Environmental Science and Pollution Research

, Volume 22, Issue 22, pp 18288–18299 | Cite as

Optimisation of dispersion parameters of Gaussian plume model for CO2 dispersion

  • Xiong Liu
  • Ajit Godbole
  • Cheng Lu
  • Guillaume Michal
  • Philip Venton
Short Research and Discussion Article


The carbon capture and storage (CCS) and enhanced oil recovery (EOR) projects entail the possibility of accidental release of carbon dioxide (CO2) into the atmosphere. To quantify the spread of CO2 following such release, the ‘Gaussian’ dispersion model is often used to estimate the resulting CO2 concentration levels in the surroundings. The Gaussian model enables quick estimates of the concentration levels. However, the traditionally recommended values of the ‘dispersion parameters’ in the Gaussian model may not be directly applicable to CO2 dispersion. This paper presents an optimisation technique to obtain the dispersion parameters in order to achieve a quick estimation of CO2 concentration levels in the atmosphere following CO2 blowouts. The optimised dispersion parameters enable the Gaussian model to produce quick estimates of CO2 concentration levels, precluding the necessity to set up and run much more complicated models. Computational fluid dynamics (CFD) models were employed to produce reference CO2 dispersion profiles in various atmospheric stability classes (ASC), different ‘source strengths’ and degrees of ground roughness. The performance of the CFD models was validated against the ‘Kit Fox’ field measurements, involving dispersion over a flat horizontal terrain, both with low and high roughness regions. An optimisation model employing a genetic algorithm (GA) to determine the best dispersion parameters in the Gaussian plume model was set up. Optimum values of the dispersion parameters for different ASCs that can be used in the Gaussian plume model for predicting CO2 dispersion were obtained.


CO2 dispersion CFD modelling Gaussian plume model Dispersion parameters Genetic algorithm 



Specific heat (J kg−1 K−1)


Concentration (kg m−3)


Roughness constant, dimensionless


A constant in the k-ε turbulence model


Gravitational acceleration (9.8 m s−2)


Release source height (m)


Specific turbulent kinetic energy (m2 s−2)


von Karman constant ≈ 0.4, dimensionless


Equivalent sand-grain roughness height (m)


Monin-Obukhov length (m)


Release source strength (kg s−1)


Bulk Richardson number, dimensionless


Temperature (K)


Wind velocity (m s−1)


Reference wind velocity (m s−1)


Friction velocity (m s−1)


Height above ground (m)


Height of atmospheric boundary layer (m)


Reference height (m)


Surface roughness length (m)

Greek letters


Specific eddy dissipation rate (m2 s−3)


‘Potential’ temperature (K)


Dispersion parameter in Gaussian model (m)


Dispersion parameter in Gaussian model (m)


Wind shear exponent, dimensionless



This work is being carried out under the aegis of the Energy Pipelines Cooperative Research Centre (EPCRC), supported through the Australian Government’s Cooperative Research Centre Program, and funded by the Department of Resources, Energy and Tourism (DRET). Cash and in-kind support from the Australian Pipelines Industries Association Research and Standards Committee (APIA RSC) is gratefully acknowledged. The authors wish to express their deep gratitude to Prof. Steven Hanna from the Harvard School of Public Health, and Dr. Joseph Chang from Homeland Security Studies and Analysis Institute, for providing the Kit Fox field experimental data.


  1. Adhikari JP, Sen GK, Tewary BK, Banerjee A, Mukherjee RN, Rajwar DP, Singh B (1990) Modification and testing of a gaussian dispersion model for particulate matter in the respirable size range. Atmospheric Environment. Part A. General Topics 24:1647–1651CrossRefGoogle Scholar
  2. Benson SM (2006) Carbon dioxide capture and storage: assessment of risk from storage of carbon dioxide in deep underground geological formations. Earth Science Division, Lawrence Berkeley National Laboratory, BerkeleyGoogle Scholar
  3. Blocken B, Stathopoulos T, Carmeliet J (2007) CFD simulation of the atmospheric boundary layer: wall function problems. Atmos Environ 41:238–252CrossRefGoogle Scholar
  4. Cao X, Roy G, Hurley W, Andrews W (2011) Dispersion coefficients for Gaussian puff models. Boundary-Layer Meteorol 139:487–500CrossRefGoogle Scholar
  5. Chang JC, Hanna SR (2004) Air quality model performance evaluation. Meteorog Atmos Phys 87:167–196CrossRefGoogle Scholar
  6. Colenbrander GW (1980) A mathematical model for the transient behaviour of dense vapour clouds, The 3rd International Symposium on Loss Prevention and Safety Promotion in the Process Industries, Basel, SwitzerlandGoogle Scholar
  7. Erbrink JJ (1991) A practical model for the calculation of σy and σz for use in an on-line gaussian dispersion model for tall stacks, based on wind fluctuations. Atmospheric Environment. Part A. General Topics 25:277–283CrossRefGoogle Scholar
  8. Ermak DL (1990) User’s manual for SLAB: an atmospheric dispersion model for denser-than-air release. Lawrence Livermore National Laboratory, LivermoreGoogle Scholar
  9. Forbes SM, Verma P, Curry TE, Friedmann SJ, Wade SM (2008) CCS guidelines: guidelines for carbon dioxide capture, transport, and storage. World Resources Institute (WRI), WashingtonGoogle Scholar
  10. Gavelli F, Bullister E, Kytomaa H (2008) Application of CFD (Fluent) to LNG spills into geometrically complex environments. J Hazard Mater 159:158–168CrossRefGoogle Scholar
  11. Gen M, Cheng R (1999) Genetic algorithms and engineering optimization (engineering design and automation). Wiley-Interscience, New YorkCrossRefGoogle Scholar
  12. Han JA, Pal S, Shen S, Lin Y (2000) An estimation of turbulent kinetic energy and energy dissipation rate based on atmospheric boundary layer similarity theory. Langley Research Center, HamptonGoogle Scholar
  13. Hanna SR (2002) Addendum to report dense gas dispersion model modifications and evaluations using the Kit Fox Field Observations. American Petroleum Institute, WashingtonGoogle Scholar
  14. Hanna SR, Briggs GA, Hosker RPJ (1982) Handbook on atmospheric diffusion. U.S. Department of EnergyGoogle Scholar
  15. Hanna SR, Britter R, Franzese P (2003) A baseline urban dispersion model evaluated with Salt Lake City and Los Angeles tracer data. Atmos Environ 37:5069–5082CrossRefGoogle Scholar
  16. Harik GR (1997) Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms. University of Michigan, MichiganGoogle Scholar
  17. Havens J (1988) A dispersion model for elevated dense gas jet chemical releases Volume II. User’s Guide. U.S. Environmental Protection Agency, North CarolinaGoogle Scholar
  18. HSE (2005) List of approved workplace exposure limits (as consolidated with amendments October 2007) – EH 40/2005. HSE, Health and Safety CommissionGoogle Scholar
  19. Hsieh KJ, Lien FS, Yee E (2013) Dense gas dispersion modeling of CO2 released from carbon capture and storage infrastructure into a complex environment. Int J Greenhouse Gas Control 17:127–139CrossRefGoogle Scholar
  20. Koopman RP, Ermak DL, Chan ST (1989) A review of recent field tests and mathematical modelling of atmospheric dispersion of large spills of Denser-than-air gases (1967). Atmos Environ 23:731–745CrossRefGoogle Scholar
  21. Labovský J, Jelemenský L (2011) Verification of CFD pollution dispersion modelling based on experimental data. J Loss Prev Process Ind 24:166–177CrossRefGoogle Scholar
  22. Lewis EV (1989) Principles of naval architecture, 2nd edn. Society of Naval Architects and Marine Engineers, AlexandriaGoogle Scholar
  23. Liu X, Godbole A, Lu C, Michal G, Venton P (2014) Source strength and dispersion of CO2 releases from high-pressure pipelines: CFD model using real gas equation of state. Appl Energy 126:56–68CrossRefGoogle Scholar
  24. Mazzoldi A (2009) Leakage and atmospheric dispersion of CO2 associated with carbon capture and storage projects. University of Nottingham, NottinghamGoogle Scholar
  25. Mazzoldi A, Hill T, Colls JJ (2008) CFD and Gaussian atmospheric dispersion models: a comparison for leak from carbon dioxide transportation and storage facilities. Atmos Environ 42:8046–8054CrossRefGoogle Scholar
  26. Mazzoldi A, Hill T, Colls JJ (2009) A Consideration of the jet-mixing effect when modelling CO2 emissions from high pressure CO2 transportation facilities. Energy Procedia 1:1571–1578CrossRefGoogle Scholar
  27. Mazzoldi A, Hill T, Colls JJ (2011) Assessing the risk for CO2 transportation within CCS projects, CFD modelling. Int J Greenhouse Gas Control 5:816–825CrossRefGoogle Scholar
  28. McBride MA, Reeves AB, Vanderheyden MD, Lea CJ, Zhou XX (2001) Use of advanced techniques to model the dispersion of chlorine in complex terrain. Process Saf Environ Prot 79:89–102CrossRefGoogle Scholar
  29. Metz B, Davidson O, Coninck HD, Loos M, Meyer L (2005) IPCC special report on carbon dioxide capture and storage. Cambridge University Press, New YorkGoogle Scholar
  30. Newman JF, Klein PM (2014) The impacts of atmospheric stability on the accuracy of wind speed extrapolation methods. Resources 3:81–105CrossRefGoogle Scholar
  31. Peterson EW, Hennessey JP (1978) On the use of power laws for estimates of wind power potential. J Appl Meteorol 17:390–394CrossRefGoogle Scholar
  32. Pontiggia M, Derudi M, Busini V, Rota R (2009) Hazardous gas dispersion: a CFD model accounting for atmospheric stability classes. J Hazard Mater 171:739–747CrossRefGoogle Scholar
  33. Pontiggia M, Landucci G, Busini V, Derudi M, Alba M, Scaioni M, Bonvicini S, Cozzani V, Rota R (2011) CFD model simulation of LPG dispersion in urban areas. Atmos Environ 45:3913–3923CrossRefGoogle Scholar
  34. Saeedi M, Fakhraee H, Sadrabadi MR (2008) A fuzzy modified Gaussian air pollution dispersion model. Res J Environ Sci 2:156–169CrossRefGoogle Scholar
  35. Scargiali F, Di Rienzo E, Ciofalo M, Grisafi F, Brucato A (2005) Heavy gas dispersion modelling over a topographically complex mesoscale: a CFD based approach. Process Saf Environ Prot 83:242–256CrossRefGoogle Scholar
  36. Scargiali F, Grisafi F, Busciglio A, Brucato A (2011) Modeling and simulation of dense cloud dispersion in urban areas by means of computational fluid dynamics. J Hazard Mater 197:285–293CrossRefGoogle Scholar
  37. Seevam PN, Race JM, Downie MJ, Hopkins P (2008) Transporting the next generation of CO2 for carbon capture and storage: the impact of impurities on supercritical CO2 pipelines, 7th International Pipeline Conference, Calgary, CanadaGoogle Scholar
  38. Sklavounos S, Rigas F (2004) Validation of turbulence models in heavy gas dispersion over obstacles. J Hazard Mater 108:9–20CrossRefGoogle Scholar
  39. Soentgen J (2010) On the history and prehistory of CO2. Found Chem 12:137–148CrossRefGoogle Scholar
  40. Stephens JC, Hering JG (2002) Comparative characterization of volcanic ash soils exposed to decade-long elevated carbon dioxide concentrations at Mammoth Mountain, California. Chem Geol 186:301–313CrossRefGoogle Scholar
  41. Tauseef SM, Rashtchian D, Abbasi SA (2011) CFD-based simulation of dense gas dispersion in presence of obstacles. J Loss Prev Process Ind 24:371–376CrossRefGoogle Scholar
  42. Tola V, Pettinau A (2014) Power generation plants with carbon capture and storage: a techno-economic comparison between coal combustion and gasification technologies. Appl Energy 113:1461–1474CrossRefGoogle Scholar
  43. Vendrig M, Spouge J, Bird A, Daycock J, Johnsen O (2003) In:Crown (ed), Risk analysis of the geological sequestration of carbon dioxide. Department of Trade and Industry’s Cleaner Coal Technology Transfer Programme, R246 DTI/Pub URN 03/1320Google Scholar
  44. Wen J, Heidari A, Xu B, Jie H (2013) Dispersion of carbon dioxide from vertical vent and horizontal releases—a numerical study. Proc Institution of Mech Eng, Part E: J Process Mech Eng 227:125–139CrossRefGoogle Scholar
  45. Western Research Institute (WRI) (1998) Final Report on the 1995 Kit Fox Project, Vol. I - Experiment Description and Data Processing, and Vol. II - Data Analysis for Enhanced Roughness Tests. Western Research Institute, Laramie, WyomingGoogle Scholar
  46. Wieringa J (1993) Representative roughness parameters for homogeneous terrain. Boundary Layer Meteorol 63:323–363CrossRefGoogle Scholar
  47. Wieringa J, Davenport AG, Grimmond CSB, Oke TR (2001) New revision of Davenport roughness classification, The 3rd European & African Conference on Wind Engineering, Eindoven, NetherlandsGoogle Scholar
  48. Witlox HWM, Harper M, Oke A (2009) Modelling of discharge and atmospheric dispersion for carbon dioxide releases. J Loss Prev Process Ind 22:795–802CrossRefGoogle Scholar
  49. Woodward JL (2010) Estimating the flammable mass of a vapor cloud: a CCPS concept book. American Institute of Chemical Engineers, New YorkGoogle Scholar
  50. Xing J, Liu Z, Huang P, Feng C, Zhou Y, Zhang D, Wang F (2013) Experimental and numerical study of the dispersion of carbon dioxide plume. J Hazard Mater 256–257:40–48CrossRefGoogle Scholar
  51. ZareNezhad B, Hosseinpour N (2009) An extractive distillation technique for producing CO2 enriched injection gas in enhanced oil recovery (EOR) fields. Energy Convers Manag 50:1491–1496CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Xiong Liu
    • 1
  • Ajit Godbole
    • 1
  • Cheng Lu
    • 1
  • Guillaume Michal
    • 1
  • Philip Venton
    • 2
  1. 1.School of Mechanical, Materials and Mechatronic EngineeringUniversity of WollongongWollongongAustralia
  2. 2.Venton and Associates Pty LtdBundanoonAustralia

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