Simulation of Air Quality using an ISCST3 Dispersion Model

Abstract

AbstractUrban air quality is an issue of major concern across many cities in India. In particular, high levels of particulate matter (both SPM and RSPM) are responsible for noncompliance to air quality standards. Air quality modeling is an effective tool to simulate the air quality of a region and to predict air quality concentrations under different scenarios. Kanpur city which is a top‐ten urban conglomerate in India (based on population) is chosen for the application of the ISCST3 model and simulation of air quality. Sectored emission loads are estimated for transport, industrial, power, and domestic sectors, which provide an estimate of the major contributors to air pollution with specific reference to particulate matter, which is a major pollutant of concern. A detailed scenario analysis is carried out to estimate the changes in emissions that would take place due to various interventions. Dispersion modeling is carried out using the ISCST3 model, to estimate the concentrations of SPM all over the city under different scenarios. Emission inventory and meteorological data served as input to the model, and the air quality is predicted for various seasons and intervention scenarios. The modeled values for the scenario without intervention results in an underestimation of 48%, which is due to unaccountable or unidentified sources, trans‐boundary movement of SPM, and model calibration errors. To overcome the error, the model is calibrated with the observed values and results are obtained for other scenarios using the calibration factor. The paper demonstrates only the research direction currently used to simulate air quality in Indian cities. However, further refinement and research is required before it could be used for more accurate predictions.