A dynamic probabilistic material flow modeling method

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.

Prospective environmental risk assessment of nanocellulose for Europe

“…Exposure was characterized by modelling the stocks and flows of NC throughout its life cycle, which includes production, manufacturing, use, and end-of-life (EoL), using a dynamic probabilistic material flow analysis (DPMFA). 30 Our system boundary was the European Union, and the timeperiod considered from 2000 to 2025. We used the model described in Sun et al 31 as a base model, which we configured to fit the system and data under consideration.…”

“…The dynamic aspect of the MFA was incorporated in order to account for the historical inputs and releases of NC, thereby modeling changes of the system over time. 30,32 Furthermore, it allows for a prospective assessment by evaluating how the system is predicted to develop in the future.…”

Incremental Material Flow Analysis with Bayesian Inference

“…One method that has been used in other studies (e.g., Bornhöft et al. ) is to sample N numbers from a uniform distribution between 0 and 1, then normalize to give . Although this seems intuitively reasonable, it turns out to produce transfer coefficients that are biased towards allocating equal fractions to each destination (figure , left), which is probably not what was intended.…”

“…For example, Glöser and colleagues () used uniformly‐distributed parameters to assess uncertainty in recycling indicators for copper, while Gottschalk and colleagues () used uniform, lognormal, and triangular distributions of parameters to find concentrations of nanoparticles in the environment, using a method they call “probabilistic MFA”. Later, Bornhöft and colleagues () extended this method to include dynamic MFA models. These methods see the process of MFA development as iterative.…”

Environmental risk assessment of engineered nano‐SiO2, nano iron oxides, nano‐CeO2, nano‐Al2O3, and quantum dots

“…Gottschalk et al () developed a probabilistic material flow analysis model based on a life‐cycle perspective, which is able to include all applications of a specific ENM and to handle the uncertainty and variability associated with the input parameters (e.g., production volume or product allocation of ENMs). Based on the same concept, a dynamic probabilistic material flow analysis model was developed by Bornhoft et al () and used to estimate the environmental release and concentrations of nano‐TiO 2 , nano‐Ag, nano‐ZnO, and carbon nanotubes in the European Union in different scenarios (Sun et al , ). Another study has predicted environmental concentrations of nano‐SiO 2 , nano iron oxides, nano‐CeO 2 , nano‐Al 2 O 3 , and quantum dots in 7 European regions using the same dynamic probabilistic material flow analysis model (Wang and Nowack ).…”