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 1Meteorological 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. The CALPUFF has run with two different dispersion options and two different vertical layers in CALMET, i.e., (i) Two layers with cell face heights of 20m and 1500m, (ii) Ten layers with default cell face heights13 of 20m, 40m, 80m, 160m, 320m,

640m, 1200m, 2000m, 3000m and 4000m. The larger Fig. 1Wind rose diagram for January, 2009 number of layers in the lower atmosphere is allowing

Fig. 2Terrain Elevation, Stacks and Receptor grid in CALPUFF KHARE et al: PERFORMANCE EVALUATION OF CALPUFF AND AERMOD 305

for greater vertical resolution near the surface where meteorological pre-processor of AERMOD, has run large gradients is observed in the meteorological using MM5 model generated site-specific data of both conditions such as wind and temperature7. The cell surface and upper air. face height of 1500 m in two-layer option is taken so that maximum mixing height includes within the top Results and Discussion layer14. It is quite logical to take larger number of The performance of CALPUFF and AERMOD layers in CALMET, but this study also attempts to with respect to monitored data are evaluated by using look at the effect of simplification in meteorological well referred statistical parameters i.e. Index of Agreement ‘d’, Fractional Bias (FB) and Normal mean characteristics in vertical direction. Further, two 5,15 different dispersion options, i.e., Pasquill Gifford square error (NMSE). The comparison between Turner with rural ISC (industrial source complex) predicted and monitored pollutants concentrations are carried out for each scenario. The performance dispersion curves (PGT-ISC) and similarity theory st based dispersion option are used to compare model evaluation results are presented in Table 3. For 1 performance with AERMOD output and monitored scenario, predicted and monitored concentrations are data. The different scenarios used for comparison in also compared in time series plot (Figure 3). The ‘d’ CALPUFF are mentioned below and listed in table 2. value of 0.54 indicates satisfactorily performance of

AERMOD for predicting the NOx concentrations. FB ● Scenario 1: Case 1 and Case 2, which consist of value is found to be within the acceptable range and same layer option in CALMET, i.e., ten layers indicating under predicted behaviour of the model. (default cell face height) with different dispersion The NMSE value is found outside the acceptable approach of PGT –ISC dispersion and similarity range (Table 3). theory based dispersion, respectively. ● Scenario 2: Case 3 and Case 4, which consist of same layer option in CALMET, i.e., 20 m & 1500 m with different dispersion approach of PGT-ISC dispersion and similarity theory based dispersion, respectively. ● Scenario 3: Case 1 and Case 3, which consist of same dispersion option, i.e., PGT-ISC dispersion with vertical layers of ten (default cell face heights) and two (20 m and 1500m). ● Scenario 4: Case 2 and Case 4, which consist of Fig. 3Time series plot of predicted and monitored concentrations same dispersion option, i.e., similarity theory based of NOX in Scenario1 dispersion with vertical layers of ten (default cell face heights) and two (20 m and 1500m). Table 3Statistical descriptors for CALPUFF and AERMOD for

predicting NOx concentrations AERMOD is also setup and run with same modelling domain as used for CALPUFF. The Models Models Options Statistical descriptors continuous air monitoring station in Vaddu village Cases ‘d’ FB NMSE (i.e. X=675.69km, Y =1679.43.) is selected for 1. AERMOD - 0.54 0.43 0.58 receptor location for both the models. It is located at 2.CALPUFF approximately 3 km from the plant in west direction Scenario 1 1 0.53 0.12 0.37 (downwind direction of plant). The AERMET, 2 0.53 0.05 0.35 Scenario 2 3 0.56 0.05 0.41 Table 2Cases used for CALPUFF 4 0.56 0.02 0.37 Scenario 3 1 0.54 0.12 0.37 Case Nos. Dispersion Option Cell Face Heights 3 0.57 0.05 0.41 Scenario 4 2 0.53 0.05 0.35 1 PGT-ISC Dispersion Default (10 Cells) 4 0.56 0.02 0.35 2 Similarity Theory Default (10 Cells) Acceptable range - >0.5* -0.5 to +0.5# ≤ 0.5# 3 PGT-ISC Dispersion 20m, 1500m 4 Similarity Theory 20m, 1500m *Moriasi et al.16, *Khare et al.5, # Kumar et al.15 306 J SCI IND RES VOL 74 MAY 2015

● Scenario 1: In both cases, CALPUFF have The comparison of models’ predicted and performed satisfactorily having ‘d’, FB and NMSE monitored concentration in terms of statistical value within the acceptable ranges. Further, PGT- descriptor indicates that performance of both models ISC dispersion option (Case 1) and similarity theory are acceptable (having d value > 0.5) for NOx based dispersion option (Case 2) of CALPUFF are prediction in all four scenarios. Further, CALPUFF compared and found that case 2 results are slightly has performed more satisfactorily in comparison with better than case 1. It is evident from the positive FB AERMOD in all four selected scenarios. Similarly, 8 values that CALPUFF under predicted for NOx, but Dresser and Huizer also found more satisfactory the extent of under-prediction is more in case1 performance of CALPUFF in comparison with (FB=0.12) as compared to case 2 (FB=0.05) AERMOD for predicting pollutants’ concentration at (Table 3). Scenario 1 results are compared with near field of a thermal power plant in Martin Creek, AERMOD results and found that CALPUFF Pennsylvania. Further, comparison in CALPUFF performed more satisfactorily than AERMOD. performance with two different dispersion CALPUFF predictions are closer to the monitored approaches (i.e. PGT-ISC dispersion curve option values and both models’ predictions trends tend to and Similarity theory based dispersion option) follow the trends of the monitored values (Figure 3). indicated that similarity theory dispersion curve ● Scenario 2: In both case 3 and case 4, CALPUFF option gives better prediction result than PGT-ISC are performed satisfactorily having ‘d’ value 0.56 dispersion option for all selected pollutants. 17 in each case. Moreover, the similarity theory based Similarly, Venkataram have reported that similarity dispersion option (FB=0.02) gives smaller under- theory based dispersion models are more accurate 18 prediction as compared to PGT dispersion option than PGT based dispersion model. Xing et al. have (FB=0.05). The result of similarity theory based found that ISCST3 (based on PG stability class) and dispersion option (NMSE=0.37) showed smaller CALPUFF predicted approximately similar results at NMSE values as compared to PGT Dispersion receptor location beyond 1 km distance from source. 19 option (NMSE=0.41). It is observed that, Further, Busini et al. have also found satisfactory CALPUFF with similarity theory option (case 4) performance of CALPUFF and AERMOD dispersion performed slightly better than PGT –ISC dispersion model for odour prediction around swine farm. The options (case 3). CALPUFF in scenario 2 also sensitivity of CALPUFF with different vertical layers performed more satisfactorily than AERMOD. are compared here, which is rarely carried out.

● Scenario 3: With same dispersion option, i.e. PGT- Conclusion ISC dispersion option, the CALPUFF with two This study has focused on the validation of layer option (Case 3) are performed more CALPUFF and AERMOD air quality dispersion satisfactory for predicting NOX (d=0.57), than ten model for predicting NOx concentrations in near field vertical layers option (Case 1) having ‘d’ value of region of a steel plant in Indian climatic conditions. 0.54. The FB value indicated that CALPUFF are Further, performance of CALPUFF has been under-predicted with ten layer options (Case1) evaluated with different dispersion option and layers when compared with result of two layer option in vertical direction. The results indicate that (Case 3). A similar trend is observed with the CALPUFF performed more satisfactorily than NMSE values for CALPUFF and AERMOD AERMOD in near-field region of point sources. performance (Table 3). Like previous scenarios, CALPUFF with similarity theory dispersion curve this scenario of CALPUFF also performed more option performed more satisfactorily than PGT-ISC satisfactorily than AERMOD. dispersion option. Similarly, CALPUFF performed ● Scenario 4: With same dispersion option, i.e. more satisfactorily with two layer option (20m and similarity theory dispersion option, two vertical cell 1500m cell face heights) than ten layer options (up to layer option (case 4) (d=0.56) performed better than 4000 m) in CALMET. The study will boost the ten layer option (d=0.53). Further, results of the two application and accuracy of CALPUFF dispersion layer option showed less FB and NMSE values than model for assessment and management of industrial ten layer option in CALPUFF and AERMOD in all air pollution in Indian climatic condition. Further, cases. In this scenario also, CALPUFF performed more simulation and sensitivity analysis with local more satisfactorily than AERMOD. topographical features and site specific monitored KHARE et al: PERFORMANCE EVALUATION OF CALPUFF AND AERMOD 307

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