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A Nonlinear Regression Model Estimating Single Source Concentrations of Primary and Secondarily Formed 2.5
Citation:
Baker, K. R. AND K. FOLEY. A Nonlinear Regression Model Estimating Single Source Concentrations of Primary and Secondarily Formed 2.5. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, 45(22):3785-3767, (2011).
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:
Various approaches and tools exist to estimate local and regional PM2.5 impacts from a single emissions source, ranging from simple screening techniques to Gaussian based dispersion models and complex grid-based Eulerian photochemical transport models. These approaches either lack a realistic chemical and physical representation of the atmosphere for secondary PM2.5 formation or. in the case of photochemical models may be too resource intensive for single source assessments. A simple nonlinear regression model has been developed to estimate annual average downwind primary and secondary PM2.5 nitrate and sulfate from a single emissions source. The statistical model is based on single emissions sources tracked with particulate source apportionment technology in a photochemical transport model. This nonlinear regression model is advantageous in that the underlying data is based on single emissions sources modeled in a realistic chemical and physical environment of a photochemical model and provides downwind PM2.5 impact information with minimal resource burden. Separate regression models are developed for total primary PM2.5, PM2.5 sulfate ion, and PM2.5 nitrate ion. Regression model inputs include facility emissions rates in tons per year and the distance between the source and receptor. An additional regression model input of receptor ammonia emissions is used to account for the variability in regional ammonia availability that is important for PM2.5 nitrate ion estimates.
URLs/Downloads:
Atmospheric Environment
A Nonlinear Regression Model Estimating Single Source Concentrations of Primary and Secondarily Formed 2.5 (PDF, NA pp, 5401 KB, about PDF)