A hybrid satellite and land use regression model of source-specific PM2.5 and PM2.5 constituents.
A hybrid satellite and land use regression model of source-specific PM2.5 and PM2.5 constituents.
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DOI：
10.1016/j.envint.2022.107233
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发表时间：
2022-04
影响因子：
11.8
通讯作者：
M. Mostafijur Rahman;G. Thurston
中科院分区：
文献类型：
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作者：
M. Mostafijur Rahman;G. Thurston
虽然PM2.5质量的来源和成分随时间和空间而变化，但大多数健康影响评估都固有地假设所有PM2.5质量具有相同的健康影响，而无论其成分如何。在全国范围内估计特定来源的PM2.5质量和成分在当地规模将允许流行病学研究和健康影响评估，考虑在其健康影响评估中的PM2.5特性的变化。作为回应，我们开发了美国2001年至2014年人口普查区水平的五个主要PM2.5来源（交通，土壤，煤炭，石油和生物质燃烧）和六种微量元素（元素碳，硫，硅，硒，镍和非土壤钾）的年度暴露模型。我们采用绝对因子分析（APCA），以获得特定源的PM2.5在监测站的影响。随机森林算法，纳入预测来自卫星，化学运输模型，人口普查区分辨率土地利用数据的交通，气象和排放量，这是严格的测试10倍交叉验证（CV），然后采用估计元素和源特定的PM2.5水平在非监测站点人口普查区在研究年。模型性能为中等至良好，CV R2范围为0.41至0.95。对于PM2.5来源，交通PM2.5的CV R2最高（CV R2= 0.73），其次是煤炭（CV R2= 0.65），石油（CV R2= 0.62），土壤（CV R2= 0.60）和生物质（CV R2= 0.41）。在各组分中，硫的CV最高（CV R2= 0.95）。我们的分析提供了2001年至2014年人口普查区域水平上年度元素和源特定PM2.5浓度的高分辨率空间估计。该数据集提供了暴露估计值，以支持未来全国范围内对特定来源的PM2.5质量和成分的长期健康影响研究，从而使流行病学研究能够解决并非所有颗粒物都相同的事实。
Although PM2.5mass varies in source and composition over time and space, most health effects assessment have made the inherent assumption that all PM2.5mass has the same health implications, irrespective of composition. Nationwide estimates of source-specific PM2.5mass and constituents at local-scale would allow for epidemiological studies and health effects assessments that consider the variability in PM2.5characteristics in their health impact assessments. In response, we developed US models of annual exposures at the census tract level for five major PM2.5sources (traffic, soil, coal, oil, and biomass combustion) and six trace elements (elemental carbon, sulfur, silicon, selenium, nickel, and non-soil potassium) for 2001 through 2014. We employed Absolute Factor Analysis (APCA) to derive the source-specific PM2.5impacts at monitoring stations. Random forest algorithms that incorporated predictors derived from satellite, chemical transport model, and census tract resolution land-use data on traffic, meteorology, and emissions, which were rigorously tested by 10-fold cross-validation (CV), were then employed to estimate elemental and source-specific PM2.5levels at non-monitoring site census-tracts over the study years. Model performances were moderate to good, with CV R2ranging from 0.41 to 0.95. For PM2.5sources, the highest CV R2was attained for traffic PM2.5(CV R2= 0.73), followed by coal (CV R2= 0.65), oil (CV R2= 0.62), soil (CV R2= 0.60), and biomass (CV R2= 0.41). Among constituents, the CV was highest for sulfur (CV R2= 0.95). Our analyses provided highly resolved spatial estimates of annual elemental and source-specific PM2.5concentrations at the census-tract level, for 2001 through 2014. This dataset offers exposure estimates in support of future nationwide long-term health effects studies of source-specific PM2.5mass and constituents, enabling epidemiological research that addresses the fact that not all particles are the same.