2004 at Xi’an, China. |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | image | Fig. 2. Time series of $\mathrm{PM}_{2.5}$ mass, organic carbon (OC), elemental carbon (EC), fraction of $\mathrm{PM}_{2.5}$ composed of $\mathrm{OC}\!\times\!1.6\!+\!\mathrm{EC}$ $(\mathrm{TCA}\%)$ , and OC/EC ratios at Xi’an from 13 September 2003 to 29 February 2004. OC is multiplied by 1.6 for the $\mathrm{TCA}\%$ calculation to account for unmeasured hydrogen and oxygen in organic material (Turpin and Lim, 2001). |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | image | Fig. 3. Relationships between OC and EC concentrations in $\mathrm{PM}_{2.5}$ and $\mathrm{PM_{10}}$ . |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | image | Fig. 4. Distribution of $\mathrm{PM}_{2.5}$ and $\mathrm{PM_{10}}$ mass concentrations during fall and winter. The valid paired samples were 17 in fall and 36 in winter. The box plots indicate the mean $24\mathrm{-h}$ concentration and the min, 1st, 25th, 50th, 75th, 99th and max percentiles. A normal curve is fitted to the measurements. |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | table | Table 2. Statistical summary of the percentage of OC, EC, and $\mathrm{TCA}\%$ in $\mathrm{PM}_{2.5}$ and $\mathrm{PM}_{10}^{\mathrm{a}}$ |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | image | Fig. 5. Abundances (mass fraction of total carbon) of eight thermally-derived carbon fractions in ambient and source samples. |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | table | Table 3. Comparison of $\operatorname{PM}_{2.5}$ OC, EC at Xi’an with other Asian cities. |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | image | Fig. 6. Periodicity of $\mathrm{PM}_{2.5}$ OC, EC, mass, and daily average wind speed. (PSD TISA on the $\mathrm{Y}$ axis refers to Power as Time-Integral Squared Amplitude.) |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | table | Table 4. APCA results of fall samples. |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | table | Table 5. APCA results of winter samples. |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | image | Fig. 7. Relative contributions of major sources to $\mathrm{PM}_{2.5}$ TC during fall and winter 2003. |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | text | Not supported with pagination yet | Characterization and source apportionment of atmospheric organic and elemental carbon during fall and winter of 2003 in Xi’an, China
J. J. $\mathbf{Cao}^{1}$ , F. $\mathbf{W}\mathbf{u}^{1,2}$ , J. C. Chow3, S. C. Lee4, Y. Li1, S. W. Chen5, Z. S. $\mathbf{A}\mathbf{n}^{1}$ , K. K. Fung6, J. G. Watson3, C. S. $\mathbf{Zhu}^{1}$ , and S. X. Liu1
1SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710075, China
2The Graduate School of Chinese academy of Sciences, Beijing 100049, China
3Desert Research Institute, Reno, Nevada, USA
4The Hong Kong Polytechnic University, Hong Kong, China
5Tongji University, Shanghai 200092, China
6Atmoslytic, Inc., Calabasas, CA, USA
Received: 14 March 2005 – Published in Atmos. Chem. Phys. Discuss.: 1 June 2005
Revised: 1 September 2005 – Accepted: 9 November 2005 – Published: 22 November 2005
Abstract. Continuous measurements of atmospheric organic and elemental carbon (OC and EC) were taken during the high-pollution fall and winter seasons at Xi’an, Shaanxi Province, China from September 2003 through February 2004. Battery-powered mini-volume samplers collected $\mathrm{PM}_{2.5}$ samples daily and $\mathrm{\bfPM}_{10}$ samples every third day. Samples were also obtained from the plumes of residential coal combustion, motor-vehicle exhaust, and biomass burning sources. These samples were analyzed for OC/EC by thermal/optical reflectance (TOR) following the Interagency Monitoring of Protected Visual Environments (IMPROVE) protocol. OC and EC levels at Xi’an are higher than most urban cities in Asia. Average $\mathrm{PM}_{2.5}$ OC concentrations in fall and winter were $34.1{\pm}18.0\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ and $61.9{\pm}33.2\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ , respectively; while EC concentrations were $11.3{\pm}6.9\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ and $12.3{\pm}5.3\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ , respectively. Most of the OC and EC were in the $\mathrm{PM}_{2.5}$ fraction. OC was strongly correlated $(\mathbf{R}{>}0.95)$ with EC in the autumn and moderately correlated $(\mathsf{R}{=}0.81)$ ) with EC during winter. Carbonaceous aerosol $(\mathrm{OC}\!\times\!1.6\!+\!\mathrm{EC})$ accounted for $48.8\%{\pm}10.1\%$ of the $\mathrm{PM}_{2.5}$ mass during fall and $45.9{\pm}7.5\%$ during winter. The average OC/EC ratio was 3.3 in fall and 5.1 in winter, with individual OC/EC ratios nearly always exceeding 2.0. The higher wintertime OC/EC corresponded to increased residential coal combustion for heating. Total carbon (TC) was associated with source contributions using absolute principal component analysis (APCA) with eight thermally-derived carbon fractions. During fall, $73\%$ of TC was attributed to gasoline engine exhaust, $23\%$ to diesel exhaust, and $4\%$ to biomass burning. During winter, $44\%$ of TC was attributed to gasoline engine exhaust, $44\%$ to coal burning, $9\%$ to biomass burning, and $3\%$ to diesel engine exhaust.
1 Introduction
This study examines temporal variations of $\mathrm{PM}_{2.5}$ and $\mathrm{\bfPM}_{10}$ concentrations of organic and elemental carbon (OC and EC) in Xi’an, China. ( $\mathrm{PM}_{2.5}$ is particulate matter with an aerodynamic diameter smaller than 2.5 micrograms $[\mu\mathrm{m}]$ , $\mathrm{{PM}_{10}}$ is particulate matter with an aerodynamic diameter smaller than $10\,\mu\mathrm{m})$ . This study also quantifies contributions of organic and elemental carbon in Xi’an from coal combustion, vehicle exhaust, fugitive dust and dust storms (Cao et al., 2005; Gao et al., 1997; Zhang et al., 1993; Zhuang et al., 1992).
With a population of five million, Xi’an, in Shaanxi Province, is the largest city in northwestern China. It has served as the capital city of 13 Chinese dynasties for more than a millennium. Since the discovery in 1974 of hundreds of buried life-size terra-cotta figures of warriors and horses, the city has been a major tourist attraction. Xi’an also experiences some of the worst air pollution among China’s cities (Zhang et al., 2001, 2002), where elevated carbonaceous aerosol components contribute to high PM levels. Several studies have been conducted in China’s well-developed coastal cities, such as Beijing, Shanghai, Guangzhou, and Hong Kong (Cao et al., 2003, 2004; He et al., 2001; Louie et al., 2005a, b; Ye et al., 2003), but few measurements are available from inland cities, such as Xi’an.
OC and EC in suspended particulate matter (PM) play important roles in health, visibility, and climate effects (ACEAsia, 1999; Cooke et al., 1999; IPCC, 2001; UNEP and NOAA, 2003; Vedal, 1997; Watson, 2002). EC, which is often equated with optically-derived, light-absorbing black carbon (BC), is known to cause heating in the air on a regional scale, thus altering atmospheric stability and vertical mixing, and affecting large-scale circulation and the hydrologic cycle (Menon et al., 2002). Since about one fourth of global BC emissions are believed to originate from China (Cooke et al., 1999), a reduction of BC emissions in China could produce positive consequences for global warming (Jacobson, 2002).
2 Sampling and analysis
2.1 Sampling site
Xi’an is located on the Guanzhong Plain at the south edge of the Loess Plateau $400\,\mathrm{m}$ above sea level at $33^{\circ}29^{\prime}–34^{\circ}44^{\prime}\,\mathrm{N}$ , $107^{\circ}40^{\prime}–109^{\circ}49^{\prime}\,\mathrm{E}$ (Fig. 1). The monitoring site was located in an urban-scale zone of representation (Chow et al., 2002) surrounded by a residential area $\mathord{\sim}15\,\mathrm{km}$ south of downtown Xi’an, where there are no major industrial activities, nor local fugitive dust sources. $\mathrm{PM}_{2.5}$ and $\mathrm{\bfPM}_{10}$ samples were obtained from 13 September 2003 to 29 February 2004 from the rooftop of the Chinese Academy of Sciences’ Institute of Earth Environment building at $10\,\mathrm{m}$ above ground. Based on local meteorological characteristics and the residential heating season (mid-November through February), the period from 13 September 2003 to 31 October 2003 was designated as fall, and the period from 1 November 2003 to 29 February 2004 was designated as winter.
2.2 Sample collection
Daily $\mathrm{PM}_{2.5}$ and every-third-day $\mathrm{\bfPM}_{10}$ samples were collected using two battery-powered mini-volume samplers (Airmetrics, Oregon, USA) operating at flow rates of 5 liters per minute $(\mathrm{L}\,\mathrm{min}^{-1}$ ; Cao, 2003). Prior to field operations, calibrated MiniVol samplers were collocated with low volume $\mathrm{PM}_{2.5}$ and $\mathsf{P M}_{10}$ Partisol samplers (model 2000, Rupprecht & Patashnick, Albany, New York, USA) at the Hong Kong Polytechnic University. The difference between the two types of samplers was less than $5\%$ for the $\mathrm{PM}_{2.5}$ and $\mathrm{\bfPM}_{10}$ mass.
PM samples were collected on $47\,\mathrm{mm}$ Whatman quartz microfiber filters (QM/A) that were pre-heated at $900^{\circ}\mathrm{C}$ for three hours before sampling. The exposed filters were stored in a refrigerator at ${\sim}4^{\circ}\mathbf{C}$ before chemical analysis to minimize the evaporation of volatile components. Quartz-fiber filters were analyzed gravimetrically for mass concentrations using a Sartorius MC5 electronic microbalance with a $\pm1\,\mu\mathrm{g}$ sensitivity (Sartorius, G¨ottingen, Germany) after 24-h equilibration at a temperature between $20^{\circ}\mathbf{C}$ and $23^{\circ}\mathbf{C}$ and a relative humidity $\left(R H\right)$ between $35\%$ and $45\%$ . Each filter was weighed at least three times before and after sampling, and the net mass was obtained by subtracting the average of pre-sampling weights from the average of postsampling weights. Differences among replicate weighings were $<\!10\,\mu\mathrm{g}$ for blanks and $<\!20\,\mu\mathrm{g}$ for samples. Sixteen field blanks were collected to correct for adsorbed gas-phase organic components. Volatilization of particle-phase organics during and immediately after sampling was not quantified. A total of $165\,\,\,\mathrm{PM}_{2.5}$ and $53\,\mathrm{\PM_{10}}$ samples were collected during the ambient sampling period. Five $\mathrm{PM}_{2.5}$ source samples were collected from residential stoves burning coal, six from alongside a major highway with heavy traffic, and five from smoke plumes when maize residue was burned after harvest.
Meteorological data were monitored continuously with a HFY-IA Wind Speed/Wind Direction Instrument (Changchun Institute of Metrological Instruments, Changchun, Jilin Province, China).
2.3 Thermal/optical carbon analysis
A $0.5\,\mathrm{cm}^{2}$ punch from each samples was analyzed for OC and EC with a Desert Research Institute (DRI) Model 2001 Thermal/Optical Carbon Analyzer (Atmoslytic Inc., Calabasas, CA, USA) for eight carbon fractions following the IMPROVE (Interagency Monitoring of Protected Visual Environments) thermal/optical reflectance (TOR) protocol (Chow et al., 1993, 2001, 2004a, 2005; Fung et al., 2002). This produces four OC fractions (OC1, OC2, OC3, and OC4 at $120^{\circ}\mathbf{C}.$ , $250^{\circ}\mathrm{C}$ , $450^{\circ}\mathrm{C}$ , and $550^{\circ}\mathrm{C}.$ , respectively, in a He atmosphere); a pyrolyzed carbon fraction (OP, determined when a reflected laser light attained its original intensity after ${\bf O}_{2}$ was added to the analysis atmosphere); and three EC fractions (EC1, EC2, and EC3 at $550^{\circ}\mathrm{C}$ , $700^{\circ}\mathrm{C}$ , and $800^{\circ}\mathrm{C}$ , respectively, in a $2\%$ $\mathrm{O}_{2}/98\%$ He atmosphere). IMPROVE OC is defined as $\mathrm{OC1+OC2+OC3+OC4+OP}$ and EC is defined as $\mathtt{E C l+E C2+E C3-O P}$ . Inter-laboratory comparisons of samples between IMPROVE protocol with the DRI Model 2001 instrument and the TMO (thermal manganese dioxide oxidation) method (done by AtmAA, Inc., Calabasas, CA) has shown a difference of ${<}5\%$ for total carbon (TC) and $10\%$ for OC/EC (Fung et al., 2002). Comparisons with other OC/EC methods (Watson et al., 2005) show that IMPROVE TOR OC and EC are near the middle of the distribution of differences for the average of all methods. Average field blanks were 1.56 and $0.4\bar{2}\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ for OC and EC, respectively. Quality Assurance/Quality Control (QA/QC) procedures have been described in Cao et al. (2003).
3 Results and discussion
3.1 Temporal variations of OC and EC
Monthly and seasonally averaged OC/EC concentrations are summarized in Table 1. $\mathrm{PM}_{2.5}$ OC and EC during winter were 1.8 and 1.1 times, respectively, of those during fall; while. $\mathrm{\bfPM}_{10}$ OC and EC during winter were 2.2 and 1.5 times, respectively, of those during fall. Monthly average OC and EC were highest during December and lowest during September. In December, OC in $\mathrm{PM}_{2.5}$ and $\mathrm{\bfPM}_{10}$ were $81.7{\pm}36.2\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ and $124.8{\pm}54.8\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ , respectively; and EC in $\mathrm{PM}_{2.5}$ and $\mathsf{P M}_{10}$ were $15.2{\pm}4.6\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ and $28.9{\pm}8.9\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ , respectively. The maximum-to-minimum ratios for $\mathrm{PM}_{2.5}$ and $\mathrm{\bfPM}_{10}$ were 3.3 and 4.2 for OC and 1.8 and 2.6 for EC, respectively. Higher variability for OC concentrations may be due to the contributions of different emission sources.
Figure 2 shows that temporal variations of $\mathrm{PM}_{2.5}$ OC coincided with mass and, to a lesser extent, with EC. OC was highly correlated with $\mathrm{PM}_{2.5}$ $(\mathrm{r}{=}0.96$ , significance level $99\%$ ) and EC was moderately correlated with $\mathrm{PM}_{2.5}$ $_{\mathrm{(r=0.72}}$ , significance level $99\%$ ). $\operatorname{PM}_{2.5}$ OC increased gradually from September to November, and reached a maximum on 14 December 2003 $(189.6\,\mu\mathrm{g}\,\mathrm{m}^{-3})$ ). Major emission sources of OC and EC in China include coal combustion (mostly residential), motor-vehicle exhaust, and biomass burning (Streets et al., 2001; Zhang et al., 2001), all of which are also evident in Xi’an. During the fall harvest season in midOctober, the residues of diverse crops like corn and rice are burned. Biofuels are also used by farmers for residential heating and cooking for both fall and winter. Zhang et al. (2001) showed that total suspended particle (TSP) in Xi’an reaches maximum levels in winter and minimum levels in summer. After the Chinese Spring Festival (22 January 2004 to 29 January 2004), OC decreases rapidly initially, then decreases further as February progresses. A similar trend was found for EC, but while EC concentration was lowest during the festival, it fluctuated at low values from 22 January 2004 to 5 February 2004.
3.2 Relationship between OC and EC
OC/EC ratios give some indication of the origins of carbonaceous $\mathrm{PM}_{2.5}$ (Chow et al., 1996; Gray et al., 1986; Turpin and Huntzicker, 1991). As shown in Fig. 3, strong OC/EC correlations (0.95–0.97) in fall suggest impacts from a combination of common source contributions (i.e. residential and commercial coal combustion, biomass burning, motorvehicle exhaust). OC/EC correlations (0.81) were lower in winter, consistent with a changing mixture of source contributions. Residential coal combustion was estimated to contribute more than $50\%$ of TSP in 1997 (Zhang, 2001). Even though many residents in Xi’an have replaced coal with natural gas, a large number of low-income families still use coal for cooking and heating. Coal-fired boilers have been banned within the second beltway in downtown Xi’an since 1998, but due to the low cost of coal, many middle- and small-scale boilers are still in use.
The slopes of OC versus EC in winter were 5.12 for $\mathrm{PM}_{2.5}$ and 3.83 for $\mathrm{\bfPM}_{10}$ , compared to those in fall (2.46) (Fig. 3), implying that OC emissions in winter increased relative to EC emissions. The difference may be ascribed to the change of emission sources between the two seasons, primarily due to the completion of burning in corn and rice fields.
3.3 Variability of OC/EC ratios
OC/EC ratios are influenced by: 1) emission sources; 2) secondary organic aerosol (SOA) formation; and 3) different OC/EC removal rates by deposition (Cachier et al., 1996). Atmospheric EC is directly emitted, while OC can be both directly emitted and formed in the atmosphere from the low vapor pressure products of chemical reactions involving emissions of volatile organic compounds (VOCs).
As shown in Table 1, monthly averaged OC/EC ratios in $\mathrm{PM}_{2.5}$ and $\mathrm{\bfPM}_{10}$ ranged from 3.0 to 3.4 in fall, and 3.6 to 6.4 in winter. The highest ratios were recorded in January, with 6.4 in $\mathrm{PM}_{2.5}$ and 5.1 in $\mathsf{P M}_{10}$ . Daily variations of $\mathrm{PM}_{2.5}$ OC/EC ratios in Fig. 2 show lower ratios and variability in fall and higher ratios and variability in winter.
Regarding source samples, the average OC/EC ratio was 12.0 for coal-combustion, 4.1 for vehicle exhaust, and 60.3 for biomass burning. These ratios are much higher than reported values elsewhere of 2.7 for coal-combustion and 1.1 for motor vehicles (Watson et al., 2001), and 9.0 for biomass burning (Cachier et al., 1989). The individual OC/EC ratios for this study exceeded 2.0 for both $\mathrm{PM}_{2.5}$ and $\mathrm{\bfPM}_{10}$ fractions (Fig. 2), which might reflect the combined contributions from coal combustion, motor-vehicle exhaust, and biomass burning sources. Elevated OC/EC ratios (8.0) during mid-December can be attributed to biomass burning and coal combustion. High OC/EC ratios (6.0–9.0) during the Chinese Spring Festival may be due to lower contributions from motor-vehicle exhaust and biomass burning during the holiday, and higher contributions from residential coal combustion.
3.4 Contributions to $\mathrm{PM}_{2.5}$ and $\mathrm{\bfPM}_{10}$ mass
Figure 4 shows a larger $\mathrm{\bfPM}_{10}$ scatter than $\mathbf{PM}_{2.5}$ in both seasons. Daily $\mathrm{{PM}_{10}}$ in winter varied by a factor of 5.7, ranging from $155\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ (06 November 2003) to $885\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ (14 December 2003), and averaging $450.6\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ . The average for fall was $261.9\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ . The $\mathrm{PM}_{2.5}$ average was $140.1\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ in fall and $258.7\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ in winter. $\mathrm{PM}_{2.5}$ accounted for $55.6\%$ of the $\mathrm{\bfPM}_{10}$ in fall, ranging between $44.3\%$ and $77.4\%$ . In winter, $\mathrm{PM}_{2.5}$ accounted for $60.4\%$ of the $\mathsf{P M}_{10}$ , with a wide range from $33.0\%$ and $97.6\%$ .
Compare to Xi’an, the percentage of $\mathrm{PM}_{2.5}$ in $\mathrm{\bfPM}_{10}$ in other Chinese cities was: Shenzhen in $2001-73.3\%$ (Cao et al., 2003); Zhuhai, $2001-70.8\%$ (Cao et al., 2003); Chongqing, $1997-65.1\%$ (Wei et al., 1999); Wuhan, 1997 — $60.5\%$ (Wei et al., 1999); Xi’an, 2003 — $60.4\%$ ; Lanzhou, 1997 — $51.9\%$ (Wei et al., 1999). In Xi’an, only five of the $17\;\mathrm{PM}_{10}$ sampling days in fall and none of the 36 sampling days in winter were in compliance with China’s legislated Class $2\,\mathrm{PM}_{10}$ standard of $150\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ (GB 3905-1996). The data depict extreme PM pollution in Xi’an despite the current substantial local government pollution control efforts.
As shown in Table 2, total carbonaceous aerosol $(\mathrm{TCA}{=}\mathrm{OC}\!\times\!1.6{+}\mathrm{EC})$ contributed $48.8\%$ of $\operatorname{PM}_{2.5}$ in fall and $45.9\%$ in winter. The percentage of TCA in $\mathrm{\bfPM}_{10}$ was lower than in $\mathbf{PM}_{2.5}$ , with an average of $34.5\%$ in fall and $37.0\%$ in winter. This may be due to higher contributions of geological matter in coarse particles. The material balance also confirmed that TCA is the dominant component of $\mathrm{PM}_{2.5}$ (Li, 2004). As shown in the time series in Fig. 2, $\mathrm{TCA}\%$ varied around the $45\%$ level during the study and did not correlate with changes of $\mathrm{PM}_{2.5}$ mass or OC/EC concentrations.
$\mathrm{PM}_{2.5}$ OC accounted for $81.8\%$ and $72.8\%$ of $\mathrm{\bfPM}_{10}$ OC during fall and winter, respectively, whereas $\mathrm{PM}_{2.5}$ EC accounted for $75.0\%$ and $59.6\%$ of $\mathrm{{PM}_{10}}$ EC in fall and winter (Table 1). Less than $60\%$ of $\mathrm{\bfPM}_{10}$ EC resided in $\mathrm{PM}_{2.5}$ in winter, possibly due to coarse soot particles in the emissions of incomplete coal combustion, or from fugitive coal dust.
3.5 The characterization of eight carbon fractions
The IMPROVE TOR protocol does not advance from one temperature to the next until a well-defined carbon peak has evolved (Chow et al., 1993, 2004a). Carbon abundances in each of these fractions differ by carbon source (Chow et al., 2004b; Watson et al., 1994). Eight carbon fractions have been used before for the source apportionment of carbonaceous aerosol (Kim et al., 2003a, b; Kim and Hopke, 2004).
The average percentages of eight carbon fractions in ambient and source samples are shown in Fig. 5. Distinct differences in carbon fractions are evident among samples from the three source types tested in this study. OC1 contributed $36.8\%$ to TC in biomass-burning samples, $2.0\%$ in coal-combustion samples, and $2.8\%$ in motor-vehicle exhaust samples. OC2 accounted for $46.9\%$ of TC in coalcombustion samples, $29.2\%$ in biomass-burning samples and $30.5\%$ in motor-vehicle samples. EC1 constituted $15.4\%$ to
TC, $5.6\%$ in coal-combustion samples and $0.4\%$ in biomassburning samples.
Monthly variations of the eight carbon fractions were related to the contributions of different emission sources. November experienced the highest contribution from biomass burning, with OC1 attaining $8.7\%$ , which was the highest value in the six months of the study. OC1 decreased to $1.7\%$ in February. OC2 increased during the six months (except for November), possibly reflecting the increased contributions of coal combustion from fall to winter. EC1 reached its lowest values in January, possibly caused by lower motor vehicle activity during the Chinese Spring Festival. OP ranged from $16.0\%$ to $22.1\%$ , with an average of $21.0\%$ . These ratios are higher than the 8.0 to $17.8\%$ OP in TC found during summer for the Pearl River Delta Region in China (Cao et al., 2004).
3.6 Periodic characteristics of OC and EC
The periodic features of emission sources and meteorological conditions can be identified from the OC/EC time series. Hies (2000) showed that domestic heating by coal combustion appears with a 365-day periodicity. In this study, traffic in Berlin, Germany contributes 3.5-, 4.6-, and 7-day peaks in the spectrum, and periodicity for elevated EC can be identified in the 13- to 42-day range.
The comparison of periodicities of OC, EC, $\mathrm{PM}_{2.5}$ mass, and daily average wind speed are illustrated in Fig. 6. These curves were obtained by AutoSignal 1.0 software (SPSS, USA). The common periodicities of OC, EC and $\mathrm{PM}_{2.5}$ were 24, 10, 7, and 5 days. Identical periodicities between $\mathrm{PM}_{2.5}$ mass and OC are consistent as they are controlled by similar processes. In agreement with Hies (2000), the periodicities of motor vehicle variations were five and seven days. Precipitation events had 10-day periodicity from September to November. This periodicity should reflect the impact of precipitation on OC and EC concentrations. Thirteen-day periodicity was a major component in the wind speed spectrum identified by Hies (2000), which also influences EC concentrations. Sixty-day peaks may be related to the change of primary emission sources.
3.7 Comparison of OC and EC with other Asian cities
Table 3 compares TC, OC, and EC concentrations in $\mathrm{PM}_{2.5}$ from 11 Asian cities. Total carbon in fall and winter at Xi’an ranked the highest. While OC and EC concentrations were similar in Beijing and Xi’an in fall, OC in Xi’an was twice that of Beijing in winter, with similar EC levels. This may be due to more motor vehicles and less coal use in Beijing (Yang et al., 2005). Winter OC levels in Xi’an were 2.7, 3.6, 4.7, 5.1, and 6.4 times those in Guangzhou, Shanghai, Shenzhen, Zhuhai, and Hong Kong, respectively (the number of motor vehicles in these coastal cities are 1.1, 0.7, 0.7, 0.3, and 0.5 million, respectively, compared with 0.2 million in Xi’an). Winter EC levels in Xi’an were 1.5, 1.5, 2.0, 2.5, and 2.6 times those of these coastal cities. The lower increment for EC may be attributed to the high emissions of motor-vehicle exhaust in the coastal cities, and the larger increment for OC may be ascribed to the lower use of coal for residential heating (there is almost no use of coal for residential heating in the coastal cities). Winter OC and EC levels in Xi’an were 12.4 and 2.9 times, respectively, those in Chongju, South Korea (Lee and Kang, 2001).
3.8 Source apportionment of carbonaceous PM
Absolute principal component analysis (APCA) (Thurston and Spengler, 1985) was applied to the eight carbon fraction concentrations to identify and quantify source contributions. The first step in APCA is the normalization of all carbon concentrations as $Z_{i k}$ . This is done by adding a zero concentration sample as case 0 (The $Z_{i0}$ is obtained by deriving the ${{Z}}$ -score for absolute zero concentrations).
$$
Z_{i k}=(C_{i k}-C_{i})/S_{i}
$$
where $C_{i k}$ is the concentration of carbon fraction $i$ in sample $k,C_{i}$ is the arithmetic mean concentration of carbon fraction $i$ , and $S_{i}$ is the standard deviation of carbon fraction $i$ for all samples included in the analysis. The normalization process allows any continuous variable, such as wind speed, to be included in future analyses along with the carbon data.
Regressing the TC data on these absolute principal component scores (APCS) gives estimates of the coefficients which convert the APCS into TC contributions from each source for each sample. For each source identified by the APCA, the weighted regression of each carbon fraction’s concentration on the predicted TC contributions yields estimates of the content of that fraction in each source, as follows:
$$
C_{i k}=b+\sum_{j=1}^{n}a_{i j}M_{j k}
$$
where $C_{i k}$ is the concentration of carbon fraction $i$ in sample $k;\,b$ is a constant; $a_{i j}$ is the mean TC fraction of source $j$ ’s particles represented by carbon fraction $i$ , and $M_{j k}$ is the TC concentration of source $j$ for observation $k$ . By repeating this weighted least-square regression for each of the $\ i{=}1$ , $2,\ldots{}\,\mathfrak{n}$ carbon fractions considered in this analysis, one can estimate the mean concentration of the carbon fractions for each factor.
Results for fall and winter are summarized in Tables 4 and 5. Factor 1 (F1) in fall was highly loaded with OC2, OC3, OC4, OP, and EC1. This factor appears to represent gasolinemotor-vehicle exhaust (Chow et al., 2004b). Factor 2 (F2) was highly loaded with high-temperature EC2 and EC3 and appears to represent diesel-vehicle exhaust (Watson et al., 1994). The high loading of OC1 in factor 3 (F3) reflects the contribution of biomass burning. In winter, the highly loaded OC2, OC3, OC4, and EC1 in F1 might represent the mixture of coal-combustion and motor-vehicle exhaust. Similar to the fall results, F2 and F3 in winter represent biomass burning and diesel-vehicle exhaust, respectively.
To simplify the estimation, it is assumed that there is no contribution of coal combustion in fall, and there are equal contributions from gasoline-powered motor vehicles in fall and winter. Coal combustion in winter is assumed to be the difference between winter F1 and fall F1, thus the source attributions can be resolved for the two seasons (Fig. 7). During fall, TC is composed of $73\%$ from gasoline exhaust, $23\%$ from diesel exhaust, and $4\%$ from biomass burning. During winter, TC receives $44\%$ from gasoline exhaust, $3\%$ from diesel exhaust, $9\%$ from biomass burning, and $44\%$ from coal burning.
4 Conclusions
Six months of continuous observations were conducted at Xi’an, Shaanxi Province, China to gain insight into the characterization and source apportionment of organic and elemental carbon (OC/EC). Major findings are as follows.
1. Average $\operatorname{PM}_{2.5}$ OC concentrations during fall and winter were $34.1{\pm}18.0\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ and $61.9{\pm}33.2\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ ; EC concentrations were 11.3±6.9 µg m−3 and
$12.3{\pm}5.3\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ , respectively. Carbonaceous aerosol accounted for $48.8{\pm}10.1\%$ and $45.9{\pm}7.5\%$ of $\mathrm{PM}_{2.5}$ and $34.5{\pm}9.3\%$ and $37{\pm}\:8.9\%$ of $\mathrm{\bfPM}_{10}$ during fall and winter, respectively. This indicates that carbonaceous aerosol is the dominant component of fine particles in Xi’an.
2. All of the OC/EC ratios exceeded 2.0, and average OC/EC ratios were 3.3 in fall and 5.1 in winter. Elevated OC/EC ratios were found during heating seasons with increased primary emissions, such as residential coal combustion. $\mathrm{PM}_{2.5}$ OC and $\mathrm{\bfPM}_{10}$ OC were highly correlated $\mathrm{R}{=}0.95–0.97_{.}$ ) during fall, and moderately correlated $_{\mathrm{R=0.81}}$ ) during winter.
3. $\mathrm{PM}_{2.5}$ total carbon source apportionment by APCA attributed $73\%$ to gasoline engine exhaust, $23\%$ to diesel engine exhaust, and $4\%$ to biomass burning during fall, and $44\%$ to gasoline engine exhaust, $44\%$ to residential coal burning, $9\%$ to biomass burning, and $3\%$ to diesel engine exhaust during winter. Motor-vehicle exhaust and coal combustion were the dominant sources for carbonaceous aerosol in Xi’an.
Acknowledgements. This project was supported by the National Basic Research Program of China (2004CB720203), National Natural Science Foundation of China (40121303, 40205018), and Research Grants Council of Hong Kong (PolyU5038/01E, PolyU5145/03E), Area of Strategic Development on Atmospheric and Urban Air Pollution (A516) funded by The Hong Kong Polytechnic University. T. Richard edited the manuscript and J. Gerard assisted in formatting and corrections.
Edited by: R. Hitzenberger
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | image | Fig. 1. Sampling site and its surroundings in a rural area in Lingcheng $37^{\circ}21^{\prime}17^{\prime\prime}\mathrm{N}$ , $116^{\circ}28^{\prime}30^{\prime\prime}\mathrm{E}$ ), a district of Dezhou City in Shandong Province, China. |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | image | Fig. 2. CMAQ modeling domains at a horizontal grid resolution of $27\,\mathrm{km}$ over China (D1, with 180 columns and 150 rows, $\sim\!1.97\times10^{7}\,\mathrm{km}^{2})$ and $9\,\mathrm{km}$ over an area in northern China (D2, with 120 columns and 111 rows, $\sim\!1.08\times10^{6}\,\mathrm{km}^{2};$ . The zoom-in area (D2) shows the regions focused on the analysis of regional contribution. |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | table | Table 1 Descriptive statistics of chemical species in $\mathrm{PM}_{2.5}$ in terms of concentrations $(\upmu\mathrm{g}/\uppi^{3})$ and percentages (in brackets, $\%$ ). |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | table | Table 2 Average concentrations of $\mathrm{PM}_{2.5}$ , $S0_{4}^{2-}$ , $\mathrm{NO}_{3}^{-}$ and $\mathrm{NH}_{4}^{+}$ in Lingcheng and other areas in China. |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | table | Table 3 The mass concentration of secondary organic carbon (SOC) during the sampling period. |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | image | Fig. 3. Temporal variations in OC and EC abundances $\left(\upmu\mathrm{g}/\uppi^{3}\right)$ and OC/EC ratios at the sampling site in Lingcheng. |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | table | Table 4 Performance statistics for $\mathrm{PM}_{2.5}$ , OC, EC, $S0_{4}^{2-}$ , $\mathrm{NO}_{3}^{-}$ and $\mathrm{NH}_{4}^{+}$ concentrations. |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | image | Fig. 4. Scatter plots of the daily simulated versus observed concentrations of $\mathrm{PM}_{2.5},$ OC, EC, $S0_{4}^{2-}$ , $\mathrm{NO}_{3}^{-}$ and $\mathrm{NH}_{4}^{+}$ during the winter sampling period in 2010. The daily simulated concentrations were calculated by the averaging the hourly simulated results from the CMAQ model. |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | image | Fig. 5. Comparison between daily simulated and observed $\mathrm{PM}_{2.5}$ , $S0_{4}^{2-}$ , $\mathrm{NO}_{3}^{-}$ , $\mathrm{NH}_{4}^{+}$ , OC and EC at the Lingcheng study site from November 21st to December 20th. Observations are shown with solid line, and simulations are shown with dashed line. The daily simulated concentrations were calculated by averaging the hourly simulated results from the CMAQ model. |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | table | Table 5 Average contributions of $\mathrm{PM}_{2.5}$ and main species from local (Lingcheng) and surrounding regions during winter and heavy haze days (in brackets) $(\%)$ . |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | image | Fig. 6. Percent contributions of $\mathrm{PM}_{2.5}$ from the four directions (north, east, west, and south; the simulation area is equally divided into four parts centered on the sampling site). |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | image | Fig. 7. The contribution of $\mathrm{PM}_{2.5}$ per unit area (contribution $/\mathrm{km}^{2}$ ). |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | image | Fig. 8. 12-Hour backward trajectories reaching the sampling site for each hour on 21–24 November and 7, 8, 16, 17, and 21 December on a Lambert conformal projection map of North China. |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | image | Fig. 9. Comparison of the $\mathrm{PM}_{2.5}$ contribution rates during the period of relatively clean days $(\mathrm{PM}_{2.5}\leq75\;\upmu\mathrm{g}/\mathrm{m}^{3})$ , haze days $(75~|\mathrm{{ug/m^{3}}<\mathrm{{PM_{2.5}}<200~|\mathrm{{ug/m^{3}}})}}$ and heavy haze days $(\mathrm{PM}_{2.5}\geq200\,\upmu\mathrm{g}/\mathrm{m}^{3})$ in each tagged area. |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | text | Not supported with pagination yet | Estimating the contribution of regional transport to $\mathsf{P M}_{2.5}$ air pollution in a rural area on the North China Plain
Dongsheng Chen a,b,⁎, Xiangxue Liu a, Jianlei Lang a,⁎⁎, Ying Zhou a, Lin Wei a, Xiaotong Wang a, Xiurui Guo a
a Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, PR China b Visiting scholar at Department of Environmental Sciences, Faculty of Science and Engineering, Macquarie University, NSW 2109, Australia
H I G H L I G H T S
• The characteristics of $\mathrm{PM}_{2.5}$ at a rural site in the Northern China Plain were analyzed.
• CMAQ-ISAM was used to identify the regional contributions of $\mathrm{PM}_{2.5}$ during winter.
$\cdot$ Results show that the $\mathrm{PM}_{2.5}$ pollution at the site is not only a local but regional issue.
• Contributions from local and nearby areas decreased with the increase of $\mathsf{P M}_{2.5}$ level.
$\cdot$ Northern and southern areas are the main contributors to the heavy haze at this site.
G R A P H I C A L A B S T R A C T
a r t i c l e i n f o
Article history:
Received 21 September 2016
Received in revised form 24 November 2016
Accepted 10 January 2017
Available online 21 January 2017
Editor: D. Barcelo
Keywords:
$\mathrm{PM}_{2.5}$
Source apportionment
CMAQ-ISAM
Regional transport
a b s t r a c t
$\mathrm{PM}_{2.5}$ air pollution in metropolises as well as some medium-sized cities in the North China Plain have aroused many researchers' interest, but less attention has been paid to the rural areas of this region. In this study, four months of daily $\mathrm{PM}_{2.5}$ samples were collected from a rural site in Lingcheng (a district of Dezhou City in Shandong Province) during different seasons in 2013 and 2014. Analysis of the samples indicates that the $\mathrm{PM}_{2.5}$ air pollution was severe over this area with the four-month average concentration of $105.9\,\upmu\mathrm{g}/\mathrm{m}^{3}$ , three times higher than China's guideline for this pollutant $(35\,\upmu\mathrm{g}/\mathrm{m}^{3})$ . In winter, the monthly average concentration was as high as $151.2\,\upmu\mathrm{g}/\mathrm{m}^{3}$ . In order to identify the potential source regions, the Integrated Source Apportionment Method within Community Multiscale Air Quality model (CMAQ-ISAM) was applied during the wintertime. The regional source apportionment results show that local emissions in Lingcheng only contributed $15.4\%$ to $\mathrm{PM}_{2.5}$ concentrations, with $12.6\%$ and $28.1\%$ from its circumjacent areas in Dezhou City and the six surrounding cities, respectively. Regional transport from areas farther away and the boundaries account for $31.6\%$ and $11.1\%$ , respectively. This indicates that the ambient $\mathrm{PM}_{2.5}$ at Lingcheng is not affected only by emissions from local and circumjacent areas; regional and long-range transport should also be considered. Further analysis indicated that with increasing degrees of pollution, the contributions from local and circumjacent regions showed a clear downward trend, while the contributions from northern and southwestern areas, which most of the trajectories passed through during periods of heavy haze, showed an obvious upward trend.
$\circledcirc$ 2017 Elsevier B.V. All rights reserved.
1. Introduction
Concerns have grown over the last several decades regarding the health and environmental effects of $\mathsf{P M}_{2.5}$ (particulate matter with an aerodynamic diameter $\leq2.5\,\upmu\mathrm{m}$ ), which is often referred to as fine particulate matter (Louie et al., 2005). $\mathsf{P M}_{2.5}$ has been demonstrated to be an important pollutant that causes haze in the atmosphere (Huang et al., 2014). Haze is regarded as having adverse effects on visibility and public health and affects climate change (Wang et al., 2015a). The composition of $\mathsf{P M}_{2.5}$ is a complex mixture of elemental and organic carbon, ammonium, nitrates, sulfates, mineral dust, trace elements, and water. It originates from both natural (e.g., dust storms and outdoor biomass burning) and anthropogenic sources (e.g., industry, vehicle emissions, residences and other human activities) (Yao et al., 2016) and includes both primary and secondary particle species (Tao et al., 2014). The concentration and chemical composition of $\mathsf{P M}_{2.5}$ can be influenced by processes that operate on local or regional scales, as well as by long-range pollution transport (Aldabe et al., 2011). As a result, some unindustrialized areas may also experience serious air pollution problems. The North China Plain (NCP) is the area that experiences the most serious air pollution in China. $\mathsf{P M}_{2.5}$ studies in this region have attracted much attention in China and internationally, but most of them are focused on the air quality in metropolises, such as Beijing and Tianjin, as well as medium-sized cities, such as some cities in Hebei Province (Song et al., 2006; Lang, et al., 2013; Wang et al., 2016; Gu, 2010; Wang et al., 2012a, 2014, 2015b). These cities with large amounts of $\mathsf{P M}_{2.5}$ pollution have large populations, and their rapid economic development has produced copious anthropogenic emissions that have caused frequent regional haze episodes over the region (Lu et al., 2015a).
It should be emphasized that air quality in rural China, where half the population lives, is also deserving of attention. China, the world's top-ranking agricultural producer, has areas of intensive agriculture and dense rural populations (Huang et al., 2012). Specifically, the NCP is regarded as having the densest rural settlements (Tian, 2003), and Li et al. (2014a) noted that coal and biomass combustion were the major sources of $\mathsf{P M}_{2.5}$ emissions in these regions. However, knowledge of $\mathsf{P M}_{2.5}$ over these rural areas is relatively limited, and a comprehensive study on the local emission sources and regional transport has not been carried out.
A few studies have been carried out on $\mathsf{P M}_{2.5}$ in the rural areas of China, and these studies can be generally categorized into three groups. (1) Most studies have concentrated on estimating the quantity of emissions (Li et al., 2016; Zhang et al., 2013a) and assessing the emission characteristics (Ni et al., 2015; Chen et al., 2014; Zhang et al., 2011) from open burning of crop residue, as well as its impact on air quality (Wang et al., 2016; Zhang et al., 2016a, b; Fu et al., 2012). (2) Some studies have focused on the emission characteristics of household biofuel combustion (Li et al., 2007; Zhang et al., 2012a) and the air pollution (both indoor and outdoor) it causes in rural areas in central (Wu et al., 2015), northwestern (Zhu et al., 2012; Zhu et al., 2015; Guo, 2015), southwestern (Ma et al., 2013) and northern China (Zhang and Chen, 2016). (3) Other studies have analyzed the chemical characteristics of $\mathsf{P M}_{2.5}$ in rural areas of China and have carried out source apportionment analyses using a positive matrix factorization (PMF) model (Li et al., 2014b) or principal components analysis (Zhou et al., 2005). The PMF model has proven to be an effective tool to apportion sources without requiring the chemical profiles of emissions. However, the credibility of this observation-based approach is built upon statistical significance, and lacks the physical basis of the source tracking results provided by the emission-based approach. Moreover, this method has difficulties in quantitatively estimating the impact of sources from different distances (e.g., local, circumjacent and remote areas) and explicitly identifying $\mathsf{P M}_{2.5}$ contributions from specific regions.
Numerical air quality models have shown several advantages in the investigation of regional transport problems. Among various models, the Community Multiscale Air Quality (CMAQ) (Byun et al., 1997)
developed by the U.S. Environmental Protection Agency (U.S. EPA) has seen extensive application in China (Chen et al., 2007a, 2007b, 2008, 2010; Cheng et al., 2007a, 2007b; Wang et al., 2010; Lang, 2013; Wang et al., 2012a, 2014, 2015b). However, the approach used in these studies was the so called Brute Force method (BFM) (Dunker et al., 1996) or zero-out method (Chen et al., 2007a), which estimates the contribution of anthropogenic emissions of specific regions or sectors by comparing the simulation results with and without (setting them to zero, or zeroing out) their emissions. This approach has two drawbacks. One is that the model has to be run multiple times under different scenarios, which is extremely computationally expensive and time consuming. The other is this method simply zeroes out the emissions of specific regions and does not take into account the effects of nonlinear factors. The results of this approach can be significantly affected by nonlinear chemistry, especially for secondary particulates of sulfate, nitrate, and organic carbon (Wang et al., 2009). Therefore, it is desirable to have an instrumented tool that simulates the physical and chemical transformations in the air quality models to calculate the contributions, but lacks the drawbacks mentioned above. The CMAQ Integrated Source Apportionment Method (CMAQ ISAM) is such an instrumented tool that was developed by the US EPA. It is implemented within the CMAQ framework to track contributions from boundary conditions, initial conditions, and user-defined combinations of emissions sectors and/or regions to ambient and deposited primary $\mathsf{P M}_{2.5}$ and secondarily formed inorganic $\mathsf{P M}_{2.5}$ as well as ozone. The implementation of CMAQ-ISAM builds upon and improves the structure developed for Tagged Species Source Apportionment (TSSA) (Wang et al., 2009). This method differs from BFM or zero-out approaches because it tracks direct mass contributions from specific emissions sources to the total $\mathsf{P M}_{2.5}$ concentrations at selected receptor sites. The CMAQ-ISAM has been extensively tested by many studies. To verify the overall mass conservation and accurate spatial and temporal distributions of the tagged sources, Kwok compared the $\mathsf{P M}_{2.5}$ (Kwok et al., 2013) and ozone (Kwok et al., 2015) source apportionment results from ISAM with zero-out simulations. The application of the CMAQ-ISAM model to acid rain studies has been carried out in the Pearl River Delta region of China (Lu et al., 2015b). This model was also used in Napelenok's study, which computed source contributions from ten categories of biomass combustion in the southeastern United States (Napelenok et al., 2014). All these applications gave increased confidence in the continued use of the model.
Based on the confidence built upon previous studies, the CMAQISAM model was selected from among the photochemical models for use in this study to investigate the potential regional influences on ambient concentrations at a rural site in the NCP. Before the application of this modeling system, semi-continuous measurement of $\mathsf{P M}_{2.5}$ was performed in four seasons during 2013–2014, and 115 daily $\mathsf{P M}_{2.5}$ samples were collected. The seasonal variations in major chemical compositions of the sampled $\mathsf{P M}_{2.5}$ were investigated. The sampling measurements were used to both (1) analyze the seasonal variation and identify when the highest concentrations of $\mathsf{P M}_{2.5}$ occur, and (2) to calibrate the CMAQ-ISAM model used in this study. The CMAQ-ISAM model was then applied to calculate the contributions from local and surrounding regions during winter, a season that was found to be prone to heavy air pollution. Moreover, backward trajectories combined with the CMAQ-ISAM modeling results were used to uncover the potential source regions contributing to the heavy hazes that occur during the winter.
2. Materials and methods
2.1. $P M_{2.5}$ sampling and analysis
Daily ambient $\mathsf{P M}_{2.5}$ samples were collected at a rural site in Lingcheng $37^{\circ}21^{\prime}17^{\prime\prime}\mathrm{N},$ , $116^{\circ}28^{\prime}30^{\prime\prime}{\mathrm{E}}$ ; a district of Dezhou City in Shandong Province with an area of $\sim\!1213\ensuremath{\,\mathrm{\km}^{2}}!$ ) during four 1-month episodes in 2013 (August and December) and 2014 (April and October), which represent summer, winter, spring and fall, respectively. The site is approximately $2\,\mathrm{m}$ above the ground in farmland, located in a mixed agricultural and residential area. The residential area lies approximately $2\;\mathrm{km}$ to the west of the site. As illustrated in Fig. 1, there are no large buildings or crops around the sampling site which may potentially influence the local air flow. The samples were collected only on fine days.
Four parallel filters (three Teflon filters and one quartz fiber filter with diameters of $47\,\mathrm{mm}$ ) were used simultaneously for each sampling. After sampling, all the samples were placed in a low temperature environment $(-18\,^{\circ}\mathrm{C})$ until they were analyzed in the laboratory. Laboratory analyses included measurements of mass concentrations, ionic species, elements, organic carbon (OC) and elemental carbon (EC). The mass concentrations of $\mathsf{P M}_{2.5}$ were obtained using an analytical balance (AX105, Mettler-Toledo, Switzerland). Ionic species $(\mathsf{N a}^{+},\mathsf{N H}_{4}^{+}$ $\mathsf{K}^{+}$ , ${\mathrm{Mg}}^{2+}$ , ${\mathsf{C}}{\mathsf{a}}^{2+}$ , $\mathsf{F}^{-}$ , $\mathrm{Cl}^{-}$ , $S0_{4}^{2-}$ and $\mathtt{N O}_{3}^{-}$ ) were analyzed using ion chromatography (IC). A Dionex-ICS2000 (USA) was used for soluble inorganic anions; a Dionex-ICS2500 was used for soluble inorganic cations. Trace elements in the digestion solutions, including Al, Fe, Na, Mg, K, Ca, Ba, Ti, Mn, Co, Ni, Cu, Zn, Mo, Cd, Pb, Ti, V, Cr, As and Rb, were analyzed using inductively coupled plasma–mass spectrometry (ICP–MS, X serial, Thermo, USA). The quartz-fiber filter was used to analyze OC and EC using a thermal/optical carbon analyzer (Sunset Laboratory, OR, USA).
datasets/ds083.2) (NCEP, 2000) as the initial and boundary meteorological conditions. The emissions inventory for the simulation was from the Multi-resolution Emission Inventory for China (MEIC) (http://www. meicmodel.org) (He, 2012).
Version 5.0.2 of the CMAQ model was used in this study. The vertical resolution is consistent with that of the WRF model, which includes 29 layers from the surface to the tropopause with corresponding sigma levels of 1.000, 0.993, 0.983, 0.970, 0.954, 0.934, 0.909, 0.880, 0.832, 0.784, 0.735, 0.687, 0.604, 0.528, 0.459, 0.398, 0.342, 0.292, 0.247, 0.207, 0.171, 0.139, 0.110, 0.086, 0.065, 0.048, 0.033, 0.020, 0.009 and 0.000. The gaseous and aerosol modules are the Carbon Bond-05 mechanism with chlorine and updated toluene chemistry (CB05tucl; Sarwar et al., 2008) and the sixth-generation modal CMAQ aerosol model with extensions for sea salt emissions and thermodynamics (AERO6; Yarwood et al., 2005; Whitten et al., 2010), respectively.
To perform regional source apportionment of $\mathsf{P M}_{2.5}$ using the ISAM module, the emissions in Domain2 were grouped into fourteen regions in the NCP: Beijing (BJ), Tianjin (TJ), Lingcheng (LnC), other areas of Dezhou (DZO), Hengshui (HS), Cangzhou (CZ), Xingtai (XT), Liaocheng (LC), Jinan (JN), Binzhou (BZ), Shanxi (SX), other areas of Hebei (HBO), other areas of Shandong (SDO), Northern Henan (NHN) and the other regions in Domain2 excluding the above fourteen source regions (OTH) (Fig. 2). These regions were tracked separately in this study.
2.2. Air quality models
2.2.1. Model configurations and inputs
In this study, the Weather Research and Forecasting (WRF) model (Skamarock et al., 2008) and the CMAQ air quality model were used to simulate $\mathsf{P M}_{2.5}$ concentrations over the sampling area during the period between 17 November and 22 December in 2013, a time of year that is prone to heavy air pollution. A two-level nested modeling domain was established (Fig. 2) with a spatial resolution of $27\ \mathrm{km}\times27\ \mathrm{km}$ for the large domain (with 180 columns and 150 rows, $\sim\!1.97\times10^{7}\,\,\mathrm{km}^{2})$ and a spatial resolution of $9\ \mathrm{km}\times9\ \mathrm{km}$ for the inner domain (with 120 columns and 111 rows, $\sim\!1.08\times10^{6}\,\mathrm{km}^{2})$ , which are denoted as D1 and D2, respectively. D1 covered most of China. D2 covered most of the NCP, including Lingcheng (denoted as LnC in Fig. 2) as well as the cities bordering it and some remote areas that may potentially influence the study area. The boundary conditions of D2 were provided by D1. The outermost boundary conditions were provided by the default profile available in CMAQ. The hourly meteorological fields required by CMAQ were simulated by the WRF model, which used the National Center for Environmental Prediction (NCEP) Final (FNL) Operational Global Analysis data (http://rda.ucar.edu/
2.2.2. Evaluation approach and criteria applied to the model
The CMAQ model has been applied and tested extensively in the NCP in recent years by ourselves (Chen et al., 2007a, 2007b, 2008, 2010; Cheng et al., 2007a, 2007b; Wang et al., 2010; Li et al., 2010; Lang, 2013), as well as other researchers (Streets et al., 2007; Wang et al., 2012a, 2014, 2015b). Confidence in the ISAM module was also established by other previous studies (Kwok et al. 2013, 2015; Lu et al., 2015b; Napelenok et al., 2014).
To evaluate the performance of the CMAQ model, similar methods to those applied by Wang et al. (2015a, 2015b) were used in this study. Specifically, the evaluation statistics included the Normalized Mean Bias (NMB), the Normalized Mean Error (NME), the Mean Fractional Bias (MFB) and the Mean Fractional Error (MFE), following guidelines set out by the U.S. Environmental Protection Agency (U.S. EPA, 2007). The model performance criteria were set to $\mathrm{MFB}\pm60\%$ and MFE $75\%$ for particulate matter modeling, following the suggestions of Boylan and Russell (2006). The concentrations of $\mathsf{P M}_{2.5}$ and its major chemical species, including $S0_{4}^{2-}$ , $\mathrm{NO}_{3}^{-}$ , $\mathsf{N H}_{4}^{+}$ , EC and OC, that were observed at the rural site in Lingcheng are compared with the model simulations to evaluate the model's ability to predict the chemical compositions of $\mathsf{P M}_{2.5}$ .
3. Results and discussion
3.1. $P M_{2.5}$ mass concentrations
The concentration and chemical composition of $\mathsf{P M}_{2.5}$ collected in the summer and winter of 2013 and the spring and autumn of 2014 are summarized in Table 1. The overall average concentration of $\mathsf{P M}_{2.5}$ was $105.9~\upmu\mathrm{g}/\mathrm{m}^{3}$ (ranging from 23.9 to $369.4~\upmu\mathrm{g/m}^{3})$ , which is $\sim\!10$ times higher than the annual mean $\mathsf{P M}_{2.5}$ regulation $:10~\upmu\mathrm{g}/\mathfrak{m}^{3}\rightleftharpoons\mathfrak{M}^{2},$ established by the World Health Organization (WHO, 2006), and $^{\sim3}$ times higher than China's guideline of $35~\ensuremath{\,\upmu\mathrm{g/m^{3}}}$ (Ministry of Environmental Protection of the People's Republic of China, 2012). The $\mathsf{P M}_{2.5}$ concentrations in spring, summer, autumn and winter are $93.3\ensuremath{~\upmu\mathrm{g}/\mathrm{m}^{3}}$ , $70.9\,\upmu\mathrm{g}/\mathrm{m}^{3}$ , $100.9\,\upmu\mathrm{g/m}^{3}$ and $151.2\,\upmu\mathrm{g}/\mathrm{m}^{3}$ , respectively. It should be noted that the $\mathsf{P M}_{2.5}$ concentrations in winter are ${>}4$ times higher than China's annual guideline. Two possible reasons are as follows. First, during the wintertime, the unfavorable meteorological conditions such as cold high pressure systems with low surface wind speeds and which are often accompanied by surface temperature inversions dominate the NCP region. This meteorological background is favorable for the formation of haze over the area (Zhao et al., 2013b). Second, the enhanced coal combustion for residential heating as well as the wide use of biofuels for residential cooking and heating in the rural areas may contribute to the severe haze during this time of year (Wang et al., 2015a). This observation is to be further investigated using the modeling described in Section 3.3.
3.2. Chemical species
3.2.1. Water-soluble ionic species
Water-soluble ionic species accounted for an average of $53.9\%$ of $\mathsf{P M}_{2.5}$ in the study area during the sampling period. It was noted that the major anions were $S0_{4}^{2-}$ , $\Nu0_{3}^{-}$ and $\mathrm{Cl}^{-}$ while the dominant cations were $\mathrm{NH_{4}^{+}}$ , $\mathsf{K}^{+}$ and $\mathsf{N a}^{+}$ , of which secondary inorganic aerosols (SIA, which refers to the sum of $\mathrm{NH}_{4}^{+}$ , $S0_{4}^{2-}$ and $\Nu0_{3}^{-}$ , which are the main components of secondary inorganic aerosols) were found to be the most abundant, making up $89.5\%$ of total ions.
The contributions of water-soluble ions to total $\mathsf{P M}_{2.5}$ mass were observed to be $53.1\%$ in spring, $69.8\%$ in summer, $50.4\%$ in autumn, and $49.6\%$ in winter (Table 1). As shown in Table 2, although the $\mathsf{P M}_{2.5}$ and SIA concentrations in Lingcheng were lower than those of most cities in the NCP (Wang et al., 2015c; Zhao et al., 2013a; Gao et al., 2011), the SIA concentrations were generally higher than in other rural areas in China (Lu et al., 2015a; Lai et al., 2016; Xu et al., 2002; Ding et al., 2011; Li et al., 2014b). Specifically, the $\mathtt{N O}_{3}^{-}$ concentration $(18.2\,\upmu\mathrm{g/m^{3}})$ was about two times higher than those observed in other rural areas in China (Lu et al., 2015a; Xu et al., 2002; Ding et al., 2011; Li et al., 2014b). This value was at the same level as those observed in some big cities, such as Beijing $(20.3\,\upmu\mathrm{g}/\mathrm{m}^{3})$ and Tianjin $(18.8\;\upmu\mathrm{g/m^{3}})$ (Zhao et al., 2013a). Nitrate forms photochemically from gaseous precursors $\left(\mathrm{NO}_{\mathrm{X}}\right)$ which are mainly emitted from power plants and vehicular sources. However, the sampling site was situated in agricultural lands and away from major roads and power plants. Therefore, it can be assumed that the high nitrate concentrations were mainly due to regional transport rather than local contributions.
Similar results are obtained for $\mathrm{NH}_{4}^{+}$ . The $\mathrm{NH}_{4}^{+}$ concentration $(13.0\;\upmu\mathrm{g/m^{3}})$ is about two times higher than those observed in other rural areas of China (Lu et al., 2015a; Xu et al., 2002; Ding et al., 2011; Li et al., 2014b). This value is comparable with that of Beijing $(18.8\,\upmu\mathrm{g/m^{3}})$ (Wang et al., 2015c), but higher than that of some big cities, such as Tianjin $(7.6\;\upmu\mathrm{g}/\mathrm{m}^{3})$ (Zhao et al., 2013a) and Shijiazhuang $(9.3\;\upmu\mathrm{g}/\mathrm{m}^{3})$ Zhao et al. (2013a). This might due to the high emissions intensity of ammonia in the NCP area (Huang et al., 2012), while other rural sites are located in areas with lower ammonia emissions rates. It should be noted that the different time periods examined by the studies listed in
Table 2 might also have an influence on the comparison to some extent. However, the sampling periods of most studies include all four seasons, and the annual average values were calculated based on the average of the data from four seasons. Therefore, the influence is relatively small and the comparison is meaningful.
3.2.2. OC and EC
The mean concentrations of OC and EC were as high as 22.5 and $2.8\;\upmu\mathrm{g}/\mathrm{m}^{3}$ (Table 3), which were equivalent to $21.3\%$ and $2.7\%$ of the $\mathsf{P M}_{2.5}$ mass, respectively. The seasonal variations of OC and EC are presented in Fig. 3. Relatively high concentrations of OC and EC appeared in winter $(35.1\ \ \upmu\mathrm{g}/\mathrm{m}^{3}$ and $3.4~\upmu\mathrm{g}/\mathrm{m}^{3}$ , respectively) and autumn $(28.4\,\upmu\mathrm{g}/\mathrm{m}^{3}$ and $4.5\,\upmu\mathrm{g}/\mathrm{m}^{3}$ , respectively) followed by spring $(19.0\,\upmu\mathrm{g}/\mathrm{m}^{3}$ and $1.4~{\upmu\mathrm{g}}/{\mathrm{m}}^{3}$ , respectively) and summer $\mathrm{[6.1\\upmug/m^{3}}$ and $1.7~{\upmu\mathrm{g}}/{\mathrm{m}}^{3}$ , respectively). OC was generally regarded as coming from combustion sources, such as coal combustion and biomass burning (Song et al., 2006). This suggests that there might be intense biomass and coal burning during winter and autumn in Lingcheng. However, high SOC/OC ratio was also observed in all four seasons. This might be due to the formation of secondary OC during regional transport.
The average ratio of OC/EC was 7.9 and ranged from 1.7 to 39.4, which is similar to that reported in Zhang et al. (2010a) study at Tengchong Mountain, in which the average ratio of OC/EC was 9.6 and ranged from 2.7 to 33.5. The seasonal variation in OC/EC is shown in Fig. 3 with the order of spring $(13.4)>$ winter $(10.4)>$ autumn $\left(6.3\right)>$ summer (3.7). The comparatively high OC/EC ratios observed in this study may be due to the following reasons. First, the higher OC/ EC ratios can be attributed to the predominance of organic carbon, which was derived from biomass burning and coal combustion. Second, the OC/EC ratios, which are fairly high, can also be attributed to the extraordinarily low EC levels in rural areas, and thus the OC/EC ratio tended to be high (Pongpiachan et al., 2015). It might be the main reason that the highest OC/EC ratio was observed during spring. Third, the formation of secondary OC via long-range transport might also be an important factor that causes the highs OC/EC ratio (Wang et al., 2012b; Zhou et al., 2012).
As shown in Table 3, the correlations (r) between OC and EC varied strongly among the four seasons: it was relatively high in autumn and winter $r=0.90$ and $r=0.92$ ) but low in spring and summer ( $r=$ 0.67 and $r=0.62_{\cdot}$ ). It indicates that OC and EC are produced from complicated contributors. One possible reason for the fluctuations in the degree of correlation between OC and EC might be the potential influence of Secondary Organic Aerosol (SOA) formation processes on OC in spring and summer, during which time strong solar radiation, high temperatures, and high humidity are suitable for SOA formation (Zhang et al., 2010b; Lai et al., 2016).
3.2.3. Trace and crustal elements
The concentrations and proportions in $\mathsf{P M}_{2.5}$ of measured crustal and trace elements are presented in Table 1. Trace elements, including Mg, P, Cr, Ni, Cu, Zn, As and $\mathsf{P b}$ , were observed, with no obvious differences among different seasons. Crustal elements, including the oxidized forms of Al, Fe, Si, Ti, Ca and Mn $\mathrm{Al}_{2}0_{3}$ , $\mathrm{Fe}_{2}0_{3}$ , $\mathrm{SiO}_{2}$ $\mathrm{TiO}_{2}$ , CaO and $\mathrm{MnO}_{2}$ ), were found to have a higher concentration in winter $(13.1~\upmu\mathrm{g}/\mathrm{m}^{3})$ , which was about two times higher than those observed in the other seasons. This trend may be due to the reduced vegetation cover on the land and the increase in bare ground during the winter, coupled with the climatic conditions. Soil dust and road re-suspended dust were more likely to occur in cold dry winters.
3.3. Regional source apportionment of $P M_{2.5}$ during winter
3.3.1. Model calibration and performance
The confidence in the application of CMAQ model in the NCP region is built upon previous studies both by ourselves (Chen et al., 2007a, 2007b, 2008, 2010; Cheng et al., 2007a, 2007b; Wang et al., 2010; Lang, 2013) and by other researchers (Wang et al., 2012a, 2014,
2015b). To further improve the performance of the modeling system, the following measures were taken in this study:
(1) To minimize the adverse impacts of emission uncertainties, the Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System (Houyoux et al., 2000) was used to generate emission inputs with high spatial and temporal resolution, as required by the CMAQ-ISAM model. Detailed road network information was collected and used to generate road emissions at high spatial resolution. Hourly meteorological data simulated by WRF model were used to calculate plume rise for all point sources, and the plumes were distributed into the vertical model layers that they intersected.
(2) Both the WRF meteorological model and the CMAQ air quality model were configured to use the exact same grid configurations and coordinate systems, to avoid spatial interpolation of either meteorological or chemical data.
(3) To abate the impact of initial chemical conditions, we tested spinup times of 1, 2, 3, …, and 9 days in our simulations, and found that a spin-up time of 7 days gave the best results.
(4) To reduce the influence of boundary chemical conditions, the 2- level nested domain was designed with the lateral boundaries of the large domain far from the inner domain.
(5) Different combinations of modeling parameters were tested and compared, including the adjustment of photochemical mechanisms and the aerosol modules, as well as the vertical resolution
layers. The parameter settings used in this study were finally determined based on their performance in these tests.
Table 4 presents the performance statistics for $\mathsf{P M}_{2.5}.$ , OC, EC, $S0_{4}^{2-}$ , $\mathrm{NO}_{3}^{-}$ and $\mathrm{NH}_{4}^{+}$ concentrations. It is noted that although the model under-predicted the concentrations of the species overall, the statistics were within the criteria of MFB $\pm60\%$ and MFE $75\%$ for particulate matter modeling suggested by Boylan and Russell (2006). The correlation coefficients (r) between the simulated and observed concentrations for $\mathsf{P M}_{2.5}$ $S0_{4}^{2-}$ , $\mathrm{NO}_{3}^{-}$ , $\mathrm{NH}_{4}^{+}$ , OC and EC were generally $>\!0.80$ This indicates that the modeling performance is acceptable (Chen et al., 2007a). Fig. 4 presents scatter plots of the simulated concentrations of $\mathsf{P M}_{2.5}$ and the major chemical species versus observations during the winter sampling period. Most of the points in these scatter plots are adjacently distributed on both sides of the ${\mathsf{y}}={\mathsf{x}}$ line; however, there are still a few dots apart from the line which indicate under-prediction. Fig. 5 shows the comparison between daily simulated and observed $\mathsf{P M}_{2.5}$ , $S0_{4}^{2-}$ , $\mathrm{NO}_{3}^{-}$ , $\mathrm{NH_{4}^{+}}$ , OC and EC at the Lingcheng study site from November 21st to December 20th. Generally, the simulated concentrations showed trends consistent with those seen in the observations. However, there were still certain deviations between the simulation results and the monitoring data. This might be due to the uncertainties inherent in emission inventories and the unavoidable deficiencies of meteorological and air quality models. Overall, the concentrations of simulated $\mathsf{P M}_{2.5},$ OC, $S0_{4}^{2-}$ , $\mathrm{NO}_{3}^{-}$ and $\mathrm{NH}_{4}^{+}$ were underestimated, but the deviation was found to be in an acceptable range when compared with similar studies (Zhang et al., 2013b; Wu et al., 2008). (See Table 5.)
NMB: The Normalized Mean Bias; NME: The Normalized Mean Error; MFB: The Mean Fractional Bias; MFE: The Mean Fractional Error; r: The correlation coefficients.
3.3.2. Regional source apportionment results
Haze is not a local issue. Its formation may be due to both local emissions and regional transport (Wang et al., 2015a). The results in Table 4 showed that local emissions of $\mathsf{P M}_{2.5}$ in Lingcheng only contributed $15.4\%$ of the $\mathsf{P M}_{2.5}$ in its ambient air. This value is much lower compared to the results from studies in big cities such as Beijing, Tianjin and Shijiazhuang, where local contributions play an important role. For example, Lang (2013) found that the local emissions in Beijing contributed $70.8\%$ to $\mathsf{P M}_{2.5}$ for January; Li et al. (2015) found that the local emissions in Tianjin contributed $>\!50\%$ to $\mathrm{PM}_{2.5}$ for January; Wang et al. (2014) found that the local emissions in Shijiazhuang contributed $>\!65.1\%$ to $\mathsf{P M}_{2.5}$ for December. These facts suggest that the ambient $\mathsf{P M}_{2.5}$ at Lingcheng is not affected only by emissions from local and circumjacent areas; regional and long-range transport should also be considered.
To quantitatively identify the contributions from each direction, the simulation area was equally divided into four parts centered on the sampling site, as shown in Fig. 6. The contributions of $\mathsf{P M}_{2.5}$ from each of the four directions were calculated. The results indicate that the emissions from the northern and southern regions contributed much more ( $29.4\%$ and $26.9\%$ , respectively) to $\mathsf{P M}_{2.5}$ concentrations in Lingcheng than the western $(19.6\%)$ and eastern $(11.8\%)$ areas.
To further investigate the contribution of $\mathsf{P M}_{2.5}$ per unit area and eliminate the influence of size, we scaled the contributions based on the areas of selected regions. As shown in Fig. 7, in terms of the contribution of $\mathsf{P M}_{2.5}$ per unit area for each tagged area, Lingcheng had the largest area-normalized contributions, followed by its circumjacent regions (DZO, CZ, LC, HS and XT). It should be noted that the area-normalized contributions from TJ, BJ and NHN were also relatively high. This is consistent with the results of the analysis about the contributions from the four directions. It is found that, although the local and circumjacent areas make relatively large area-normalized contributions, remote regions may also make large area-normalized contributions.
The high regional contributions in Lingcheng could also be attributed to its location. Lingcheng is surrounded by many heavily polluted cities, such as Jinan, Dezhou, Cangzhou, Liaocheng, Hengshui and Xingtai, which are known as transportation hubs and centers of heavy industries (Wang et al., 2012b; Xiong et al., 2016; Hu, 2015; Li, 2009). Thus, the regional emissions of $\mathsf{P M}_{2.5}$ and its precursors could be geographically extended and accumulated under unfavorable meteorological and geographical conditions, and even areas with relatively small local emissions (such as rural areas) can suffer from haze pollution. Another reason might be the small size of Lingcheng (only $\sim\!1213\;\mathrm{km}^{2}.$ ). The intensity of emissions in this small rural area is relatively less than its surrounding cities, thus the pollutants tend to move in a direction against the concentration gradient, which leads to the relatively lower local contribution.
The average contribution rate from the circumjacent areas in Dezhou City (DZO) was $12.6\%$ and the values for the six surrounding cities were $9.9\%$ (CZ), $5.4\%$ (HS), $2.7\%$ (XT), $8.6\%$ (LC), $1.1\%$ (JN), and $0.4\%$ (BZ), respectively. Among these regions, CZ was the greatest contributor, followed by LC and HS. For areas farther away, HBO made the largest contribution $(10.7\%)$ , followed by NHN $(7.0\%)$ , SDO $(4.3\%)$ , TJ $(3.2\%)$ , BJ $(1.9\%)$ and SX $(1.2\%)$ .
As for the species in $\mathsf{P M}_{2.5}$ , similar results were found for $S0_{4}^{2-}$ . Local emissions accounted for $11.4\%$ and circumjacent regions accounted for $11.4\%$ (DZO). CZ was still the largest contributor among the six surrounding cities with $10.4\%$ on average, followed by LC, HS, XT, JN, and BZ. For $\mathrm{NH_{4}^{+}}$ , $68.3\%$ was identified as coming from the local (LnC) and surrounding areas (DZO, CZ, HS, LC, XT, JN and BZ). LnC $(19.9\%)$ and DZO $(17.1\%)$ were the two largest contributors. The local and circumjacent areas contributed $68.6\%$ and $73.1\%$ of the OC and EC, respectively, similar to the pattern displayed by $\mathrm{NH}_{4}^{+}$ . $\mathtt{N O}_{3}^{-}$ , however, showed obvious characteristics of long-range transport. Local emissions made up only $1.9\%$ , while the contributions from distant areas were relatively large $[40.1\%$ in total), such as HBO $(11.4\%)$ and NHN $(7.2\%)$ . The boundary inflow (BCON) made the largest contribution $(32.4\%)$ .
3.3.3. Heavy haze period in winter
Lingcheng tends to experience more serious $\mathsf{P M}_{2.5}$ air pollution during winter. Among the 30 sampling days in winter, the haze days $(75\,\upmu\mathrm{g}/$ $\mathrm{m^{3}}<\mathrm{PM}_{2.5}<200\;\upmu\mathrm{g/m^{3}})$ accounted for $47\%$ of the samples, and the heavy haze days $(\mathrm{PM}_{2.5}\geq200\,\upmu\mathrm{g/m^{3}})$ ) accounted for $>\!30\%$ The average concentrations of $\mathsf{P M}_{2.5}$ were $131.0\;\upmu\mathrm{g}/\mathrm{m}^{3}$ and $267.6~\upmu\mathrm{g/m}^{3}$ for haze and heavy haze episodes, respectively. Thus, it would be meaningful to further identify the sources that led to these severely polluted days in winter, when the concentration of $\mathrm{PM}_{2.5}\,\mathrm{WaS}\!>\!200\,\upmu\mathrm{g/m}^{3}$ .
The backward air trajectory technique can be used to identify the source regions impacting the site. The HYSPLIT-4 (Hybrid Single-Particle Lagrangian Integrated Trajectory) model developed by NOAA/ARL (National Oceanic and Air Administration/Air Resources Laboratory, USA) was applied to calculate the trajectories using the hourly meteorological data simulated by the WRF model. In this study, 12-h backward trajectories were calculated twenty-four times each day (i.e., one trajectory was calculated every hour) during heavy haze days in winter. As shown in Fig. 8, the backward trajectories began at the location $(37^{\circ}21^{\prime}17.0^{\prime\prime}\mathrm{N},$ , $116^{\circ}28^{\prime}30.0^{\prime\prime}\mathrm{E})$ ) at an altitude of $200~\mathrm{m}$ above ground level (AGL). On heavy haze days, nearly half of the air trajectories $(48.9\%)$ arriving at the site came from the north, a region with many high emissions cities (such as CZ, BJ, TJ, etc.). Approximately $19.6\%$ and $19.2\%$ of the trajectories came from the south and southwest, respectively. These trajectories mostly passed through heavily polluted regions such as XT, LC, NHN and the area between XT and NHN. The rest of the trajectories $(12.3\%)$ shown in Fig. 8 came from the northeast (BZ). However, it should be noted that trajectories from the west (SX) and east (most of the SDO, except for the southwest areas in SDO) were rarely identified.
Fig. 9 presents the comparison of the $\mathsf{P M}_{2.5}$ contribution rates during periods with relatively clean days $(\mathrm{PM}_{2.5}\leq75\;\upmu\mathrm{g}/\mathrm{m}^{3})$ , haze days $(75~\upmu\mathrm{g/m^{3}}~<~\mathrm{PM_{2.5}}~<~200~\upmu\mathrm{g/m^{3}})$ and heavy haze days $(\mathrm{PM}_{2.5}\,{\ge}\,200\,\upmu\mathrm{g}/\mathrm{m}^{3})$ in each tagged area. With increases in pollution, the contributions from local (LnC) and circumjacent regions (DZO) showed a clear downward trend, while the contributions from northern (BJ, TJ, CZ) and southwestern (NHN, LC and SDO) areas, which most of the trajectories passed through during periods of heavy haze, showed an obvious upward trend. However, the relative contribution is still much less than those of the local area (LnC) and adjacent cities. This implies that, although regional transport plays a more important role during heavy haze days, the emissions from local and the circumjacent areas are still the largest contributor. The contributions from western (SX, HBO, XT, HS) areas decreased with increasing $\mathsf{P M}_{2.5}$ concentrations, but no distinct trends were observed for the eastern (BZ and JN) areas. These apportionment results were consistent with the air trajectory analysis.
Therefore, it can be concluded that the severe haze in the study area was caused to a large extent by the impacts of regional transport, and $\mathsf{P M}_{2.5}$ control policies should place emphasis on both local and regional sources, especially for those areas with relatively low local emissions compared to their background.
4. Conclusions
In this study, four months of daily $\mathsf{P M}_{2.5}$ samples were collected from a rural site in Lingcheng, Shandong Province during different seasons in 2013 and 2014. The results showed that the overall average concentration of $\mathsf{P M}_{2.5}$ was three times above China's guideline for this pollutant, and more than four times during the wintertime. The concentrations of SIA were also higher than in other rural areas in China, and the high OC concentrations indicated that coal combustion and biomass burning were common in this area.
The results of regional source apportionment using CMAQ-ISAM indicated that the $\mathsf{P M}_{2.5}$ air pollution in Lingcheng was not only a local but also a regional issue. $\mathrm{NO}_{3}^{-}$ showed the characteristics of long distance transport, while $S0_{4}^{2-}$ , $\mathsf{N H}_{4}^{+}$ , OC and EC were more influenced by local and surrounding areas. The results of backward trajectory analysis were found to be consistent with the CMAQ-ISAM simulations. With increasing degrees of pollution, the contributions from local (LnC) and circumjacent regions (DZO) showed a clear downward trend, while the contributions from northern (BJ, TJ, CZ) and southwestern (NHN, LC and SDO) areas, which most of the trajectories passed through during periods of heavy haze, showed an obvious upward trend. This indicates that, even in rural areas, air pollution involving $\mathsf{P M}_{2.5}$ cannot be neglected. The $\mathsf{P M}_{2.5}$ air pollution at this rural site is not only driven by emissions from local and circumjacent areas; regional and long-range transport is also important. Regionally coordinated emissions controls are key to improving the air quality.
As for the methodology used in this study, the CMAQ-ISAM model showed acceptable performance and has once again been shown to be a promising tool for carrying out regional source apportionments. However, there is still room for improvement in the modeling approach. For example, (1) the overall $\mathsf{P M}_{2.5}$ and main chemical species concentrations were somewhat underestimated; (2) the uncertainties in estimated emissions are still large for both local and regional sources, which might be the main reason for the underestimation; (3) a global atmospheric chemistry model could be used to provide more realistic chemical boundary conditions, as required by the CMAQ model; (4) the meteorological fields simulated by the WRF model could be further improved, especially for the surface wind fields; (5) since the air quality model can provide hourly concentrations of $\mathsf{P M}_{2.5}$ and the main chemical species, observations should be made at higher temporal resolution to permit better model performance evaluation.
Acknowledgments
This paper was supported by the Natural Sciences Foundation of China (No. 51578017 & 51408014) and the Public Welfare Projects for Environmental Protection of China (201409002 & 201509005).
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atmosphere | 0004 | 10.1016/j.jes.2015.05.023 | image | Fig. 1 – Temporal variations in (a) air temperature, relative humidity, precipitation, (b) wind speed, wind direction, and (c) $\bf{P M}_{10}$ and $\mathbf{PM}_{2.5}$ mass concentrations and their ratio at the investigated rural site in 2012. |
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atmosphere | 0004 | 10.1016/j.jes.2015.05.023 | table | Table 1 – Atmospheric PM10 and PM2.5 mass concentrations and major chemical components of PM10 at the investigated |
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atmosphere | 0004 | 10.1016/j.jes.2015.05.023 | image | Fig. 2 – Diurnal variations of airborne $\bf{P M}_{10}$ percent concentrations during the crop tillage period in May, the vegetation period from mid-June to mid-September and the harvest period in October at the farm site. |
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atmosphere | 0004 | 10.1016/j.jes.2015.05.023 | image | Fig. 3 – Temporal variations in (a) secondary aerosol-related ions $(\mathbf{NH}_{4}^{+},\mathbf{NO}_{3}^{-}$ and $\mathbf{so}_{4}^{2-})$ , (b) dust-related elements (Al, Ca, Fe and $\mathbf{M}\mathbf{g})$ , (c) carbonaceous species (OC and EC) and (d) biomass burning marker $(\mathbb{K}^{+})$ at the investigated rural site in 2012. OC: organic carbon; EC: elemental carbon. |
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atmosphere | 0004 | 10.1016/j.jes.2015.05.023 | image | Fig. 4 – Temporal variations of the individual contributions of dust (dust), carbonaceous species (carbon), secondary aerosol (SA) and other to the atmospheric $\mathbf{PM_{10}}$ at the rural site and the corresponding average of the four compositions during the tillage period (26 April–15 June), vegetative period (16 June–25 September) and harvest period (26 September–31 October) in 2012. Secondary aerosols (SAs) include ammonium, nitrate and sulfate. Mineral dust (dust) was calculated from the oxide content of Al, Si, Ca, Fe, Mg and K. Carbonaceous species (carbon) represent the sum of organic carbon (OC) and elemental carbon (EC). |
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atmosphere | 0004 | 10.1016/j.jes.2015.05.023 | image | Fig. 5 – Estimated chemical profiles of field tilling-induced and straw burning-induced $\bf{P M}_{10}$ emission. |
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atmosphere | 0004 | 10.1016/j.jes.2015.05.023 | image | Fig. 6 – Diurnal profile of planetary boundary layer (PBL)-adjusted field tilling- and crop burning-induced $\mathbf{PM_{10}}$ emission. |
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atmosphere | 0004 | 10.1016/j.jes.2015.05.023 | text | Not supported with pagination yet | Temporal variability of atmospheric particulate matter and chemical composition during a growing season at an agricultural site in northeastern China
Weiwei Chen1,⁎, Daniel Tong2, Shichun Zhang1, Mo Dan3, Xuelei Zhang1, Hongmei Zhao1
1. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 100029, China
2. National Oceanic and Atmospheric Administration (NOAA), MD 20740, USA
3. Beijing Municipal Institute of Labor Protection, Beijing 100054, China
A R T I C L E I N F O
A B S T R A C T
Article history: Received 6 January 2015 Revised 14 May 2015 Accepted 14 May 2015 Available online xxxx
Keywords:
$\mathrm{PM}_{10}$
$\mathrm{PM}_{2.5}$
Emission factor
Agricultural inventory
Tillage
Harvest
Burning
This study presents the observations of $\mathrm{PM}_{10}$ and $\mathrm{PM}_{2.5}$ concentrations at an agricultural site from April to October 2012 in Dehui city, China. Ambient air was sampled by filter-based samplers and online PM monitors. The filter samples were analyzed to determine the abundance of ionic/inorganic elements, organic carbon (OC) and elemental carbon (EC). The daily $\mathrm{PM}_{10}$ concentrations varied significantly over the monitoring period, with an average of $168\pm63$ (in the range of 52–277) $\upmu\mathrm{g}/\mathrm{m}^{3}$ during the land preparation/planting period (26 April– 15 June), $85\pm65$ (36–228) $\upmu\mathrm{g/m}^{3}$ during the growing season (16 June–25 September), and $^{207\pm}$ 88 (103–310) $\upmu\mathrm{g/m}^{3}$ during the harvest period (26 September–31 October). $\mathrm{PM}_{2.5}$ accounted for $44\%$ , $56\%$ and $66\%$ of atmospheric $\mathrm{PM}_{10}$ during these periods, respectively. The $\mathrm{PM}_{10}$ diurnal variation showed a distinct peak from 16:00 to 21:00 (LST) during the growing and harvesting seasons, while a gradual increase throughout the daytime until 17:00 was observed during tilling season. Mineral dust elements (Al, Ca, Fe, and $\mathrm{Mg})$ dominated the $\mathrm{PM}_{10}$ chemical composition during the tilling season; OC, $\mathrm{NO}_{3}^{-}$ $S O_{4}^{2-}$ and $\mathrm{NH_{4}^{+}}$ during the growing season; and carbonaceous species (i.e., OC and EC) during the harvesting season. Our results indicate that the soil particles emitted by farm tillage and organic matter released from straw burning are the two most significant sources of $\mathrm{PM}_{10}$ emissions contributing to the recurring high pollution events in this region. Therefore, development of agricultural PM inventories from soil tillage and straw burning is prioritized to support air quality modeling.
$\circledcirc$ 2015 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.
Introduction
Over the last decades, the frequency of regional atmospheric haze events has rapidly increased in China (Kan et al., 2012). As one of the primary atmospheric pollutants, the issue of particulate matter (PM) emission has gradually grown in importance (Zhang et al., 2012). In agriculture-dominated regions, farming activities can significantly affect local air quality or contribute to regional haze events, especially in arid and semiarid regions with intense field activities (Hinz and Tamoschat-Depolt, 2007). However, studies have shown large temporal–spatial differences in agricultural PM emissions because of the various field operations, crop types, soil properties and climate conditions (Carvacho et al., 2004; Funk et al., 2008; Holmén et al., 2008; Aneja et al., 2009; Aimar et al., 2012).
Agricultural activities emit considerable primary PM and gaseous precursors (e.g., ammonia, VOC and chemical substances) of secondary PM (Hinz and Tamoschat-Depolt, 2007). Soil tillage, crop harvesting and straw burning have been identified as the three largest anthropogenic sources of agricultural PM emission. Nordstrom and Hotta (2004) have noted that aeolian transport of cultivated and grazed soil is a global problem, and the disturbance of soil and plants could increase the frequency of dust events. Previous studies have indicated that the magnitude of the mechanically generated PM emission might be several times that of wind erosion of cropland, although these activities only occurred over a few days or weeks (Goossens et al., 2001). Agricultural burning releases a significant number of airborne fine particles that can rapidly spread, thus receiving more attention (Zhang et al., 2007). In many agricultural regions, spring plowing, fall harvesting and straw burning are responsible for regional haze events (Zhang et al., 2010; Qin and Xie, 2011).
Northeastern China is a major region for crop production and covers approximately $20\%$ of the total arable land area in China (National Bureau of Statistics of China, 2011). Bare soils in the region are exposed to the atmosphere for up to seven months because the single cropping pattern is dominant. Natural wind erosion and agricultural disturbance could increase the potential of PM emissions from the soil (Nordstrom and Hotta, 2004) and decrease the soil organic matter content (Harper et al., 2010). Additionally, most straw residues are openly burned to start the farming season and to reduce the cost of recycling in this region. However, the information about rural air quality and the mechanism, magnitude, and patterns of agricultural emission sources of PM is scarce (e.g., Han et al., 2010; Huang et al., 2011). As air quality concerns become increasingly prominent in northeastern China, the lack of information may impede air quality modeling and control measures in agricultural activities.
This study presents our results on rural air quality and estimated farm activity roles on PM emissions in a maizedominant area of northeastern China. Using portable filterbased samplers and real-time monitors, we measured the atmospheric concentrations of $\mathrm{PM}_{10}$ and $\mathrm{PM}_{2.5}$ at a farmland site near Changchun City from the end of April to the end of October 2012. The major chemical components of the sampled $\mathrm{PM}_{10}$ filters were also analyzed in the laboratory. Our primary obective was to understand the relationships among the characteristics of the PM concentration levels, their chemical compositions and the field farming activities in northeastern China.
1. Materials and methods
1.1. Sampling site
The sampling site was located at a farmland in the black-soil protection agro-technical station (ATS, $44^{\circ}12^{\prime}29^{\prime\prime}\mathrm{N},$ , $125^{\circ}34^{\prime}04^{\prime\prime}$ E). This farm station is on the edge of a village and is approximately $50\;\mathrm{km}$ away from the center of Changchun City, the capital of Jilin Province, China. Note that this station is adjacent $(\sim30~\mathrm{m})$ to a local small chicken house with length of $20~\mathrm{m}$ and width of $^{10}\,\mathrm{m}$ , and chicken manure consistently accumulates in the ditch close to the station. The National Road 102 is approximately $300\;\mathrm{m}$ away from the station. The local climate is characterized as a semi-humid temperate continental monsoon climate (Chen et al., 2015). The long-term (30 year) mean annual temperature is $4.4^{\circ}\mathrm{C}$ , with a mean January temperature of $-15.1^{\circ}C$ and a July mean of $23.1^{\circ}\mathrm{C}$ . The annual precipitation is $522–615\mathrm{~mm}$ , of which more than $70\%$ falls in the summer from June to August. The soil type at the investigated site is clay loam soil (Typic hapludoll), with a mean organic carbon content of $1.65\%$ , a mean $\mathrm{pH}$ of 6.48, and a mean bulk density of $1.24\;\mathrm{g/cm}^{3}$ at the depth of $0.05\;\mathrm{m}$ . Maize is the dominant upland crop, accounting for $53\%$ of the total arable land in Jilin Province (Bureau of Statistic of Jilin Province, 2012). In this region, spring tillage begins at the end of April and ends in mid-June, the vegetation period is from June to September, and crops or straw are harvested or burned in October.
1.2. Atmospheric PM sampling
The sampling period lasted for six months from the end of April to the end of October 2012. The sampling heights were approximately $^{3}\,\mathrm{m}$ (i.e., top of the support frame of the sampler) at the ATS site. Using a filter-based gravimetric sampling method, the atmospheric $\mathrm{PM}_{10}$ and $\mathrm{PM}_{2.5}$ were sampled every one or two days during tillage and harvest periods and twice a month during the vegetation period. Portable samplers (Model Omni, BGI Inc., USA) were used with a flow-rate of $5\;\mathrm{L/min}$ on a 24-hr basis. Two types of $47\;\mathrm{mm}$ Teflon and Quartz filters (Whatman PTFE and QM-A, General Electric Co., Maidstone, UK) were applied to collect atmospheric particles. The mass concentrations of PM were calculated by division of the weight increase in the filter and the standard sampling air volume, which was converted using the actual air volumes and, periodically, the air temperature and pressure. These filters were weighed on an electronic microbalance with a precision of $0.01~\mathrm{mg}$ (Model XS105DU, Mettler Toledo Inc., Zurich, Switzerland). Before and after filter sampling, the filters were stored in a dessicator at $20{-}25^{\circ}\mathrm{C}$ and $35\%{-}45\%$ relative humidity for $48\,\mathrm{hr}$ . Subsequently, the sampled filters were stored in a refrigerator at $4^{\circ}\mathrm{C}$ until chemical component analysis.
Meteorological data, including the daily precipitation, air temperature, relative humidity, visibility, wind speed and direction, were obtained from the Changchun Meteorological Bureau.
1.3. Chemical component analysis of ${\tt P M}_{10}$
Teflon $\mathrm{PM}_{10}$ filters were used to measure the ionic speciation, including anions (i.e., $\mathtt{F}^{-}$ , $\mathrm{Cl^{-}}$ , $\mathrm{NO}_{3}^{-}$ and $S0_{4}^{2-}$ ), cations $(\mathrm{Na}^{+},\mathrm{NH}_{4}^{+},\mathrm{K}^{+}$ ${\mathrm{Mg}}^{2+}$ and $\mathsf{C a}^{2+}$ ), and inorganic elements (i.e., Al, Ca, Fe, Mg, K, Mn, Ni, Cu, $Z\mathrm{n}$ , As, Se, Sr, Ba, Cd, Cr, Nd, and $\mathrm{Pb}$ ). The concentrations of anions and cations were determined by ion chromatography (ICS-1000, Dionex Inc., Sunnyvale, CA, USA). The eluent used for the anions was a 3.5 mmol $\mathrm{Na_{2}C O_{3}/1.0-m m o l\ N a H C O_{3}}$ solution, whereas a 20 mmol methane sulfonic acid (MSA) solution was used as the cation eluent. The ion chromatography method had a detection limit of $0.05~\mathrm{mg/L}$ and an uncertainty of $\pm10\%$ for all of the ions. For the inorganic elements, the Teflon filter was extracted for $0.5\;\mathrm{hr}$ using $6~\mathrm{mL}$ $\mathrm{HNO}_{3}$ and $2~\mathrm{mL}$ HCl in a microwave laboratory system with a power of $1400~\mathrm{W}$ , $170^{\circ}\mathrm{C}$ as the maximal temperature and an ultimate pressure of 20 bar before the determination. The extracted solution was injected into an inductively coupled plasma-atomic emission spectrometer (ICP-AESIRIS Intrepid II, Thermo Electron Corp., Beverly, MA, USA) to obtain the element concentrations. The precision and bias of the element concentrations of the ICP-AESIRIS method were typically less than $10\%$ . The concentration of mineral dust was calculated by summing the content of the oxides of Al, Si, Ca, Fe, Mg and K, (i.e., 1.89 $\mathrm{Al}+2.14$ $\mathrm{Si}+1.40$ $\mathsf{C a}+1.43$ $\mathrm{Fe}\mathrm{~+~}1.66~\mathrm{Mg}\mathrm{~+~}1.21~\mathrm{K})$ (Hueglin et al., 2005). The Si concentration was estimated according to the average ratio of Si/Al (3.6) in the earth's crust (Hueglin et al., 2005) because Si was not determined by the ICP-AESIRIS method in this study.
Quartz filters were used to determine the particulate EC and OC concentrations using a thermal-optical carbon aerosol analyzer (Sunset-OCEC RT-4, Sunset Lab Inc., Tigard, OR, USA) (Aneja et al., 2006). This method is based on the thermal desorption/oxidation of particulate carbon to ${\mathrm{CO}}_{2}$ which is then reduced to methane and, subsequently, measured using a flame-ionization detector. The analysis sequence was initialized in a nonoxidizing atmosphere (helium) with a 10 sec purge followed by four temperature ramps to a maximum of $900^{\circ}\mathrm{C}$ . A cooling blower was then used to decrease the temperature to $600^{\circ}\mathrm{C}$ before oxygen was added. The temperature was maintained at this point until the transmittance or reflectance returned to the initial value before the sample was heated. This point determines the distinction between the OC and EC, i.e., all of the carbon measured up to this point is OC, whereas all of the carbon measured after this point is EC. The total carbon in this study refers to the sum of EC and OC. The precision is 0.19 at $1\ \upmu\mathrm{g}$ of carbon and 0.01 at $^{10-72}\,\upmu\mathrm{g}$ of carbon.
1.4. Diurnal variation of the $\mathtt{P M}_{10}$ measurement
During each period, i.e. tillage period (TP), vegetation period (VP) and harvest period (HP), three or four sunny days were selected to measure the diurnal $\mathrm{PM}_{10}$ concentrations using a real-time DUSTTRAK™Aerosol Monitor (Model 8520, TSI Inc., Shoreview, MN, USA). The monitor is based on light scattering technology and was regulated to record data at a frequency of 1 min (Yanosky et al., 2002).
An aerosol sample is drawn into the sensing chamber in a continuous stream, and particles in the aerosol stream scatter light in all directions. A laser beam collects a portion of the scattered light and focuses it onto a photodetector. The detection circuitry converts the light into a voltage; this voltage is proportional to the amount of light scattered, which is proportional to the mass concentration of the aerosol. The portability and low-power-supply requirement of this monitor ensures that the PM determination could be performed simultaneously at four sites, which is especially useful at rural sites without good infrastructure.
1.5. Data analysis
Pearson correlations were obtained between the PM and the components and among the components. The significance of the differences in the PM concentrations and the chemical components were investigated using the independent-samples t test. All of the statistical procedures were performed using the software SigmaPlot 10.0 (SPSS Inc., Chicago, IL, USA).
2. Results
2.1. Meteorological factors and farming activities
The mean air temperature $(17.4^{\circ}\mathrm{C})$ , relative humidity $(65\%)$ and wind speed $(2.7\;\mathrm{m/sec})$ from May to October 2012 were comparable to the ten-year average values for the same period from 2004 to 2013 (Fig. 1a and b). The total precipitation $\mathrm{453~mm}]$ ) was higher than the long-term mean $(405\;\mathrm{mm})$ , indicating a slightly wet year.
Farming activities, including soil tillage, crop harvesting and straw burning, are related to precipitation. Most of the soil tillage and crop plant operations were conducted before May 11 because of the fifteen continuous sunny days that occurred before that date. Frequent rain occurred throughout the vegetation period (June to September), and an extreme rain event $(107\;\mathrm{mm})$ ) occurred from 28 to 29 August. In October, rainfall occurred every three or four days, although the sum of the precipitation was similar to the average.
2.2. Temporality of atmospheric PM
(1) Seasonal pattern: The daily concentrations of $\mathrm{PM}_{10}$ and $\mathrm{PM}_{2.5}$ at the farm site ranged from 44 to $313~\upmu\mathrm{g}/\mathrm{m}^{3}$ and from 21 to $205~\upmu\mathrm{g}/\mathrm{m}^{3}$ during the sampling period, respectively (Fig. 1c). The temporal coefficients of variation (CV) were $55\%$ for $\mathrm{PM}_{10}$ and $63\%$ for $\mathrm{PM}_{2.5}$ . The ratios of $\mathrm{PM}_{2.5}$ to $\mathrm{PM}_{10}$ varied from $36\%$ to $77\%$ during the investigated period and averaged $44\%\pm9\%$ , $55\%\pm7\%$ and $66\%\pm2\%$ at TP, VP and HP, respectively. Compared with VP $(85\pm65~\upmu\mathrm{g}/\mathrm{m}^{3})$ , significantly higher PM concentrations occurred, with an average of $168\pm63$ and $207\pm88~\upmu\mathrm{g/m}^{3}$ , respectively (Table 1). Although low average PM concentrations were determined during VP, two high values, i.e. 253 and $258~\upmu\mathrm{g}/\mathrm{m}^{3}$ , were observed on 7 July and 1 September.
(2) Diurnal pattern: The diurnal variations of the $\mathrm{PM}_{10}$ percent concentration at the farm site were different during TP, VP and HP (Fig. 2). During the soil tillage, the $\tt P M_{10}$ concentrations had a unimodal pattern, with a gradual increase in the daytime, and reached the highest values at 16:00–18:00 local time. The vegetation period had a typical bimodal pattern of $\mathsf{P M}_{10}$ variations, and two peaks appeared at 7:00 and $18{:}00{-}^{\cdot}19{:}00$ . The $\tt P M_{10}$ levels at other times were significantly lower than these two times during VP. Similar to VP, the $\mathrm{PM}_{10}$ pattern during HP showed that the $\mathrm{PM}_{10}$ concentrations were low from 10:00–18:00, increased rapidly to a peak from 20:00 to 22:00 and then gradually decreased. There was another minor peak in the $\mathrm{PM}_{10}$ concentrations at approximately 7:00.
2.3. Chemical composition of atmospheric ${\tt P M}_{10}$
Fig. 3 presents the temporal variations of the four types of components, i.e. secondary aerosol-related ions, mineral dust-related elements, carbonaceous species and $\mathtt{K}^{+}$ . The ranges of the $\mathtt{N O}_{3}^{-}$ , $S O_{4}^{2-}$ and $\mathrm{NH_{4}^{+}}$ concentrations were 0–13, 1–25 and $1{-}34~\upmu\mathrm{g}/\mathrm{m}^{3}$ respectively (Fig. 3a). The peaks of these three ion peaks were consistent with the highest PM concentrations during the vegetation period. Mineral dust-related elements had a similar temporal trend, with the highest values of 11 $\upmu\mathrm{g}/\mathrm{m}^{3}$ Al, $10~\upmu\mathrm{g}/\mathrm{m}^{3}$ Ca, $4~{\upmu\mathrm{g}}/{\mathrm{m}}^{3}$ Fe, and $2~\upmu\mathrm{g}/\mathrm{m}^{3}~\mathrm{Mg}$ observed at soil tilling and planting before mid-June (Fig. 3b). For carbonaceous species, the OC concentrations significantly varied from 13 to $163\ \upmu\mathrm{g}/\mathrm{m}^{3}$ , and the EC varied over the range of $2{-}18\ \upmu\mathrm{g}/\mathrm{m}^{3}$ . There were significantly higher OC levels during the harvest period, and two high values appeared during the vegetation period (Fig. 3c). The maximum of $\mathtt{K}^{+}$ $\cdot(4~\upmu\mathrm{g/m}^{3})$ was observed in October followed by May and June.
Table 1 and Fig. 4 summarize the averages of the major chemical components and their ratios during the three periods. During TP, mineral dust $(72\pm34\;\upmu\mathrm{g}/\mathrm{m}^{3})$ and OC $(35\pm12\;\upmu\mathrm{g}/\mathrm{m}^{3})$ were the primary contributors, accounting for $41\%$ and $24\%$ of the sampled $\mathrm{PM}_{10},$ respectively. The vegetation period significantly reduced the mineral dust components $(5\pm7~\upmu\mathrm{g}/\mathrm{m}^{3})$ , maintained a similar level of OC, and slightly increased the secondary aerosol-related ions. The contributions of the carbonaceous species $\mathrm{(OC+EC)}$ and the secondary aerosol ions $(\mathrm{NO}_{3}^{-},$ $S0_{4}^{2-}$ and $\mathrm{NH_{4}^{+}})$ to $\mathrm{PM}_{10}$ were $41\%$ and $16\%$ , respectively. During HP, the carbonaceous species averaged $112\pm55~\upmu\mathrm{g}/\mathrm{m}^{3}$ , which was the dominant $\mathrm{PM}_{10}$ $(53\%)$ followed by mineral dust $(10\%)$ and secondary aerosol ions $(8\%)$ .
3. Discussion
This study provides a preliminary insight into the temporal trends, pollution levels, and chemical composition of rural PM in northeastern China. This information also aids in the identification of the PM emission sources and their characteristics (e.g., chemical and diurnal profiles) in the typical agricultural region.
These levels of daily $\mathrm{PM}_{10}$ $(44{-}313~\upmu\mathrm{g}/\mathrm{m}^{3})$ or $\mathrm{PM}_{2.5}$ concentrations $(21{-}205~\upmu\mathrm{g}/\mathrm{m}^{3})$ fall in the range of reported values at the Tongyu rural site (Spring $\mathrm{PM}_{2.5}$ : $23–1630~\upmu\mathrm{g}/\mathrm{m}^{3})$ (Zhang et al., 2008) and are comparable with those at the Longfengshan rural site (Annual $\mathrm{PM}_{10}$ : $82\ \upmu\mathrm{g}/\mathrm{m}^{3})$ (Zhang et al., 2012) in northeastern China. The 24-hr mean of the $\mathrm{PM}_{10}$ and $\mathrm{PM}_{2.5}$ concentrations provided by the secondary standard of the National Ambient Air Quality Standard in China (NAAQS) is 150 and $75~\upmu\mathrm{g}/\mathrm{m}^{3}$ , respectively (Ministry of Environmental Protection of People's Republic of China, 2012). Judging by the NAAQS standards of China, the maximum $\mathrm{PM}_{10}$ and $\mathrm{PM}_{2.5}$ concentrations were double and triple the standard levels, respectively. During the soil tillage and crop harvest periods, approximately $60\%$ of the samples exceeded the standard, while $80\%$ of the PM values met the standard during the crop growing season. The rural PM concentrations were generally dependent on the local background level, and the increased portions came primarily from the periodic agriculture-induced PM releases. Conventional field tilling practices, e.g. plowing, disking, listing, compacting and planting, can generate minerals (soil origin) or a combination of mineral and organic dust (plant origin) due to bare soil disturbance (Bogman et al., 2005). These emission sources represented the most significant contribution of mineral elements $(41\%)$ of airborne $\mathrm{PM}_{10}$ samples, and the $\mathrm{PM}_{2.5}/\mathrm{PM}_{10}$ ratios $(40\%)$ were low during soil tillage (Figs. 1c and 4). Moreover, recurring dust from farm vehicles moving on unpaved or paved roads in the surrounding area may be significant in the fugitive $\mathrm{PM}_{10}$ concentrations. The second highest contribution to the PM levels came from OC and EC during this period (Fig. 4), which were most likely from fuel consumption due to the increased usage of agricultural machinery, and spring straw burning or roadside garbage burning based on the detected ${\tt K}^{+}$ (Fig. 3d). Continuously high Fe levels in the soil tillage period compared with the other periods (Fig. 3b) were mostly attributed to the soil/mineral dust, as Fe is one of the common crustal elements. In addition, the increase of agricultural vehicles or machines during soil tillage could enhance Fe emission because the coarse particle mode of Fe may be emitted or formed in the aging process by vehicles using gasoline and diesel fuel (Fang et al., 2003).
During the vegetation period, the PM concentrations significantly decreased, and the ratio of fine particles in $\mathrm{PM}_{10}$ obviously increased. The mineral dust emissions can be ignored with the end of field activities and the increase in the soil coverage by crop growth and rainfall. Frequent rainfall events significantly improve the air quality by removing the air particles by wet deposition. Two high PM events were observed on 7 July and 1 September, which presented a high loading of $\mathrm{NO}_{3}^{-}$ , $S0_{4}^{2-}$ , and $\mathrm{NH_{4}^{+}}$ along with OC. These ions represent the basic characteristics of nitrate and sulfate, which are formed during photochemical reaction-induced secondary pollution (Cao et al., 2005). The July 7 episode was on the third day after a 10-day moderate rain period, and the mean wind speed was $3~\mathrm{m/sec}$ , indicating a calm air flow condition. The weather on this day was suitable for farm weeding and foliage spraying, and most activities were conducted by farm machines. On the one hand, agricultural activities in the field and vehicular emissions from National Road 102 could directly contribute to PM emissions by farm machinery and soil/crop disturbance; on the other hand, PM emissions from industrial or transportation sources that generated gas precursors for secondary aerosols were accumulated in weather conditions unfavorable to air diffusion. In addition, high ammonium concentrations most likely arose from chicken manure fermentation, because the manure consistently accumulated around the observation station. Therefore, both enhanced emission sources and unfavorable diffusion conditions were the contributors to this high PM event. For the second event, there were no corresponding agricultural activities in this period. The high contents of $\mathrm{NO}_{3}^{-}$ and OC may have resulted from the regional photochemical smog. On the observation day, the visibility $(7\ \mathrm{km})$ from the Changchun Weather Bureau was significantly lower than on the other sunny day $(11{-}16~\mathrm{km})$ .
Field operations during the crop harvest include reaping maize, straw burning, transporting maize stalks to homes for fuel during the winter, and fall plowing. These activities were directly affected by rainfall events. In October, we did not observe a large-scale straw burning phenomenon because the crop straw was not sufficiently dry for burning under the conditions of short intervals between rainfalls (three or four days). During the crop harvest, the PM samples over four days were above the Chinese standard. During these days, the $\mathsf{P M}_{10}$ concentrations were comparable to those in the soil tillage period, while the $\mathrm{PM}_{2.5}$ concentrations were significantly higher than those in the other periods (Fig. 1c). Carbonaceous species dominated the principal components of airborne $\mathrm{PM}_{10},$ with a contribution of $\sim\!50\%$ . Even the burning events were not strong in the observed period, which could elevate the OC contents. Furthermore, as the tracer of biomass burning, the increase of ${\tt K}^{+}$ and ${\tt K}$ confirmed its emission in agricultural burning operations. Previous studies have reported that particles from biogenic burning would become finer with age, and a high content of $\mathrm{NO}_{3}^{-}$ and $S0_{4}^{2-}$ was found in aged particles (Hays et al., 2005; Zhang et al., 2007). Similarly, increasing the $\mathrm{NO}_{3}^{-}$ and $S O_{4}^{2-}$ concentrations at the post-harvest stage also supported the importance of biomass burning to secondary aerosol formation. The mineral dust-related element concentrations in this harvest season were significantly lower than that during soil tilling, although agricultural machines and vehicles were also used in the field. The weak mineral dust emissions should be ascribed to the limited fugitive dust under the wet topsoil condition and the reduction in the use of the harvesting machinery due to frequent rainfall events.
Local chemical and temporal profiles induced by agricultural operations are of significant importance in air quality models, providing the key parameters (Hinz and Tamoschat-Depolt, 2007). In this study, the particle chemical profile of the field operations was not directly determined under controlled conditions. We indirectly estimated the chemical profile induced by soil tillage and crop straw burning by calculating the increased $\mathrm{PM}_{10}$ concentrations and components on the obviously polluted days with field operations and without field operations, i.e., pre-tilling before 1 May or pre-harvest days from September to October. Based on the field tilling-induced chemical profile of $\mathrm{PM}_{10}$ (Fig. 5), the operation raised the mineral-related elements by more than $53\%$ (i.e., Si, Al, Ca, $\mathbf{M}\boldsymbol{\ g},$ and Fe) by disturbance of the soil, and released considerable OC $(\sim\!21\%)$ through fuel consumption by diesel machinery. The straw burning primarily produced OC $(\sim\!56\%)$ and EC $(\sim\!5\%)$ , while secondary aerosol-related $\mathrm{NO}_{3}^{-}$ , $S0_{4}^{2-}$ and $\mathrm{{NH_{4}^{-}}}$ were formed in the aging process.
We assumed that the $\mathrm{PM}_{10}$ concentrations in the vegetation period represented the background (except for 4 July and 1 September). The differences in the hourly $\mathrm{PM}_{10}$ concentrations between the tillage period/harvest period and the background values were calculated as tilling- and burning-induced emissions. Because the high concentrations generally depend on the emission strength and the planetary boundary layer (PBL) height, we adjusted the diurnal profiles using PBL heights simulated from the Weather Research and Forecasting model (Fig. 6). According to these results, field tilling operations strongly emitted $\mathrm{PM}_{10}$ from 10:00 to 17:00, while the $\mathrm{PM}_{10}$ release from crop burning primarily occurred from 14:00 to 18:00. The estimated diurnal profiles from the field activities appear coincident with the local conventional practice, according to the farmers.
Our in-situ investigations confirm that soil tilling in May and crop burning in October are the most significant regional sources for particulate matter emission in the maize-dominant area in northeastern China. Creating an inventory of agricultural PM emission will improve the accuracy of load forecasting in local or even regional air quality. However, estimated parameter patterns concerning the chemical and diurnal profiles of these operations require further research because we were unable to provide sufficient replicates of the agricultural regions and the temporal coverage of $\mathrm{PM}_{10}$ (i.e., real-time observation) to precisely account for the diurnal variations resulting from infrastructural constraints.
4. Conclusions
Farmland tillage and crop straw burning from a maize-dominated region in northeastern China were identified as the most significant agricultural operations for area sources of PM emissions. Mineral dust and OC were the primary chemical compositions during the tillage period, while straw burning generated substantial OC during the harvest period. The short interval between rainfall events in May and October may significantly reduce the PM emissions by regulating the agricultural operations and increasing the soil moisture. The estimated diurnal profile of the $\mathrm{PM}_{10}$ emissions showed that soil tillage and planting practices were conducted nearly the entire day, while crop straw appeared to be burned mostly in the afternoon. The chemical and diurnal profiles of PM emission from field tilling and straw burning are useful to model local or regional air quality. In addition, our study revealed $\mathrm{PM}_{10}$ and $\mathrm{PM}_{2.5}$ events that were two times the level from these activities, indicating that other agricultural practices in the countryside, such as animal production or unconventional activities, are also potential sources of PM emission. However, the observation frequency of the PM concentrations and the chemical component analysis were insufficient to further identify these sources.
Acknowledgments
This study was financially supported by the National Natural Science Foundation of China (Nos. 41205106, 41275158). We would also like to thank the staff at the sampling sites for their support in the field experiments and for providing agricultural information.
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atmosphere | 0005 | 10.1016/j.scitotenv.2013.12.069 | image | Fig. 1. (a) Sampling location of the Cheng-Yu region (the shaded area) in China. (b) Location of the sampling site (the triangle) in Chengdu. |
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atmosphere | 0005 | 10.1016/j.scitotenv.2013.12.069 | table | Table 1 Measurement parameters and instruments adopted in the sampling site. |
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atmosphere | 0005 | 10.1016/j.scitotenv.2013.12.069 | image | Fig. 2. (a) Times-series of temperature $(\mathrm{T},{}^{\circ}\mathrm{C})$ , dew point $(^{\circ}C)$ , and pressure (P, hPa) during 14–21 May in Chengdu. (b) Times-series of relative humidity $(\mathrm{RH},\%)$ and wind speed $\left(\mathfrak{m}\;\mathbf{s}^{-1}\right)$ ) during 14–21 May in Chengdu. (c) Times-series of PM concentrations $\left(\upmu\mathrm{g}\:\mathsf{m}^{-3}\right)$ ) and visibility $(\mathrm{km})$ during 14–21 May in Chengdu. (d) Times-series of wind direction (WD, °) during 14–21 May in Chengdu. |
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atmosphere | 0005 | 10.1016/j.scitotenv.2013.12.069 | image | Fig. 3. Times-series of $S0_{2}$ , $\mathrm{NO}_{\mathrm{x}}$ $0_{3}$ and CO concentrations $\left(\upmu\mathrm{g}\:\mathfrak{m}^{-3}\right)$ ) during 14–21 May in Chengdu. (Missing data were due to the malfunction of instruments or power failure). |
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atmosphere | 0005 | 10.1016/j.scitotenv.2013.12.069 | image | Fig. 4. (a) Regional distribution of aerosol optical depth (AOD, $550~\mathrm{nm}$ ) retrieved from MODIS during 18–21 May in the Cheng-Yu region. (b) Fire spots retrieved from MODIS on 18 May in Chengdu. (c) Fire spots retrieved from MODIS on 19 May in Chengdu. (Chengdu, Chongqing, Deyang, Ziyang, Meishan, and Dujiangyan are denoted by CD, $_{\mathrm{CQ},}$ DY, ZY, MS, and DJY, respectively). |
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atmosphere | 0005 | 10.1016/j.scitotenv.2013.12.069 | table | Table 2 Comparison of the average concentrations of $S0_{2}$ $\mathrm{NO}_{\mathrm{x}},0_{3}$ and CO during 14 May to 21 May in Chengdu. |
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atmosphere | 0005 | 10.1016/j.scitotenv.2013.12.069 | image | Fig. 5. Correlation analysis between gaseous pollutants (CO, $S0_{2},$ $\mathrm{NO}_{\mathrm{x}})$ and $\mathrm{PM}_{2.5}$ during the haze episode in Chengdu. |
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atmosphere | 0005 | 10.1016/j.scitotenv.2013.12.069 | image | Fig. 6. The 3-h average mixing height during 14–21 May in Chengdu. |
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atmosphere | 0005 | 10.1016/j.scitotenv.2013.12.069 | image | Fig. 8. Three-day forward matrix trajectories terminated at $12{\cdot}00\ {\sf a.m.}$ (16:00 UTC) for 24-h intervals from 18 May to 21 May (matrix points $30^{\circ}$ , 30.5°, $31^{\circ}\,\mathrm{N}$ by $103^{\circ}$ , 103.5°, $104^{\circ}\mathrm{E}_{\mathrm{,}}$ . |
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atmosphere | 0005 | 10.1016/j.scitotenv.2013.12.069 | table | Table 3 Mass concentrations of $\mathrm{PM}_{10}$ $\mathrm{PM}_{2.5}$ and $\mathrm{PM}_{2.5}$ species in Chengdu. |
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atmosphere | 0005 | 10.1016/j.scitotenv.2013.12.069 | image | Fig. 9. Comparison of the enrichment factors (EFs) during and after the haze episode in Chengdu. |
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atmosphere | 0005 | 10.1016/j.scitotenv.2013.12.069 | text | Not supported with pagination yet | Characteristics and formation mechanism of a heavy air pollution episode caused by biomass burning in Chengdu, Southwest China
Yuan Chen, Shao-dong Xie ⁎
College of Environmental Science and Engineering, Peking University, No. 5 Yiheyuan Rd., Haidian District, Beijing 100871, PR China
H I G H L I G H T S
• Formation characteristics of a biomass burning event in Chengdu were analyzed.
• CO levels increased by two times, and were highly correlated with $\mathrm{PM}_{2.5}.$ • Burning habit, ineffective dispersion, and northeast wind led to nighttime peaks.
• The plume could affect large regions in northern and eastern China.
• High $\mathrm{PM}_{2.5}/\mathrm{PM}_{10},$ OC/EC, and K/EC ratios and OM levels were indices for biomass burning.
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 15 October 2013
Received in revised form 13 December 2013
Accepted 15 December 2013
Available online 3 January 2014
Keywords:
Biomass burning
Heavy pollution
Formation mechanism
$\mathrm{PM}_{2.5}$
Chengdu
To track the chemical characteristics and formation mechanism of biomass burning pollution, the hourly variations of meteorological factors and pollutant concentrations during a heavy pollution on 18–21 May, 2012 in Chengdu are presented in this study. The episode was the heaviest and most long-lasting pollution event in the historical record of Chengdu caused by a combination of stagnant dispersion conditions and enhanced $\mathsf{P M}_{2.5}$ emission from intensive biomass burning, with peak values surpassing $500~\upmu\mathrm{g}~\mathrm{m}^{-3}$ . The event was characterized by three nighttime peaks, relating to the burning practice and decreased boundary layer height at night. The prevailing northeasterly wind during nighttime preferentially brought more pollutants to the urban regions from northern suburbs of Chengdu, where dense fire spots were observed. Due to the obstruction of hilly topography and weak wind speed, minor regional features were reflected from the $\mathrm{PM}_{10}$ variations in nearby cities, whereas the long-distance transport of the plume impacted extensive regions in northern and eastern China. Carbon monoxide (CO) concentrations increased by more than $200\%$ , while exceptionally high $\mathrm{PM}_{2.5}$ levels of 190.1 and $268.4~\upmu\mathrm{g}~\mathrm{m}^{-3}$ on 17 May and 18 May, were observed and showed high correlation with CO $\mathrm{i}=0.751$ ). The relative contribution of biomass burning smoke to organic carbon was estimated from OC/EC ratios (organic carbon/elemental carbon) and elevated to $81.3\%$ during the episode, indicating a significant impact on urban aerosol levels. The occurrence of high $\mathrm{PM}_{2.5}/\mathrm{PM}_{10}$ ratios $(>\!0.80)$ and $\mathrm{K^{+}/E C}$ ratios $(>1.0)$ , along with the increased carbonaceous concentrations and their fraction in $\mathrm{PM}_{2.5}\left(>\!40\%\right)$ and high OC/EC ratios (about 8), could be used as immediate indicators for biomass burning pollution in cities. In addition, the heavy pollution involved a mixture of anthropogenic sources, reflected from the high SOR and NOR values and increases in the EFs (enrichment factors) of Mo, Zn, Cd, and Pb.
$\circledcirc$ 2013 Elsevier B.V. All rights reserved.
1. Introduction
Characterizing pollutant features and identifying the possible source region have been key issues in the study of biomass burning pollution, which poses as a great threat to air quality, atmospheric radiative budget, and human health (Andreae and Merlet, 2001; Arola et al., 2007; Lu et al., 2011; IPCC, 2007; Zheng et al., 2005). Particulate matter and gaseous species from biomass burning are studied over many parts of the world, focusing on tropical forest regions, such as Savanna, the
Amazon, and the Southeast Asia (Andreae et al., 1998; Andreae et al., 1996; Ramanathan et al., 2001; Yamasoe et al., 2000). In southern Australia, the impacts of biomass burning on rural communities are substantial but dependent on season, fire activity, and duration of plume strikes (Reisen et al., 2011), while open fires together with trash burning contributed $35\%$ of POA (primary organic aerosol) in the Mexico City Metropolitan Area (Lei et al., 2013). In many urban regions of China, the influence of biomass burning is mostly from open field burning of agricultural residues after harvest, sometimes causing severe deterioration of regional air quality (Duan et al., 2004; Qin and Xie, 2011). In Guangzhou of China, the biomass or biofuel smoke is mostly from upwind during the changes of two prevailing wind directions due to approaching tropical cyclones and the estimated contribution to organic carbon (OC) reaches up to $32\%$ (Zhang et al., 2010). The emissions of open crop residue burning in the nearby five provinces contribute as much as $26\%\,0_{3}$ , $62\%$ CO, $79\%$ BC (black carbon), and $80\%$ OC at the summit of Mount Tai (Yamaji et al., 2010), while $30\%{-}60\%$ of OC in Beijing is attributable to typical open burning aerosols from surrounding rural regions (Zheng et al., 2005).
Located in southwestern China, the Cheng-Yu region (also called the Sichuan Basin) covers $260,\!000\ \mathrm{km}^{2}$ with low altitudes of about $500~\mathrm{m}$ and is surrounded by mountains and a plateau higher than $4\;\mathrm{km}$ (Fig. 1). The region has long been recognized as a low visibility area with high PM levels (Che et al., 2007; Chen and Xie, 2012). The city of Chengdu is one of the two megacities of the region (the other one is Chongqing) with $\mathsf{P M}_{2.5}$ concentration varying a wide range from 49.2 to more than $400\,\upmu\mathrm{g}\,\mathfrak{m}^{-3}$ (Tao et al., 2013; Wang et al., 2013), mostly higher than the daily average of $75~\upmu\mathrm{g}~\mathrm{m}^{-3}$ in National Ambient Air Quality Standards (NAAQS, available at http://www.zhb.gov.cn/). Of the various pollution sources, biomass burning is an important contributor to airborne particles in the Cheng-Yu region owning to the widespread burning activity after harvest and large consumption of agricultural residues for energy source (Streets et al., 2001; Wang et al., 2013; Yang et al., 2012). It is estimated that $9.9~\mathrm{Tg}$ of biomass is burned per year in the Sichuan Province, many of which are open burning of crop residues (Streets et al., 2003). The impacts of biomass burning may be more influential on air quality of Chengdu, as it is surrounded by $4.3\times10^{3}~\mathrm{km}^{2}$ cultivated land at the heartland of the Chuanxi agricultural base (Chengdu Bureau of Statistics, 2010, http:// www.sc.stats.gov.cn/sctj/) and the harvest periods of different plants cover a long span from February to October. In addition, the local dispersion conditions are generally suppressed by adjacent mountains. Therefore, there is a great need to characterize the biomass burning aerosols and formation features of the pollution in the region. Compared with northern China, YRD (Yangtze River Delta), and PRD (Pearl River Delta) with intensive field campaigns (Andreae et al., 2008; Du et al., 2011; Fu et al., 2008; Huang et al., 2012; Ma et al., 2012; Wang et al., 2006; Yao et al., 2003; Zhang et al., 2008b), none of the studies have discussed the formation mechanism of haze in Chengdu and knowledge about local biomass burning is extremely limited.
According to the daily API released for public notification in 2012 (available at http://www.zhb.gov.cn/), there were four heavy pollution days in May (18–21 May) with API higher than 150 caused by open burning of biomass to increase soil fertility after spring harvest. This event was captured during a one-year $\mathsf{P M}_{2.5}$ sampling campaign (May, 2012–April, 2013), providing a rare chance to evaluate its influence in Chengdu. Therefore, one of the aims of the study was to explore the formation mechanism of this heavy pollution by analyzing the hourly variations of PM, gaseous pollutants, and meteorological factors during the episode. The source regions of the burning smoke and its possible influenced regions were identified by air mass trajectories. Furthermore, the chemical characteristics of the biomass burning influenced aerosol were also investigated.
2. Material and methods
2.1. Sampling site and description
Chengdu, with a population of 14.0 million and an area of $12{,}390~\mathrm{km}^{2}$ lies on the midstream of Minjiang River and west of the Sichuan Basin. It is a rare flat region in mountainous Southwestern China. Due to the obstruction of cold air by mountains in the north, Chengdu has a subtropical monsoon climate with high temperature and relative humidity, but with extremely low wind speed. From 1981 to 2010, the lowest monthly average temperature was $6.1~^{\circ}\mathrm{C}$ in January and the highest temperature was $25.8~^{\circ}\mathrm{C}$ in July (China Meteorological Administration, 1981–2010, http://cdc.cma.gov.cn/home.do). The monthly average RH was higher than $70\%$ while wind speed varied between 1.0 and $1.7~\mathrm{m/s}$ and no prevailing wind was observed.
The sampling site in Chengdu is on the roof of a sixth-floor building of Sichuan Environmental Monitoring Center (CD, $104^{\circ}6^{\prime}\mathrm{E}$ , $30^{\circ}36^{\prime}\mathrm{N}$ shown in Fig. 1b) with a height of $28\textrm{m}$ . It is only $20\;\mathrm{m}$ from a main road with high traffic density (Renmin South Road of Chengdu), and therefore the air quality is influenced by local vehicular emission, as well as residential emission and regional pollution, which is typical for the Chengdu urban area.
2.2. On-line monitoring of aerosols, gaseous pollutants and meteorological data
Continuous on-line measurements of $\mathsf{P M}_{10}.$ $\mathsf{P M}_{2.5},$ gaseous pollutants $S0_{2}$ , $\mathsf{N O}_{\mathrm{x}},$ CO, $0_{3}$ ), and five-factor meteorological meters (temperature, relative humidity, pressure, wind speed, and wind direction) were carried out synchronously at the sampling site in Chengdu. The QA/QC is performed weekly by trained staff of local environmental monitoring center according to the Automated Methods for Ambient Air Quality Monitoring (HJ/T 193–2005) released by Ministry of Environmental Protection in China. Table 1 lists the measured parameters and the instrument used in the sampling site. Visibility data in Chengdu was collected from http://www.wunderground.com. Surface synoptic patterns over eastern Asia were collected from Korea Meteorological Administration (http:// web.kma.go.kr/eng/).
2.3. Manual sampling and chemical analysis of PM
Information about the chemical compositions of PM was collected from our research aiming to study the long-term particulate pollution in Chengdu. The research started from May, 2012 and collected aerosol samples for $24~\mathrm{h}$ (from $10~\mathsf{a.m}$ . to $10~\mathsf{a.m}$ . of next day) every six days using a four-channel sampler made by Tianhong Instrument Co., Ltd. in Wuhan of China (model: TH-16A, flow rate: $16.7~\mathrm{L/min}$ for each channel). The measurement uncertainty of the sampler for $\mathsf{P M}_{2.5}$ concentrations is less than $10\%$ The flow rate of each channel was calibrated regularly during the sampling. $\mathsf{P M}_{10}$ samples were collected on Teflon filters ( $47~\mathrm{mm}$ , $2\;\upmu\mathrm{m}$ pore size, Whatman Inc., Maidstone, UK) through one of the channels equipped with a $10\;\upmu\mathrm{m}$ cyclone. Three channels equipped with $2.5\;\upmu\mathrm{m}$ cyclones collected $\mathsf{P M}_{2.5}$ samples on two Teflon filters and one quartz filter. The collected filter samples were stored in a freezer at $-18\,^{\circ}\mathrm{C}$ to avoid possible volatilization. During the episode, two groups of four samples (one group from $10~\mathsf{a.m}$ . on 17 May to $9{\cdot}30\ {\mathrm{a.m}}$ . on 18 May, and one from 10 a.m. to 6 p.m. on 18 May) were collected. Samples collected from 10 a.m. on 23 May to 10 a.m. on 24 May after the haze were used for comparison.
Teflon filters before and after sampling were weighted using an electronic balance (Metller Toledo AX105 DR) with a precision of $10^{-5}\:\mathrm{g}$ after balancing for $24~\mathrm{h}$ in a super-clean environment with constant temperature $(20\pm1~^{\circ}\mathrm{C})$ and relative humidity $(40\pm3\%)$ . Mass concentrations of $\mathrm{PM}_{2.5}/\mathrm{PM}_{10}$ were then obtained by dividing the mass difference before and after sampling by sampled air volume. The quartz filters were preheated at $550~^{\circ}\mathrm{C}$ for $5.5~\mathrm{h}$ in a muffle furnace to remove the absorbed organic vapors prior to sampling. Sampled filters were further analyzed for ions, elements, and carbon factions of PM. Simultaneous analyses of blank filter were performed and the blank values were subtracted from sample concentrations.
Samples on one Teflon filter were extracted ultrasonically using $10~\mathrm{mL}$ ultrapure water ( $18.5~\mathrm{M\Omega\cm}^{-1}$ ) for $30\;\mathrm{min}$ . The aqueous extracts were filtered through a $47\ \upmu\mathrm{m}$ water-filter and the ion concentrations $^{\leftmoon}\right.^{\leftmoon}$ , $\mathrm{NH}_{4}^{+}$ , $\mathsf{K}^{+}$ , ${\mathrm{Mg}}^{2+}$ , ${\mathsf{C}}{\mathsf{a}}^{2+}$ , $\mathtt{N O}_{3}^{-}$ , $S0_{4}^{2-}$ , and $\mathsf{C l}^{-}$ ) were determined using ion chromatography (Dionex, model: ICS 2000). An analytical column of AS11-HC (Dionex Ionpac, $4\;\mathrm{mm}$ ) with a guard column of AG11-HC (Dionex Ionpac, $4\;\mathrm{mm}$ ) was used for inorganic anion analysis using KOH eluent and a self-generating suppressed conductivity detector. A cation analytical column of CS12A (Dionex Ionpac, $4\;\mathrm{mm}$ ) with a guard column of CG12A (Dionex Ionpac, $4\;\mathrm{mm}$ ) was used to analyze inorganic cations with an eluent of $20\;\mathrm{mM}$ methyl sulfonic acid (MSA). Standard reference materials produced by the National Institute of Metrology, China were analyzed for quality assurance purpose.
$\mathsf{P M}_{2.5}$ samples on the other Teflon filter were digested in Teflon vessels with 3 mL $\mathrm{HNO}_{3}$ $.65\%)$ , 1 mL HCl $(38\%)$ , and $0.2~\mathrm{mL}$ HF $(48\%)$ at $175~^{\circ}\mathrm{C}$ for $1\mathrm{~h~}$ . After cooling, the solutions were then diluted with ultrapure water to $100~\mathrm{mL}$ and subjected to inductively coupled plasma mass spectrometry (ICP-MS, Agilent 7500c, Agilent Technologies Co., Ltd., USA) analysis. The measured elements included Al, Na, Mg, P, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Ag, Cd, Ba, Tl, and Pb.
The quartz filters were used for OC and EC (elemental carbon) analysis by an OC/EC analyzer produced by Sunset Laboratory, USA with the thermal/optical transmission (TOT) method (Chow et al., 2001) and a detection limit of $0.2~{\upmu\mathrm{g}}~{\mathsf{C}}~{\mathsf{c m}}^{-2}$ . A filter punch of $1.45~\mathsf{c m}^{2}$ in area was removed from the $47~\mathrm{mm}$ quartz filter and then analyzed following the NOISH thermal/optical transmission (TOT) protocol for ten carbon factions (four OC fractions at low temperature in a helium atmosphere, a pyrolyzed carbon fraction, and five EC factions at high temperature in a $2\%$ oxygen/ $198\%$ helium atmosphere). A He–Ne laser was used to monitor the sample transmission and correct the OC pyrolysis to EC at high temperature.
2.4. Model simulation and satellite observation
The mixing-layer heights during the episode were computed using the NCEP Global Data Assimilation System model (http://www.arl.
noaa.gov/READYamet.php). Both backward and forward trajectories, calculated by the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, were used to track the transport pathways of the airflow. In addition, spatial distribution of aerosol optical depth (AOD) at $550~\mathrm{nm}$ was provided by the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument (http://disc.sci.gsfc.nasa.gov/). Total fire spots retrieved from MODIS were also shown (http:// earthdata.nasa.gov/).
3. Results and discussion
3.1. Hourly variations of meteorological factors and pollutant concentrations
3.1.1. Before the episode
Figs. 2 and 3 depict the time-series of the various meteorological parameters, particulate concentrations, and trace gases ( $S0_{2}$ , $\Nu0_{\mathrm{x}}$ , CO, and $0_{3}$ ) before and during the episode. Results of the MODIS, providing important information about the regional distribution of AOD and fire spots at global scale, are also discussed (Fig. 4). During the period from 14 May to 16 May before the episode, no fire spots were observed in Chengdu, and it was recognized that less burning occurred and the air quality was largely influenced by other local anthropogenic pollution. The rain coupled with the high wind speed of $4.2~\mathrm{m/s}$ on 14 May of Chengdu removed the particles effectively from the air, and $\mathsf{P M}_{2.5}$ concentrations were preserved at low levels of 57 and $52\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ on 14 and 15 May (Fig. 2c), respectively. From early morning of 16 May, PM concentrations started to increase, as a surface high pressure formed upon Chengdu and degraded wind speed to $1.4\;\mathrm{m/s}$ (Fig. 2b). A few fire spots were observed on 17 May in north of Chengdu, but did not impact local air quality greatly. $\mathsf{P M}_{10}$ concentrations on 17 May decreased again and stabilized around $150\;\upmu\mathrm{g}\;\mathsf{m}^{-3}$ , when the high pressure gradually moved southeast and finally out of Chengdu (Fig. S1a,b).
Average concentrations of $S0_{2}$ , $\Nu0_{\mathrm{x}}$ , $0_{3}$ and CO before the haze (14 May to $17\;\mathrm{\;May})$ were 26.85, 97.8, 71.6 and $1172\ \upmu\mathrm{g}\ \mathrm{m}^{-3}$ (Table 2), respectively. The $S0_{2}$ levels were within $15\%$ difference reported by the local environmental monitoring center. The annual average concentration of $S0_{2}$ in urban Chengdu has decreased from $76.3~\upmu\mathrm{g}~\mathrm{m}^{-3}$ in 2005 to $30.1~\upmu\mathrm{g}~\mathrm{m}^{-3}$ in 2010, owing to the shutdown of several small coal-fired power plants, desulfurization of coal-fired factories, and the promotion of clean fuels (Sichuan Environmental Protection Bureau, http://www.schj.gov.cn/). Instead, the concentrations of $\Nu0_{2}$ in urban region have shown a slight increase to $51.0\,\upmu\mathrm{g}\mathrm{~m}^{-3}$ in 2010 in accompany with the increased use of gasoline and diesel-fueled vehicles.
3.1.2. Impact of biomass burning on air quality
Reflected from the spatial distribution of AOD at the wavelength of $550~\mathrm{nm}$ (Fig. 4a), a high AOD zone was observed in the Cheng-Yu region during the episode from 18 to 21 May, and Chengdu was obviously at the center of the zone with a high value of over 1.5. From 17 May to the early morning of 18 May, greatly enhanced PM pollution was observed and $\mathsf{P M}_{2.5}$ concentrations increased sharply to a peak of $433~\upmu\mathrm{g}~\mathrm{m}^{-3}$ . Over the next three days, daily $\mathsf{P M}_{2.5}$ ranged from 154.2 to $271.4~\upmu\mathrm{g}~\mathrm{m}^{-3}$ with significant hourly fluctuations, but even the minimum hourly concentration of $74~\upmu\mathrm{g}~\mathrm{m}^{-3}$ was close to the daily $\mathsf{P M}_{2.5}$ criteria of $75\,\upmu\mathrm{g}\;\mathrm{m}^{-3}$ in China. Daily visibility varied around $3.5\;\mathrm{km}$ with the lowest visibility of $1.3\;\mathrm{km}$ at 9:00 a.m. on 20 May. Daily average wind speeds were from 1.1 to $1.6~\mathrm{m/s}$ , and over $50\%$ of the hourly values were lower than $1.0\;\mathrm{m/s}$ .
During the biomass burning in Chengdu, concentrations of $S0_{2}$ $\mathsf{N O}_{\mathrm{x}},$ $0_{3}$ and CO (18 May to 20 May) ranged 31.8–44.7, 91.7–127.4, 52.1–78.4 and $2180{-}2700\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ , respectively (Table 2). The average concentrations of $S0_{2}$ and $\Nu0_{\mathrm{x}}$ (38.5 and $104.8~{\upmu\mathrm{g}}~\mathrm{m}^{-3}$ , respectively) were slightly higher than the period before the haze, and no significant difference was observed in the level of $0_{3}$ , whereas CO was greatly enhanced. It was expected that large amounts of gaseous pollutants were released during the incomplete combustion of biomass burning. A significant correlation between CO and $\mathsf{P M}_{2.5}$ with a high coefficient of 0.75 was observed during the pollution, indicating their similarity in sources (Fig. 5). However, both $S0_{2}$ and $\Nu0_{\mathrm{x}}$ had weaker correlations with $\mathsf{P M}_{2.5}$ $\mathrm{~\r~}_{\mathrm{r}}=0.62$ and $\mathrm{r}=0.31$ respectively) than CO did, meaning that biomass burning contributes little to the $S0_{2}$ and $\Nu0_{\mathrm{x}}$ emission and vehicle and coal combustion played minor roles in the aerosol formation of the event. CO is emitted predominantly by the smoldering combustion during biomass burning and is usually chosen as a reference gas for estimating the emission ratios for other species emitted mostly during smoldering (Andreae and Merlet, 2001). The estimated emission factor of CO in the burning of agricultural residues was $92\pm84\mathrm{\,g\,kg^{-1}}$ , far above than those of $S0_{2}$ $(0.40\mathrm{~g~kg^{-1}})$ and $\mathrm{NO_{x}}$ $2.5\pm1.0\mathrm{~g~kg}^{-1}\$ (Andreae and Merlet, 2001).
3.1.3. Nighttime peaks of PM during the episode
Temporal variations of PM during the episode were characterized by three periods with typical peaks at nighttime (Fig. 2c): (1) In the early morning (12:00 a.m. to $8{\mathrm{:}}00\ {\mathrm{a.m.}}$ ) of $18\;\mathrm{May}$ , the $\mathsf{P M}_{2.5}$ levels sharply increased from about $100\;\upmu\mathrm{g}\;\mathsf{m}^{-3}$ to more than $300\;\upmu\mathrm{g}\;\mathsf{m}^{-3}$ within $^{8\mathrm{~h~}}$ , companied by the slump of visibility to only $2~\mathrm{km}$ . The peak values of $\mathsf{P M}_{10}$ reached more than $500\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ . At the same time, the surface high pressure persisted over Chengdu (Fig. S1) and the dispersion conditions were unusually stagnant. (2) During the second period from $10{\mathrm{:}}00\ {\mathrm{p.m.}}$ . on 18 May to 2:00 p.m. on 19 May, the average $\mathsf{P M}_{10}$ concentrations reached $317\,\upmu\mathrm{g}\mathrm{~m}^{-3}$ with peak values around $400\,\upmu\mathrm{g}\;\mathsf{m}^{-3}$ and average wind speed of $1.5\;\mathrm{m}/\mathrm{s}$ . (3) During the period from afternoon of 20 May to early morning of 21 May, peak $\mathsf{P M}_{10}$ concentrations of $505~\upmu\mathrm{g}~\mathrm{m}^{-3}$ and $578~\upmu\mathrm{g}~\mathrm{m}^{-3}$ were observed at $2{:}00\ \mathrm{p.m}$ on 20 May and 3:00 a.m. on 21 May, respectively.
Two reasons could possibly explain the nighttime peaks during the episode. Firstly, most of the crop residues were burnt during afternoon or night, as local farmer tends to dry the straws during daytime when the solar radiation is intense. The reduced supervision from local authority at nighttime also added to the burning events. Fire spots retrieved from MODIS verified the peaks occurred on $18{-}19\ \mathrm{May},$ , as large numbers of fire spots were observed at the suburbs and only a few kilometers away from downtown (Fig. 4b, c). However, few fire spots were observed on 20–21 May, which may due to the blur of high cloud amount. Furthermore, the higher nighttime PM levels could be partially explained by the reduced dispersion and inadequate vertical mixing resulting from the weak wind speed and low mixing layer height at night. For instance, the wind speed in the early morning of 18 May was as low as $0.28~\mathrm{m/s}$ , and the mixing layer height was lower than $100~\mathrm{m}$ (Fig. 6). The different prevailing wind direction between daytime and nighttime influenced the dispersion of the air pollutants as well. During the episode, weak northeasterly wind was prevailing at night when PM levels were high (Fig. 2c), followed by a change in wind direction to southwesterly or southeasterly during daytime. As the burning area was mostly concentrated in north suburbs of Chengdu (see Section 3.2) at night, wind in northern direction preferentially brought more pollutants to the urban regions. Typically, decrease in the first peak values in the afternoon of 18 May was accompanied by the change in the wind direction to southeast and southwest.
It was reflected that the haze episode was caused by a combination of intensive pollution and stagnant dispersion conditions. The $\mathsf{P M}_{2.5}$ levels rapidly increased to a high level during the occurrence of biomass burning, and then lasted for more than 3 days with contributions from other local sources. During 19 May and 20 May, Chengdu was at the edge of the high pressure system and dispersion conditions only improved slightly. The average wind speed was $1.5~\mathrm{m/s}$ , and the air was rather wet with increasing dew point and average $\mathrm{RH}\!>\!60\%$ . The persistent burning plus the weak wind speed and high relative humidity also provided a longer contact time for atmospheric components and promoted the gas-to-particle transformation in the air. Afterwards, the cold air from plateau on northwest Chengdu gradually moved eastward, while the warm air from southeast has brought much moisture. Owning to the confluence of cold and warm air over Chengdu, it started to rain at about 3 a.m. on 21 May (Fig. S1d). The PM concentrations fell to the level of normal air quality within a few hours owning to the effective scavenging of precipitation (Fig. 2).
3.2. Source regions of PM and the long-range transport of air mass
Hourly variations of the $\mathsf{P M}_{10}$ concentrations at six other monitoring stations in Chengdu provide important information about the transportation of biomass burning pollution in the city. In the early morning of 18 May when the burning started, sharp increase of $\mathsf{P M}_{10}$ concentrations was firstly observed at Jinniuba (JNB, Fig. 7a) north of the city, then at Liangjiaxiang northeast of the city (LJX, Fig. 7a), followed by Caotangsi (CTS), Renmingongyuan (RMGY), and Shilidian (SLD) of downtown area (Fig. 7b). The rise of $\mathsf{P M}_{10}$ concentrations was last observed at Shahepu (SHP, Fig. 7a) southeast of the city. It was expected that the smoke was originated from north side of the city, in accordance with the observed fire spots in Qingbaijiang and Guanghan regions in north suburbs of Chengdu, which were reported to have wide burning activity in May due to the harvest of rape and wheat. The transport pathways were also in agreement with the prevailing northeasterly wind during the early morning of 18 May. The daily average $\mathsf{P M}_{10}$ concentrations in Chengdu rose from $36.5–113.6~\upmu\mathrm{g}\,\,\mathrm{m}^{-3}$ on 14 May to $328.7–504.4\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ on 18 May, and then declined to 20.1– $102\ \upmu\mathrm{g}\ \mathrm{m}^{-3}$ on 22 May.
The variations of $\mathsf{P M}_{10}$ concentrations in nearby cities, including Ziyang (ZY, $87~\mathrm{km}$ away from southeast of Chengdu), Meishan (MS, $70~\mathrm{km}$ away from southern Chengdu), Deyang (DY, $60~\mathrm{km}$ away from northeast Chengdu), and Dujiangyan (DJY, $50~\mathrm{km}$ away from northwest of Chengdu), were also shown in Fig. 7c. Variations of $\mathsf{P M}_{10}$ levels in DY roughly followed the variation patterns of PM in Chengdu, while the increases in other cities were relatively small. It was demonstrated that the pollutant transport was mostly toward northern directions, as northeast wind was prevailing wind at night with speed lower than ${<}1.0\ \mathrm{m}/{s}$ and replaced rapidly by a complete change to southwesterly or southeasterly at daytime. Moreover, the mountains in southern Chengdu have inhibited the transport of pollutants toward ZY and MS, exhibiting weak regional characteristics.
As the biomass burning in Chengdu was rather serious, it might influence other regions as a possible source. To track the transport of the plume, the three-day matrix trajectories terminated at 12:00 a.m. (16:00 UTC) for 24-h intervals from 18 May to 21 May at the height of $100\;\mathrm{m}$ , were also shown (Fig. 8a–d). The matrix points were evenly distribution and covered the whole city ( $30^{\circ}$ , $30.5^{\circ}$ , $31^{\circ}\,\mathrm{N}$ by $103^{\circ}$ , 103.5°, $104^{\circ}\mathrm{E}_{\mathrm{,}}$ . It showed that the air masses on 18–19 May from Chengdu all traveled in the northeastern direction, passing by Deyang city and further influencing Gansu, Ningxia, and Inner Mongolia (Fig. 8a–b). From 20 May, the air masses rotated clockwise and influenced vast regions in east China (Fig. 8c–d), including Hubei, Henan, and the Yangtze River Delta. On 21 May, the air masses firstly traveled southeast and caused the increase of PM levels in Meishan and Ziyang, then turned abruptly to the east. It is interesting to note that high levels of PM were not observed at a large scale despite of the long-distance transport of the air masses. The possible explanation was that, the air masses from the surface layer of Chengdu elevated rapidly and reached the height of exceeding $1000~\mathrm{m}$ after 24-h travel, which might not threaten the ground air quality immediately at the receptor site.
3.3. Chemical characteristics of burning influenced aerosols
3.3.1. Enhanced $P M_{I O}$ and $P M_{2.5}$ mass concentrations
Table 3 compares the manual sampling results during (17 May and $18\;\mathrm{May})$ and after (23 May) the episode in Chengdu. Greatly enhanced aerosol pollution was observed on 17 May and 18 May with $\mathsf{P M}_{10}$ concentrations reaching 237.6 and $315.8~{\upmu\mathrm{g}}~\mathrm{m}^{-3}$ , respectively. These values not only were much higher than the annual $\mathsf{P M}_{10}$ average concentrations of $119.1~\upmu\mathrm{g}~\mathrm{m}^{-3}$ reported by local environmental monitoring center, but also exceeded the Grade II NAAQS of $150~\upmu\mathrm{g}~\mathrm{m}^{-3}$ . The observed $\mathsf{P M}_{2.5}$ concentrations during the episode were 190.1 and $268.4\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ , respectively, four to seven times higher than the $\mathsf{P M}_{2.5}$ daily average concentrations of $39.4~\upmu\mathrm{g}~\mathrm{m}^{-3}$ on 23 May after the episode. The $\mathrm{PM}_{2.5}/\mathrm{PM}_{10}$ ratios in Chengdu increased to 0.80 and 0.85 on 17–18 May, respectively, while the ratio was 0.76 on May 23 after the episode, suggesting that fine aerosol accounted for the majority of $\mathsf{P M}_{10}$ during biomass burning pollution.
3.3.2. Carbonaceous species and ratios of OC/EC
Of the various PM species with increased concentrations, organic aerosols including OC and EC were the most significantly enhanced group. OC in $\mathsf{P M}_{2.5}$ averaged 62.2 and $74.0\;\upmu\mathrm{g}\;\mathfrak{m}^{-3}$ on 17 May and 18
May, respectively, while EC averaged 7.5 and $8.1~{\upmu\mathrm{g}}~\mathrm{m}^{-3}$ , respectively. OC concentrations were seven to eight times higher than normal days $\mathbf{\Psi}^{\left(8.9\right)}\,\upmu\mathrm{g}\,\,\mathrm{m}^{-3}$ , Table 2), and OM (organic matter) contributed $52.4\%$ and $44.1\%$ to $\mathsf{P M}_{2.5}$ mass on 17 May and 18 May, respectively, using an OM/OC ratio of 1.6. The reported annual average OC and EC concentrations in Chengdu during 2009–2010 were 20.7 and $5.7~\upmu\mathrm{g}\mathrm{~m}^{-3}$ (Tao et al., 2013). Thus, the elevated level in OC was more significant than that in EC. The ratio of OC to EC is on the order of 3 for urban locations in China, and the reported annual average value in Chengdu is about 3.3 (Cao et al., 2007; Zhang et al., 2008a). However, the OC/EC ratios during the episode reached 8, even higher than many rural sites. The high OC/EC ratio in biomass burning related with the high organic matter content in vegetation and was verified by other studies (Watson et al., 2001). For instance, OC/EC ratio as high as 7.1 was calculated in open biomass burning in field (Cao et al., 2006), and the burning of wheat residue and maize generated a low EC to OC ration of 0.09 and 0.10 (Li et al., 2009). OC/EC ratios measured in field experiment of Chengdu reached 16.1–16.6 for wheat straw, 2.3 for rape straw burning, and an average of 9.3 (Tao et al., 2013). Using the average OC/EC ratio of 9.3 for wheat and rape straw burning and 3.3 for other urban sources, it is estimated that $81.3\%$ of the total OC was contributed by biomass burning in this episode.
3.3.3. Ionic species
The major soluble species from biomass burning were $\mathsf{C l}^{-}$ , $\mathtt{N O}_{3}^{-}$ , $S0_{4}^{2-}$ , $\mathrm{NH}_{4}^{+}$ , and $\mathsf{K}^{+}$ (Table 3). Comparing with the $36.8\%$ contribution on 23 May, the mass ratio of SNA (secondary inorganic aerosol, sum of sulfate, nitrate, and ammonium) to $\mathsf{P M}_{2.5}$ during the episode was relatively low $\!\!\!\,26.7\!-\!\!26.9\%\!\!\!$ ) because of the significant increase of carbonaceous aerosols, but the absolute concentrations of the SNA species were much higher (Table 3). $\mathsf{K}^{+}$ of PM is a good indicator for estimating biomass burning emission and tracing aerosol long-range transportation in the atmosphere (Cachier et al., 1995). $\mathsf{K}^{+}$ concentrations reached as high as 8.7 and $15.1~\upmu\mathrm{g}~\mathrm{m}^{-3}$ on 17 May and 18 May, respectively, which was 17 to 30 times higher than the clean day after the episode (Table 3). The total potassium accounted for $4.5–5.6\%$ of $\mathsf{P M}_{2.5}$ mass during the episode, of which more than $90\%$ was from biomass burning emission using a K/Fe ratio of 0.56 to estimate the contribution of mineral and dust (Malm et al., 1994). The average concentrations of $C1^{-}$ were also remarkably higher, in accordance with the high emission factors of KCl from rape and wheat straw burning (Tao et al., 2013). $\mathsf{K}^{+}$ was primarily generated from biomass burning and EC was produced by both fossil fuel combustion and biomass burning in the city, and the ratio of Kexcess (total potassium minus soil potassium) to EC could be used as an indicator for source identification (Andreae, 1983). The calculated $\mathrm{K}_{\mathrm{excess}}/$ EC during the episode reached 1.09–1.60, within the range of 0.85–2.04 for smoke particles from wheat fields in Chengdu (Tao et al., 2013), but higher than the ratios in previous biomass burning episodes (i.e. 0.21 to 0.46 in Brazil; 0.70 and 0.84 in Chengdu; 0.24 and 0.29 in Guangzhou) (Andreae, 1983; Tao et al., 2013; Wang et al., 2013; Zhang et al., 2010).
As $\mathsf{K}^{+}$ is steady, it remains in particulate phase during transportation and provides more surfaces for atmospheric secondary reaction, and gaseous $S0_{2}$ and $\mathsf{N O}_{2}$ tend to condense on pre-existing KCl and convert into sulfate and nitrate (Du et al., 2011; Eq. (1)). As a result, on 18 May, the sulfate and nitrate concentrations enhanced greatly to 42.8 and $14.9~\upmu\mathrm{g}~\mathrm{m}^{-3}$ . The SOR (sulfur oxidation ratio, defined as $n{-}{\mathsf{S O}}_{4}^{2-}\ ,$ $(n{-}50_{4}^{2-}\,+\,n{-}50_{2})$ ) and NOR (nitrogen oxidation ratio, defined as $n{\mathrm{-}}{\mathrm{NO}}_{3}^{\mathrm{-}}\ /$ $n_{}\mathrm{NO}_{3}^{-}\,+\,n_{}\mathrm{-NO}_{2})$ ) reached 0.54 and 0.12 on 17 May and 0.45 and 0.14 on 18 May, respectively, much higher than the values on the clean day of 23 May $\mathrm{^SOR}=0.05\$ and $\mathrm{NOR}=0.03$ ). The high SOR and NOR values verified the enhanced secondary transformation process in the atmosphere, and corroborated the high SNA concentrations during the episode.
$$
\mathrm{SO}_{2}(\mathrm{NO}_{2})+\mathrm{KCl}+\mathrm{H}_{2}\mathrm{O}\mathrm{\rightarrowK}_{2}\mathrm{SO}_{4}+\mathrm{KHSO}_{4}(\mathrm{KNO}_{3})+\mathrm{HCl}
$$
3.3.4. Enrichment factors of elements
Enrichment factor (EF) is defined as $\mathrm{EF}=[\mathrm{X}\ /\ \mathrm{Ref}]_{\mathrm{sample}}\ /\ [\mathrm{X}\ /$ Ref]source, where $[\mathrm{X}\,/\,\mathrm{Ref}]_{\mathrm{sample}}$ denotes the concentration ratio of interested element to the reference element in the sample, and [X / Ref]source represents the concentration ratio of interested element to the reference element in the source. In our study, Al was used as a reference element from crustal source. The EFs of Ti, Fe, Mg, V, Ba, Na, Ca, Ni, and Mn were less than 10 and these elements were considered mainly natural dust-derived, whereas the EFs of Cu, Cr, As, Mo, Zn, Se, Cd, and $\mathsf{P b}$ were much higher than 10 and they were from anthropogenic sources. During the episode, the EFs of $\mathsf{K}^{+}$ increased to 41.0 and 60.1 on 17 May and 18 May, respectively, contrasting to the low value of 2.8 on 23 May. Moreover, most of the elements from anthropogenic sources were also enriched, whereas variations in the EFs of natural sources were negligible (Fig. 9). The EFs of Mo, Zn, Cd, and Pb increased to 284.6, 652.4, 3358.4, and 22,239.4 on 18 May, respectively, which were 1.6 to 6.7 times of the values on clean day. Mo, Zn, and Cd are from metallurgy and metal production and traffic emission, and $\mathsf{P b}$ is related with coal combustion. The increase in these elements suggested that the biomass burning episode involved in a complex mix and interaction with aerosols and gaseous precursors from local anthropogenic pollution.
4. Conclusion
The major findings of this study are summarized as follows:
(1) Using a synergy of on-line measurement, manual sampling, and remote sensing, the formation of the heavy pollution episode during 18–21 May in Chengdu was identified to be caused by a combination of increased PM emission from biomass burning and poor meteorological conditions. During the episode, a high AOD zone with dense fire spots was observed around Chengdu with peak concentrations of PM reaching more than $500\,\upmu\mathrm{g}\mathrm{\;m}^{-3}$ , which was unprecedented in previous research. The pollution was characterized by dramatic build-up of PM concentrations at nighttime, when intensive burning of crop residues was carried out at night by local farmers and the prevailing northeasterly wind brought resultant pollutants from northern suburbs. The almost stagnant dispersion conditions with wind speed lower
than $1.0\;\mathrm{m/s}$ and extremely low mixing layer at night also contributed to the higher nighttime PM levels.
(2) Temporal variation of $\mathsf{P M}_{10}$ in monitoring stations of Chengdu confirmed that the burning regions concentrated in northern suburbs of Chengdu, consistent with the observed prevailing northeasterly wind at night. However, the stagnant weather and hilly topography led to ineffective dispersion of pollutants on a regional scale and prolonged the heavy-pollution period in Chengdu.
(3) Pollution during the haze was characterized by high PM levels, high $\mathrm{PM}_{2.5}/\mathrm{PM}_{10}$ and OC/EC ratios, and greatly enhanced organic aerosol and CO concentrations. OM accounted for $52.4\%$ and $44.1\%$ of the $\mathsf{P M}_{2.5}$ mass concentrations on 17 May and 18 May (190.1 and $268.4~\upmu\mathrm{g}~\mathrm{m}^{-3}$ , respectively), of which $81.3\%$ was attributed to biomass burning, suggesting significant impact on urban aerosols. CO concentrations
increased to $2180{-}2700\ \upmu\mathrm{g}\ \mathrm{m}^{-3}$ , more than twice of those before the haze, and showed high correlation with $\mathsf{P M}_{2.5}$ $\mathrm{~r~}=0.75$ ). The enrichment in the EFs of anthropogenic sources suggested that local industrial and traffic emission played minor roles in the episode.
Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2013.12.069.
Conflict of interest
We do not have any conflict of interest that could possibly influence our publication of the submitted manuscript.
Acknowledgments
This study was funded by the Major State Basic Research Development Program of China (973 Program) (No. 2010CB955608), the public grant from the Ministry of Environmental Protection of China (Characteristics and Controlling Measures of Atmospheric Haze in the ChengYu region, No. 201009001), and the scholarship of the US Energy Foundation. We gratefully acknowledge NASA for the MODIS data, NOAA Air Resources Laboratory for providing the HYSPLIT trajectory model, and READY website for calculation of mixing layer height. We would also like to thank the Sichuan Environmental Monitoring Center for the help in sample collection and the use of aerosol and gas data.
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atmosphere | 0006 | 10.3390/atmos10020078 | image | Figure 1. (a) Location of Sichuan and Chongqing in China; (b) Sampling sites of Chengdu (CD), Neijiang (NJ), and Chongqing (CQ). |
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atmosphere | 0006 | 10.3390/atmos10020078 | table | Table 1. Particle and water-soluble inorganic ion (WSII) concentrations in CD, NJ, and CQ (2012–2013). |
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atmosphere | 0006 | 10.3390/atmos10020078 | image | Figure 2. Seasonal variations of $\mathrm{PM}_{2.5}$ and WSIIs in CD, NJ, and CQ. |
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atmosphere | 0006 | 10.3390/atmos10020078 | image | Figure 3. Scatter plots of total anions vs. total cations in (a) CD, (b) NJ, and (c) CQ. |
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atmosphere | 0006 | 10.3390/atmos10020078 | image | Figure 4. Scatter plots of ammonium and the major acidic anions in $\mathrm{PM}_{2.5}$ of $(\mathbf{a},\mathbf{d},\mathbf{g})$ CD, $({\bf b},{\bf e},{\bf h})\ \mathrm{NJ}.$ , and (c,f,i) CQ. |
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atmosphere | 0006 | 10.3390/atmos10020078 | table | Table 2. The correlation coefficients $(R)$ between $\mathrm{NO}_{3}^{\ensuremath{-}}$ and cations in $\mathrm{PM}_{2.5}$ of CD, NJ, and CQ. |
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atmosphere | 0006 | 10.3390/atmos10020078 | table | Table 3. Seasonal variation of sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR) in CD, NJ, and CQ. |
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atmosphere | 0006 | 10.3390/atmos10020078 | table | Table 4. $\mathrm{NO}_{3}^{\mathrm{~-~}}/\mathrm{SO}_{4}^{\mathrm{~2-~}}$ ratios and NOR/SOR ratios under four different $\mathrm{PM}_{2.5}$ levels. |
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atmosphere | 0006 | 10.3390/atmos10020078 | image | Figure 5. $[\mathrm{NO}_{3}^{\mathrm{~-}}]/[\mathrm{SO}_{4}^{\mathrm{~2-}}]$ ratio as a function of $[\mathrm{NH_{4}}^{+}]/[\mathrm{SO_{4}}^{2-}]$ in (a) CD, (b) NJ, and (c) CQ. |
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atmosphere | 0006 | 10.3390/atmos10020078 | image | Figure 6. $\mathrm{NO}_{3}^{\mathrm{~-~}}$ concentration as a function of excess $\mathrm{NH_{4}}^{+}$ in (a) CD, (b) NJ, and (c) CQ. |
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atmosphere | 0006 | 10.3390/atmos10020078 | image | Figure 7. Relationship between $[\mathrm{NO}_{3}^{\mathrm{~-}}]/[\mathrm{SO}_{4}^{\mathrm{~2-}}]$ and $[\mathrm{NH_{4}}^{+}]/[\mathrm{SO_{4}}^{2-}]$ under different (a) acidity and (b) RH. |
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atmosphere | 0006 | 10.3390/atmos10020078 | table | Table 5. PCA factor loadings of WSIIs in CD, NJ, and CQ. |
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atmosphere | 0006 | 10.3390/atmos10020078 | text | Not supported with pagination yet | Article Characteristics and Sources of Water-Soluble Ions in PM2.5 in the Sichuan Basin, China
Yuan Chen 1, Shao-dong Xie 2,\*, Bin Luo 3 and Chongzhi Zhai 4
School of Safety and Environmental Engineering, Capital University of Economics and Business, NO.121 Zhangjialukou Rd, Fengtai District, Beijing 100070, China; [email protected]
2 College of Environmental Science and Engineering, Peking University, NO.5 Yiheyuan Rd, Haidian District, Beijing 100871, China
3 Sichuan Provincial Environmental Monitoring Center, NO.88 3rd East Guanghua Rd, Qingyang Districat, Chengdu 610041, China; [email protected] Chongqing Environmental Monitoring Center, NO.252 Qishan Rd, Yubei District, Chongqing 401147, China; [email protected] Correspondence: [email protected]; Tel.: +86-10-6275-5852
Received: 19 January 2019; Accepted: 7 February 2019; Published: 15 February 2019
Abstract: To track the particulate pollution in Sichuan Basin, sample filters were collected in three urban sites. Characteristics of water-soluble inorganic ions (WSIIs) were explored and their sources were analyzed by principal component analysis (PCA). During 2012–2013, the $\mathrm{PM}_{2.5}$ concentrations were $86.7\pm49.7~\upmu\mathrm{g}~\mathrm{m}^{-3}$ in Chengdu (CD), $78.6\pm36.8~\upmu\mathrm{g}~\mathrm{m}^{-3}$ in Neijiang (NJ), and $71.7\pm36.9\;\upmu\mathrm{g}\,\mathrm{m}^{-3}$ in Chongqing (CQ), respectively. WSIIs contributed about $50\%$ to $\operatorname{PM}_{2.5},$ and $90\%$ of them were secondary inorganic ions. $\mathrm{NH_{4}}^{+}$ and $\mathrm{NO}_{3}^{\ensuremath{-}}$ roughly followed the seasonal pattern of $\mathrm{PM}_{2.5}$ variations, whereas the highest levels of $^{\mathrm{SO}_{4}}{}^{2-}$ appeared in summer and autumn. $\mathrm{PM}_{2.5}$ samples were most acidic in autumn and winter, but were alkaline in spring. The aerosol acidity increased with the increasing level of anion equivalents. $\mathrm{SO}_{4}{}^{2-}$ primarily existed in the form of $(\mathrm{NH}_{4})_{2}\mathrm{SO}_{4}$ . Full neutralization of $\mathrm{NH_{4}}^{+}$ to $\mathrm{NO}{_3}^{-}$ was only observed in low levels of $\mathrm{SO}_{4}{}^{2-}+\mathrm{NO}_{3}{}^{-}$ , and $\mathrm{NO}_{3}^{\ensuremath{-}}$ existed in various forms. $\mathrm{SO}_{4}{}^{2-}$ and $\mathrm{NO}_{3}^{\mathrm{~-~}}$ were formed mainly through homogeneous reactions, and there was the existence of heterogeneous reactions under high relative humidity. The main identified sources of WSIIs included coal combustion, biomass burning, and construction dust.
Keywords: $\operatorname{PM}_{2.5;}$ water-soluble ions; sources; Sichuan Basin
1. Introduction
Water-soluble inorganic ions (WSIIs) are a major part of fine particles $(\mathrm{PM}_{2.5},$ particulate matter with an aerodynamic diameter less than $2.5~{\upmu\mathrm{m}}$ ). Of the various components, secondary inorganic ions (SII), including sulfate $({\mathrm{SO}}_{4}{}^{2-})$ , nitrate $\left(\mathrm{NO}_{3}-\right)$ , and ammonium $\left(\mathrm{NH_{4}}^{+}\right)$ , are the predominant species and account for more than $90\%$ of WSIIs [1]. Nationwide, SII contribute about $25\%{-}48\%$ to $\mathrm{PM}_{2.5}$ mass, and are attributable to about $60\%$ of the visibility reduction in China [2]. Moreover, they also play important roles in atmospheric acidification and climate change [3,4]. Characteristics of the WSII pollution in many cities of China have been studied, and the formation of SII has always been a focus [5]. Sulfate is primarily formed through homogeneous gas-phase oxidation of sulfur dioxide, while heterogeneous transformation processes, i.e., metal-catalyzed oxidation, $\mathrm{H}_{2}\mathrm{O}_{2}/\mathrm{O}_{3}$ oxidation, and in-cloud process, are also reported [6] (p. 348), [7]. Both homogeneous reaction via $\mathrm{NO}_{2}$ oxidation by OH radical and $\mathrm{O}_{3}$ , and the heterogeneous hydrolysis of $\mathrm{N}_{2}\mathrm{O}_{5}$ on preexisting aerosols, are important pathways of nitric acid formation [8].
The Sichuan Basin (also called the Cheng-Yu region) is located in southwestern China (Figure 1a), covering the eastern part of Sichuan Province and most regions of Chongqing city. The basin is an important city agglomeration in western China with two megacities, namely, Chengdu and Chongqing, and 14 small and medium-sized cities. In total, the region has a population of more than 100 million. As a region with high RH, low wind speed, and unfavorable dispersion conditions, the Sichuan Basin has suffered from low visibility and air quality deterioration for a long period [9]. With its rapid urbanization, soaring industrialization, and booming population in the last two decades, air pollution is of great concern in the basin. The limited studies about particulate pollution in the Sichuan Basin have focused on Chengdu, and some of them covered Chongqing while a few of them studied the small cities in the basin. Source apportionment concluded that secondary inorganic aerosols contributed $37\%$ to $\mathrm{PM}_{2.5}$ mass and were the predominant sources of particles in Chengdu [10]. In Chongqing, WSIIs generally accounted for more than $40\%$ of $\mathrm{PM}_{2.5}$ and displayed an acidic feature [11–14]. Water content and RH played more critical roles than aerosol acidity in the heterogeneous formation of nitrate in Chongqing [12,15]. After the implementation of the Clean Air Action Plan in 2013, the $\mathrm{PM}_{2.5}$ levels in most of the cities of the Sichuan Basin have shown remarkable decreases (i.e., $52.2\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ for Chengdu and $44.0\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ for Chongqing in 2017) (Ranking of Air Quality in China, 2017). However, the current $\mathrm{PM}_{2.5}$ levels still exceed the Grade II National Ambient Air Quality Standard (NAAQS-II) $(35~\upmu\mathrm{g}\textrm{m}^{-3}$ for annual average). Meanwhile, long-term observations of the local WSII pollution, especially in medium or small cities, are still lacking. As for the production of sulfate and nitrate, this varies from site to site, depending on the emission of gaseous precursors ( $S O_{2}$ and $\mathrm{{NO}_{x}}$ ), levels of gaseous and aqueous oxidants, characteristics of existing particles, and meteorological conditions [16]. Therefore, more studies addressing the WSII characteristics and the formation of secondary aerosols in the region, with specific geographical and meteorological conditions, are needed to supply effective guidance on local control strategies.
During 2012–2013, an intensive haze campaign was carried out in the region and detailed information about fine particle pollution was gathered from Chengdu, Chongqing, and Neijiang (a medium-sized city in the Sichuan Basin). The one-year field sampling data is summarized, here, to provide a comprehensive study of the WSIIs in $\mathrm{PM}_{2.5}$ of the Sichuan Basin. The WSII property is analyzed from annual and seasonal perspectives, and the acidity characteristics of the sulfate–nitrate–ammonium system and nitrate formation are investigated in the three cities of different sizes. The indicators reflected from the WSIIs are also explored to track the sources of $\mathrm{PM}_{2.5}$ in the region.
2. Experiments
2.1. Site Description and Field Sampling
Locations of the three sampling sites in the Sichuan Basin are denoted in Figure 1b. Chengdu is the provincial capital of Sichuan Province and surrounded by many small and medium-sized cities in the west of Sichuan basin. The sampling site in Chengdu is on the roof of a sixth-floor building with a height of $28\,\mathrm{m}$ (CD, $104^{\circ}6^{\prime}$ E, $30^{\circ}36^{\prime}\,\mathrm{N})$ , which stands beside a main road with high traffic density. The site is representative of urban air quality, combining the influence of local vehicular emission, residential emission and regional pollution.
Chongqing is a municipality that is directly administrated by the Central Government. The sampling site in Chongqing is on the roof of a commercial building (CQ, $29^{\circ}37^{\prime}\mathrm{~N~}$ , $106^{\circ}30^{\prime}$ E) in Yubei District in a downtown region (Figure 1b), and the sampling height is $35\;\mathrm{m}$ . The site is surrounded by main roads and office buildings. The third sampling site in Neijiang is on the roof of Neijiang Environmental Monitoring Center (NJ, $105^{\circ}4^{\prime}$ E, $29^{\circ}42^{\prime}\,\mathrm{N})$ with a height of $25\,\mathrm{m}$ . Neijiang is located $150\,\mathrm{km}$ southeast of Chengdu and $145\,\mathrm{km}$ west of Chongqing with an area of $75\,\mathrm{km}^{2}$ and a population of about 1 million in the downtown region.
During May 2012 to May 2013, particulate samples were synchronously collected at the above three sites. Both $\mathrm{PM}_{2.5}$ and $\mathrm{PM_{10}}$ were sampled once every six days on $47\;\mathrm{mm}$ teflon filters using a four-channel sampler at a flow rate of $16.7~\mathrm{L/min}$ (model: TH-16A, Tianhong Instrument $C_{0}$ ., Ltd., Wuhan). The sampling process was carried out by trained staff of a local monitoring station, and the flow rate of the sampler was checked monthly using a bubble flow meter (Gilian Gilibrator 2, Sensidyne, US) to ensure collection efficiency. The sampled filters were stored in refrigerator at $-18\,^{\circ}C$ and transported by air. The fliters were balanced and weighted in a superclean laboratory with controlled temperature $(20\pm1^{\circ}\mathrm{C})$ and RH $(40\pm3\%)$ , both before and after the sampling.
2.2. Water-Soluble Ion Measurement
Samples on teflon filters were firstly extracted ultrasonically using $10\,\mathrm{mL}$ ultrapure water (18.5 $\mathrm{M}\Omega\,\mathrm{cm}^{-1};$ ) for 30 minutes. The aqueous extract was flitered through a $0.45~{\upmu\mathrm{m}}$ water fliter and the ion concentrations were determined using ion chromatography (Dionex, ICS 2000). A Dionex separator column of AS11-HC with KOH eluent was used for anion analysis $\left(\mathrm{NO}_{3}^{\mathrm{~-~}}$ , ${\mathrm{SO}}_{4}{}^{2-}$ , and $C1^{-}$ ), and a cation analytical column of CS12A and an eluent of $20\;\mathrm{mM}$ methyl sulfonic acid was used to analyze inorganic cations $(\mathrm{Na}^{+},\mathrm{NH}_{4}^{+},\mathrm{K}^{+},\mathrm{Mg}^{2+},\mathrm{Ca}^{2+})$ . Careful quality assurance and quality control (QA/QC) procedure was applied. Reference materials from the National Institute of Metrology, China were used as standards. Blank and standard samples were repeated every ten samples. Examples of a calibration curve of standard samples are displayed in the Supplementary Material.
3. Results and Discussion
3.1. Concentrations of $P M_{10},\,P M_{2.5},$ and WSIIs
Table 1 summarizes the observed particle and WSII concentrations in the three sampling sites. During 2012–2013, the annual average $\mathrm{PM_{10}}$ and $\mathrm{PM}_{2.5}$ concentrations were $125.8\,\pm\,74.4$ and $86.7\pm49.7\:\upmu\mathrm{g}\:\mathrm{m}^{-3}$ in CD, $116.3\pm54.7$ and $78.6\pm36.8~\upmu\mathrm{g}~\mathrm{m}^{-3}$ in $\mathrm{NJ}$ , and $101.0\,\pm\,51.7$ and 71.7 $\pm\,36.9\;\upmu\mathrm{g}\,\mathrm{m}^{-3}$ in $C Q,$ respectively. Obviously, they all exceeded the latest NAAQS-II issued in 2012, and the annual $\mathrm{PM}_{2.5}$ concentrations were more than 2 times the $35\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ limit. Daily $\mathrm{PM}_{2.5}$ levels in more than one-third of the sampling days surpassed the daily $75\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ criteria, and there were 8, 3, and 3 heavy pollution days in CD, NJ, and CQ with $\mathrm{PM}_{2.5}$ higher than $150\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ . The average $\mathrm{PM}_{2.5}/\mathrm{PM}_{10}$ ratios were 0.72 in CD, 0.69 in NJ, and 0.71 in CQ, indicating a predominance of fine particulate pollution in the Sichuan Basin.
The annual averages of total WSIIs were $43.0\,\pm\,27.9$ , $36.2\,\pm\,18.4,$ and $35.4\,\pm\,18.4~\upmu\mathrm{g}~\mathrm{m}^{-3}$ in CD, NJ, and CQ (Table 1), which accounted for $49.9\%$ , $46.1\%_{,}$ , and $49.4\%$ of the $\mathrm{PM}_{2.5}$ mass, respectively. Although the average WSIIs in $\mathrm{PM}_{2.5}$ showed minor differences among sites, there were large fluctuations within each site, from a few to a couple of hundred $\upmu\mathrm{g}\,\mathsf{m}^{-3}$ . For instance, WSII levels in CD were lower than $10\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ during clean period $(\mathrm{PM}_{2.5}<35~\upmu\mathrm{g}~\mathrm{m}^{-3}),$ , and they increased to higher than $90~\upmu\mathrm{g}~\mathrm{m}^{-3}$ under heavy polluted period $(\mathrm{PM}_{2.5}>150~\upmu g~\mathrm{m}^{-3})$ . $\mathrm{SO}_{4}{}^{2-}$ was the most abundant species of WSIIs, with an average concentration of $17.7\pm11.2~\upmu\mathrm{g}\,\mathrm{m}^{-3}$ in CD, $18.1\pm10.0$ $\upmu\mathrm{g}\:\mathrm{m}^{-3}$ in NJ, and $17.6\pm9.6~\upmu\mathrm{g}\,\mathrm{m}^{-3}$ in CQ. Annual average concentrations of the other ions were ranked in the order of $\mathrm{NO_{3}}^{-}>\mathrm{NH_{4}}^{+}>\mathrm{Cl}^{-}>\mathrm{K}^{+}>\mathrm{Na}^{+}>\mathrm{Ca}^{2+}>\mathrm{Mg}^{2+}$ in CD, whereas the order was $\mathrm{NH_{4}}^{+}>\mathrm{NO_{3}}^{-}>\mathrm{K}^{+}>\mathrm{Cl}^{-}>\mathrm{Ca}^{2+}>\mathrm{Na}^{+}>\mathrm{Mg}^{2+}$ in both NJ and CQ (Table 1). The secondary inorganic components, in total, constituted about $90\%$ of the total WSIIs $\left(89.3\%\right.$ in CD, $92.4\%$ in $\mathrm{NJ}$ , and $94.2\%$ in CQ), and the rest of the ions each had a minor contribution.
Among the three cities, $\mathrm{PM}_{2.5}$ and WSII levels were highest in CD, followed by NJ and CQ. The three cities have roughly the same $\mathrm{SO}_{4}{}^{2-}$ levels, while CD was characterized by higher $\mathrm{NO}_{3}^{\mathrm{~-~}}$ and $\mathrm{NH_{4}}^{+}$ concentrations, indicating that the sampling site in CD was more affected by motor vehicles from its surrounding road. Specifically, concentration of chloride was also the highest in CD, which might be associated with coal combustion. CD and NJ suffered from higher loadings of ${\bf K}^{+}$ (Table 1), and it was a diagnostic tracer for intensive biomass burning in the suburban regions [17].
When compared with the observed values in 2011, the levels of WSIIs in Chengdu have shown a downward trend except for $\mathrm{NO}_{3}^{\mathrm{~-~}}$ and $C1^{-}$ [8]. In Chongqing, the $\mathrm{PM}_{2.5}$ and $\mathrm{SO}_{4}{}^{2-}$ levels have decreased by $57.3\%$ and $31\%$ compared to those in 2005–2006, whereas the $\mathrm{NO}{_3}^{-}$ level increased by $43\%$ (7.8 vs. $5.46~{\upmu\mathrm{g}}~\mathrm{m}^{-3},$ ) [13]. The similar trend of decreasing $\mathrm{SO}_{4}{}^{2-}$ and increasing $\mathrm{NO}_{3}^{\mathrm{~-~}}$ in both Chengdu and Chongqing were related to the strict enforcement of desulfurization engineering and the soaring of the vehicular population in large cities of China [18]. After 2013, the decrease of $\mathrm{PM}_{2.5}$ concentration in Chongqing was minor and stabilized around $67.5~\upmu\mathrm{g}~\mathrm{m}^{-3}$ from 2015 to 2016 [15]. However, there was still an increase in $\mathrm{NO}{_3}^{-}$ $(10.9\;\upmu\mathrm{g}\,\mathsf{m}^{-3})$ [15], which further highlights the importance of vehicular emissions in Chongqing.
Results of the study were also compared with previous measurements conducted in other cities of China. CD displayed lower loadings of $\mathrm{PM}_{2.5}$ and $\mathrm{SO}_{4}{}^{2-}$ than Deyang, which was a medium-sized city in the Sichuan Basin located on the diffusion air pathways of Chengdu [19]. WSII levels in the Sichuan Basin were generally lower than cities in northern China, i.e., Handan (2013–2014) [20], Shijiazhuang (2009–2010) [21], and Taiyuan (2009–2010) [22]. However, the $\mathrm{SO}_{4}{}^{2-}$ and $\mathrm{NH_{4}}^{+}$ levels were higher than those of Beijing during 2009–2010 [23], despite lower $\mathrm{PM}_{2.5}$ and $\mathrm{NO}{_3}^{-}$ levels. When compared with cities in southern China, like Shanghai and the Pearl River Delta (PRD) [24], the pollution situation of $\mathrm{PM}_{2.5}$ and WSIIs was more serious in the Sichuan Basin.
3.2. Seasonal Variations of $P M_{2.5}$ and WSIIs
The seasonal variations of $\mathrm{PM}_{2.5}$ and WSIIs at the three sites are depicted in Figure 2. Notably, winter has the highest $\mathrm{PM}_{2.5}$ levels (108.1, 97.6, and $97.5~\upmu\mathrm{g}\;\mathrm{m}^{-3}$ in $\mathrm{CD},\mathrm{NJ}$ , and $C Q,$ respectively), and was the most heavily polluted season in the Sichuan Basin. Autumn in CD and NJ also recorded high $\mathrm{PM}_{2.5}$ concentrations $(101.6~\upmu\mathrm{g}\,\mathbf{m}^{-3}$ in CD and $81.2\;\upmu\mathrm{g}\,\mathrm{m}^{-3}$ in NJ), whereas spring and summer were relatively clean (spring: $72.2\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ in CD and $67.5\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ in NJ; summer: $68.3~{\upmu\mathrm{g}}~\mathrm{m}^{-3}$ in CD and $68.7\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ in NJ). In $C Q,$ $\mathrm{PM}_{2.5}$ concentrations in spring $(57.3\;\upmu\mathrm{g}\,\mathrm{m}^{-3})$ ), summer $(64.9\,\upmu\mathrm{g}\,\mathrm{m}^{-3})$ , and autumn $(68.0\;\upmu\mathrm{g}\;\mathrm{m}^{-3})$ were rather close to each other.
The seasonal patterns of WSIIs were attributable to local and regional source variations between seasons, as well as meteorological factors, which affected their formation, transformation, and transport. The seasonal variations of $\mathrm{NH_{4}}^{+}$ followed the changes of $\mathrm{PM}_{2.5}$ in each city, and $\mathrm{NH_{4}}^{+}$ levels were highest in winter. Undoubtedly, the sulfate concentrations were also high in winter, which were due to poor dispersion in the cold season, enhanced in-cloud process under high RH, and long contact time for gas–liquid reaction under stable meteorology. Specifically, NJ and CQ suffered from the highest loading of sulfate in summer, though the $\mathrm{PM}_{2.5}$ concentrations were low. As a typical secondary ion, $\mathrm{SO}_{4}{}^{2-}$ formation via homogeneous gas phase reaction was greatly enhanced at high temperature and intensive solar radiation in summer. It is worthy to note that autumn samples in CD showed an elevated level of sulfate with respect to summer (Figure 2, $17.9\;\upmu\mathrm{g}\,\mathrm{m}^{-3}$ in summer vs. $19.5~{\upmu\mathrm{g}}\,\mathrm{m}^{-3}$ in autumn), which might be ascribed to the elevated $\mathrm{PM}_{2.5}$ loading in the season.
Different from sulfate, the season pattern of nitrate was characterized by winter maxima, autumn medium, and spring/summer minima at the three sites (Figure 2). Temperature and relative humidity are two important meteorological factors influencing the thermodynamic features of nitrate, and high temperature and low RH is highly favorable for nitrate volatilization [25]. Therefore, the low temperature and high RH in winter and autumn are beneficial for nitrate stabilization. Moreover, the high loadings of $\mathrm{PM}_{2.5}$ in winter provided more aerosol surfaces for heterogeneous formation of nitrate [26].
3.3. Stoichiometric Analysis of Cations and Anions
To examine the ion balance and acidity of $\mathrm{PM}_{2.5}$ samples, the ion mass concentrations $(\upmu\mathrm{g}\,\\\\mathrm{m}^{-3})$ are converted into microequivalents $\left(\upmu\mathrm{mol}\,\mathrm{m}^{-3}\right)$ ) by the following equations.
$$
\mathrm{AE(anion\;equivalent)}=\frac{S O_{4}^{2-}}{96}\times2+\frac{N O_{3}^{-}}{62}+\frac{C l^{-}}{35.5},
$$
$$
\mathrm{CE(cation\;equivalent)}=\frac{N a^{+}}{23}+\frac{N H_{4}^{+}}{18}+\frac{K^{+}}{39}+\frac{M g^{2+}}{24}\times2+\frac{C a^{2+}}{40}\times2.
$$
Figure 3 illustrates the scatter plots of AE vs. CE in four seasons of CD, NJ, and CQ. Strong correlations between anion and cation equivalents were found for all the three cities $({\sim}1.0).$ , supporting that the measured eight ions were dominant species in the $\mathrm{PM}_{2.5}$ ionic components. In CD, most of the samples in autumn and winter were above the 1:1 (AE/CE) line, indicating an acidic feature. By contrast, the majority of the samples in spring fall below the 1:1 line, demonstrating a deficiency of anions which might be associated with more alkaline dust particles. $C O_{3}{}^{2-}$ and $\mathrm{HCO}_{3}{}^{-}$ were not measured by the method, and also contributed to the anion deficiency. In the summer of CD, most of the samples generally showed a balance between anions and cations, while some of them also denoted acidic features which might result from the enhanced formation of sulfate and loss of cations from the volatilization of nitrate and ammonium. Similar seasonal patterns of $\mathrm{PM}_{2.5}$ acidity were also observed in NJ and CQ (Figure 3b, Figure 3c). Interestingly, it was reflected from the scatter plots (Figure 3) that aerosol acidity increased with the level of AE, indicating $\mathrm{PM}_{2.5}$ samples under heavy pollution were mostly acidic. Tian et al. [15] also found increased acidity with aerosol pollution level. High humidity and low wind speed were common meteorological conditions for heavy pollution [27]. They were unfavorable for horizontal dispersion and vertical mixing of pollutants but beneficial for the formation of nitrate and sulfate, therefore resulting in the acidic feature.
3.4. Chemical Forms of Nitrate and Sulfate
The scatter plots of $\mathrm{NH_{4}}^{+}$ vs. $\mathrm{SO}_{4}{}^{2-}$ , $\mathrm{SO}_{4}{}^{2-}+\mathrm{NO}_{3}{}^{-}.$ , and $\mathrm{SO}_{4}^{\,2-}+\mathrm{NO}_{3}^{\,-}+\mathrm{Cl}^{-}$ (all the above denote electron equivalent concentrations) in CD, NJ, and CQ are depicted in Figure 4. As $(\mathrm{NH}_{4})_{2}\mathrm{SO}_{4}$ is less volatile and preferentially formed compared to $\mathrm{NH}_{4}\mathrm{NO}_{3}$ and $\mathrm{NH_{4}C l},$ the relationships between $\mathrm{NH_{4}}^{+}$ and $\mathrm{SO}_{4}{}^{2-}$ are firstly explored to investigate the chemical forms of sulfate and nitrate. It is reflected from Figure $4\mathsf{a}-\mathsf{c}$ that $\mathrm{NH_{4}}^{+}$ was closely related with $\mathrm{SO}_{4}{}^{2-}$ , and the data were mostly above the 1:1 $(\mathrm{NH}_{4}^{+}/\mathrm{SO}_{4}^{2-})$ ) line, suggesting the complete neutralization of $\mathrm{SO}_{4}{}^{2-}$ by $\mathrm{NH_{4}}^{+}$ , and $(\mathrm{NH}_{4})_{2}\mathrm{SO}_{4}$ was the major species. However, there were a few exceptions below the 1:1 $(\mathrm{NH}_{4}^{+}/\mathrm{SO}_{4}^{2-})$ line in the summer of NJ and CQ (Figure $\mathrm{4a-c}\mathrm{)}$ ). It was understandable that the formation of $\mathrm{SO}_{4}{}^{2-}$ was greatly enhanced under the high temperature of summer while $\mathrm{NH_{4}}^{+}$ was more easily removed by decomposition. Therefore, the samples did not have sufficient $\mathrm{NH_{4}}^{+}$ to fully neutralize $\mathrm{SO}_{4}{}^{2-}$ , and $\mathrm{NH_{4}H S O_{4}}$ existed in summer.
When it came to nitrate, the samples in spring mostly occupied enough $\mathrm{NH_{4}}^{+}$ to neutralize both $\mathrm{SO}_{4}{}^{2-}$ and $\mathrm{NO}_{3}^{\ensuremath{-}}$ (Figure 4d–f) and formed $(\mathrm{NH}_{4})_{2}\mathrm{SO}_{4}$ and $\mathrm{NH}_{4}\mathrm{NO}_{3},$ in spite of a few exceptions. In other seasons, $\mathrm{NH_{4}}^{+}$ was able to neutralize the secondary anions when their levels were low $\scriptstyle(\mathrm{SO}_{4}{}^{2-}$ $+\;\mathrm{NO}_{3}^{\;-}<0.5\;\upmu\mathrm{mol}\;\mathrm{m}^{-3})$ (Figure 4d–f). In fact, the abundance of $\mathrm{NH_{4}}^{+}$ almost equaled to the sum of $\mathrm{SO}_{4}{}^{2-},\mathrm{NO}_{3}{}^{-},$ , and $C1^{-}$ under low PM loadings (Figure $^{4}\mathrm{g-i})$ , and dominant anions existed in the form of $(\mathrm{NH}_{4})_{2}\mathrm{SO}_{4}$ , $\mathrm{NH}_{4}\mathrm{NO}_{3}$ , and $\mathrm{NH_{4}C l}$ . However, under high $\mathrm{SO}_{4}{}^{2-}$ and $\mathrm{NO}_{3}^{\mathrm{~-~}}$ levels $({\mathrm{SO}}_{4}{}^{2-}+$ $\mathrm{NO}_{3}^{\,-}>0.5\;\upmu\mathrm{mol}\;\mathrm{m}^{-3};$ ), $\mathrm{NH_{4}}^{+}$ was far from fully neutralized (Figure 4d–f). It was observed in most previous studies that high $\mathrm{NO}_{3}^{\mathrm{~-~}}$ levels were associated with high levels of $\mathrm{NH_{4}}^{+}$ [28]. In contrast, the relatively high $\mathrm{NO}_{3}^{\,-}$ observed in the present study was with moderate levels of $\mathrm{NH_{4}}^{+}$ , suggesting that the formation rate of nitrate may be much higher than other ions. The high $\mathrm{NO}_{3}^{\mathrm{~-~}}$ might also be associated with high levels of $\mathrm{NO}_{2}$ under heavy pollution.
The correlation coefficients between $\mathrm{NO}_{3}^{\mathrm{~-~}}$ and other cations in $\mathrm{PM}_{2.5}$ were further calculated in Table 2 to identify the chemical forms of nitrate. $\mathrm{Na^{+}}$ and ${\bf K}^{+}$ were found to be correlated with $\mathrm{NO}{_3}^{-}$ in most seasons, and $\mathrm{NaNO}_{3}$ and $\mathrm{KNO}_{3}$ were also the major chemical species in aerosol particles. Notably, there were exceptionally high levels of $\mathrm{NO}_{3}^{\mathrm{~-~}}$ in the winter of CD, and $\mathrm{NO}_{3}^{\mathrm{~-~}}$ were significantly correlated with all cations and existed in various forms of $\mathrm{NH}_{4}\mathrm{NO}_{3}$ , $\mathrm{NaNO}_{3}.$ , $\mathrm{KNO}_{3}$ , $\mathrm{Mg}(\mathrm{NO}_{3})_{2},$ and ${\mathrm{Ca}}({\mathrm{NO}}_{3})_{2}$ .
3.5. Formation Mechanism of Nitrate and Sulfate
Sulfur oxidation ratio (SOR), defined as $\mathrm{n}{-}\mathrm{SO}_{4}{}^{2-}/(\mathrm{n}{-}\mathrm{SO}_{2}+\mathrm{n}{-}\mathrm{SO}_{4}{}^{2-})$ , and nitrogen oxidation ratio (NOR) defined as $\mathrm{n}\mathrm{-}\mathrm{NO}_{3}\mathrm{^{-}/(n-N O_{2}+n-N O_{3}\mathrm{^{-}})}$ , in CD, NJ, and $C Q,$ are listed in Table 3 and used to indicate the secondary transformation processes. Generally, the SOR values were much higher than 0.10 (Table 3), demonstrating the occurrence of strong secondary oxidation of $S O_{2}$ to $\mathrm{SO}_{4}{}^{2-}$ throughout the year [29]. The interseasonal variation of SOR peaked in summer in both CD and CQ, and it was explicable by the accelerated homogenous gas-phase oxidation of $S O_{2}$ under high temperature [30]. Moreover, the increased oxidizing capacity from the greater production of ozone in summer also promoted $\mathrm{SO}_{4}{}^{2-}$ formation. By contrast, the highest SOR in NJ appeared in autumn instead of summer, and a good correlation was found between SOR and RH $(r=0.58)$ ). This suggested that high RH also increased the formation of $\mathrm{SO}_{4}{}^{2-}$ by prompting $S O_{2}$ oxidation through heterogeneous reaction [31], i.e., metal-catalyzed $\mathrm{H}_{2}\mathrm{O}_{2}/\mathrm{O}_{3}$ oxidation and in-cloud process. The good correlation between SOR and RH in winter of CD ${'r}=0.66)$ and CQ $(r=0.71)$ also confirmed the existence of heterogeneous reaction. On days with elevated RH, the hygroscopic growth of sulfate would increase the liquid water content, and the aqueous phase on the aerosol surface could provide heterogeneous transformation vectors for gaseous pollutants $({\mathrm{SO}}_{2})$ . Therefore, the elevated RH would largely promote the secondary formation of sulfate [32,33].
The annual average NOR in CD, NJ, and CQ all surpassed 0.1 (Table 3), indicating the existence of secondary oxidation of $\mathrm{NO}_{2}$ to $\mathrm{NO}_{3}^{\mathrm{~-~}}$ [29]. Reflected in Table 3, the NOR values had a different seasonal pattern from SOR, and reached their maxima in winter. Although the absolute concentrations of sulfate and nitrate both increased with $\mathrm{PM}_{2.5}$ levels, their relative importance changed under different pollution levels. Table 4 listed the variations of $\mathrm{NO}_{3}^{\mathrm{~-~}}/\mathrm{SO}_{4}^{\mathrm{~2-~}}$ ratios and NOR/SOR ratios as a function of different $\mathrm{PM}_{2.5}$ levels. The continuous increase of $\mathrm{NO}_{3}^{\mathrm{~-~}}/\mathrm{SO}_{4}^{\mathrm{~2-~}}$ ratio as a function of $\mathrm{PM}_{2.5}$ concentrations (Table 4), as well as the increase of NOR/SOR ratio, indicated that $\mathrm{NO}_{2}$ oxidation under heavy pollution was more significant than $S O_{2}$ , and nitrate formation might play an important role in haze in the Sichuan Basin. The above result is also supported by the findings of Hewitt [34] and Tian et al. [15]. It is worth noting that the high concentrations of nitrate in the study were mostly collected during hazy and humid weather with high sulfate and acidity. Thus, the details of nitrate formation are discussed below.
Figure 5 shows the molar ratio of nitrate-to-sulfate $([\mathrm{NO}_{3}^{-}]/[\mathrm{SO}_{4}^{2-}])$ as a function of the ammonium-to-sulfate molar ratio $(\mathrm{[NH_{4}^{+}]/[S O_{4}^{\;2-}]})$ in ${\mathrm{CD}},\ {\mathrm{NJ}},$ and CQ. The majority of the $[\mathrm{NH_{4}}^{+}]/[\mathrm{SO_{4}}^{2-}]$ ratios in the Sichuan Basin were ${>}1.5$ (one exception in CD, and two exceptions in CQ). Thus, these samples were categorized into $\mathrm{NH_{4}}^{+}$ -rich conditions $(\mathrm{[NH_{4}^{+}]/[S O_{4}^{\,2-}]}$ ratio $>1.5\AA$ ). According to Pathak et al. [28], the relationship between $[\mathrm{NO}_{3}^{-}]/[\mathrm{SO}_{4}^{\;2-}]$ and $[\mathrm{NH_{4}}^{+}]/[\mathrm{SO_{4}}^{2-}]$ could be used to show the formation pathways of $\mathrm{NO}_{3}^{\ensuremath{-}}$ . Under $\mathrm{NH_{4}}^{+}$ -rich conditions, a linear relationship exists between them, suggesting homogeneous gas-phase formation for $\mathrm{NO}_{3}^{\mathrm{~-~}}$ , and, otherwise, hydrolysis of $\operatorname{NOx}$ on preexisting aerosols is responsible for the high $\mathrm{NO}_{3}^{\,-}$ level [12,15,28]. In Figure 5, the relative abundance of nitrate $([\mathrm{NO}_{3}^{-}]/[\mathrm{SO}_{4}^{2-}])$ increased as the $[\mathrm{NH_{4}}^{+}]/[\mathrm{SO_{4}}^{2-}]$ ratio increased $(r=0.81$ in CD, $r=0.75$ in NJ, and $r=0.68$ in CQ), suggesting that nitrate formation via gas-phase reaction became evident in the $\mathrm{NH_{3}{-}H^{+}{-}S O_{4}{}^{2}{-}{-}H_{2}O}$ system in aerosol [35,36]. In fact, the good relationship between the excess ammonium (excess $[\mathrm{NH_{4}}^{+}]\mathrm{~=~[NH_{4}}^{+}]\mathrm{~-~}1.5[\mathrm{SO_{4}}^{2-}])$ and nitrate (Figure 6) further confirmed that the homogeneous gas-phase formation of nitrate was significant. Reflected from Figure 6, the increase of nitrate rate seemed to surpass the increase of excess $\mathrm{[NH_{4}}^{+}]$ under high concentrations $(\mathrm{NO}_{3}^{\mathrm{~-~}}\mathrm{>}\,0.25\mathrm{\\mumol\,m^{-}}^{3})$ . As more $\mathrm{NO}_{3}^{\mathrm{~-~}}$ led to more ions, it could further explain the observed high acidity of $\mathrm{PM}_{2.5}$ under high pollution levels in Section 3.3.
However, it is hard to ignore that some plots are rather scattered in Figure 5, and the relationship between $[\mathrm{NO}_{3}^{-}]/[\mathrm{SO}_{4}^{\;2-}]$ and $[\mathrm{NH_{4}}^{+}]/[\mathrm{SO_{4}}^{2-}]$ were further explored under different acidity and RH (Figure 7). The scatter plots of $[\mathrm{NO}_{3}^{-}]/[\mathrm{SO}_{4}^{\;2-}]$ and $[\mathrm{NH_{4}}^{+}]/[\mathrm{SO_{4}}^{2-}]$ for both $\mathrm{AE/CE}>1$ and AE/CE $<1$ displayed significant linear relationships, highlighting the importance of homogeneous gas-phase reaction. However, the correlation of $R^{2}\,=\,0.45$ between $[\mathrm{NO}_{3}^{-}]/[\mathrm{SO}_{4}^{\;2-}]$ and $[\mathrm{NH_{4}}^{+}]/[\mathrm{SO_{4}}^{2-}]$ for samples with $\mathrm{RH}>75\%$ was lower than samples with $\mathrm{RH}<75\%$ $\mathit{\Omega}[R^{2}\mathrm{~=~}0.65)$ , and the plots were more scattered (Figure 7b). The result was consistent with other research in Suzhou and Chongqing [15,16], where they also tended to have better linear correlation under lower RH conditions $(-75\%)$ . The scattered plots under $\mathrm{RH}>75\%$ implied the existence of different mechanisms other than the homogeneous reaction. The critical parameters to heterogeneous formation of nitrate via $\mathrm{N}_{2}\mathrm{O}_{5}$ hydrolysis on pre-existing particles include particulate hygroscopicity, surface area, and acidity. On the one hand, high RH relates to greater water content and surface areas of aerosols, which may promote ${\tt N}_{2}{\tt O}_{5}$ uptake on the aerosol surfaces. On the other hand, the high concentrations of $\mathrm{PM}_{2.5}$ mass and large fractions of WSIIs with acidic conditions would favor the hydrolysis of $\mathrm{N}_{2}\mathrm{O}_{5}$ [34].
3.6. Source Analysis of WSIIs
Principal component analysis (PCA) is applied in the study using SPSS version 16.0 software packages to explore the sources of WSIIs in CD, NJ, and CQ. In the analysis, all the WSIIs are considered as variables, and factors explaining more than $80\%$ of the total variance are extracted. Varimax rotation is then used to redistribute the variance and provide a more interpretable pattern of the factors. Three factors in each city, with their component loadings, eigenvalues, and explained variance are displayed in Table 5.
Factor 1 in CD covered $61.1\%$ of the total variance, and had high loadings of $\mathrm{Na^{+}}$ (0.74), $\mathrm{NH_{4}}^{+}$ (0.96), $C1^{-}$ (0.77), $\mathrm{SO}_{4}{}^{2-}$ (0.85), and $\mathrm{NO}_{3}^{\mathrm{~-~}}$ (0.93). $\mathrm{NH_{4}}^{+}$ , $\mathrm{SO}_{4}{}^{2-}$ , and $\mathrm{NO}_{3}^{\mathrm{~-~}}$ are typical secondary ions, and $C1^{-}$ is a tracer for coal combustion. Thus, factor 1 in CD was recognized as a mixture of secondary aerosols and coal combustion, and the good correlation between $\mathrm{Na^{+}}$ and $C1^{-}$ indicated their similar origins or coexistence in aerosols. In factor 2, $14.9\%$ of the total variance was explained and loadings of $\mathsf{K}^{+}$ and $C a^{2+}$ were much higher than other variables, indicating the contribution from biomass burning and natural dust. Factor 3 was responsible for $8.5\%$ of total variance, and was heavily loaded by ${\mathrm{Mg}}^{2+}$ (0.89). ${\mathrm{Mg}}^{2+}$ was from construction dust, and the factor was related with the wide reconstruction work in urban Chengdu.
$\ln\mathrm{{NJ}}$ , the loadings of $\mathrm{Na}^{+}$ (0.70), $\mathsf{K}^{+}$ (0.68), $\mathrm{Mg}^{2+}$ (0.81), and $C a^{2+}\ (0.83)$ in factor 1 were high, and these were probably from natural dust and biomass burning. Factor 2 had high loadings of $\mathrm{NO}_{3}^{\,-}$ (0.88) and $C1^{-}$ (0.88), indicating the influence from secondary nitrate and coal combustion. In factor 3, both loadings of $\mathrm{SO}_{4}{}^{2-}$ (0.96) and $\mathrm{NH_{4}}^{+}$ (0.84) were high, and they were tracers for secondary sulfate.
In $C Q,$ factor 1, explaining $57.8\%$ of total variance, had high loadings of $\mathrm{NH_{4}}^{+}$ (0.79), $\mathsf{K}^{+}$ (0.79), and $\mathrm{SO}_{4}{}^{2-}$ (0.89), and reflected a combination of biomass burning and secondary sulfate. Factor 2 was mainly affected by $\mathrm{Na}^{+}$ (0.61), $\mathrm{NH_{4}}^{+}$ (0.55), $C1^{-}$ (0.92), and $\mathrm{NO}_{3}^{\mathrm{~-~}}$ (0.87), and was a mixed influence from coal combustion and secondary nitrate. Similar to CD, factor 3 in CQ was heavily loaded by dust elements of ${\mathrm{Mg}}^{2+}$ and $C a^{2+}$ , which were tracers for construction and road dust, respectively.
4. Conclusions
The annual average $\mathrm{PM}_{2.5}$ concentrations were $86.7\pm49.7\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ in CD, $78.6\pm36.8\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ in $\mathrm{NJ}$ , and $71.7\pm36.9\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ in CQ from 2012 to 2013. The total WSIIs contributed $49.9\%$ , $46.1\%$ , and $49.4\%$ to the $\mathrm{PM}_{2.5}$ mass, respectively. Secondary inorganic ions dominated WSIIs, accounting for $89.3\%$ of WSIIs in CD, $92.4\%$ in NJ, and $94.2\%$ in CQ. In the recent decade, a trend of decreasing $\mathrm{SO}_{4}{}^{2-}$ and increasing $\mathrm{NO}_{3}^{\mathrm{~-~}}$ was observed in both Chengdu and Chongqing, which was probably due to the performance of desulfurization projects and elevated vehicular population.
The seasonal variations of $\mathrm{PM}_{2.5}$ were significant in the Sichuan Basin, and winter was the most heavily polluted season. $\mathrm{NH_{4}}^{+}$ and $\mathrm{NO}_{3}^{\mathrm{~-~}}$ exhibited similar seasonal pattern of $\mathrm{PM}_{2.5}$ variations, whereas peaks of sulfate appeared in summer of NJ and ${\mathrm{CQ}},$ and in autumn of CD. The $\mathrm{PM}_{2.5}$ samples also showed pronounced seasonal variations with acidic feature in autumn and winter, and were alkaline in spring. The aerosol acidity tended to increase with the increasing level of anion equivalents. $\mathrm{SO}_{4}{}^{2-}$ was fully neutralized and existed mainly in the form of $(\mathrm{NH}_{4})_{2}\mathrm{SO}_{4}.$ , but there was $\mathrm{NH_{4}H S O_{4}}$ in summer when $\mathrm{SO}_{4}{}^{2-}$ formation was enhanced. Full neutralization of $\mathrm{NH_{4}}^{+}$ to $\mathrm{NO}_{3}^{\,-}$ was only observed in low levels of $\mathrm{SO}_{4}{}^{2-}+\mathrm{NO}_{3}{}^{-}$ $\left(<0.5\;\upmu\mathrm{mol}\;\mathrm{m}^{-3}\right)$ ), and mostly $\mathrm{NO}_{3}^{\,-}$ existed in a variety forms of $\mathrm{NH_{4}N O_{3},N a N O_{3},K N O_{3},M g(N O_{3})_{2}},$ and $C a(\mathrm{NO}_{3})_{2}$ .
Homogeneous reactions played an important role in the formation of $\mathrm{SO}_{4}{}^{2-}$ , and there was the existence of heterogeneous reactions in winter with high RH. The formation of $\mathrm{NO}_{3}^{\mathrm{~-~}}$ was enhanced under heavy pollution, and the mechanisms were more complex. Generally, $\mathrm{PM}_{2.5}$ samples in the basin were under $\mathrm{NH_{4}}^{+}$ -rich conditions, and homogeneous reactions dominated nitrate formation. The scatter plots between $[\mathrm{NO}_{3}^{-}]/[\mathrm{SO}_{4}^{\,2-}]$ and $[\mathrm{NH_{4}}^{+}]/[\mathrm{SO_{4}}^{2-}]$ denoted possible heterogeneous reactions, especially under $\mathrm{RH}>75\%$ . Principal component analysis (PCA) showed that sources of WSIIs in the Sichuan Basin were the mixture of secondary origins and coal combustion, biomass burning, and construction dust.
Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4433/10/2/78/s1, the sampling data used in the manuscript are in the ‘Supplementary Material $1^{\prime}$ , and the calibration curve used in Section 2.2 is in the ‘Supplementary Material $2^{\prime}$ .
Author Contributions: Formal analysis, Y.C.; Funding acquisition, S.-d.X.; Methodology, Y.C.; Project administration, B.L. and C.Z.; Supervision, S.-d.X.; Writing—original draft, Y.C.
Acknowledgments: This study was funded by the National Key R & D Program of China (Demonstration of integrated air pollution control technology in the Cheng-Yu region, NO. 2018YFC0214000), the Special Funds
from the Ministry of Environmental Protection, China (NO. 201009001) and the Funds for Excellent Teachers from Capital University of Economics and Business (2016). We also would like to thank the staff in Sichuan Provincial Environmental Monitoring Center, Neijiang Environmental Monitoring Station and Chongqing Environmental Monitoring Center for the help in the sample collection.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
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atmosphere | 0007 | 10.1016/j.atmosenv.2015.06.054 | table | 3 Table 5 4 Verification statistics of meteorological and chemical simulations during dry and wet seasons. |
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atmosphere | 0007 | 10.1016/j.atmosenv.2015.06.054 | table | 1 Table 7 2 Comparisons of $\mathrm{PM}_{2.5}$ source apportionment results between this study and three studies published before. |
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atmosphere | 0007 | 10.1016/j.atmosenv.2015.06.054 | image | Figure 1 Framework of the source apportionment method used in this study |
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atmosphere | 0007 | 10.1016/j.atmosenv.2015.06.054 | image | Figure 2 Location of $\mathrm{PM}_{2.5}$ sampling site and WRF/Chem modelling domains |
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atmosphere | 0007 | 10.1016/j.atmosenv.2015.06.054 | image | Figure 3 Proportions of seven major components in $\mathrm{PM}_{2.5}$ concentration in dry and wet seasons of 2013 in Guangzhou |
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atmosphere | 0007 | 10.1016/j.atmosenv.2015.06.054 | image | 2013 in Guangzhou |
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atmosphere | 0007 | 10.1016/j.atmosenv.2015.06.054 | image | Figure 5 Analytical results of the 24h air mass back trajectory at $100\mathrm{m}$ elevation during simulated days in wet (a) and dry (b) season, which were run four times per day |
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atmosphere | 0007 | 10.1016/j.atmosenv.2015.06.054 | image | Figure 6 Locations of sizeable industrial and power plants in PRD region |
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atmosphere | 0007 | 10.1016/j.atmosenv.2015.06.054 | text | Not supported with pagination yet | Accepted Manuscript
Source apportionment of $\mathsf{P M}_{2.5}$ in Guangzhou combining observation data analysis and chemical transport model simulation
Hongyang Cui, Weihua Chen, Wei Dai, Huan Liu, Xuemei Wang, Kebin He
PII: S1352-2310(15)30195-3
DOI: 10.1016/j.atmosenv.2015.06.054
Reference: AEA 13932
To appear in: Atmospheric Environment
Received Date: 4 February 2015
Revised Date: 27 May 2015
Accepted Date: 29 June 2015
Please cite this article as: Cui, H., Chen, W., Dai, W., Liu, H., Wang, X., He, K., Source apportionment of PM2.5 in Guangzhou combining observation data analysis and chemical transport model simulation, Atmospheric Environment (2015), doi: 10.1016/j.atmosenv.2015.06.054.
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Source apportionment of PM2.5 in Guangzhou combining observation data analysis and chemical transport model simulation 5 Hongyang Cuia, Weihua Chenb, Wei Daib, Huan Liuc,\*, Xuemei Wangb,\* \*, Kebin Hec 6 7 a Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System 8 Science, Tsinghua University, Beijing 100084, China 9 b School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, 10 China
11 c School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution
12 Control (SKLESPC), Tsinghua University, Beijing 100084, China
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31 \* Corresponding author. Tel./fax: +86 10 62771679
32 \*\* Corresponding author. Tel.: +86 20 84112293; fax: +86 20 84110692
33 E-mail addresses: [email protected] (H. Liu), [email protected] (X. Wang).
1 ABSTRACT
2 A hybrid method combining observation data analysis and chemical transport model
3 simulation was used in this study to provide the $\mathrm{PM}_{2.5}$ source apportionment result of
4 Guangzhou. Four main anthropogenic emission sectors in PRD region were taken into
5 consideration, including mobile, power, industrial and residential. The proportions (Ps)
6 of six major components (sulfate, nitrate, ammonium, SOA, POA and EC) in PM2.5
7 were acquired by analyzing the daily $\mathrm{PM}_{2.5}$ monitoring data collected in the year of
8 2013 at an urban sampling site in Guangzhou. WRF/Chem model was used to get the
9 contribution ratios (CRs) of each emission sector to the concentrations of six related
0 primary pollutants, including $\mathrm{SO}_{2}$ , $\mathrm{NO}_{\mathrm{X}}$ , $\mathrm{NH}_{3}$ , VOCs, POA and EC. Then the CRs of
1 the four sources to Guangzhou’s $\mathrm{PM}_{2.5}$ mass were calculated. It was found that
2 stationary sources (industrial and power) still had the largest contribution $(22.2\%$ in
3 dry season, $44.4\%$ in wet season) to $\mathrm{PM}_{2.5}$ in Guangzhou. Mobile sector was the
4 predominant single contributor, with an average contribution of $20.7\%$ in dry season
5 and $37.4\%$ in wet season. Almost all the $\mathbf{PM}_{2.5}$ concentration in Guangzhou was
6 caused by the emissions within PRD region in wet season. In dry season, however, the
7 emissions emitted within PRD region and the pollutants transported from the areas
8 north of PRD region both played important roles.
Keywords
Source apportionment; PM2.5; Observation data analysis; WRF/Chem modeling 3
1 1. Introduction
2 Guangzhou (GZ), with a permanent population of over 13.1 million by 2014
3 (Guangzhou Statistical Bureau, 2015), is one of the megacities suffering from the
4 severest fine particulate (particulate matter less than $2.5\upmu\mathrm{m}$ in aerodynamic diameter;
5 $\operatorname{PM}_{2.5})$ air pollution in the world (van Donkelaar et al., 2010). The annual average
6 concentration of $\mathrm{PM}_{2.5}$ from 2008 to 2014 in Guangzhou was 62, 50, 55, 41, 51, 53
7 and $49\upmu\mathrm{g}/\mathrm{m}^{3}$ , respectively (Zhu et al., 2013; Peng et al., 2011; Guangzhou
8 Environmental Protection Bureau, 2011-2014), which significantly exceeded the
9 WHO Air Quality Guidelines (annual mean: $10\upmu\mathrm{g}/\mathrm{m}^{3}.$ ). The high $\mathrm{PM}_{2.5}$ concentration
10 poses great danger to human respiratory system, reduces the atmospheric visibility
11 tremendously and has a complex impact on global climate change (Romieu et al.,
12 1996; Lighty et al., 2000; Wu et al., 2007; Abbey et al., 1994; Huang et al., 2009;
13 IPCC, 2013). Facing enormous public pressure for a cleaner air, the government is
14 showing stronger willingness and enthusiasm against $\mathrm{PM}_{2.5}$ air pollution than ever
15 before (Zhang et al., 2012). For policy makers, a comprehensive and accurate
16 description of the $\mathrm{PM}_{2.5}$ sources and their respective contributions to the ambient
17 $\mathrm{PM}_{2.5}$ concentration is critical to the design of the corresponding treatment measures
18 and control strategies.
19 In the past several years, some research groups have made great efforts to identify the
20 sources of $\mathrm{PM}_{2.5}$ in cities of Pearl River Delta (PRD) region (Guo et al., 2009; He et
21 al., 2011; Xiao et al., 2011; Huang et al., 2014). As for Guangzhou, however, the
22 focus of source apportionment studies has been on $\mathrm{{PM}_{10}}$ (Wang et al., 2006), volatile
23 organic compounds (VOCs) (Liu et al., 2008) and polycyclic aromatic hydrocarbons
24 (PAHs) (Li et al., 2005; Yang et al., 2010; Tan et al., 2011); limited information on
25 comprehensive sources of $\mathrm{PM}_{2.5}$ has been provided. Wang et al. (2007) evaluated the
26 contribution of biomass burning to $\mathrm{PM}_{2.5}$ concentration in Guangzhou using
27 acetonitrile and levoglucosan as tracers. Liu et al., (2014) explored the possible
28 sources of $\mathrm{PM}_{2.5}$ carbonaceous aerosols using radiocarbon and organic tracers.
29 Andersson et al. (2015) provided the source apportionment results of
1 combustion-derived black carbon (BC) using dual carbon isotope analysis. However,
2 these studies only concentrated on one or two specific emission sources or specific
3 chemical components of $\mathrm{PM}_{2.5}$ . Only Wu et al., (2013), to the best of our knowledge,
4 has given a relatively complete $\mathrm{PM}_{2.5}$ source apportionment result of Guangzhou,
5 using CAMx model with particulate source apportionment technology (PSAT).
6 Various source apportionment technologies have been adopted to apportion the
7 sources of fine particulate matters in the world. Among them, receptor model is a
8 mainstream approach. Zheng et al. (2005) applied chemical mass balance (CMB)
9 model to calculate the contributions of different sources to $\mathrm{PM}_{2.5}$ in Beijing, using
10 organic molecular as markers. Lewis et al. (2003) utilized UNMIX model to estimate
11 five kinds of sources’ impacts on $\mathrm{PM}_{2.5}$ in Phoenix. Besides, positive matrix
2 factorization (PMF) model has also been used to investigate the main $\mathrm{PM}_{2.5}$ sources in
3 many recent publications (e.g. Song et al., 2006; Hwang et al., 2011; Herrera et al.,
V
14 2012; Zhang et al., 2013). However, these receptor models cannot identify the sources
15 of secondary components in $\mathrm{PM}_{2.5}$ , which is a huge limitation. Another mainstream
16 source apportionment method at present, chemical transport model (CTM), can solve
17 this problem. For example, Lang et al. (2013) employed the MM5/CMAQ model to
18 obtain the trans-boundary contribution of Tianjin and Hebei to $\mathrm{PM}_{2.5}$ in Beijing. Bove
19 et al. (2014) adopted a source-oriented WRF/CAMx model to investigate the $\mathrm{PM}_{2.5}$
20 sources in the city of Genoa, Italy. These CTMs can be used to analyze the sources of
21 the complete PM2.5 concentration, including the primary components and the
22 secondary components as well. Nonetheless, restricted by the accuracy of emission
23 inventory and its poor ability to simulate secondary aerosols, CTM often gives a
24 $\mathrm{PM}_{2.5}$ source apportionment result containing notable error. Based on the idea that
25 CTMs can apportion the sources of the primary aerosols (e.g., EC, POA) and the
26 gaseous precursors of the secondary aerosols (e.g., $\mathrm{SO}_{2}$ , $\mathrm{NO}_{\mathrm{X}}$ , $\mathrm{NH}_{3}$ and VOCs)
27 relative accurately, Cheng et al. (2013) came up with a new method combining
28 monitoring, simulation and factor analysis (FA) and applied it to study the vehicles’
9 contribution to $\mathrm{PM}_{2.5}$ in Beijing. Having avoided the evident drawbacks of single
1 receptor model and CTM, this hybrid source apportionment method is relatively more
2 accurate and applicable.
3 The objective of this study is to provide the $\mathrm{PM}_{2.5}$ source apportionment result of
4 Guangzhou using a modified version of this new hybrid method, taking both sectoral
5 contributions and regional transport impacts into account. Four main anthropogenic
6 emission sectors (mobile, power, industrial and residential) in PRD region were
7 considered. $\mathrm{PM}_{2.5}$ was divided into six major components, including sulfate, nitrate,
8 ammonium, POA, SOA and EC. Observation data of 2013 was analyzed to find the
9 proportions of the six components in $\mathrm{PM}_{2.5}$ concentration. WRF/Chem model was
10 chosen to simulate the impacts of the four emission sectors on the concentrations of
11 the corresponding six chemical pollutants. Then the eventual PM2.5 source
12 apportionment outcome was calculated.
13 2. Methodology
14 2.1 Source apportionment framework
15 Framework of the hybrid source apportionment method used in this study is presented
16 in Figure 1. The contribution ratio (CR) of one specific emission sector (e.g., Mobile)
17 in PRD region to the $\mathrm{PM}_{2.5}$ concentration in Guangzhou was calculated using the
18 equation shown in the figure. $C R_{p r i m a r y,m}$ , , , , and
19 $C R_{P M_{2.5},m}$ are the CRs of sector m to the primary, secondary, unidentified and the
20 total $\mathrm{PM}_{2.5}$ components. $P_{i}$ and $P_{k}$ are the proportions of primary component i and
21 secondary component k in the total $\mathrm{PM}_{2.5}$ mass. $C R_{i,m}$ , $C R_{k,m}$ and $C R_{j,m}$ are the CRs
22 of sector m to the primary $\mathrm{PM}_{2.5}$ component i, the secondary $\mathrm{PM}_{2.5}$ component $\mathbf{k}$ and
23 the gaseous precursor of secondary $\mathrm{PM}_{2.5}$ component j, respectively. The details of
24 different subscripts $(\mathrm{i,j,k,m})$ are shown in Table 1.
25 One emission sector’s CRs to the concentrations of different secondary $\mathrm{PM}_{2.5}$
26 components (sulfate, nitrate, ammonium and SOA) were assumed to be equal to those
27 to the concentrations of the corresponding gaseous precursors $(\mathrm{SO}_{2},\,\mathrm{NO}_{\mathrm{X}},\,\mathrm{NH}_{3}$ and
28 VOCs). $\mathrm{PM}_{2.5}$ samples were collected and chemically analyzed to get $P_{i}$ and $P_{k}$ .
1 WRF/Chem model was simulated to give $C R_{i,m}$ and $C R_{j,m}$ . Then CRs of sector m
2 to the identified $\mathrm{PM}_{2.5}$ components (sulfate, nitrate, ammonium, POA, SOA and EC)
3 were calculated by making weighted summation of $C R_{i,m}$ and $C R_{k,m}$ using the
4 relevant $P_{i}$ and $P_{k}$ as the weighted factors. By assuming that CRs of sector m to the
5 unidentified components were equal to those to the identified components, $C R_{P M_{2.5},m}$
6 was finally obtained. Details on how to obtain Ps and CRs are introduced in the
7 following section 2.2 and 2.3.
8 Previous source apportionment studies in northern Chinese cities (e.g., Beijing,
9 Tianjin, Jinan) usually paid close attention to the variation of contributions in spring,
0 summer, autumn and winter (Zhu et al., 2005; Yu et al., 2013; Bi et al., 2007).
1 However, the change between four seasons in Guangzhou is not that distinct. Instead,
2 there are obvious alternating dry and wet seasons. Hence the target year was divided
3 into dry season (January to March and October to December) and wet season (April to
4 September) to be analyzed and compared here. Since regional transport is a
5 non-ignorable factor to determine the air quality of Guangzhou, the four emissions in
6 the whole PRD region instead of those only in Guangzhou were treated as the target
7 emission sources. As such, the source apportionment result in this study could be used
8 to guide the settings of the $\mathrm{PM}_{2.5}$ joint prevention and control policy in PRD region.
2.2 Observation data collection
0 Sampling was conducted during the period of January 2013 to December 2013 on the
1 rooftop of the Guangzhou Environmental Monitoring Center (GZEMC) office
2 building $(23^{\circ}07^{\prime}49^{\prime\prime}\mathrm{N}$ , $113^{\circ}15^{\prime}56^{\prime\prime}$ ) (Figure 2), which is about $15\mathrm{m}$ above ground
3 level. GZMEC is located in a residents-commercial-transportation mixed district,
4 crowded and surrounded by many urban buildings and busy roads. Therefore it could
5 be highly representative of the urban Guangzhou. Daily $\mathrm{PM}_{2.5}$ samples were collected
6 simultaneously on the $47\mathrm{mm}$ PTFE filters (Whatman Inc., UK) and the
7 $20.3\mathrm{cm}{\times}24.5\mathrm{cm}$ quartz filters (Whatman Inc., UK) by a RP2300 four-channel
8 low-volume sampler $\left\lfloor16.7\right\mathrm{~\L/min}\right\rceil$ ) and an Andersen high-volume sampler (1130
9 L/min), respectively.
1 Before and after sampling, the PTFE filters were equalized for $24\mathrm{h}$ in driers and then
2 weighed at $25\,^{\circ}\mathrm{C}$ temperature and $50\%$ relative humidity, using a microbalance with
3 an accuracy of $0.01\,\mathrm{~mg~}$ to calculate the mass concentrations of $\mathrm{PM}_{2.5}$ . The quartz
4 filters were prepared for the analysis of OC/EC and water-soluble ions. OC/EC was
5 analyzed using thermo-optical transmittance (TOT) method (NOISH, 1999) by an
6 OC/EC Analyzer (Sunset Laboratory Inc., USA). Then OC/EC minimum method
7 (Castro et al., 1999) was adopted to calculate the amount of POC and SOC, which
8 were further multiplied by 1.6 respectively to get the amount of POA and SOA
9 (Turpin et al., 2001). The minimum OC/EC ratios used in this study were 2.103 and
10 1.732 for dry and wet seasons, respectively. Water-soluble ions including sulfate,
11 nitrate and ammonium were measured by 883 Basic IC plus (Metrohm, Switzerland).
12 Totally, 70 samples in dry season and 51 samples in wet season in 2013 were
13 collected and analyzed.
14 2.3 WRF/Chem model description
15 WRF/Chem v3.4.1 was applied in this study to estimate the CRs of different emission
16 sectors to the concentrations of the primary $\mathrm{PM}_{2.5}$ components and the precursors of
17 the secondary $\mathrm{PM}_{2.5}$ components. WRF/Chem model has been widely used in the
18 previous studies of PRD region and its performance has been recognized as
19 acceptable (Wang et al., 2009; Li et al., 2013). The detailed physics configurations of
20 WRF/Chem model, including the physical and chemical schemes chosen for this study,
21 are listed in Table 2. The experiment in this study employed two nested
22 computational domains, which were run in two-way interactive mode. On a Lambert
23 map (shown in Figure 2), the coarse domain (D01) consisted of $145\!\times\!142$ grid points
24 with a spatial resolution of $9\mathrm{km}{\times}9\mathrm{km}$ , while the nested domain (D02) contained
25 $145\!\times\!124$ grid points with a spatial resolution of $3\mathrm{km}{\times}3\mathrm{km}$ . D02 covered the whole
26 PRD region, centered at $22.9^{\circ}\mathrm{N}$ and $113.7^{\circ}\mathrm{E}$ with 32 vertical layers up to $50\mathrm{hPa}$ . The
27 target area consisted of Guangzhou (GZ), Foshan (FS), Dongguan (DG), Zhongshan
28 (ZS), Shenzhen (SZ), Zhuhai (ZH), Huizhou (HZ), Jiangmen (JM) and the
29 south-eastern part of Zhaoqing (ZQ). Other cities in D02, including Shanwei (SW),
1 Heyuan (HY), Shaoguan (SG), Qingyuan (QY), Yangjiang (YJ) and the north-western
2 part of ZQ were not studied since they do not belong to PRD region.
3 Two representative periods were chosen to conduct the simulation in this study. June
4 $15^{\mathrm{th}}$ 2013 to July $15^{\mathrm{th}}~\;2013$ and November $15^{\mathrm{th}}~\;2013$ to December $15^{\mathrm{th}}$ 2013
5 represented wet season and dry season, respectively. The two reasons why we chose
6 these two periods were: (1) the most important characteristics of wet and dry seasons
7 were prevailing wind direction and precipitation. During the period of June $15^{\mathrm{th}}$ to
8 July $15^{\mathrm{th}}$ , continuous and heavy rainfall occurred in the region and the prevailing wind
9 was from the ocean. On the contrary, rain rarely fell from November $15^{\mathrm{th}}$ to December
10 $15^{\mathrm{th}}$ and the prevailing wind was from the mainland. Therefore, the two time periods
11 chosen could reflect the main features of wet and dry seasons well. (2) $\mathrm{PM}_{2.5}$ samples
12 were collected densely (once a day) during the chosen time periods. The sampling in
13 other periods was taken at a 6-day interval. The WRF/Chem simulation started from
14 00UTC June $13^{\mathrm{th}}~2013$ to 18UTC July $15^{\mathrm{th}}$ 2013, and from 00UTC November $13^{\mathrm{th}}$
15 2013 to18UTC July $15^{\mathrm{th}}\;2013.\;48\mathrm{h}$ spin up time was used for each run. Then the two
16 one-month-long (00UTC June $15^{\mathrm{th}}\,2013$ to 18UTC July $15^{\mathrm{th}}$ 2013, 00UTC November
17 $15^{\mathrm{th}}\;2013$ to 18UTC December 15th 2013) simulation results were analyzed to get the
18 relevant CRs in dry and wet seasons, respectively. The limitation of representativeness
19 could not be avoided without running full-year simulation. This could be one of the
20 future improvements of the study.
21 A highly resolved spatial anthropogenic emission inventory for PRD was provided by
22 South China University of Technology in the base year 2006 (Zheng et al., 2009).
23 The regional emission inventory was updated to 2010 using the statistical emission
24 data (Liu et al., 2013b). Five scenarios were simulated using a zero-out method to
25 investigate the CRs of mobile, power, industrial and residential emissions’ impacts on
26 the concentrations of the referred seven kinds of pollutants. The details of the five
27 scenarios are shown in Table 3.
8 3. Results and Discussion
29 3.1 Observation data analysis
1 Based on the method described in section 2.2, the $\mathrm{PM}_{2.5}$ mass and chemical speciation
2 data were obtained. Table 4 presents the statistical results. The average $\mathrm{PM}_{2.5}$
3 concentrations in dry and wet seasons in Guangzhou were 68.4 and $50.7\,\upmu\mathrm{g}/\mathrm{m}^{3}$ ,
respectively. $34\%$ of the sampling days in dry season exceeded the recommended
5 WHO Interim Target 1 (24-hour mean: $75\upmu\mathrm{g}/\mathrm{m}^{3\cdot}$ ), compared with $6\%$ in wet season.
6 The average concentrations of all the chemical species analyzed were higher in dry
7 season. Figure 3 gives the calculated proportions of different kinds of components in
8 the airborne $\mathrm{PM}_{2.5}$ mass. The seasonal variation was not that obvious. The seven
9 major components explained about $75\%$ of the total $\mathrm{PM}_{2.5}$ mass in both dry and wet
0 seasons. The secondary components (sulfate, nitrate, ammonium and SOA) accounted
1 for $54.7\%$ in dry season and $55.6\%$ in wet season, about three times higher than the
2 proportions of the primary components (EC and POA). The average OC/EC ratios in
13 dry and wet seasons were severally 5.72 and 4.42, which extended far beyond 2,
4 indicating the abundance of SOA in $\mathrm{PM}_{2.5}$ (Turpin et al, 1990; Chow et al., 1996). The
5 results of observation data analysis also confirmed this point, showing that SOA
6 accounted for the largest fraction of the $\mathrm{PM}_{2.5}$ concentration, with a proportion of
7 $20.5\!-\!23.2\%$ . $\mathrm{SO}_{4}^{\ 2\cdot}$ -, $\mathrm{NO}_{3}^{\,-}$ and $\mathrm{NH_{4}}^{+}$ were the dominant inorganic ions. Referring to
18 Arimoto et al. (1996), several studies used the $\mathrm{NO}_{3}^{-}/\mathrm{SO}_{4}^{2-}$ mass ratio to indicate the
9 relative importance of mobile sources (e.g., mobile) versus stationary sources (e.g.,
0 industrial and power) in mega-cities of China (Yao et al., 2002; Wang et al., 2008a;
1 Han et al., 2014). The average $\mathrm{NO}_{3}^{-}/\mathrm{SO}_{4}^{2-}$ ratio in Guangzhou was 0.74 in 2013,
2 nearly four times higher than the measured ratio (0.2) in 2006 (Han et al., 2014). The
3 growth of $\mathrm{NO}_{3}^{-}/\mathrm{SO}_{4}^{2-}$ ratio suggested that the contribution of mobile source to
4 Guangzhou’s air quality was increasing. This might be mainly caused by the rapid
5 increase of vehicle population and the effective control of $\mathrm{SO}_{2}$ emissions in PRD
6 region in the last few years (Wang et al., 2013). However, the ratio was still lower
7 than 1, indicating that coal-burning stationary sources remained the predominant
8 source of Guangzhou $\mathrm{PM}_{2.5}$ pollution.
3.2 WRF/Chem model performance
1 The meteorological observation data of 13 meteorological sites (Figure 2) from China
2 Meteorological Data Sharing Service System (http://cdc.cma.gov.cn/home.do),
3 including Fogang(FG), Lianping(LP), Guangning(GN), Gaoyao(GY),
4 Guanghzou(GZ), Dongyuan(DY), Zengceng (ZC), Huiyang(HY), Wuhua(WH),
5 Taishan(TS), Shenzhen(SZ), Shanwei(SW) and Yangjiang(YJ) and the air quality
6 monitoring data of Guangzhou’s 10 state-controlled sites from GZEMC were used to
7 evaluate the performance of WRF/Chem model. Daily results of model simulation
8 were compared with the observation data mentioned above. The statistics of
9 verification included bias, mean absolute errors (MAE), root mean square error
10 (RMSE) and correlation coefficient (R) of both meteorological parameters ( pressure
11 (P), $10\mathrm{m}$ wind speed (WS), $2\mathrm{m}$ temperature (T), $2\mathrm{m}$ relative humidity (RH) ) and
12 chemical pollutants’ concentrations ( $\mathrm{{SO}}_{2}$ , $\mathrm{NO}_{2}$ , $\mathrm{PM}_{2.5}$ and maximum 8-h average ${\bf{O}}_{3}$ ).
13 The detailed model verification results of wet and dry season were listed in Table 5.
14 Relative higher correlation coefficient (0.74-0.98) and lower bias (0.2-3.5 in absolute
15 terms) suggested that WRF/Chem model simulated the four meteorological
16 parameters reasonably well in this study. For chemical pollutants, more discussions
17 were needed. First of all, the modeling performance of $\mathrm{SO}_{2}$ and $\mathrm{NO}_{2}$ was better than
18 that of $\mathrm{PM}_{2.5}$ , especially in dry season. This was the important reason why the six
19 primary pollutants instead of $\mathrm{PM}_{2.5}$ were chosen to be analyzed in this hybrid
20 method—to maximally avoid errors introduced by relatively poor performance on
21 $\mathrm{PM}_{2.5}$ modeling. Although the biases of $\mathrm{PM}_{2.5}$ concentrations were comparatively
22 higher, the $\mathrm{PM}_{2.5}$ modeling performance of this study still met the criteria suggested
23 by Boylan et al., (2006). Secondly, our simulation showed that the modeling
24 performance of gaseous pollutants was within acceptable limits. Mean fractional bias
25 (MFB) and mean fractional error (MFE) were recommended by U.S. EPA (2007) as
26 the performance statistical indicators of PM modeling. Wang et al. (2014) used an
27 $\mathrm{MFB<=\pm60\%}$ and an $\mathrm{MFB}<=75\%$ as the criteria for the modeling performance
28 evaluation of gaseous species. In this study, the MFB and MFE values of $\mathrm{SO}_{2}$ were
29 $-46\%$ and $63\%$ in dry season, while those in wet season were $21\%$ and $71\%$ ,
1 respectively. For $\mathrm{NO}_{2}$ , the MFB and MFE values were $-14\%$ and $52\%$ in dry season,
2 while those in wet season were $18\%$ and $42\%$ , respectively. Both of the two indicators
3 of $\mathrm{SO}_{2}$ and $\mathrm{NO}_{2}$ met the criteria, indicating that the WRF/Chem simulation in this
4 study was satisfactory and reliable, and could be further used in the following
5 calculations. Thirdly, in dry season, the concentrations of $\mathrm{SO}_{2}$ and $\mathrm{NO}_{2}$ were
6 underestimated. It was mainly due to the uncertainties in emissions (Wang et al., 2014)
7 and the overestimation of wind speed (Liu et al., 2015; Table 5). However, the biases
8 of $\mathrm{SO}_{2}$ and $\mathrm{NO}_{2}$ concentrations in wet season were in a different direction. The
9 overprediction of $\mathrm{SO}_{2}$ and $\mathrm{NO}_{2}$ concentrations in wet season was mainly resulted
10 from the underestimation of precipitation. The simulated mean precipitation in the 13
11 meteorological sites was $17\%$ under its observation mean value. For Guangzhou
12 meteorological site, the underestimation of precipitation reached $118\mathrm{mm}$ . The
13 phenomenon that the biases of gaseous species were in opposite directions in different
14 seasons was consistent with the previous studies (Liu et al., 2010; Wang et al., 2014;
15 Liu et al., 2015).
16 The simulation deviations, which could not be avoided when chemical transport
17 models were used to identify the sources of $\mathrm{PM}_{2.5}$ , would lead to unquantifiable
18 uncertainties in the source apportionment results. Efforts in the following two aspects
19 could be made to solve this problem gradually in the future. Firstly, a more accurate
20 regional emission inventory with higher resolution should be provided, trying to take
21 other major emission sectors (e.g., marine, biomass burning, construction dust,
22 cooking) into consideration. Secondly, the chemical transport model’s simulation
23 ability, especially in heterogeneous chemical mechanisms, should be improved step
24 by step. Currently, the point was to ensure that the modeling performance was within
25 acceptable limits.
26 3.3 WRF/Chem model simulation results
27 The simulated contributions of four emission sectors in PRD region to the
28 concentrations of EC, POA, $\mathrm{SO}_{2}$ , $\mathrm{NO}_{\mathrm{X}}$ , $\mathrm{NH}_{3}$ and VOCs in Guangzhou are listed in
9 Table 6. Mobile (road dust included) and industrial sectors were found to be the two
1 main contributors to most pollutants. Mobile sector was the predominant source of
2 $\mathrm{{NO}_{X}}$ concentration in Guangzhou, contributing $56.0\%$ and $68.6\%$ in dry and wet
3 seasons, respectively, which exceeded the summation of the other three sectors’
4 contributions. One reason for that was mobile sector’s huge amount of $\mathrm{NO}_{\mathrm{X}}$ emissions.
5 Another reason was that mobile sources emitted pollutants at a lower height compared
6 with the other major $\mathrm{NO}_{\mathrm{X}}$ emitters (e.g., power sector). Besides, mobile also had the
7 most significant impacts on VOCs, EC and POA among the four anthropogenic
8 emission sources. Industrial sector remained the largest contributor to $\mathrm{SO}_{2}$
9 concentration, with an average CR of $41.2\%$ in dry season and $60.7\%$ in wet season.
0 The influence of residential sector and power sector was relatively low compared with
1 the first two sectors. But it has to be pointed out that residential sector was an
2 important source of VOCs in Guangzhou, which should not be ignored. The total
3 contribution of the four analyzed emission sectors to $\mathrm{NH}_{3}$ was only $17.7\%$ in dry
4 season and $41.4\%$ in wet season. It was mainly caused by the fact that $\mathrm{NH}_{3}$ was an
5 agriculture-dominated pollutant (Yin et al., 2010). Similarly, considering that biogenic
6 emissions had a great influence on VOCs concentration, the low value of the four
7 sources’ total contribution to VOCs could be explained here. The summation of the
8 four emission sectors’ contributions to $\mathrm{SO}_{2}$ and $\mathrm{{NO}_{X}}$ was higher than $100\%$ , resulting
9 from the nonlinear response of pollutant concentrations to pollutant emissions in the
0 simulation process. But the uncertainties were acceptable compared with the previous
1 studies using zero-out method (Wang et al., 2008b). The referred four emission
2 sectors in PRD region were not the complete sources of the six pollutants, especially
3 in dry season. The unexplained part was caused by the sectors that were not analyzed
24 using zero-out method (agriculture, biogenic, airport and farm machinery) and the
5 trans-boundary impacts from the areas outside PRD region, which was further
6 discussed in section 3.4.
3.4 Source apportionment results and discussion
Figure 4 presents Guangzhou’s $\mathrm{PM}_{2.5}$ source apportionment results in dry and wet seasons of 2013. The CRs of four main emission sectors (mobile, power, industrial
1 and residential) in PRD region to Guangzhou’s $\mathrm{PM}_{2.5}$ concentration were computed
2 from the results discussed in section 3.1 and 3.2 using the equations introduced in
3 section 2.1.
4 Mobile sector was found to be the largest single contributor to Guangzhou’s $\mathrm{PM}_{2.5}$
5 concentration, with a contribution ratio of $20.7\%$ in dry season and $37.4\%\mathrm{~in~}$ wet
6 season. It was mainly resulted from mobile sector’s significant contributions to $\mathrm{NO}_{\mathrm{X}}$
7 $(56.0\%$ in dry season, $68.6\%$ in wet season), VOCs $23.4\%$ in dry season, $28.6\%$ in
8 wet season) and POA $(39.0\%$ in dry season, $46.5\%$ in wet season) concentrations.
9 Industrial sector followed mobile to be the second greatest contributor. As the main
10 emission source of $\mathrm{SO}_{2}$ ( $41.2\%$ in dry season, $60.7\%$ in wet season), POA $22.4\%$ in
11 dry season, $30.2\%$ in wet season) and VOCs ( $13.3\%$ in dry season, $20.4\%$ in wet
12 season), industrial sector was responsible for $19.8\%$ and 32.2% of the total $\mathrm{PM}_{2.5}$
13 mass in dry and wet seasons, respectively. Compared with the above two emission
14 sources, power and residential sectors contributed relatively less. The contribution of
15 power sector was lower than that of residential sector in dry season, but higher in wet
16 season. Stationary sources (including industrial sector and power sector) were still the
17 dominant contributor to $\mathrm{PM}_{2.5}$ in Guangzhou, with a contribution of $22.2\%$ in dry
18 season and $44.4\%$ in wet season, which was a little bit larger than that of mobile
19 sector. This result was consistent with the conclusion from the $\mathrm{NO}_{3}^{-}/\mathrm{SO}_{4}^{2-}$ ratio
20 analysis in section 3.1. Hence, industrial and power sector would still be the focus of
21 the air quality controlling strategies in PRD region. However, more attention should
22 be paid on the mobile sector in the future.
23 It can be easily concluded that the seasonal variation of four sectors’ contributions to
24 $\mathrm{PM}_{2.5}$ mass was highly significant. The CRs of mobile, power, industrial and
25 residential in wet season were all 1.5-5.1 times higher than those in dry season. The
26 impacts of the four sectors in PRD region explained $88.4\%$ of Guangzhou’s $\mathrm{PM}_{2.5}$
27 concentration in wet season. However, the value in dry season was only $47.1\%$ . In this
28 study, the $100\%$ contribution to $\mathrm{PM}_{2.5}$ could only be distributed among the emission
29 sources that were included in the emission inventory. The unexplained $11.6\%$ in wet
1 season and $52.9\%$ in dry season here should be attributed to those emission sources
2 that were considered in the base scenario but were not turned off in the S1-S4
3 scenarios, including the agriculture, biogenic, airport and farm machinery emissions
4 within PRD region and the emissions transported from the areas outside PRD region.
5 To further explore the impacts of regional transport, backward trajectory air mass
6 analysis was applied.
7 As is shown in Figure 5, 24-h backward trajectories of June $15^{\mathrm{th}}$ to July $15^{\mathrm{th}}$ and
8 November $15^{\mathrm{th}}$ to December $15^{\mathrm{th}}$ were performed using NOAA HYSPLIT Model
9 (www.arl.noaa.gov/ready/) to explore the origin of the air mass of Guangzhou. It was
0 run four times per day. In wet season, Guangzhou was mainly affected by the
1 emissions from the south, including PRD region and the ocean areas. But in dry
2 season, more emissions were transported from the north, especially the provinces to
3 the northeast of Guangzhou. Figure 6 shows the locations of sizeable industrial and
4 power plants in PRD region. It can be seen clearly that the overwhelming majority of
5 those plants are located in the southern part of Guangzhou and in the cities south of
6 Guangzhou. On the contrary, far fewer plants could be found in the northern part of
7 Guangzhou and in the cities north of Guangzhou. Human and vehicle population
8 distribution shows the same pattern. Considering the prevailing wind direction and the
9 locations of the emission sources shown in Figure 5 and Figure 6, it can be
0 concluded that in wet season, almost all the $\mathrm{PM}_{2.5}$ pollution in Guangzhou was caused
1 by the emissions in PRD region, especially the cities on the south of Guangzhou. In
2 dry season, however, the $\mathrm{PM}_{2.5}$ concentration in Guangzhou was the result of the
3 emissions emitted in PRD region combined with the pollutants transported from the
4 areas north of PRD region. The effects of the regional transport could not be ignored.
5 Joint prevention and control mechanism has been come up within PRD region for
6 several years and has obtained great success (Liu et al., 2013a). However, the source
7 apportionment result in this study indicated that the inter-regional cooperation
8 between PRD region and the provinces north of Guangdong is also of great
9 importance in order to improve the air quality in Guangzhou, especially in dry season.
1 To get the specific contribution of each related province in the north, more scenarios
2 should be set in the zero-out analysis process, which can be done in the future
3 research.
4 The $\mathrm{PM}_{2.5}$ source apportionment result of this study was compared with those of three
5 previously published studies which also focused on cities (Guangzhou, Hong Kong,
6 Shenzhen) in PRD region (Table 7). Vehicular emission was the source that all the
7 four researches analyzed, which could be used to provide external evaluation on this
8 hybrid method. The seasonal contributions of vehicular emissions provided by Wu et
9 al., (2013) and our study were quite similar, and the annual average contributions
10 given by the other two previous studies were both between the values in dry and wet
11 seasons of this study, which showed that the results were comparable in general.
12 4. Conclusion
13 In this research, we provided the $\mathrm{PM}_{2.5}$ source apportionment result of Guangzhou
14 using a hybrid method combining observation data analysis and chemical transport
15 model simulation. Four main anthropogenic emission sectors (mobile, power,
16 industrial and residential) in PRD region were considered. $\mathrm{PM}_{2.5}$ was divided into six
17 major components, including sulfate, nitrate, ammonium, SOA, POA and EC. The
18 proportions of the six components in $\mathrm{PM}_{2.5}$ were acquired by analyzing the daily
19 $\mathrm{PM}_{2.5}$ monitoring data collected in 2013 at an urban sampling site in Guangzhou.
20 WRF/Chem model was used to get the impacts of the four emission sectors on the
21 concentrations of six related primary pollutants, including $\mathrm{SO}_{2}$ , $\mathrm{NO}_{\mathrm{X}}$ , $\mathrm{NH}_{3}$ , VOCs,
22 POA and EC. Then the contribution ratios of the four sources to Guangzhou’s $\mathrm{PM}_{2.5}$
23 mass were finally calculated. The results indicated that stationary sources (including
24 industrial sector and power sector) still had the largest contribution $22.2\%$ in dry
25 season, $44.4\%$ in wet season) to $\mathrm{PM}_{2.5}$ in Guangzhou. But mobile sector had become
26 the predominant single contributor, with an average contribution of $20.7\%$ in dry
27 season and $37.4\%$ in wet season. Hence, mobile emissions should be paid more
28 attention when setting up the air quality control strategies in the future. Almost all the
29 $\mathrm{PM}_{2.5}$ pollution in Guangzhou was resulted from the emissions within PRD region in
1 wet season. In dry season, however, the emissions emitted within PRD region and the
2 pollutants transported from the areas north of PRD region both played important roles.
3 Therefore, not only the joint prevention and control mechanism within PRD region
4 but also the inter-regional cooperation between PRD region and the provinces north of
5 Guangdong was of great importance to the air quality in Guangzhou.
6 Acknowledgement
7 This work was supported by the National Science Fund for Distinguished Young
8 Scholars (41425020), the National Program on Key Basic Research Project
9 (2014CB441301) and National Natural Science Foundation of Guangdong Province
0 as key project (S2012020011044). The authors would like to thank Professor Junyu
1 Zheng of South China University of Technology for providing the PRD emission
2 inventory and thank Guangzhou Environmental Monitoring Center for providing the
3 $\mathrm{PM}_{2.5}$ monitoring data. This work was also partly supported by the high-performance
4 grid-computing platform of Sun Yat-sen University and Toyota Motor Corporation.
5
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6
1 List of Tables
2
3 Table 1
4 Meanings of different subscripts $(\mathrm{i,j,k,m})$ in the source apportionment framework
5
6 Table 2
7 Physics configuration of WRF/Chem model
9
10
11
12
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16
17
18
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3 Table 3
4 Five scenarios and their implications for assessing the contributions of four emission sectors
5 to the concentrations of seven major components in $\mathrm{PM}_{2.5}$
6
7
8
9
10
11
12
13
14
15
Table 4
Maximum, mean and minimum concentrations of $\mathrm{PM}_{2.5}$ and different chemical species 16 observed in dry and wet seasons of 2013 in Guangzhou (unit: $\upmu\mathrm{g}/\mathrm{m}^{3})$
1
2
5
6 Table 6
7 Contribution ratios of four main sectors in PRD region to the concentrations of six major
8 pollutants in Guangzhou simulated by WRF/Chem model $(\%)$
1 List of Figures
2
3
4
5
6
7
8
9
1
2
3
4
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7
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Figure 4 Seasonal contribution ratios of four emission sectors in PRD region to the total $\mathbf{PM}_{2.5}$ mass in
Highlights
The $\mathrm{PM}_{2.5}$ source apportionment result of Guangzhou was provided using a new hybrid method.
The contributions of four major emission sectors to $\mathrm{PM}_{2.5}$ mass in dry and wet seasons were obtained.
Mobile sector was the largest single contributor to $\mathrm{PM}_{2.5}$ in Guangzhou.
The pollutants transported from the areas north of PRD region were important to Guangzhou’s air quality in dry season.
|
atmosphere | 0008 | 10.1016/j.envpol.2018.04.111 | image | Fig. 1. (a) Geography of the Wei valley (cross-sectional view) and (b) locations of the six sampling sites on the topographic map (four urban sites: SS, SF, CA, JK, one urban background site: YL and rural site: CT). Xi'an ${\sim}400\,\mathrm{m}$ a.s.l) lies on the Guanzhong plain, which borders the southern foot of the Loess Plateau $({\sim}900{-}1200\,\mathrm{m}$ a.s.l) and the northern foot of the Qinling Mountains $({\sim}2000{-}2800\,\mathrm{m}$ a.s.l). The topographical features of the Wei valley can trap and facilitate the accumulation of air pollutants, resulting in severe air pollution under stable weather conditions. |
|
atmosphere | 0008 | 10.1016/j.envpol.2018.04.111 | image | Fig. 2. Time series of meteorological parameters (a), concentrations of gas pollutants and PM (b, c, d), sulfur and nitrogen oxidized ratios (the molar ratios of $[S O_{4}^{2-}]$ to $[S O_{4}^{2-}+S O_{2}]$ , $[N O_{3}^{-}]$ to $[N O_{3}^{-}+N O_{2}]$ , respectively) (e), and mass concentrations and fractions of $\mathrm{PM}_{2.5}$ species (f, g), SNA denotes the sum of sulfate, nitrate and ammonium. The grey shadows (left) represent three pollution episodes in winter, spring and autumn, the narrower bisque shadows (right) denote two dust-driven pollution events observed in summer. |
|
atmosphere | 0008 | 10.1016/j.envpol.2018.04.111 | image | Fig. 3. Box plots for seasonal variation of crustal material, EC, POC, SOC, sulfate, nitrate, ammonium and trace elements in $\mathrm{PM}_{2.5}$ and $\mathsf{P M}_{10}$ |
|
atmosphere | 0008 | 10.1016/j.envpol.2018.04.111 | image | Fig. 4. Time evolution of the concentrations of main species in $\mathrm{PM}_{2.5}$ and the ratio of $\mathrm{NO}_{3}^{-}$ and $S0_{4}^{2-}$ in the winter of 2003 (Cao et al., 2012a), 2006 (Xu et al., 2016), 2008 (Xu et al., 2016), 2010 (Xu et al., 2016) and 2014 (this study). |
|
atmosphere | 0008 | 10.1016/j.envpol.2018.04.111 | image | Fig. 5. Annual average mass reconstruction of $\mathrm{PM}_{2.5}$ and $\mathsf{P M}_{10}$ collected from six sites over time. Mass concentrations are labeled outside the pie chart ring. |
|
atmosphere | 0008 | 10.1016/j.envpol.2018.04.111 | image | Fig. 6. Average concentrations (bars) and fractions (pies) of each species (crustal material, trace elements, OM, $S0_{4}^{2-}$ $\mathtt{N O}_{3}^{-}$ , $\mathrm{NH_{4}^{+}}$ , EC and others) in $\mathrm{PM}_{2.5}$ and $\mathrm{PM}_{10}$ over six sites are shown as a function of different PM levels. The cyan lines denote the trend of SNA against PM concentrations. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) |
|
atmosphere | 0008 | 10.1016/j.envpol.2018.04.111 | image | Fig. 7. Scatter plot of daily OC and EC concentrations in PM samples (color bar indicates the daily $0_{\mathrm{x}}\left(\mathrm{NO}_{2}{+}0_{3}\right)$ concentrations). The black line is the upper edge of the whole data and the red line is the regression line of data that used to calculate primary OC/EC. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) |
|
atmosphere | 0008 | 10.1016/j.envpol.2018.04.111 | image | Fig. 8. Scatter plots of SOC vs EC (a), sulfate vs EC (b) for PM in winter. Data were colored by concentrations of arsenic (As). Black lines are the upper and bottom edges of winter data. |
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atmosphere | 0008 | 10.1016/j.envpol.2018.04.111 | image | Fig. 9. Scatter plots of SOC vs $K^{+}$ EC vs $K^{+}$ in the spring, summer, autumn and winter. |
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atmosphere | 0008 | 10.1016/j.envpol.2018.04.111 | text | Not supported with pagination yet | Chemical nature of PM2.5 and $\mathsf{P M}_{10}$ in Xi'an, China: Insights into primary emissions and secondary particle formation
Qili Dai a, Xiaohui Bi a, \*, Baoshuang Liu a, Liwei Li a, 1, Jing Ding a, Wenbin Song b, Shiyang Bi c, Benjamin C. Schulze c, Congbo Song a, Jianhui Wu a, Yufen Zhang a, Yinchang Feng a, Philip K. Hopke d, e
a State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport
Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
b Xi'an Environmental Monitoring Station, Xi'an, Shaanxi, 710054, China
c Department of Civil and Environmental Engineering, Rice University, Houston, TX, 77005, USA
d Center for Air Resources Energy and Science, Clarkson University, Potsdam, NY, 13699, USA
e Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 23 December 2017
Received in revised form
17 April 2018
Accepted 23 April 2018
Keywords:
$\mathrm{PM}_{2.5}$
Chemical species
Primary sulfate
Residential coal combustion
In Xi'an, a city that frequently experiences serious PM pollution in northern China, 1476 $\mathsf{P M}_{10}$ and 1464 $\mathsf{P M}_{2.5}$ valid daily filter samples were collected at six sites from December 2014 to November 2015 and analyzed for 29 species. The annual mean $\mathsf{P M}_{10}$ and $\mathrm{PM}_{2.5}$ concentrations were $149.4\pm93.1\$ $108.0\pm70.9\,\upmu\mathrm{g/m}^{3}$ , respectively. Organic carbon (OC) is the predominant $\mathrm{PM}_{2.5}$ component while crustal material predominated in $\mathsf{P M}_{10}$ . Sulfate concentrations, which was the largest component in Xi'an PM in previous studies, were lower than nitrate. Winter sulfate, OC, and elemental carbon (EC) have decreased since 2003, while nitrate remained constant in recent years and the ratio of $\mathrm{NO}_{3}^{-}/\mathrm{SO}_{4}^{2-}$ increased from 0.4 in 2006 to 1.3 in 2014. This result suggests that the motor vehicle contribution to PM has increased relative to coal-fired power plant emissions over the past decade. The mass fractions of crustal material, sulfate, and EC in $\mathrm{PM}_{2.5}$ decreased as the $\mathsf{P M}_{2.5}$ concentrations increased from “clean” days $(<\!50\ \upmu\mathrm{g}/\mathrm{m}^{3})$ to the highest values, while nitrate significantly increased. Despite forming through secondary reactions, the high concentrations of SOC and $S0_{4}^{2-}$ in winter are attributed to primary emissions and particularly to residential heating and cooking with coal. Primary SOC and $S0_{4}^{2-}$ accounted for $33\%$ and $42\%$ of their total $\mathrm{PM}_{2.5}$ concentrations in winter, respectively. Therefore, control measures applied to these primary sources can substantially improve air quality.
$\copyright$ 2018 Published by Elsevier Ltd.
1. Introduction
As one of the areas of the world with the severest air pollution, China's airborne particulate matter pollution has drawn much attention from the government, scientists, and the public (Chan and Yao, 2008; Guo et al., 2014; He et al., 2002; Shao et al., 2006; Song et al., 2017a; Wang et al., 2015b). The high particulate matter concentrations have adverse impacts on visibility, climate change, and human health (Huang et al., 2014; Pope, 2002; Song et al.,
2017b). The Chinese State Council released the “Action Plan on Prevention and Control of Atmospheric Pollution” on September 10, 2013 after the unprecedented pollution event in early 2013 (China State Council, 2013). This plan aims to reduce PM concentrations across the country, for example, reducing $\mathsf{P M}_{2.5}$ in Beijing-TianjinHebei region by up to $25\%$ by 2017 relative to 2012. Although control policies have been developed and implemented, haze pollution is still severe in most cities in China. In 2013, only $4.1\%$ of the Chinese cities attained the annual average $\mathsf{P M}_{2.5}$ standard (Wang et al., 2017).
Xi'an is a megacity in northwest China with a residential population of 8.7 million and covers an area of about 10,100 square kilometers (Xi'an Statistical Yearbook, 2016), located in the center of the Guanzhong plain, one of the most seriously polluted regions in China (Liu et al., 2016; van Donkelaar et al., 2010; Wang et al.,
2015a). The annual average $\mathsf{P M}_{2.5}$ and $\mathsf{P M}_{10}$ concentrations in Xi'an have never attained the annual grade-Ⅱlimit values set by the national ambient air quality standards (NAAQS) of China since it was promulgated in 2012 (GB3095-2012). Severe pollution from both fine and coarse particles in Xi'an, have received widespread attention from the scientific community (Cao et al., 2012b; Chen et al., 2016; Elser et al., 2016; Huang et al., 2014, 2012; Li et al., 2017, 2016; Liu et al., 2017; Shen et al., 2010; Wang et al., 2014b, Wang et al., 2015a; Xu et al., 2016). Generally, the heating-season runs from November 15 to next March 15, and aggravates the local air pollution in Xi'an (Huang et al., 2012). Huang et al. (2014) reported the source apportionment result of $\mathsf{P M}_{2.5}$ during a severe haze pollution event in winter in Xi'an and found that a dustrelated source contributed up to $46\%$ of $\mathsf{P M}_{2.5}$ mass. Cao et al. (2005) reported the measurement of atmospheric carbonaceous materials during the fall and winter of 2003 in Xi'an, and found the contribution of coal combustion increased the total winter carbon concentration by up to $44\%$ . The significance of coal combustion primarily used for residential heating and cooking was also reported by Xu et al. (2016), based on the measurement of $\mathsf{P M}_{2.5}$ chemical composition during the winter of 2006, 2008, and 2010 in Xi'an. Their results showed the contribution of coal combustion decreased from $31\%$ in 2006 to $24\%$ in 2010 but still dominated the $\mathsf{P M}_{2.5}$ mass. However, all these studies were conducted at a single sampling site in Xi'an for a limited time. These results were augmented by adding information on the spatial and seasonal variability of $\mathsf{P M}_{2.5}$ with six sampling sites and four seasonal measurement campaigns as reported by Wang et al. (2015a). A recent epidemiologic study conducted in Xi'an suggested that spatial and temporal varying factors might play important roles in modifying the $\mathsf{P M}_{2.5}$ -mortality association (Huang et al., 2012).
Particulate matter concentrations and compositions vary with the nature of the sources and related human activities. The government has strengthened its mitigation strategies to reduce the pollution. Thus, continuing development of effective and efficient mitigation strategies must be based on an updated understanding of the chemical nature of the PM, including data with detailed chemical and spatial scale information. Therefore, the current study utilized four urban sites, one urban background site, and one rural site to determine the ambient PM concentrations across Xi'an and examine the tempo-spatial patterns of particle composition to provide insights into the PM sources.
2. Methods
2.1. PM sampling
Ambient $\mathsf{P M}_{2.5}$ and $\mathsf{P M}_{10}$ samples were collected simultaneously from six sites in Xi'an, including four urban sites (SS, SF, CZ, and JZ), one urban background site (YZ), and one rural site (CT) (Fig. 1). The details of sampling sites are provided in Table S1 in the Supplemental Information (SI). All sampling sites were close to the corresponding locations of national air quality monitoring stations. Daily concentrations of $\mathsf{P M}_{2.5}$ and $\mathsf{P M}_{10}$ were measured at all sampling sites for 11 consecutive days each month from December 2014 to November 2015. Four channel samplers (TH-16A, Wuhan Tianhong company, China) were used to collect $\mathsf{P M}_{10}$ and $\mathsf{P M}_{2.5}$ samples from $10{:}30\,\mathsf{a.m}$ . to $08{:}30\,\mathrm{a.m}$ . (22h) at a flow rate of $16.7\,\mathrm{L}/$ min using four parallel filters (two $47{-}\mathrm{mm}$ quartz fiber filters and two $47{-}\mathrm{mm}$ Teflon filters). The Teflon filters were used for measurement of PM gravimetric mass and elemental concentrations. The quartz-fiber filters were analyzed for water-soluble ions and carbonaceous materials.
Fifty-two filter samples were invalidated due to filter fracture, sampler malfunction, and/or other unexpected accidents during the sampling campaign. Detailed information regarding the sampling and quality assurance/quality control (QA/QC) are available in the SI. These efforts resulted in $1464\ \mathrm{PM}_{2.5}$ and 1476 $\mathsf{P M}_{10}$ valid filter samples available for chemical characterization.
2.2. Sample treatment and analysis
The water-soluble ions $\left(\mathsf{N O}_{3}^{-}$ , $S0_{4}^{2-}$ , $C1^{-}$ , $\mathsf{F}^{-}$ , $\mathsf{N H}_{4}^{+}$ , $\mathtt{N a}^{+}$ , $\mathsf{K}^{+}$ , $C a^{2+}$ and ${\mathrm{Mg}}^{2+}$ ) collected on the quartz filters were analyzed by ion chromatography (Thermo ICS5000, USA). Half of each filters (cut with ceramic scissors) was used for extraction at $40\,^{\circ}\mathrm{C}$ with $20\,\mathrm{ml}$ purified water (specific resistivity $=18.2\,\mathsf{M\Omega C m}^{-1},$ ) for $20\,\mathrm{min}$ . After the extract was cooled to room temperature, the supernatant was filtered through a $0.22\,\upmu\mathrm{m}$ microfiltration membrane and analyzed.
The best analytic methods were chosen after assessing five different dissolution methods used in Inductively Coupled PlasmaAtomic Emission Spectrometry (ICP-AES) analysis. The results were compared with two X-ray fluorescence (namely Epsilon 5, Quant’X) instruments. The Teflon filter samples were digested with $10\,\mathrm{ml}$ $\mathrm{HNO}_{3}$ -HCl (1:3) mixture in a closed vessel at $140\pm5\,^{\circ}\mathrm{C}$ for $4\,\mathrm{h}$ . The leaching solution was then transferred to a volumetric flask and diluted to $50\,\mathrm{mL}$ with purified water. Na, Mg, Al, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Pb, As and Cd were determined by ICP-AES (Thermo IRIS IntrepidⅡXSP, USA) (Tian et al., 2014), while Si was determined by X-ray fluorescence (Thermo ARL Quant’X, USA) coupled with a fundamental parameter (FP) quantification method. The carbonaceous materials (OC and EC) of each quartz-fiber filter were determined by a Thermal Optical Analyzer (Sunset Lab Model 4L NDIR) following the NIOSH 5040 protocol (Phuah et al., 2009). More detailed information on the chemical analysis procedures and ${\tt Q A}/$ QC can be found in the SI.
Gas-phase air pollutant data, including $S0_{2}$ , $\mathsf{N O}_{2}$ , $\Nu0_{\tt x}$ , $0_{3}$ , and CO, and local meteorological data for each sampling site were recorded every $1\,\mathrm{h}$ at the nearest national air quality monitoring stations.
2.3. Mass reconstruction
determined using the lowest OC/EC ratios. The main concern of this method is that the minimum OC/EC ratios vary among emission sources. Thus, the calculated POC is from a mixture of sources rather than a single source. Selecting the samples with OC/EC ratios smaller than the 10th percentile produced satisfactory OC-EC correlations $\lceil r^{2}\,{=}\,0.96$ , $p<0.01^{\prime}$ (Table S2 and Fig. 3 (a)), indicating these data are likely emitted from primary sources.
PM mass reconstruction was performed by taking the sum of primary organic aerosol (POA), secondary organic aerosol (SOA), EC, crustal material, trace elements, sulfate, nitrate, ammonium, and others.
$$
\begin{array}{r l}{[C r u s t a l\;m a t e r i a l]=1.89^{*}A l+2.14^{*}S i+1.4^{*}C a+1.67^{*}T i}&{}\\ {+\;1.43^{*}F e+1.2^{*}K+1.67^{*}M g+1.29^{*}M n}\\ {+\;1.35^{*}N a}\end{array}
$$
Previous studies have reported the OC/EC ratios from primary emission sources, such as OC/EC ratios of biomass burning ranging from 2.6 to 5.7 (Schmidl et al., 2008), and OC/EC ratios in particle emissions during efficient fossil fuel combustion are generally lower than 1 (Handler et al., 2008). Thus, the minimum ratio of OC/ EC of 5.3 derived from OC/EC ratios smaller than the 10th percentile used in this study is assumed to come from a mix of combustion sources like coal plants, household coal burning, biomass burning etc., where OC and EC can be co-emitted with likely dominance by residential and biomass combustion that involves a substantial fraction of smoldering conditions. Our results would likely underestimate the fraction of SOC given the high primary OC that would be estimated. We will examine if the minimum ratio method can be used to reflect sharp primary-secondary OC separation by measuring the OC/EC ratio in real source emissions. However, this additional work has not yet begun and was beyond the scope of the present study.
2.3.1. POA, SOA, and EC
Organic mass (OM) was estimated by multiplying OC by a factor $(f)$ to account for non-C atoms (S, N, H, O) in organic compounds. $f$ can range from 1.27 to 2.2 (Aiken et al., 2008; Chow et al., 2015; Philip et al., 2014). The $f$ multiplier is expected to be lower in POA dominating urban areas than in rural areas with oxidation and/or addition of SOA. However (Chow et al., 2015), summarized the OM/ OC ratio determined directly in various studies at urban and remote locations and found that the results do not show systematic variations. Although site-specific $f$ values should be measured, POA was calculated by multiplying POC by a relatively low $f$ of 1.4 at all sites in this study (Chow et al., 1996). The multiplier $f$ was adopted as 1.8 for converting SOC to SOA (Aiken et al., 2008; Philip et al., 2014).
The concentrations of the mineral oxides ( $S\mathrm{iO}_{2}$ , $\mathrm{Ti}_{2}0$ , $\tt M g O$ $\mathrm{Fe}_{2}0_{3})$ were derived directly from their elemental concentrations. For those elements (Na, K, Ca, Mn) that have both crustal and pollution origin, their mineral oxides $\mathrm{Na}_{2}0$ , $\mathrm{K}_{2}\mathrm{O}$ , CaO, MnO) were calculated based on their elemental ratios to Al from the Earth's average upper crustal composition (Taylor and McLennan, 1995). Their individual excess masses were assumed to be of anthropogenic origin. Crustal material was calculated as follows:
The split between primary OC (POC) and secondary OC (SOC) was estimated based on the EC-tracer method, assuming EC and POC have same origins with good correlations. (Hu et al., 2012; Lim and Turpin, 2002; Lin et al., 2009):
2.3.2. Crustal material
$$
{\mathsf{S O C}}=O C_{t o t}-E C\times\left({\frac{O C}{E C}}\right)_{m i n}
$$
where POC is estimated by the second term. The $(\mathrm{OC}/\mathrm{EC})_{\mathrm{min}}$ was
2.3.3. Trace elements
Trace elements concentrations were estimated by adding the concentrations of all metal species determined in this study and the anthropogenic portions of Na, K, Ca, and Mn. Generally, Na and $C1^{-}$ were markers for sea salt based on the elemental composition of sea salt. Since Xi'an is an inland city about $1000\,\mathrm{km}$ from the sea, the contribution from sea salt was not calculated in this study. The unaccounted mass of PM is regarded as “Other” was attributed to particle-bound water, errors in the gravimetric measurement of the filters and component determination from the chemical analyses, and improper OC/OM multipliers (Chow et al., 2015; Malm et al., 2011). Additionally, chlorine was counted in “Other” in this study. In this study, the sum of mineral material, OM, EC, sulfate, nitrate, and trace elements explained $78.0{-}92.9\%$ of the PM mass across all sites.
3. Results and discussion
3.1. $P M_{l0}$ and $P M_{2.5}$ mass concentrations
As shown in Fig. S1, the PM concentrations measured on Teflon filters and on quartz-fiber filters match well (slopes close to unity and high correlations). However, since quartz-fiber filters tend to adsorb water or shred fibers during sample handling (Tian et al., 2014), concentrations measured on the Teflon filters were used for PM concentrations.
Annual mean concentrations and monthly variations of $\mathsf{P M}_{10}$ and $\mathsf{P M}_{2.5}$ measured at the six sampling sites are shown in Fig. S2. The ensemble average concentrations and standard deviations of $\mathsf{P M}_{10}$ and $\mathsf{P M}_{2.5}$ at all sites during the sampling campaign were $149.4\pm93.1\$ , $108.0\pm70.9\,\upmu\mathrm{g/m}^{3}$ , respectively. The $\mathsf{P M}_{10}$ and $\mathsf{P M}_{2.5}$ concentrations on average exceeded the limit values set by the air quality standards of China by $113\%$ and $209\%$ . The annual mean concentrations of $\mathsf{P M}_{10}$ and $\mathsf{P M}_{2.5}$ at each site exceeded the corresponding limit values and significantly exceeded the stricter World Health Organization air quality guidelines of 20 and $10\,\upmu\mathrm{g}/\mathrm{m}^{3}$ for $\mathsf{P M}_{10}$ and $\mathsf{P M}_{2.5}$ (World Health Organization, 2006), and the U.S. NAAQS values of $12\,\upmu\mathrm{g}/\mathrm{m}^{3}$ for $\mathsf{P M}_{2.5}$ (US Federal Register, 2013). Our results showed that the annual mean PM concentrations at the urban sites (SS-U, SF-U, CA-U, JK-U) were close to those in urban background site (YL-UB), but higher than those in rural site (CT-R). A Kruskal-Wallis ANOVA for the six sites was significant $(p<0.01)$ . The Dunn-Bonferroni test was performed for all possible post-hoc pairwise comparisons and the results can be found in Table S3.
The monthly mean PM concentrations have a similar seasonal pattern at all sites, decreasing from winter to fall to spring to summer, which is consistent with Wang et al. (2015a). The highest PM concentration occurred in January, decreased in February but then increased in March due to the potential influence of transported desert dust from the Gobi desert and northwest part of China (Wang et al., 2014b). However, the PM concentrations then dropped to a relatively lower value, which lasted from April to September. Thereafter, there was an increase in October at all sites. All sites were characterized by higher fall-winter and lower springsummer PM values, probably due to the huge coal consumption during the heating season coupled with the atmospheric stagnation that favored the accumulation of the local PM emissions in winter.
The annual average $\mathrm{PM}_{2.5}/\mathrm{PM}_{10}$ ratios were ${\sim}0.75$ for SS-U, JK-U and YL-UB, 0.69 for CA-U and SF-U, and 0.67 for CT-R. The differences between sites were tested by Dunn-Bonferroni post hoc pairwise comparisons (Table S3). A nearby construction site $\left(\sim\!150\,\mathrm{m}\right)$ had potential impacts on the SF-U site (mainly for the coarse sizes), and the resuspension of road dust from a construction road near the CT-R site was expected to lower the $\mathrm{PM}_{2.5}/\mathrm{PM}_{10}$ ratio. As shown in Fig. S2, the monthly variations of $\mathrm{PM}_{2.5}/\mathrm{PM}_{10}$ ratios are similar at all sites. $\mathrm{PM}_{2.5}/\mathrm{PM}_{10}$ ratios reached their lowest values in April and August, and peak values were observed in June,
September, and winter months. The low $\mathrm{PM}_{2.5}/\mathrm{PM}_{10}$ ratios in April and August were attributed to transported desert dust episodes in spring and summer.
The squared correlation coefficients $(\boldsymbol{\mathbf{r}}^{2})$ for PM mass among the urban-site pairs (e.g. SS-U v.s. SF-U) are higher than 0.85. These values are higher than that between any urban site and the urban background site (YL-UB) or the rural site (CT-R). The lowest $\Gamma^{2}$ value was observed for that between YL-UB and CT-R (0.55). These results indicate that the spatial patterns of PM mass collected among urban sites are very similar, but different from the urban background and rural sites.
3.2. Temporal variations in PM speciation
OC was the most abundant species of $\mathsf{P M}_{2.5}$ collected at any site with annual mean concentrations of $19.73\pm15.03\,\upmu\mathrm{g/m^{3}},$ followed by nitrate $(16.18\pm15.58\;\upmu\mathrm{g/m^{3}})$ , sulfate $(14.86\pm9.28\;\upmu\mathrm{g/m^{3}})$ , crustal material $(13.76\pm13.55\,\upmu\mathrm{g/m^{3}})$ , ammonium $(8.24\pm6.81\;\upmu\mathrm{g}/\mathrm{m}^{3}).$ trace elements $(1.86\pm1.27\,\upmu\mathrm{g}/\mathrm{m}^{3})$ , and EC $(1.86\pm1.00\,\upmu\mathrm{g/m^{3}})$ (Fig. 5). For $\mathsf{P M}_{10}$ , the most abundant species was crustal material $(40.00\pm31.47\,\upmu\mathrm{g/m^{3}})$ followed by OC $(22.47\pm17.42\,\upmu\mathrm{g/m^{3}})$ , nitrate $(20.41\pm18.46\,\upmu\mathrm{g/m^{3}})$ sulfate $(19.45\pm11.65\,\upmu\mathrm{g/m^{3}})$ , ammonium $(8.71\pm6.81\;\upmu\mathrm{g/m^{3}})$ , trace elements $(2.46\pm1.96\,\upmu\mathrm{g/m^{3}})$ , and EC $(2.23\pm1.42\,\upmu\mathrm{g/m^{3}})$ .
The seasonal average concentrations of crustal material in $\mathsf{P M}_{2.5}$ were in the order: spring>winter $>$ summer>autumn similar to $\mathsf{P M}_{10}$ . Xi'an is characterized by dry springs and winters, and the seasonal average wind speed was relatively elevated in spring giving rise to dust suspension. Additionally, dust transported by northwesterly monsoonal winds from the arid and semi-arid regions in northwest China to the Guanzhong plain often occur in spring (Niu et al., 2016). Those factors may have a cumulative effect on the high crustal material concentrations in spring. Fig. 4 shows there are outliers in the box plot for crustal material in the summer, indicating that dust-driven pollution events occurred. The bisque shadowed areas in summer (Fig. 2) show two summer dust-driven pollution events associated with low $\mathrm{{PM}}_{2.5}/\mathrm{{PM}}_{10}$ ratios $(<\!0.4)$ , high mass fractions of crustal material $(\mathord{\sim}0.6)$ , and low fractions of SNA in PM $(<\!0.2)$ .
POC, SOC, and EC show similar seasonal patterns with the highest average concentrations recorded in winter and lowest in summer. Similar seasonal patterns were found for sulfate, nitrate, and ammonium. The average concentrations for these species were in the order: winter>autumn $>$ spring $>$ summer. Table S4 shows that the relative standard deviation of sulfate was lower than for nitrate, suggesting lower seasonal variations of sulfate compared with nitrate indicating a steady source of sulfate not related to atmospheric conversion. As shown in Fig. 2 (g), the mass fraction of sulfate was highest in the summer. The sulfur conversion ratio (SOR, molar ratio of $[S O_{4}^{2-}]$ to $[S O_{4}^{2-}+S O_{2}]$ ) peaks in the summer consistent with previously results in Xi'an (Zhang et al., 2011) and Beijing (Hu et al., 2014).
In winter, high $\mathsf{N O}_{2}$ concentrations resulted from increased emissions from fossil fuel combustion, as well as lower temperatures, and shallower planetary boundary layers that facilitate the formation of secondary nitrate (Heo et al., 2009; Wang et al., 2015a). The relatively high contribution of nitrate in the autumn agrees with previous results reported by Wang et al. (2015a), who observed that nitrate levels were highest in the autumn. They concluded that stable weather conditions and heterogeneous reactions favored the formation of nitrate. In the present study, the highest concentration of secondary nitrate was observed in winter, followed by autumn. The short sampling time scale of two weeks in Wang's study might also have produced their inconsistent nitrate seasonality.
Most species attained their highest concentrations in winter (Fig. 3). The average concentrations of OC, EC, $\mathrm{SO}_{4}^{2-}$ , $\mathrm{NO}_{3}^{-}$ , $\mathsf{N H}_{4}^{+}$ in $\mathsf{P M}_{2.5}$ in the winters of 2003 (Cao et al., 2012a), 2006, 2008, and 2010 (Xu et al., 2016) were compared with 2014 (this study) to investigate the evolution of these species (Fig. 4). The average $\mathsf{P M}_{2.5}$ concentrations decreased from $356.3\pm118.4\,\upmu\mathrm{g}/\mathrm{m}^{3}$ in 2003 to $155.8\pm82.3\,\upmu\mathrm{g}/\mathrm{m}^{3}$ in 2014. Sulfate concentrations declined almost monotonically from $53.8\pm25.6\,\upmu\mathrm{g}/\mathrm{m}^{3}$ in 2003 to $16.2\pm10.3\,\upmu\mathrm{g}/\mathrm{m}^{3}$ in 2014, with an annual reduction rate of $24.3\%$ . This decline was likely caused by the countrywide implementation of flue gas desulfurization in coal-fired power plants beginning in 2006 as defined by the air pollution control goals in China's 11th 5-year plan (Wang et al., 2012, 2013; Xu et al., 2016). Nitrate concentrations decreased from $29.0\pm10.0\,\upmu\mathrm{g}/\mathrm{m}^{3}$ in 2003 to $20.6\pm13.9\,\upmu\mathrm{g}/\mathrm{m}^{3}$ in 2006. There were then constant values until 2010. The average OC concentration decreased by $62.5\%$ from $95.8\pm27.7\;\upmu\mathrm{g}/\mathrm{m}^{3}$ in 2003 to $35.9\pm19.2\,\upmu\mathrm{g}/\mathrm{m}^{3}$ in 2006, continued decreasing to $30.2\pm13.9\,\upmu\mathrm{g}/\mathrm{m}^{3}$ in 2008, and then increased to $37.5\pm23.9\,\upmu\mathrm{g}/\mathrm{m}^{3}$ in 2010. A relatively lower concentration was observed during the winter of 2014. The $\Nu0_{\mathrm{x}}$ emission control policies for vehicles and coal-fired plants likely compensated for the increased number of vehicles over the past decade. As a result, OC became the most abundant component of $\mathsf{P M}_{2.5}$ during the 2014-15 sampling campaign. Similar to OC, the EC concentrations decreased from 2003 to 2006, then changed only slightly up to 2010, but showed lower concentrations in 2014. The OC and EC in 2014 were determined by TOT method that led to higher OC values and lower EC values than those determined by TOR methods in the previously reported studies.
The $\mathrm{NO}_{3}^{-}/\mathrm{SO}_{4}^{2-}$ ratio has been used as an indicator of the importance of stationary emissions versus mobile emissions (Arimoto et al., 1996; Wang et al., 2005; Xu et al., 2016). The rising ratio of $\mathrm{NO}_{3}^{-}/\mathrm{SO}_{4}^{2-}$ from 0.4 in 2006 to 1.3 in 2014 indicates an increasing contribution from mobile vehicles, relative to coal-fired power plants due to implementation of $S0_{2}$ scrubbers.
Additionally, previous studies have suggested that sulfate is the main component of annual average $\mathsf{P M}_{2.5}$ in 2010 in Xi'an, followed by OC and nitrate, revealing the most important sources are the emissions from fossil fuel combustion (Wang et al., 2015a). In the present study, OC is the dominant component of $\mathsf{P M}_{2.5},$ followed by nitrate (Table S4). Sulfate is the third largest component, indicating that the source contributions of PM have changed substantially in recent years.
3.3. Spatial variation of PM speciation
Urban-rural variation of species provides insights into identification of local/regional sources. Fig. 5 shows the relative high mass of POA at urban sites compared to the urban background site and rural site, suggesting the role of anthropogenic emissions in urban area. Higher mass and percentage of SOA at the background site indicates that regionally transported, aged organic aerosol probably had a major influence on this site because it was located in an upwind area and surrounded by cornfields. There were no directly emitted anthropogenic sources near the CT-R site, which likely explains the low mass concentration of POA. The relative high percentage of SOA $(19\%)$ at CT-R site could be attributed to the oxidization of large quantities of biogenic VOCs that are emitted from forests in the Qinling Mountains (with the forest coverage rate as $48\%$ ) as described previously (Wang et al., 2015a).
The total OM $^{\prime}{\mathsf{P O A}}{+}S0A)$ was the dominate part of PM mass. The highest percentage of OM was recorded at the SS-U site, probably due to the contribution from coal combustion and cooking activities, as this site is very close to a Chinese traditional food street that has many coal-fired restaurants. The highest levels of EC in $\mathsf{P M}_{2.5}$ and $\mathsf{P M}_{10}$ were both recorded at JK-U site, which probably results from coal combustion in a district heat-supply facility and a China Datang power plant that were $^{-5}$ and $10\,\mathrm{km}$ away from this site, respectively. This attribution is consistent with having the lowest OM/EC ratios at JK-U site (15.9). The relatively high OM/EC ratio at YL-UB may result from the influence of the regional transported SOA and higher contribution from biomass burning and biogenic emissions to PM levels, since biomass burning and biogenic emissions both can elevate the OM/EC ratio (Amato et al., 2016; Puxbaum et al., 2007). This hypothesis was supported by the high level of SOA at this site (see the discussion before).
The nitrate concentrations at each site are very similar and show low spatial variability except for the CT-R site. Nitrate was not a sitespecific species and played an important role in Xi'an even in the upwind area. Seventy-nine percent of the nitrate in $\mathsf{P M}_{10}$ was in $\mathsf{P M}_{2.5}$ . Wang et al. (2014b) suggested that the significant coarse fraction nitrate may due to the reaction of mineral dust with gaseous $\mathrm{HNO}_{3}$ .
Similar to nitrate, the sulfate concentrations were very similar among the urban sites and the urban background site (Fig. 5). SNA had an average contribution to $\mathsf{P M}_{2.5}$ mass of $36.5\%$ . The highest level of SNA in $\mathsf{P M}_{2.5}$ was at the rural site $(38.2\%)$ , indicative of the characteristic feature of regional SNA pollution in Xi'an. About $81\%$ of SNA in $\mathsf{P M}_{10}$ are present in $\mathsf{P M}_{2.5}.$ implying the fine mode of occurrence of most SNA.
Since crustal material mass was mainly distributed in the coarse mode, it is the main component of $\mathsf{P M}_{10}$ mass loading at all sites $(20{-}30\%$ , see Fig. 5). Crustal material concentrations were much lower in $\mathsf{P M}_{2.5}$ , with a range of $9{-}14\%$ of $\mathsf{P M}_{2.5}$ mass. The highest fraction of crustal material was recorded at CT-R site ( $14\%$ , $30\%$ of $\mathsf{P M}_{2.5}$ and $\mathsf{P M}_{10}$ concentrations, respectively). The lowest fraction of crustal material was found at SS-U $9\%$ $20\%$ of $\mathsf{P M}_{2.5}$ and $\mathsf{P M}_{10}$ concentrations), indicating dust was the dominant rural site contributor of PM compared to the urban sites. The percentages of crustal material in $\mathsf{P M}_{2.5}$ measured in this study were higher than measured in Beijing $(7.1\!-\!8.0\%)$ and Chongqing $(6.0{-}7.5\%)$ during March 2005 through February 2006 (Yang et al., 2011), and also higher than that determined in five AIRUSE cities from southern Europe $(4{-}10\%)$ (Amato et al., 2016).
Trace elements accounted for comparably small fractions in PM mass at all sites (Fig. 5), of which the sum range from 1.22 to 2.20 $\upmu\mathrm{g}/\mathrm{m}^{3}$ in $\mathrm{PM}_{2.5},\,1.79{-}3.04\,~\upmu\mathrm{g/m}^{3}$ in $\mathsf{P M}_{10}$ . Lower trace element concentrations were observed at the rural site with a decline of $24\%$ in $\mathsf{P M}_{2.5}$ and $\mathsf{P M}_{10}$ respectively, compared to the urban sites and urban background site. The high abundance of trace elements was assumed to be related to industrial emissions such as iron and steel production and coal-fired power plant emissions (Dai et al., 2015). The urban-rural variation in trace element abundance shows a much lower contribution of anthropogenic activities in the rural area compared to the urban area. The highest concentrations of trace elements in $\mathsf{P M}_{2.5}$ were observed at SF-U and JK-U sites, showing that the factories nearby both sites had direct impacts on local air quality.
Fig. 5 shows the total of SNA and SOA dominated PM mass by contributing $49\mathrm{-}62\%$ to $\mathsf{P M}_{2.5}$ , $40{-}53\%$ to $\mathsf{P M}_{10}$ . The contributions from secondary aerosol were larger at all sites, which is suggestive of a regional, rather than local nature.
3.4. Particle composition at different PM concentrations
As marked in Fig. 2, five pollution events where PM concentration exceeded the NAAQS limit values were observed. There were two dust-driven pollution events observed in the summer. The other three pollution episodes were recorded in winter, spring and autumn (grey shadows in Fig. 2), respectively. During the three pollution events, the relative humidity was gradually increasing (Fig. 2(a)), the mass fraction of SNA, and the molar ratios of $[S O_{4}^{2-}]$ $\mathrm{to}[S O_{4}^{2-}+\,S O_{2}]$ , $[N O_{3}^{-}]$ to $[N O_{3}^{-}+N O_{2}]$ increased simultaneously. Therefore, $\mathrm{PM}_{2.5}/\mathrm{PM}_{10}$ increased since SNA was the predominate species of $\mathsf{P M}_{2.5}$ .
Fig. 6 presents the average mass concentrations and fractions of species at different PM concentrations. The mass concentrations of all species show an increasing trend as the PM increased from clean days $(<\!50\ \upmu\mathrm{g}/\mathrm{m}^{3})$ to its highest values. The crustal material concentrations increased to only a small extent in both $\mathsf{P M}_{2.5}$ and $\mathsf{P M}_{10}$ . The mass fraction of sulfate decreased from $18\%$ to $10\%$ , $15\%{-}10\%$ in $\mathsf{P M}_{2.5}$ and $\mathsf{P M}_{10}$ , respectively. Trace elements and EC also decreased. The mass fractions of OM and ammonium remained constant as $\mathsf{P M}_{2.5}$ and $\mathsf{P M}_{10}$ mass increased. When the PM concentrations increased from ${<}50\ \upmu\mathrm{g}/\mathrm{m}^{3}$ level to the highest concentration, nitrate increased from $9\%$ to $16\%$ in $\mathsf{P M}_{2.5}$ (from 3.36 to $50.96\;\upmu\mathrm{g/m}^{3})$ and $8\%{-}16\%$ in $\mathrm{PM}_{10}\,(3.40{-}68.41~\upmu\mathrm{g/m^{3}})$ , while the mass fractions of other species decreased.
3.5. Primary emissions of secondary species
Generally, sulfate and SOC have been considered to be totally secondary species (Cao et al., 2012b; LAI et al., 2007; Li et al., 2016; Xu et al., 2016). However, as mentioned in Section 3.2, there is much more SOC and sulfate in winter than summer (Fig. 3). Given the weak photochemical conditions, cold temperatures, and lack of sufficient oxidants in the winter, the data suggests that a significant fraction of the measured SOC and sulfate must be primary. Both edges of the scatter points in Fig. 7 provide insights into the OC sources. The bottom edge (red line) represent the data that were used to calculate primary OC/EC ratio and are likely from traffic emissions and high temperature combustion sources. The data points along the upper edge (black line) are probably from smoldering biomass burning and residential coal combustion with low temperature since both sources can produce oxidized POC. The upper edge in Fig. 8(a) shows the strong relationship between SOC and EC, revealing that SOC emitted from combustion sources. The data points in Fig. 8(a) are colored by their arsenic concentrations since arsenic is a common tracer of coal combustion (Tian et al., 2010). High arsenic concentration are obviously close to the upper edge, indicating these data points are from residential coal combustion. Thus, fresh EC emitted from coal combustion in household stoves was co-emitted with semivolatile and low volatility organic compounds that can rapidly condense on the surface of the EC and forming “SOC”. The data points along with the upper edge in Fig. 8 (b) are also from coal combustion because of their high arsenic concentration, while the data points along the bottom edge in Fig. 8(b) with small amounts of co-emitted sulfate are potentially from diesel vehicles. The China Ⅳemission standards for on-road diesel vehicles has been implemented in Xi'an during sampling campaign, required the sulfur content in fuel to be no more than $50\,\mathrm{ppm}$ . Therefore, the sulfur trioxide emitted from diesel engines could rapidly form sulfate in ambient air. Recent studies reported that sulfate accounted for $3.27\%$ of $\mathsf{P M}_{2.5}$ mass from on-road trucks (Cui et al., 2017) and $4.80\%$ of PM emitted from
diesel vehicles measured in tunnel (Cui et al., 2016).
Thus, we calculated the primary “secondary species” by using the EC-tracer method based on the assumption that primary species and EC originated from the same sources, as it has been used for calculating SOC in Eq (2):
$$
[S_{p r i m a r y}]=E C\times\left(\frac{S}{E C}\right)_{m i n}
$$
where $S_{p r i m a r y}$ is the primary part of secondary species S, and $(S/E C)_{m i n}$ is determined by the lowest $S/{\tt E C}$ ratio. The results show values below the 25th and 5th percentiles of SOC/EC and $\mathrm{SO}_{4}^{2-}/\mathrm{EC}$ ratios produced satisfactory SOC-EC, $\mathsf{S O}_{4}^{2-}$ -EC correlations, respectively (Table S5), indicating these samples are most dominated by conventional primary source emissions.
On average, the primary SOC and $\mathrm{SO}_{4}^{2-}$ accounted for up to $33\%$ and $42\%$ of total SOC and $\dot{\mathrm{S}}0_{4}^{2-}$ in winter, respectively. These results are consistent with Huang et al. (2014) who reported that $\sim\!37\%$ of sulfate is directly emitted from coal burning in Xi'an during haze events in January 2013. This value is comparable with the estimated fraction of primary $S0_{4}^{2-}$ in this study. Meanwhile, Wang et al. (2014a) found primary $\mathrm{SO}_{4}^{2-}$ was ${\sim}10~\upmu\mathrm{g}/\mathrm{m}^{3}$ in the vicinity surrounding point sources, accounting for ${\sim}6{-}10\%$ of $\mathsf{P M}_{2.5}$ mass simulated using the WRF/CAMQ modeling system for that haze event in central Xi'an. Similarly, primary $\mathrm{SO}_{4}^{2-}$ contributed $4.3\%$ of $\mathsf{P M}_{2.5}$ mass on average in winter for our data.
$\mathsf{K}^{+}$ is commonly used as the indicator of biomass burning. The highest squared correlation coefficients between $\mathsf{K}^{+}$ and EC $(\mathsf{r}^{2}\,\!=\!0.53)$ , $\mathsf{K}^{+}$ and SOC $(\mathbf{r}^{2}\,{=}\,0.78)$ were observed in autumn (Fig. 9), suggesting the impact of smoldering biomass on the PM composition in Xi'an during the harvest season because it produces white smoke with oxidized carbon and little EC. Yokelson et al. (1997) reported that about one half of the detected organic emissions arose from smoldering combustion of biomass that produces smoke rich in oxygenated organic compounds.
4. Conclusions
Although control measures have been implemented in the past decade particularly on coal-fired power plants and motor vehicles, atmospheric particulate pollution remains a serious challenge in Xi'an since annual mean concentrations of $\mathsf{P M}_{2.5}$ and $\mathsf{P M}_{10}$ were $149.4\pm93.1\$ , $108.0\pm70.9\,\upmu\mathrm{g/m}^{3}$ during the sampling campaign, respectively (far exceeding the corresponding WHO air quality guidelines). OC is the predominant component of $\mathsf{P M}_{2.5}$ and accounted for $15.4{-}18.1\%$ at all six sampling sites, while crustal material is the predominant component of $\mathsf{P M}_{10}$ $(20.1\substack{-30.5\%})$ . Sulfate concentrations that had been the largest component in Xi'an PM in previous studies, were lower than nitrate in this study. Sulfate, OC, and EC have decreased during wintertime since 2003, while nitrate remained constant in recent years. The ratio of $\Nu0\Bar{3}/$ $S0_{4}^{2-}$ increased from 0.4 in 2006 to 1.3 in 2014. These results indicate that the source contributions of PM have changed remarkably. Specifically, the contribution of motor vehicles to PM has increased relative to coal-fired power plants in the last decade.
The urban-rural variation of species revealed the intensive anthropogenic emissions in urban areas and profound influence of regional transported particles on rural sites. High POA and SOA mass values were observed at the urban sites and the background site, respectively. The mass fractions of crustal material, sulfate, EC in $\mathsf{P M}_{2.5}$ decreased when the $\mathsf{P M}_{2.5}$ levels rose from clean days $_{<50}$ $\upmu\mathrm{g}/\mathfrak{m}^{3})$ to the highest level, while nitrate significantly increased.
Species that are normally considered to be secondary are actually primary. The highest SOC and $\mathrm{SO}_{4}^{2-}$ observed in winter cannot be only attributed to secondary formation, but to primary emissions such as residential heating and cooking by coal. Primary SOC and $\mathrm{SO}_{4}^{2-}$ accounted for up to $33\%$ and $42\%$ of the total amounts of SOC and $\mathrm{SO}_{4}^{2-}$ in the winter, respectively. Therefore, control measures on these primary sources can improve air quality.
Acknowledgements
This work was financially supported by the National Key R&D Program of China (Grant No. 2016YFC0208500 (No. 2016YFC0208501)), National Natural Science Foundation of China project (Grant No. 21407081), Tianjin Science and Technology Foundation (16YFZCSF00260), and the Fundamental Research Funds for the Central Universities of China. The authors thank Yahong Wang, Yuehong Hu, Xuejuan Song, Jing Han, Naiwang Yang (staffs at Xi'an Environmental Monitoring Station) for their assistances in the field sampling and chemical analysis of particle samples. The scholarships provided by China Scholarship Council to two of the authors, Qili Dai and Xiaohui Bi, are also gratefully acknowledged.
Appendix A. Supplementary data
Supplementary data related to this article can be found at https://doi.org/10.1016/j.envpol.2018.04.111.
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atmosphere | 0009 | 10.1016/j.atmosres.2012.12.004 | image | Fig. 1. Geographical map of the sampling site. |
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atmosphere | 0009 | 10.1016/j.atmosres.2012.12.004 | table | Table 1 Method detection limits, precision, recovery ratio and field blank concentrations of WSIs and PAHs in $\mathrm{PM}_{2.5}$ in the suburb of Shenzhen. |
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atmosphere | 0009 | 10.1016/j.atmosres.2012.12.004 | table | Table 2 Seasonal mean values of meteorological data at the sampling sites. |
|
atmosphere | 0009 | 10.1016/j.atmosres.2012.12.004 | image | Fig. 2. Seasonal mean mass concentrations of $\mathrm{PM}_{2.5}$ in the suburb of Shenzhen. |
|
atmosphere | 0009 | 10.1016/j.atmosres.2012.12.004 | image | Fig. 3. 72-hour air mass backward trajectories of air mass reaching Shenzhen. |
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