Evaluating Hangzhou's urgent source-specific regulatory policies for the 2024 New Year haze: A receptor model and machine learning approach.
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| Title: | Evaluating Hangzhou's urgent source-specific regulatory policies for the 2024 New Year haze: A receptor model and machine learning approach. |
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| Authors: | Liang, Weizhao1,2 (AUTHOR), Li, Yuan1,2 (AUTHOR), Liu, Xuan1,2 (AUTHOR), Yan, Renchang3 (AUTHOR), Zhu, Wubin1,2 (AUTHOR), Li, Yunshan1,2 (AUTHOR), Shen, Jiandong1,3 (AUTHOR) shenjiandonghzem@163.com, Bi, Xiaohui1,2 (AUTHOR), Zhang, Yufen1,2 (AUTHOR), Dai, Qili1,2 (AUTHOR) daiql@nankai.edu.cn, Feng, Yinchang1,2 (AUTHOR) |
| Source: | Journal of Environmental Sciences (Elsevier). Jul2026, Vol. 165, p460-467. 8p. |
| Subject Terms: | *Emergency management, *Air pollution control, *Particulate matter, *Emission control, Haze, Random forest algorithms, Particulate nitrate, Machine learning |
| Geographic Terms: | China, Hangzhou (China) |
| Abstract: | China's PM 2.5 levels have significantly declined over the past decade due to stringent air pollution controls. However, severe autumn and winter haze events persist, posing short-term acute health risks. Since 2016, seasonal air pollution mitigation campaigns have been implemented, yet an unexpected large-scale haze event during the 2023–2024 New Year in eastern China raised public concerns about the effectiveness of current measures. This study, using a meteorological normalization method based on random forest, found a sharp decrease in PM 2.5 emission strength on January 1, 2024, which is causally linked to emergency control measures. Dispersion-normalized PMF analysis identified secondary nitrate (47.4 %), secondary sulfate (17.4 %), and vehicle emissions (10.1 %) as major PM 2.5 sources, indicating a typical chemistry-dominant haze event driven by secondary formation. The meteorologically normalized PM 2.5 levels dropped 17 % after the implementation of emergency emission reduction measures, leading to a general decrease in the concentrations of most PM 2.5 chemical species and source contributions. Particularly, emergency control measures accounted for 73 % and 66 % of the PM 2.5 reduction from coal combustion and industrial emissions, respectively. These findings demonstrate that short-term, targeted interventions—particularly those aimed at key stationary emission sources—can effectively suppress peak pollution concentrations and mitigate the severity of haze events, even under unfavorable meteorological conditions. This study offers actionable insights for improving emergency air pollution control strategies and supports more accountable, evidence-based environmental governance in future haze episodes. [Display omitted] [ABSTRACT FROM AUTHOR] |
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| Database: | GreenFILE |
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