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Simulation of Gaseous Species and their Sensitivity towards the Regional Emission Inventory over Delhi, India using the Polyphemus modeling System

Author Affiliations

  • 1Department of Mathematics, Braj Mohan Das College, Dayalpur, Babasaheb Bhimrao Ambedkar Bihar University, Muzaffarpur, Bihar, India and Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi, India

Int. Res. J. Environment Sci., Volume 14, Issue (3), Pages 50-73, July,22 (2025)

Abstract

Air quality models are invaluable instruments for estimating and analyzing the concentration of air pollutants in the atmosphere. This paper evaluates the impact of regional emission inventory and global inventory EDGAR through the simulated air quality. The present study utilized the Polyphemus chemical transport model (CTM) over Delhi, India to simulate various chemical species. In particular, the impact of regional emission inventory on the simulations of gaseous species like Ozone (O3), Nitrogen dioxide (NO2), Nitrous oxide (NO), Carbon monoxide (CO) and Sulphur dioxide (SO2) are analyzed over the national capital of India. In order to analyse the impact of regional emission data on simulated gaseous species diurnal variation, time-series plots and statistical metrics are analyzed at multiple monitoring stations situated in the simulation domain. Regional emission inventory prepared for the base year 2010, takes into account of various sectors such as Industry, Road Transport, Waste disposal and Burning, Brick Kilns, vehicles etc. Anthropogenic impact on gaseous species as NO, NO2, CO and SO2concentration levels are observed. EDGAR global inventory for the base year 2010 is also used to analyze the comparative performance of megacity regional inventory. The ground level modelled concentration of O3, NO, NO2, CO and SO2 are compared with the monitored value at various locations. A regional emission inventory is able to capture NO, NO2 and CO concentration, whereas O3 is better predicted with EDGAR. The impact of the emission reduction scenario of major precursors NOX and VOCs on simulated O3 concentration has been analyzed. It will expedite the model response towards the emission changes that can be conducive to policy support also.

References

  1. Guttikunda, S.K. and Calori, G. (2013)., A GIS Based Emissions Inventory at 1 km x 1 km Spatial Resolution for Air Pollution Analysis in Delhi, India., Atmos. Environ., 67, 101–111.
  2. Gurjar, B.R., van Aardenne, J.A., Lelieveld, J. and Mohan, M. (2004)., Emission estimates and trends (1990–2000) for megacity Delhi and implications., Atmos. Environ., 38, 5663–5681.
  3. Kansal, A., Khare, M and Sharma, C.S. (2011)., Air quality modelling study to analyse the impact of the World Bank emission guidelines for thermal power plants in Delhi., Atmos. Pollut. Res., 2, 99–105.
  4. Sahu, S.K., Beig, G. and Parkhi, N.S. (2011)., Emissions Inventory of Anthropogenic PM2.5 and PM10 in Delhi during Commonwealth Games 2010., Atmos. Environ., 45, 6180–6190.
  5. Nagpure, A.S., Gurjar, B.R. and Martel, J.C. (2014)., Human health risks in national capital territory of Delhi due to air pollution., Atmos. Pollut. Res., 5(3), 371-380.
  6. Daniels MJ, Dominici F, Samet JM and Zeger SL (2000)., Estimating particulate matter-mortality dose-response curves and threshold levels: an analysis of daily time-series for the 20 largest US cities., Am J Epidemiol; 152, 397–406.
  7. Dockery, D.W. and Pope, C.A. (1994)., Acute Respiratory Effects of Particulate Air Pollution., Annu. Rev. Public Health, 15, 107-132.
  8. Schwartz, J. (1994)., Air Pollution and Daily Mortality: A Review and Metaanalysis., Environ. Res., 64, 36-52.
  9. Davidson, A. (1993)., Update on ozone trends in California’s south coast air basin., Air & Waste, 43(2), 226-240.
  10. Wakamatsu, S., Ohara, T. & Uno, I. (1996)., Recent trends in precursor concentrations and oxidant distributions in the Tokyo and Osaka areas., Atmospheric Environment, 30(5), 715-721.
  11. Jacob, D. J., & Winner, D. A. (2009)., Effect of climate change on air quality., Atmospheric environment, 43(1), 51-63.
  12. Oke T.R. (1995)., The heat island of the urban boundary layer: characteristics, causes and effects., In: Cermak JE, Davenport AG, Plate EJ, Viegas DX (eds) Wind Climate in Cities. NATO ASI Series E: Applied Sciences - Vol. 277, Boston: Kluwer Academic Publishers, 81-108.
  13. Martilli, A., Clappier, A., and Rotach, M.W. (2002)., An urban surface exchange parameterization for mesoscale models., Boundary-Layer Meteorology, 104, 261-304.
  14. Vautard, R., Honoré, C., Beekmann, M., and Rouil, L. (2005)., Simulation of ozone during the August 2003 heat wave and emission control scenarios., Atmos. Environ. 39, 2957–2967.
  15. Sillman, S. and Samson, P. J. (1995)., Impact of temperature on oxidant photochemistry in urban, polluted rural and remote environments., J. Geophys. Res. Atmos., 100, 11497-11508.
  16. Rubin, J. I., Kean, A. J., Harley, R. A., Millet, D. B. and Goldstein, A. H. (2006)., Temperature dependence of volatile organic compound evaporative emissions from motor vehicles, J. Geophys., Res.-Atmos., 111, d03305, doi: 10.1029/2005JD006458.
  17. Strassburger, A., & Kuttler, W. (1998)., Diurnal courses of ozone in an inner urban park., Meteorologische Zeitschrift, 7.
  18. Guttikunda, S. (2009)., Photochemistry of air pollution in Delhi, India., Sim air working paper series, 25.
  19. Lam, K. S., Wang, T. J., Wu, C. L., & Li, Y. S. (2005)., Study on an ozone episode in hot season in Hong Kong and transboundary air pollution over Pearl River Delta region of China., Atmospheric Environment, 39(11), 1967-1977.
  20. Chan, C. Y., Li, Y. S., Tang, J. H., Leung, Y. K., Wu, M. C., Chan, L. Y., ... & Liu, S. C. (2007)., An analysis on abnormally low ozone in the upper troposphere over subtropical East Asia in spring 2004., Atmospheric Environment, 41(17), 3556-3564.
  21. Crutzen, P. J. (1973)., A discussion of the chemistry of some minor constituents in the stratosphere and troposphere., Pageoph., 106-108: 1385-99.
  22. Crutzen, P. J. (1979)., The role of NO and NO2 in the chemistry of the troposphere and stratosphere., Ann. Rev. Earth Planet. Sci., 7: 443-72.
  23. Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C. and Eder, B. (2005)., Fully coupled “online” chemistry within the WRF model., Atmos. Environ., 39, 6957–6975.
  24. Zhang, Y. (2008)., Online-coupled meteorology and chemistry models: history, current status, and outlook., Atmos. Chem. Phys., 8, 2895–2932, doi: 10.5194/acp-8-2895-2008
  25. Sindhwani, R., Goyal, P., Kumar, S., & Kumar, A. (2015)., Anthropogenic Emission Inventory of Criteria Air Pollutants of an Urban Agglomeration-National Capital Region (NCR), Delhi., Aerosol and Air Quality Research, 15(4), 1681-1697.
  26. Sindhwani, R. and Goyal, P (2014)., Assessment of traffic-generated gaseous and particulate matter emissions and trends over Delhi (2000−2010)., Atmos. Pollut. Res., 5(3), 438-446.
  27. Nagpure, A.S., Sharma, K. and Gurjar, B.R. (2013)., Traffic induced emission estimates and trends (2000–2005) in megacity Delhi., Urban Climate, 4, 61–73.
  28. Kumar, A., & Goyal, P. (2011)., Forecasting of daily air quality index in Delhi., Science of the Total Environment, 409(24), 5517-5523.
  29. Mallet, V., Quélo, D., Sportisse, B., Ahmed de Biasi, M., Debry, É., Korsakissok, I., Wu, L., Roustan, Y., Sartelet, K., Tombette, M. and Foudhil, H. (2007)., Technical Note: The air quality modeling system Polyphemus., Atmos. Chem. Phys., 7, 5479-5487.
  30. Mallet, V., & Sportisse, B. (2004)., 3-D chemistry-transport model Polair: numerical issues, validation and automatic-differentiation strategy., Atmospheric Chemistry and Physics Discussions, 4(2), 1371-1392.
  31. Mallet, V and B. Sportisse (2005)., A comprehensive study of ozone sensitivity with respect to emissions over Europe with a chemistry-transport model., J. Geophys. Res., 110, D22302, doi: 10.1029/2005JD006234.
  32. Que ́lo, D., V. Mallet, and B. Sportisse (2005)., Inverse modeling of NOx emissions at regional scale over northern France: Preliminary investigation of the second-order sensitivity., J. Geophys. Res., 110, D24310, doi: 10.1029/2005JD006151.
  33. Louis, J. F. (1979)., A parametric model of vertical eddy fluxes in the atmosphere., Boundary-Layer Meteorology, 17, 187-202.
  34. Troen, I.B. and Mahrt, L., (1986)., A simple model of the atmospheric boundary layer; sensitivity to surface evaporation., Boundary-Layer Meteorology, 37, 129-148.
  35. Wesely, M.L. (1989)., Parameterization of surface resistances to gaseous dry deposition in regional-scale numerical models., Atmospheric Environment, 23, 1293-1304.
  36. Zhang, L., Gong, S., Padro, J. and Barrie, L., (2001)., A size-seggregated particle dry deposition scheme for an atmospheric aerosol module., Atmospheric Environment, 35, 549–560.
  37. Sportisse, B. and Dubois, L. (2002)., Numerical and theoretical investigation of a simplified model for the parameterization of below-cloud scavenging by falling raindrops., Atmospheric Environment, 36, 5719-5727.
  38. Zhang, L., Brook, J. and Vet, R., (2003)., A revised parameterization for gaseous dry deposition in air quality models., Atmospheric Chemistry and Physics Discussions, 3, 2067-2082.
  39. Yarwood, G., Rao, S., Yocke, M., and Whitten, G. Z. (2005)., Updates to the Carbon Bond Chemical Mechanism: CB05., Tech. rep., US Environmental Protection Agency.
  40. Gurjar, B. R., Van Aardenne, J. A., Lelieveld, J., & Mohan, M. (2004)., Emission estimates and trends (1990–2000) for megacity Delhi and implications., Atmospheric Environment, 38(33), 5663-5681.
  41. Energy Statistics of India (2018). http://mospi.nic.in/sites/default/files/publication_reports/Energy_Statistics_2018.pdf, undefined, undefined
  42. Maithel, S., Uma, R., Bont, T., Baum, E. and Thao, V.T.K. (2012)., Brick Kilns Performance Assessment, Emissions Measurements, & a Roadmap for Cleaner Brick Production in India., Study prepared by Green Knowledge Solutions, New Delhi, India.
  43. Shankar, S. (2014)., Study of Air Pollution in Delhi Due to Diesel Generator Sets Used in Telecommunication., 1–42. Centre for Atmospheric Sciences, Indian Institute of Technology, Delhi, M.Tech Dissertation.
  44. Emmons, L. K., Walters, S., Hess, P. G., Lamarque, J. F., Pfister, G. G., Fillmore, D., Granier, C., Guenther, A., Kinnison, D., Laepple, T., Orlando, J., Tie, X., Tyndall, G., Wiedinmyer, C., Baughcum, S. L., and Kloster, S. (2010)., Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4)., Geosci. Model Dev., 3, 43-67, doi: 10.5194/gmd-3-43-2010.
  45. Verwer, J. G., Hundsdorfer, W., and Blom, J. G. (1998):, Numerical time integration for air pollution models., technical report, CWI.
  46. Verwer, J. G., Spee, E. J. Blom, J. G. and Hundsdorfer, W (1999)., A second-order Rosenbrock method applied to photochemical dispersion problems., SIAM J. Sci. Comput., 20, 1456–1480.
  47. Chen, F. and Dudhia, J. (2001)., Coupling an advanced land- surface/hydrology model with the Penn State/NCAR MM5 modeling system., Part I: Model description and implementation, Mon. Weather Rev., 129, 569–585.
  48. Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A. (1997)., Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for longwave., J. Geophys. Res., 102(D14), 16663–16682.
  49. Lin, Y. L., R. D. Farley, and H. D. Orville (1983)., Bulk parameterization of the snow field in a cloud model., J. Clim. Appl. Meteorol., 22, 1065–1092, doi: 10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO; 2.
  50. Hong, S.Y., Noh, Y. and Dudhia, J. (2006)., A new vertical diffusion pack- age with an explicit treatment of entrainment processes., Mon. Weather Rev., 134, 2318–2341.
  51. Chou, M. D. and Suarez, M. J. (1994)., An efficient thermal infrared radiation parameterization for use in general circulation models., NASA Tech. Memo. 104606, 85.
  52. Stockwell, W.R., Middleton, P., Chang, J.S. and Tang, X., (1990)., second-generation regional acid deposition model chemical mechanism for regional air quality modeling., Journal of Geophysical Research, 95, 16343–16367.
  53. Stockwell, W.R., Kirchner, F., Kuhn, M. and Seefeld, S., (1997)., A new mechanism for regional atmospheric chemistry modeling., Journal of Geophysical Research, 102, 25847–25879.
  54. Wild, O., Zhu, X., and Prather, M. J. (2000)., Fast-J: Accurate simulation of in and below cloud photolysis in tropospheric chemical models., J. Atmos. Chem., 37, 245–282.
  55. Monin, A. S., and Obukhov, A. M. (1954)., Basic laws of turbulent mixing in the surface layer of the atmosphere., Tr. Akad. Nauk SSSR Geofiz. Inst., 24, 163–187.
  56. Kain, J.S. (2004)., The Kain–Fritsch convective parameterization: an update., Journal of Applied Meteorology, 43, 170–181.
  57. G. Janssens-Maenhout et al. (2011)., EDGAR-HTAP: a harmonized gridded air pollution emission dataset based on national inventories., doi:10.2788/14102.
  58. De Meij, A., Krol, M., Dentener, F., Vignati, E., Cuvelier, C. and Thunis, P (2006)., The sensitivity of aerosol in Europe to two different emission inventories and temporal distribution of emissions., Atmos. Chem. Phys. 6, 4287–4309.
  59. Pregger, T. and Friedrich, R. (2009)., Effective pollutant emission heights for atmospheric transport modelling based on real-world information., Environ. Poll., 157, 552–560, doi:10.1016/j.envpol.2008.09.027.
  60. Thunis, P. and Clappier, A. (2014)., Indicators to support the dynamic evaluation of air quality models., Atmos. Environ., 98, 402-409.
  61. Seinfeld, J. and Pandis, S. (1998)., Atmospheric chemistry and physics: From air pollution to climate change (2nd ed.)., Hoboken, New Jersey: Wiley.
  62. Seinfeld, J.H. (1989)., Urban air pollution: State of science., Science, 243, 745-753.
  63. Logan, J.A., M.J. Prather, S.C. Wofsy and M.B. McElroy (1981)., Tropospheric chemistry: A global perspective., J. Geophys. Res., 86, 7210-7254.
  64. Brewer, D.A., T.R. Augustsson, and J.S. Levine. (1983)., The photochemistry of anthropogenic nonmethane hydrocarbons in the troposphere., J. Geophys. Res., 88, 6683-6695.
  65. Finlayson-Pitts, B.J. and J.N. Pitts, Jr. (1986)., Atmospheric Chemistry: Fundamentals and Experimental Techniques., New York: Wiley-Inter science Publication. 1098pp.
  66. Sillman, S., J. A. Logan, and S. C. Wofsy (1990)., The sensitivity of ozone to nitrogen oxides and hydrocarbons in regional ozone episodes., J. Geophys. Res., 95(D2), 1837-1851. doi:10.1029/JD095iD02p01837.
  67. Folberth, G.A., Rumbold, S.T., Collins, W.J. and Butler, T.M. (2012)., Global radiative forcing and megacities., Urban Clim. 1 (2012), 4–19.
  68. Mena-Carrasco, M., Carmichael, G.R., Campbell, J.E., Zimmerman, D., Tang, Y., Adhikary, B., D’allura, A., Molina, L.T., Zavala, M., García, A., Flocke, F., Campos, T., Weinheimer, A.J., Shetter, R., Apel, E., Montzka, D.D., Knapp, D.J., Zheng, W., (2009)., Assessing the regional impacts of Mexico City emissions on air quality and chemistry., Atmos. Chem. Phys., 9, 3731–3743. http:// dx.doi.org/10.5194/acp-9-3731-2009.
  69. Tarrason L., Jonson J.E., Fagerli H., Benedictow A., Wind P., Simpson D. and Klein H., (2003)., Transboundary Acidification, Eutrophication and Ground Level Ozone in Europe, Part III., Source-receptor relationships, 2003. EMEP MSC-W Report 1/ 2003, Norwegian Meteorological Institute, Oslo, Norway.
  70. deMeij, A., Thunis, P., Bessagnet, B. and Cuvelier, C. (2009)., The sensitivity of the CHIMERE model to emissions reduction scenarios on air quality in Northern Italy., Atmos. Environ. 43, 1897–1907.
  71. Finardi, S., Silibello, C., D’Allura, A., & Radice, P. (2014)., Analysis of pollutants exchange between the Po Valley and the surrounding European region., Urban Climate, 10, 682-702.
  72. Guttikunda, S.K., Tang, Y., Carmichael, G.R., Kurata, G., Pan, L., Streets, D.G., Woo, J.-H., Thongboonchoo, N., Fried, A., (2005)., Impacts of Asian megacity emissions on regional air quality during spring 2001., J. Geophys. Res., 110, D20301. http://dx.doi.org/10.1029/2004JD004921