Abstract
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.
Highlights
Assessing environmental flows and concentrations of anthro‐ pogenic pollutants is a crucial step in determining emerging ecological risks of these pollutants
The significant flow processes of carbon nanotubes (CNTs) through the techno‐ sphere into the environment are represented as a Dynamic probabilistic material flow analysis (DPMFA) model [12]
The flows between the com‐ partments are determined by local transfer coefficients (TCs) that define the flow from one compartment to another as a rate of its total outflow
Summary
Assessing environmental flows and concentrations of anthro‐ pogenic pollutants is a crucial step in determining emerging ecological risks of these pollutants. Because for many pol‐ lutants quantitative measurements are not feasible, material flow analysis (MFA) [1] and environmental fate modeling [2] have been developed to provide indirect means for expo‐ sure assessment. Diverging assumptions about the (true) value of a model parameter are weighted based on the modeler’s degrees of belief and combined into a probabilistic parameter distri‐ bution. The results derived from Bayesian models are con‐ cluded based on the assumptions and their weighting. In Bayesian networks [7, 8], which are the most widespread Bayesian models, parameters are represented by discrete sets of values and assigned probabilities
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