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Improving Intra-Urban Prediction of Atmospheric Fine Particles Using a Hybrid Deep Learning Approach

Authors :
Zhengyu Zhang
Jiuchun Ren
Yunhua Chang
Source :
Atmosphere, Vol 14, Iss 3, p 599 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Growing evidence links intra-urban gradients in atmospheric fine particles (PM2.5), a complex and variable cocktail of toxic chemicals, to adverse health outcomes. Here, we propose an improved hierarchical deep learning model framework to estimate the hourly variation of PM2.5 mass concentration at the street level. By using a full-year monitoring data (including meteorological parameters, hourly concentrations of PM2.5, and gaseous precursors) from multiple stations in Shanghai, the largest city in China, as a training dataset, we first apply a convolutional neural network to obtain cross-domain and time-series features so that the inherent features of air quality and meteorological data associated with PM2.5 can be effectively extracted. Next, a Gaussian weight calculation layer is used to determine the potential interaction effects between different regions and neighboring stations. Finally, a long and short-term memory model layer is used to efficiently extract the temporal evolution characteristics of PM2.5 concentrations from the previous output layer. Further comparative analysis reveals that our proposed model framework significantly outperforms previous benchmark methods in terms of the stability and accuracy of PM2.5 prediction, which has important implications for the intra-urban health assessment of PM2.5-related pollution exposures.

Details

Language :
English
ISSN :
20734433
Volume :
14
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Atmosphere
Publication Type :
Academic Journal
Accession number :
edsdoj.6a7f720b20c6403b9f88e84cea51cf5b
Document Type :
article
Full Text :
https://doi.org/10.3390/atmos14030599