A dynamic probabilistic material flow modeling method. (February 2016)

• Record Type: Journal Article
• Authors: Bornhöft, Nikolaus A.; Sun, Tian Yin; Hilty, Lorenz M.; Nowack, Bernd
• Is Part Of: Environmental modelling & software. Volume 76(2016:Feb.)
• Page Start: 69
• Page End: 80
• Publication Date: 2016-02
• Subjects: Material flow modeling -- Simulation -- Bayesian modeling -- Uncertainty handling -- Prediction model -- Environmental exposure assessment; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.11.012
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Material flow modeling constitutes an important approach to predicting and understanding the flows of materials through the anthroposphere into the environment. The new "Dynamic Probabilistic Material Flow Analysis (DPMFA)" method, combining dynamic material flow modeling with probabilistic modeling, is presented in this paper. Material transfers that lead to particular environmental stocks are represented as systems of mass-balanced flows. The time-dynamic behavior of the system is calculated by adding up the flows over several consecutive periods, considering changes in the inflow to the system and intermediate delays in local stocks. Incomplete parameter knowledge is represented and propagated using Bayesian modeling. The method is implemented as a simulation framework in Python to support experts from different domains in the development of their application models. After the introduction of the method and its implementation, a case study is presented in which the framework is applied to predict the environmental concentrations of carbon nanotubes in Switzerland. Highlights: Presentation of a probabilistic modeling method for representing dynamic mass flows. Implementation as simulation framework. Case study: Exposure modeling of carbon nanotubes for Switzerland.