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Methane emissions from natural gas vehicles in China
Da Pan ORCID: orcid.org/0000-0002-1618-73891,2,
Lei Tao1,2,
Kang Sun ORCID: orcid.org/0000-0002-9930-75093,4,
Levi M. Golston1,2,
David J. Miller ORCID: orcid.org/0000-0002-3456-44161,2,5,
Tong Zhu6,
Yue Qin ORCID: orcid.org/0000-0003-1664-45167,8,
Yan Zhang ORCID: orcid.org/0000-0003-2152-13699,
Denise L. Mauzerall1,10 &
Mark A. Zondlo ORCID: orcid.org/0000-0003-2302-95541,2
Nature Communications volume 11, Article number: 4588 (2020) Cite this article
Climate-change impacts
Natural gas vehicles (NGVs) have been promoted in China to mitigate air pollution, yet our measurements and analyses show that NGV growth in China may have significant negative impacts on climate change. We conducted real-world vehicle emission measurements in China and found high methane emissions from heavy-duty NGVs (90% higher than current emission limits). These emissions have been ignored in previous emission estimates, leading to biased results. Applying our observations to life-cycle analyses, we found that switching to NGVs from conventional vehicles in China has led to a net increase in greenhouse gas (GHG) emissions since 2000. With scenario analyses, we also show that the next decade will be critical for China to reverse the trend with the upcoming China VI standard for heavy-duty vehicles. Implementing and enforcing the China VI standard is challenging, and the method demonstrated here can provide critical information regarding the fleet-level CH4 emissions from NGVs.
From 2000 to 2017, the population of natural gas vehicles (NGVs) in China increased from 6000 to 6.08 million (Fig. 1 and Supplementary Table 1)1. This rapid growth of NGVs in China is primarily driven by environmental considerations and associated economic incentives. Natural gas (NG) is considered to be a clean-burning fuel characterized by relatively low carbon content and low air pollutant emissions2, making it less costly for NGVs to meet increasingly stringent PM2.5 and NOx emission standards in China compared to gasoline and diesel counterparts3,4. Currently, the payback time of the additional cost for purchasing a NGV relative to gasoline and diesel counterparts are two to three years for a taxi driver3, and around one year for a truck driver in China (see Supplementary Fig. 1 for details). China aims to increase the population of NGVs to 10 million by 20205, and more growth is expected beyond 2020 for heavy-duty applications (buses and trucks) where vehicle electrification remains difficult6. In 2017, 6.37 million heavy-duty trucks (all fuel-types combined) accounted for 3.1% of the total vehicles in China and contributed 53 and 60% of the vehicular NOx and particulate matter (PM) emissions7. To improve the situation, the State Council of the People's Republic of China set the goal to retire heavy-duty vehicles meeting just China III emission standards and specifically mentioned the goal of promoting the use of heavy-duty NGVs for the first time in 20188. In the first half of 2019, 85,000 heavy-duty NG trucks were sold in China accounting for 13% of the total heavy-duty vehicles sold in the same period and were 27% higher than the annual sales of heavy-duty NG trucks in 20189.
Fig. 1: Population of natural gas vehicles (NGVs) and their share in total vehicle stock in China from 2000 to 2017.
Supplementary Table S1 lists the number of NGVs in each category. Source data are provided as a Source Data file.
Although NGVs are cost-effective alternatives to achieve the desired NOx, PM, and CO2 emission reductions4,10,11, their unintended methane (CH4) emissions could compromise their potential climate benefits. CH4 is a potent greenhouse gas (GHG) with a global warming potential (GWP) of 28–34 over a 100-year time horizon (84–86 over a 20-year time horizon)12. Despite having the largest NGV fleet in the world, emission factors (EFs) of NGVs in China have not been carefully quantified, and CH4 emissions from NGVs are not included in the CH4 emission inventory for China13,14,15. Previous studies of NGVs mainly focused on CH4 emissions from the upstream stages in China, such as extraction, processing, and distribution of NG, using life-cycle analysis (LCA). Emissions related to vehicle operation have been ignored in these life-cycle analyses because of the lack of direct measurements in China14,16,17,18. CH4 can be emitted from NGVs as unburnt fuels from tailpipes. CH4 is the hydrocarbon most resistant to catalytic oxidation. Therefore, CH4 removal rates in the exhaust of NGVs are highly variable and strongly depend on the combination of engine and after-treatment technology2. CH4 also has been observed in the blowby gas from spark ignited (SI) engines with an open crankcase system and in the vented gas from High-Pressure Direct Injection (HPDI) engines19. Occasionally, CH4 can be released directly from on-board fuel storage tanks as well due to manual or pressure relief venting19. Clark et al.20 estimated that vehicle emissions account for about 80% and 40–60% of pump-to-wheels (PTW) and well-to-wheels (WTW) CH4 emissions, respectively, based upon observations for NGVs in the US. For WTW GHG emissions from NGVs in China, only Huo et al.16 adopted the CH4 emission factor (EF) of light-duty NGVs developed for the US. Other life-cycle analyses for China have ignored vehicle CH4 emissions from NGVs entirely14,17,21,22.
In fact, CH4 emissions from NGVs in China may be significantly higher than the results from the US and other regions. In China, about 80% of NGVs are retrofitted from conventional vehicles with engines and after-treatment equipment not designed for NG23. In 2018, Hu et al.23 reported the overall CH4 emission factor of NGVs in China (3.0 ± 0.5% of NG consumed, (mean ± standard error)) is about eight times the emission factor for NGVs given by the IPCC (0.4% of NG consumed). Without distinguishing light- and heavy-duty NGVs (Supplementary Fig. 2), they attributed the high CH4 emissions to the retrofitted light-duty NGVs23. However, heavy-duty NG buses and trucks produced in China are equipped with lean-burn (LB) engines and oxidation catalysts (OC)2. The exhaust temperature of LB engines is usually lower than the ideal temperature for OC (450 °C) to effectively remove CH42,24. The mean value of fuel-specific EFs found in previous studies for LB engines with OC is six times higher than that of stoichiometric (SM) engines equipped with a three-way catalyst (TWC, Supplementary Table 2 lists CH4 EFs for different technologies)25. Finally, although CH4 emissions from heavy-duty NGVs are regulated by emission standards in China, the low removal rate at low exhaust temperature may lead to significantly elevated real-world emissions than the certified emission limit. Such discrepancies have been reported widely for NOx emissions from heavy-duty vehicles equipped with selective catalytic reduction (SCR) systems, which are also temperature sensitive10,26. Because measurements of CH4 emissions from NGVs are lacking, however, it is unclear whether such a discrepancy exists for CH4 emissions from NGVs in China and to what extent it impacts their GHG emissions.
During the 2014 CAREBEIJING North China Plain field campaign, we deployed a mobile laboratory to quantify CH4 EFs of NGVs in China. We updated the well-to-wheels GHG emissions for NGVs with the observed EFs and developed a detailed bottom-up CH4 emission inventory of NGVs in China for 2000–2017. Starting July 1st, 2019, heavy-duty NGVs sold in China must be certified for the China VI emission standard for heavy-duty vehicles27. We designed three scenarios to assess the potential impacts of the implementation of the new standard on CH4 emissions from NGVs. Our results show that CH4 emissions from heavy-duty NGVs were high and switching to NGVs from conventional vehicles in China has led to a net increase of 77 Mt CO2eq from 2000–2017. Our scenario analyses demonstrate that strictly implementing the upcoming China VI standard could reduce GHG emissions by 509 Mt CO2eq for 2020–2030.
On-road CH4 emissions from NG taxis and buses
CH4 emissions from exhaust and leakage from NG buses and taxis in Baoding and Shijiazhuang were measured by our mobile laboratory equipped with fast-response sensors. We measured 26 h on-road, covering around 600 km in these two cities in June 2014 (details about instruments and spatial coverage can be found in Supplementary Table 3 and Supplementary Fig. 3). The fast-response sensors (10 Hz) allowed the use of the plume-chasing method to measure on-road emissions from vehicles. Several criteria, including sufficient CO2 and CH4 enhancements, correlations between CH4 and CO2 and videos recorded on the road, were developed to identify plumes from NGVs. Supplementary Movie 1 provides an example of the on-road measurements. A Gaussian puff model was used to investigate the effectiveness of our method to minimize the influence of the exhaust of nearby vehicles, and the results show our method can significantly reduce interferences caused by the emissions from other vehicles28. Using the plume-chasing method, we were able to capture emissions from 73 NG buses and 63 NG taxis during the field campaign. The observed CH4 and CO2 mixing ratios were used to derive CH4:CO2 enhancement and emission ratios. The emission ratios were then converted to fuel-specific CH4 emission factors. Similar methods have been used to estimate vehicular NH3 emissions29,30. More details and discussion about the uncertainty of the method can be found in the "Method" section and Supplementary Discussion. Figure 2 shows the on-road fuel-specific CH4 EFs (presented as % of NG consumed) derived from CH4:CO2 emission ratios measured in China as well as previously reported EFs.
Fig. 2: Fuel-specific emission factors as % of NG consumed for light-duty natural gas vehicles, heavy-duty natural gas vehicles, and fuel-specific well-to-pump (WTP) NG leakage rate.
The boxes and whiskers for our observations show 5th, 25th, 50th, 75th, and 95th percentiles of the observed EFs. Black dots and bars show the average values and standard errors of corresponding EFs measured in China. Black dots and bars show the average values and standard deviation (S.D.) of corresponding EFs measured in China. Numbers of independent samples (vehicles) used to derive EFs and the standard errors are listed in the labels. Red dots and bars show the venting-emission and seasonality adjusted values of corresponding EFs for China. Gray dots and bars show the average values and standard errors of corresponding EFs measured in other regions. The star and the associated bar show the estimated EF and its uncertainty for heavy-duty NG trucks equipped with a lean-burn engine and oxidation catalyst (determination of the uncertainty can be found in method section). Xie et al. and Guo et al. measured total hydrocarbon (THC) emissions instead of CH423,52. We converted their results to CH4 emissions assuming 90% of THC is CH4 as suggested by Xie et al. and Hu et al.23,52. The observed EF for heavy-duty vehicles is 85% higher than the current standard (China V). "LB + OC", "SM + TWC", "SM + TWC w. CC", "HPDI", and "HPDI w. DV" stand for a lean-burn engine with oxidation catalyst, stoichiometric engine with three-way catalyst, stoichiometric engine with a three-way catalyst with crankcase emissions, high-pressure direct injection (HPDI), and HPDI with dynamic venting emissions. Source data are provided as a Source Data file.
Sixty-three NG taxis with clear NGV labels were sampled to represent light-duty NGVs in China, which had an average EF of 1.7 ± 0.5%. The EF is 16 times higher than the values reported for light-duty NGVs in the US and EU (0.10 ± 0.3%), but the EF agrees with the tailpipe CH4 EF measured in the exhaust of NG taxis by Hu et al.23 (1.7 ± 0.8%). The CH4 EF measured from 73 NG buses in China is 2.9 ± 0.5%, which is 90% higher than the CH4 limit of the China V standard for heavy-duty vehicles31. We were able to distinguish buses powered by liquified natural gas (LNG) and compressed natural gas (CNG) by checking the label of the buses. No statistically significant difference was found between the EFs of LNG buses (39 buses, 2.8 ± 0.4 %) and CNG buses (34 buses, 3.1 ± 0.5 %). The NG buses in these two cities were equipped with LB engine and OC, and they were certified for the China VI and China V standards, respectively. We also observed low NH3 emissions from NG buses (Supplementary Fig. 4), consistent with the reported pattern for NGVs with LB engine with OC32,33. The observed EF of NG buses is more consistent with the overall on-road CH4 EF measured by Hu et al.23 (3.0 ± 0.5%) than the observed EF of light-duty NGVs. To validate our method, we conducted additional measurements by following NG buses in Atlantic City, US, in the spring of 2015. The observed EF agrees with previously reported tailpipe CH4 emissions for NG buses in the US as well as the CH4 emissions used in the GREET model18.
Estimation of CH4 emissions from heavy-duty NG trucks
Identifying NG trucks in China was more difficult than NG buses since they were not labeled as clearly as the NG buses. Therefore, we could not derive CH4 EF for heavy-duty NG trucks using our observations. Our survey shows that NG trucks certified for China IV and V from the major manufacturers in China are equipped with similar LB engines and OC but with slightly larger displacements than the engines on NG buses (Supplementary Table 4). This type of engine is rarely used on trucks in other countries, and therefore, no CH4 EF have been reported for NG trucks equipped with LB engines. Previous studies suggested driving conditions of the vehicles may have larger impacts on CH4 emissions rather than the chassis2,19. Comparing CH4 EFs reported for NG buses and trucks equipped with similar SM engines and TWC, we did not find a significant difference for both the tailpipe and the crankcase CH4 emissions (Fig. 2 and Supplementary Table 2)33,34,35,36,37,38. Therefore, the measured CH4 EF of NG buses is used to estimate CH4 emissions from heavy-duty NG trucks. Since NG trucks may operate on the highway more frequently than NG buses, we assigned a larger error to the lower-bound uncertainty of EFs of NG trucks, which equals to the lower-bound uncertainty of the previously reported CH4 EF of LB engines with OC (Fig. 2 and Supplementary Table 2).
Venting-emission and seasonality adjustment
Because low CO2 enhancements and correlations between CH4 and CO2 mixing ratio enhancements are used to remove impacts from other CH4 sources, our method can capture operations related CH4 emissions from tailpipes and crankcases but may miss sporadic venting events directly from the on-board fuel tanks that are not fed to the engine. Clark et al.19 found these emissions are difficult to be characterized by in-field observations because of the large volume of methane vented in single events and their intermittent nature. Using tank pressure and liquid fuel level (%) differences before and after venting, they estimated the fuel-specific emission rate of these venting events is 0.1% of NG consumed in the US (about 8.4% of total pump-to-wheels CH4 emissions for NGVs in the US)19. The same emission rate is adopted in our study to account for the venting emissions. Our observations were made in June with an average ambient temperature of 30 °C, which may underestimate CH4 emissions during cold seasons, especially for the cold-start emissions. Among the studies reviewed, only two studies reported the cold-start CH4 emissions for heavy-duty NGVs at low temperatures. The ratio of cold- and hot-start for CH4 EFs at around 0 °C ranges from 1.08 for vehicles with a fuel-specific EF of 11.2% to 2.69 for vehicles with a fuel-specific EF of 0.2% (Supplementary Table 5)37,39. To account for the potential impact of cold-start emissions at low temperature, we adjusted the observed EFs using a cold-start/hot-start emission ratio of 1.5 and a weighting factor of 14% for cold-start emissions as listed in the testing procedure for the China VI standard (see "Method" section for details). The adjusted EFs are 1.9 [−0.7, +0.9] %, 3.2 [−0.8, +1.0] %, and 3.2 [−1.7, +1.0] % for NG taxies, heavy-duty NG buses, and heavy-duty NG trucks as shown by the red dots and bars in Fig. 2.
Technological pathways for the China VI standard
Figure 2 also shows the EFs for SM engines equipped with TWC and the high-pressure direct injection (HPDI) engines. Both have the potential to meet the CH4 limit of the China VI standard. However, high CH4 emissions from the crankcases of SM engines have been observed as NG could pass through the gaps between the piston rings and the cylinders19. When crankcase CH4 emissions are considered, it will be difficult for SM engines to meet the China VI standard unless a complicated, closed crankcase ventilation system (CCV) is installed2. No crankcase CH4 emission has been reported for the HPDI engines, but HPDI engines require venting of the high-pressure fuel to balance NG and diesel fueling pressures, leading to dynamic venting CH4 emissions19. The dynamic venting CH4 emissions could far outweigh the tailpipe CH4 emissions during urban operation and could be equivalent to tailpipe emissions during highway operation19.
Well-to-wheels GHG emissions of NGVs in China
Previous studies have estimated the WTW GHG emissions for NGVs in China with limited consideration of CH4 emissions from NGVs (see Supplementary Table 6 for studies reviewed)14,16,22. Ou et al.22 investigated multiple pathways of CNG and LNG in China and reported a WTP leakage rate about 0.6% of NG consumed in the Tsinghua Life Cycle Analysis Model. Huo et al. assumed the technologies in China for production and distribution of CNG and LNG are similar to the ones used in other regions and adopted the rates of 1.93% of NG consumed for extraction and production and 0.007% of NG transported per km via pipeline from the GREET model16,18. The difference of WTP GHG emissions between CNG and LNG (1%) is lower than the variation caused by the CH4 leakage from pipeline distribution (standard deviation of 7%) since the transport distance ranges from 200 to 4400 km for different provinces. Therefore, the same WTP GHG emission factor (28 ± 6 CO2eq MJ−1) and the same WTP CH4 leakage rate (1.65 ± 1.05% of NG consumed) are used for both LNG and CNG. The overall WTP leakage rate is about the same as the CH4 EF of light-duty NGVs and is 40% lower than the CH4 EF of heavy-duty NGVs (Fig. 2).
The distance-specific WTW GHG EFs for NGVs are derived in this study by combining previously reported upstream GHG EFs, distance-specific fuel consumption, and adjusted CH4 EFs of NGVs (shown in Fig. 3). The uncertainty of the national level WTW GHG EF for NGVs in China is large because of the variation in NG transport distance via pipeline (from 200 km to 4400 km). For provincial analysis, as demonstrated by Huo et al.16, the uncertainty could be reduced. With the observed CH4 emissions, both light-duty NGVs and NG buses are unlikely to reduce GHG emissions compared to their counterparts. For NG buses, the WTW GHG emissions are likely to be higher than diesel buses even if they satisfy the China VI standard CH4 limit because of their increased fuel consumption (Supplementary Table 7). Switching from diesel trucks to current generation NG trucks equipped with LB engines and OC as the measured NG buses is likely to increase GHG emissions by 160 [−200, +180] g CO2eq km−1. Only the ones operating mostly on the highways in the near-source regions may have lower WTW GHG EF compared to diesel trucks.
Fig. 3: Well-to-wheels GHG emissions of vehicles powered by gasoline, diesel, and NG in China.
Panels a and b show the well-to-wheels GHG emissions for light- and heavy-duty vehicles, respectively, in China. Blue bars show the WTW GHG emissions without CH4 contribution. Green and orange bars are the CO2 equivalents of WTP CH4 emissions and CH4 emissions from NGVs (a GWP of 30 over a time-scale of 100 years is used for CH4 from fossil fuel consumption according to IPCC AR5)12. For cars and buses, NGVs may not bring GHG emission mitigation. NG trucks that meet China VI standard have lower GHG emissions compared to diesel trucks. The black error bars indicate high and low estimates derived using error propagation of uncertainties of multiple input parameters (e.g., life-cycle GHG emissions, CH4 emission factors, and fuel consumptions). Uncertainty estimates (standard deviation, S.D.) of individual parameters are listed in Supplementary Tables 1, 6, 7, and 11. Source data are provided as a Source Data file.
For trucks equipped with SM engines and TWC or HPDI engines, the WTW GHG emissions are similar to diesel trucks. It should be noted that the fuel consumption of trucks equipped with SM engines and TWC is assumed to be the same as trucks with LB engines. Operating at lean conditions is an effective way to improve fuel efficiency compared to a pure stoichiometric operation40. However, the fuel economy of SM engines can be significantly improved by operating the engine with diluted mixtures through exhaust gas recirculation (EGR) systems, which also can significantly reduce NOx emissions35,40. Hajbabaei et al.35 compared the fuel consumption of a SM engine with an EGR system and two LB engines. They found the SM engine with EGR had very similar fuel consumption compared to the LB engines. For the NG trucks to be certified for the China VI standard, SM engines are likely to be used with an EGR system to be competitive in the market in terms of fuel economy and to be in compliance with the China VI NOx emission limit and the China Stage 3 fuel consumption limits41. The same fuel consumption was scaled by 0.95 to approximate the fuel consumption of HPDI engines because Thiruvengadam et al.32 reported the fuel consumption of HPDI engines was 4% lower than that of SM engines with EGR systems.
If the China VI standard is stringently the enforced with the real-world emissions being the same as the CH4 emission limit, switching from diesel trucks to NG trucks will lead to a GHG reduction of 100 ± 150 g CO2eq km−1, and upstream CH4 leakages will become the limiting factor for lowering the WTW GHG emissions from NGVs in China. Although having real-world emissions in line with certified emission limits is challenging, it has been shown to be technically achievable at least for NOx emissions from Euro VI trucks, to which the China VI standard is equivalent26.
CH4 emissions from NGVs in China
NG consumption of the Transport, Storage, and Post sector reported in the China Statistical Yearbook (CSYB) does not have the detailed categorical information for estimating CH4 emissions from NGVs in China42. Therefore, we estimated NG consumption of NG taxis, light-duty NGVs (non-taxi), NG buses, and NG trucks in China as the product of vehicle population (Supplementary Table 1), distance-specific fuel consumption (Supplementary Table 7), and annual mileage traveled (Supplementary Table 8). The four categories are determined based on fuel consumption and emission characteristics and availability of the population data. Figure 4a shows the estimated NG consumption and reported NG consumption in the CSYB42. Personal light-duty NGVs (light-duty NGVs except for NG taxis) should be excluded when comparing the estimated NG consumption and the CSYB reported values since fuel consumed by personal vehicles are not included in the Transport, Storage, and Post sector in the CSYB43. The sum of NG consumption of NG taxis, buses, and trucks is slightly lower than the CSYB reported consumption because NG consumption of cargo ships is included in the CSYB but not included in our estimates. For 2017, our estimate is closer to the reported consumption of CSYB likely due to the NG shortage in China in the winter of 2017. In 2017, NG buses and trucks consumed about 70% of the total NG consumption of NGVs.
Fig. 4: NG consumption, total CH4 emissions from NGVs, and changes of WTW GHG emissions of switching to NGVs in China from 2000 to 2030.
Estimated (bars or solid lines) and projected (dashed lines) NG consumption (a), total CH4 emissions from NGVs (b), and changes of WTW GHG emissions of switching to NGVs (c) in China from 2000 to 2030. Gray line in a shows the reported NG consumption for the Transport, Storage, and Post sector reported in the China Statistical Yearbook (CSYB). When comparing the estimated NG consumption and NG consumption from CSYB, light-duty vehicles (without taxis) should be excluded (light blue bar in a). The error bars in a and b and the gray area in c indicate high and low estimates derived using error propagation of uncertainties of multiple input parameters. Uncertainty estimates (standard deviation, S.D.) of individual parameters are listed in Supplementary Table 1, 6, 7, 8, and 11. Source data are provided as a Source Data file.
Total CH4 emissions and changes in WTW GHG emissions are calculated by multiplying the corresponding emission factors (venting-emission and seasonality adjusted) to the NG consumption (see "Method" section for more details). Figure 4b, c shows the estimated and the projected total CH4 emissions from NGVs in China and the changes in WTW GHG emissions of switching to NGVs from gasoline and diesel counterparts for 2000–2030. The annual CH4 emissions from NGVs in China increased from 0.0014 [−0.0004, +0.0004] Mt in 2000 to 0.77 [−0.28, +0.22] Mt in 2017. Switching to NGVs has increased the GHG emissions by 83 Mt CO2eq for 2000–2017. More than 80% of CH4 emissions from NGVs are emitted by NG buses and trucks in 2017 because of their high fuel consumption and high EFs. Therefore, the implementation of the CH4 limit of the China VI standard for heavy-duty vehicles is critical for mitigating future CH4 emissions from NGVs.
Three scenarios were designed to assess different pathways regarding the implementation of the China VI standard. Table 1 lists the major features of these scenarios. The population estimates are adapted from the projection by Wu et al.6, where aggressive electrification for applicable fleets was considered (see Supplementary Table 9 for projected vehicle population for the three scenarios). The fuel consumption of heavy-duty vehicles (both NGVs and conventional gasoline or diesel vehicles) purchased after 2021 is lowered by 15% assuming that the Stage 3 China Fuel Consumption Standard will be implemented successfully41.
Table 1 Scenarios for projections of future CH4 emission and changes in GHG emissions of switching to NGVs.
The high-emission scenario represents the pathway that retrofitting light-duty vehicles is allowed. In addition, this scenario assumes that the CH4 limit of China VI standard is loosely enforced, which has been the case for previous standards as demonstrated here. Although LB engines with OC are considered the last generation technology, they could meet the NOx limit of China VI standard if SCR is implemented11. If the CH4 limit of the China VI standard is loosely implemented, LB engines may dominate the heavy-duty vehicle market because of their advantages in terms of upfront cost, since SM engines require precise air–fuel ratio control strategies and an exhaust gas recirculation system40. Under this scenario, annual CH4 emissions from NGVs in China would increase to 3.3 Mt, equivalent to 8% of the estimated total anthropogenic CH4 emissions and 17% of CH4 emissions related to fossil fuel production and consumption in China in 201013. Cumulatively, switching to NGVs from counterparts would increase the WTW GHG emissions by 432 Mt CO2eq from 2020 to 2030 under this scenario (the integrated area under the orange curve in Fig. 4b from 2020 to 2030).
The medium-emission scenario represents the pathway that retrofitting is prohibited, and heavy-duty NGVs sold after 2019 are equipped with SM or HPDI engines. Because of the increased cost, the penetration rate of NGVs is lower than the high-emission scenario. Under this scenario, CH4 emissions from NGVs in China would increase at a slower rate, reaching 1.3 Mt in 2030 and the cumulative changes in the WTW GHG emissions from 2020 to 2030 would increase by 117 Mt CO2eq.
The low-emission scenario assumes that the EF of the heavy-duty NGVs purchased after 2019 is the same as the CH4 limit of China VI standard. The growth of NGVs is assumed to be localized within source regions where NG price is low, and the leakage CH4 emissions related to NG distribution are lower than the medium- and high-emission scenarios. The annual CH4 emissions from NGVs in China would gradually decrease to 0.7 Mt in 2030 and reduce the WTW GHG emissions by 77 Mt CO2eq cumulatively from 2020 to 2030 under this scenario. Comparing the cumulative WTW GHG changes between the high- and the low-emission scenarios, we find that stringently enforcing the China VI standard for heavy-duty vehicles could generate a GHG reduction of 509 Mt CO2eq for 2020– 2030, equivalent to eliminating GHG emissions of 12 million passenger cars with the current GHG emission level.
NGVs have been promoted in China as a cost-effective alternative to mitigate air pollution and to reduce GHG emissions from vehicles, especially for heavy-duty applications where vehicle electrification remains difficult. Previous studies suggested that the WTW GHG emissions of NGVs were 6–25% lower than gasoline and diesel counterparts, but tailpipe and crankcase CH4 emissions from NGVs were mostly ignored14,16,17,18. Hu et al.23 sampled on-road CH4 emissions from NGVs in China and reported an overall fuel-specific EF of 3.0% and attributed the high on-road CH4 emissions to leakage from converted light-duty NGVs. However, our sampling results of 63 NG taxis show similar tailpipe CH4 emissions and no significant leakage from light-duty NGVs. Our observations of CH4 emissions from 73 NG buses indicate that heavy-duty NGVs contributed more to the high overall on-road CH4 emissions in China compared to light-duty NGVs. With the current level of CH4 emissions from NGVs, switching to NGVs in China has not brought a reduction of GHG emissions.
With the China VI standard, the heavy-duty transportation sector in China will be "gasified" rapidly because it is less costly for NGVs than diesel and heavy-duty electric vehicles to meet the stringent limits for air pollutant emissions4. The rapid growth of heavy-duty NGVs without stringent enforcement of the China VI standard CH4 limit, however, would increase CH4 emissions as demonstrated by the high-emission scenario. Strictly implementing the China VI standard as in the low-emission scenario will require a closed crankcase ventilation system for SM engines with TWC for heavy-duty NGVs. This pathway would reduce CH4 emissions by around 70% in 2030 compared to the high-emission scenario and generate a significant GHG emission reduction, making it a "win-win" option for air quality and climate. Although this measure could lead to an increased price barrier for purchasing NGVs, innovations in the design of engines and after-treatment devices could lower the barrier. For example, catalysts that can remove CH4 more effectively from LB engines are being pursued44,45,46,47. China's successful implementation of the China VI standard on such a large scale could inspire climatically beneficial NGV development in other regions facing similar challenges, especially in developing countries where natural gas is already considered as an economical alternative for transportation48,49.
Substantial uncertainties still exist in the estimates and the projections for CH4 and WTW GHG emissions from NGVs in China due to lacking detailed vehicle population and NG consumption data as well as the uncertainties related to CH4 EFs of NGVs. With categorized and regional NGV data, the uncertainties for both the CH4 emissions and the WTW GHG emissions could be significantly reduced. Despite the large sample size from our campaign, the observed EFs for light-duty NGVs and NG buses may be underestimated because of missing cold-start CH4 emissions at low temperature and sporadic venting emissions. In addition, no CH4 EF has been reported for current generation NG trucks that are equipped with LB engine and OC and the CH4 EF could be different under various driving conditions for the NG trucks. Finally, CH4 emissions from NGVs in China in the next decade could vary substantially, depending on the implementation of the China VI standard, as demonstrated in our scenario analyses. Therefore, more observations are needed not only to constrain CH4 emissions from current generation NGVs but also to enforce the CH4 limit of the China VI standard.
As our measurements demonstrate, there is a discrepancy between real-world CH4 emissions and the associated emission limits. For China III, IV, and V, the compliance testing for CH4 emissions from NGVs is conducted with the European Transient Cycle (ETC)31. The ETC has been criticized for not representing real-world driving conditions since it has relatively high average engine loads over the entire test; consequently, exhaust temperatures during the ETC test are relatively high50. Under real-world driving conditions, especially in urban areas, the exhaust temperature from NGVs equipped with LB and OC may be lower than the ideal temperature for CH4 removal leading to elevated CH4 emissions. This discrepancy highlights the challenges in ensuring that the desired CH4 emission levels for NGVs are met in reality and the need for more real-world measurements. For the China VI standard, the compliance testing will be conducted using the World Harmonized Steady-State Cycle (WHSC), which should be more representative of the full range of real-world driving conditions27. In addition, starting in 2021, model compliance testing will include a real-world emission test conducted with a portable emissions measurement system (PEMS) attached to the vehicles27. However, crankcase and venting emissions may not be captured by the PEMS method. Besides, the results of PEMS testing often show considerable variation because of different vehicle and traffic conditions, as can be seen from EFs measured using PEMS from 25 light-duty NGVs by Xie et al. (Fig. 2), limiting its use to estimate overall emissions51,52. Increasing the sample size can reduce the variability but will lead higher costs and difficulties in getting access to the vehicles. Remote sensing or plume-chasing methods, similar to the method used in this study or the method demonstrated by Hu et al.23, can provide critical information regarding the fleet-level CH4 emissions from NGVs in China to researchers in other fields and the stakeholders. Finally, our method could be applied to other regions as well. For example, a majority of taxis, buses, and autorickshaws in 11 (out of 29) Indian states are powered with CNG due to Indian Supreme Court decisions53,54. However, similar to the case in China, CH4 emissions from these vehicles have rarely been quantified despite the large fleet size, and EFs developed for other regions were used to investigate climatic impacts of NGVs in India55. Our method could be implemented at relatively low cost and, combined with fuel consumption estimates, used to quantify CH4 emissions from NGVs in India and total impacts on GHG emissions.
Derivation of CH4:CO2 emission ratio
We used a method similar to Sun et al.30 to calculate the CH4:CO2 emission ratio (in the unit of (ppmv CH4) (ppmv CO2)−1). Observed CH4 and CO2 mixing ratios were first separated into localized vehicle emission signals (local enhancements) from the urban backgrounds by finding their 2nd percentiles within a 3-min window. Since CH4 and CO2 are relatively stable (lifetimes about are 12 and 30–95 years for CH4 and CO2), and we were close to the emission sources, the CH4:CO2 emission ratio at a given time can be approximated by the slope of orthogonal regression of CH4 and CO2 enhancements measured within a time window of ±1 s. The short time window was chosen to capture instantaneous changes of CH4 emissions. Increasing the time window to ±2.5 s only changed the results by less than 2%.
The plume-chasing method assumes that CH4 and CO2 are co-emitted from the same sources of interest and are transported and dispersed in the same way. To make sure this assumption is valid, we only used observations when the mobile laboratory was directly following NGVs. With the videos, we were able to distinguish buses powered by liquified natural gas (LNG) and compressed natural gas (CNG) by checking the label of the buses and our on-road videos. No significant difference was observed between the LNG and CNG buses (see Supplementary Table 2 for values), and, therefore, we did not distinguish LNG and CNG buses in the life-cycle analysis and emission scenario analyses.
An example is provided in Supplementary Movie 1, and the associated observations are also shown in Supplementary Fig. 5. We were typically less than 50 m away from the NGVs. Although we did not measure the distance between the NGVs and our mobile laboratory, we used the ΔCO2 and ΔCH4 thresholds (10 ppmv for ΔCO2 and 0.2 ppmv for ΔCH4) to determine if the mobile laboratory was within the plume of NGVs. To prevent the influence of the emissions from our mobile platform, we excluded observations with a driving speed smaller than 5 km h−1 based on our previous studies29,56. As shown in Supplementary Movie 1, there were still vehicles in other lanes even when our mobile laboratory was directly behind the NGVs. Since most of the vehicles were not powered by NG (only 2.6% of the vehicles were powered by NG on a national scale), emissions from other vehicles would contribute to the observed ΔCO2, which could potentially lower the estimated emission ratios. To reduce such interference, the correlation between ΔCO2 and ΔCH4 was used as a criterion to remove observations potentially influenced by other vehicles. Data with R2 < 0.5 within the ±1 s time window were excluded. The rationale behind this criterion is that high-frequency fluctuations (10 Hz) of CO2 and CH4 concentrations were caused by turbulent movements of the plumes, and these changes should be correlated if CO2 and CH4 plumes are emitted from the same source and go thru the same atmospheric transport and dispersion. In addition, using the slope of orthogonal regression between ΔCH4 and ΔCO2 (instead of the quotient) to estimate enhancement ratio helps to remove the ΔCO2 offsets caused by emissions from other vehicles. To investigate the effectiveness of correlation criterion and orthogonal regression, we used a Gaussian puff model (PUFFER) to simulate on-road CO2 and CH4 concentrations measured by our mobile laboratory (see Supplementary Discussion for more details)28. Our results highlight the importance of capturing high-frequency variations. When high-frequency fluctuation is present, which was usually the case for busy roads when our measurements were influenced by other vehicles, no statistically significant difference between the true and the estimated ΔCH4:ΔCO2 ratios is found, indicating that the combination of R2 filtering and orthogonal regression effective in minimizing the interference.
As shown in Supplementary Movie 1, the emission ratio could vary by a factor of three following single bus. This variability is related to driving mode changes and leads to the skewness of observed EFs shown in Fig. 2. Similar variability has been reported by previous studies using the chassis dynamometer method33,57. This variability was largely reduced by using fleet-wise mean emission ratios. During our field campaign in China, we had three days of observations and we treated observations from each day as an individual fleet sample. The relative standard deviations of the daily averaged EFs for NG buses and taxis are 3 and 12%. The major source of uncertainty in our method is the plume-identification process. Our results are insensitive to the choice of temporal window for background removal and the thresholds for ΔCO2 and R2. However, our results are sensitive to the threshold for ΔCH4. For example, changing the threshold for R2 to 0.25 or 0.75 from 0.5 leads to 2–8% changes in the mean ER, much smaller than the impacts of the CH4 cutoff (see Supplementary Table 10 for details). In addition, R2 filtering is less sensitive as the CH4 threshold increases. With a CH4 threshold of 0.4 ppmv, the daily mean ER changed <2% when increasing R2 threshold from 0.25 to 0.75. Therefore, we estimated our standard errors of ER related to plume identification as the difference of the results without a cutoff for CH4 and the results with a CH4 cutoff of 0.4 ppmv. Finally, we estimated the uncertainty of our observation using sample-size weighted uncertainty propagation combining the plume-identification uncertainty and sample uncertainty (see Supplementary Table 10 for details).
Derivation of the fuel-specific CH4 emission factors
The observed fuel-specific CH4 emission factor (EF) is defined as the ratio of CH4 emission rate and fuel consumption rate. Fuel consumption rate can be estimated by the carbon balance method
$${\mathrm{EF}}_{{\mathrm{CH}}_4,{\mathrm{obs}}}^{{\mathrm{fuel}}}(\%\, {\mathrm{of}}\,{\mathrm{NG}}\,{\mathrm{consumed}}) =\, \frac{{{\Delta} {\mathrm{CH}}_4}}{{{\Delta} {\mathrm{CO}}_2 + {\Delta} {\mathrm{CH}}_4}} \cdot \frac{{M_{{\mathrm{CH}}_{\mathrm{4}}}}}{{M_{\mathrm{C}}}} \cdot w_{\mathrm{c}} \cdot 100\% \\ =\, \frac{{{\mathrm{ER}}}}{{1 + {\mathrm{ER}}}} \cdot \frac{{M_{{\mathrm{CH}}_{\mathrm{4}}}}}{{M_{\mathrm{C}}}} \cdot w_c \cdot 100\%,$$
where ΔCH4 and ΔCH2 are CH4 and CO2 enhancements in ppmv, \(M_{{\mathrm{CH}}_{\mathrm{4}}}\) and MC are the molar weights of CH4 and carbon, wc = 0.75 is the carbon content of NG, and ER is the emission ratio. Since \(w_{\mathrm{c}}\,M_{{\mathrm{CH}}_4}/M_{\mathrm{C}} = 1\) for CH4 emissions from NGVs, Eq. (1) can be simplified as:
$${\mathrm{EF}}_{{\mathrm{CH}}_4,{\mathrm{obs}}}^{{\mathrm{fuel}}}(\% \,{\mathrm{of}}\,{\mathrm{NG}}\,{\mathrm{consumed}}) = \frac{{{\mathrm{ER}}}}{{1 + {\mathrm{ER}}}} \cdot 100\%.$$
CH4 emissions and CO2 emissions from other studies are converted to fuel-specific emission factor using the same method when they are not provided (Supplementary Table 2). CO was neglected in this study to be consistent with past literature and because CO emissions are often not available. Adding the observed CO concentrations to Eq. (1) would decrease the fuel-specific CH4 emission factors by 2%. The fuel-specific emission factors that include CO emissions can be found in Supplementary Table 2.
Venting-emission and seasonality adjusted fuel-specific CH4 emission factors
The observed EFs are adjusted to account for venting emissions and seasonality as
$${\mathrm{EF}}_{{\mathrm{CH}}_{\mathrm{4}}{\mathrm{,adj}}}^{{\mathrm{fuel}}} = {\mathrm{EF}}_{{\mathrm{CH}}_{\mathrm{4}}{\mathrm{,obs}}}^{{\mathrm{fuel}}} \cdot (1.5 \times 0.14 + 1 \times 0.86) + 0.1\%,$$
where cold-start emissions are estimated using a cold-start/hot-start emission ratio of 1.5. The cold-start/hot-start emission ratio is determined as the average cold-start/hot-start emission ratio for an EF near 3% of fuel consumed (Supplementary Table 5). Cold-start and hot-start are averaged with weighting factors of 14% and 86% (adapted from the testing procedure listed in the China VI standard), respectively27. Venting CH4 emissions from the on-board tank are not related to the combustion process and are not co-emitted with CO2 emissions. Therefore, these emissions were not captured by our method and should be compensated. Therefore, 0.1% of NG consumed is added to compensate the venting EF as reported by Clark et al.19. The lower-bound uncertainty remains unchanged as the lower-bound uncertainty of the observed ERs to account for the possibility that ambient temperature and venting events have no impact on CH4 emissions. The upper-bound uncertainty (\({\mathrm{UBEF}}_{{\mathrm{CH}}_{\mathrm{4}}{\mathrm{,adj}}}^{{\mathrm{fuel}}}\)) is estimated as
$${\mathrm{UBEF}}_{{\mathrm{CH}}_{\mathrm{4}}{\mathrm{,adj}}}^{{\mathrm{fuel}}} = {\mathrm{UBEF}}_{{\mathrm{CH}}_{\mathrm{4}}{\mathrm{,obs}}}^{{\mathrm{fuel}}} \cdot (2.7 \times 0.14 + 1 \times 0.86) + 0.1\%,$$
to account for the possibility of large temperature impact on CH4 emissions as reported by Olofsson et al.37 for a lower EF.
Estimation of WTW GHG emissions
The GHG emissions considered in this study include CO2, CH4, and nitrous oxide (N2O). The CO2 equivalents for CH4 and N2O were calculated with 100-year GWPs of 30 and 268 for fossil fuel combustion, respectively12. Fuel life-cycle analyses have been conducted for gasoline, diesel and NG in the transportation sector in China, and the reported GHG emissions per fuel (MJ) consumed are listed in Supplementary Table 6. Vehicle CH4 emissions from NGVs were largely neglected in previous studies and were included here. Therefore, WTW GHG emissions per km traveled from NGVs can be estimated as
$${\mathrm{WTW}}\,{\mathrm{GHG}} = {\mathrm{FC}} \cdot \left( {{\mathrm{GEF}} + {\mathrm{EF}}_{{\mathrm{CH}}_{\mathrm{4}}{\mathrm{,adj}}}^{{\mathrm{fuel}}} \cdot \frac{{M_{{\mathrm{CH}}_{\mathrm{4}}}}}{{M_{{\mathrm{CO}}_{\mathrm{2}}}}}{\mathrm{EF}}_{{\mathrm{CO}}_{\mathrm{2}}}^{{\mathrm{energy}}} \cdot {\mathrm{GWP}}_{{\mathrm{CH}}_{\mathrm{4}}}} \right),$$
where FC is the fuel consumption (MJ km−1), GEF is the previously reported fuel life-cycle GHG emissions (kg CO2eq MJ−1), \(M_{{\mathrm{CH}}_{\mathrm{4}}}\) and MC are the molar weights of CH4 and CO2, \({\mathrm{EF}}_{{\mathrm{CO}}_{\mathrm{2}}}^{{\mathrm{energy}}} = 55.72\) (kg CO2 MJ−1) is the CO2 emission factor per MJ natural gas consumed recommended by the IPCC58, and \({\mathrm{GWP}}_{{\mathrm{CH}}_{\mathrm{4}}} = 30\) is the 100-year GWP for CH4. Fuel consumption can be found in Supplementary Table 7. The \({\mathrm{EF}}_{{\mathrm{CO}}_{\mathrm{2}}}^{{\mathrm{energy}}}\) includes CO2 oxidized from the escaped fuel carbons (in the form of CO, CH4, and other hydrocarbons)58. Carbon emissions in the form soot is ignored since the reported soot emissions are extremely low for NGVs (<1 × 10−3%)59. In addition, we also assigned 5% uncertainty to \({\mathrm{EF}}_{{\mathrm{CO}}_{\mathrm{2}}}^{{\mathrm{energy}}}\) to account for potential changes in the oxidation rate as well as potential variation in the composition of natural gas (previously reported value ranges from 55.54 to 57.7 kg CO2 MJ−1 for China)14,17.
Compilation of the bottom-up CH4 emission inventory for NGVs in China
The total CH4 emissions from NGVs in China are estimated by multiplying the category-specific ERs to the corresponding CO2 emissions. The CO2 emissions from NGVs are calculated using the estimated NG consumption based on vehicle population and annual mileage traveled in each category. The estimated NG consumption is consistent with the official statistics (Fig. 3a). Four categories are defined: NG taxis, private light-duty NGVs, NG buses, and heavy-duty NG trucks. We estimate CH4 emissions from NG taxis and light-duty NGVs separately because of the significant difference between their annual mileage traveled (Supplementary Table 8). Therefore, CH4 emissions from NGVs can be described as
$${\mathrm{Emis}}_{{\mathrm{CH}}_{\mathrm{4}}} = \mathop {\sum}\limits_{i = 1}^4 {\mathop {\sum}\limits_{y = 1}^{y_{{\mathrm{retire}}}} {{\mathrm{EF}}_{{\mathrm{CH}}_{\mathrm{4}}{\mathrm{,adj,}}i}^{{\mathrm{fuel}}}} } \cdot \frac{{M_{{\mathrm{CH}}_{\mathrm{4}}}}}{{M_{{\mathrm{CO}}_{\mathrm{2}}}}} \cdot {\mathrm{AMT}} \cdot {\mathrm{FC}}_i \cdot {\mathrm{EF}}_{{\mathrm{CO}}_{\mathrm{2}}}^{{\mathrm{energy}}} \cdot {\mathrm{VP}}_{i,y},$$
where i is the vehicle category, y is the age of the vehicles (compulsory retirement age (yr) ranges from 8 to 15, see Supplementary Table 9 for more details), AMTi,y is the annual mileage traveled by vehicles in category i with age y (km, see Supplementary Table 9 for details), FCi is the fuel consumption for category i (MJ km−1, see Supplementary Table 7), \({\mathrm{EF}}_{{\mathrm{CO}}_{\mathrm{2}}}^{{\mathrm{energy}}}\) is the energy-specific CO2 emission factor58, and VPi,y is the vehicle population for category i with age y. Uncertainty of annual emissions are calculated using uncertainty propagation, and relative standard errors for these parameters can be found in Supplementary Table 11.
Calculating GHG emission changes of switching to NGVs
The GHG emission changes of switching to NGVs are calculated similar to total CH4 emissions from NGVs
$${\Delta} {\mathrm{WTW}}\,{\mathrm{GHG}} = \mathop {\sum}\limits_{i = 1}^4 {\mathop {\sum}\limits_{y = 1}^{y_{retire}} {({\mathrm{WTW GHG}}_{{\mathrm{NG}},i} - {\mathrm{WTW GHG}}_{{\mathrm{CG}}/{\mathrm{CD}},i}) \cdot } } {\mathrm{AMT}}_{i,y} \cdot {\mathrm{VP}}_{i,y},$$
where WTW GHGNG,I is the WTW GHG emission of NGVs in category i, and WTW GHGCG/CD,I is the WTW GHG emission of conventional gasoline or diesel vehicles in category i.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Time series of raw observations used for emission ratio calculations (10 Hz), time series of ΔCH4:ΔCO2 and ΔNH3:ΔCO2, and their determination coefficients (R2) are included in Supplementary Data 1. The data are also available from DataSpace at Princeton University [https://doi.org/10.34770/t009-7064]. Other data related to emission calculation are listed in the main text or in Supplementary Information. Source data are provided with this paper.
The source codes for calculating enhancement ratios and R2 and for Gaussian puff model are provided within Supplementary Data 1. The codes are also archived in https://github.com/dp7-PU/CH4_from_NGV_in_China.
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We acknowledge Levi Stanton, Victor Fu, and the CAREBeijing/NCP science team for their support during the field sampling. The research was supported by the Council for International Teaching and Research at Princeton University, the National Geographic Air and Water Conservation Fund (GEFC16-13), NSF Center for Mid-Infrared Technologies for Health and the Environment (MIRTHE, NSF EEC-0540832). Special thanks to LI-COR Biosciences (environmental division) for providing a set of LI-COR sensors for the mobile laboratory.
Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, 08544, USA
Da Pan, Lei Tao, Levi M. Golston, David J. Miller, Denise L. Mauzerall & Mark A. Zondlo
Center for Mid-Infrared Technologies for Health and The Environmental, NSF-ERC, Princeton, NJ, 08544, USA
Da Pan, Lei Tao, Levi M. Golston, David J. Miller & Mark A. Zondlo
Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY, 14260, USA
Kang Sun
Research and Education in eNergy, Environment and Water (RENEW) Institute, University at Buffalo, Buffalo, NY, 14260, USA
Currently at Environmental Defense Fund, New York, NY, 10010, USA
David J. Miller
State Key Joint Laboratory for Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, 100871, Beijing, China
Tong Zhu
Department of Geography, The Ohio State University, Columbus, OH, 43210, USA
Yue Qin
Sustainability Institute, The Ohio State University, Columbus, OH, 43210, USA
Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI, 48824, USA
Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, 08544, USA
Denise L. Mauzerall
Da Pan
Lei Tao
Levi M. Golston
Mark A. Zondlo
D.P. contributed to the design of the work, acquisition of the data, data analysis, and interpretation of data and led the writing. L.T. contributed to the design of the work, acquisition of the data, and interpretation of data. K.S. contributed to the design of the work, acquisition of the data, and interpretation of data. L.M.G. contributed to the acquisition of the data, data analysis, and interpretation of data. D.J.M. the contributed to acquisition of the data and interpretation of data. T.Z. contributed to the design of the work. Y.Q. contributed to the interpretation of data. Y.Z. contributed to the design of the work. D.L.M. contributed to the design of the work and interpretation of data. M.A.Z. contributed to the design of the work, data analysis, interpretation of data, and writing.
Correspondence to Da Pan or Mark A. Zondlo.
Peer review information Nature Communications thanks Oliver Krocher and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Supplementary Data 1
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Da Pan, Tao, L., Sun, K. et al. Methane emissions from natural gas vehicles in China. Nat Commun 11, 4588 (2020). https://doi.org/10.1038/s41467-020-18141-0
Near and long-term perspectives on strategies to decarbonize China's heavy-duty trucks through 2050
Nina Khanna
Hongyou Lu
Nan Zhou


text:
Istanbul's Sultanahmet district
by Anabright Hay / 31 March, 2012
Rich lives can be found among the gravestones of Istanbul's Sultanahmet district.
In Istanbul, the dead and living enjoy a surprisingly peaceful coexistence. Whether neglected or cherished, the gravestones and tombs of the common and the great quietly compete for space in this ever-expanding metropolis. Just metres from where tour buses daily disgorge thousands of animated tourists who file through the 400-year-old Blue Mosque lies the grand tomb of the sultan who commissioned it.
Part of the mosque's outer complex, the Tomb of Sultan Ahmed is like a mini Blue Mosque, as its 17th century interior is adorned with spectacular blue Iznik tiles. I join the small group of visitors blinking away the midday sun to peer into its majestic interior where stained glass windows and low-hanging lights illuminate large caskets covered in green fabric. Each is topped with a lavishly stuffed white turban.
The tomb also contains the caskets of other, mainly 17th century, sultans. Removing our shoes we pad around the tiled walls and gaze at the shoulder-to-shoulder caskets in reverent silence. No strangers to this neck of the woods when they were alive, the sultans now lie just a short walk from their old home, the Topkapi Palace. The palace was built by their ancestor, Mehmed II, between 1459 and 1465, shortly after his conquest of Constantinople.
The tomb is maintained by Turcek, an organisation with equally lofty aims. It seeks to preserve, perpetuate and promote movable and immovable assets, tombs, fountains and sacred relics. Meanwhile, in Sultan Ahmet Square, the ebb and flow of commerce goes on. Carpet sellers politely try to entice lunchtime visitors into their shops and a man selling horse chestnuts from his cart enjoys a steady flow of customers.
Before leaving the tomb for my next appointment with the dead, I scan the brochure I picked up at the entrance. In their prime these sultans were busy men who did a lot more than laze around on the soft furnishings of the Topkapi Palace waiting for the next cooling breeze from the Bosphorus. They not only commissioned buildings but were highly educated, implemented radical policy reforms and commanded armies and navies that brought new territory into the empire.
So where did this 700-year-old empire end and the present Turkish republic begin? Everyone has their own story but I think it ended just around the corner in the Çemberlitaş district at the new marble grave of Ertuğrul Osman, who died in 2009. The last heir to the throne to have been born in Istanbul, he was banished from Turkey in 1924. Though only a child, he was seen as a threat to the legitimacy of the young republic.
After years working in the US mining industry, he was allowed to return to Turkey in 1992, and spent the rest of his life living quietly in Istanbul. A friendly young university student, whose directions I had sought on the street, guides me to the cemetery where Osman is buried. We soon find the grave I am looking for nestled beside a large octagonal mausoleum housing the tomb of Sultan Mahmud II and other 19th century sultans.
My youthful guide is anxious to find out what I believe and launches into a deep theological discussion about the difference between believers and non-believers. I am anxious to avoid such heavy topics and am more interested in enjoying the beauty of the many old gravestones pointing skywards at rakish angles. "You have to be careful in this city where an angel can actually be a devil in disguise," my new friend tells me, as he shoos away a scruffy man who my protector believes is hoping I will give him money. I am feeling increasingly mellow.
My guidebook tells me the Ottoman cemetery is a garden of the dead where the living can happily stroll among the graves. The decorations on the gravestones can reveal a lot about the deceased. A woman's headstone has a fl ower carved on it for each child; a turban carved in stone atop a man's grave may refl ect his high status; and a stone fez can denote a public servant.
I notice that the flowers planted on Ertugrul Osman's grave are freshly weeded. Born in Istanbul in 1912, Osman was the last surviving grandson of any serving Ottoman emperor. Before being exiled, he roamed the corridors of the 285-room 19th-century Dolmabahçe Palace, which lines an impressive expanse of the European side of the Bosphorus. As an old man, Osman visited the palace as an ordinary member of the public on a guided tour. Istanbul's many cemeteries, tombs and graves don't just tell the story of a cityand its generations of inhabitants.
They tell a tale of a vast empire that peaked in 1683 when it got as far as the gates of Vienna. After the failure of that siege, Ottoman power declined. The clean and tidy grave of Osman, a modern man with imperial blood who never ascended the throne of one of the world's greatest empires, is a poignant reminder of what might have been.

redpajama_set_name: RedPajamaCommonCrawl


text:
Hypatima meliptila is a moth in the family Gelechiidae. It was described by Edward Meyrick in 1926. It is found in Papua New Guinea, where it has been recorded from New Ireland.

The wingspan is 12–13 mm. The forewings are pale grey suffusedly irrorated (sprinkled) with white and with an ochreous-yellow basal dot beneath the costa, as well as scattered small light ochreous-yellow tufts except towards the apex, especially two rather larger placed transversely at the end of the cell. There are small blackish spots on the fold at one-fourth, on the dorsum below this, and near the dorsum before the middle a semi-oval blackish spot on the costa before the middle, beneath this some dark grey suffusion including the blackish plical and first discal stigmata, the former anterior. The apical third is irregularly suffused with dark grey and sprinkled with black. The hindwings are grey, lighter anteriorly.

References

Hypatima
Taxa named by Edward Meyrick
Moths described in 1926

redpajama_set_name: RedPajamaWikipedia


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text:
Q: Uploading data to trimble via pathfinder I was sent via email many different file formats: .dd, .gic, .gip, .gis, .giw, .gix, .obs, .obx, .car
I need to upload this to my trimble to go out and complete a survey. How do you create one "waypoint" file from all of these different formats that can be transferred onto the trimble? Or would it be considered a "data file" when sending the data to the trimble in the data transfer process? Is this whole process completed in pathfinder?
I am new to this process.

A: Those files are what TerraSync actually uses so assuming you have the correct TerraSync version for the SSF files, just copy the set of files into the "My Documents\TerraSync" directory on your data collector and you'll be able to see a data file in TerraSync and open it in TerraSync.
Background:
When you use Pathfinder Office to transfer an SSF or DDF file etc onto your data collector, it splits the SSF into DD and GIC and GIP etc. files.
When you use PFO to transfer a data file from data collector to your PC, it combines the DD and GIC and GOP etc. files into a single SSF file.

redpajama_set_name: RedPajamaStackExchange


In June 2012, serial ecopreneur, Chris Morrison, was wondering just how many All Good Bananas he could sell into banana-hungry New Zealand. From trials bringing Fairtrade bananas to New Zealand in 2008, All Good Bananas had grown to take 5% of the market share in a fiercely competitive industry dominated by large multi-national corporations with a legacy of poor environmental and social practices in the countries where the bananas were grown. All Good Bananas sold Fairtrade accredited bananas into one of the country's two major supermarket chains and to independent stores. It used guerilla marketing and social media to spread the Fairtrade message. Chris Morrison and the other two owners saw potential in bringing other Fairtrade products to the market under the All Good brand as well. Chris Morrison was aware that All Good Bananas had the first mover advantage in the New Zealand Fairtrade banana market and that its staff of five were adept users of social marketing – but with the introduction of Dole's "ethical choice" bananas and with more and more corporations moving to social media to engage with the market, he knew price would be an important factor to reach the company's goal to grow to a 10% market share.
Download We will make the download of the inspection copy available again shortly.


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A while back I saw an ad for Romance to the Rescue, a collection of ten stories by famous romance writers that was published to benefit Tails of Joy Animal Rescue.
The first book in the anthology is Fool for Love by Marie Force. This is the second in the series, but you can easily read it and not feel lost in the narrative.
Janey McCarthy has been with David Lawrence since they were in high school. Thirteen years. Of course, he has been in Boston, in medical school, for the last six years, while she did as he bade and stayed on Gansett Island as a vet tech instead of going to veterinary school in Ohio, which she really wanted, and was more than qualified for. But surely it would all be worth it when he came home and Janey got her white picket fence and four children? And surely his wanting to save them from having two sets of medical school bills is really for the best. Right?
Only, when Janey shows up in Boston as a surprise and finds a nasty little surprise of her own – namely David flat on his back in the bed they bought together – with a buxom blond riding cowgirl – all that love and devotion is suddenly gone. Maybe it isn't just the whole 'watching as your fiance bangs a whore' thing, but all the phone calls he never returned, the times he blew off visits, and all the other glaring road markers leading to "wake the hell up and smell that coffee" that built up. Whatever the case, Janey is done, and it is time to get on with her life. And time for her to consider what, or make that who, was under her nose all the time. A HEA is upcoming, and more than Janey could have ever expected. It made me smile to watch her stand up, straighten her spine, and go for everything she ever wanted – as well as everything she never expected to have.
I doubt I will read the historical stories, just not my thing, but my purchase will hopefully help the cause and five books for $2.99? Yep. It's all good!
If you enjoy my reviews please check the "Yes" box at Amazon.com – I appreciate your support!


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