Predicting fine-scale daily NO
NO2
XGBoost
air pollution
ensemble modeling
random forest
Journal
Atmospheric pollution research
ISSN: 1309-1042
Titre abrégé: Atmos Pollut Res
Pays: Turkey
ID NLM: 101554506
Informations de publication
Date de publication:
Jun 2023
Jun 2023
Historique:
pmc-release:
01
06
2024
pubmed:
17
5
2023
medline:
17
5
2023
entrez:
16
5
2023
Statut:
ppublish
Résumé
In recent years, there has been growing interest in developing air pollution prediction models to reduce exposure measurement error in epidemiologic studies. However, efforts for localized, fine-scale prediction models have been predominantly focused in the United States and Europe. Furthermore, the availability of new satellite instruments such as the TROPOsopheric Monitoring Instrument (TROPOMI) provides novel opportunities for modeling efforts. We estimated daily ground-level nitrogen dioxide (NO
Identifiants
pubmed: 37193345
doi: 10.1016/j.apr.2023.101763
pmc: PMC10168642
mid: NIHMS1894851
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : NIEHS NIH HHS
ID : P30 ES023515
Pays : United States
Organisme : NIEHS NIH HHS
ID : R01 ES013744
Pays : United States
Organisme : NICHD NIH HHS
ID : T32 HD049311
Pays : United States
Déclaration de conflit d'intérêts
Competing Interests The author(s) declare that they have no competing interests. Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.