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• W3119335550 endingPage "2162" @default.
• W3119335550 startingPage "2152" @default.
• W3119335550 abstract "Exposure to fine particulate matter (PM2.5) has become a major global health concern. Although modeling exposure to PM2.5 has been examined in China, accurate long-term assessment of PM2.5 exposure with high spatiotemporal resolution at the national scale is still challenging. We aimed to establish a hybrid spatiotemporal modeling framework for PM2.5 in China that incorporated extensive predictor variables (satellite, chemical transport model, geographic, and meteorological data) and advanced machine learning methods to support long-term and short-term health studies. The modeling framework included three stages: (1) filling satellite aerosol optical depth (AOD) missing values; (2) modeling 1 km × 1 km daily PM2.5 concentrations at a national scale using extensive covariates; and (3) downscaling daily PM2.5 predictions to 100-m resolution at a city scale. We achieved good model performances with spatial cross-validation (CV) R2 of 0.92 and temporal CV R2 of 0.85 at the air quality sites across the country. We then estimated daily PM2.5 concentrations in China from 2013 to 2019 at 1 km × 1 km grid cells. The downscaled predictions at 100 m resolution greatly improved the spatial variation of PM2.5 concentrations at the city scale. The framework and data set generated in this study could be useful to PM2.5 exposure assessment and epidemiological studies." @default.
• W3119335550 created "2021-01-18" @default.
• W3119335550 creator A5022883128 @default.
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• W3119335550 date "2021-01-15" @default.
• W3119335550 modified "2023-10-16" @default.
• W3119335550 title "High-Resolution Spatiotemporal Modeling for Ambient PM_2.5 Exposure Assessment in China from 2013 to 2019" @default.
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• W3119335550 doi "https://doi.org/10.1021/acs.est.0c05815" @default.
• W3119335550 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33448849" @default.
• W3119335550 hasPublicationYear "2021" @default.
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