A combined model–observation approach to estimate historic gridded fields of PM2.5 mass and species concentrations |
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| Authors: | Christian Hogrefe Barry Lynn Richard Goldberg Cynthia Rosenzweig Eric Zalewsky Winston Hao Prakash Doraiswamy Kevin Civerolo Jia-Yeong Ku Gopal Sistla PL Kinney |
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| Institution: | 1. Atmospheric Sciences Research Center, University at Albany, 251 Fuller Road, Albany, NY 12203, USA;2. Weather It Is, LTD, Efrat, Israel;3. NASA-Goddard Institute for Space Studies, 2880 Broadway # 1, New York, NY 10025, USA;4. New York State Department of Environmental Conservation, 625 Broadway, Albany, NY 12233-3259, USA;5. Mailman School of Public Health, Columbia University, 60 Haven Ave., New York, NY 10032, USA;1. Center for Air Quality, Stocker 159, Chemical and Biomolecular Engineering, Ohio University, Athens, OH 45701, USA;2. Environmental Science Division, Argonne National Laboratory, Argonne, IL, USA |
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| Abstract: | This paper introduces a methodology for estimating gridded fields of total and speciated fine particulate matter (PM2.5) concentrations for time periods and regions not covered by observational data. The methodology is based on performing long-term regional scale meteorological and air quality simulations and then integrating these simulations with available observational data. To illustrate this methodology, we present an application in which year-round simulations with a meteorological model (the National Center for Atmospheric Research/Penn State Mesoscale Model, hereafter referred to as MM5) and a photochemical air quality model (the Community Multiscale Air Quality Model, hereafter referred to as CMAQ) have been performed over the northeastern United States for 1988–2005. Model evaluation results for total PM2.5 mass and individual species for the time period from 2000 to 2005 show that model performance varies by species, season, and location. Therefore, an approach is developed to adjust CMAQ output with factors based on these three variables. The adjusted model values for total PM2.5 mass for 2000–2005 are compared against independent measurements not utilized for the adjustment approach. This comparison reveals that the adjusted model values have a lower root mean square error (RMSE) and higher correlation coefficients than the original model values. Furthermore, the PM2.5 estimates from these adjusted model values are compared against an alternate method for estimating historic PM2.5 values that is based on PM2.5/PM10 ratios calculated at co-located monitors. Results reveal that both methods yield estimates of historic PM2.5 mass that are broadly consistent; however, the adjusted CMAQ values provide greater spatial coverage and information for PM2.5 species in addition to total PM2.5 mass. Finally, strengths and limitations of the proposed approach are discussed in the context of potential uses of this method. |
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