健康風險評估中不確定性之量化與降低

Abstract

近年評估一個污染場址對於人體健康的威脅，是否嚴重到需要進一步整治時，已普遍地應用風險評估方法，作為決策輔助工具。風險評估因為考慮實地性、多介質與機率性等概念，整個評估方法因此更趨成熟，但是也因為複雜度的提高，同時必須面對許多不確定性的問題，所以近年風險評估的文獻，都聚焦於不確定性的研究。 在風險評估各類的不確定性中，參數不確定性因為其容易分析與量化的特性，所以有許多文獻進行研究探討，也能有效地加以量化與降低，但是關於其他來源的不確定性，包括模式與情境不確定性，研究則相當缺乏，僅有一些研究提出這些不確定性的初步量化方法，證明對風險結果有很大的影響，但是卻缺乏有效降低這些不確定性的相關研究。有鑑於不同模式之間的結果必然存在差異，導致模式選擇這個步驟會產生大量的不確定性，因此，如何刪除不適模式或是選擇最適模式，以降低模式差異所造成的不確定性就成為本研究的重點。 本研究第一部分，在於建立一個可以比較各模式之情境適合度的篩選流程，此流程以數個候選的多介質模式，分別結合蒙地卡羅方法進行不確定性分析，得到各多介質模式具機率性的暴露途徑與風險值之後，比較各模式與情境之間的適合度，來刪除不適合模式，此流程以MEPAS、MMSOILS與CalTOX三個多介質模式，應用於一個地下水污染場址來做介紹。結果顯示，經由此流程可以刪去不適合情境假設的模式，降低因為模式差異所造成的不確定性。 但是即使透過以上篩選流程選出適合情境的模式後，一個決策者仍然難以客觀地在數個模式之間選出有助於決策的模式，因此，為了能進行一個有效且客觀的整治決策，第二部份的研究，納入風險因子之外的成本考量，建立一個以降低參數不確定性至不干擾決策之最小花費為基準，來選擇出最適合整治決策之多介質模式的方法。首先，此方法結合二維蒙地卡羅、排序相關係數法，與風險之決策準則，找出各模式中參數分布會干擾決策的關鍵不確定性參數；接著透過分布收縮率，計算每個模式中為了降低不確定性所需的花費，其中分布收縮率為每個關鍵參數之分布所需要的縮小程度，最後，以分布收縮率最小與成本花費最少為依據，選出最適合決策進行的客觀模式。此部份研究同樣沿用以上地下水污染場址案例，來展現此方法。結果顯示，此方法可以在考慮不確定性的影響之下，客觀地選擇出有助於決策的最適模式。 以上兩部分的研究，皆證明客觀的模式選擇方法，可以有效降低模式不確定性，但是不同的模式選擇方法各有其選擇基礎，其差異可視為情境不確定性的來源，因此，最後一部分的研究，建立總不確定性量化方法，不僅可以量化風險評估中各類的不確定性，尤其是不同模式選擇方法所造成的情境不確定性，更可以清楚呈現模式選擇前後，對總不確定性降低的影響。

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.