Predicting PM2.5 atmospheric air pollution using deep learning with meteorological data and ground-based observations and remote-sensing satellite big data-Reference-Cited by-同舟云学术

Predicting PM2.5 atmospheric air pollution using deep learning with meteorological data and ground-based observations and remote-sensing satellite big data

Published:2021-11-23 Issue: Volume: Page:
ISSN:1873-9318
Container-title:Air Quality, Atmosphere & Health
language:en
Short-container-title:Air Qual Atmos Health

Author:

Muthukumar Pratyush^ORCID,Cocom Emmanuel,Nagrecha Kabir,Comer Dawn,Burga Irene,Taub Jeremy,Calvert Chisato Fukuda,Holm Jeanne,Pourhomayoun Mohammad

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Management, Monitoring, Policy and Law,Atmospheric Science,Pollution

Link

https://link.springer.com/content/pdf/10.1007/s11869-021-01126-3.pdf

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4. Cocom E, Muthukumar P, Holm J, Comer D, Lyons A, Burga I, Hasenkopf CA, Calvert C, Pourhomayoun M (2020) Particulate Matter Forecasting in Los Angeles County with Sparse Ground-based Sensor Data Analytics. In: AGU Fall Meeting Abstracts, vol 2020, p A061–0004

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