Optimization of wind speed on dispersion of pollutants using coupled receptor and dispersion model

Abstract

Air pollutants emission from various source categories can be quantified through mass balance (receptor model) techniques, multivariate data analysis and dispersion model. The composition of particulate matter from various emission points (emission inventory) and the massive analysis of the composition in the collected samples from various locations (receptor) are used to estimate quantitative source contribution through receptor models. In dispersion model, on the other hand the emission rates (μ g/m 3) from various sources together with particle size, stack height, topography, meteorological conditions (temperature, humidity, wind speed and directions, etc.) will affect the pollutant concentration at a point or in a region. The parameters used in dispersion model are not considering in receptor models but have been affecting indirectly as difference concentration at various receptor locations. These differences are attributed and possible erroneous results can be viewed through coupled receptor-dispersion model analysis. The current research work proposed a coupled receptor-dispersion model to reduce the difference between predicted concentrations through optimized wind velocity used in dispersion model. The converged wind velocities for various error percentages (10%, 40%, 60% and 80%) in receptor concentration have been obtained with corresponding increase in the error. The proposed combined approaches help to reconcile the differences arise when the two models used in an individual mode.