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A nonlinear regression model estimating single source concentrations of primary and secondarily formed PM2.5
Abstract 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 non-linear regression model has been developed to estimate annual average downwind primary and secondarily formed PM2.5 nitrate and sulfate from single emissions sources. The statistical model is based on single emissions sources tracked with particulate source apportionment technology in a photochemical transport model. This non-linear 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 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.
Highlights ► Photochemical model source apportionment used to track single sources of PM2.5. ► A new non-linear regression model relating facility emissions to downwind PM2.5. ► Model relates SO2 to PM2.5 sulfate, source NOx & receptor NH3 to PM2.5 nitrate. ► Generalized model form for plants not included in analysis.
A nonlinear regression model estimating single source concentrations of primary and secondarily formed PM2.5
Abstract 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 non-linear regression model has been developed to estimate annual average downwind primary and secondarily formed PM2.5 nitrate and sulfate from single emissions sources. The statistical model is based on single emissions sources tracked with particulate source apportionment technology in a photochemical transport model. This non-linear 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 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.
Highlights ► Photochemical model source apportionment used to track single sources of PM2.5. ► A new non-linear regression model relating facility emissions to downwind PM2.5. ► Model relates SO2 to PM2.5 sulfate, source NOx & receptor NH3 to PM2.5 nitrate. ► Generalized model form for plants not included in analysis.
A nonlinear regression model estimating single source concentrations of primary and secondarily formed PM2.5
Baker, Kirk R. (author) / Foley, Kristen M. (author)
Atmospheric Environment ; 45 ; 3758-3767
2011-03-21
10 pages
Article (Journal)
Electronic Resource
English
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