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Advancing methodologies for applying machine learning and evaluating spatiotemporal models of fine particulate matter (PM(2.5)) using satellite data over large regions
Reconstructing the distribution of fine particulate matter (PM(2.5)) in space and time, even far from ground monitoring sites, is an important exposure science contribution to epidemiologic analyses of PM(2.5) health impacts. Flexible statistical methods for prediction have demonstrated the integrat...
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| Pubblicato in: | Atmos Environ (1994) |
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| Autori principali: | , , , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
2020
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7591135/ https://ncbi.nlm.nih.gov/pubmed/33122961 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.atmosenv.2020.117649 |
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