Two-stage model for estimating the spatiotemporal distribution of hourly PM1.0 concentrations over central and east China

摘要

Widespread and severe PM1.0 (particulate matter = 1.0 mu m) pollution in China has a significant negative influence on human health. However, knowledge of the regional spatiotemporal distribution of PM1.0 has been hindered by sparsely distributed PM1.0 concentration data. In this work, a two-stage model (linear mixed effect-bagged tree model) was proposed for estimating hourly PM1.0 pollution levels from July 2015 to June 2017 over central and east China by using Himawari-8 aerosol products and coincident geographic data, meteorology, and site-based PM1.0 concentrations from ground monitoring network. The cross-validation for the developed model displayed R-2 and mean absolute error value of 0.80 and 9.3 mu g/m(3), respectively. Validation demonstrated that themodel accurately estimated hourly PM1.0 concentrations with high R-2 of 0.63-0.85 and low bias of 8.7-10.1 mu g/m(3). The estimated PM1.0 concentrations on daily scale showed peaks with PM1.0 of 36.9 +/- 8.4 mu g/m(3) at rush hours during daytime. Seasonal distribution displayed that summer was cleanest with an average PM1.0 of 20.9 +/- 6.8 mu g/m(3) and winter was the most polluted season with an average PM1.0 of 45.6 +/- 16.8 mu g/m(3). These results indicated that the proposed satellite-based model can estimate reliable spatial distribution of PM1.0 concentrations over a large-scale region. (C) 2019 Published by Elsevier B.V.