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Methods for reducing biases and errors in regional photochemical model outputs for use in emission reduction and exposure assessments
Citation:
Porter, P., S. Rao, C. Hogrefe, E. Gego, AND R. Mathur. Methods for reducing biases and errors in regional photochemical model outputs for use in emission reduction and exposure assessments. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, 112:178-188, (2015).
Impact/Purpose:
The National Exposure Research Laboratory’s Atmospheric Modeling 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:
In the United States, regional-scale photochemical models are being used to design emission control strategies needed to meet the relevant National Ambient Air Quality Standards (NAAQS) within the framework of the attainment demonstration process. Previous studies have shown that the current generation of regional photochemical models can have large biases and errors in simulating absolute levels of pollutant concentrations. To avoid the errors and biases in absolute model predictions, model applications for examining the effectiveness of different emission reduction strategies typically combine the relative model response with observational data. However, recent studies have revealed that regional air quality models were not always accurately reproducing even the relative changes in ozone air quality stemming from changes in emissions because of uncertain emission inventories, initial and boundary conditions, and inadequate representation of physics and chemistry. Even if all other model inputs, physics and chemistry, and numerical methods were perfect, there would still be inherent uncertainty associated with model results because imperfect knowledge of the initial state of the atmosphere impairs prediction of its future state. These issues suggest that the model outputs may need to be constrained by observations to provide robust methods for using regional air quality models for estimating future air quality and performing exposure assessment.
URLs/Downloads:
PORTER ET AL_FINAL FINALACCEPTEDVERSION.PDF (PDF, NA pp, 514.646 KB, about PDF)Atmospheric Environment
