Using satellite data on remote transportation of air pollutants for PM2.5 prediction in northern Taiwan.
PLoS One
; 18(3): e0282471, 2023.
Article
em En
| MEDLINE
| ID: mdl-36897845
ABSTRACT
Accurate PM2.5 prediction is part of the fight against air pollution that helps governments to manage environmental policy. Satellite Remote sensing aerosol optical depth (AOD) processed by The Multi-Angle Implementation of Atmospheric Correlation (MAIAC) algorithm allows us to observe the transportation of remote pollutants between regions. The paper proposes a composite neural network model, the Remote Transported Pollutants (RTP) model, for such long-range pollutant transportation that predicts more accurate local PM2.5 concentrations given such satellite data. The proposed RTP model integrates several deep learning components and learns from the heterogeneous features of various domains. We also detected remote transportation pollution events (RTPEs) at two reference sites from the AOD data. Extensive experiments using real-world data show that the proposed RTP model outperforms the base model that does not account for RTPEs by 17%-30%, 23%-26% and 18%-22% and state-of-the-art models that account for RTPEs by 12%-22%, 12%-14%, and 10%-11% at +4h to +24h, +28h to +48 hours, and +52h to +72h hours respectively.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Contexto em Saúde:
2_ODS3
Problema de saúde:
2_quimicos_contaminacion
Assunto principal:
Poluentes Atmosféricos
/
Poluição do Ar
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
País/Região como assunto:
Asia
Idioma:
En
Revista:
PLoS One
Assunto da revista:
CIENCIA
/
MEDICINA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Taiwan