Article pubs.acs.org/est
Oxidative Potential and Inflammatory Impacts of Source Apportioned Ambient Air Pollution in Beijing Qingyang Liu,†,§ Jill Baumgartner,‡ Yuanxun Zhang,*,† Yanju Liu,§ Yongjun Sun,§ and Meigen Zhang∥ †
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China Institute for Health and Social Policy & Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, Quebec, Canada H3A 0G4 § Beijing Center for Physical and Chemical Analysis, Beijing 100089, China ∥ State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China ‡
S Supporting Information *
ABSTRACT: Air pollution exposure is associated with a range of adverse health impacts. Knowledge of the chemical components and sources of air pollution most responsible for these health effects could lead to an improved understanding of the mechanisms of such effects and more targeted risk reduction strategies. We measured daily ambient fine particulate matter ( 0.05; S2, r = 0.23, p > 0.05), which differs from the results of several previous studies on this topic.11,43,44 Results from Verma et al.45 suggest a substantial contribution of hydrophobic compounds (e.g., humic-like substances, HULIS) to the oxidative properties of PM, as measured with the DTT assay. Thus, it is possible that the organic fraction lost during PM extraction was HULIS.45 Lin and Yu46 found similar results where DTT consumption relied on HULIS-mediated redoxactive transition metal sites and that transition metal could oxidize the majority of DTT loss. These may explain the low correlation between PM mass and water-soluble ROS activity observed in our study. The dust storm events on Mar. 28 and Apr. 28 considerably impacted the ambient PM concentrations in our study. Based on backward trajectories from the NOAA hybrid single particles Lagrangian integrated trajectories (HYSPLIT) model, the dust storms appear to be influenced by arid parts of northwestern China24 (SI Figures S1 and S2). Mean PM2.5 concentrations on dust storm days were 100 μg m−3 at S1 and 350 μg m−3 at S2. Similarly, ROS activities induced by the extracts of PM collected on dust storm days were twice that of nondust storm days at both S1 (0.29−0.49 nmol m−3 min−1 versus 0.12−0.28 nmol m−3 min−1) and S2 (0.19−0.31 nmol m−3 min−1 versus 0.10−0.24 nmol m−3 min−1). The differences in PM-induced ROS activity from samples collected at S1 versus S2 are likely attributable to differences in PM composition from different source contributors in these two distinct urban environments.30 Many studies have observed significant spatial distribution in the composition of air pollution in Beijing (e.g., including pollutants such as NOx, PM, SO2, Pb, and carbonaceous aerosol),47−50 and these concentrations were
strongly determined by the distance of the air sampling location to the emission sources. Potential source contribution function (PSCF) results51 indicate that sulfate, nitrate, ammonium, and carbonaceous aerosols are higher in urban Beijing than in the suburbs, whereas higher concentrations of mineral aerosol were found in the suburbs. These findings directly support the reliability of our chemical composition results (SI Table S1) and indirectly support the spatial distribution of ROS activity that we observed in Beijing. Chemical Composition of Ambient PM. The chemical composition of ambient PM at both sites during the study period is provided in Table 1. The PM2.5 chemical components were significantly higher during dust storm days compared with nondust storm days at both sites. Daily ambient mean concentrations of water-soluble PM components (i.e., Na+, K+, Ca2+, Mg2+, NH4+, Cl−, SO42−, and NO3−) were not significantly different (p > 0.05) throughout the study period at both sites, with the exception of the two dust storm days where they were 1.1−2.1 times higher (p < 0.05). The higher concentrations of SO42−, NO3−, and NH4+ on dust storm days were likely due to faster conversion of their gaseous precursors (SO2, NOx and NH3) on the surface of dust aerosols.28 The mean concentrations of metal PM components were 2− 3 times higher on dust storm days at both sites, indicating that direct emissions may also have been larger on dust storm days. Compared with the S1 site, we found higher concentrations of Al, Cu, and Zn species at the S2 site throughout the study period. Al is a chemical fingerprint for dust, whereas Cu and Zn are associated with motor vehicle emissions. These differences can be used as simple indicators to compare the source differences between the two sites.28 The average WSOC concentrations were higher on dust storm days compared with nondust storm days (S1, 5.20 μg m−3 versus 4.49 μg m−3; S2, 6.52 μg m−3 versus 4.91 μg m−3) and were primarily associated with higher atmospheric oxidation D
dx.doi.org/10.1021/es5029876 | Environ. Sci. Technol. XXXX, XXX, XXX−XXX
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Table 2. Expression of Interleukin-6 (IL-6) and Tumor Necrosis Factor-α (TNF-α) in Cultured A549 Lung Epithelial Cells (ng L−1) Exposed to Water Extracts of Ambient Beijing PM2.5a IL-6
TNF-α
n
geometric mean (SD)
rangeb
n
geometric mean (SD)
rangeb
exposure time 24 h 48 h
27 27
50.7 (16.9) 83.2 (30.1) (pc < 0.001)
28.9−90.7 41.2−152.1
27 27
72.4 (19.4) 96.4 (19.0) (pc < 0.001)
42.6−118.1 71.9−136.8
exposure time for 24 h N D
18 9
43.5 (10.1) 68.6 (14.7) (pc < 0.001)
28.9−63.9 48.7−90.7
18 9
63.2 (12.7) 94.0 (13.4) (pc < 0.001)
42.6−89.4 42.6−89.4
exposure time for 48 h N D
18 9
70.0 (19.1) 117.4 (22.5) (pc < 0.001)
41.2−112.7 86.5−152.1
18 9
86.9 (10.3) 118.7 (13.6) (pc < 0.001)
71.9−103.1 93.0−136.9
a SD, standard deviation; N, Nondust storm days (n = 18); D, dust storm days (n = 9). bIL-6 and TNF-α expression levels for “unexposed” samples were 20.2 ± 1.4 and 25.4 ± 3.2 ng L−1, respectively. cp-value for the difference in geometric means of IL-6 and TNF-α expression levels, stratified by different subgroups.
Source Apportionment of ROS Activity from Beijing Ambient PM. We found moderate to strong correlations (r = 0.47−0.86) between exposure to water-soluble chemical species and DTT activity (SI Table S2), though other water-soluble elements including K+, Na+, Mg2+, and NH4+ were not correlated with DTT activity. Total metals, EC, OC, and secondary organic carbon (SI Table S1) in PM2.5 were also not strongly correlated with DTT activity (SI Table S3). We found that WSOC was highly correlated with DTT loss (r > 0.60), consistent with results from Verma et al.45 (r = 0.86). Cheung et al.21 evaluated the potential generation of ROS from coarse PM using the DTT assay and concluded that most ROS activity was highly correlated with water-soluble metals, including V and Cu (r > 0.60), a result that is consistent with our findings. The relationship between organic pollutants and ROS has been examined by Ntziachristos et al.,40 who found that Fe, Pb, Zn, and Cu were highly corrected with ROS generation (r > 0.90) and concluded that some species such as Pb and Zn may elicit oxidative stress via ROS formation and electrophilic chemistry.56,57 Ca was highly correlated with DTT activity (r > 0.70), a relationship also reported by Verma et al. (r > 0.68).32 NO3− and SO42− are formed by a secondary chemical reaction in atmosphere, so these results are consistent with the fact that ROS in the urban atmosphere is mostly derived from combustion and secondary reactions.32 Cl, Al, and Ni also have a strong correlation with DTT loss (r > 0.65) in our study, which indicates that these elements may be confounders of WSOC (r > 0.60) that mechanistically influence DTTmeasured oxidative activity. In order to interpret the ROS source profiles results as realworld PM sources, we analyzed the ambient PM2.5 samples from Beijing in a PMF model to obtain source profiles. SI Figures S3 and S4 illustrate the estimated source profiles for the PM2.5 mass concentrations at our two study sites, with the daily variation in the PM2.5 source contribution estimates presented in SI Figures S5 and S6. We identified 6 PM sources at our sampling sites that contribute to ROS activity, including zinc factor, aluminum factor, lead factor, secondary source, iron source, and soil dust source (SI Figures S7 and S8). The factor contributions to these sources at the S1 site during the entire measurement period and during nondust storm days are
rates. WSOC accounts for 20−70% of atmospheric organic carbon, which is dominated by secondary sources and thus commonly used as a proxy for SOAs.52 Associations between PM Exposure and Pro-inflammatory Cytokine Levels. In comparison with the “unexposed” control samples, we observed significantly higher expression of IL-6 and TNF-α in cultured lung cells following exposure to the extracts of ambient Beijing PM2.5 (Table 2), supporting previous evidence of PM’s inflammatory potential.2,6,7 IL-6 levels ranged from 28.9 to 90.7 ng L−1 (geometric mean (GM), 53.2) and 41.2−152.1 ng L−1 (GM, 88.1) after exposure to PM extracts for 24 and 48 h, respectively. Cell expression of TNF-α ranged from 42.6 to 118.1 ng L−1 (GM, 74.6) and from 71.9 to 136.8 ng L−1 (GM, 98.2) following 24 and 48 h of PM exposure, respectively. We observed significantly higher production of cytokines after 48-h exposure compared with 24-h exposure (IL-6, 83.2 ng L−1 versus 50.7 ng L−1, p < 0.001; TNF-α, 96.4 ng L −1 versus 72.4 ng L−1, p < 0.001). Day-to-day comparisons show the highest expressions of IL-6 (GM, 152.1 ng L−1) and TNF-α (GM, 136.9 ng L−1) in cells were detected after 48-h exposure to PM2.5 extracts from dust storm days, a result which may be due to the altered chemical composition of the PM2.5 extracts during dust storms discussed earlier. We also found that lung cells expressed significantly higher levels of IL-6 and TNF-α on the first day of the dust storm compared with the second day, suggesting PM induced a clear concentration increase in IL-6 and TNF-α excretion. Overall, our findings are consistent with the ROS activity results from the DTT assay, which showed a positive association between redox activity and in vivo expression of inflammatory cytokines (β = 0.803−0.951, p < 0.001). Although many previous experiments have targeted A549 as the initiators of inflammatory responses to inhaled particles,53,54 the focus of research has largely shifted to investigating the direct effects of ambient particle on alveolar macrophages.20,55 We speculate that the different effects observed in the two models may be related to the different origins of the cells, i.e., bronchiolar versus alveolar. Future studies could improve upon our in vivo results by conducting these experiments in primary human cells. E
dx.doi.org/10.1021/es5029876 | Environ. Sci. Technol. XXXX, XXX, XXX−XXX
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clear relationship on dust storm days. The secondary source in this study was composed of mass fractions of WSOC. Previous work by Chung et al.61 found that quinones are strongly correlated with DTT consumption (r = 0.73−0.89). Quinones and hydroquinones are likely to either be cogenerated with polycyclic aromatic hydrocarbon (PAHs) or photochemically formed from PAHs in the atmosphere. Therefore, we defined this source as a “secondary source”. The difference in secondary source contribution between our study sites may be attributable to the higher road traffic emissions, and thus higher concentrations of NOx and SOA precursors, at S2. Iron Source. The PMF factor containing most of the measured iron was identified as an iron source. Gildemeister et al.58 estimated this source as steel emissions due to the high percentage of Fe and positive contributions of Cl, Ca, and OC. At both sampling sites, the iron source contributed to
