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An Overview of a Multimedia Benchmarking Analysis for Three Risk Assessment Models: RESRAD, MMSOILS, and MEPAS
Author(s) -
Laniak Gerard F.,
Droppo James G.,
Faillace Ernest R.,
Gnanapragasam Emmanuel K.,
Mills William B.,
Strenge Dennis L.,
Whelan Gene,
Yu Charley
Publication year - 1997
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/j.1539-6924.1997.tb00859.x
Subject(s) - benchmarking , computer science , conceptualization , process (computing) , offset (computer science) , risk assessment , agency (philosophy) , mathematical model , operations research , management science , risk analysis (engineering) , statistics , engineering , artificial intelligence , mathematics , medicine , philosophy , computer security , epistemology , marketing , business , programming language , operating system
Multimedia modelers from the United States Environmental Protection Agency (EPA) and the United States Department of Energy (DOE) collaborated to conduct a detailed and quantitative benchmarking analysis of three multimedia models. The three models—RESRAD (DOE), MMSOILS (EPA), and MEPAS (DOE)—represent analytically‐based tools that are used by the respective agencies for performing human exposure and health risk assessments. The study is performed by individuals who participate directly in the ongoing design, development, and application of the models. Model form and function are compared by applying the models to a series of hypothetical problems, first isolating individual modules (e.g., atmospheric, surface water, groundwater) and then simulating multimedia‐based risk resulting from contaminant release from a single source to multiple environmental media. Study results show that the models differ with respect to environmental processes included (i.e., model features) and the mathematical formulation and assumptions related to the implementation of solutions. Depending on the application, numerical estimates resulting from the models may vary over several orders‐of‐magnitude. On the other hand, two or more differences may offset each other such that model predictions are virtually equal. The conclusion from these results is that multimedia models are complex due to the integration of the many components of a risk assessment and this complexity must be fully appreciated during each step of the modeling process (i.e., model selection, problem conceptualization, model application, and interpretation of results).