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Air quality modeling in Warsaw Metropolitan Area

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Decision support of air quality management needs to connect several categories of the input data with the analytical process of air pollution dispersion. The aim of the respective model of air pollution is to provide a quantitative assessment of environmental impact of emission sources in a form of spatial/temporal maps of pollutants’ concentration or deposition in the domain. These results are in turn used in assessment of environmental risk and supporting respective planning actions. However, due to the complexity of the forecasting system and the required input data, such environmental prognosis and related decisions contain many potential sources of imprecision and uncertainty. The main sources of uncertainty are commonly considered meteorological and emission input data. This paper addresses the problem of emission uncertainty, and impact of this uncertainty on the forecasted air pollution concentrations and adverse health effects. The computational experiment implemented for Warsaw Metropolitan Area, Poland, encompasses one-year forecast with the year 2005 meteorological dataset. The annual mean concentrations of the main urban pollutants are computed. The impact of uncertainty in emission field inventory is also considered. Uncertainty assessment is based on the Monte Carlo technique where the regional scale CALPUFF model is the main forecasting tool used in air quality analysis.
Rocznik
Strony
56--69
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
autor
  • Systems Research Institute of the Polish Academy of Sciences, Warsaw
autor
  • Systems Research Institute of the Polish Academy of Sciences, Warsaw
Bibliografia
  • [1] ApSimon H.M., Warren R.F., Kayin S. Addressing uncertainty in environmental modeling: a case study of integrated assessment of strategies to combat long-range transboundary air pollution. Atmospheric Environment, 36, 2002, 5417–5426.
  • [2] Calori G., Clemente M., De Maria R., Finardi S., Lollobrigida F., Tinarelli G. Air quality integrated modelling in Turin urban area. Environmental Modelling & Software, 21, 2006, 468–476.
  • [3] Carnevale C., Finzi G., Pisoni E., Volta M., Guariso G., Gianfreda R., Maffeis G., Thunis P., White L., Triacchini G. An integrated assessment tool to define effective air quality policies at regional scale. Environmental Modelling & Software, 38, 2012, 306–315.
  • [4] Cofała J., Amann M., Asman W., Bertok I., Heyes C., Hoeglund I.L., Klimont Z., Schoepp W., Wagner F. Integrated assessment of air pollution and greenhouse gasses mitigation in Europe. Archives of Environmental Protection, 36, 2010, 29–39.
  • [5] Fisher B. Fuzzy environmental decision-making: applications to air pollution. Atmospheric Environment, 37, 2003, 1865–1877.
  • [6] Hanna S.R., Chang J.C., Fernau M.E. Monte Carlo estimates of uncertainties in predictions by photochemical grid model (UAM-IV) due to uncertainties in input variables. Atmospheric Environment, 32, 1998, 3619–3628.
  • [7] Hanna S.R., Lu Z., Frey H.C., Wheeler N., Vukovich J., Arunachalam S., Fernau M., Hansen D.A. Uncertainties in predicted ozone concentrations due to input uncertainties for the UAM-V photochemical grid model applied to the July 1995 OTAG domain. Atmospheric Environment, 35, 2001, 891–903.
  • [8] Holnicki P. Air Pollution Transport Models in Air Quality Control (in Polish). EXIT Publishers, 2006, Warsaw, Poland.
  • [9] Holnicki P., Nahorski Z., Tainio M. Uncertainty in air quality forecasts caused by emission uncertainty. Proceedings of HARMO 13th Conference on Harmonisation within Atmospheric Dispersion Modelling, 2010, 119–123.
  • [10] Holnicki P., Nahorski Z. Uncertainty in Integrated Systems of Air Quality Assessment (in Polish). Report RB/6/2011, 2011, Systems Research Institute PAS, Warsaw.
  • [11] Holnicki P. Uncertainty in Integrated Modeling of Air Quality. In: Advenced Air Pollution. INTECH Publishers, 2011, Rijeka, 239–260.
  • [12] Jacobson M.Z. Fundamentals of Atmospheric Modeling. Cambridge University Press, 2005, Cambridge, U.K.
  • [13] Juda-Rezler K. New Challenges in Air Quality and Climate Modeling. Archives of Environmental Protection, 36, 2010, 3–28.
  • [14] Kelly J.A. An Overview of the RAINS Model. Environmental Research Centre Report, Environmental Protection Agency, 2006, Dublin, Ireland.
  • [15] Markiewicz T.M. Podstawy modelowania rozprzestrzeniania się zanieczyszczeń w powietrzu atmosferycznym. Oficyna Wyd. Politechniki Warszawskiej, 2004, Warszawa.
  • [16] Mediavilla-Sahagún A., ApSimon H.M. Urban scale integrated assessment for London: Which emission reduction strategies are more effective in attaining prescribed PM10 air quality standards by 2005? Environmental Modelling & Software, 21, 2006, 501–513.
  • [17] Moore G.E., Londergan R.J. Sampled Monte Carlo uncertainty analysis for photochemical grid models. Atmospheric Environment, 35, 2001, 4863–4876.
  • [18] Oxley T., Valiantis M., Elskkaki A., ApSimon H.M. Background, Road and Urban Transport modeling of Air quality Limit Values (The BRUTAL model). Environmental Modelling & Software, 24, 2009, 1036–1050.
  • [19] Page T., Whyatt J.D., Beven K.J., Metcalfe S.E. Uncertainty in modeled estimates of acid deposition across Wales: a GLUE approach. Atmospheric Environment, 38, 2003, 2079–2090.
  • [20] Park S.-K., Cobb C.E., Wade K., Mulholland J., Hu Y., Russel A.G.) Uncertainty in air quality model evaluation for particulate matter due to spatial variations in pollutant concentrations. Atmospheric Environment, 40, 2006, S563–S573.
  • [21] Russel A., Dennis D. NASTRO critical review of photochemical models and modeling. Atmospheric Environment, 34, 2000, 2283–2324.
  • [22] Sax T., Isakov V. A case study for assessing uncertainty in local-scale regulatory air quality modeling applications. Atmospheric Environment, 37, 2003, 3481–3489.
  • [23] Scire J.S., Strimaitis D.G., Yamartino R.J. A User’s Guide for the CALPUFF Dispersion Model. Earth Technology Inc., 2000.
  • [24] Sportisse B. A review of current issues in air pollution modeling and simulation. Computational Geosciences, 11, 2007, 159–181.
  • [25] www.windandpower.com
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-612d25a8-813e-4d20-af0d-a17611c08d6c
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