Machine Learning Modeling Reveals Divergent Air Pollutant Responses to Stringent Emission Controls in the Yangtze River Delta Region

Machine Learning Modeling Reveals Divergent Air Pollutant Responses to Stringent Emission Controls in the Yangtze River Delta Region

Ozone (O<sub>3</sub>) and fine particulate matter (PM<sub>2.5</sub>) are critical atmospheric pollutants whose complex chemical coupling presents significant challenges for multi-pollutant control strategies. This study investigated the spatiotemporal variations and driving m...

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Bibliographic Details
Main Authors: Qiufang Yao, Linhao Wang, Wenjing Qiu, Yutong Shi, Qi Xu, Yanping Xiao, Jiacheng Zhou, Shilong Li, Haobin Zhong, Jinsong Liu
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Atmosphere
Subjects:
machine learning
air pollution control
partial dependency analysis
regional transport
Yangtze River Delta region
Online Access:https://www.mdpi.com/2073-4433/16/6/710
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