A dynamic probabilistic material flow modeling method

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. Presentation of a probabilistic modeling method for representing dynamic mass flows.Implementation as simulation framework.Case study: Exposure modeling of carbon nanotubes for Switzerland.

[1]  D. W. Scott,et al.  Multivariate Density Estimation, Theory, Practice and Visualization , 1992 .

[2]  F. Christensen,et al.  Modeling Flows and Concentrations of Nine Engineered Nanomaterials in the Danish Environment , 2015, International journal of environmental research and public health.

[3]  B. Nowack,et al.  Exposure modeling of engineered nanoparticles in the environment. , 2008, Environmental science & technology.

[4]  Roland W. Scholz,et al.  Exposure modeling of engineered nanoparticles , 2009 .

[5]  Fadri Gottschalk,et al.  The release of engineered nanomaterials to the environment. , 2011, Journal of environmental monitoring : JEM.

[6]  T. Louis,et al.  Bayes and Empirical Bayes Methods for Data Analysis. , 1997 .

[7]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[8]  David W. Scott,et al.  Multivariate Density Estimation: Theory, Practice, and Visualization , 1992, Wiley Series in Probability and Statistics.

[9]  Fadri Gottschalk,et al.  Stochastic fate analysis of engineered nanoparticles in incineration plants , 2014 .

[10]  Rolf Widmer,et al.  Modeling metal stocks and flows: a review of dynamic material flow analysis methods. , 2014, Environmental science & technology.

[11]  Konrad Hungerbühler,et al.  The State of Multimedia Mass-Balance Modeling in Environmental Science and Decision-Making , 2010 .

[12]  H. Norppa,et al.  Risk assessment of engineered nanomaterials and nanotechnologies--a review. , 2010, Toxicology.

[13]  Roland W. Scholz,et al.  Probabilistic material flow modeling for assessing the environmental exposure to compounds: Methodology and an application to engineered nano-TiO2 particles , 2010, Environ. Model. Softw..

[14]  Stefan Seeger,et al.  Industrial production quantities and uses of ten engineered nanomaterials in Europe and the world , 2012, Journal of Nanoparticle Research.

[15]  Peter Wick,et al.  A brief summary of carbon nanotubes science and technology: a health and safety perspective. , 2011, ChemSusChem.

[16]  Gaël Varoquaux,et al.  The NumPy Array: A Structure for Efficient Numerical Computation , 2011, Computing in Science & Engineering.

[17]  K. Hungerbühler,et al.  Comprehensive probabilistic modelling of environmental emissions of engineered nanomaterials. , 2014, Environmental pollution.

[18]  Wassily Leontief.  Input-Output Economics , 1966 .

[19]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[20]  Mark R Wiesner,et al.  The use of Bayesian networks for nanoparticle risk forecasting: model formulation and baseline evaluation. , 2012, The Science of the total environment.

[21]  Paola Vicard,et al.  The Use of Bayesian Networks for Imputation , 2004 .

[22]  B. Page,et al.  Simulating discrete event systems with UML and JAVA , 2006 .

[23]  A. Rukhin.  Bayes and Empirical Bayes Methods for Data Analysis , 1997 .

[24]  Lorenz M. Hilty,et al.  Material Flow Modelling for Environmental Exposure Assessment - A Critical Review of Four Approaches Using the Comparative Implementation of an Idealized Example , 2013, EnviroInfo.

[25]  P. Brunner,et al.  Metabolism of the Anthroposphere , 1991 .

[26]  R. Scholz,et al.  Modeled environmental concentrations of engineered nanomaterials (TiO(2), ZnO, Ag, CNT, Fullerenes) for different regions. , 2009, Environmental science & technology.

[27]  Michel Boissière,et al.  潜在的な発光および磁気2モード画像化プローブとしてのポリオール合成Zn0.9Mn0.1ナノ粒子:合成,特性評価,および毒性研究 , 2012 .

[28]  Qiang Shen,et al.  Learning Bayesian networks: approaches and issues , 2011, The Knowledge Engineering Review.