Validation and calibration of aerosol optical depth and classification of aerosol types based on multi-source data over China

Jing Wang; Yusi Liu; Li Chen; Yaxin Liu; Ke Mi; Shuang Gao; Jian Mao; Hui Zhang; Yanling Sun; Zhenxing Ma

doi:10.1016/j.scitotenv.2023.166603

Validation and calibration of aerosol optical depth and classification of aerosol types based on multi-source data over China

Sci Total Environ. 2023 Dec 10:903:166603. doi: 10.1016/j.scitotenv.2023.166603. Epub 2023 Sep 2.

Authors

Jing Wang¹, Yusi Liu², Li Chen³, Yaxin Liu¹, Ke Mi¹, Shuang Gao¹, Jian Mao¹, Hui Zhang¹, Yanling Sun¹, Zhenxing Ma¹

Affiliations

¹ School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China.
² State Key Laboratory of Severe Weather & Key Laboratory for Atmospheric Chemistry of China Meteorology Administration, Chinese Academy of Meteorological Sciences, Beijing 100081, China.
³ School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China. Electronic address: amychenli1981@126.com.

PMID: 37660811
DOI: 10.1016/j.scitotenv.2023.166603

Abstract

A refined classification of aerosol types is essential to identify and control air pollution sources. This study focused on improving the resolution and accuracy of aerosol optical depth (AOD) and further refining the classification of aerosol types in China. We validated the accuracy of the AOD acquired using the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) and Copernicus Atmosphere Monitoring Service (CAMS) by comparing it with that acquired using from the Aeronet Robotic Network (AERONET). We simulated the AOD with high spatial resolution and accuracy based on the extremely randomized trees (ERT), adaptive boosting (AdaBoost), and gradient boosting decision trees (GBDT) models and identified aerosol types based on the Angstrom Exponent (AE) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the calibrated AOD. The results showed that CAMS overestimates AOD (21.4 %) and MERRA2 underestimates AOD (-17.3 %). Among the three machine learning models, the ERT model performed best, with a determination coefficient (R²) of 0.825 and the root-mean-square error (RMSE) of 0.174. Biomass burning/urban-industrial aerosols dominated China, with the largest contributions to southern, eastern, and central China in spring and summer. Clean continental aerosols contributed the most to southwestern China in fall and winter, whereas desert dust aerosols contributed the most to northwestern and eastern China in spring.

Keywords: Aerosol optical depth; Aerosol types; Extremely randomized trees; Reanalysis.