Performance evaluation of ISCST3, adms-urban and aermod for urban air quality management in a mega city of India

S. Gulia, S.M. Shiva Nagendra, M. Khare
2014 International Journal of Sustainable Development and Planning  
Urban air quality has deteriorated in last few decades in the mega cities of both developed and developing countries. Many mathematical models have been widely used as prediction tool for urban air quality management in developed countries. However, applications of these models are limited in developing countries including India due to lack of suffi cient validation studies. In this paper, three state-of-the-art air quality models namely AERMOD, ADMS-Urban and ISCST3 have been used to predict
more » ... n used to predict the air quality at an intersection in Delhi city, India, followed by their performance evaluation and sensitive analysis under different meteorological conditions. The models have been run for different climatic conditions, i.e. summer and winter season to predict the concentration of carbon monoxide (CO), nitrogen dioxide (NO 2 ) and PM 2.5 (diameter size less than 2.5 µm). The ISCST3 has performed satisfactorily (d = 0.69) for predicting CO concentrations when compared with AERMOD (d = 0.50) and ADMS-Urban (d = 0.45) for winter period. The ADMS-Urban (d = 0.49) has performed satisfactorily for predicting NO 2 concentration when compared with ISCST3 (d = 0.36) and AERMOD (d = 0.32). The AERMOD, ISCST3 and ADMS-Urban have performed satisfactorily for predicting PM 2.5 concentrations having d values as 0.46, 0.45 and 0.43 respectively. All three models have performed satisfactorily for predicting CO concentrations when wind speed was in the range of 0.5-3 m/s and wind direction in the range 90-180 degrees, i.e. downwind direction. The difference in model's performance may be due to differences in model formulation and the treatment of terrain features. The causal nature of these Gaussian based models may be one of the reasons for difference in performance of the models, because these are sensitive to quality and quantity of input data on meteorology and emission sources.
doi:10.2495/sdp-v9-n6-778-793 fatcat:gxikxiinwfcxziqbhcqe2vpx5m