Advertisement

Numerical simulation of pollutant dispersion in the residential areas with continuous grass barriers

  • A. IssakhovEmail author
  • P. Omarova
Original Paper
  • 27 Downloads

Abstract

In this paper, numerical modeling of the movement and dispersion of pollutant emissions between houses as well as the effect of a continuous barrier on the spread of pollutants was considered. To solve this problem, a system of Reynolds-averaged Navier–Stokes equations was used, and various turbulent models were applied to close this equation system. To test the mathematical model, the numerical algorithm and the choice of the optimal turbulent model, the test problem was solved numerically. The obtained numerical results were compared with experimental data and the results of modeling by other authors. After checking the mathematical model, the numerical algorithm and the choice of the optimal turbulent model, the main problem describing the process of pollutant emissions between houses using continuous grass barriers was solved. In this problem, the distribution of pollutants was considered when using herbal barriers and the optimal height for these barriers was chosen. For the simulation, the RNG k-epsilon turbulence model was applied, which was chosen from the obtained results of the test problem. The numerical simulation results were compared with the obtained calculated data using different heights of solid grass barriers. And it was found that when using a solid grass barrier with a height of 0.1 m, the concentration value drops by more than 1.5 times compared with that without using a barrier, and further increasing the height of the barrier does not give the desired result.

Keywords

Residential areas Pollutant dispersion SIMPLE algorithm Large eddy simulation (LES) Reynolds-averaged Navier–Stokes (RANS) 

Notes

Acknowledgements

This work is supported by the grant from the Ministry of education and science of the Republic of Kazakhstan.

Compliance with ethical standards

Conflict of interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

References

  1. Assimakopoulos VD, ApSimon HM, Moussiopoulos N (2003) A numerical study of atmospheric pollutant dispersion in different two-dimensional street canyon configurations. Atmos Environ 37:4037–4049CrossRefGoogle Scholar
  2. Baik JJ, Kang YS, Kim JJ (2007) Modeling reactive pollutant dispersion in an urban street canyon. Atmos Environ 41:934–949CrossRefGoogle Scholar
  3. Baker J, Walker HL, Cai X (2004) A study of the dispersion and transport of reactive pollutants in and above street canyons—a large eddy simulation. Atmos Environ 38:6883–6892CrossRefGoogle Scholar
  4. Buccolieri R, Sandberg M, Di Sabatino S (2010) City breathability and its link to pollutant concentration distribution within urban-like geometries. Atmos Environ 44:1894–1903CrossRefGoogle Scholar
  5. Cai XM, Barlow JF, Belcher SE (2008) Dispersion and transfer of passive scalars in and above street canyons: large-eddy simulations. Atmos Environ 42:5885–5895CrossRefGoogle Scholar
  6. Chao Y, Ruiqin S, Yangyang Z (2019) Multilayer urban canopy modelling and mapping for traffic pollutant dispersion at high density urban areas. Sci Total Environ 647:255–267CrossRefGoogle Scholar
  7. Chavez M, Hajra B, Stathopoulos T, Bahloul A (2011) Near-field pollutant dispersion in the built environment by CFD and wind tunnel simulations. J Wind Eng Ind Aerodyn 99:330–339CrossRefGoogle Scholar
  8. Cui PY, Li Z, Tao WQ (2016) Wind-tunnel measurements for thermal effects on the air flow and pollutant dispersion through different scale urban areas. Build Environ 97:137–151CrossRefGoogle Scholar
  9. Deardorff J (1970) A numerical study of three-dimensional turbulent channel flow at large Reynolds numbers. J Fluid Mech 41(2):453–480CrossRefGoogle Scholar
  10. Efthimiou GC, Berbekar E, Harms F, Bartzis JG, Leitl B (2015) Prediction of high concentrations and concentration distribution of a continuous point source release in a semi-idealized urban canopy using CFD-RANS modeling. Atmos Environ 100:48–56CrossRefGoogle Scholar
  11. Gousseau P, Blocken B, Stathopoulos T, van Heijst GJF (2011a) CFD simulation of near-field pollutant dispersion on a high-resolution grid: a case study by LES and RANS for a building group in downtown Montreal. Atmos Environ 45(2):428–438CrossRefGoogle Scholar
  12. Gousseau P, Blocken B, van Heijst GJF (2011b) CFD simulation of pollutant dispersion around isolated buildings: on the role of convective and turbulent mass fluxes in the prediction accuracy. J Hazard Mater 194:422–434CrossRefGoogle Scholar
  13. Grawe D, Cai XM, Harrison RM (2007) Large eddy simulation of shading effects on NO2 and O3 concentrations within an idealised street canyon. Atmos Environ 41:7304–7314CrossRefGoogle Scholar
  14. Gromke C, Ruck B (2007) Influence of trees on the dispersion of pollutants in an urban street canyon: experimental investigation of the flow and concentration field. Atmos Environ 41:3287–3302CrossRefGoogle Scholar
  15. Gromke C, Buccolieri R, Di Sabatino S, Ruck B (2008) Dispersion study in a street canyon with tree planting by means of wind tunnel and numerical investigations: evaluation of CFD data with experimental data. Atmos Environ 42:8640–8650CrossRefGoogle Scholar
  16. Gu ZL, Zhang YW, Cheng Y, Lee SC (2011) Effect of uneven building layout on air flow and pollutant dispersion in non-uniform street canyons. Build Environ 46:2657–2665CrossRefGoogle Scholar
  17. Hajra B, Stathopoulos T (2012) A wind tunnel study of the effect of downstream buildings on near-field pollutant dispersion. Build Environ 52:19–31CrossRefGoogle Scholar
  18. Hajra B, Stathopoulos T, Bahloul A (2011) The effect of upstream buildings on near-field pollutant dispersion in the built environment. Atmos Environ 45:4930–4940CrossRefGoogle Scholar
  19. Huber AH (1991) Wind tunnel and Gaussian plume modeling of building wake dispersion. Atmos Environ 25:1237–1249CrossRefGoogle Scholar
  20. Issakhov A (2014) Modeling of synthetic turbulence generation in boundary layer by using zonal RANS/LES method. Int J Nonlinear Sci Numer Simul 2014:15–120Google Scholar
  21. Issakhov A (2016) Mathematical modeling of the discharged heat water effect on the aquatic environment from thermal power plant under various operational capacities. Appl Math Model 40(2):1082–1096CrossRefGoogle Scholar
  22. Issakhov A, Imanberdiyeva M (2019) Numerical simulation of the movement of water surface of dam break flow by VOF methods for various obstacles. Int J Heat Mass Transf 136:1030–1051CrossRefGoogle Scholar
  23. Issakhov A, Mashenkova A (2019) Numerical study for the assessment of pollutant dispersion from a thermal power plant under the different temperature regimes. Int J Environ Sci Technol 16(10):6089–6112.  https://doi.org/10.1007/s13762-019-02211-y CrossRefGoogle Scholar
  24. Issakhov A, Zhandaulet Y (2019a) Numerical simulation of thermal pollution zones’ formations in the water environment from the activities of the power plant. Eng Appl Comput Fluid Mech 13(1):279–299Google Scholar
  25. Issakhov A, Zhandaulet Y (2019b) Numerical study of technogenic thermal pollution zones’ formations in the water environment from the activities of the power plant. Environ Model Assess.  https://doi.org/10.1007/s10666-019-09668-8 CrossRefGoogle Scholar
  26. Issakhov A, Zhandaulet Y, Nogaev A (2018) Numerical simulation of dam break flow for various forms of the obstacle by VOF method. Int J Multiph Flow 109:191–206CrossRefGoogle Scholar
  27. Issakhov A, Bulgakov R, Zhandaulet Y (2019a) Numerical simulation of the dynamics of particle motion with different sizes. Eng Appl Comput Fluid Mech 13(1):1–25Google Scholar
  28. Issakhov A, Bulgakov R, Zhandaulet Y (2019b) Numerical study of the dynamics of particles motion with different sizes from coal-based thermal power plant. Int J Nonlinear Sci Numer Simul.  https://doi.org/10.1515/ijnsns-2018-0182 CrossRefGoogle Scholar
  29. Kikumoto H, Ooka R (2012a) A study on air pollutant dispersion with bimolecular reactions in urban street canyons using large-eddy simulations. J Wind Eng Ind Aerodyn 2012:104–106Google Scholar
  30. Kikumoto H, Ooka R (2012b) A numerical study of air pollutant dispersion with bimolecular chemical reactions in an urban street canyon using large-eddy simulation. Atmos Environ 54:456–464CrossRefGoogle Scholar
  31. Kikumoto H, Ooka R (2018) Large-eddy simulation of pollutant dispersion in a cavity at fine grid resolutions. Build Environ 127:127–137CrossRefGoogle Scholar
  32. Kim JJ, Baik JJ (2003) Effects of inflow turbulence intensity on flow and pollutant dispersion in an urban street canyon. J Wind Eng Ind Aerodyn 91:309–329CrossRefGoogle Scholar
  33. Kim JJ, Baik JJ (2004) A numerical study of the effects of ambient wind direction on flow and dispersion in urban street canyons using the RNG ke turbulence model. Atmos Environ 38:3039–3048CrossRefGoogle Scholar
  34. Kwak KH, Baik JJ (2012) A CFD modeling study of the impacts of NOx and VOC emissions on reactive pollutant dispersion in and above a street canyon. Atmos Environ 46:71–80CrossRefGoogle Scholar
  35. Li Y, Li X (2015) Natural ventilation potential of high-rise residential buildings in northern China using coupling thermal and airflow simulations. Build Environ 8:51–64Google Scholar
  36. Li X, Xue F (2018) Bayesian inversion of inflow direction and speed in urban dispersion simulations. Build Environ.  https://doi.org/10.1016/j.buildenv.2018.08.042 CrossRefGoogle Scholar
  37. Li XX, Liu CH, Leung DYC (2008) Large-eddy simulation of flow and pollutant dispersion in high-aspect-ratio urban street canyons with wall model. Bound Layer Meteorol 129:249–268CrossRefGoogle Scholar
  38. Li C, Li X, Su Y, Zhu Y (2012) A new zero-equation turbulence model for micro-scale climate simulation. Build Environ 47:243–255CrossRefGoogle Scholar
  39. Ma J, Li X, Zhu Y (2012) A simplified method to predict the outdoor thermal environment in residential district. Build Environ 5:157–167Google Scholar
  40. Metin B, Ulas I, Alper U (2019) Evaluation of impact of residential heating on air quality of megacity Istanbul by CMAQ. Sci Total Environ 651:1688–1697CrossRefGoogle Scholar
  41. Michioka T, Sato A, Takimoto H, Kanda M (2011) Large-eddy simulation for the mechanism of pollutant removal from a two-dimensional street canyon. Bound Layer Meteorol 138:195–213CrossRefGoogle Scholar
  42. Oikawa S, Meng Y (1998) A wind-tunnel study of peak concentration in the near-wake region of a cubical model building. J Jpn Soc Atmos Environ 33:151–163Google Scholar
  43. Oke TR (1988) Street design and urban canopy layer climate. Energy Build 11:103–113CrossRefGoogle Scholar
  44. Pavageau M, Schatzmann M (1999) Wind tunnel measurements of concentration fluctuations in an urban street canyon. Atmos Environ 33:3961–3971CrossRefGoogle Scholar
  45. Ramponi R, Blocken B, de Coo LB, Janssen WD (2015) CFD simulation of outdoor ventilation of generic urban configurations with different urban densities and equal and unequal street widths. Build Environ 92:152–166CrossRefGoogle Scholar
  46. Rathna R, Varjani S, Nakkeeran E (2018) Recent developments and prospects of dioxins and furans remediation. J Environ Manag 223:797–806CrossRefGoogle Scholar
  47. Salim SM, Cheah SC, Chan A (2011) Numerical simulation of dispersion in urban street canyons with avenue-like tree plantings: comparison between RANS and LES. Build Environ 46:1735–1746CrossRefGoogle Scholar
  48. Sanchez B, Santiago JL, Martilli A, Martin F, Borge R, Quaassdorff C, de la Paz D (2017) Modelling NOx concentrations through CFD-RANS in an urban hot-spot using high resolution traffic emissions and meteorology from a mesoscale model. Atmos Environ 163:155–165CrossRefGoogle Scholar
  49. Sato A, Sada K (2002) A wind tunnel experiment on tracer gas concentration fluctuation near a cubical model building. Dob Gakkai Ronbunshu 2002:41–49CrossRefGoogle Scholar
  50. Smagorinsky J (1963) General circulation experiments with the primitive equations. Mon Weather Rev 91(3):99–164CrossRefGoogle Scholar
  51. Spalart PR (1997) Comments on the feasibility of LES for wing and on a hybrid RANS/LES approach. In: 1st ASOSR conference on DNS/LES. Arlington, TXGoogle Scholar
  52. Tan W, Li C, Wang K, Zhu G, Wang Y, Liu L (2018) Dispersion of carbon dioxide plume in street canyons. Process Saf Environ Prot 116:235–242CrossRefGoogle Scholar
  53. Tominaga Y, Stathopoulos T (2010) Numerical simulation of dispersion around an isolated cubic building: model evaluation of RANS and LES. Build Environ 45:2231–2239CrossRefGoogle Scholar
  54. Tominaga Y, Stathopoulos T (2011) CFD modeling of pollution dispersion in a street canyon: comparison between LES and RANS. J Wind Eng Ind Aerodyn 99:340–348CrossRefGoogle Scholar
  55. Tominaga Y, Stathopoulos T (2016) Ten questions concerning modeling of near-field pollutant dispersion in the built environment. Build Environ 105:390–402CrossRefGoogle Scholar
  56. Tominaga Y, Iizuka S, Imano M, Kataoka H, Mochida A, Nozu T, Ono Y, Shirasawa T, Tsuchiya N, Yoshie R (2013) Cross comparisons of CFD results of wind and dispersion fields for MUST experiment: evaluation exercises by AIJ. J Asian Archit Build Eng 12:117–124CrossRefGoogle Scholar
  57. Xue F, Li X (2017) The impact of roadside trees on traffic released PM10 in urban street canyon: aerodynamic and deposition effects. Sustain Cities Soc 30:195–204CrossRefGoogle Scholar
  58. Yassin MF (2011) Impact of height and shape of building roof on air quality in urban street canyons. Atmos Environ 45:5220–5229CrossRefGoogle Scholar
  59. Yuan C, Ng E, Norford LK (2014) Improving air quality in high-density cities by understanding the relationship between air pollutant dispersion and urban morphologies. Build Environ 71:245–258CrossRefGoogle Scholar

Copyright information

© Islamic Azad University (IAU) 2019

Authors and Affiliations

  1. 1.al-Farabi Kazakh National UniversityAlmatyRepublic of Kazakhstan
  2. 2.Kazakh British Technical UniversityAlmatyRepublic of Kazakhstan

Personalised recommendations