Luo Yuzhou, Yang Xiusheng
Department of Natural Resources Management and Engineering, University of Connecticut, Storrs, CT 06269, USA.
Chemosphere. 2007 Jan;66(8):1396-407. doi: 10.1016/j.chemosphere.2006.09.026. Epub 2006 Nov 13.
This paper presented a framework for analysis of chemical concentration in the environment and evaluation of variance propagation within the model. This framework was illustrated through a case study of selected organic compounds of benzo[alpha]pyrene (BAP) and hexachlorobenzene (HCB) in the Great Lakes region. A multimedia environmental fate model was applied to perform stochastic simulations of chemical concentrations in various media. Both uncertainty in chemical properties and variability in hydrometeorological parameters were included in the Monte Carlo simulation, resulting in a distribution of concentrations in each medium. Parameters of compartmental dimensions, densities, emissions, and background concentrations were assumed to be constant in this study. The predicted concentrations in air, surface water and sediment were compared to reported data for validation purpose. Based on rank correlations, a sensitivity analysis was conducted to determine the influence of individual input parameters on the output variance for concentration in each environmental medium and for the basin-wide total mass inventory. Results of model validation indicated that the model predictions were in reasonable agreement with spatial distribution patterns, among the five lake basins, of reported data in the literature. For the chemical and environmental parameters given in this study, parameters associated to air-ground partitioning (such as moisture in surface soil, vapor pressure, and deposition velocity) and chemical distribution in soil solid (such as organic carbon partition coefficient and organic carbon content in root-zone soil) were targeted to reduce the uncertainty in basin-wide mass inventory. This results of sensitivity analysis in this study also indicated that the model sensitivity to an input parameter might be affected by the magnitudes of input parameters defined by the parameter settings in the simulation scenario. Therefore, uncertainty and sensitivity analyses for environmental fate models was suggested to be conducted after the model output was validated based on an appropriate input parameter settings.
本文提出了一个用于分析环境中化学物质浓度以及评估模型内方差传播的框架。通过对大湖地区选定的有机化合物苯并[a]芘（BAP）和六氯苯（HCB）的案例研究对该框架进行了说明。应用了一个多介质环境归趋模型来对各种介质中的化学物质浓度进行随机模拟。蒙特卡罗模拟中既包括了化学性质的不确定性，也包括了水文气象参数的变异性，从而得出了每种介质中浓度的分布。在本研究中，假设隔室尺寸、密度、排放和背景浓度等参数是恒定的。将空气、地表水和沉积物中的预测浓度与报告数据进行比较以进行验证。基于秩相关性，进行了敏感性分析，以确定各个输入参数对每种环境介质中浓度以及全流域总质量存量的输出方差的影响。模型验证结果表明，模型预测与文献中报告数据在五个湖盆中的空间分布模式具有合理的一致性。对于本研究中给出的化学和环境参数，针对与气-地分配相关的参数（如表层土壤湿度、蒸气压和沉积速度）以及土壤固体中的化学分布（如有机碳分配系数和根区土壤中的有机碳含量），以降低全流域质量存量的不确定性。本研究的敏感性分析结果还表明，模型对输入参数的敏感性可能会受到模拟场景中参数设置所定义的输入参数大小的影响。因此，建议在基于适当的输入参数设置对模型输出进行验证之后，再对环境归趋模型进行不确定性和敏感性分析。
