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Air quality modeling for Warsaw agglomeration

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Warianty tytułu
PL
Zanieczyszczenie powietrza w skali miejskiej : analiza jakości powietrza w Warszawie w roku 2012
Języki publikacji
EN
Abstrakty
EN
The paper investigates the air quality in the urban area of Warsaw, Poland. Calculations are carried out using the emissions and meteorological data from the year 2012. The modeling tool is the regional CALMET/CALPUFF system, which is used to link the emission sources with the distributions of the annual mean concentrations. Several types of polluting species that characterize the urban atmospheric environment, like PM10, PM2.5, NOx, SO2, Pb, B(a)P, are included in the analysis. The goal of the analysis is to identify the most polluted districts and polluting compounds there, to check where the concentration limits of particular pollutants are exceeded. Then, emission sources (or emission categories) which are mainly responsible for violation of air quality standards and increase the adverse health effects, are identified. The modeling results show how the major emission sources – the energy sector, industry, traffic and the municipal sector – relate to the concentrations calculated in receptor points, including the contribution of the transboundary inflow. The results allow to identify districts where the concentration limits are exceeded and action plans are needed. A quantitative source apportionment shows the emission sources which are mainly responsible for the violation of air quality standards. It is shown that the road transport and the municipal sector are the emission classes which substantially affect air quality in Warsaw. Also transboundary inflow contributes highly to concentrations of some pollutants. The results presented can be of use in analyzing emission reduction policies for the city, as a part of an integrated modeling system.
PL
W pracy przedstawiono wyniki analizy jakości powietrza w Warszawie. Obliczenia przeprowadzono dla danych emisyjnych i meteorologicznych z roku 2012. Jako narzędzie modelowania wykorzystano regionalny system CALMET/CALPUFF, którego zadaniem było powiązanie danych emisyjnych z rozkładami stężeń średniorocznych. Analiza dotyczy podstawowych zanieczyszczeń atmosferycznych, charakteryzujących aglomeracje miejskie, jak np.: PM10, PM2.5, NOx, SO2, Pb, B(a)P, metale ciężkie. Celem analizy było zidentyfikowanie najbardziej zanieczyszczonych obszarów miasta oraz zanieczyszczeń, których stężenia przekraczają poziomy dopuszczalne. Ponadto, wskazanie źródeł emisji (lub kategorii emisyjnych), które głównie odpowiadają za te przekroczenia, powodując negatywne skutki zdrowotne. Wyniki modelowania pokazują, w jakim stopniu główne źródła emisyjne – związane z sektorami energii, przemysłu, transportu lub komunalno-bytowym – odpowiadają za wartości stężeń w receptorach. Uwzględniono przy tym udział transgranicznego napływu zanieczyszczeń. Wyniki pozwalają wskazać dzielnice, w których zostały przekroczone poziomy dopuszczalne stężeń i konieczne są odpowiednie działania naprawcze. Dokonano ilościowej oceny udziału źródeł emisji głównie odpowiedzialnych za przekroczenia standardów. Ruch uliczny oraz sektor komunalno-bytowy wskazano jako kategorie emisyjne, które mają decydujący wpływ na pogarszanie jakości powietrza w Warszawie. W przypadku niektórych zanieczyszczeń (np. PM) bardzo istotny jest również udział napływu zewnętrznego. Wyniki mogą być przydatne przy wyborze strategii ograniczania emisji oraz jako część zintegrowanego systemu modelowania.
Rocznik
Strony
48--64
Opis fizyczny
Bibliogr. 43 poz., rys., tab.
Twórcy
autor
  • Systems Research Institute, Polish Academy of Sciences, Poland
autor
  • Systems Research Institute, Polish Academy of Sciences, Poland
autor
  • Systems Research Institute, Polish Academy of Sciences, Poland
  • Warsaw School of Information Technology, Poland
  • Warsaw School of Information Technology, Poland
autor
  • EKOMETRIA, Office of the Studies & Ecological Measurements, Poland
Bibliografia
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  • [7]. Carnevale, C., Finzi, G., Pisoni, E., Volta, M., Guariso, G., Gianfreda, R., Maffeis, G., Thunis, P. White, L. & Triacchini, G. (2012). An integrated assessment tool to define effective air quality policies at regional scale, Environmental Modelling & Software, 38, pp. 306–315.
  • [8]. Chafe, Z.A., Braue,r M., Klimont, Z., van Dingenen, R., Mehta, S., Rao, S., Riahi K., Dentener, F. & Smith, K.R. (2014). Household cooking with solid fuels contributes to ambient PM2.5 air pollution and the burden of disease, Environmental Health Perspectives & Software, 122, pp. 1–30.
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  • [10]. Dimitriou, K. & Kassomenos, P. (2014). A study on the reconstitution of daily PM10 and PM2.5 levels in Paris, Atmospheric Environment, 98, pp. 648–654.
  • [11]. Dresser, A.L. & Huizer, R.D. (2011). CALPUFF and AERMOD model validation study in the near field: Martins Creek revisited, Journal of the Air & Waste Management Association, 61, pp. 641–657.
  • [12]. EEA (2012). European Environment Agency. Air quality in Europe – Report No 4/2012.
  • [13]. EEA (2014). European Union emission inventory report 1990–2012 under the UNECE Convention on Long-range Transboundary Air Pollution (LRTAP). EEA Technical report No 12/2014.
  • [14]. Elbir, T. (2003). Comparison of model predictions with the data of an urban air quality monitoring network in Izmir, Turkey, Atmospheric Environment, 37, pp. 2149–2157.
  • [15]. EMEP/EEA (2013). Air pollutant emission inventory guidebook 2013.
  • [16]. ETC/ACM (2013). Technical Paper 2013/11 (R. Rouil, B. Bessagnet, eds). How to start with PM modelling for air quality assessment and planning relevant to AQD.
  • [17]. Holmes, N.S. & Morawska, L. (2006). A review of dispersion modelling and its application to the dispersion of particles: An overview of different dispersion models available, Atmospheric Environment, 40, pp. 5902–5928.
  • [18]. Holnicki, P. & Nahorski, Z. (2013). Air quality modeling in Warsaw Metropolitan Area, Journal of Theoretical and Applied Computer Science, 7, pp. 56–69.
  • [19]. Holnicki, P. & Nahorski Z. (2015). Emission data uncertainty in urban air quality modeling – case study, Environmental Modeling and Assessment, 20, pp. 583–597.
  • [20]. Holnicki, P., Kałuszko, A. & Trapp, W. (2016). An urban scale application and validation of the CALPUFF model, Atmospheric Pollution Research, 7, pp. 393–402.
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  • [24]. Kiesewetter, G., Borken-Kleefeld, J., Schöpp, W., Heyes, C., Thunis, P., Bessagnet, B., Terrenoire, E. & Amann, M. (2014). Modelling street level PM10 concentrations across Europe: source apportionment and possible futures, Atmospheric Chemistry and Physics Discussions, 14, pp. 18315–18354.
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  • [26]. Maxim, L. & van der Sluijs J. (2011). Quality in environmental science for policy: Assessing uncertainty as a component of policy analysis, Environmental Science & Policy, 14, pp. 482–492.
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  • [28]. ME (2012). Ministry of the Environment. Decree 1031, 24 Aug. 2012, On the admissible levels of some substances in the air. (in Polish)
  • [29]. NCAR (2008). A description of the advanced research WRF Version 3. NCAR Technical Note, TN–475+STR. Boulder, Colorado, USA June 2008.
  • [30]. Oshan, R., Kumar, A. & Anand, M. (2006). Application of the USEPA’s CALPUFF model to an urban area, Environmental Progress & Sustainable Energy, 25, pp. 12–17.
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  • [32]. Park, S.-K., Cobb, C.E., Wade, K., Mulholland, J., Hu Y. & Russel, A.G. (2006). Uncertainty in air quality model evaluation for particulate matter due to spatial variations in pollutant concentrations, Atmospheric Environment, 40, pp. S563–S573.
  • [33]. Patton, A.P., Perkins, J., Zamore, W., Levy, J.I., Brugge, D. & Durant, J.L. (2014). Spatial and temporal differences in traffic-related air pollution in three urban neighborhoods near an interstate highway, Atmospheric Environment, 99, pp. 309–321.
  • [34]. Pisoni, E., Carnevale, C. & Volta, M. (2010). Sensitivity to spatial resolution of modeling systems designing air quality control policies, Environmental Modelling & Software, 25, pp. 66–73.
  • [35]. Rogula-Kozłowska, W., Kozielska, B., Klejnowski, K. & Szopa, S. (2013). Hazardous compounds in urban PM in the central part of Upper Silesia (Poland) in Winter, Archives of Environmental Protection, 39, pp. 53–65.
  • [36]. Rzeszutek, M. & Bogacki, M. (2016). The evaluation of the air pollution dispersion model (OSPM): case study, Poland, Kraków, Rocznik Ochrona Środowiska, 18, pp. 351–362. (in Polish)
  • [37]. Sax, T. & Isakov, V. (2003). A case study for assessing uncertainty in local-scale regulatory air quality modeling applications, Atmospheric Environment, 37, pp. 3481–3489.
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  • [39]. Tartakovsky, D., Broday, D. M. & Stern, E. (2013). Evaluation of AERMOD and CALPUFF for predicting ambient concentrations of total suspended particulate matter (TSP) emissions from a quarry in complex terrain, Environmental Pollution, 179, pp. 138–145.
  • [40]. Thunis, P., Rouil, L., Cuvelier, R., Stern, R., Kerschbaumer, A., Bessagnet, B., Schaap, M., Builtjes, P., Tarrason, L., Douros, J., Moussiopoulos, N., Pirovano, G & Bedogni, M. (2007). Analysis of model responses to emission-reduction scenarios within the CityDelta project, Atmospheric Environment, 41, pp. 208–220.
  • [41]. Trapp, W. (2010). The application of CALMET/CALPUFF models in air quality assessment system in Poland, Archives of Environmental Protection, 36, pp. 63–79.
  • [42]. Villasenor, R., Lopez-Villegas, M. T., Eidels-Dubovoi, S., Quintanar, A. & Gallardo, J.C. (2003). A mesoscale modeling study of windblown dust on the Mexico City Basin, Atmospheric Environment, 37, pp. 2451–2462.
  • [43]. WIOŚ (2012). Environment Quality in Mazovian Voivodship in the year 2012. Voivodship Inspectorate of Environment Protection (WIOŚ). Report for the year 2012 (in Polish).
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b2ee343f-c373-48ce-befd-edc5c878922d
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