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Key Factors for Improving the Carcinogenic Risk Assessment of PAH Inhalation Exposure by Monte Carlo Simulation

Author

Listed:
  • Ning Qin

    (School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Ayibota Tuerxunbieke

    (School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Qin Wang

    (Chinese Center for Disease Control and Prevention, China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Beijing 100021, China)

  • Xing Chen

    (School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Rong Hou

    (School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Xiangyu Xu

    (School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Yunwei Liu

    (School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Dongqun Xu

    (Chinese Center for Disease Control and Prevention, China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Beijing 100021, China)

  • Shu Tao

    (Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China)

  • Xiaoli Duan

    (School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China)

Abstract

Monte Carlo simulation (MCS) is a computational technique widely used in exposure and risk assessment. However, the result of traditional health risk assessment based on the MCS method has always been questioned due to the uncertainty introduced in parameter estimation and the difficulty in result validation. Herein, data from a large-scale investigation of individual polycyclic aromatic hydrocarbon (PAH) exposure was used to explore the key factors for improving the MCS method. Research participants were selected using a statistical sampling method in a typical PAH polluted city. Atmospheric PAH concentrations from 25 sampling sites in the area were detected by GC-MS and exposure parameters of participants were collected by field measurement. The incremental lifetime cancer risk (ILCR) of participants was calculated based on the measured data and considered to be the actual carcinogenic risk of the population. Predicted risks were evaluated by traditional assessment method based on MCS and three improved models including concentration-adjusted, age-stratified, and correlated-parameter-adjusted Monte Carlo methods. The goodness of fit of the models was evaluated quantitatively by comparing with the actual risk. The results showed that the average risk derived by traditional and age-stratified Monte Carlo simulation was 2.6 times higher, and the standard deviation was 3.7 times higher than the actual values. In contrast, the predicted risks of concentration- and correlated-parameter-adjusted models were in good agreement with the actual ILCR. The results of the comparison suggested that accurate simulation of exposure concentration and adjustment of correlated parameters could greatly improve the MCS. The research also reveals that the social factors related to exposure and potential relationship between variables are important issues affecting risk assessment, which require full consideration in assessment and further study in future research.

Suggested Citation

  • Ning Qin & Ayibota Tuerxunbieke & Qin Wang & Xing Chen & Rong Hou & Xiangyu Xu & Yunwei Liu & Dongqun Xu & Shu Tao & Xiaoli Duan, 2021. "Key Factors for Improving the Carcinogenic Risk Assessment of PAH Inhalation Exposure by Monte Carlo Simulation," IJERPH, MDPI, vol. 18(21), pages 1-14, October.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:21:p:11106-:d:662410
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    References listed on IDEAS

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    2. Kimberly M. Thompson & David E. Burmaster & Edmund A.C. Crouch3, 1992. "Monte Carlo Techniques for Quantitative Uncertainty Analysis in Public Health Risk Assessments," Risk Analysis, John Wiley & Sons, vol. 12(1), pages 53-63, March.
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    4. Yunwei Liu & Ning Qin & Weigang Liang & Xing Chen & Rong Hou & Yijin Kang & Qian Guo & Suzhen Cao & Xiaoli Duan, 2020. "Polycycl. Aromatic Hydrocarbon Exposure of Children in Typical Household Coal Combustion Environments: Seasonal Variations, Sources, and Carcinogenic Risks," IJERPH, MDPI, vol. 17(18), pages 1-14, September.
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