Probabilistic material flow modeling for assessing the environmental exposure to compounds: Methodology and an application to engineered nano-TiO2 particles

  • Authors:

  • Fadri Gottschalk;Roland W. Scholz;Bernd Nowack

  • Affiliations:

  • ETH Zurich, Institute for Environmental Decisions, Natural and Social Science Interface, 8092 Zurich, Switzerland and Empa - Swiss Federal Laboratories for Materials Testing and Research, Technolo ...;ETH Zurich, Institute for Environmental Decisions, Natural and Social Science Interface, 8092 Zurich, Switzerland;Empa - Swiss Federal Laboratories for Materials Testing and Research, Technology and Society Laboratory, CH-9014 St. Gallen, Switzerland

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2010

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Abstract

An elementary step towards a quantitative assessment of the risks of new compounds or pollutants (chemicals, materials) to the environment is to estimate their environmental concentrations. Thus, the calculation of predicted environmental concentrations (PECs) builds the basis of a first exposure assessment. This paper presents a probabilistic method to compute distributions of PECs by means of a stochastic stationary substance/material flow modeling. The evolved model is basically applicable to any substance with a distinct lack of data concerning environmental fate, exposure, emission and transmission characteristics. The model input parameters and variables consider production, application quantities and fate of the compounds in natural and technical environments. To cope with uncertainties concerning the estimation of the model parameters (e.g. transfer and partitioning coefficients, emission factors) as well as uncertainties about the exposure causal mechanisms (e.g. level of compound production and application) themselves, we utilized and combined sensitivity and uncertainty analysis, Monte Carlo simulation and Markov Chain Monte Carlo modeling. The combination of these methods is appropriate to calculate realistic PECs when facing a lack of data. The proposed model is programmed and carried out with the computational tool R and implemented and validated with data for an exemplary case study of flows of the engineered nanoparticle nano-TiO"2 in Switzerland.