Uncertainties in human health risk assessment of environmental contaminants: A review and perspective

被引:104
|
作者
Dong, Zhaomin
Liu, Yanju
Duan, Luchun
Bekele, Dawit
Naidu, Ravi [1 ]
机构
[1] Univ Newcastle, Fac Sci & Informat Technol, Callaghan, NSW 2308, Australia
关键词
Uncertainties; Exposure; Hazard; Human health risk assessment; Modelling; PHARMACOKINETIC PBPK MODEL; NEXT-GENERATION; DOSE-RESPONSE; COMPUTATIONAL TOXICOLOGY; EXPOSURE ASSESSMENT; HUMAN VARIABILITY; LEAD-EXPOSURE; TOXICITY; TRICHLOROETHYLENE; METAANALYSIS;
D O I
10.1016/j.envint.2015.09.008
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Addressing uncertainties in human health risk assessment is a critical issue when evaluating the effects of contaminants on public health. A range of uncertainties exist through the source-to-outcome continuum, including exposure assessment, hazard and risk characterisation. While various strategies have been applied to characterising uncertainty, classical approaches largely rely on how to maximise the available resources. Expert judgement, defaults and tools for characterising quantitative uncertainty attempt to fill the gap between data and regulation requirements. The experiences of researching 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) illustrated uncertainty sources and how to maximise available information to determine uncertainties, and thereby provide an 'adequate' protection to contaminant exposure. As regulatory requirements and recurring issues increase, the assessment of complex scenarios involving a large number of chemicals requires more sophisticated tools. Recent advances in exposure and toxicology science provide a large data set for environmental contaminants and public health. In particular, biomonitoring information, in vitro data streams and computational toxicology are the crucial factors in the NexGen risk assessment, as well as uncertainties minimisation. Although in this review we cannot yet predict how the exposure science and modem toxicology will develop in the long-term, current techniques from emerging science can be integrated to improve decision-making. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:120 / 132
页数:13
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