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A Comparison of Statistical Techniques for Combining Modeled and Observed Concentrations to Create High-Resolution Ozone Air Quality Surfaces
Citation:
GARCIA, V., K. FOLEY, D. Holland, S. T. RAO, AND E. Gego. A Comparison of Statistical Techniques for Combining Modeled and Observed Concentrations to Create High-Resolution Ozone Air Quality Surfaces. Journal of Exposure Science and Environmental Epidemiology . Nature Publishing Group, London, Uk, 60(5):586-595, (2010).
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:
Air quality surfaces representing pollutant concentrations across space and time are needed for many applications, including tracking trends and relating air quality to human and ecosystem health. The spatial and temporal characteristics of these surfaces may reveal new information about the associations between emissions, pollution levels, and human exposure and health outcomes that may not have been discernable before. This paper presents four techniques, ranging from simple to complex, to statistically combine observed and modeled daily maximum 8-hr ozone concentrations for a domain covering the greater New York State area for the summer of 2001. Cross-validation results indicate that, for the domain and time period studied, the simpler techniques (additive and multiplicative bias adjustment) perform as well as or better than the more complex techniques. However, the spatial analyses of the resulting ozone concentration surfaces revealed some problems with these simpler techniques in limited areas where the model exhibits difficulty in simulating the complex features such as those observed in the New York City area.
URLs/Downloads:
A Comparison of Statistical Techniques for Combining Modeled and Observed Concentrations to Create High-Resolution Ozone Air Quality Surfaces (PDF, NA pp, 5632 KB, about PDF)Journal of the Air & Waste Management Association
