Evaluation of Using Satellite-Derived Aerosol Optical Depth in Land Use Regression Models for Fine Particulate Matter and Its Elemental Composition

Evaluation of Using Satellite-Derived Aerosol Optical Depth in Land Use Regression Models for Fine Particulate Matter and Its Elemental Composition

This study introduced satellite-derived aerosol optical depth (AOD) in land use regression (LUR) modeling to predict ambient concentrations of fine particulate matter (PM<sub>2.5</sub>) and its elemental composition. Twenty-four daily samples were collected from 17 air quality monitoring...

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Bibliographic Details
Main Authors:	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
Format:	Article
Language:	English
Published:	MDPI AG 2021-08-01
Series:	Atmosphere
Subjects:	air pollution elemental composition land use regression aerosol optical depth
Online Access:	https://www.mdpi.com/2073-4433/12/8/1018

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