The determination of the environmental concentration of a pollutant is a crucial step in the risk assessment of anthropogenic substances. Dynamic probabilistic material flow analysis (DPMFA) is a method to predict flows of substances to the environment that can be converted into environmental concentrations. In cases where direct quantitative measurements of concentrations are impossible, environmental stocks are predicted by reproducing the flow processes creating these stocks in a mathematical model. Incomplete parameter knowledge is represented in the form of stochastic distributions and propagated through the model using Monte Carlo simulation. This work discusses suitable means for the model design and the representation of system knowledge from several information sources of varying credibility as model parameter distributions, further evaluation of the simulation outcomes using sensitivity analyses, and the impacts of parameter uncertainty on the total uncertainty of the simulation output. Based on a model developed in a case study of carbon nanotubes in Switzerland, the modeling process, the representation and interpretation of the simulation results are described and approaches to sensitivity and uncertainty analyses are demonstrated. Finally, the overall approach is summarized and provided in the form of a set of modelling and evaluation rules for DPMFA studies.

确定污染物的环境浓度是人为物质风险评估的关键步骤。动态概率物质流分析 (DPMFA) 是一种预测物质流向环境的方法，该流可以转换为环境浓度。在不可能直接定量测量浓度的情况下，通过在数学模型中重现创建这些库存的流动过程来预测环境库存。不完整的参数知识以随机分布的形式表示，并使用蒙特卡罗模拟通过模型传播。这项工作讨论了模型设计和系统知识表示的合适方法，这些信息来自几个不同可信度的信息源，如模型参数分布，使用敏感性分析进一步评估模拟结果，以及参数不确定性对模拟输出总不确定性的影响。基于在瑞士碳纳米管案例研究中开发的模型，描述了建模过程、模拟结果的表示和解释，并展示了敏感性和不确定性分析的方法。最后，总结并以一套用于 DPMFA 研究的建模和评估规则的形式提供了总体方法。描述了模拟结果的表示和解释，并展示了敏感性和不确定性分析的方法。最后，总结并以一套用于 DPMFA 研究的建模和评估规则的形式提供了总体方法。描述了模拟结果的表示和解释，并展示了敏感性和不确定性分析的方法。最后，总结并以一套用于 DPMFA 研究的建模和评估规则的形式提供了总体方法。