Identifying heterogeneous air pollution exposure using machine learning models with dynamic traffic and population data

Identifying heterogeneous air pollution exposure using machine learning models with dynamic traffic and population data

Accurate identification of high-pollution areas is critical for equitable air quality (AQ) management and public health protection in megacities. However, regulatory air monitoring stations are limited and may not fully represent population exposure at the community level. This study leverages dynam...

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Bibliographic Details
Main Authors: Ruoxi Wu, Yifan Wen, Xiaomeng Wu, Cheng Huang, Qingyan Fu, Qingyao Hu, Hongli Wang, Ye Wu, Shaojun Zhang
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Environmental Research: Infrastructure and Sustainability
Subjects:
air pollution
traffic
machine learning
exposure
high resolution
simulation
Online Access:https://doi.org/10.1088/2634-4505/aded34
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https://doi.org/10.1088/2634-4505/aded34

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