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Air Pollution Data for Model Evaluation and Application
Citation:
RUSSELL, A. G., J. BOYLAN, AND S. NAPELENOK. Air Pollution Data for Model Evaluation and Application. EM: AIR AND WASTE MANAGEMENT ASSOCIATION'S MAGAZINE FOR ENVIRONMENTAL MANAGERS. Air & Waste Management Association, Pittsburgh, PA, May 2012:30-035, (2012).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
One objective of designing an air pollution monitoring network is to obtain data for evaluating air quality models that are used in the air quality management process and scientific discovery.1.2 A common use is to relate emissions to air quality, including assessing the effectiveness of emissions controls, estimating the impacts of specific sources, and providing information to assess pollutant exposures and potential effects. Scientific applications center on developing a better understanding of how chemical species evolve and interact with exposed ecosystems. Given their role, it is important to evaluate air quality models at a level commensurate with their use, which requires considering what data are desirable for each specific evaluation exercise. The atmosphere is a very complex system and models cannot fully capture all relevant physical and chemical processes; hence, all models are inherently incomplete 3or, as it is often said, “all models are wrong, some are useful.” A model evaluation that compares model results to measurements is one mechanism to increase confidence in their usefulness.4,5 Two issues must be addressed in considering the data requirements for a specific model evaluation: (1) what specific model is being evaluated, and (2) what is the purpose of the evaluation. A model that has already undergone extensive evaluation of model performance and peer review, and has been open to public scrutiny, requires less evaluation than a new, less tested model. However, using a well-tested model for a new purpose requires more extensive evaluation to develop the desired level of confidence.
URLs/Downloads:
EM Magazine
Air Pollution Data for Model Evaluation and Application (PDF, NA pp, 1947 KB, about PDF)