Models in Environmental Regulatory Decision Making (2007) / Chapter Skim
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1 Study Background
1 Study Background
Pages 18-39
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This report looks at a specific aspect of computational modeling, the use of environmental models in federal regulatory activities, particularly at the U.S. Environmental Protection Agency (EPA)
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. An early example of the level of sophistication possible in computational models is Arrhenius's climate model for assessing the greenhouse effect (Arrhenius 1896)
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TRENDS IN ENVIRONMENTAL REGULATORY MODEL USE The past 25 years has seen a vast increase in the number, variety, and complexity of computational models available for regulatory purposes at EPA. Models have increased in capabilities and sophistication through advances in computer technology, data availability, developer creativity, and increased understanding of environmental processes.
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Due to the nonlinearity of atmospheric chemistry, the selection of the emission-control strategy has traditionally relied on air quality models. One of the first attempts to include the complexity of atmospheric ozone chemistry in the decision-making process was a simple observations-based model, the so-called Appendix J curve (36 Fed.
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22 Models in Environmental Regulatory Decision Making the late 1970s and early 1980s (Atkinson and Lloyd 1984) to simulate the airshed of interest, assuming that it is a well-mixed box.
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The development of three-dimensional grid models capable of simulating the dynamics and spatial variability of ozone formation (commonly termed 3D chemical transport models or CTMs) also began in the 1970s, although computational demands prevented their use in regulatory activities.
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. Regional scale modeling is an integral part of understanding the science behind new ambient air quality standards for ozone and fine particulate matter.
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This committee's task in looking at model use in the regulatory process is 1 and 2, the use of models in understanding environmental impacts and developing and evaluating policy alternatives, that are most prominent. Such analysis of relations and regulatory proposals form the core of regulatory modeling analysis.
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In addition, all regulatory model applications have assumptions and default parameters incorporated into them, some of which may include science policy judgments (NRC 1994; EPA 2004a)
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For examples, models of climate change, regional groundwater contaminant transport, or human health impacts may make estimates for time scales where observations are not available. Other models, such as air and water quality models, may produce average pollutant concentrations for a wide spatial extent (a grid cell within the model)
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. In addition, models and their results can be prominent in the litigation that results from environmental regulatory activities.
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In 2005, the National Research Council (NRC) established the Committee on Models in the Regulatory Decision Process.
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The task statement asks the committee to address the following specific issues: • What scientific and technical factors should be considered in developing model acceptability and application criteria that address the needs of EPA, as well as those of interested and affected parties? • How can the agency provide guidance on procedures for appropriate use, peer review, and evaluation of models that is applicable across the range of interdisciplinary regulatory activities undertaken by EPA?
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To help differentiate environmental regulatory models from other models, the committee defines four basic terms: model, conceptual model, computational model, and environmental regulatory model. Recognizing the wide usage of the term in academia, policy making, and elsewhere, the committee defines a model as a simplification of reality that is constructed to gain insights into se lect attributes of a particular physical, biological, economic, or so cial system.
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Although the question of whether a mouse is an appropriate analog model for estimating human health impacts is not part of this study, issues related to the statistical models that are used to extrapolate from mice to humans are part of this study. The process of building computational, physical, and other models begins with a basic conceptualization of a system.
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A subset of all models are those that use measurable variables, numerical inputs and mathematical relationships to produce quantitative outputs. The committee defines a computational model as a model that is expressed in formal mathematics using equations, statistical relationships, or a combination of the two.
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The committee defines an environmental regulatory model as a computational model used to inform the environmental regulatory process. Some models are independent of a specific regulation, such as water quality or air quality models that are used in an array
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Note that several possible statistical models are fit to each data set. Source: NRC 2001b, from Morales et al.
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The approaches can range from single parameter linear relationship models to mod els with thousands of separate components and many billions of calculations. Environmental regulatory models range from those that come complete with source code, documentation, and cellophane packaging to those that are simply a system of algebraic equations or statistical operations.
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This includes the use of models in the assessment and regulation of toxic substances, the setting of emissions and environmental standards, and the development of mitigation plans. For example, models are used to • Assess exposures to contaminants and effects, as well as the relationships between them.
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These models vary widely in complexity. One of the simplest environmental regulatory modeling applications is the use of one-dimensional groundwater flow equations in the assessment of regulatory actions for leaking underground storage tanks (Weaver 2004)
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. However, computational models, particularly statistical dose-response models, that are used to extrapolate laboratory animal data to humans are included.
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