Estimating the Impact of COVID-19 on the PM Levels in China with a Satellite-Driven Machine Learning Model.

China implemented an aggressive nationwide lockdown procedure immediately after the COVID-19 outbreak in January 2020. As China emerges from the impact of COVID-19 on national economic and industrial activities, it has become the site of a large-scale natural experiment to evaluate the impact of COVID-19 on regional air quality. However, ground measurements of fine particulate matters (PM) concentrations do not offer comprehensive spatial coverage, especially in suburban and rural regions. In this study, we developed a machine learning method with satellite aerosol remote sensing data, meteorological fields and land use parameters as major predictor variables to estimate spatiotemporally resolved daily PM concentrations in China. Our study period consists of a reference semester (1 November 2018-30 April 2019) and a pandemic semester (1 November 2019-30 April 2020), with six modeling months in each semester. Each period was then divided into subperiod 1 (November and December), subperiod 2 (January and February) and subperiod 3 (March and April). The reference semester model obtained a 10-fold cross-validated R (RMSE) of 0.79 (17.55 μg/m) and the pandemic semester model obtained a 10-fold cross-validated R (RMSE) of 0.83 (13.48 μg/m) for daily PM predictions. Our prediction results showed high PM concentrations in the North China Plain, Yangtze River Delta, Sichuan Basin and Xinjiang Autonomous Region during the reference semester. PM levels were lowered by 4.8 μg/m during the pandemic semester compared to the reference semester and PM levels during subperiod 2 decreased most, by 18%. The southeast region was affected most by the COVID-19 outbreak with PM levels during subperiod 2 decreasing by 31%, followed by the Northern Yangtze River Delta (29%) and Pearl River Delta (24%).