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

Transcription

1 Journal of Scientific & Industrial Research Vol. 74, May 2015, pp Performance evaluation of CALPUFF and AERMOD dispersion models for air quality assessment of an industrial complex S Gulia 1, A Kumar 2 and M Khare 3 * 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 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. 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 conditions 1-5. However, only few studies are available in literature regarding application and *Author for correspondence kharemukesh@yahoo.co.in 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.

2 KHARE et al: PERFORMANCE EVALUATION OF CALPUFF AND AERMOD 303 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 states 10. 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 (h c ).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 AERMOD 7. 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 ( 0 K) 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 m/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 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 guidelines 12. The NO x concentration data collected from continuous air quality monitoring station at Vaddu village (X=675.69km, Y = ) are used for models Table 1 Meteorological parameters for the study area Parameters Wind Speed (m/s) Temperature ( o C) Relative Humidity (%) Cloud cover (Tens) Atmospheric Pressure (Milibars) Min. 0 (calm) Max Average Std. Dev

3 304 J SCI IND RES VOL 74 MAY 2015 validation. The 24-hour average NO x concentration during the study period is found to be 28 ±16 μg/m 3. Fig. 1 Wind rose diagram for January, 2009 Model Setup and Run CALPUFF and AERMOD have setup and run to predict 24-hour average NO x concentrations for winter period of 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 (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 NO x 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 heights 13 of 20m, 40m, 80m, 160m, 320m, 640m, 1200m, 2000m, 3000m and 4000m. The larger number of layers in the lower atmosphere is allowing Fig. 2 Terrain Elevation, Stacks and Receptor grid in CALPUFF

4 KHARE et al: PERFORMANCE EVALUATION OF CALPUFF AND AERMOD 305 for greater vertical resolution near the surface where large gradients is observed in the meteorological conditions such as wind and temperature 7. The cell face height of 1500 m in two-layer option is taken so that maximum mixing height includes within the top layer 14. 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 = ) 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 2 Cases used for CALPUFF Case Nos. Dispersion Option Cell Face Heights 1 PGT-ISC Dispersion Default (10 Cells) 2 Similarity Theory Default (10 Cells) 3 PGT-ISC Dispersion 20m, 1500m 4 Similarity Theory 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 1 st 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 NO x 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. 3 Time series plot of predicted and monitored concentrations of NO X in Scenario1 Table 3 Statistical descriptors for CALPUFF and AERMOD for predicting NO x concentrations Models Models Options Statistical descriptors Cases d FB NMSE 1. AERMOD CALPUFF Scenario Scenario Scenario Scenario Acceptable range - >0.5* -0.5 to +0.5 # 0.5 # *Moriasi et al. 16, * Khare et al. 5, # Kumar et al. 15

5 306 J SCI IND RES VOL 74 MAY 2015 Scenario 1: In both cases, CALPUFF have performed satisfactorily having d, FB and NMSE value within the acceptable ranges. Further, PGT- ISC 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 NO x, 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. PGT- ISC dispersion option, the CALPUFF with two layer option (Case 3) are performed more satisfactory for predicting NO X (d=0.57), than ten vertical layers option (Case 1) having d value of 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 NO x prediction in all four scenarios. Further, CALPUFF has performed more satisfactorily in comparison with AERMOD in all four selected scenarios. Similarly, Dresser and Huizer 8 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, Venkataram 17 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 NO x 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

6 KHARE et al: PERFORMANCE EVALUATION OF CALPUFF AND AERMOD 307 meteorological data will help in improving model s accuracy. Reference 1 Caputo M, Giménez M & Schlamp M, Inter-comparison of atmospheric dispersion models, Atmos Environ, 37(18) (2003) Chang J C & Hanna S R, Air quality model performance evaluation. Meteorol Atmos Phys, 87 (2004) Holmes N S & Morawska L A, A review of dispersion modelling and its application to the dispersion of particles : An overview of different dispersion models available, Atmos Environ, 40(2) (2006) Langner C & Klemm O, A comparison of model performance between AERMOD and AUSTAL2000, J Air & Waste Manage Assoc, 61(2011) Khare M, Nagendra S & Gulia S, Performance evaluation of air quality dispersion models at urban intersection of an Indian city: a case study of Delhi city, WIT Trans Eco & Environ, 157 (2012) Walker J I, Scaplen M & George F, ISCST3, AERMOD and CALPUFF: A Comparative Analysis in the Environmental Assessment of a Sour Gas Plant, Jacques Whitford Environ Ltd (JWEL) report paper, paper no: 25 (2002). 7 Scire J, Strimaitis D & Yamartino R, A user s guide for the CALPUFF dispersion model, Earth Tech., Inc., Dresser A L & Huizer R D, CALPUFF and AERMOD model validation study in the near field: Martins Creek Revisited. J Air & Waste Manage Assoc, 61(6) (2011) Bluett J, Gimson N & Fisher G, Good practice guide for atmospheric dispersion modelling, National Institute of Water and Atmospheric Research, Aurora Pacific Limited and Earth Tech Incorporated for the Ministry for the Environment, Cimorelli A J, Perry S G, Venktaram A, Weil J C & Paine R J, AERMOD: A dispersion model for industrial source applications. Part I: General model formulation and boundary layer characterization, J Appl Meteorol, 44 (2004) USEPA, A Comparison of CALPUFF with ISC3, EPA-454/R , United State Environmental Protection Agency, MoEF, National ambient air quality standards, The Gazette of India, notification number 860, dated 16 th November, Ministry of Environment and Forest, Fox, Clarification on EPA-FLM Recommended Settings for CALMET, Memorandum, Air quality modelling group, United State Environment Protection Agency, Attri S D, Singh S, Mukhopadhyay B & Bhatnagar A K, Atlas of hourly mixing height and assimilative capacity of atmosphere in India. Met Monograph No. Environmental Meteorology-01/2008, Indian Meteorological Department, New Delhi, Govt. of India, Kumar A, Dixit S, Varadarajan C, Vijayan A & Masuraha A, Evaluation of the AERMOD dispersion model as a function of atmospheric stability for an urban area, Environ Prog, 25(2) (2006) Moriasi D N, Arnold J G, Liew V M W, Bingner R L, Harmel R D & Veith T L, Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, Am Soc Agric Bio Engg, 50(3) (2007) Venkataram A, An examination of the Pasquill-Gifford- Turner dispersion scheme, Atmos Environ, 30 (8) (1996) Xing Y, Guo H, Feddes J, Yu Z, Shewchuck S & Predicala B, Sensitivities of four air dispersion models to climatic parameters for Swine odor dispersion, Am Soc Agric Bio Engg, 50(3) (2007) Busini V, Capelli L, Sironi S, Nano G, Rossi A N & Bonat S, Comparison of CALPUFF and AERMOD models for odour dispersion simulation, Chem Engg Trans, 30 (2012)