Geospatial Methods for Reducing Uncertainties in Environmental Health Risk Assessment: Challenges and Opportunities
Geospatial Methods for Reducing Uncertainties in Environmental Health Risk Assessment: Challenges and Opportunities
复制标题
减少环境健康风险评估不确定性的地理空间方法：挑战和机遇
DOI：
10.1080/00045608.2012.674900
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
N. Lam
中科院分区：
文献类型：
--
作者：
N. Lam
与许多涉及不同数据和方法的研究一样，环境健康风险评估研究很容易出现错误和不确定性。不幸的是，与研究结果相关的不确定性很少能够消除，政策决策往往需要根据不确定的结果做出。因此，有助于减少与研究结果相关的不确定性的方法将是最有用的，并且需要开发。本文通过案例研究强调了不确定性的四个主要来源，包括数据、分析方法、研究结果的解释以及对研究结果的反应的不确定性。确定了五组地理空间方法，可以帮助减少四种来源组合所带来的不确定性，包括可视化和测量、聚类检测、暴露建模、尺度分析和决策支持方法。文章的结论是，未来的努力应该更多地关注决策工具的开发，以便能够根据不确定的发现做出决策。
As in many studies involving disparate data and methodologies, environmental health risk assessment research is prone to errors and uncertainties. Unfortunately, uncertainties associated with the research findings can seldom be eliminated, and policy decisions often need to be made based on the uncertain findings. Hence methods that can help reduce uncertainties associated with the research findings would be most useful and need to be developed. This article highlights, through a case study, four major sources of uncertainties, which include uncertainties from data, methods of analysis, interpretations of the findings, and reactions to the findings. Five groups of geospatial methods that can help reduce the uncertainties as a result of combinations of the four sources are identified, including methods for visualization and measurement, cluster detection, exposure modeling, scale analysis, and decision support. The article concludes that future effort should focus more on the development of decision tools to enable decision making based on uncertain findings.