Deep neural networks for spatiotemporal PM<sub>2.5</sub> forecasts based on atmospheric chemical transport model output and monitoring data.

Kow, Pu-Yun; Chang, Li-Chiu; Lin, Chuan-Yao; Chou, Charles C-K; Chang, Fi-John

Deep neural networks for spatiotemporal PM_2.5 forecasts based on atmospheric chemical transport model output and monitoring data.

Kow, Pu-Yun; Chang, Li-Chiu; Lin, Chuan-Yao; Chou, Charles C-K; Chang, Fi-John.

Afiliação

Kow PY; Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 10617, Taiwan.
Chang LC; Department of Water Resources and Environmental Engineering, Tamkang University, New Taipei City, 25137, Taiwan.
Lin CY; Research Center for Environmental Changes, Academia Sinica, Taipei, 11529, Taiwan.
Chou CC; Research Center for Environmental Changes, Academia Sinica, Taipei, 11529, Taiwan.
Chang FJ; Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 10617, Taiwan. Electronic address: changfj@ntu.edu.tw.

Environ Pollut ; 306: 119348, 2022 Aug 01.

Article em En | MEDLINE | ID: mdl-35487466

Assuntos

Poluentes Atmosféricos; Poluição do Ar; Poluentes Atmosféricos/análise; Poluição do Ar/análise; Monitoramento Ambiental/métodos; Redes Neurais de Computação; Material Particulado/análise

Palavras-chave

Atmospheric chemical transport model; Convolutional neural network (CNN); Deep neural network (DNN); PM(2.5) forecast; Regional air quality forecast

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Poluentes Atmosféricos / Poluição do Ar Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Poluentes Atmosféricos / Poluição do Ar Idioma: En Ano de publicação: 2022 Tipo de documento: Article