Performance evaluation of CALPUFF and AERMOD dispersion models for air quality assessment of an industrial complex

Abstract

Air quality model (AQM) is an essential tool for management of air quality in near field region of an industrial complex. Model validation study using site specific input data can boost the consistency on accuracy of model’s performance for air quality management. This study describes the validation of CALPUFF and AERMOD for assessment of NO_x concentrations in near field region of a steel industry in Bellary district of Karnataka state in India. Relative model performances are evaluated by comparing monitored and predicted pollutants using well referred statistical descriptors. Further, the performance of CALPUFF has evaluated with different dispersion options (i.e., PGT-ISC dispersion curve and similarity theory) and vertical layers option (i.e., two and ten vertical layers) in CALMET, meteorological pre-processor of CALUFF. Both models performed satisfactorily for predicting NO_x concentrations. Further, CALPUFF with different dispersion options performed more satisfactorily than AERMOD. CALPUFF with PGT- ISC dispersion curve option performed more satisfactorily than similarity theory based dispersion option for the selected pollutant. In addition to this, CALPUFF with two vertical layers option performed better than ten vertical layers option. The satisfactory performance of CALPUFF over AERMOD might be due to its predicting capability in calm condition, in which all plume dispersion models failed.