Using land use variable information and a random forest approach to correct spatial mean bias in fused CMAQ fields for particulate and gas species.

Air pollution CMAQ Gas species Particulate species Random forest model Spatiotemporal pollutant fields

Journal

Atmospheric environment (Oxford, England : 1994)
ISSN: 1352-2310
Titre abrégé: Atmos Environ (1994)
Pays: England
ID NLM: 9888534

Informations de publication

Date de publication:
01 Apr 2022
Historique:
medline: 1 4 2022
pubmed: 1 4 2022
entrez: 22 12 2023
Statut: ppublish

Résumé

Accurate spatiotemporal air pollution fields are essential for health impact and epidemiologic studies. There are an increasing number of studies that have combined observational data with spatiotemporally complete air pollution simulations. Land-use, speciated gaseous and particulate pollutant concentrations and chemical transport modeling are fused using a random forest approach to construct daily air quality fields for 12 pollutants (CO, NOx, NO

Identifiants

DOI: 10.1016/j.atmosenv.2022.118982 PMID: 38131016 PMC: PMC10735214
pubmed: 38131016
doi: 10.1016/j.atmosenv.2022.118982
pmc: PMC10735214
pii:
doi:

Types de publication

Journal Article

Langues

eng

Auteurs

Niru Senthilkumar (N)

School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.

Mark Gilfether (M)

School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.

Howard H Chang (HH)

Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, USA.

Armistead G Russell (AG)

School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.

James Mulholland (J)

School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.

Classifications MeSH