Source apportionment of particulate matter (PM2.5) in an urban area using dispersion,receptor and inverse modelling |
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| Authors: | Herdis Laupsa Bruce Denby Steinar Larssen Jan Schaug |
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| Institution: | 1. Center for Health and Global Environment, Department of Environmental Health, Harvard School of Public Health, Boston, MA 02215, United States;2. Department of Environmental Health, Harvard School of Public Health, Boston, MA 02215, United States;3. Department of Civil and Environmental Engineering, University of Tennessee at Knoxville, Knoxville, TN 37996-2010, United States;4. Department of Environmental Health, Boston University School of Public Health, Boston, MA 02115, United States;1. Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India;2. Physics and Dynamics of Tropical Cloud Group, Indian Institute of Tropical Meteorology, Pune, India;1. SKL-ESPC and BIC-ESAT, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China;2. State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China |
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| Abstract: | Air pollution emission inventories are the basis for air quality assessment and management strategies. The quality of the inventories is of great importance since these data are essential for air pollution impact assessments using dispersion models. In this study, the quality of the emission inventory for fine particulates (PM2.5) is assessed: first, using the calculated source contributions from a receptor model; second, using source apportionment from a dispersion model; and third, by applying a simple inverse modelling technique which utilises multiple linear regression of the dispersion model source contributions together with the observed PM2.5 concentrations. For the receptor modelling the chemical composition of PM2.5 filter samples from a measurement campaign performed between January 2004 and April 2005 are analysed. Positive matrix factorisation is applied as the receptor model to detect and quantify the various source contributions. For the same observational period and site, dispersion model calculations using the Air Quality Management system, AirQUIS, are performed. The results identify significant differences between the dispersion and receptor model source apportionment, particularly for wood burning and traffic induced suspension. For wood burning the receptor model calculations are lower, by a factor of 0.54, but for the traffic induced suspension they are higher, by a factor of 7.1. Inverse modelling, based on regression of the dispersion model source contributions and the PM2.5 concentrations, indicates similar discrepancies in the emissions inventory. In order to assess if the differences found at the one site are generally applicable throughout Oslo, the individual source category emissions are rescaled according to the receptor modelling results. These adjusted PM2.5 concentrations are compared with measurements at four independent stations to evaluate the updated inventory. Statistical analysis shows improvement in the estimated concentrations for PM2.5 at all sites. Similarly, inverse modelling is applied at these independent sites and this confirms the validity of the receptor model results. |
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