Development of transferable neighborhood land use regression models for predicting intra-urban ambient nitrogen dioxide (NO
Land use regression (LUR) models
Macro-scale predictor variables
Micro-scale predictor variables
Neighborhoods
Nitrogen dioxide (NO2)
Transferability
Journal
Environmental science and pollution research international
ISSN: 1614-7499
Titre abrégé: Environ Sci Pollut Res Int
Pays: Germany
ID NLM: 9441769
Informations de publication
Date de publication:
Jun 2022
Jun 2022
Historique:
received:
29
11
2021
accepted:
05
02
2022
pubmed:
13
2
2022
medline:
24
6
2022
entrez:
12
2
2022
Statut:
ppublish
Résumé
Land use regression (LUR) models have been extensively used to predict air pollution exposure in epidemiological and environmental studies. The lack of dense routine monitoring networks in big cities places increased emphasis on the need for LUR models to be developed using purpose-designed neighborhood-scale monitoring data. However, the unsatisfactory model transferability limits these neighborhood LUR models to be then applied to other intra-urban areas in predicting air pollution exposure. In this study, we tackled this issue by proposing a method to develop transferable neighborhood NO
Identifiants
pubmed: 35150420
doi: 10.1007/s11356-022-19141-x
pii: 10.1007/s11356-022-19141-x
doi:
Substances chimiques
Air Pollutants
0
Particulate Matter
0
Nitrogen Dioxide
S7G510RUBH
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
45903-45918Subventions
Organisme : National Natural Science Foundation of China
ID : 41871317
Organisme : Performance-Based Research Fund
ID : 9450/71710/A6B8
Organisme : State Key Laboratory of Urban and Regional Ecology
ID : SKLURE2021-2-6
Organisme : Shaanxi Provincial Science and Technology Department
ID : 2021JM-388/01
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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