Quantifying the pollution changes and meteorological dependence of airborne trace elements coupling source apportionment and machine learning.

Journal: The Science of the total environment
Published Date: Jul 2, 2024

Abstract

Airborne trace elements (TEs) present in atmospheric fine particulate matter (PM) exert notable threats to human health and ecosystems. To explore the impact of meteorological conditions on shaping the pollution characteristics of TEs and the associated health risks, we quantified the variations in pollution characteristics and health risks of TEs due to meteorological impacts using weather normalization and health risk assessment models, and analyzed the source-specific contributions and potential sources of primary TEs affecting health risks using source apportionment approaches at four sites in Shandong Province from September to December 2021. Our results indicated that TEs experience dual effects from meteorological conditions, with a tendency towards higher TE concentrations and related health risks during polluted period, while the opposite occurred during clean period. The total non-carcinogenic and carcinogenic risks of TEs during polluted period increased approximately by factors of 0.53-1.74 and 0.44-1.92, respectively. Selenium (Se), manganese (Mn), and lead (Pb) were found to be the most meteorologically influenced TEs, while chromium (Cr) and manganese (Mn) were identified as the dominant TEs posing health risks. Enhanced emissions of multiple sources for Cr and Mn were found during polluted period. Depending on specific wind speeds, industrialized and urbanized centers, as well as nearby road dusts, could be key sources for TEs. This study suggested that attentions should be paid to not only the TEs from primary emissions but also the meteorology impact on TEs especially during pollution episodes to reduce health risks in the future.

Authors

  • Haolin Wang
    College of Medical Informatics, Chongqing Medical University, Chongqing 400016, People's Republic of China.
  • Xu Guan
    College of Information Science and Engineering, Ocean University of China, Qingdao, China. guanxu@stu.ouc.edu.cn.
  • Jiao Li
    CAS Key Laboratory of Tropical Marine Bio-resources and Ecology, South China Sea Institute of Oceanology, Chinese Academy of Sciences Guangzhou 510301 China yinhao@scsio.ac.cn.
  • 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.
  • Guoqiang Wang
    School of Management, Hefei, Anhui, China.
  • Qingzhu Zhang
    The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China.
  • Tianshuai Li
    Academician Workstation for Big Data Research in Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, China.
  • Xinfeng Wang
    Institute for Global Public Policy, Fudan University, Shanghai 200433, China.
  • Qingpeng Meng
    Academician Workstation for Big Data Research in Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, China.
  • Jiaqi Chen
    Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
  • Min Zhao
    Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • 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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