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Volume 14, issue 19
Atmos. Chem. Phys., 14, 10619-10630, 2014
https://doi.org/10.5194/acp-14-10619-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: The community version of the Weather Research and Forecasting...

Atmos. Chem. Phys., 14, 10619-10630, 2014
https://doi.org/10.5194/acp-14-10619-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 09 Oct 2014

Research article | 09 Oct 2014

Air quality in Delhi during the Commonwealth Games

P. Marrapu1,2, Y. Cheng2,3, G. Beig4, S. Sahu5, R. Srinivas4, and G. R. Carmichael1,2 P. Marrapu et al.
  • 1Department of Chemical and Biochemical Engineering, University of Iowa, Iowa city, USA
  • 2Center for Global and Regional Environmental Research, University of Iowa, Iowa city, USA
  • 3Multiphase Chemistry Department, Max Planck Institute Chemistry, Hahn-Meitner-Weg 1, Mainz, Germany
  • 4Indian Institute of Tropical Meteorology (Ministry of Earth Sciences, Govt. of India) Dr. Homi Bhabha Road, Pashan, Pune, India
  • 5Forschungszentrum Julich Gmbh, IEk-8:Troposphere, 52425 Julich, Germany

Abstract. Air quality during the Commonwealth Games (CWG, held in Delhi in October 2010) is analyzed using a new air quality forecasting system established for the games. The CWG stimulated enhanced efforts to monitor and model air quality in the region. The air quality of Delhi during the CWG had high levels of particles with mean values of PM2.5 and PM10 at the venues of 111 and 238 μg m−3, respectively. Black carbon (BC) accounted for ~ 10% of the PM2.5 mass. It is shown that BC, PM2.5 and PM10 concentrations are well predicted, but with positive biases of ~ 25%. The diurnal variations are also well captured, with both the observations and the modeled values showing nighttime maxima and daytime minima. A new emissions inventory, developed as part of this air quality forecasting initiative, is evaluated by comparing the observed and predicted species-species correlations (i.e., BC : CO; BC : PM2.5; PM2.5 : PM10). Assuming that the observations at these sites are representative and that all the model errors are associated with the emissions, then the modeled concentrations and slopes can be made consistent by scaling the emissions by 0.6 for NOx, 2 for CO, and 0.7 for BC, PM2.5, and PM10. The emission estimates for particles are remarkably good considering the uncertainty in the estimates due to the diverse spread of activities and technologies that take place in Delhi and the rapid rates of change.

The contribution of various emission sectors including transportation, power, domestic and industry to surface concentrations are also estimated. Transport, domestic and industrial sectors all make significant contributions to PM levels in Delhi, and the sectoral contributions vary spatially within the city. Ozone levels in Delhi are elevated, with hourly values sometimes exceeding 100 ppb. The continued growth of the transport sector is expected to make ozone pollution a more pressing air pollution problem in Delhi. The sector analysis provides useful inputs into the design of strategies to reduce air pollution levels in Delhi. The contribution for sources outside of Delhi on Delhi air quality range from ~ 25% for BC and PM to ~ 60% for day time ozone. The significant contributions from non-Delhi sources indicates that in Delhi (as has been show elsewhere) these strategies will also need a more regional perspective.

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