Quantification and Reduction of Uncertainty in Health Risk Assessment
Date Issued
2007
Date
2007
Author(s)
Chen, Yen-Chuan
DOI
zh-TW
Abstract
The decision as to whether a contaminated site poses a threat to human health and should be cleaned up relies increasingly upon the use of risk assessment models. However, the more sophisticated risk assessment models become, through inclusion of such concepts as stochasticity, multimedia transfer, and site-specificity, the greater the concern with the uncertainty in, and thus the credibility of, risk assessment. It has been demonstrated in the literature that model uncertainty may significantly affect the assessment result, but no research has provided the practical methods on how to analyze and decrease them. Therefore, how to eliminate unsuitable model or select right model in order to reduce model uncertainty is an important issue in the research.
Based on the relationship between exposure pathways and estimated risk results, this study develops a screening procedure to compare the relative suitability between potential multimedia models, which would facilitate the reduction of uncertainty due to model selection. MEPAS, MMSOILS, and CalTOX models, combined with Monte Carlo simulation, are applied to a realistic groundwater-contaminated site to demonstrate the process. The results reveal that this procedure can decrease model uncertainty by eliminating unsuitable model.
In particular, when there are several equally plausible models, decision makers are confused by model uncertainty and perplexed as to which model should be chosen for making decisions objectively. When the correctness of different models is not easily judged after objective analysis has been conducted, the cost incurred during the processes of risk assessment has to be considered in order to make an efficient decision. In order to support an efficient and objective remediation decision, this study develops a methodology to cost the least required reduction of uncertainty and to use the cost measure in the selection of candidate models. The focus is on identifying the efforts involved in reducing the input uncertainty to the point at which the uncertainty would not hinder the decision in each equally plausible model. First, this methodology combines a nested Monte Carlo simulation, rank correlation coefficients, and explicit decision criteria to identify key uncertain inputs that would influence the decision in order to reduce input uncertainty. This methodology then calculates the cost of required reduction of input uncertainty in each model by convergence ratio, which measures the needed convergence level of each key input’s spread. Finally, the most appropriate model can be selected based on the convergence ratio and cost. A case of a contaminated site is used to demonstrate the methodology. The outcome shows that this methodology can efficiently and objectively select the best model to support decision with considering the influence from uncertainty.
Although the previous two model comparison methods have both proved that an objective model selection method could effectively reduce model uncertainty, different model selection method based on different consideration and criteria would cause different results that can be seen as the source of scenario uncertainty. Therefore, this study finally develops a framework of total uncertainty to not only quantify scenario uncertainty due to different model selection methods but also explicitly reveal the reduction of total uncertainty resulting from model selection.
Subjects
模式不確定性
不確定性
敏感度分析
蒙地卡羅
多介質模式
Uncertainty
Model uncertainty
Sensitivity analysis
Monte Carlo
Multimedia model
SDGs
Type
thesis