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Influence of AOD remotely sensed products, meteorological parameters, and AOD–PM2.5 models on the PM2.5 estimation by Yuelei Xu; Yan Huang; Zhongyang Guo is a Environmental Science article available to read on EtoBox.

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Air pollution has emerged as one of the most urgent problems worldwide over the past few years. Especially, fine particulate matter with a diameter smaller than 2.5 micrometres (PM 2.5 ) has seriously endangered the human life and health worldwide. Thus, monitoring accurate and spatio-temporal continuously PM 2.5 concentrations is essential for controlling air pollution. The key to accurately estimating PM 2.5 concentrations is to build a reliable aerosol optical depth (AOD)-PM 2.5 model, which is mainly determined by the AOD remotely sensed products, meteorological parameters, and estimating methods. However, no systematic evaluation and analysis of the influence of these three variables on AOD-PM 2.5 model has yet been reported. Based on Terra and Aqua Moderate Resolution Imaging Spectroradiometer AOD products, reanalysis meteorological data, and ground-based PM 2.5 data during 2014-2015 in eastern China, we analyzed the influence of above mentioned variables on the accuracy of the AOD-PM 2.5 model. The four modeling methods investigated are stepwise multiple linear regression (SMLR), backpropagation neural network (BPNN), classification and regression tree (CART), and random for

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Author

Yuelei Xu; Yan Huang; Zhongyang Guo

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Environmental Science (Physical Sciences)