A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM
aerosol optical depth
fine particulate matter
machine learning
random forest
reanalysis
satellite
Journal
Remote sensing
ISSN: 2072-4292
Titre abrégé: Remote Sens (Basel)
Pays: Switzerland
ID NLM: 101624426
Informations de publication
Date de publication:
Nov 2020
Nov 2020
Historique:
entrez:
7
1
2021
pubmed:
8
1
2021
medline:
8
1
2021
Statut:
epublish
Résumé
Epidemiological studies on the health effects of air pollution usually rely on measurements from fixed ground monitors, which provide limited spatio-temporal coverage. Data from satellites, reanalysis, and chemical transport models offer additional information used to reconstruct pollution concentrations at high spatio-temporal resolutions. This study aims to develop a multi-stage satellite-based machine learning model to estimate daily fine particulate matter (PM
Identifiants
pubmed: 33408882
doi: 10.3390/rs12223803
pmc: PMC7116547
mid: EMS104896
doi:
Types de publication
Journal Article
Langues
eng
Pagination
3803Subventions
Organisme : Medical Research Council
ID : MR/M022625/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/R013349/1
Pays : United Kingdom
Déclaration de conflit d'intérêts
Conflicts of Interest: The authors declare no conflict of interest.
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