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 & *3
Civil 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 With the advent of rapid industrialization and increasing air pollution load, the need to accurately assess the ambient air quality has become quite essential to reduce the air pollution exposure. To manage air quality around the industrial activities, there is a need to evaluate the impact of different emission sources by using efficient air quality prediction tools. Air quality modelling is an important tool to quantify the impacts of emission sources on ambient air quality. Many air quality models (AQMs) have been used worldwide to evaluate the impacts of industrial air pollution. Validation of AQMs by using site specific information, prior to its practical application, is quite essential for accurate prediction and forecasting of air pollution load. In the recent past, a number of studies have been carried out evaluating and comparing predictive performance of AQMs such as AERMOD, ISCST3, ADMS-Urban in different environmental conditions1-5. However, only few studies are available in literature regarding application and *Author for correspondence E-mail:
[email protected]
performance evaluation of CALPUFF. Scire et al,6 reported that CALPUFF has advantages over plume models like AERMOD, in dealing with calm winds and stagnant conditions. Further, Walker et al.7 have compared ISCST3, CALPUFF, and AERMOD to predict pollutants concentrations around a plant located in Nova Scotia, Canada. They found that CALPUFF performed satisfactorily for large simulation domain of 400 by 600 km followed by AERMOD and ISCST3. Dresser and Huizer,8 also validated and compared the CALPUFF and AERMOD to assess the ambient air quality of near field region of two coal fired thermal power plant in Martins Creek, Pennsylvania. They found performance of CALPUFF was superior to AERMOD. Such difference in model’s performance can be attributed to certain limitations of the Gaussian plume model as pointed out by Bluett et al.9.The present study focuses on the validation of CALPUFF model for predicting air pollutants concentrations around a steel industrial complex in Bellary region of Karnataka state in India. Further, the performance of CALPUFF model with different dispersion options, has been compared with AERMOD.
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Materials and Methods Model description
AERMOD is an advanced version of ISCST3 that incorporates the effects of vertical variations in the planetary boundary layer (PBL) on the dispersion of pollutants. The plume growth is determined by turbulence profiles that vary with height. AERMOD calculates the convective and mechanical mixing height. It includes the concept of a dividing streamlines and the plume is modeled as a combinations of terrain– following and terrain-impacting states10. It incorporates AERMET (Meteorological Preprocessor) and AERMAP (terrain pre-processors). Input data for AERMET includes hourly cloud cover observations, surface meteorological observations, such as wind speed and direction, temperature, dew point, humidity and sea level pressure, and twice-aday upper air soundings. The AERMAP uses gridded terrain data (digital elevations model data) to calculate a representative terrain-influence height (hc).CALPUFF is a non-steady-state puff dispersion model that can simulate the effects of temporal and spatial variability of micrometeorological conditions on pollutant transport, transformation and removal. It consists of three sub-components, namely, CALMET (meteorological pre-processor that combines meteorological data and geophysical data to generate a 3-D wind field), CALPUFF (predict concentration at receptor locations based on CALMET output and source information) and CALPOST (post processor which summarizes CALPUFF output in tabulated and graphical form)11. The puffs are tracked within the modelling domain while calculating dispersion, transformation and removal along the way. CALPUFF has an advantage of performing satisfactorily in calm wind condition relative to AERMOD7. Site description
The selected site is a steel plant in Vijaynagar, located in the heartland of the high-grade iron ore belt at Toranagallu in the Bellary-hotspot area of Karnataka. It has a semi-arid climate and located in rain shadow of western ghat. The topography of the study area is gently sloping from South to North. The area is surrounded by small mountain ranges with elevation range of 430 m to 850 m above mean sea level. Emission source
The capacity of the selected steel plant is 16 MTPA. The industrial complex consists of many air
pollution generating sub units such as pellet plant, coke oven battery, sinter plant, blast furnace, steel melting shop, continuous casting facility, captive power plants, lime calcinations plants and other small units. The source emission data are obtained from various point sources within the plant region. All 46 stacks are considered in the industrial complex having height range of 30m to 275m (average stack height =73.24m). Other important information regarding emission sources, such as stack location (X,Y-coordinates), pollutant emission rate (g/s), gas exit velocity (m/s), stack height (meter), stack diameter (meter) and exit gas temperature (0K) are collected and used in model’s setup. The exit gas velocity, stack diameter and gas temperature are found in range of 7.01m/s - 27.63m/s, 5m - 8.7m and 313 K to 660 K, respectively. Meteorological data
The MM5 model generated surface and upper air sounding meteorological data of 2009 are used in this study. The hourly averaged surface meteorological parameters such as wind speed, wind direction, temperature, cloud cover, relative humidity, atmospheric pressure, solar radiation and precipitation; and upper meteorological parameters such as wind speed, direction; temperature and atmospheric pressure are used in both the models. The upper air sounding data are recorded only two times a day, i.e. in morning and afternoon. The models are setup and run for one month in winter season of 2009. Some prominent details are given in table 1. In addition to this, figure 1 showed the wind rose diagram for the study period which indicates the dominant wind direction is East and South East (blowing from). Air quality monitoring data
Air quality monitoring are carried around the industrial complex as per NAAQS guidelines12. The NOx concentration data collected from continuous air quality monitoring station at Vaddu village (X=675.69km, Y =1679.43) are used for models Table 1Meteorological parameters for the study area Parameters Wind Speed Temperature Relative Cloud Atmospheric (m/s) (oC) Humidity cover Pressure (%) (Tens) (Milibars) Min. Max. Average Std. Dev.
0 (calm) 7.4 3.31 1.37
14.6 28 21.10 2.90
46 100 74.17 13.86
2 10 3.51 1.59
942 952 946.57 2.22
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validation. The 24-hour average NOx concentration during the study period is found to be 28 ±16 μg/m3. Model Setup and Run
Fig. 1Wind rose diagram for January, 2009
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 number of layers in the lower atmosphere is allowing
Fig. 2Terrain Elevation, Stacks and Receptor grid in CALPUFF
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for greater vertical resolution near the surface where large gradients is observed in the meteorological conditions such as wind and temperature7. The cell face height of 1500 m in two-layer option is taken so that maximum mixing height includes within the top layer14. It is quite logical to take larger number of layers in CALMET, but this study also attempts to look at the effect of simplification in meteorological characteristics in vertical direction. Further, two different dispersion options, i.e., Pasquill Gifford Turner with rural ISC (industrial source complex) dispersion curves (PGT-ISC) and similarity theory based dispersion option are used to compare model performance with AERMOD output and monitored data. The different scenarios used for comparison in CALPUFF are mentioned below and listed in table 2. ● Scenario 1: Case 1 and Case 2, which consist of same layer option in CALMET, i.e., ten layers (default cell face height) with different dispersion approach of PGT –ISC dispersion and similarity 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 same dispersion option, i.e., similarity theory based dispersion with vertical layers of ten (default cell face heights) and two (20 m and 1500m). AERMOD is also setup and run with same modelling domain as used for CALPUFF. The continuous air monitoring station in Vaddu village (i.e. X=675.69km, Y =1679.43.) is selected for receptor location for both the models. It is located at approximately 3 km from the plant in west direction (downwind direction of plant). The AERMET, Table 2Cases used for CALPUFF Case Nos. 1 2 3 4
Dispersion Option
Cell Face Heights
PGT-ISC Dispersion Similarity Theory PGT-ISC Dispersion Similarity Theory
Default (10 Cells) Default (10 Cells) 20m, 1500m 20m, 1500m
meteorological pre-processor of AERMOD, has run using MM5 model generated site-specific data of both surface and upper air. Results and Discussion The performance of CALPUFF and AERMOD with respect to monitored data are evaluated by using well referred statistical parameters i.e. Index of Agreement ‘d’, Fractional Bias (FB) and Normal mean square error (NMSE).5,15 The comparison between predicted and monitored pollutants concentrations are carried out for each scenario. The performance evaluation results are presented in Table 3. For 1st scenario, predicted and monitored concentrations are also compared in time series plot (Figure 3). The ‘d’ value of 0.54 indicates satisfactorily performance of AERMOD for predicting the NOx concentrations. FB value is found to be within the acceptable range and indicating under predicted behaviour of the model. The NMSE value is found outside the acceptable range (Table 3).
Fig. 3Time series plot of predicted and monitored concentrations of NOX in Scenario1 Table 3Statistical descriptors for CALPUFF and AERMOD for predicting NOx concentrations Models 1. AERMOD 2.CALPUFF Scenario 1 Scenario 2 Scenario 3 Scenario 4 Acceptable range
Models Options Cases 1 2 3 4 1 3 2 4 -
Statistical descriptors ‘d’ 0.54
FB 0.43
NMSE 0.58
0.53 0.12 0.37 0.53 0.05 0.35 0.56 0.05 0.41 0.56 0.02 0.37 0.54 0.12 0.37 0.57 0.05 0.41 0.53 0.05 0.35 0.56 0.02 0.35 >0.5* -0.5 to +0.5# ≤ 0.5#
*Moriasi et al.16, *Khare et al.5, # Kumar et al.15
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● Scenario 1: In both cases, CALPUFF have performed satisfactorily having ‘d’, FB and NMSE value within the acceptable ranges. Further, PGTISC dispersion option (Case 1) and similarity theory based dispersion option (Case 2) of CALPUFF are compared and found that case 2 results are slightly better than case 1. It is evident from the positive FB values that CALPUFF under predicted for NOx, but the extent of under-prediction is more in case1 (FB=0.12) as compared to case 2 (FB=0.05) (Table 3). Scenario 1 results are compared with AERMOD results and found that CALPUFF performed more satisfactorily than AERMOD. CALPUFF predictions are closer to the monitored values and both models’ predictions trends tend to follow the trends of the monitored values (Figure 3). ● Scenario 2: In both case 3 and case 4, CALPUFF are performed satisfactorily having ‘d’ value 0.56 in each case. Moreover, the similarity theory based dispersion option (FB=0.02) gives smaller underprediction as compared to PGT dispersion option (FB=0.05). The result of similarity theory based dispersion option (NMSE=0.37) showed smaller NMSE values as compared to PGT Dispersion option (NMSE=0.41). It is observed that, CALPUFF with similarity theory option (case 4) performed slightly better than PGT –ISC dispersion options (case 3). CALPUFF in scenario 2 also performed more satisfactorily than AERMOD. ● Scenario 3: With same dispersion option, i.e. PGTISC dispersion option, the CALPUFF with two layer option (Case 3) are performed more satisfactory for predicting NOX (d=0.57), than ten vertical layers option (Case 1) having ‘d’ value of 0.54. The FB value indicated that CALPUFF are under-predicted with ten layer options (Case1) when compared with result of two layer option (Case 3). A similar trend is observed with the NMSE values for CALPUFF and AERMOD performance (Table 3). Like previous scenarios, this scenario of CALPUFF also performed more satisfactorily than AERMOD. ● Scenario 4: With same dispersion option, i.e. similarity theory dispersion option, two vertical cell layer option (case 4) (d=0.56) performed better than ten layer option (d=0.53). Further, results of the two layer option showed less FB and NMSE values than ten layer option in CALPUFF and AERMOD in all cases. In this scenario also, CALPUFF performed more satisfactorily than AERMOD.
The comparison of models’ predicted and monitored concentration in terms of statistical descriptor indicates that performance of both models are acceptable (having d value > 0.5) for NOx prediction in all four scenarios. Further, CALPUFF has performed more satisfactorily in comparison with AERMOD in all four selected scenarios. Similarly, Dresser and Huizer8 also found more satisfactory performance of CALPUFF in comparison with AERMOD for predicting pollutants’ concentration at near field of a thermal power plant in Martin Creek, Pennsylvania. Further, comparison in CALPUFF performance with two different dispersion approaches (i.e. PGT-ISC dispersion curve option and Similarity theory based dispersion option) indicated that similarity theory dispersion curve option gives better prediction result than PGT-ISC dispersion option for all selected pollutants. Similarly, Venkataram17 have reported that similarity theory based dispersion models are more accurate than PGT based dispersion model. Xing et al.18 have found that ISCST3 (based on PG stability class) and CALPUFF predicted approximately similar results at receptor location beyond 1 km distance from source. Further, Busini et al.19 have also found satisfactory performance of CALPUFF and AERMOD dispersion model for odour prediction around swine farm. The sensitivity of CALPUFF with different vertical layers are compared here, which is rarely carried out. Conclusion This study has focused on the validation of CALPUFF and AERMOD air quality dispersion model for predicting NOx concentrations in near field region of a steel plant in Indian climatic conditions. Further, performance of CALPUFF has been evaluated with different dispersion option and layers in vertical direction. The results indicate that CALPUFF performed more satisfactorily than AERMOD in near-field region of point sources. CALPUFF with similarity theory dispersion curve option performed more satisfactorily than PGT-ISC dispersion option. Similarly, CALPUFF performed more satisfactorily with two layer option (20m and 1500m cell face heights) than ten layer options (up to 4000 m) in CALMET. The study will boost the application and accuracy of CALPUFF dispersion model for assessment and management of industrial air pollution in Indian climatic condition. Further, more simulation and sensitivity analysis with local topographical features and site specific monitored
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meteorological data will help in improving model’s accuracy. Reference
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