Water, Air, & Soil Pollution: Focus

, Volume 9, Issue 1–2, pp 27–37

The Use of Modern Third-Generation Air Quality Models (MM5-EMIMO-CMAQ) for Real-Time Operational Air Quality Impact Assessment of Industrial Plants

  • R. San José
  • J. L. Pérez
  • J. L. Morant
  • R. M. González Barras
Article

Abstract

In many cases, a substantial proportion of large industrial emissions are located in the surrounding areas of cities and are the cause of an important part of air concentrations over the city and surrounding areas. The need to have a tool to analyze and manage these concentrations is the main objective of this contribution. In this paper, we show the implementation of an adapted version of the MM5-CMAQ (Byun et al. 1998, Description of the Models-3 Community Multiscale Air Quality (CMAQ) model. Proceedings of the American Meteorological Society 78th Annual Meeting Phoenix, AZ, Jan. 11–16, 264–268, 1998)—PSU/NCAR and EPA (US) models—modeling system for a large combined cycle power plant located near Madrid city (Spain). The system called TEAP (EUREKA project)—a Tool to Evaluate the Air Quality Impact of Industrial Plants—allows the assessment of the impact of each individual power group (400 MW) in real-time and forecasting mode. As a consequence, the industrial plant and authorities are having a period of time (≈16 h) to decide to switch off one power group or several, to minimize or avoid the possible exceedance of European Union (EU) limits—as declared in the EU directives. The quantification of the impact of these possible exceedances of EU Directives due to emissions produced by the power plant is essential for decision making according to the daily forecasts. We will show the implementation of the TEAP system and operation in two real applications which are operating since summer 2005 and January, 2007 in the surrounding area of Madrid (Spain).

Keywords

Air quality industrial impact Mesoscale modeling Air quality modeling 

References

  1. Byun, D. W., & Ching, J. K. S. (1999). Science algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) modelling system (EPA/600/R-99/030). Research Triangle Park: Atmospheric Modeling Division, National Exposure Research Laboratory, US Environmental Protection Agency.Google Scholar
  2. Byun, D. W., Young, J., Gipson, G., Godowitch, J., Binkowsky, F., Roselle, S. et al. (1998). Description of the Models-3 Community Multiscale Air Quality (CMAQ) model. In Proceedings of the American Meteorological Society 78th Annual Meeting Phoenix, AZ, Jan. 11–16, 264–268.Google Scholar
  3. Carmichael, G. R., Uno, I., Phadnis, M. J., Zhang, Y., & Sunwoo, Y. (1998). Tropospheric ozone production and transport in the springtime in east Asia. Journal of Geophysical Research, 103, 10649–10671.CrossRefGoogle Scholar
  4. Carmichael, G. R., Calori, G., Hayami, H., Uno, I., Cho, S. Y., Engardt, M., et al. (2002). The MICS-Asia study: model intercomparison of long-range transport and sulfur deposition in East Asia. Atmospheric Environment, 36, 175–199.CrossRefGoogle Scholar
  5. CIESIN, Center for International Earth Science Information Network (CIESIN) (2004). Global Rural-Urban Mapping Project (GRUMP): Urban/Rural population grids. Palisades: CIESIN, Columbia University. http://sedac.ciesin.columbia.edu/gpw/.Google Scholar
  6. Ebel, A., Memmesheimer, M., Jakobs, H. J., Kessler, C. H., Piekorz, G., & Weber, M. (2001). Air pollution modeling and its application. In S. Gryning (Ed.), Simulation of photochemical smog episodes in Europe using nesting techniques and different model evaluation approaches (pp. 145–153). New York: Kluwer Academic/Plenum.Google Scholar
  7. Ehrhard, J., Khatib, I. A., Winkler, C., Kunz, R., Moussiopoulos, N., & Ernst, G. (2000). The microscale model MIMO: Development and assessment. Journal of Wind Engineering and Industrial Aerodynamics, 85, 163–176.CrossRefGoogle Scholar
  8. Flassak, T. (1989). Ein nicht-hydrostatisches mesoskaliges Modell zur Beschreibung der Dynamik der planetaren Grenzschicht. Düsseldorf: VDI Verlag, Verlag des Vereins Deutcher Ingenieure. ISSN: 0178-9589, ISBN: 3-18-147415-0. Hauptreferent: Prof. Dr.-Ing. Habil. N. Moussiopoulos.Google Scholar
  9. Flatøy, Ø. H., & Schlanger, H. (2000). Chemical forecasts used for measurement flight planning during the POLINAT 2. Geophysical Research Letters, 27, 951–954.CrossRefGoogle Scholar
  10. Grell, G., Dudhia, J., & Stauffer, D. (1997). A description of the fifty generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note, TN-398 + STR.Google Scholar
  11. Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., et al. (2005). Fully coupled online chemistry within the WRF model. Atmospheric Environment, 39, 6957–6975.CrossRefGoogle Scholar
  12. Guenther, A. B., Zimmerman, P. R., Harley, P. C., Monson, R. K., & Fall, R. (1993). Isoprene and monoterpene emission rate variability: model evaluations and sensitivity analyses. Journal of Geophysical Research, 98D, 12609–12617.CrossRefGoogle Scholar
  13. Houyoux, Mark, R., Vukovich, J. M., Coats Jr., C. J., Wheeler, N. J. M., & Kasibhatla, P. S. (2000). Emission inventory development and processing for the Seasonal Model for Regional Air Quality (SMRAQ) project. Journal of Geophysical Research, 105, 9079–9090.CrossRefGoogle Scholar
  14. IPCC (2001). Climate Change 2001: The Scientific Basis. Contribution of Working Group 1 to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.Google Scholar
  15. Iversen, T. (1987). A model for long-range transport of sulphur dioxide and particulate sulphate in the atmosphere—A technical description. NILU-OR 82/86. pp. 63.Google Scholar
  16. Juang, H.-M. H. (1992). A spectral fully compressible nonhydrostatic mesoscale model in hydrostatic sigma coordinates: Formulation and preliminary results. Meteorology and Atmospheric Physics, 50, 75–88.CrossRefGoogle Scholar
  17. Kallberg, P. (Ed.) (1989). The HIRLAM level 1 system. Documentation manual, 160 pp. [Available from SMHI, S 60176, Norrkoping, Sweden].Google Scholar
  18. Langner, J., Persson, C., & Robertson, L. (1995). Concentration and deposition of acidifying air pollutants over Sweden: Estimates for 1991 based on the MATCH model and observations. Water Air and Soil Pollution, 85, 2021–2026.CrossRefGoogle Scholar
  19. Moussiopoulos, N., Ossanlis, I., & Barmpas, P. H. (2005). A study of heat transfer effects on air pollutants dispersion in street canyons by numerical simulations. International Journal of Environment and Pollution, 25(1–2–3–4), 131–144.CrossRefGoogle Scholar
  20. Pospisil, J., Katolicky, J., & Jicha, M. (2004). A comparison of measurements and CFD model predictions for pollutant dispersion in cities. Science of the Total Environment, 334–335, 185–195.CrossRefGoogle Scholar
  21. Pullen, J., Bori, J. P. S., Young, T., Patnaik, G., & Iselin, J. (2005). A comparison of contaminant plume statistics from a Gaussian puff and urban CFD model for two large cities. Atmospheric Environment, 39, 1049–1068.CrossRefGoogle Scholar
  22. San José, R., Prieto, J. F., Castellanos, N., & Arranz, J. M. (1997). Sensitivity study of dry deposition fluxes in ANA air quality model over Madrid mesoscale area. In R. San José, & C. A. Brebbia (Eds.), Measurements and modelling in environmental pollution (pp. 119–130). Southampton: Computational Mechanics Publications. ISBN: 1 85312 461 3.Google Scholar
  23. San José, R., Rodriguez, M. A., Pelechano, A., & González, R. M. (1999). Sensitivity study of dry deposition fluxes. In R. San José (Ed.), Measuring and modelling investigation of environmental process (pp. 205–246). Southampton: WIT, ISSN: 1460-1427, ISBN: 1 85312 566 0.Google Scholar
  24. San José, R., Peña, J. I., Pérez, J. L., & González, R. M. (2004a). EMIMO: an emission model. In R. Friedlich (Ed.), Emissions of air pollutants—measurements, calculations and uncertainties (pp. 292–298). Heidelberg: Springer, ISBN: 3-540-00840-3.Google Scholar
  25. San José, R., Peña J. I., Pérez, J. L. & González, R. M. (2004b). Emissions of air pollutants – measurements, calculations and uncertainties, GENEMIS. EUROTRAC-2 Subproject Final Report (pp. 292–298). R. Friedrich (Ed.) Stuttgart: University of Stuttgart (IER). ISBN: 3-540-00840-3.Google Scholar
  26. San José, R., Pérez, J. L., González, R. M. (2006). The use of MM5-CMAQ for an Incinerator Air Quality Impact Assessment for metals, PAH, Dioxins and Furans: Spain case study. Lecture Notes, Large-Scale Scientific Computations, pp. 498–505. ISSN: 0302-9743, Springer GmbH. Subject: Computer Science. vol. 3743/2006.Google Scholar
  27. San José, R., Pérez, J. L., & González, R. M. (2007a). An operational real time air quality modelling system for industrial plants. Environmental Modelling and Software Journal, 22, 297–307. ISSN: 1364-8152.CrossRefGoogle Scholar
  28. San José, R., Pérez, J. L., Morant, J. L. & González, R. M. (2007b). CFD (MICROSYS) and mesoscale air quality (MM5-CMAQ-EMIMO) model integration for operational Internet street level pollution applications: Las Palmas de Gran Canaria (Canary Islands, Spain) case study. In A. Kungolos, K. Aravossis, A. Karagiannidis, P. Samaras (Eds.), Proceedings of SECOTOX Conference and the International Conference on Environmental Management Engineering, Planning and Economics. Skiathos (Greece), June, 24–28, 2007. ISBN: 978-960-89090-8-3. 1, 409-414.Google Scholar
  29. Schaap, M., Roemer, M., Sauter, F., Boersen, G., Timmermans, R., & Builtjes, P. J. H. (2005). LOTOS–EUROS: documentation. The Netherlands: Apeldoorn. Report number B&O-A R2005/297.Google Scholar
  30. Simons, A. J., Burridge, D., Jarraud, M., Girard, M. C., & Wergen, W. (1989). The ECMWF medium-range prediction models. Development of the numerical formulations and the impact of increased resolution. Meteorology and Atmospheric Physics, 40, 28–60.CrossRefGoogle Scholar
  31. Van Egmond, N. D., & Kesseboom, H. (1981). Numerike vespreidingsmodellen voor de interpretatie van de meetresultaten van het nationaal meetnet voor luchtverontreininging, RIVM report 227905048 (in Dutch). Bilthoven: National Institute of Public Health and the Environment.Google Scholar
  32. Vautard, R., Bessagnet, B., Chin, M., & Menut, L. (2005). On the contribution of natural Aeolian sources to particulate matter concentrations in Europe: Testing hypotheses with a modelling approach. Atmospheric Environment, 39(18), 3291–3303.CrossRefGoogle Scholar
  33. Yarwood, G., Whitten, G. Z., & Rao, S. (2005). Updates to the Carbon Bond 4 Photochemical Mechanism. Des Plains, IL: Report prepared for the Lake Michigan Air Directors Consortium.Google Scholar
  34. Zlatev, Z., Christensen, J., & Hov, O. (1992). An Eulerian air pollution model for Europe with nonlinear chemistry. Journal of Atmospheric Chemistry, 15, 1–37.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • R. San José
    • 1
  • J. L. Pérez
    • 1
  • J. L. Morant
    • 1
  • R. M. González Barras
    • 2
  1. 1.Environmental Software and Modelling Group, Computer Science SchoolTechnical University of Madrid (UPM)MadridSpain
  2. 2.Department of Meteorology and Geophysics, Faculty of PhysicsComplutense University of Madrid (UCM)MadridSpain

Personalised recommendations