Masri Shahir, Li Lianfa, Dang Andy, Chung Judith H, Chen Jiu-Chiuan, Fan Zhi-Hua Tina, Wu Jun
Program in Public Health, College of Health Sciences, University of California, Irvine, CA, 92697, U.S.A.
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
Atmos Environ (1994). 2018 Mar;177:175-186. doi: 10.1016/j.atmosenv.2018.01.014. Epub 2018 Jan 8.
Airborne exposures to polycyclic aromatic hydrocarbons (PAHs) are associated with adverse health outcomes. Because personal air measurements of PAHs are labor intensive and costly, spatial PAH exposure models are useful for epidemiological studies. However, few studies provide adequate spatial coverage to reflect intra-urban variability of ambient PAHs. In this study, we collected 39-40 weekly gas-phase PAH samples in southern California twice in summer and twice in winter, 2009, in order to characterize PAH source contributions and develop spatial models that can estimate gas-phase PAH concentrations at a high resolution. A spatial mixed regression model was constructed, including such variables as roadway, traffic, land-use, vegetation index, commercial cooking facilities, meteorology, and population density. Cross validation of the model resulted in an R of 0.66 for summer and 0.77 for winter. Results showed higher total PAH concentrations in winter. Pyrogenic sources, such as fossil fuels and diesel exhaust, were the most dominant contributors to total PAHs. PAH sources varied by season, with a higher fossil fuel and wood burning contribution in winter. Spatial autocorrelation accounted for a substantial amount of the variance in total PAH concentrations for both winter (56%) and summer (19%). In summer, other key variables explaining the variance included meteorological factors (9%), population density (15%), and roadway length (21%). In winter, the variance was also explained by traffic density (16%). In this study, source characterization confirmed the dominance of traffic and other fossil fuel sources to total measured gas-phase PAH concentrations while a spatial exposure model identified key predictors of PAH concentrations. Gas-phase PAH source characterization and exposure estimation is of high utility to epidemiologist and policy makers interested in understanding the health impacts of gas-phase PAHs and strategies to reduce emissions.
通过空气传播接触多环芳烃（PAHs）与不良健康后果相关。由于对PAHs进行个人空气测量既耗费人力又成本高昂，因此空间PAH暴露模型对流行病学研究很有用。然而，很少有研究能提供足够的空间覆盖范围来反映城市环境中PAHs的内部变异性。在本研究中，我们于2009年夏季和冬季在南加州每周采集39 - 40份气相PAH样本，各采集两次，以便确定PAH的来源贡献，并开发能够高分辨率估算气相PAH浓度的空间模型。构建了一个空间混合回归模型，纳入了诸如道路、交通、土地利用、植被指数、商业烹饪设施、气象和人口密度等变量。该模型的交叉验证结果显示，夏季的R值为0.66，冬季为0.77。结果表明冬季的总PAH浓度更高。热解源，如化石燃料和柴油废气，是总PAHs的最主要贡献者。PAH来源随季节变化，冬季化石燃料和木材燃烧的贡献更高。空间自相关在冬季（56%）和夏季（19%）的总PAH浓度方差中占很大比例。在夏季，解释方差的其他关键变量包括气象因素（9%）、人口密度（15%）和道路长度（21%）。在冬季，交通密度（16%）也对方差有解释作用。在本研究中，来源特征确定了交通和其他化石燃料源对测得的总气相PAH浓度的主导作用，而空间暴露模型确定了PAH浓度的关键预测因子。气相PAH来源特征和暴露估计对于有兴趣了解气相PAHs的健康影响及减排策略的流行病学家和政策制定者具有很高的实用价值。
