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Using satellite data on remote transportation of air pollutants for PM2.5 prediction in northern Taiwan.
- Source :
- PLoS ONE; 3/10/2023, Vol. 17 Issue 3, p1-22, 22p
- Publication Year :
- 2023
-
Abstract
- Accurate PM<subscript>2.5</subscript> 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 PM<subscript>2.5</subscript> 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- AIR pollutants
OPTICAL remote sensing
DEEP learning
ENVIRONMENTAL policy
POLLUTANTS
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 17
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- PLoS ONE
- Publication Type :
- Academic Journal
- Accession number :
- 162353121
- Full Text :
- https://doi.org/10.1371/journal.pone.0282471