| 大气可吸入颗粒物中多环芳烃的健康风险预测模型 |
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| 引用本文： | 魏全伟,刘钰,田在锋,李洪波,赵琪,王靖飞,王路光. 大气可吸入颗粒物中多环芳烃的健康风险预测模型[J]. 武汉大学学报(理学版), 2010, 56(1) |
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| 作者姓名： | 魏全伟 刘钰 田在锋 李洪波 赵琪 王靖飞 王路光 |
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| 作者单位： | 河北省环境科学研究院/河北省水环境科学实验室,河北,石家庄,050051 |
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| 基金项目： | 国家自然科学基金(40332015); 河北省自然科学基金(D2007001026)资助项目; 河北省科学技术研究与发展计划项目(06276715,06649125D,06276715) |
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| 摘 要： | 通过对河北省40个采样点的大气可吸入颗粒物(PM10)中16种多环芳烃(PAHs)污染物的环境浓度以及其所致的苯并(a)芘等效毒性(BEQ)进行分析,发现大气可吸入颗粒物中PAHs污染物随季节变换而发生变化,且在低温冬季时,各PAHs环境浓度和所致BEQ均高于其他季节;日常仅监测苯并(a)芘(BaP),苯并(k)荧蒽(BkF)和稠二萘(CHR)的环境浓度,即可对大气可吸入颗粒物中PAHs污染物所致BEQ带来的健康风险进行评价,缩减了日常监测的工作量和运算过程,提高了效率.
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| 关 键 词： | 可吸入颗粒物 多环芳烃 健康风险 因子分析 预测模型 |
Predictive Modeling on the Risk Assessment of Polycyclic Aromatic Hydrocarbons (PAHs) in PM_(10) |
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| WEI Quanwei,LIU Yu,TIAN Zaifeng,LI Hongbo,ZHAO Qi,WANG Jingfei,WANG Luguang. Predictive Modeling on the Risk Assessment of Polycyclic Aromatic Hydrocarbons (PAHs) in PM_(10)[J]. JOurnal of Wuhan University:Natural Science Edition, 2010, 56(1) |
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| Authors: | WEI Quanwei LIU Yu TIAN Zaifeng LI Hongbo ZHAO Qi WANG Jingfei WANG Luguang |
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| Affiliation: | WEI Quanwei,LIU Yu,TIAN Zaifeng,LI Hongbo,ZHAO Qi,WANG Jingfei,WANG Luguang(Hebei Provincial Academy of Environmental Sciences/Hebei Provincial Key Laboratory of Aquatic Environment,Shijiazhuang 050051,Hebei,China) |
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| Abstract: | 16 polycyclic aromatic hydrocarbons (PAHs) in PM_(10) (the particulate matter(≤10 μm)) gathering from 40 sample points in Hebei province were determined by GC/MS. Through the analysis of the concentration and benzo(a) pyrene equivalent toxicity (BEQ) of the PAHs pollutions, we concluded that the distribution characters of PAHs in four seasons were different from each other, and the concentra-tions of PAHs and BEQ in winter all were quite more than those of the others probably because of the rela-tively low temperature and the heating system. In addition, we create a predictive modeling by factor mathematic method using three factors such as benzo(a) pyrene (BaP), benzo(k) fluorathene (BkF) and chrysene (CHR). This modeling could greatly reduce the workload of routine monitor and the arithmetic process, leading to the efficient prediction. |
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| Keywords: | PM_(10) (the particulate matter(≤10 μm)) polycyclic aromatic hydrocarbons risk assess-ment factor analysis predictive modeling |
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