Advancing methodologies for applying machine learning and evaluating spatiotemporal models of fine particulate matter (PM
Aerosol optical depth
Air pollution
MAIAC
PM2.5
Spatial cross-validation
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:
15 Oct 2020
15 Oct 2020
Historique:
entrez:
30
10
2020
pubmed:
31
10
2020
medline:
31
10
2020
Statut:
ppublish
Résumé
Reconstructing the distribution of fine particulate matter (PM
Identifiants
pubmed: 33122961
doi: 10.1016/j.atmosenv.2020.117649
pmc: PMC7591135
mid: NIHMS1615982
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 : R00 ES023450
Pays : United States
Organisme : NIH HHS
ID : UG3 OD023337
Pays : United States
Organisme : NIH HHS
ID : UH3 OD023337
Pays : United States
Déclaration de conflit d'intérêts
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.
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