Mapping Blood Lead Levels in China during 1980-2040 with Machine Learning.

Journal: Environmental science & technology
PMID: 38625742

Abstract

Lead poisoning is globally concerning, yet limited testing hinders effective interventions in most countries. We aimed to create annual maps of county-specific blood lead levels in China from 1980 to 2040 using a machine learning model. Blood lead data from China were sourced from 1180 surveys published between 1980 and 2022. Additionally, regional statistical figures for 15 natural and socioeconomic variables were obtained or estimated as predictors. A machine learning model, using the random forest algorithm and 2973 generated samples, was created to predict county-specific blood lead levels in China from 1980 to 2040. Geometric mean blood lead levels in children (i.e., age 14 and under) decreased significantly from 104.4 μg/L in 1993 to an anticipated 40.3 μg/L by 2040. The number exceeding 100 μg/L declined dramatically, yet South Central China remains a hotspot. Lead exposure is similar among different groups, but overall adults and adolescents (i.e., age over 14), females, and rural residents exhibit slightly lower exposure compared to that of children, males, and urban residents, respectively. Our predictions indicated that despite the general reduction, one-fourth of Chinese counties rebounded during 2015-2020. This slower decline might be due to emerging lead sources like smelting and coal combustion; however, the primary factor driving the decline should be the reduction of a persistent source, legacy gasoline-derived lead. Our approach innovatively maps lead exposure without comprehensive surveys.

Authors

  • Yanni Zhang
    Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.
  • Mengling Tang
    Department of Public Health, Fourth Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
  • Shuyou Zhang
    The State Key Laboratory of Fluid Power and Mechatronic System, Zhejiang University, Hangzhou, 310027, China.
  • Yaoyao Lin
    Department of Public Health, Fourth Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
  • Kaixuan Yang
    Department of Public Health, Fourth Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
  • Yadi Yang
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Jiangjiang Zhang
    Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210024, China.
  • Jun Man
    Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.
  • Iason Verginelli
    Laboratory of Environmental Engineering, Department of Civil Engineering and Computer Science Engineering, University of Rome "Tor Vergata", 00133 Rome, Italy.
  • Chaofeng Shen
    Department of Environmental Engineering, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; Zhejiang Provincial Key Laboratory for Water Pollution Control and Environmental Safety, Hangzhou 310058, China. Electronic address: ysxzt@zju.edu.cn.
  • Jian Luo
    School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
  • Yongming Luo
    Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.
  • Yijun Yao
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.

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