National Empirical Models of Air Pollution Using Microscale Measures of the Urban Environment.
Empirical models
exposure assessment
machine learning
street-level features
urban form
Journal
Environmental science & technology
ISSN: 1520-5851
Titre abrégé: Environ Sci Technol
Pays: United States
ID NLM: 0213155
Informations de publication
Date de publication:
16 11 2021
16 11 2021
Historique:
pubmed:
6
11
2021
medline:
26
11
2021
entrez:
5
11
2021
Statut:
ppublish
Résumé
National-scale empirical models of air pollution (e.g., Land Use Regression) rely on predictor variables (e.g., population density, land cover) at different geographic scales. These models typically lack microscale variables (e.g., street level), which may improve prediction with fine-spatial gradients. We developed microscale variables of the urban environment including Point of Interest (POI) data, Google Street View (GSV) imagery, and satellite-based measures of urban form. We developed United States national models for six criteria pollutants (NO
Identifiants
pubmed: 34739226
doi: 10.1021/acs.est.1c04047
doi:
Substances chimiques
Air Pollutants
0
Particulate Matter
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM