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EPA Issues New Guidance on Environmental Models

crem-logo2The Environmental Protection Agency has issued a new set of guidances on developing environmental models. This may affect companies evaluating the toxicity of their products, as well as carbon-emitting installations such as manufacturing plants or utilities.

EPA uses environmental models to simulate the impact of pollutants and to estimate pollution’s effect on public health and the environment. Environmental models also are used to compare the costs and benefits of alternative policies or procedures.

The new guidance (PDF) provides more transparency about EPA’s process, and how it applies environmental models once an environmental issue has been identified. It was prepared by EPA’s Council for Regulatory Environmental Modeling.

Using white papers, the National Research Council’s Models in Environmental Regulatory Decision Making and peer-reviewed literature, the EPA has identified an approach that accounts for model development, evaluation and application.

According to EPA, the model development and evaluation processes “conform to protocols or standards that help ensure the utility, scientific soundness, and defensibility of the models and their outputs for decision making.” This is to ensure a better understanding of the process for companies whose products or projects are subject to EPA review.

The guidance details EPA’s position on best practices, transparency, corroboration, sensitivity analysis and uncertainty analysis.

EPA says that that model developers and users should:

  1. subject their model to credible, objective peer review;
  2. assess the quality of the data they use;
  3. corroborate their model by evaluating the degree to which it corresponds to the system being modeled;
  4. perform sensitivity and uncertainty analyses. According to EPA, sensitivity analysis evaluates the effect of changes in input values or assumptions on a model’s results. Uncertainty analysis investigates the effects of lack of knowledge and other potential sources of error in the model.
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