Ambient PM2.5 Estimates and Variations during COVID-19 Pandemic in the Yangtze River Delta Using Machine Learning and Big Data

Wanliu Mao,Jing Wei,Lilin Zheng,Wu Xiao,Debin Lu,Liang Zhang

doi:10.3390/rs13081423

Abstract

The lockdown of cities in the Yangtze River Delta (YRD) during COVID-19 has provided many natural and typical test sites for estimating the potential of air pollution control and reduction. To evaluate the reduction of PM2.5 concentration in the YRD region by the epidemic lockdown policy, this study employs big data, including PM2.5 observations and 29 independent variables regarding Aerosol Optical Depth (AOD), climate, terrain, population, road density, and Gaode map Point of interesting (POI) data, to build regression models and retrieve spatially continuous distributions of PM2.5 during COVID-19. Simulation accuracy of multiple machine learning regression models, i.e., random forest (RF), support vector regression (SVR), and artificial neural network (ANN) were compared. The results showed that the RF model outperformed the SVR and ANN models in the inversion of PM2.5 in the YRD region, with the model-fitting and cross-validation coefficients of determination R2 reached 0.917 and 0.691, mean absolute error (MAE) values were 1.026 μg m−3 and 2.353 μg m−3, and root mean square error (RMSE) values were 1.413 μg m−3, and 3.144 μg m−3, respectively. PM2.5 concentrations during COVID-19 in 2020 have decreased by 3.61 μg m−3 compared to that during the same period of 2019 in the YRD region. The results of this study provide a cost-effective method of air pollution exposure assessment and help provide insight into the atmospheric changes under strong government controlling strategies.

Highlights

IntroductionOn 20 January 2021, COVID-19 has been known to cause more than two million deaths worldwide, with a global mortality rate of 3.4%
Coronavirus disease 2019 (COVID-19), as an infectious disease, was identified in the city of Wuhan, China, and spread to nearly every country around the globe [1,2,3].On 20 January 2021, COVID-19 has been known to cause more than two million deaths worldwide, with a global mortality rate of 3.4%
The results indicate that the random forest (RF) estimation should be a good approximation to the true state of PM2.5 concentrations in the Yangtze River Delta

Summary

Introduction

On 20 January 2021, COVID-19 has been known to cause more than two million deaths worldwide, with a global mortality rate of 3.4%. In response to the outbreak of COVID-19, a nation-wide lockdown of cities was proposed by the Chinese government after January. Almost all production activities, such as transportation, construction, and industries were completely restricted [7,8,9]. Such unprecedented stagnation of industrial production and residents’ consumption has effectively reduced air pollution emission, providing natural and typical test sites for estimating the impacts of human activities controlling on the air pollution control and reduction [10,11,12,13]. Such unprecedented stagnation of industrial production and residents’ consumption has effectively reduced air pollution emission, providing natural and typical test sites for estimating the impacts of human activities controlling on the air pollution control and reduction [10,11,12,13]. 4.0/).

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