Volume 14, No. 5, August 2014, Pages 1487-1499 PDF(1.01 MB)
Air Quality Prediction of PM10 through an Analytical Dispersion Model for Delhi
Anikender Kumar, P. Goyal
Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi-110016, India
An analytical dispersion model for point, line and area sources is formulated in this work. The analytical solution of an advection-diffusion equation with the Neumann (total reflection) boundary conditions for a bounded domain is obtained for point sources using the separation of variable and wind speed as a power law profile of vertical height above the ground. The downwind and vertical eddy diffusivities are considered as an explicit function of downwind distance and vertical height. The formulations for line and area sources are obtained by integrating the point source formulation in crosswind, crosswind and downwind directions, respectively. A gridded emissions inventory of Delhi City for the year 2008–09 has been developed to estimate the strength of emissions from various sources, namely vehicles, industries, power plants and domestic sources, and this has been made using primary and secondary data of PM10 (particulate matter with aerodynamic diameter ≤ 10 µm). Dispersion models generally require steady and horizontally homogeneous hourly surface and upper air meteorological observations. However, hourly meteorological observations are not easily available for most of the locations in India. To overcome this limitation, meteorological variables are computed using the Weather Research and Forecast (WRF) model (version 3.1.1) developed by the National Center for Atmospheric Research (NCAR). The performance of this analytical model is evaluated through concentrations of PM10 monitored at different locations in Delhi. The observed data for the period December 2008 has been obtained from the Central Pollution Control Board (CPCB). The model’s predicted values are found to be in good in agreement with the observed values. However, the model results depend on the accuracy of source strength data, i.e., the estimated values of emission rates for the various air pollutants.
Analytical model; PM10; Weather Research and Forecast (WRF); Emission inventory.