Significant spatiotemporal changes in atmospheric particulate mercury pollution in China: Insights from meta-analysis and machine-learning.

Journal: The Science of the total environment
PMID: 39454773

Abstract

PM bound mercury (PBM) in the atmosphere is a major component of total mercury, which is a pollutant of global concern and a potent neurotoxicant when converted to methylmercury. Despite its importance, comprehensive macroanalyses of PBM on large scales are still lacking. To explore the driving factors, spatiotemporal pollution distribution, and associated health risks, we compiled a comprehensive dataset consisting of PBM concentrations and spatiotemporal information across China from 2000 to 2023 that was collected from the published scientific literature with valid data. By incorporating corresponding multidimensional predicting variables, the best-fitted random forest model was applied to predict PBM concentrations with a high spatial resolution of 0.25° × 0.25°, and the health risk assessment model was used for subsequent health risk assessment. Our results indicated that population density and PM emissions from power generation were the main contributors to PBM concentrations. In 2020, the pollution was primarily concentrated in northern, central, and eastern China, with the highest annual average concentration of 815.91 pg/m in Shanghai. Beijing experienced the most significant seasonal increase, with PBM concentrations rising by 146.92 % from summer to winter. Nationally, the annual average PBM pollution decreased extensively and markedly from 2015 to 2020. The non-carcinogenic risk of PBM alone was negligible in 2020, with HQ values generally <0.02 in winter. This study may provide an important assessment of the effectiveness of China's measures against mercury pollution and offer valuable insights for future prevention and control of PBM pollution.

Authors

  • Haolin Wang
    College of Medical Informatics, Chongqing Medical University, Chongqing 400016, People's Republic of China.
  • Tianshuai Li
    Academician Workstation for Big Data Research in Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, China.
  • Guoqiang Wang
    School of Management, Hefei, Anhui, China.
  • Yanbo Peng
    Academician Workstation for Big Data Research in Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, China; Key Laboratory of Land and Sea Ecological Governance and Systematic Regulation, Shandong Academy for Environmental Planning, Jinan 250101, China. Electronic address: pengyanbo@mail.sdu.edu.cn.
  • Qingzhu Zhang
    The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China.
  • Xinfeng Wang
    Institute for Global Public Policy, Fudan University, Shanghai 200433, China.
  • Yuchao Ren
    Academician Workstation for Big Data Research in Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, China.
  • Ruobing Liu
    Faculty of Technology, Policy and Management, Delft University of Technology, Delft, South Holland, Netherlands.
  • Shuwan Yan
    Academician Workstation for Big Data Research in Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, China.
  • Qingpeng Meng
    Academician Workstation for Big Data Research in Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, China.
  • Yujia Wang
    School of EECS, Penn State, University Park, PA, 16802, U.S.A. upcheers@gmail.com.
  • Qiao Wang
    Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang R & D Centre for Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang University, Hangzhou 310058, China; Department of Food Science and Nutrition, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, Zhejiang, China.

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