Improve ground-level PM<sub>2.5</sub> concentration mapping using a random forests-based geostatistical approach.

Liu, Ying; Cao, Guofeng; Zhao, Naizhuo; Mulligan, Kevin; Ye, Xinyue

Improve ground-level PM_2.5 concentration mapping using a random forests-based geostatistical approach.

Liu, Ying; Cao, Guofeng; Zhao, Naizhuo; Mulligan, Kevin; Ye, Xinyue.

Afiliação

Liu Y; Department of Geosciences, Texas Tech University, Lubbock, TX 79409, USA; Center for Geospatial Technology, Texas Tech University, Lubbock, TX 79409, USA.
Cao G; Department of Geosciences, Texas Tech University, Lubbock, TX 79409, USA; Center for Geospatial Technology, Texas Tech University, Lubbock, TX 79409, USA. Electronic address: guofeng.cao@ttu.edu.
Zhao N; Center for Geospatial Technology, Texas Tech University, Lubbock, TX 79409, USA.
Mulligan K; Department of Geosciences, Texas Tech University, Lubbock, TX 79409, USA; Center for Geospatial Technology, Texas Tech University, Lubbock, TX 79409, USA.
Ye X; Department of Geography, Kent State University, Kent, OH 44240, USA.

Environ Pollut ; 235: 272-282, 2018 Apr.

Article em En | MEDLINE | ID: mdl-29291527

Assuntos

Poluentes Atmosféricos/análise; Poluição do Ar/estatística & dados numéricos; Monitoramento Ambiental/métodos; Material Particulado/análise; Geografia; Humanos; Fatores Socioeconômicos; Análise Espacial; Estados Unidos

Palavras-chave

Air pollution; Geostatistics; PM(2.5); Random forests; Remote sensing

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Monitoramento Ambiental / Poluentes Atmosféricos / Poluição do Ar / Material Particulado Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google