Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data

Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data

This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative hum...

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Bibliographic Details
Main Authors: Jingyi Zhang, Bin Li, Yumin Chen, Meijie Chen, Tao Fang, Yongfeng Liu
Format: Article
Language:English
Published: MDPI AG 2018-06-01
Series:International Journal of Environmental Research and Public Health
Subjects:
fine particulate matter (PM2.5)
spatial effect
eigenvector spatial filtering method
regression model
Online Access:http://www.mdpi.com/1660-4601/15/6/1228

Internet

http://www.mdpi.com/1660-4601/15/6/1228

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