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Temporal Collinearity Amongst Modeled and Measured Pollutant Concentrations and Meteorology
Citation:
Garcia, V., P. Porter, E. Gego, AND S. Rao. Temporal Collinearity Amongst Modeled and Measured Pollutant Concentrations and Meteorology. Chapter 9, Air Pollution Modeling and its Application XXII. Springer, Heidelburg, Germany, 2014:53-57, (2013).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL′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:
The results from epidemiology time series models that relate air quality to human health are often used in determining the need for emission controls in the United States. These epidemiology models, however, can be sensitive to collinearity among co-variates, potentially magnifying biases in the parameter estimates caused by exposure misclassification error or other deficiencies in the time series models by orders of magnitude. As a result, we examined collinearity among several covariates typically used in air quality epidemiology time series studies (ozone, fine particulate matter and its species, and temperature). In addition, we examined the ability of a bias-correction technique applied to estimates simulated by the Community Multiscale Air Quality (CMAQ) model to “fill-in” for the spatial and temporal limitations of observations for purposes of reducing exposure misclassification. Specifically, we evaluated whether the bias-adjusted CMAQ estimates could replicate the correlation among variables seen in the observations. The results presented are for a domain east of the Rocky Mountains for the entire 2006 year and indicate that collinearity among covariates varies across space.