Performance evaluation of CALINE 4 dispersion model for an urban highway corridor in Delhi

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1 SHARMA et al: PERFORMANCE EVALUATION OF CALINE 4 DISPERSION MODEL IN DELHI Journal of Scientific & Industrial Research Vol. 72, August 2013, pp Performance evaluation of CALINE 4 dispersion model for an urban highway corridor in Delhi Niraj Sharma 1*, Sunil Gulia 2, Rajni Dhyani 3 and Anil Singh 4 1, 4 Environmental Science Division, CSIR-Central Road Research Institute, New Delhi , India 2 Civil Engineering Department, IIT Delhi, India 3 Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Road Research Institute, New Delhi , India Received 27 July 2012; revised 04 February 2013; accepted 29 April 2013 The paper focuses on the performance evaluation of CALINE 4 model for predicting carbon monoxide (CO) concentrations along an urban highway corridor passing through the city of Delhi. The model was applied using two different sets of Indian emission factors for different categories of vehicles specified by Central Pollution Control Board (CPCB), and Automotive Research Association of India (ARAI) in conjunction with on-site traffic and micro-meteorological inputs. The modelling results indicate that the dispersion of CO along the corridor was limited to a distance of ~150m from the edge of the mixing zone width (road width+3m on each side of the road). The concentrations were found to be 12% higher in the case of ARAI emission factors as compared to the CPCB emission factors. The correlation coefficient values (r 2 ) between predicted and observed 1-hour concentrations for CPCB and ARAI emission factors were found to be 0.60 and 0.65 respectively. In addition, the estimated index of agreement (d) values of 0.86 and 0.85 respectively indicate satisfactory model performance. However, the estimated fractional bias (FB) values of 0.04 for CPCB and 0.06 for ARAI emission factors indicate that the CALINE 4 model under predicts the concentrations in both the cases. Keywords: Line source models, CALINE 4, urban traffic, Emission factors, Statistical Descriptors, Performance evaluation Introduction The rapid growth of motor vehicles ownership and activities in Indian cities are causing serious health, environmental and socio-economic impacts 1. The rapidly growing vehicle fleet, distance travelled by each vehicle and change in land use pattern are some of the primary causes of vehicular air pollution and consequently urban air pollution 2. The motor vehicle population in India has increased from nearly 0.3 million in 1951 to 115 million in 2009, of which, two wheelers accounted for approximately 70% of the total vehicles 3. The vehicular pollution is the major contributor of urban air pollution in most of the cities of India and estimated to account for approximately 70% CO, 50% HC, 30%-40% NO x, 30% of SPM and 10% of SO 2 of the total pollution load of which two third is contributed by two wheelers alone 4. In a recent study it has been reported that the road transport sector is contributing about 72% of total air pollution in Delhi and contributes around 14% emissions of green house gases (primarily CO 2 ) 3. The line source emission modelling is an important tool in control and *Author for correspondence neeraj.crri@nic.in management of vehicular exhaust emissions (VEE) in urban environment. Line source dispersion models can provide current and future air quality levels to facilitate control and manage the vehicular/ urban air pollution. The line sources models are used to simulate the dispersion of vehicular pollutants near r oads where vehicles continuously emit pollutants. Various line source dispersion models are used to predict the pollutants concentration along the roads/ highways. However, CALINE 4 the latest in CALINE series model is most widely used vehicular pollution model to predict air pollutions concentration along the highway under rural (i.e. open) and semi urban conditions without street canyon effects 5. CALINE 4 offers several advantages over other models and has been used in various Indian cities to predict concentration of vehicular pollutants along the roads/ highways 6, 7. Earlier, Nirjar et al. 8 had used CALINE 4 to predict the concentration of CO along the urban and semi-urban roads in Delhi and the study results showed under prediction and moderate r 2 correlation values between observed and predicted concentrations. Further, Gramotnev et al. 9 used CALINE 4 for the analysis aerosols of (fine and ultra-fine particles) generated by vehicles on a busy road and found good

2 522 J SCI IND RES VOL 72 AUGUST 2013 Ashram chowk NH-2 Ashram chowk CRRI road corridor of NH-2 CRRI (Length of Corridor = 2.7 km; 06 lane; carriageway width ~24m and median width =3m) Fig. 1 Base map of Ashram Chowk-CRRI highway corridor of NH- 2 agreement between observed and predicted concentrations. Levitin et al. 10 found that the performance of CALINE 4 was better at a distance of 34m compared with that at a distance of 17m. The study also analyzed the performance of the model vis-à-vis observed data in terms of the wind speed and direction. Anjaneyulu et al. 11 studied the CO concentrations of Calicut city (India) using CALINE 4 and IITLS and linear regression models. In California, CALINE 4 has been widely accepted for many years as the standard modelling tool to evaluate project-level CO impacts 12. Although most of the studies involving CALINE 4 models have been limited to predict CO concentrations along roads and highways, however some recent studies have also been carried out to predict concentrations of fine particulates and NO/NO x concentrations in Florida 13. Further, Yura et al. 14 have reported that the CALINE 4 does not perform well in densely populated areas and differences in topographyto: could be a decisive factor when the model was used to predict concentrations of PM 2.5. Majumdar et al. 15 reveal that CALINE 4 with correction factors (0.37) can be applied reasonably well for the prediction of CO in the city of Kolkata. Ganguly et al. 16 compared CALINE 4 model and GFLSM code 17 and evaluated their comparative performance. They observed that CALINE 4 has performed better as compared to GFLSM code. They also found good agreement between observed and predicted concentrations at receptor points close to the road which decreased with increasing distance from the road for both the models. In India, various line source dispersion models have been routinely used to predict CO concentrations along the highway, under open terrain and urban conditions, without calibrating and validating these models under Indian climatic and heterogeneous traffic conditions. Moreover, various input parameters used in the models are not defined well, leading to incorrect or sometimes even unreliable predictions. Greatest inaccuracy in vehicular pollution modelling exercise in India occurs due to the use of improper emission factors for different categories of vehicles. Another source of inaccuracy in these models pertains to non-availability of on-site micro meteorological data. Most often modelers in India rely on nearest Indian Meteorological Department (IMD) data, which does not reflect actual field conditions and adds to inaccurate prediction estimates 18. The present study focuses on the performance evaluation of CALINE 4 model for predicting CO concentrations under mixed traffic conditions on an urban road corridor (a section of the national highway-2) in Delhi using two different sets of emission factors for different categories of vehicles presently available and used in India 19, 20 and on-site meteorological data. Methodology Site Characteristics Ashram Chowk - CRRI highway road corridor of NH-2 (generally referred as Delhi-Mathura Road) has been selected for the present study (Fig. 1). The total length of the road corridor is about 2.7 km and connects adjoining Faridabad and Noida city to Delhi. The road

3 SHARMA et al: PERFORMANCE EVALUATION OF CALINE 4 DISPERSION MODEL IN DELHI 523 No. of Vehicles W Auto Cars LCV HCV Buses Hour (s) Fig. 2 Hourly traffic variations along Ashram Chowk CRRI (NH-2) Table 1 Age profile of vehicles based on fuel station survey 21 Year Two Wheelers (2W) Four Wheelers Buses Auto (Cars) (CNG) (3W) (CNG) LCV HCV 100% 100% 100% 100% 2-Stroke 4-Stroke Petrol Diesel CNG Diesel CNG Diesel CNG (40%) (60%) (60%) (38%) (2%) Total (%) corridor is a six lane road corridor with carriageway width of ~24m and median width of 3m. Traffic Data The 24-hr classified traffic volume data on the road corridor has been collected by manual counting. The observed diurnal pattern of traffic flow has been shown in fig. 2. The peak traffic volumes were observed during hours (morning peak hours) and hours (evening peak hours). However, the numbers of heavy goods/ commercial vehicles (HGV or HCV) were found to be maximum during night times as the road corridor is part of the highway corridor (NH-2) and used for interstate traffic including goods traffic. The traffic on the road corridor is dominated by cars (~42%) followed by two wheeler (2W) (~33%), thr ee wheelers (3Ws; auto rickshaws) (~12%), light commercial vehicles (LCVs) (~4%) and heavy commercial vehicles (HCVs) (~6%) respectively.

4 524 J SCI IND RES VOL 72 AUGUST 2013 Table 2 Summary of the on-site meteorological parameters in the vicinity of CRRI (March, 2010) 21 Time Temp Humidity Wind Speed (m/s) Wind Direction Stability Mixing Ventilation (hrs) ( o C) (%) (10m Distance Above Class Height (m) Coefficient (m 2 /s) GL)* (P-G) S F S F S F SSW F SSW F SSW F SSW F SSW B WSW B SW B SW B WSW A WSW A WSW A W B W B W B S B S D S D S F S F S F % 0.21 S F *The wind speed at 10m above GL has been found out by using velocity power law equation given below: U 1 Z1 = U ( 2 Z ) The value of corresponding to urban terrain conditions have been taken as 0.33 (Counihan, 1975) Fuel station survey at different petrol pumps and CNG filling stations along the road corridor was also carried out to find out the age profile of the vehicles plying on the road (Table 1) 21. This included finding out percentage of two stroke (2S) and four stroke (4S) vehicles in two wheeler and percentage of petrol and diesel and CNG driven vehicles in four wheeler (i.e. car categories). The above survey was carried out to estimate weighted/ composite emission factors (WEF or CEF), used as an input in the CALINE 4 model. It has been further assumed that the vehicles plying on the road, could be represented by vehicles (in terms of their age profile, engine technology and composition) captured during the fuel station survey. The percentages of 2S and 4S vehicles in two wheeler category, based on fuel station survey, were found to be 40% and 60% respectively. Whereas, in 4W (cars) category, the percentage of petrol driven cars was (~60%), followed by diesel driven cars (~38%) and CNG driven cars (~2%). Meteorological data The on-site meteorological data such as winds speed, wind direction, temperature and relative humidity has been taken from another study carried out by CRRI 21 and used for the present study. These meteorological data were obtained by using Soundbook (Mechanical) with Samurai Software with Weather Station Option (Sinus Messtechnik gmbh, Germany). A summary of meteorological data and estimated stability classes (P-G) and ventilation coefficient (mixing height x wind velocity) values has been shown in Table Based on the hourly mixing height values were obtained from the Indian Meteorological Department (IMD) 22. CALINE 4 Model Description CALINE 4 model is the fourth generation simple line source Gaussian plume dispersion model, a significant improvement over CALINE 3 model 5. It predicts the concentrations of carbon monoxide (CO), nitrogen dioxide

5 SHARMA et al: PERFORMANCE EVALUATION OF CALINE 4 DISPERSION MODEL IN DELHI 525 (NO 2 ), and suspended particles (PM 10 / PM 2.5 ) near roadways. It employs a mixing zone concept to characterize pollutant dispersion over the roadway due to vehicles plying on the road corridor. The CALINE 4 can predict the pollutant concentrations for receptors located within 200 m under given traffic and meteorological conditions of road/ highway. The important input parameters required for CALINE 4 model include, classified traffic volume (number of vehicles per hour), meteorological parameters (wind speed, wind direction, ambient temperature, mixing height and stability class), emission parameters (weighted emission factor, WEF), road geometry (road width, median width, road elevation), type of terrain (rural or urban), background CO concentration (ppm or µg/m 3 ) at pre-identified receptor locations along the road corridors. The CO was measured by using CO 11M Monitor - IR Gas Filter Correlation Detector (Environment SA, Poissy, France) fitted in mobile air pollution laboratory. The monitoring of CO was carried out as monitoring protocol and method of measured stipulated in National Ambient Air Quality Standards (NAAQS, 2009) by Central Pollution Control Board (CPCB) of India. Instrument calibration was performed before the start of measurements using CO Calibration gas mixture of 20ppm and changing sample inlet PTFE filter followed by base line correction in case of the drift. The CO measurements were recorded after stabilization of the instrument. The analyzer record the instantaneous changed (real-time) 15 minute or 30 minute or as 01hour averaging intervals depending on the choice and requirement of the study. For the present study 15 minute aver aging time was chosen to record the CO concentrations (average of all changes in concentrations during this period). Model Setup and Run The CALINE 4 model has been used to predict CO concentrations along the Ashram Chowk-CRRI section of road corridor of national highway NH-2 under prevailing traffic and meteorological conditions. The CO being indicator pollutant for vehicular activities was chosen for the present study. The CALINE 4 model was run using two different weighted emission factor (WEF) calculated based on emission factors specified by CPCB (Table 3) 19 and ARAI (Table 4) 20 for Indian vehicles. The WEF (input parameter for CALINE 4) is a function of vehicle emission factor (as a function of vehicle category, type, fuel type, age profile, vintage etc.) and Table 3 Emission factors for carbon monoxide for different categories of vehicle (CPCB, 2000) Year of Registration Type CO (gm/km) (Vintage) CO W2 stroke W4 stroke W2 stroke PCG* PCD* LCV HCV Bus *PCG/PCD - Passenger car (Gasoline/Diesel driven), ** LCV Light Commercial Vehicles vehicle activity (traffic volume). The methodology used for calculation of WEF 23 is given below: WEF = [ Σ (j) Σ (ky) N (j, ky). EF (i, j, ky)] / Total No. of Vehicles... (1)

6 526 J SCI IND RES VOL 72 AUGUST 2013 Table 4 Emission factor for Carbon Monoxide (in gm/km) as per ARAI (2008) 20 Year Two Wheelers Four Wheelers LCV HCV 100% 100% 100% 100% 2-Stroke 4-Stroke Petrol Diesel CNG Buses Auto Diesel CNG Diesel CNG (40%) (60%) (60%) (38%) (2%) (CNG) (CNG) (Compiled; Source: Sharma, 2010) 25 Table 5 Input parameters used in CALINE 4 model S.No. Parameters Values /Units Source (i) Traffic Data (24-hour) 24 hourly Manual Count (ii) Weighted Emission Factor (WEF) g/mile (a) Based on CPCB Emission Factors 2.67 Calculated (CPCB, 2000) (b) Based on ARAI Emission Factors 2.96 Calculated (ARAI, 2008) (iii) Terrain Type Urban Physically observed (iv) Road Geometry (a) Mixing Zone Width (carriage width 30.3 Physically measured + 3 on both sides) (b) Road Alignment Straight Google Map (c) Road Type At-grade Physical observed (v) Meteorological Data CRRI (2010) 21 (a) Wind Speed m/s On-site Measurement* (b) Wind Direction Degree Pre-identified Points Across the Road Corridor* (c) Mixing Height Meters (m) Attri et al. (2008) 22 (d) Stability Class 1,2,3,4,5,6 or 7 Pasquill (P-G) Stability Class (vi) Background CO Concentration** ppm CRRI (2010) 21 (vii) Monitored CO Concentrations** ppm CRRI (2010) 21 * Soundbook (with Samurai Software with Weather Station Option) Sinus Messtechnik GmbH, Germany **CO 11M, Environment SA, Poissy, France - IR Gas Filter Correlation Detector; wavelength µm; Range(s): / 25 / 50 / 100 / 200 ppm; Lower Detectable Limit (2s): 50 ppb; Zero Drift: < 0.1 ppm / 24 h or < 0.1 ppm / 7 days; Span Drift: < 1 % / 24 h or < 1 % / 7 days; Linearity: ±1 % of F.S. Where, WEF = Weighted emission factor(g/km) (to be estimated for given emission factor for 19, 20 different categories of vehicles N (j, ky) = Number of vehicles of a particular type j and age ky in year y ( average daily traffic)

7 SHARMA et al: PERFORMANCE EVALUATION OF CALINE 4 DISPERSION MODEL IN DELHI 527 Table 6 Incremental increase in co concentration due difference in emission factor of CPCB (2000) and ARAI (2008) Run Type Duration CO Concentrations (µg/m 3 ) % Difference EF- CPCB (2000) EF- ARAI (2008) Standard Run (1 Hour) (8 Hours) Worse Case (1 Hour) (8 Hours) CO concentration (µg / m 3 ) Distance from edge of mixing zone width (m) Distance from edge of mixing zone width (m) (a) Standard case (b) Worse case Fig. 3 Predicted CO concentration (peak hour ) along Ashram Chowk - CRRI (a) Standard case and (b) Worse case CO concentration (µg / m 3 ) EF(i,j,ky) = Emission factor for component i for the vehicle type j and age ky in Year y (g/km) i = Pollutant component (viz., CO) j = Type of vehicle [i.e. 2W, 3W (Auto rickshaw), cars, bus, truck etc.] ky = Age of the vehicle in year y The 18 receptor points (9 points on each side of the highway corridor) were selected at per-identified receptor location with a specified distance from the edge of the mixing zone width (road width + 3 m on each side of the road corridor) i.e. 1.0m, 2m, 5m, 10m, 15m, 25m, 50m, 100m, 150m from the edge of the road on both the side. The summary of various input parameters used in the CALINE 4 model along with their sources has been shown in Table 5. Results and discussion Prediction of CO Concentrations using CPCB and ARAI Emission Factors At present, emission factor prescribed by CPCB 19 and ARAI 20 for different categories of vehicles (as a function type & category of vehicles and their vintage, type of fuel used etc.) are used by researchers in India. Thus, in order to know their impact on predicated CO concentrations, the CO predictions were carried out by using same set of traffic, meteorological road geometry and other relevant input data set but with two different sets of emission factors, one specified by CPCB and another by ARAI. In this exercise, the prediction of CO concentrations was made to find out 1-hour (Standard and Worst-case run) and 8-hours (Multi Run and Multi- Run/ Worst-Case Hybrid Scenario) average CO concentrations. Under Standard Case (1-hr) and Multi- Run Case (8-hr) conditions, the model uses given wind speed and wind direction input and predicts CO concentrations at pre-identified receptor points. Whereas, in Worst Case (1-hr) and Multi Run/ Worst Case Hybrid scenario (8-hr), the model takes the given wind speed and selects wind direction which gives maximum CO concentration at pre-identified receptor locations. The background CO concentrations (X) were assumed to be zero (i.e., X = 0), hence predicted CO concentrations ( X) r eflected the incremental increase in CO concentrations due to vehicular activities only. Table 6 shows the maximum predicted CO concentrations at receptor location (at a distance of 1m from the edge of mixing zone width) and incremental increase in 1-hour and 8-hour CO concentrations while using CPCB 19 and ARAI 20 emission factors. From Table 6 it is evident that the ARAI emission factors results in higher predicted values for CO as compared to CPCB emission factors. Thus, ARAI emission factors overestimated CO values by +12% vis-à-vis CPCB emission factors indicating that the predicted CO values will be on higher side when ARAI emission factors are used than that of CPCB. The predicted 1-hour CO concentrations during peak hour at pre-identified receptor location have been shown in fig. 3 for standard and worse case conditions respectively. It is evident from the model results that the

8 528 J SCI IND RES VOL 72 AUGUST Es t im at e d C O O b s e r v e d C O CO Concentration (µg / m 3 ) H o u r (s) Hour (s) Fig. 4 Diurnal pattern of measured and predicted CO concentrations Predicted concentration ( µg / m 3 ) y = 0.662x R 2 = Observed concentration (µg / m 3 ) (a) CPCB Emission Factor Predicted concentration ( µg / m 3 ) y = 0.695x R 2 = Observed concentration (µg / m 3 ) (b) ARAI Emission Factor Fig. 5 Correlation between predicted (using Caline 4) and observed 1- hour CO concentrations using (a) CPCB (2000) emission factors (standard case) and (b) ARAI (2008) emission factors (standard case) impact of vehicular activities on air quality is confined to ~150m on both the sides of the road, depending upon traffic and on-site meteorological conditions. Beyond this distance, the air quality approaches to the background air quality in the area, and do not show any impact/ effect of vehicular traffic. Performance Evaluation of CALINE 4 Model The diurnal variation of measured and predicted CO concentrations at pre-identified receptor locations along Ashram Chowk- CRRI highway corridor of NH-2 were carried out and has been shown in fig. 4. It is evident that the predicted and measured CO concentrations follow the similar trend. This is supported by moderately high r 2 value 0.60 and 0.65 for CPCB and ARAI emission factors between the predicted and the monitored CO concentrations fig. 5. The performance of the model can be deemed acceptable if: 0.4 < d < 1.0, NMSE < 0.5, -0.5 < FB < Therefore, from the estimated Table 7 Statistical parameters for model results Parameters EF(ARAI, 2008) EF(CPCB, 2000) Index of Agreement (d) Fractional Bias (FB) NMSE statistical descriptor values, it can be concluded that CALINE-4 model is predicting satisfactorily for CO under given traffic and meteorological and terrain condition/ land use pattern which is urban (but without urban street canyon effect) in this case. Further, the performance of model predictions were tested with statistical descriptors i.e. Index of Agreement (d), Fractional Bias (FB) and Normal Mean Square Error (NMSE) and were found to be 0.85, 0.04 and for ARAI emission factors; and for CPCB emission factors and found to be similar (Table 7).

9 SHARMA et al: PERFORMANCE EVALUATION OF CALINE 4 DISPERSION MODEL IN DELHI 529 Conclusions The performance of CALINE 4 model has been evaluated for predicting CO concentration on an urban highway corridor in Delhi using two different sets of Indian emission factors. The results have indicated that the dispersion of predicted CO concentrations is limited to a distance of ~150m from the edge of the mixing zone width. The model predicted ~12% higher CO concentration is case of ARAI vis-à-vis CPCB emission factors. The correlation coefficient values (r 2 ) of 0.60 and 0.65 as well as index of agreement (d) values of 0.86 and 0.85 between predicted and observed 1-hour CO concentrations respectively for CPCB and ARAI emission factors affirm the satisfactorily performance of the model. However, the estimated fractional bias (FB) values of 0.04 and 0.06 indicate that the model has under predicted CO values for both the sets of emission factors. It could be concluded that CO prediction along an urban highway corridor (without street canyon effect) could be performed satisfactorily using CALINE 4 model under Indian meteorological and heterogeneous traffic conditions. Both the sets of emission factors underpredicts the CO values but ARAI emission factors predict ~12% higher values as compared to CPCB emission factors. The application of CALINE 4 model with ARAI emission factors will be more realistic (i.e. more closer to the actual values) than the CPCB emission factors, however, this need to be corroborated by further studies. Nevertheless, comparatively accurate prediction capabilities and user-friendly nature of CALINE 4 model could be effectively used as a tool for vehicular pollution management in urban road corridors in Indian cities. 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