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Performance Evaluation of CALPUFF and AERMOD Dispersion Models for Air Quality Assessment of an Industrial Complex

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Performance Evaluation of CALPUFF and AERMOD Dispersion Models for Air Quality Assessment of an Industrial Complex Journal of Scientific & Industrial Research Vol. 74, May 2015, pp. 302-307 Performance evaluation of CALPUFF and AERMOD dispersion models for air quality assessment of an industrial complex S Gulia1, A Kumar2 and M Khare3* 1,2 & *3Civil Engineering Department, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India Received 18 December 2013; revised 7 October 2014; accepted 23 April 2015 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 NOx 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 NOx 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. Keywords: Industrial air pollution, Air quality dispersion models, CALPUFF, AERMOD, Performance evaluation. Introduction performance evaluation of CALPUFF. Scire et al,6 With the advent of rapid industrialization and reported that CALPUFF has advantages over plume increasing air pollution load, the need to accurately models like AERMOD, in dealing with calm winds assess the ambient air quality has become quite and stagnant conditions. Further, Walker et al.7 have essential to reduce the air pollution exposure. To compared ISCST3, CALPUFF, and AERMOD to manage air quality around the industrial activities, predict pollutants concentrations around a plant there is a need to evaluate the impact of different located in Nova Scotia, Canada. They found that emission sources by using efficient air quality CALPUFF performed satisfactorily for large prediction tools. Air quality modelling is an simulation domain of 400 by 600 km followed by important tool to quantify the impacts of emission AERMOD and ISCST3. Dresser and Huizer,8 also sources on ambient air quality. Many air quality validated and compared the CALPUFF and models (AQMs) have been used worldwide to AERMOD to assess the ambient air quality of near evaluate the impacts of industrial air pollution. field region of two coal fired thermal power plant in Validation of AQMs by using site specific Martins Creek, Pennsylvania. They found information, prior to its practical application, is quite performance of CALPUFF was superior to essential for accurate prediction and forecasting of air AERMOD. Such difference in model’s performance pollution load. In the recent past, a number of studies can be attributed to certain limitations of the have been carried out evaluating and comparing Gaussian plume model as pointed out by Bluett et predictive performance of AQMs such as AERMOD, al.9.The present study focuses on the validation of ISCST3, ADMS-Urban in different environmental CALPUFF model for predicting air pollutants conditions1-5. However, only few studies are concentrations around a steel industrial complex in available in literature regarding application and Bellary region of Karnataka state in India. Further, the performance of CALPUFF model with different *Author for correspondence dispersion options, has been compared with E-mail: [email protected] AERMOD. KHARE et al: PERFORMANCE EVALUATION OF CALPUFF AND AERMOD 303 Materials and Methods pollution generating sub units such as pellet plant, Model description coke oven battery, sinter plant, blast furnace, steel AERMOD is an advanced version of ISCST3 that melting shop, continuous casting facility, captive incorporates the effects of vertical variations in the power plants, lime calcinations plants and other small planetary boundary layer (PBL) on the dispersion of units. The source emission data are obtained from pollutants. The plume growth is determined by various point sources within the plant region. All 46 turbulence profiles that vary with height. AERMOD stacks are considered in the industrial complex calculates the convective and mechanical mixing having height range of 30m to 275m (average stack height. It includes the concept of a dividing streamlines height =73.24m). Other important information and the plume is modeled as a combinations of terrain– regarding emission sources, such as stack location following and terrain-impacting states10. (X,Y-coordinates), pollutant emission rate (g/s), gas It incorporates AERMET (Meteorological Pre- exit velocity (m/s), stack height (meter), stack processor) and AERMAP (terrain pre-processors). diameter (meter) and exit gas temperature (0K) are Input data for AERMET includes hourly cloud cover collected and used in model’s setup. The exit gas observations, surface meteorological observations, velocity, stack diameter and gas temperature are such as wind speed and direction, temperature, dew found in range of 7.01m/s - 27.63m/s, 5m - 8.7m and point, humidity and sea level pressure, and twice-a- 313 K to 660 K, respectively. day upper air soundings. The AERMAP uses gridded terrain data (digital elevations model data) to Meteorological data calculate a representative terrain-influence height The MM5 model generated surface and upper air (hc).CALPUFF is a non-steady-state puff dispersion sounding meteorological data of 2009 are used in this model that can simulate the effects of temporal and study. The hourly averaged surface meteorological spatial variability of micrometeorological conditions parameters such as wind speed, wind direction, on pollutant transport, transformation and removal. It temperature, cloud cover, relative humidity, consists of three sub-components, namely, CALMET atmospheric pressure, solar radiation and precipitation; (meteorological pre-processor that combines and upper meteorological parameters such as wind meteorological data and geophysical data to generate speed, direction; temperature and atmospheric a 3-D wind field), CALPUFF (predict concentration pressure are used in both the models. The upper air at receptor locations based on CALMET output and sounding data are recorded only two times a day, i.e. source information) and CALPOST (post processor in morning and afternoon. The models are setup and which summarizes CALPUFF output in tabulated and run for one month in winter season of 2009. Some graphical form)11. The puffs are tracked within the prominent details are given in table 1. In addition to modelling domain while calculating dispersion, this, figure 1 showed the wind rose diagram for the transformation and removal along the way. CALPUFF study period which indicates the dominant wind has an advantage of performing satisfactorily in calm direction is East and South East (blowing from). wind condition relative to AERMOD7. Air quality monitoring data Site description Air quality monitoring are carried around the 12 The selected site is a steel plant in industrial complex as per NAAQS guidelines . Vijaynagar, located in the heartland of the high-grade The NOx concentration data collected from iron ore belt at Toranagallu in the Bellary-hotspot continuous air quality monitoring station at Vaddu area of Karnataka. It has a semi-arid climate and village (X=675.69km, Y =1679.43) are used for models located in rain shadow of western ghat. The Table 1Meteorological parameters for the study area topography of the study area is gently sloping from South to North. The area is surrounded by small Parameters Wind Speed Temperature Relative Cloud Atmospheric (m/s) (oC) Humidity cover Pressure mountain ranges with elevation range of 430 m to (%) (Tens) (Milibars) 850 m above mean sea level. Min. 0 (calm) 14.6 46 2 942 Emission source Max. 7.4 28 100 10 952 The capacity of the selected steel plant is 16 Average 3.31 21.10 74.17 3.51 946.57 MTPA. The industrial complex consists of many air Std. Dev. 1.37 2.90 13.86 1.59 2.22 304 J SCI IND RES VOL 74 MAY 2015 validation. The 24-hour average NOx concentration during the study period is found to be 28 ±16 μg/m3. Model Setup and Run CALPUFF and AERMOD have setup and run to predict 24-hour average NOx concentrations for winter period of 2009. Both models have different structure and input data requirement as mentioned above in model description section. In CALPUFF, the modelling domain has setup by 40km × 40km horizontal grid with each grid cell spacing of 0.8 km, to provide adequate resolution of terrain features. The number of grid cells developed for modelling are 50 × 50 (i.e, 2500) along X and Y axes. The terrain elevation, source and receptor locations are shown in figure 2.The CALPUFF performances are evaluated using different dispersion approaches and further compared with AERMOD output and monitored NOx concentrations.
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