Prediction of daily mean and one-hour maximum PM
Air pollution
Air quality management
Environmental modeling
Machine-learning model
Particulate matter
Remote sensing
Journal
Journal of exposure science & environmental epidemiology
ISSN: 1559-064X
Titre abrégé: J Expo Sci Environ Epidemiol
Pays: United States
ID NLM: 101262796
Informations de publication
Date de publication:
11 2022
11 2022
Historique:
received:
22
03
2022
accepted:
15
08
2022
revised:
15
08
2022
pubmed:
11
9
2022
medline:
15
12
2022
entrez:
10
9
2022
Statut:
ppublish
Résumé
Machine-learning algorithms are becoming popular techniques to predict ambient air PM Our goal was to develop a machine-learning model to predict mean PM We present a new modeling approach based on extreme gradient boosting (XGBoost) and inverse-distance weighting that uses AOD, meteorology, and land-use variables. We also investigated applications of our mean PM Our models for mean and max PM Machine learning algorithms can be used to predict highly spatiotemporally resolved PM Our PM
Sections du résumé
BACKGROUND
Machine-learning algorithms are becoming popular techniques to predict ambient air PM
OBJECTIVE
Our goal was to develop a machine-learning model to predict mean PM
METHODS
We present a new modeling approach based on extreme gradient boosting (XGBoost) and inverse-distance weighting that uses AOD, meteorology, and land-use variables. We also investigated applications of our mean PM
RESULTS
Our models for mean and max PM
SIGNIFICANCE
Machine learning algorithms can be used to predict highly spatiotemporally resolved PM
IMPACT
Our PM
Identifiants
pubmed: 36088418
doi: 10.1038/s41370-022-00471-4
pii: 10.1038/s41370-022-00471-4
pmc: PMC9731899
mid: NIHMS1830291
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
917-925Subventions
Organisme : NIEHS NIH HHS
ID : R01 ES031295
Pays : United States
Organisme : NIEHS NIH HHS
ID : R01 ES021357
Pays : United States
Organisme : NIEHS NIH HHS
ID : R01 ES032242
Pays : United States
Organisme : NIEHS NIH HHS
ID : R01 ES013744
Pays : United States
Organisme : NIEHS NIH HHS
ID : R24 ES028522
Pays : United States
Organisme : NIEHS NIH HHS
ID : P30 ES023515
Pays : United States
Organisme : NIEHS NIH HHS
ID : R01 ES014930
Pays : United States
Organisme : NICHD NIH HHS
ID : T32 HD049311
Pays : United States
Informations de copyright
© 2022. The Author(s).
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