GIS-Based Emission Inventory, Dispersion Modeling, and Assessment for Source Contributions of Particulate Matter in an Urban Environment

• Published in: Water, air, and soil pollution Vol. 218; no. 1-4; pp. 423 - 436
• Main Authors: Behera, Sailesh N., Sharma, Mukesh, Dikshit, Onkar, Shukla, S. P.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.06.2011; Springer; Springer Nature B.V
• Subjects: Air pollution; Air quality; Air sampling; Airborne particulates; Applied sciences; Atmospheric Protection/Air Quality Control/Air Pollution; Cities; Civil engineering; Climate Change/Climate Change Impacts; Dust; Earth and Environmental Science; Emission inventories; Emissions; Environment; Environmental monitoring; Exact sciences and technology; Fuels; Geographic information systems; Humidity; Hydrogeology; Industrial plant emissions; Industrial pollution; Inventories; Outdoor air quality; Particulate matter; Point sources; Pollutants; Pollution; Power plants; Rain; Remote sensing; Roads; Sampling; Soil Science & Conservation; Stockpiling; Studies; Summer; Urban areas; Urban environments; Water Quality/Water Pollution; Weather; Asia; India; Kanpur India; India, Uttar Pradesh, Unnao Dist., Kanpur; GIS; Model evaluation; Index of agreement; ISCST3; The Ganga basin; Secondary particulate formation; Short term; Performance evaluation; Combustion; Identification; Motor vehicle; Urban area; Modeling; Coarse particle; Emission inventory; Sampling; Point source; Receptor model; Air quality; Dust; Geographic information system; Pollution source; Aerosols; Coal power plant; Pollution source inventory; Unpaved road; Air pollution; Road traffic; Industrial area; Performance
• ISSN: 0049-6979; 1573-2932
• DOI: 10.1007/s11270-010-0656-x

The Industrial Source Complex Short Term (ISCST3) model was used to discern the sources responsible for high PM 10 levels in Kanpur City, a typical urban area in the Ganga basin, India. A systematic geographic information system-based emission inventory was developed for PM 10 in each of 85 grids of 2 × 2 km. The total emission of PM 10 was estimated at 11 t day −1 with an overall breakup as follows: (a) industrial point sources, 2.9 t day −1 (26%); (b) vehicles, 2.3 t day −1 (21%); (c) domestic fuel burning, 2.1 t day −1 (19%); (d) paved and unpaved road dust, 1.6 t day −1 (15%); and the rest as other sources. To validate the ISCST3 model and to assess air-quality status, sampling was done in summer and winter at seven sampling sites for over 85 days; PM 10 levels were very high (89–632 μg m −3 ). The results show that the model-predicted concentrations are in good agreement with observed values, and the model performance was found satisfactory. The validated model was run for each source on each day of sampling. The overall source contribution to ambient air pollution was as follows: vehicular traffic (16%), domestic fuel uses (16%), paved and unpaved road dust (14%), and industries (7%). Interestingly, the largest point source (coal-based power plant) did not contribute significantly to ambient air pollution. The reason might be due to release of pollutant at high stack height. The ISCST3 model was shown to produce source apportionment results like receptor modeling that could generate source apportionment results at any desired time and space resolution.