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通过蒙特卡洛模拟改善多环芳烃吸入暴露致癌风险评估的关键因素

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

作者信息

Qin Ning, Tuerxunbieke Ayibota, Wang Qin, Chen Xing, Hou Rong, Xu Xiangyu, Liu Yunwei, Xu Dongqun, Tao Shu, Duan Xiaoli

机构信息

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

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

出版信息

Int J Environ Res Public Health. 2021 Oct 22;18(21):11106. doi: 10.3390/ijerph182111106.

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.

摘要

蒙特卡罗模拟(MCS)是一种广泛应用于暴露和风险评估的计算技术。然而,基于MCS方法的传统健康风险评估结果一直受到质疑,原因在于参数估计中引入的不确定性以及结果验证的困难。在此,利用来自个体多环芳烃(PAH)暴露大规模调查的数据,探索改进MCS方法的关键因素。在一个典型的PAH污染城市中,采用统计抽样方法选取研究参与者。通过气相色谱 - 质谱联用仪(GC - MS)检测该地区25个采样点的大气PAH浓度,并通过现场测量收集参与者的暴露参数。根据测量数据计算参与者的终生癌症风险增量(ILCR),并将其视为人群的实际致癌风险。基于MCS的传统评估方法以及包括浓度调整、年龄分层和相关参数调整的蒙特卡罗方法在内的三种改进模型对预测风险进行评估。通过与实际风险进行比较,定量评估模型的拟合优度。结果表明,传统和年龄分层的蒙特卡罗模拟得出的平均风险比实际值高2.6倍,标准差比实际值高3.7倍。相比之下,浓度调整和相关参数调整模型的预测风险与实际ILCR吻合良好。比较结果表明,准确模拟暴露浓度和调整相关参数可以大大改进MCS。该研究还表明,与暴露相关的社会因素以及变量之间的潜在关系是影响风险评估的重要问题,在评估中需要充分考虑,并在未来研究中进一步探讨。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e954/8583189/4a9c786892f9/ijerph-18-11106-g001.jpg

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