over the past decades , a large body of 
 literature has provided 
 evidence for associations between exposures to ambient particulate 
 matter ( pm ) and cardiorespiratory morbidity and mortality .
the vast majority of the epidemiological studies have assessed the 
 relationships between health outcomes and pm2.5 or pm10 mass concentrations . unlike a single gaseous pollutant , 

atmospheric pm is a mixture of heterogeneous components ; and particles 
 of different sizes may have different physicochemical and toxicological 
 properties . in a simplistic and practical 
 fashion , pm2.5 can be considered the sum of two distinct 
 components , namely ultrafine particles ( ufps , 100 nm in aerodynamic 
 diameter ) and accumulation - mode particles ( amps , 100 nm to 
 1.0 m ) .
furthermore , results from animal studies have suggested that inhaled 
 ufps deposit more deeply into the lung and may even directly translocate 
 into the circulatory system , thereby exerting adverse health effects 
 via different pathophysiological pathways than larger particles . since the early 1990s ,
studies using various 
 approaches , including 
 toxicological ( in vitro and in vivo ) , controlled human exposure , and 
 epidemiological methods , have been conducted to examine health effects 
 of ufps . to date
a few epidemiological studies observed associations 
 of ufps with acute respiratory symptoms in infants and in adults with 
 asthma , while other studies did not observe associations 
 between ufps and emergency department visits .
a major explanation for inconsistent findings across studies is 
 that different studies may have different accuracies in capturing 
 ufp exposure using central - site monitoring data , as number concentrations of ufps generally have a large 
 spatial variation , declining rapidly with distances from sources such 
 as roadways . at the present time
, experimental 
 data are limited to support the notion that the ultrafine fraction 
 of pm2.5 would affect pm - induced pathophysiologic pathways 
 differently than the coarser fraction that dominates pm2.5 mass concentrations . during the 2008 beijing olympics , aggressive 
 air pollution control 
 measures
were implemented to temporarily improve beijing s 
 air quality , leading to substantial reductions 
 in air pollutant levels . by taking advantage of this unique opportunity
, 
 we conducted a study to examine relationships between substantial 
 changes in air pollutant concentrations and changes in levels of biomarkers 
 reflecting inflammation , oxidative stress , hemostasis , and autonomic 
 function in a panel of beijing residents .
findings on the associations 
 between these biomarkers and pm2.5 mass ( and gaseous pollutants ) 
 have been published . in the present paper ,
we aim to associate the same 
 set of biomarkers with ufp number concentrations , and compared ufp 
 and pm2.5 in their associations with the biomarkers in 
 terms of effect size and lag pattern , respectively .
the panel study was 
 conducted before ( june 2 to july 7 , 2008 ) , during ( july 28 to august 
 29 , 2008 ) , and after ( september 29 to october 30 , 2008 ) the 2008 beijing 
 summer olympics .
briefly , 125 nonsmoking individuals , 2227 years of age , 
 were recruited from the pool of the medical residents at peking university 
 first hospital ( pkh ) .
all study participants worked on the campus 
 of the hospital and most ( 92% ) resided in dormitories of the nearby 
 ( < 5 km from the hospital ) peking university health sciences center .
air pollutants ( except particle 
 number concentrations ) and meteorological conditions were monitored 
 at the top of a seven - floor building located on the campus of the 
 hospital ( pkh ) throughout the entire study period .
the measurement technologies for the air pollutants were 
 reported in previous publications and a brief summary 
 was included in the supporting information ( si ) ( appendix 1 ) .
particle number concentrations were monitored 
 on the roof of a six - floor building at the main campus of peking university 
 ( pku ) , about 9 km from the pkh location , for the same time period 
 ( june 2 to october 30 , 2008 ) .
particle number concentrations were 
 measured using a twin differential mobility particle sizer system 
 ( tdmps ) , consisting of two hauke - type differential mobility analyzers 
 and two condensation particle counters ( models 3010 and 3025 ; tsi 
 inc . , st .
the tdmps measured particle number concentrations 
 in 26 size bins ( ranges ) within a range of 13.0 to 764.7 nm at a 10 
 min interval . in addition , particle number concentrations were 
 measured at the 
 pkh site for 30 days ( october 130 , 2008 ) , allowing us to compare 
 the particle number concentrations between the pku site and pkh site . 

the particle measurement at the pkh site was conducted using a scanning 
 mobility particle sizer system ( smps , model 3080 , tsi inc . , st .
paul , 
 mn ) , consisting of a long differential mobility analyzer ( tsi model 
 3081 ) and a condensational particle sizer ( tsi model 3025a ) .
the smps 
 measured the number concentrations of particles from 14.1 to 736.5 
 nm at a 5 min interval . in order to compare the results obtained 
 by the two different systems , 
 the tdmps and the smps were colocated ( side - by - side ) to measure particles 
 simultaneously for seven days
participants 
 were invited for clinical visits twice in each of the pre- , during- , 
 and post - olympic periods , and the two visits in each period were two 
 weeks apart . based on physiological function
autonomic 
 function indicators included heart rate , systolic blood pressure ( sbp ) 
 and diastolic blood pressure ( dbp ) .
pulmonary inflammation and oxidative 
 stress were assessed using exhaled breath condensate ( ebc ) markers , 
 including ph values , nitrite , malondialdehyde ( mda ) , and fractional 
 exhaled nitric oxide ( feno ) .
hemostasis biomarkers included von willebrand 
 factor ( vwf ) , soluble cd40 ligand ( scd40l ) and p - selectin ( scd62p ) . 

systemic inflammation and oxidative stress biomarkers included plasma 
 fibrinogen , white blood cells ( wbc ) , urinary mda , and urinary 8-hydroxy-2-deoxyguanosine 
 ( 8-ohdg ) .
the selection basis and measurement methods for these biomarkers 
 have been described in detail previously and in the si ( appendix 
 2 ) .
linear mixed - effect models were 
 applied to examine associations between biomarkers and particle number 
 concentrations , as done previously . in 
 each single - pollutant model ,
temperature , relative humidity ( rh ) , 
 gender , and day of week were adjusted . 
best fits 
 for temperature and rh , in the 24 h prior to biomarker measurements 
 and moving averages up to 7 days , were obtained by running the natural 
 splines function with up to 3 degrees of freedom and determined by 
 akaike information criterion ( aic ) .
measurements of heart rate , ebc 
 nitrite , feno , scd62p , scd40l , urinary mda , and 8-ohdg were log - transformed 
 in the mixed - effect models , because the values of these biomarkers 
 were right - skewed .
pollutant concentrations measured 17 
 days prior to biomarker measurement were used to assess the lag pattern 
 of the effects as follows : lag 0 ( 023 h ) , lag 1 ( 2447 
 h ) , lag 2 ( 4871 h ) , and so on , up to lag 6 ( 144167 
 h ) . for all pollutant - biomarker combinations
lag 
 plots  representing the percent changes in biomarker levels 
 associated with one interquartile range ( iqr ) increase in pollutant 
 for lags 0 through lag 6 .
we used two - pollutant models to examine 
 whether the biomarker - pollutant 
 association , obtained in the single - pollutant model as described above , 
 can be retained after controlling for a second copollutant . to maximize 
 the amount of variation in the biomarkers that would be accounted 
 for by the added second pollutant , we chose the lag demonstrating 
 the strongest statistical significance ( smallest p value ) for both the pollutant of primary consideration and the copollutant 
 in the two - pollutant models analysis .
subject , temperature , rh , gender , 
 and day of week were adjusted in the two - pollutant models in the same 
 way as in the single - pollutant models .
we then compared the estimated 
 biomarker changes associated with pm2.5 and ufps from the 
 single pollutant models to those from the two - pollutant models .
particle number 
 concentrations were measured by two different systems , 
 that is , tdmps and smps , at the same location for seven days , and 
 the results were compared in figure 1 .
number 
 concentrations were derived separately for ufps ( 13.0108.2 
 nm ) and accumulation - mode particles ( amps : 108.3764.7 nm ) . 

measurements made by the two systems had strong correlations ( r > 0.94 ) for both ufps and amps .
however , 
 particle 
 number concentrations measured by the tdmps were about 35 
 times higher than those measured by the smps ( figure 1 ) .
one explanation for tdmps providing higher ufp concentrations 
 was that the particle size range tdmps measured was from 13.0 to 764.7 
 nm which was slightly wider than that ( 14.1 to 736.5 nm ) smps measured . 

another possible reason might be due to the intrinsic properties of 
 the two different systems in counting particles .
ufp and accumulation - mode 
 particle number concentrations measured 
 by two systems colocated for seven days .
figure 2 shows the daily average concentration 
 of ufps and amps for the 30-day period when particle number concentrations 
 were simultaneously measured at the pku and pkh locations .
as shown , 
 a similar day - to - day changing pattern was observed for both ufps and 
 amps between the two locations .
concentrations of amps between the 
 two locations tracked each other better than those of ufps .
concentrations 
 of ufps measured at the pkh site appeared to be systematically lower 
 ( by 34% on average ) than those measured at pku site .
the difference 
 may be explained by the fact that the tdmps provided a higher particle 
 number concentration than the smps as shown in figure 1 .
daily 
 average concentrations of ufps and amps at two locations , 
 that is , the pku and pkh sites .
linear regression analyses were conducted to compare the 
 number 
 concentrations of ufps and amps between the two locations .
we found 
 a higher spearman correlation ( r = 0.80 ) for amps 
 than for ufps ( r = 0.45 ) .
more data are shown in 
 the si ( figure s1 ) . at the pku site
, number 
 concentrations were measured for particles 
 with a size ranging from 13.0 to 764.7 nm for 94 days .
the size distribution 
 of particles was shown in si ( figure s2 ) . 

the means  standard deviations for 24 h averaged concentrations 
 were 10623  4313 cm for ufps and 5156  
 2076 cm for amps .
a 16% reduction in ufp concentrations 
 and a 32% reduction in amp concentrations were observed from the pre- 
 to during - olympic period .
larger increases ( 66% in ufps and 46% in 
 amps ) were observed from the during- to post - olympic period .
spearmen correlation coefficients between pollutant pairs and the 
 meteorological parameters were summarized in table 1 .
ufp was positively correlated with no2 ( r = 0.65 , p < 0.001 ) , elemental carbon 
 ( ec ) ( r = 0.43 , p
< 0.001 ) , so2 ( r = 0.31 , p = 0.026 ) , 
 amp ( r = 0.20 , p = 0.128 ) , and co 
 ( r = 0.18 , p = 0.166 ) , or but negatively 
 ( and weakly ) correlated with pm2.5 ( r=-0.18 , p = 0.168 ) . in contrast , pm2.5 was generally more strongly 
 correlated with amp ( r = 0.79 , p < 0.001 ) , so2 ( r = 0.73 , p < 0.001 ) , co ( r = 0.62 , p
< 0.001 ) , and ec ( r = 0.59 , p < 0.001 ) .
temp , temperature ; rh , relative 
 humidity ; amp , accumulation - mode particles ( 108.3764.7 nm ) ; 
 ufp , ultrafine particles ( 13.0108.2 nm ) . *
denotes statistical significance ( p < 0.01 ) . * * denotes statistical 
 significance ( p < 0.001 ) .
figure 3a - d and table s1s4 in the si showed 
 the estimated change in each individual biomarker associated with 
 an iqr increase in pm2.5 or ufp concentrations from one 
 to seven lag days .
these associations were presented 
 again here so that we can compare the size of the estimated biomarker 
 changes associated with iqr increases in ufp and pm2.5 concentrations .
estimated 
 means and 95% confident intervals for the percent changes 
 in the biomarkers reflecting ( a ) autonomic function , ( b ) hemostasis , 
 ( c ) pulmonary inflammation and oxidative stress , and ( d ) systemic 
 inflammation and oxidative stress associated with interquartile range 
 changes in ufps and pm2.5 , controlling for temperature , 
 rh , sex , and day of the week .
blocks numbered with 16 were 
 presented in an enlarged scale under the main plot .
for the three parameters related to autonomic function 
 ( figure 3a and si table s1 ) , systolic 
 blood pressure ( sbp ) showed significant associations with pm2.5 and ufps at lag 3 and lag 4 , respectively ; diastolic blood pressure 
 ( dbp ) was associated with neither pm2.5 nor ufps ; and heart 
 rate showed a significant association with pm2.5 at lag 
 1 , while its association with ufps appeared marginally significant 
 at lag 0 then decreased in the following lag days .
as described in 
 the materials and methods section , the percent 
 changes in biomarkers were standardized to the scale of an iqr increase 
 in ufps or pm2.5 .
the largest percent changes in sbp associated 
 with ufps and pm2.5 were 1.38 ( 95% ci : 0.67 , 2.09 ) at lag 
 4 and 1.03 ( 95% ci : 0.36 , 1.70 ) at lag 3 , respectively . the largest 
 percent changes in heart rate associated with ufps and pm2.5 were 1.12 ( 95% ci : 0.01 , 2.26 ) at lag 0 and 1.49 ( 95% ci : 
 0.24 , 2.75 ) at lag 1 , respectively . for the biomarkers pertinent 
 to hemostasis ( figure 3b and si table s2 ) , scd62p showed 
 significant associations with both ufps and pm2.5 at lag 
 0 and 1 , and the association between scd62p and pm2.5 continued 
 to be significant until lag 4 .
in contrast , scd40l was significantly 
 associated with ufps at lag 0 , while its association with pm2.5 was significant at lag 3 and 4 .
vwf began to have a significant 
 association with pm2.5 from lag 0 and remained significant 
 until lag 4 , while it was only significantly positively associated 
 with ufps at lag 5 .
the largest percent changes in scd62p associated 
 with iqr increases in ufps at lag 1 and pm2.5 at lag 2 
 were 9.05 ( 95% ci : 4.55 , 13.74 ) and 11.44 ( 95% ci : 8.28 , 14.70 ) , respectively . 
 the largest percent changes in scd40l per iqr increases were 5.87 
 ( 95% ci : 1.83 , 10.08 ) with ufps at lag 0 and 3.53 ( 95% ci : 0.99 , 6.14 ) 
 with pm2.5 at lag 4 . the largest percent changes in vwf 
 were 5.25 ( 95% ci : 1.70 , 8.80 ) with ufps at lag 5 and 5.26 ( 95% ci : 
 3.04 , 7.47 ) with pm2.5 at lag 3 . for biomarkers
related 
 to pulmonary inflammation and oxidative 
 stress ( figure 3c and si table s3 ) , feno was significantly associated with ufps at lag 0 , 
 while its association with pm2.5 was significant at all 
 seven lag days .
ebc ph value showed a significant association with 
 ufps at lag 1 , and it was significantly associated with pm2.5 from lag 0 to lag 5 .
ebc nitrite appeared to have opposite patterns 
 of the associations with ufps versus those with pm2.5 . 

ebc mda was significantly associated with pm2.5 at lag 
 3 , 4 , and 5 , while no significant and positive associations were observed 
 between ebc mda and ufps at any lags .
after the standardization to 
 the iqr scale , the largest percent change in feno associated with 
 ufps was 25.34 ( 95% ci : 12.96 , 39.09 ) at lag 0 , and with pm2.5 it was 40.71 ( 95% ci : 26.10 , 57.02 ) at lag 0 .
the largest percent 
 changes in ebc ph value were 1.54 ( 95% ci : 0.79 , 2.28 ) associated 
 with ufps and 1.21 ( 95% ci : 0.39 , 2.03 ) associated with pm2.5 both at lag 1 . the largest percent change in ebc nitrite associated 
 with ufps at lag 6 was 25.64 ( 95% ci : 16.12 , 35.94 ) , and was 21.90 
 ( 95% ci : 12.04 , 32.63 ) in association with pm2.5 at lag 
 0 . for biomarkers related to systemic inflammation and oxidative 
 stress 
 ( figure 3d and si table s4 ) , the lag patterns of urinary mda and 8-ohdg were different 
 for ufps than those for pm2.5 . the associations of these 
 two biomarkers with pm2.5 began as significant at the first 
 two lag days and then decreased to null , while their associations 
 with ufps were nonsignificant at the first three lags and then became 
 significant starting from lag 3 .
plasma fibrinogen showed no significant 
 association with ufps , while it was significantly associated with 
 pm2.5 at lag 3 .
wbc showed a significant association with 
 ufps at lag 0 , but no association was observed between wbc and pm2.5 .
the largest percent changes in urinary mda per iqr increase 
 were 10.89 ( 95% ci : 0.56 , 22.28 ) for ufps at lag 3 and 15.27 ( 95% 
 ci : 3.44 , 28.44 ) for pm2.5 at lag 0 . the largest percent 
 changes in 8-ohdg were 28.56 ( 95% ci : 4.08 , 59.53 ) for ufps at lag 
 3 and 57.58 ( 95% ci : 26.06 , 96.99 ) for pm2.5 at lag 1 .
figure 4a and 4b presented the largest percent changes 
 in the 12 biomarkers per iqr increases in ufps and pm2.5 estimated by single- and two - pollutant models .
dbp and fibrinogen 
 were not included in the two - pollutant models since they did not show 
 significant associations with either ufps or pm2.5 in single 
 pollutant models estimated means and 95% confident intervals for the percent 
 change 
 in the biomarkers with one iqr increase in ( a ) ufps and ( b ) pm2.5 , controlling for temperature , rh , sex , day of the week , 
 and a second pollutant , including amp , sulfur dioxide ( so2 ) , nitrogen dioxide ( no2 ) , carbon monoxide ( co ) , and elemental 
 carbon ( ec ) .
figure 4a showed that the largest percent 
 changes in the biomarkers of autonomic function and hemostasis , including 
 sbp , heart rate , scd62p , scd40l , and vwf , associated with increases 
 in ufp were not notably changed after controlling for any of the five 
 copollutants regarding to the effect size and the significance , except 
 for the changes in vwf after controlling for so2 as the 
 copollutant .
the changes in wbc associated with ufps were not remarkably 
 changed in the two - pollutant models as well .
the largest changes in 
 vwf , ebc ph , and ebc mda associated with ufps were significantly reduced 
 in both the effect size and the significance after controlling for 
 so2 in the two - pollutant models . the largest percent change 
 in feno associated with the increase in ufps increased after controlling 
 for ec as the copollutant . for ebc nitrite , urinary mda and 8-ohdg , 
 their largest changes associated with increases in ufp were reduced 
 in the effect size in copollutant models , with some of them became 
 nonsignificant .
figure 4b showed that 
 the largest percent 
 changes in all the biomarkers associated with increases in pm2.5 were reduced both in the effect size and the statistical 
 significance by controlling for copollutants .
for example , the changes 
 in sbp were reduced in two - pollutant models after controlling for 
 amp , no2 , or ec , so were the changes in scd62p and vwf 
 after controlling for so2 as the copollutant .
the largest 
 changes in ebc ph , vwf , ebc nitrite , and ebc mda associated with increases 
 in pm2.5 were remarkably reduced in both the effect size 
 and significance after controlling for amp , so2 , or ec . 
 for feno and 8-ohdg , their changes associated with increases in pm2.5 were reduced but remained significant in the two - pollutant 
 models .
in the current study , 
 we utilized central - site concentrations as 
 a surrogate of ufp exposure for study subjects residing and working 
 within a 9 km radius .
similar to the associations between pm2.5 and the biomarkers , significant associations were consistently observed 
 between ufps and the biomarkers related to multiple physiological 
 pathways .
this suggests the usability of central - site ufp data to 
 estimate exposures of pathophysiological relevance in our study participants .
central - site monitoring data have been commonly used as surrogates 
 for population exposures to pm2.5 and pm10 mass 
 in epidemiologic studies . however , due to large spatial variability in ufp number concentrations , 
 there is a concern on the usage of the ufp monitoring data from a 
 limited number of central sites even in relatively small areas . in a health effects institute 

report , the investigators pointed out that ufp number concentrations 
 monitored at different locations within cities were reasonably correlated 
 in time , with similar patterns of rising and falling over the course 
 of a day .
moore and colleagues ( 2009 ) 
 measured number concentrations of total particles and found hourly 
 median correlation coefficients varied from 0.3 to 0.56 across 14 
 cities in the los angeles area . consistently 
 found in the current study , daily average concentrations of ufps at 
 two locations in beijing changed in a similar pattern across the 30 
 monitoring days with a moderate correlation coefficient of 0.45 . even 
 though the correlations of ufps between two locations within beijing 
 city were not as strong as those observed for the accumulation - mode 
 particles ( r = 0.80 ) , they might be usable to support 
 epidemiologic studies on the short - term effects of ufps on human health .
another concern on the studies of ufp 
 health effects is the correlation 
 between ufps and other traffic - related pollutants .
the effects of ufps observed in the current study were 
 likely to be independent from pm2.5 since we observed a 
 weak correlation between ufps and pm2.5 ( r = 0.18 ) . in rochester , new york with relatively low ambient 
 pm2.5 and ufp concentrations ( mean  sd concentration 
 of pm2.5 and ufps were 8.67  6.06 g / m and 4049  2168 particles / cm ) , rich et al . 

( 2012 ) observed that ufps and pm2.5 were also poorly correlated 
 ( r = 0.11 ) .
( 2006 ) suggested inverse correlations 
 between ufps and pm2.5 as the processes they experience 
 in the atmosphere , that is , coagulation and condensation , can transfer 
 materials from ufps size to the accumulation mode size .
given that some traffic - related pollutants were correlated 
 with 
 ufps and/or pm2.5 ( table 1 ) , we 
 examined whether the associations observed through single - pollutant 
 models were robust by adding a copollutant .
associations of biomarkers 
 with ufps obtained from the single - pollutant models seems more robust 
 than with pm2.5 after controlling for the copollutants , 
 in terms of either the size of association estimates or statistical 
 significance . on one hand ,
the percent changes of the biomarkers 
 associated with 
 increases in ufps were reduced in a smaller extent of the magnitude 
 than those associated with increases in pm2.5 by controlling 
 for copollutants ( a 26% average reduction in the 60 biomarker - ufp 
 association estimates versus a reduction of 60% in the 60 biomarker - pm2.5 association estimates ) . on the other hand ,
the statistical 
 significance was less affected for the biomarker - ufp associations 
 because only 13 out of the 60 biomarker - ufp associations in contrast 
 to 30 out of the 60 biomarker - pm2.5 associations ( figure 4a and 4b ) lost statistical 
 significance by controlling for the copollutants .
it is also notable 
 that , by controlling for amp , only two biomarkers associations 
 ( urinary mda and 8-ohdg ) with ufps showed considerable reductions , 
 while seven biomarkers associations with pm2.5 were 
 significantly reduced by controlling for amp ( figure 4b ) .
this is not surprising because pm2.5 showed 
 higher correlations with the copollutants ( except no2 ) 
 than ufps ( table 1 ) .
the reductions in the 
 effect size and the loss of statistical significance of the associations 
 between biomarkers and the two measures of particles were substantial 
 after controlling for copollutants , especially for pm2.5 .
therefore , the covariation between the two particle measures and 
 other traffic - related pollutants should be considered to interpret 
 the association results from single - pollutant models .
( 2012 ) in findings 
 on the associations of some biomarkers with ufps and pm2.5 in short - term exposure , even though the subjects and exposure levels 
 of particles of the two studies were not comparable . in both of the studies , increases in sbp and fibrinogen 
 were associated with increases in ufps and pm2.5 at lag 
 34 , but no clear pattern of associations between dbp and ufps 
 or pm2.5 were observed . while no significant association 
 was observed between wbc and ufps in the study of rich et al .
( 2012 ) , 
 a significant increase in wbc was associated with increases in ufps 
 in the current study . based on a limited number of human studies conducted 
 to date
, the evidence is not sufficiently strong to support the notion 
 that there are substantial differences in the effects of short - term 
 exposure to ufps from those of pm2.5 . in the current study by comparing ufps to pm2.5 in their associations with different biomarkers
, we found that the 
 percent changes in biomarkers associated with iqr increases in the 
 two measures of particles were comparable in magnitude for most of 
 the biomarkers ( figure 3 ) .
however , we 
 observed differences between upfs and pm2.5 in the changing 
 patterns of their associations with biomarkers reflecting 
 hemostasis , pulmonary inflammation , and oxidative stress , and systemic 
 inflammation and oxidative stress .
association pattern 
 with ufps from lag 0 to 6 , while those with pm2.5 were 
 quite the reverse for scd62p and scd40l ( figure 3b ) ; ebc nitrite showed a declining association with pm2.5 from lag 0 to lag 6 , while its largest association with ufps occurred 
 at lag 6 ( figure 3c ) ; and urinary mda and urinary 
 8-ohdg were significantly associated with ufps starting from lag 3 , 
 while their associations with pm2.5 were significant from 
 lag 0 ( figure 3d ) .
furthermore , wbc showed 
 significant association with ufps at lag 0 but not with pm2.5 throughout the 7 lag days ( figure 3d ) , while 
 ebc mda showed a significant association with pm2.5 at 
 lag 35 but not with ufps ( figure 3c ) . 

the comparisons suggest potential differences in timing of  actions  
 between ufps and larger particles ( which dominate pm2.5 mass concentration ) on the respiratory and the circulatory systems .
the differences between ufps and pm2.5 in the associations 
 with the biomarkers are likely to reflect their differences in deposition , 
 clearance , and translocation after inhalation .
compared with larger particles , ufps appear 
 to be cleared more slowly and retained longer within the lung after 
 deposition .
biological effects mediated by 
 ufps may be cumulative due to their longer retention in the lung than 
 those mediated by larger particles .
this 
 assumption may partly explain the difference between pm2.5 and ufps in the variation of their associations with some of the 
 biomarkers across lag days .
for instance , the association of ebc nitrite 
 with ufps stayed significant for most of the lag days and showed an 
 increasing pattern from lag 0 to 6 ( except lag 3 and 4 ) , whereas its 
 association with pm2.5 became nonsignificant after lag 
 1 and decreased from lag 1 to 6 ( figure 3c ) . 

the findings on ebc nitrite may suggest that ufps were retained longer 
 in the lung than larger particles after the deposition .
it has 
 also been hypothesized that some of the systemic effects 
 of pm are due to a spillover of the pulmonary effects .
if this is the case , we should expect to observe a more 
 prolonged effect of ufps than pm2.5 .
our data on systemic 
 oxidative stress ( urinary mda and 8-ohdg ) and inflammation ( vwf ) indeed 
 appear to support this hypothesis .
the associations of the two urinary 
 biomarkers with ufps became significant from lag 3 to lag 5 , in contrast , 
 their associations with pm2.5 were  immediately  
 significant from lag 0 and then decreased ( figure 3d ) .
vwf showed a significant association with pm2.5 at lag 0 but with ufps at lag 1 ( figure 3b ) .
however , some of the biomarkers seemed to provide results against 
 this assumption ; for example , scd40l showed a significant association 
 with ufps at lag 0 , and the association decreased to nonsignificant 
 afterward ( figure 3b ) .
this is perhaps due 
 to another mechanism that  competes  with the spillover 
 hypothesis , as discussed below .
translocation of particles in 
 the body might be another reason 
 for the differences between ufps and pm2.5 in their associations 
 with the biomarkers .
animal studies have found translocations of ufps , 
 but not fine or coarse particles ( i.e. , particles > 100 nm in size ) , 
 into the circulatory system , even though the mechanism 
 for ufp translocation is still unclear .
we assume that the blood markers 
 will have quicker and/or stronger associations with ufps than with 
 pm2.5 due to the translocation of ufps into bloodstream , 
 although the assumption was not fully supported by all the blood biomarkers . 

we observed that wbc was significantly associated with ufps at lag 
 0 but not with pm2.5 throughout the seven lag days ( figure 3d ) .
scd40l was significantly associated with ufps 
 at lag 0 , but its association with pm2.5 started to be 
 significant from lag 3 to 4 ( figure 3b ) .
the 
 difference in biomarker responses to ufps verses to pm2.5 may reflect the inherent differences in underlying biology , measurement 
 error , sampling variation , or the confounding effects of the other 
 correlated pollutants .
for example , the associations of scd62p and 
 scd40l with ufps were not remarkably changed in term of the significance 
 when controlling for any of the copollutants ; whereas the associations 
 of vwf with ufps became nonsignificant in the copollutant models ( figure 4b ) .
speculation on the reasons for these inconsistent 
 findings may provide insights to reveal the underlying mechanisms 
 through which ufps and pm2.5 adversely affect human health .
the major limitation of the current study is the characterization 
 of ufp exposure . just like in any study using central - site monitoring 
 data
, there exists a possible nondifferential exposure misclassification 
 due to utilization of central - site air pollution levels rather than 
 personal exposure assessment .
second , due to practical constraints , 
 both ufps and pm2.5 were measured at a height around 20 
 m above ground .
even so , we do not tend to attribute the observed differences in 
 biomarker - pollutant associations between ufps and pm2.5 to potential vertical differences in pollutant concentrations , as 
 our study design was a within - person comparison and no within - person 
 changes in elevations would be expected across the six visits . other 
 limitations for this type of observational study
our findings suggest that 
 number concentrations of ufps monitored 
 in a central site may be useful in a panel study design that mainly 
 relies on within - person comparisons and when subjects work and resides 
 within a relatively small area ( < 9 km radius ) . because ufps and 
 pm2.5 were poorly correlated , the lag - pattern differences 
 between upfs and pm2.5 suggest that the ultrafine size 
 fraction ( 100 nm ) and the fine size fraction ( 1002.5 
 m ) of pm2.5 are likely to affect pm - induced pathophysiological 
 pathways independently .
this finding suggests that controlling policies 
 need to consider both the ultrafine and the fine size fraction of 
 pm2.5 in order to protect human health .
 using a quasi - experimental opportunity 
 offered by greatly restricted 
 air pollution emissions during the beijing olympics compared to before 
 and after the olympics , we conducted the current study to compare 
 ultrafine particles ( ufps ) and fine particles ( pm2.5 ) in 
 their associations with biomarkers reflecting multiple pathophysiological 
 pathways linking exposure and cardiorespiratory events . 
 number concentrations 
 of particles ( 13.0764.7 nm ) and mass concentrations of pm2.5 were measured at two locations within 9 km from the residence 
 and workplace of 125 participating beijing residents . 
 each participant 
 was measured 6 times for biomarkers of autonomic function ( heart rate , 
 systolic and diastolic blood pressures ) , hemostasis ( von willebrand 
 factor , soluble cd40 ligand , and p - selectin ) , pulmonary inflammation 
 and oxidative stress ( exhaled nitric oxide and exhaled breath condensate 
 ph , malondialdehyde , and nitrite ) , and systemic inflammation and oxidative 
 stress ( urinary malondialdehyde and 8-hydroxy-2-deoxyguanosine , 
 plasma fibrinogen , and white blood cells ) . 
 linear mixed models were 
 used to estimate associations of biomarkers with ufps and pm2.5 measured 17 days prior to biomarker measurements ( lags ) . 
 
 we found that the correlation coefficient for ufps at two locations 
 ( 9 km apart ) was 0.45 , and at the same location , the correlation 
 coefficient for pm2.5 vs ufps was 0.18 . 
 changes 
 in biomarker levels associated with increases in ufps and pm2.5 were comparable in magnitude . 
 however , associations of certain biomarkers 
 with ufps had different lag patterns compared to those with pm2.5 , suggesting that the ultrafine size fraction ( 100 
 nm ) and the fine size fraction ( 100 nm to 2.5 m ) of 
 pm2.5 are likely to affect pm - induced pathophysiological 
 pathways independently . 
Introduction
Materials and
Methods
Results
Discussion