Evaluation of using satellite-derived aerosol optical depth in land use regression models for fine particulate matter and its elemental composition

Chun Sheng Huang, Ho Tang Liao, Tang Huang Lin, Jung Chi Chang, Chien Lin Lee, Eric Cheuk Wai Yip, Yee Lin Wu, Chang Fu Wu

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

This study introduced satellite-derived aerosol optical depth (AOD) in land use regression (LUR) modeling to predict ambient concentrations of fine particulate matter (PM2.5 ) and its elemental composition. Twenty-four daily samples were collected from 17 air quality monitoring sites (N = 408) in Taiwan in 2014. A total of 12 annual LUR models were developed for PM2.5 and 11 elements, including aluminum, calcium, chromium, iron, potassium, manganese, sulfur, silicon, titanium, vanadium, and zinc. After applied AOD and a derived-predictor, AOD percentage, in modeling, the number of models with leave-one-out cross-validation R2 > 0.40 significantly increased from 5 to 9, indicating the substantial benefits for the construction of spatial prediction models. Sensitivity analyses of using data stratified by PM2.5 concentrations revealed that the model performances were further improved in the high pollution season.

原文English
文章編號1018
期刊Atmosphere
12
發行號8
DOIs
出版狀態Published - 2021 8月

All Science Journal Classification (ASJC) codes

  • 環境科學(雜項)

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