Eigenvector Spatial Filtering Regression Modeling of Ground PM(2.5) Concentrations Using Remotely Sensed Data

This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM(2.5) concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative h...

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Detalles Bibliográficos
Autores principales: Zhang, Jingyi, Li, Bin, Chen, Yumin, Chen, Meijie, Fang, Tao, Liu, Yongfeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6025436/
https://www.ncbi.nlm.nih.gov/pubmed/29891785
http://dx.doi.org/10.3390/ijerph15061228

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6025436/
https://www.ncbi.nlm.nih.gov/pubmed/29891785
http://dx.doi.org/10.3390/ijerph15061228

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