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In Environment international ; h5-index 0.0

High spatiotemporal resolution fine particulate matter (PM2.5) simulations can provide important exposure data for the assessment of long-term and short-term health effects. Satellite-based aerosol optical depth (AOD) data, meteorological data, and topographic data have become key variables for PM2.5 estimation. In this study, a random forest model was developed and used to estimate the highest resolution (0.01° × 0.01°) daily PM2.5 concentrations in the Beijing-Tianjin-Hebei region. Our model had a suitable performance (cv-R2 = 0.83 and test-R2 = 0.86). The regional test-R2 value in southern Beijing-Tianjin-Hebei was higher than that in northern Beijing-Tianjin-Hebei. The model performance was excellent at medium to high PM2.5 concentrations. Our study considered meteorological lag effects and found that the boundary layer height of the one-day lag had the most important contribution to the model. AOD and elevation factors were also important factors in the modeling process. High spatiotemporal resolution PM2.5 concentrations in 2010-2016 were estimated using a random forest model, which was based on PM2.5 measurements from 2013 to 2016.

Zhao Chen, Wang Qing, Ban Jie, Liu Zhaorong, Zhang Yayi, Ma Runmei, Li Shenshen, Li Tiantian

2020-Jan

High spatiotemporal resolution, Human exposure, Machine learning, PM(2.5) estimation