Combining Google traffic map with deep learning model to predict street-level traffic-related air pollutants in a complex urban environment.

Wei, Peng, Hao, Song, Shi, Yuan ORCID: 0000-0003-4011-8735, Anand, Abhishek, Wang, Ya, Chu, Mengyuan and Ning, Zhi (2024) Combining Google traffic map with deep learning model to predict street-level traffic-related air pollutants in a complex urban environment. Environment international, 191. p. 108992. ISSN 0160-4120, 1873-6750

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Abstract

Traffic-related air pollution (TRAP) is a major contributor to urban pollution and varies sharply at the street level, posing a challenge for air quality modeling. Traditional land use regression models combined with data from fixed monitoring stations may be unable to predict and characterize fine-scale TRAP, especially in complex urban environments influenced by various features. This study aims to estimate fine-scale (50 m) concentrations of nitrogen oxides (NO and NO₂) in Hong Kong using a deep learning (DL) structured model. We collected data from mobile air quality sensors on buses and crowd-sourced Google real-time traffic status as a proxy for real-time traffic emissions. Our DL model was compared with existing machine learning models to assess performance improvements. Using an interpretable machine learning method, we hierarchically evaluated the global, local, and interaction effects for different features. Our DL model outperformed existing machine learning models, achieving R2 values of 0.72 for NO and 0.69 for NO₂. The incorporation of traffic status as a key predictor improved model performance by 9% to 17%. The interpretable machine learning method revealed the importance of traffic-related features and their pairwise interactions. The results indicate that traffic-related features significantly contribute to TRAP and provide insights and guidance for urban planning. By incorporating crowd-sourced Google traffic information, we assessed traffic abatement scenarios that could inform targeted strategies for improving urban air quality.

Item Type:	Article
Additional Information:	Source info: ENVINT-D-24-01381
Uncontrolled Keywords:	Machine learning, Deep learning, Mobile measurement, Street-level pollution, Air quality sensor, Crowd-sourced, Crowd-sourced
Divisions:	Faculty of Science & Engineering Faculty of Science & Engineering > School of Environmental Sciences
Depositing User:	Symplectic Admin
Date Deposited:	23 Sep 2024 09:44
Last Modified:	28 Feb 2026 00:50
DOI:	10.1016/j.envint.2024.108992
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3184701
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