High-resolution spatiotemporal prediction of PM 2.5 concentration based on mobile monitoring and deep learning.

Autor: Wang YZ; Center for Intelligent Transportation Systems and Unmanned Aerial Systems Applications, State-Key Laboratory of Ocean Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Data-Driven Management Decision Making Lab, Shanghai Jiao Tong University, Shanghai 200240, China., He HD; Center for Intelligent Transportation Systems and Unmanned Aerial Systems Applications, State-Key Laboratory of Ocean Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. Electronic address: hongdihe@sjtu.edu.cn., Huang HC; Center for Intelligent Transportation Systems and Unmanned Aerial Systems Applications, State-Key Laboratory of Ocean Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China., Yang JM; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China., Peng ZR; International Center for Adaptation Planning and Design, College of Design, Construction and Planning, University of Florida, Florida, 32611-5706, USA; Healthy Building Research Center, Ajman University, Ajman, United Arab Emirates.
Jazyk: angličtina
Zdroj: Environmental pollution (Barking, Essex : 1987) [Environ Pollut] 2025 Jan 01; Vol. 364 (Pt 2), pp. 125342. Date of Electronic Publication: 2024 Nov 18.
DOI: 10.1016/j.envpol.2024.125342
Abstrakt: Obtaining the high-resolution distribution characteristics of urban air pollutants is crucial for effective pollution control and public health. In order to fulfill it, mobile monitoring offers a novel and practical approach compared to traditional fixed monitoring methods. However, the sparsity of mobile monitoring data still makes it a challenge to recover the high-resolution pollutant concentration across an entire area. To tackle the sparsity issue and fulfill a prediction of the spatiotemporal distribution of PM 2.5 , a high-resolution urban PM 2.5 prediction method was proposed based on mobile monitoring data in this study. This method enables prediction with a spatial resolution of 500m × 500m and a temporal resolution of 1 h. First, a Light Gradient Boosting Machine (LightGBM) was trained using mobile monitoring of PM 2.5 concentration and exogenous features to obtain complete spatiotemporal PM 2.5 concentration. Second, a model consisting of Convolutional Neural Network and Transformer (CNN-Transformer) with a customised loss function was established to predict high-resolution PM 2.5 concentration based on complete spatiotemporal data. The method was validated using real-world data collected from Cangzhou, China. The numerical results from cross-validation showed an R 2 of 0.925 for imputation and 0.887 for prediction, demonstrating this method is suitable for high-resolution spatiotemporal prediction of PM 2.5 concentration based on mobile monitoring data.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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