Insight into VOCs source profiles by machine learning: Role of commonalities in synergistic pollution controls.

Journal: Journal of hazardous materials
Published Date: Apr 8, 2025

Abstract

Under the trend of low-carbon and cost-reduction, achieving efficient control requires identifying the commonalities in volatile organic compounds (VOCs) source profiles and implementing collaborative emissions reduction strategies. This study focuses on the analysis of common pollution characteristics in chemical industrial clusters, examining the emission behaviors of VOCs from nearly 200 emission outlets across 14 industries. A total of 593 VOCs were identified, including 488 new species. The highest concentration of newly discovered VOCs is 240 × 10 μg/m, accounting for 91 %. The identical emission behavior of different components and isomers of industrial sources in several industries is revealed. The dominant species were redefined based on three dimensions. Using machine learning (ML), the maximum incremental reactivity (MIR) values of 488 VOCs were simulated, and based on the common characteristics of VOCs and photochemistry, VOC factor groups were identified that represent 75 %-80 % of the emission sources in the chemical industrial cluster. The average percentage of oxygenated volatile organic compounds (OVOCs) in this study was 28 % higher than in other studies. This study follows the trend of synergistic emission reduction, reduces the blindness of large-scale establishment and updating of source profiles, and provides an efficient control method of VOCs.

Authors

  • Shuwei Zhang
    State Key Laboratory of Fine Chemicals, Dalian University of Technology, Dalian, Liaoning 116024, China. Electronic address: zswei@dlut.edu.cn.
  • Song Gao
    Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Bo Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Zhukai Ning
    School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China.
  • Lingning Meng
    School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China.
  • Ming Hu
    Department of Civil and Environmental Engineering and Earth Sciences, College of Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA.
  • Xiang Che
    Shanghai Environmental Monitoring Center, Shanghai 200235, China.
  • Zheng Jiao
    School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China. Electronic address: zjiao@shu.edu.cn.

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