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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.
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]
Copyright of Journal of Environmental Sciences (Elsevier) is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: GreenFILE
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DbLabel: GreenFILE
An: 194169050
AccessLevel: 6
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PubTypeId: academicJournal
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  Data: 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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  Data: <searchLink fieldCode="AR" term="%22Liang%2C+Weizhao%22">Liang, Weizhao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Yuan%22">Li, Yuan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Xuan%22">Liu, Xuan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yan%2C+Renchang%22">Yan, Renchang</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Wubin%22">Zhu, Wubin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Yunshan%22">Li, Yunshan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shen%2C+Jiandong%22">Shen, Jiandong</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> shenjiandonghzem@163.com</i><br /><searchLink fieldCode="AR" term="%22Bi%2C+Xiaohui%22">Bi, Xiaohui</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yufen%22">Zhang, Yufen</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dai%2C+Qili%22">Dai, Qili</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> daiql@nankai.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Feng%2C+Yinchang%22">Feng, Yinchang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Environmental+Sciences+%28Elsevier%29%22">Journal of Environmental Sciences (Elsevier)</searchLink>. Jul2026, Vol. 165, p460-467. 8p.
– Name: Subject
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  Data: *<searchLink fieldCode="DE" term="%22Emergency+management%22">Emergency management</searchLink><br />*<searchLink fieldCode="DE" term="%22Air+pollution+control%22">Air pollution control</searchLink><br />*<searchLink fieldCode="DE" term="%22Particulate+matter%22">Particulate matter</searchLink><br />*<searchLink fieldCode="DE" term="%22Emission+control%22">Emission control</searchLink><br /><searchLink fieldCode="DE" term="%22Haze%22">Haze</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Particulate+nitrate%22">Particulate nitrate</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink><br /><searchLink fieldCode="DE" term="%22Hangzhou+%28China%29%22">Hangzhou (China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Environmental Sciences (Elsevier) is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.jes.2025.06.016
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
        StartPage: 460
    Subjects:
      – SubjectFull: Emergency management
        Type: general
      – SubjectFull: Air pollution control
        Type: general
      – SubjectFull: Particulate matter
        Type: general
      – SubjectFull: Emission control
        Type: general
      – SubjectFull: Haze
        Type: general
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Particulate nitrate
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: China
        Type: general
      – SubjectFull: Hangzhou (China)
        Type: general
    Titles:
      – TitleFull: 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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            NameFull: Liang, Weizhao
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            NameFull: Li, Yuan
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            NameFull: Liu, Xuan
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            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
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