Meyer Torsten, Wania Frank, Breivik Knut
Department of Chemical Engineering and Applied Chemistry, University of Toronto at Scarborough, 1265 Military Trail, Toronto, Ontario, Canada M1C 1A4.
Environ Sci Technol. 2005 May 1;39(9):3186-96. doi: 10.1021/es048728t.
Variations of model predictions of the environmental fate of organic contaminants are usually analyzed for only one or at most a few selected chemicals, even though parameter sensitivity and contribution to uncertainty are widely different for different chemicals. A graphical method is introduced that allows for the comprehensive investigation of model sensitivity and uncertainty for all persistent organic nonelectrolytes at the same time. This is achieved by defining a two-dimensional hypothetical "chemical space" as a function of the equilibrium partition coefficients between air, water, and octanol (KOW, KAW, KOA), and plotting sensitivity and/or uncertainty of a specific model result to each input parameter as a function of this chemical space. The approach is illustrated for the bulk phase concentrations in air, water, soil, and sediment calculated by a level III model. Colored contour maps facilitate the identification of those input parameters that cause a high output variation of hypothetical and real chemicals. They also allow for the easy categorization of chemicals in terms of common parameter sensitivities, and thus comparable environmental behavior. Sensitivity varies with the mode of emission and the degradability of the chemicals, making it necessary to develop multiple sets of contour maps. Comparison of these sets of maps in turn allows the investigation of how parameter sensitivities change as a result of changes in mode of emission and persistence. The presented method can be used for investigating the sensitivity of any prediction obtained with any linear fate model that characterizes the partitioning behavior of organic chemicals with KAW, KoW, and KOA. Once the sensitivity maps have been constructed for a given environmental scenario, it is possible to perform a sensitivity analysis for a specific chemical by simple. placement of the substances' partitioning combinations within the chemical space. The maps can further contribute to the mechanistic understanding of a model's behavior, can aid in explaining observations of divergent environmental behavior of related substances, and can provide a rationale for grouping chemicals with similar model behavior, or for selecting representative example chemicals for a model investigation. They can also help in deciding when accurate and precise knowledge of physical chemical property data is crucial and when approximate numbers suffice to conduct a model investigation.
尽管不同化学物质的参数敏感性和对不确定性的贡献差异很大，但通常仅针对一种或至多几种选定的化学物质分析有机污染物环境归宿的模型预测变化情况。本文介绍了一种图形方法，可同时对所有持久性有机非电解质的模型敏感性和不确定性进行全面研究。这是通过将二维假设的“化学空间”定义为空气、水和辛醇之间的平衡分配系数（KOW、KAW、KOA）的函数，并将特定模型结果对每个输入参数的敏感性和/或不确定性作为该化学空间的函数进行绘制来实现的。通过三级模型计算的空气、水、土壤和沉积物中的体相浓度对该方法进行了说明。彩色等高线图有助于识别那些导致假设和实际化学物质输出变化较大的输入参数。它们还允许根据共同的参数敏感性轻松对化学物质进行分类，从而得出可比的环境行为。敏感性随化学物质的排放方式和降解性而变化，因此有必要绘制多组等高线图。反过来，比较这些图集可以研究参数敏感性如何因排放方式和持久性的变化而改变。所提出的方法可用于研究任何通过表征有机化学物质与KAW、KoW和KOA分配行为的线性归宿模型获得的预测的敏感性。一旦为给定的环境情景构建了敏感性图，就可以通过简单地将物质的分配组合放置在化学空间内来对特定化学物质进行敏感性分析。这些图可以进一步有助于对模型行为的机理理解，可以帮助解释相关物质不同环境行为的观测结果，并可以为将具有相似模型行为的化学物质分组或为模型研究选择代表性示例化学物质提供依据。它们还可以帮助确定何时对物理化学性质数据的准确和精确了解至关重要，以及何时近似数字足以进行模型研究。
