Skip to main content

Advertisement

SpringerLink
Log in

2-D simulation of atmospheric boundary layer in and around Delhi to determine air pollution scenarios during winter morning

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

In this paper, a 2-D turbulent closure model, based on the pollutant mass conservation equation, is adopted to estimate the local and background pollutants in the predominant wind direction for the stable atmosphere during winter mornings. The background concentration of pollutants can severely affect the regional pollution level, and its monitoring is a challenging task. Here, the turbulent closure model is employed across three cities in India, viz., Patiala, Delhi, and Agra, to estimate SO2 and NOx concentration along the predominant wind direction to demonstrate the potential of numerical models. The direction of the prevailing wind in this area during January 2003 was NNW (330°). Patiala is followed by Delhi and then Agra in the predominant wind direction. The sensitivity analysis of surface temperature on pollutant concentration reveals that concentration would increase by its square as temperature dips. So, during low or no horizontal wind, pollution episodes will be inevitable. Thus, the pollution hotspots are also identified in these three cities. Delhi had a high pollution load. So, the impact of local pollution in Delhi, through dispersion, was found significant in Agra. NOx hot spots (exceed the 30 μg/m3 limit) are found all across Delhi, except IGI Airport and two other locations. However, no SO2 hotspot (exceed the 60 μg/m3 limit) is found in Delhi. The proposed model output is verified with the WRF-CFD model results. Compared to the WRF-CFD model, the proposed model has overestimated NOx and SO2 concentration maximum by 14.4% and 23.5%, respectively. The overestimation occurred primarily due to ignoring atmospheric chemical reactions (e.g., acid condensation, etc.) for which the atmospheric factors were not so conducive.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

Data will be made available on reasonable request.

References

  • Abdurrahman, M. I., Chaki, S., & Saini, G. (2020). Stubble burning: Effects on health & environment, regulations and management practices. Environmental Advances, 2, 100011. https://doi.org/10.1016/j.envadv.2020.100011

  • Agarwal, M., & Tandon, A. (2010). Modeling of the urban heat island in the form of mesoscale wind and of its effect on air pollution dispersal. Applied Mathematical Modelling, 34, 2520–2530.

    Article  Google Scholar 

  • Angelevska, B., Atanasova, V., & Andreevski, I. (2021). Urban Air Quality Guidance Based on Measures Categorization in Road Transport. Civil Engineering Journal, 7(2), 253–267.

    Article  Google Scholar 

  • Bhanarkar, A. D., Goyal, S. K., Shivacoumar, R., & Chalapati Rao, C. V. (2005). Assessment of contribution of SO2 and NO2 from different sources in Jamshedpur region, India. Atmospheric Environment, 39, 7745–7760.

    Article  CAS  Google Scholar 

  • Bicheng, C., Shuhua, L., & Yucong, M. (2013). Construction and validation of a computational fluid dynamics approach for flow and dispersion modeling in urban area. Journal of Meteorological Research, 27(6), 923–941.

    Google Scholar 

  • CEA (Central Electrical Authority). (2011). Energy wise – Performance Status All India Region, www.cea.nic.in/reports, Accessed in January 2012.

  • CPCB (Central Pollution Control Board). (2010). AIR QUALITY DATABASE. URL:http://cpcbenvis.nic.in/airpollution/air%20data%202011-2007/AIRTAB.html, last visited on 25 May, 2020.

  • Chen, F., & Dudhia, J. (2001). Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Monthly Weather Review, 129(4), 569–585.

  • Cusworth, D. H., Mickley, L. J., Sulprizio, M. P., Liu, T., Marlier, M. E., DeFries, R. S., Guttikunda, S. K., & Gupta, P. (2018). Quantifying the influence of agricultural fires in north-west India on urban air pollution in Delhi India. Environmental Research Letters, 13, 044018. https://doi.org/10.1088/1748-9326/aab303

  • Dudhia, J. (1989). Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. Journal of the Atmospheric Sciences, 46(20), 3077–3107.

    Article  Google Scholar 

  • Durre, I., Vose, R. S., & Wuertz, D. B. (2006). Overview of the integrated global radiosonde archive. Journal of Climate, 19(1), 53–68.

    Article  Google Scholar 

  • Feng, X., Wei, S., & Wang, S. (2020). Temperature inversions in the atmospheric boundary layer and lower troposphere over the Sichuan Basin, China: Climatology and impacts on air pollution. Science of the Total Environment, 138579.

  • Gao, M., Beig, G., Songa, S., Zhang, H., Hu, J., Ying, Q., Liang, F., Liu, Y., Wang, H., Lua, X., Zhu, T., Carmichael, G. R., Nielsen, C. P., & McElroy, M. B. (2018). The impact of power generation emissions on ambient PM2.5 pollution and human health in China and India. Environment International, 121, 250–259.

    Article  CAS  Google Scholar 

  • Gibergans-Báguena, J., Hervada-Sala, C., & Jarauta-Bragulat, E. (2020). The quality of urban air in Barcelona: A new approach applying compositional data analysis methods. Emerging Science Journal, 4(2), 113–121.

    Article  Google Scholar 

  • Grisogono, B., & Oerlemans, J. (2002). Justifying the WKB approximation in pure katabatic flows. Tellus A: Dynamic Meteorology and Oceanography, 54(5), 453–462.

    Article  Google Scholar 

  • Harrison, R. M. (2018). Urban atmospheric chemistry: A very special case for study. npj Climate and Atmospheric Science, 1(1), 1–5.

    Article  Google Scholar 

  • Hong, S. Y., Dudhia, J., & Chen, S. H. (2004). A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Monthly Weather Review, 132(1), 103–120.

    Article  Google Scholar 

  • Hudson, S. R., & Brandt, R. E. (2005). A look at the surface-based temperature inversion on the Antarctic Plateau. Journal of Climate, 18(11), 1673–1696.

    Article  Google Scholar 

  • Jain, S., & Khare, M. (2008). Urban air quality in mega cities: A case study of Delhi City using vulnerability analysis. Environ Monitoring Assessment, 136, 257–265.

    Article  CAS  Google Scholar 

  • Janjić, Z. I. (1990). The step-mountain coordinate: Physical package. Monthly Weather Review, 118(7), 1429–1443.

    Article  Google Scholar 

  • Janjić, Z. I. (1994). The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Monthly Weather Review, 122(5), 927–945.

    Article  Google Scholar 

  • Jeričević, A., Kraljević, L., Grisogono, B., Fagerli, H., & Večenaj, Ž. (2010). Parameterization of vertical diffusion and the atmospheric boundary layer height determination in the EMEP model. Atmospheric Chemistry and Physics, 10(2), 341.

    Article  Google Scholar 

  • Kain, J. S. (2004). The Kain-Fritsch convective parameterization: an update. Journal of Applied Meteorology, 43(1), 170–181.

    Article  Google Scholar 

  • Kansal, A., Khare, M., & Sharma, C. S. (2011). Air quality modelling study to analyse the impact of the World Bank emission guidelines for thermal power plants in Delhi. Atmospheric Pollution Research, 2, 99–105.

    Article  CAS  Google Scholar 

  • Khlystova, I., Buchwitz, M., Richter, A., Wittrock, F., Bovensmann, H., & Burrows, J. P. (2009). SCIAMACHY simultaneous measurements over biomass burning source regions. In Proceedings of Atmospheric Science Conference, (pp. 7–11).

  • Kleinman, L. I. (1994). Low and high NOx tropospheric photochemistry. Journal of Geophysical Research: Atmospheres, 99, 16831–16838.

    Article  CAS  Google Scholar 

  • Kusaka, H., & Kimura, F. (2004). Coupling a single-layer urban canopy model with a simple atmospheric model: Impact on urban heat island simulation for an idealized case. Journal of the Meteorological Society of Japan Ser II, 82(1), 67–80.

    Article  Google Scholar 

  • Kusaka, H., Kondo, H., Kikegawa, Y., & Kimura, F. (2001). A simple single-layer urban canopy model for atmospheric models: Comparison with multi-layer and slab models. Boundary-layer meteorology, 101(3), 329–358.

    Article  Google Scholar 

  • Lee, C. Y., Lee, Z. J., Huang, J. Q., Ye, F. L., Ning, Z. Y., & Yang, C. F. (2019). Urban Air Quality Analysis and Forecast Based on Intelligent Algorithm with Parameter Optimization and Decision Rules. MDPI Applied Sciences, 9, 5445. https://doi.org/10.3390/app9245445

    Article  CAS  Google Scholar 

  • Liao, T., Wang, S., Ai, J., Gui, K., Duan, B., Zhao, Q., & Sun, Y. (2017). Heavy pollution episodes, transport pathways and potential sources of PM2. 5 during the winter of 2013 in Chengdu (China). Science of the Total Environment, 584, 1056–1065.

    Article  Google Scholar 

  • Lien, F. -S., & Yee, E. (2004). Numerical modelling of the turbulent flow developing within and over a 3-d building array, part I: A high-resolution Reynolds-averaged Navier-Stokes approach. Boundary-Layer Meteorology, 112, 427–466.

    Article  Google Scholar 

  • Liu, T., Marlier, M. E., DeFries, R. S., Westervelt, D. M., Xia, K. R., Fiore,  A. M., Mickley, L. J., Cusworth, D. H., & Milly, G. (2018). Seasonal impact of regional outdoor biomass burning on air pollution in three Indian cities: Delhi Bengaluru, and Pune. Atmospheric Environment, 172, 83–92.

    CAS  Google Scholar 

  • Mammeri, A. & Lallam, M. (2019). Elaboration of an Analytical Formula for the Calculation of the Surface Temperature. Civil Engineering Journal, 5(10), 2260–2269. https://doi.org/10.28991/cej-2019-03091409

  • Miao, Y., Liu, S., Chen, B., Zhang, B., Wang, S., & Li, S. (2013). Simulating urban flow and dispersion in Beijing by coupling a CFD model with the WRF model. Advances in Atmospheric Sciences, 30(6), 1663–1678.

    Article  Google Scholar 

  • Miao, Y., Liu, S., Zheng, H., Zheng, Y., Chen, B., & Wang, S. (2014). A multi-scale urban atmospheric dispersion model for emergency management. Advances in Atmospheric Sciences, 31(6), 1353–1365.

    Article  CAS  Google Scholar 

  • Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., & Clough, S. A. (1997). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the long-wave. Journal of Geophysical Research: Atmospheres, 102(D14), 16663–16682.

    Article  CAS  Google Scholar 

  • Mohan, M., Kikegawa, Y., Gurjar, B. R., Bhati, S., & Kolli, N. R. (2013). Assessment of urban heat island effect for different land use–land cover from micrometeorological measurements and remote sensing data for megacity Delhi. Theoretical and Applied Climatology, 112, 647–658. https://doi.org/10.1007/s00704-012-0758-z

    Article  Google Scholar 

  • Mohan, M. (2010). Dynamics of urban pollution: A curative strategy using RS and GIS Techniques – A case study of Megacity Delhi, Project Completion Report, Indian Institute of Technology, Delhi.

  • Mohan, M., & Payra, S. (2009). Influence of aerosol spectrum and air pollutants on fog formation in urban environment of megacity Delhi, India. Environmental monitoring and assessment, 151(1–4), 265–277.

    Article  CAS  Google Scholar 

  • Ning, G., Wang, S., Yim, S. H. L., Li, J., Hu, Y., Shang, Z., & Wang, J. (2018). Impact of low-pressure systems on winter heavy air pollution in the north-west Sichuan Basin, China. Atmospheric Chemistry and Physics, 18(18), 13601–13615.

    Article  CAS  Google Scholar 

  • Okedere, O. B., Olalekan, A. P., Fakinle, B. S., Elehinafe, F. B., Odunlami, O. A., & Sonibare, J. A. (2019). Urban air pollution from the open burning of municipal solid waste. Environmental Quality Management, 28, 67–74. https://doi.org/10.1002/tqem.21633

    Article  Google Scholar 

  • Pandey, P., Kumar, D., Prakash, A., Jamson Masih, J., Singh, M., Kumar, S., Jain, V. K., & Kumar, K. (2012). A study of urban heat island and its association with particulate matter during winter months over Delhi. Science of the Total Environment, 414, 494–507. https://doi.org/10.1016/j.scitotenv.2011.10.043

    Article  CAS  Google Scholar 

  • Pianosi, F., Keith, B., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B., & Wagener, T. (2016). Sensitivity analysis of environmental models: A systematic review with practical workflow. Environmental Modelling and Software, 79, 214–232.

    Article  Google Scholar 

  • Sanchez, K. A., Foster, M., Nieuwenhuijsen, M. J., May, A. D., Ramani, T., Zietsman, J., & Khreis, H. (2020). Urban policy interventions to reduce traffic emissions and traffic-related air pollution: Protocol for a systematic evidence map. Environment International, 142, 105826.

    Google Scholar 

  • Sánchez-Barroso, G., & Sanz-Calcedo, J. G. (2019). Application of predictive maintenance in hospital heating, ventilation and air conditioning facilities. Emerging Science Journal, 3(5), 337–343.

    Article  Google Scholar 

  • Suhail, M., Khan, M. S., & Faridi, R. A. (2019). Assessment of urban heat islands effect and land surface temperature of Noida, India by using Landsat satellite data. MAPAN-Journal of Metrology Society of India, 34(4), 431–441.

    Google Scholar 

  • Tewari, M., Kusaka, H., Chen, F., Coirier, W. J., Kim, S., Wyszogrodzki, A. A., & Warner, T. T. (2010). Impact of coupling a microscale computational fluid dynamics model with a mesoscale model on urban scale contaminant transport and dispersion. Atmospheric Research, 96(4), 656–664.

    Article  CAS  Google Scholar 

  • Upadhyay, S., Das, S. K., & Ojha, C. S. P. (2019). Probabilistic comparison between turbulence closure model and Bulk Richardson Number approach for ABL height estimation using copula. Dynamics of Atmospheres and Oceans, 87, 101094. https://doi.org/10.1016/j.dynatmoce.2019.05.003

    Article  Google Scholar 

  • Wu, F., Lu, Y., Wang, M., Zhang, X., & Yang, C. (2019). Catalytic removal of ozone by Pd/ACFs and optimal design of ozone converter for air purification in aircraft cabin. Civil Engineering Journal, 5(8), 1656–1671.

    Article  Google Scholar 

  • Zheng, Y., Miao, Y., Liu, S., Chen, B., Zheng, H., & Wang, S. (2015). Simulating flow and dispersion by using WRF-CFD coupled model in a built-up area of Shenyang, China. Advances in Meteorology, 2015.

  • Zuo, W., Zhang, X., & Li, Y. (2020). Review of flue gas acid dew-point and related low temperature corrosion. Journal of the Energy Institute, 93, 1666–1677.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors hereby acknowledge the financial support provided by the INSPIRE Faculty Award programme of DST, Government of India. The IGRA data are downloaded from the Web site: https://www.ncdc.noaa.gov/data-access/weather-balloon-data.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Shikhar Upadhyay, Sarit K. Das & C. S. P. Ojha

Authors
  1. Shikhar Upadhyay
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sarit K. Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. C. S. P. Ojha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sarit K. Das.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Upadhyay, S., Das, S.K. & Ojha, C.S.P. 2-D simulation of atmospheric boundary layer in and around Delhi to determine air pollution scenarios during winter morning. Environ Monit Assess 193, 295 (2021). https://doi.org/10.1007/s10661-021-09065-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-021-09065-3

Keywords

Search

Navigation