Skip to main content
Log in

Dispersion modeling of air pollutants in the atmosphere: a review

  • Research Article
  • Published:
Central European Journal of Geosciences

Abstract

Modeling of dispersion of air pollutants in the atmosphere is one of the most important and challenging scientific problems. There are several natural and anthropogenic events where passive or chemically active compounds are emitted into the atmosphere. The effect of these chemical species can have serious impacts on our environment and human health. Modeling the dispersion of air pollutants can predict this effect. Therefore, development of various model strategies is a key element for the governmental and scientific communities. We provide here a brief review on the mathematical modeling of the dispersion of air pollutants in the atmosphere. We discuss the advantages and drawbacks of several model tools and strategies, namely Gaussian, Lagrangian, Eulerian and CFD models. We especially focus on several recent advances in this multidisciplinary research field, like parallel computing using graphical processing units, or adaptive mesh refinement.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Flight disruptions cost airlines $1.7bn, says IATA, BBC News, http://news.bbc.co.uk/2/hi/business/8634147.stm

  2. Stull R. B., An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers, 1988

    Google Scholar 

  3. Kumar P., Sharan M., Parameterization of the eddy diffusivity in a dispersion model over homogenous terrain in the atmospheric boundary layer, Atmos. Res., 106, 2012, 30–43

    Google Scholar 

  4. Seidel D. J., Ao. C. O., Li K., Estimating climatological planetary boundary layer heights from radiosonde observations: Comparison of methods and uncertainty analysis, J. Geophys. Res., 115, 2010, D16113, doi: 10.1029/2009JD013680

    Google Scholar 

  5. Sriram G., Krishna Mohan N., Gopalasamy V., Sensitivity study of Gaussian dispersion models, Journal of Scientific and Industrial Research, 65, 2006, 321–324

    Google Scholar 

  6. Turner D. B., The long lifetime of the dispersion methods of Pasquill in U.S. regulatory air modeling, J. Appl. Meteorol., 36, 1997, 1016–1020

    Google Scholar 

  7. Luna R. E., Church H. W., A Comparison of Turbulence Intensity and Stability Ratio Measurements to Pasquill Stability Classes, J. Appl. Meteorol., 11, 1972, 663–669

    Google Scholar 

  8. Galperin B., Sukoriansky S., Anderson P. S., On the critical Richardson number in stably stratified turbulence, Atmos. Sci. Lett., 8, 2007, 65–69

    Google Scholar 

  9. Cimorelli A. J., Perry S. G., Venkatram A., Weil J. C., Paine R. J., Wilson R. B., Lee R. F., Peters W. D., Brode R. W., AERMOD: A dispersion model for industrial source applications. Part I: General model formulation and boundary layer characterization, J. Appl. Meteorol., 44(5), 2005, 682–693

    Google Scholar 

  10. Perry S. G., CTDMPLUS: A dispersion model for sources near complex topography. Part I: Technical Formulations, J. Appl. Meteorol., 31, 1992, 633–645

    Google Scholar 

  11. Foken T., 50 years of the Monin-Obukhov similarity theory. Bound-Lay. Meteorol., 2006, 119, 431–447

    Google Scholar 

  12. Draxler R. R., Hess G.D., An overview of HYSPLIT_4 modelling system for trajectories, dispersion and deposition, Aust. Meteorol. Mag., 47, 1998, 295–308

    Google Scholar 

  13. Johansson C., Smedman A-S., Högström U., Critical test of the validity of Monin-Obukhov similarity during convective conditions, J. Atmos. Sci., 58, 2001, 1549–1566

    Google Scholar 

  14. Stohl A., Forster C., Frank A., Seibert P., Wotawa, G., Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2, Atmos. Chem. Phys., 5, 2005, 4739–4799

    Google Scholar 

  15. Woodward J. L., Estimating the Flammable Mass of a Vapor Cloud: A CCPS Concept Book Appendix A, doi: 10.1002/9780470935361, 1999

    Google Scholar 

  16. Lagzi I., Kármán D., Turányi T., Tomlin A. S., Haszpra L., Simulation of the dispersion of nuclear contamination using an adaptive Eulerian grid model, J. Environ. Radioact., 75, 2004, 59–82

    Google Scholar 

  17. Mészáros R., Zsély I. G., Szinyei D., Vincze C., Lagzi I., Sensitivity analysis of an ozone deposition model, Atmos. Environ., 43, 2009, 663–672

    Google Scholar 

  18. Mészáros R., Szinyei D., Vincze C., Lagzi I., Turányi T., Haszpra L., Tomlin A.S., Effect of the soil wetness state on the stomatal ozone fluxes over Hungary, Int. J. Environ. Pollut., 36, 2009, 180–194

    Google Scholar 

  19. Sportisse B., A review of parameterizations for modelling dry deposition and scavenging of radionuclides, Atmos. Environ., 41, 2007, 2683–2698

    Google Scholar 

  20. Baklanov A., Sørensen J. H., Parameterisation of radionuclide deposition in atmospheric long-range transport modelling, Phys. Chem. Earth B., 26, 2001, 787–799

    Google Scholar 

  21. Stockie J.M., Mathematics of atmospheric dispersion modelling, SIAM Rev., 53, 2011, 349–372

    Google Scholar 

  22. Namdeo A., Mitchell G., Dixon R., TEMMS: an integrated package for modelling and mapping urban traffic emissions and air quality, Environ. Model. Softw., 17, 2002, 177–188

    Google Scholar 

  23. Sharan, M. and Gopalakrishnan, S. G., Bhopal gas accident: a numerical simulation of the gas dispersion event, Environ. Model. Softw., 12, 1997, 135–141

    Google Scholar 

  24. Li Z., Briggs G. A., Simple PDF models for convectively driven vertical diffusion, Atmos. Environ., 22, 1988, 55–74

    Google Scholar 

  25. Schulman L. L., Strimaitis D. G., Scire J. S., Development and evaluation of the PRIME plume rise and building downwash model, J. Air Waste Manage. Assoc., 50, 2000, 378–390

    Google Scholar 

  26. Abu-Allaban M., Abu-Qudais, H., Impact assessment of ambient air quality by cement industry: a case study in Jordan, Aerosol Air, Qual. Res., 11, 2011, 802–810

    Google Scholar 

  27. Lee S-S., Keener T. C., Dispersion modeling of mercury emissions from coal-fired power plants at Coshocton and Manchester, Ohio. The Ohio J. Sci, 2008, 108, 65–69

    Google Scholar 

  28. Bajwa K. S., Arya S. P., Aneja, V. P., Modeling studies of ammonia dispersion and dry deposition at some hog farms in North Carolina, J. Air Waste Manage. Assoc., 58, 2008, 1198–1207

    Google Scholar 

  29. Krzyzanowski, J., Approaching cumulative effects through air pollution modelling, Water. Air Soil Pollut., 214, 2011, 253–273

    Google Scholar 

  30. Carruthers D. J., Holroyd R. J., Hunt J. C. R., Weng W-S., Robins A. G., Thomson D. J., Smith, F. B., UKADMS, a new approach to modelling dispersion in the earth’s atmospheric boundary layer, J. Wind Eng. Ind. Aerod., 52, 1994, 139–153

    Google Scholar 

  31. Carruthers D. J., Dyster S. J., McHugh C. A., Factors affecting inter-annual variability of NOx and NO2 concentrations from single point sources, Clean Air and Environmental Protection, 33, 2003, 15–20

    Google Scholar 

  32. McHugh C. A., Carruthers D. J., Edmunds H. A., ADMS-Urban: an air quality management system for traffic, domestic and industrial pollution, Int. J. Environ. Pollut., 8, 1997, 666–674

    Google Scholar 

  33. Holmes N. S., Morawska L., A review of dispersion modelling and its application to the dispersion of particles: An overview of different dispersion models available, Atmos. Environ., 40, 2006, 5902–5928

    Google Scholar 

  34. Rama Krishna T. V. B. P. S., Reddy M. K., Reddy R. C., Singh R. N., Impact of an industrial complex on the ambient air quality: Case study using a dispersion model, Atmos. Environ., 39(29), 2005, 5395–5407

    Google Scholar 

  35. Silverman, K. C., Tell, J. G., Sargent, E. V. and Qiu, Z., Comparison of the Industrial Source Complex and AERMOD dispersion models: Case study for human health risk assessment, J. Air Waste Manage. Assoc., 57, 2007, 1439–1446

    Google Scholar 

  36. Athanassiadou M., Baker J., Carruthers D., Collins W., Girnary S., Hassell D., Hort M., Johnson C., Johnson K., Jones R., Thomson D., Trought N., Witham C., An assessment of the impact of climate change on air quality at two UK sites, Atmos. Environ., 44, 2010, 1877–1886

    Google Scholar 

  37. Leelossy Á., Mészáros R., Lagzi I., Short and long term dispersion patterns of radionuclides in the atmosphere around the Fukushima Nuclear Power Plant, J. Environ. Radioact., 102, 2011, 1117–1121

    Google Scholar 

  38. Bubbico R., Mazzarotta, B., Accidental release of toxic chemicals: influence of the main input parameters on consequence calculation, J. Hazard. Mater., 151, 2008, 394–406

    Google Scholar 

  39. Zhang J., Hodgson J., Erkut, E., Using GIS to assess the risks of hazardous materials transport in networks, Eur. J. Oper. Res., 121, 2000, 316–329

    Google Scholar 

  40. Pudykiewicz J., Numerical simulation of the transport of radioactive cloud from the Chernobyl nuclear accident, Tellus B, 40B, 1988, 241–259

    Google Scholar 

  41. Piedelievre J. P., Musson-Genon, L., Bompay, F., MEDIA — An Eulerian model of atmospheric dispersion: First validation on the Chernobyl release, J. Appl. Meteorol., 29, 1990, 1205–1220

    Google Scholar 

  42. Dacre H. F., Grant A. L. M., Hogan R. J., Belcher S. E., Thomson D. J., Devenish B. J., Marenco F., Hort M. C., Haywood J. M., Ansmann A., Mattis I., Clarisse L., Evaluating the structure and magnitude of the ash plume during the initial phase of the 2010 Eyjafjallajökull eruption using lidar observations and NAME simulations, J. Geophys. Res., 116, 2011, D00U03, doi: 10.1029/2011JD015608

    Google Scholar 

  43. Mészáros R., Vincze C., Lagzi I., Simulation of accidental release using a coupled transport (TREX) and numerical weather prediction (ALADIN) model, Idojárás, 114, 2010, 101–120

    Google Scholar 

  44. Srinivas C. V., Venkatesan R., Baskaran R., Rajagopal V., Venkatraman B., Regional scale atmospheric dispersion simulation of accidental releases of radionuclides from Fukushima Dai-ichi reactor, Atmos. Environ., 61, 2012, 66–84

    Google Scholar 

  45. Brandt J., Mikkelsen T., Thykier-Nielsen S., Zlatev Z., Using a combination of two models in tracer simulations, Math. Comput. Model., 23, 1996, 99–115

    Google Scholar 

  46. Oettl D., Uhmer U., Development and evaluation of GRAL-C dispersion model, a hybrid Eulerian-Lagrangian approach capturing NO-NO2-O3 chemistry, Atmos. Environ., 45, 2011, 839–847

    Google Scholar 

  47. Pozorski J., Minier J-P., On the Lagrangian turbulent dispersion models based on the Langevin equation, Int. J. Multiphas. Flow, 24, 1998, 913–945

    Google Scholar 

  48. Williams M., Yamada T., A microcomputer-based forecasting model: potential applications for emergency response plans and air quality studies, J. Air Waste Manage. Assoc., 40, 1990, 1266–1274

    Google Scholar 

  49. Mikkelsen T., Alexandersen S., Astrup P., Champion H. J., Donaldson A. I., Dunkerley F. N., Gloster J., Sorensen J. H., Thykier-Nielsen S., Investigation of airborne foot-and-mouth disease virus transmission during low-wind conditions in the early phase of the UK 2001 epidemic, Atmos. Chem. Phys., 3, 2003, 2101–2110

    Google Scholar 

  50. Sorensen J. H., Sensitivity of the DERMA long-range Gaussian dispersion model to meteorological input and diffusion parameters, Atmos. Environ., 32, 1998, 4195–4206

    Google Scholar 

  51. Lepicard S., Heling R., Maderich V., POSEIDON/RODOS models for radiological assessment of marine environment after accidental releases: application to coastal areas of the Baltic, Black and North Seas, J. Environ. Radioact., 72, 2004, 153–161

    Google Scholar 

  52. Ghannam K., El-Fadel M., Emissions characterization and regulatory compliance at an industrial complex: An integrated MM5/CALPUFF approach, Atmos. Environ., 69, 2013, 156–169

    Google Scholar 

  53. Levy J. I., Spengler J. D., Hlinka D., Sullivan D., Moon, D., Using CALPUFF to evaluate the impacts of power plant emissions in Illinois: model sensitivity and implications, Atmos. Environ., 36, 2002, 1063–1075

    Google Scholar 

  54. Prueksakorn K., Kim T., Kim S., Kim H., Kim K. Y., Son W., Vongmahadlek C., Review of air dispersion modelling approaches to assess the risk of windborne spread of foot-and-mouth disease virus, J. Environ. Prot., 3, 2012, 1260–1267

    Google Scholar 

  55. Zhou Y., Levy J. I., Hammitt J. K., Evans, J. S., Estimating population exposure to power plant emissions using CALPUFF: a case study in Beijing, China, Atmos. Environ., 37, 2003, 815–826

    Google Scholar 

  56. Yamada T., Bunker S., and Moss M., Numerical simulations of atmospheric transport and diffusion over coastal complex terrain, J. Appl. Meteorol., 31, 1992, 565–578

    Google Scholar 

  57. Wang G., Ostoja-Starzewski M., Influence of topography on the Phoenix CO2 dome: a computational study, Atmos. Sci. Lett., 5, 2004, 103–107

    Google Scholar 

  58. Wu J., Lu C-H., Chang S-J., Yang Y-M, Chang B-J., Teng J-H., Three-dimensional dose evaluation system using real-time wind field information for nuclear accidents in Taiwan, Nucl. Instrum. Methods Phys. Res. A, 565, 2006, 812–820

    Google Scholar 

  59. Yamada T., Merging CFD and atmospheric modeling capabilities to simulate airflows and dispersion in urban areas, Comput. Fluid Dyn. J., 2004, 13, 329–341

    Google Scholar 

  60. Garner M. G., Hess G. D., Yang, X., An integrated modelling approach to assess the risk of wind-borne spread of foot-and-mouth disease virus from infected premises, Environ. Model. Assess., 11, 2006, 195–207

    Google Scholar 

  61. Long N. Q., Truong Y., Hien P. D., Binh N. T., Sieu L. N., Giap T. V., Phan N. T., Atmospheric radionuclides from the Fukushima Dai-ichi nuclear reactor accident observed in Vietnam, J. Environ. Radioact., 111, 2012, 53–58

    Google Scholar 

  62. McGowan H., Clark A., Identification of dust transport pathways from Lake Eyre, Australia using HYSPLIT, Atmos. Environ., 42, 2008, 6915–6925

    Google Scholar 

  63. Shan W., Yin Y., Lu H., Liang S., A meteorological analysis of ozone episodes using HYSPLIT model and surface data. Atmos. Res., 2009, 93, 767–776

    Google Scholar 

  64. Challa V. S., Indrcanti J., Baham J. M., Patrick C., Rabarison M. K., Young J. H., Hughes R., Swanier S. J., Hardy M. G., Yerramilli A., Sensitivity of atmospheric dispersion simulations by HYSPLIT to the meteorological predictions from a meso-scale model, Environ. Fluid. Mech., 8, 2008, 367–387

    Google Scholar 

  65. Wain A. G., Lee S., Mills G. A., Hess G. D., Cope M. E., Tindale N., Meteorological overview and verification of HYSPLIT and AAQFS dust forecasts for the duststorm of 22–24 October 2002, Aust. Meteorol. Mag., 55, 2006, 35–46

    Google Scholar 

  66. Stohl A., Hittenberger M., Wotawa G., Validation of the Lagrangian particle dispersion model FLEXPART against large-scale tracer experiment data, Atmos. Environ., 32, 1998, 4245–4264

    Google Scholar 

  67. Ryall D. B., Maryon R. H., Validation of the UK Met Office’s NAME model against the ETEX dataset, Atmos. Environ., 32, 1998, 4256–4276

    Google Scholar 

  68. de Foy B., Burton S. P., Ferrare R.A., Hostetler C. A., Hair J. W., Wiedinmyer C., Molina, L. T., Aerosol plume transport and transformation in high spectral resolution lidar measurements and WRF-FLEXPART simulations during the MILAGRO Field Campaign, Atmos. Chem. Phys., 11, 2011, 3543–3563

    Google Scholar 

  69. Warneke C., Froyd K. D., Brioude J., Bahreini R., Brock C. A., Cozic J., de Gouw J. A., Fahey D. W., Ferrare R., Holloway J. S., Middlebrook A. M., Miller L., Montzka S., Schwarz J. P., Sodemann H., Spackman J. R., Stohl, A., An important contribution to springtime Arctic aerosol from biomass burning in Russia, Geophys. Res. Lett., 37, 2010, L01801, doi: 10.1029/2009GL041816

    Google Scholar 

  70. Stohl A., Seibert P., Wotawa G., Arnold D., Burkhart J. F., Eckhardt S., Tapia C., Vargas A., Yasunari T. J., Xenon-133 and caesium-137 releases into the atmosphere from the Fukushima Dai-ichi nuclear power plant: determination of source term, atmospheric dispersion, and deposition, Atmos. Chem. Phys., 11, 2011, 28319–28394

    Google Scholar 

  71. Koracin D., Vellore R., Lowenthal D. H., Watson J. G., Koracin J., McCord T., DuBois D. W., Chen L-W. A., Kumar N., Knipping E. M., Wheeler N. J. M., Craig K., Reid S., Regional source identification using Lagrangian stochastic particle dispersion and HYSPLIT backward-trajectory models, J. Air Waste Manage. Assoc., 61, 2011, 660–672

    Google Scholar 

  72. Povinec P.P., Sykora I., Gera M., Holy K., Brestaková L., Kovácik A., Fukushima-derived radionuclides in ground-level air of Central Europe: a comparison with simulated forward and backward trajectories, J. Radioanal. Nucl. Ch., 295, 2013, 1171–1176

    Google Scholar 

  73. Bey I., Jacob D., Yantosca M., Logan J., Field B., Fiore A., Li Q, Liu H, Mickley L, Schultz M., Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation, J. Geophys.Res., 106, 2001, 23073–23096

    Google Scholar 

  74. Grell G. A., Peckham S. E., McKeen S., Schmitz R., Frost G., Skamarock W. C., Eder B., Fully coupled “online” chemistry within the WRF model, Atmos. Environ., 39, 2005, 6957–6975

    Google Scholar 

  75. Wang K., Zhang Y., Jang C., Phillips S., Wang B., Modeling intercontinental air pollution transport over the trans-Pacific Region in 2001 using Community Multiscale Air Quality modeling system, J. Geophys. Res., 114, 2009, D04307

    Google Scholar 

  76. Garcia-Menendez F., Odman M. T., Adaptive grid use in air quality modeling, Atmosphere, 2, 2011, 484–509

    Google Scholar 

  77. Ghorai S., Tomlin A. S., Berzins M., Resolution of pollutant concentrations in the boundary layer using a fully 3D adaptive gridding technique, Atmos. Environ., 34, 2000, 2851–2863

    Google Scholar 

  78. Lagzi I., Tomlin A. S., Turányi T., Haszpra L., Mészáros R., Berzins M., The simulation of photochemical smog episodes in Hungary and Central Europe using adaptive gridding models, Lect. Notes Comp. Sci., 2074, 2001, 67–77

    Google Scholar 

  79. Lagzi I., Tomlin S. A., Turányi T., Haszpra, L., Modelling photochemical air pollutant formation in Hungary using an adaptive grid technique, Int. J. Environ. Pollut., 36, 2009, 44–58

    Google Scholar 

  80. Tomlin A. S., Ghorai S., Hart G., Berzins M., 3-D Multi-scale air pollution modelling using adaptive unstructured meshes, Environ. Model. Softw., 15, 2000, 681–692

    Google Scholar 

  81. Zegeling P. A., R-refinement with finite elements or finite differences for evolutionary PDE models, Appl. Numer. Math., 26, 1998, 97–104

    Google Scholar 

  82. Zegeling P. A., Lagzi I., Izsak F., Transition of Liesegang precipitation systems: simulations with an adaptive grid PDE method, Commun. Comput. Phys., 10, 2011, 867–881

    Google Scholar 

  83. Ascher U., Numerical methods for evolutionary differential equations. Computational science and engineering. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, 2008

    Google Scholar 

  84. Grossmann C., Roos H., Stynes M., Numerical Treatment of Partial Differential Equations. Universitext, Springer, Berlin, 2007

    Google Scholar 

  85. Thomas J. W., Numerical partial differential equations: finite difference methods, volume 22 of Texts in Applied Mathematics. Springer-Verlag, New York, 1995

    Google Scholar 

  86. Versteeg H., Malalasekera W., An introduction to computational fluid dynamics: the finite volume method. Pearson Education Australia, 2007

    Google Scholar 

  87. Huebner K., The Finite Element Method for Engineers. A Wiley-Interscience publication. Wiley, New York, 2001

    Google Scholar 

  88. Nair R. D., Thomas S. J., Loft R. D., A discontinuous Galerkin transport scheme on the cubed sphere. Mon. Weather Rev., 2005, 133, 814–828

    Google Scholar 

  89. Faragó I., Havasi Á., Operator splitting and their applications, Mathematics Research Development Series, Nova Science Publishers, Inc., New York, 2009

    Google Scholar 

  90. Lanser D., Verwer J. G., Analysis of operator splitting for advection-diffusion-reaction problems in air pollution modelling, J. Compute. Appl. Math., 111, 1999, 201–216

    Google Scholar 

  91. Marchuk G. I., Methods of Splitting. Nauka, Moscow, 1988 (in Russian)

    Google Scholar 

  92. Yanenko N. N., On convergence of the splitting method for heat equation with variable coefficients. Journal of Computational Mathematics and Mathematical Physics 2, 1962 (in Russian)

  93. Zlatev Z., Computer Treatment of Large Air Pollution Models, Kluwer Academic Publisher, 1995

    Google Scholar 

  94. Dimov I., Faragó I., Havasi Á., Zlatev Z., Operator splitting and commutativity analysis in the Danish Eulerian Model, Math. Comput. Simul, 67, 2003, 217–233

    Google Scholar 

  95. Dimov I., Faragó I., Havasi Á., Zlatev, Z., Different splitting techniques with application to air pollution models, Int. J. Environ. Pollut., 32(2), 2008, 174–199

    Google Scholar 

  96. Strang G., On the construction and comparison of difference schemes, SIAM J. Numer. Anal., 5, 1968, 506–517

    Google Scholar 

  97. Csomós P., Havasi Á., Faragó I., Weighted sequential splittings and their analysis, Comp. Math. Appl., 50, 2005, 1017–1031

    Google Scholar 

  98. Strang G., Accurate partial difference methods I: Linear Cauchy problems, Arch. Ration. Mech. An., 12, 1963, 392–402

    Google Scholar 

  99. Foster I., Kesselman C., Tuecke S., The anatomy of the grid, Intl. J. High Perf. Comput. Appl, 15, 2001, 200–222

    Google Scholar 

  100. Jacob B., Brown M., Fukui K., Trivedi N., Introduction to Grid computing. IBM Redbooks, Vervante, Springville, Utah, 2005

    Google Scholar 

  101. Sterling T. L, Bell G., Beowulf Cluster Computing With Linux, MIT Press, 2002

    Google Scholar 

  102. Adiga N. R., Blumrich M. A., Chen D., Coteus P., Gara A., Giampapa M. E., Heidelberger P., Singh S., Steinmacher-Burow B. D., Takken T., Tsao M., Vranas P., Blue Gene/L torus interconnection network, IBM J. Res. Dev., 49, 2005, 265–276

    Google Scholar 

  103. Hempel R., The MPI standard for message passing. Proc. Intl. Conf. and Exhibit. On High Perf. Comp and Networking II, 1994, 247–252

    Google Scholar 

  104. Sunderam V. S., PVM: A framework for parallel distributed computing, Concurrency-Pract. Ex., 2, 1990, 315–339

    Google Scholar 

  105. Sun X.-H., Chen Y., Reevaluating Amdahl’s law in the multicore era, J. Parallel Distrib. Comput., 70, 2010, 183–188

    Google Scholar 

  106. General Purpose Computation on Graphics Hardware, http://gpgpu.org/

  107. Mészáros R., Molnár F., Izsák F., Kovács T., Dombovári P., Steierlein Á., Nagy R., Lagzi I., Environmental modeling using graphical processing unit with CUDA, Idojárás, 116, 2012, 237–251

    Google Scholar 

  108. Molnár F., Szakály T., Mészáros R., Lagzi I., Air pollution modelling using a Graphics Processing Unit with CUDA, Comput. Phys. Commun., 181, 2010, 105–112

    Google Scholar 

  109. Pardyjak E. R., Singh B., Norgren A., Willemsen P., Using video gaming technology to achieve low-cost speed up of emergency response urban dispersion simulations, in: Seventh Symposium on the Urban Environment, University of Utah, Salt Lake City and University of Minnesota, Duluth, 2007

    Google Scholar 

  110. Senocak I., Thibault J., Caylor M., Rapid-response urban CFD simulations using a GPU computing paradigm on desktop supercomputers, in: Eighth Symposium on the Urban Environment, Phoenix, Arizona, 2009, J19.2

    Google Scholar 

  111. Simek V., Dvorak R., Zboril F., Kunovsky J., Towards accelerated computation of atmospheric equations using CUDA, in: Proceedings of the UK Sim 2009. 11th International Conference on Computer Modelling and Simulation, 2009, 449–454

    Google Scholar 

  112. Januszewski M., Kostur M., Accelerating numerical solution of stochastic differential equations with CUDA, Comput. Phys. Commun., 181, 2010, 183–188

    Google Scholar 

  113. Michéa D., Komatitsch D., Accelerating a threedimensional finite-difference wave propagation code using GPU graphics cards, Geophys. J. Int., 182, 2010, 389–402

    Google Scholar 

  114. Micikevicius P., 3D Finite difference computation on GPUs using CUDA. Proc. 2nd Workshop General Purpose Processing on Graphics Processing Units, ACM, 2009, 79–84

    Google Scholar 

  115. Molnár F., Izsák F., Mészáros R., Lagzi I., Simulation of reaction-diffusion processes in three dimensions using CUDA, Chemometr. Intell. Lab., 108, 2011, 76–85

    Google Scholar 

  116. Sanderson A. R., Meyer M. D., Kirby R. M., Johnson C. R., A framework for exploring numerical solutions of advection-reaction-diffusion equations using a GPU-based approach, Comput. Vis. Sci., 12, 2009, 155–170

    Google Scholar 

  117. Che S., Boyer M., Meng J., Tarjan D., Sheaffer J. W., Skadron K., A performance study of general purpose applications on graphics processors using CUDA. J. Parallel Distr. Com., 2008, 68, 1370–1380

    Google Scholar 

  118. Garland M., Le Grand S., Nickolls J., Anderson J., Hardwick J., Morton S., Phillips E., Zhang Y., Volkov, V., Parallel computing experiences with CUDA, Micro IEEE, 28, 2008, 13–27

    Google Scholar 

  119. Krishnaprasad S., Uses and abuses of Amdahl’s law, J. Comp. Sci. Coll., 17, 2001, 288–293

    Google Scholar 

  120. Gustafson J., Re-evaluating Amdahl’s law, Communications of the ACM, 31, 1988, 532–533

    Google Scholar 

  121. El-Nashar A. I., To Parallelize or not to parallelize, speed up issue, Int. J Dist. Parallel Syst., 2, 2011, 2

    Google Scholar 

  122. Ostromsky T., Zlatev Z., Parallel and GRID implementation of a large scale air pollution model. Numerical Methods and Applications Lect., Notes Comput. Sc., 4310, 2007, 475–482

    Google Scholar 

  123. Todorova A., Syrakov D., Gadjhev G., Georgiev G., Ganev K.G., Prodanova M., Miloshev N., Spiridonov V., Bogatchev A., Slavov K., Grid computing for atmospheric composition studies in Bulgaria, Earth Sci. Inf., 3, 2010, 259–282

    Google Scholar 

  124. Roberti D. R., Souto R, P., de Campos Velho H. F., Degrazia G. A., Anfossi D., Parallel implementation of a Lagrangian stochastic model for pollutant dispersion, Int. J. Parallel Program., 33, 2005, 485–498

    Google Scholar 

  125. Srinivas C. V., Venkatesan R., Muralidharan N. V., Das S., Dass H., Kumar P.E., Operational mesoscale atmospheric dispersion prediction using a parallel computing cluster, J. Earth Syst. Sci., 115, 2006, 315–332

    Google Scholar 

  126. Alexandrov V. N., Owczarz W., Thomson P. G., Zlatev Z., Parallel runs of a large air pollution model on a grid of Sun computers, Math. Comput. Simul., 65, 2004, 557–577

    Google Scholar 

  127. Georgiev K., An algorithm for parallel implementations of an Eulerian smog model. Numerical Methods and Applications Lect., Notes Comput. Sc., 2542, 2003, 463–470

    Google Scholar 

  128. Georgiev K., Ostromsky T., Zahari Z., New parallel implementation of an air pollution computer model — performance study on an IBM blue gene/p computer. Large-Scale Scientific Computing Lect. Notes Comput. Sc., 7116, 2012, 283–290

    Google Scholar 

  129. Ostromsky T., Zlatev Z., Parallel implementation of a large-scale 3-D air pollution model. Large-Scale Scientific Computing Lect, Notes Comput. Sc., 2179, 2001, 309–316

    Google Scholar 

  130. Philippe C., Coppalle A., Atmospheric dispersion and chemical pollutant transformation simulated with parallel calculations using two PC clusters, Int. J. Environ. Pollut., 22, 2004, 133–143

    Google Scholar 

  131. Chen Q., Prediction of room air motion by Reynoldsstress models. Build. Environ., 1996, 31(3), 233–244

    Google Scholar 

  132. Rossi R., Iaccarino G., Numerical simulation of scalar dispersion downstream of a square obstacle using gradient-transport type models, Atmos. Environ., 43, 2009, 2518–2531

    Google Scholar 

  133. Baklanov A., Application of CFD methods for modelling in air pollution problems: possibilities and gaps, Environ. Monit. Assess., 65, 2000, 181–189

    Google Scholar 

  134. Cheng W. C., Liu, C-H., Large-eddy simulation of flow and pollutant transports in and above twodimensional idealized street canyons, Bound-Lay. Meteorol., 139, 2011, 411–437

    Google Scholar 

  135. Li X-X., Liu C-H., Leung D. Y. C., Large-eddy simulation of flow and pollutant dispersion in high-aspectratio urban street canyons with wall model, Bound-Lay. Meteorol., 129, 2008, 249–268

    Google Scholar 

  136. Balczó M., Balogh M., Goricsán I., Nagel T., Suda J. M., Lajos T., Air quality around motorway tunnels in complex terrain: computational fluid dynamics modeling and comparison to wind tunnel data, Idojárás, 115, 2011, 179–204

    Google Scholar 

  137. Di Sabatino S., Buccolieri R., Pulvirenti B., Britter R. E., Flow and pollutant dispersion in street canyons using FLUENT and ADMS-Urban. Environ. Model. Assess., 13, 2008, 369–381

    Google Scholar 

  138. Milliez M., Carissimo B., Numerical simulations of pollutant dispersion in an idealized urban area, for different meteorological conditions, Bound-Lay. Meteorol., 122(2), 2007, 321–342

    Google Scholar 

  139. Tominaga Y., Mochida A., Yoshie R., Kataoka H., Nozu T., Yoshikawa M., Shirasawa, T., AIJ guidelines for practical applications of CFD to pedestrian wind environment around buildings, J. Wind Eng. Ind. Aerod., 96, 2008, 1749–1761

    Google Scholar 

  140. Tewari M., Kusaka H., Chen F., Coirier W.J., Kim S., Wyszogrodzki A. A., Warner, T. T., Impact of coupling a microscale computational fluid dynamics model with a mesoscale model on urban scale contaminant transport and dispersion, Atmos. Res., 96, 2010, 656–664

    Google Scholar 

  141. Van Dop, H., Addis, R., Fraser, G., Girardi, F., Graziani, G., Inoue, Y., Kelly, N., Klug, W., Kulmala, A., Nodop, K., Pretel, J., ETEX: A Europian Tracer Experiment; Observations, dispersion modelling and emergency response, Atmos. Environ. 32, 1998, 4089–4094

    Google Scholar 

  142. Zhang, Y: Online-coupled meteorology and chemistry models: history, current status, and outlook, Atmos. Chem. Phys., 8, 2008, 2895–2932

    Google Scholar 

  143. Molteni, F.; Buizza, R.; Palmer, T. N.; Petroliagis, T., The ECMWF Ensemble Prediction System: Methodology and validation, Q. J. Roy, Meteor. Soc., 122, 1996, 73–119

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Róbert Mészáros.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Leelőssy, Á., Molnár, F., Izsák, F. et al. Dispersion modeling of air pollutants in the atmosphere: a review. cent.eur.j.geo. 6, 257–278 (2014). https://doi.org/10.2478/s13533-012-0188-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.2478/s13533-012-0188-6

Keywords

Navigation