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244 Int. J. Environment and Pollution, Vol. 52, Nos. 3/4, 2013
Copyright © 2013 Inderscience Enterprises Ltd.
Performance evaluation of CALINE 4 model in a hilly terrain – a case study of highway corridors in Himachal Pradesh (India)
Rajni Dhyani* Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Road Research Institute (CRRI), New Delhi-110025, India E-mail: [email protected] *Corresponding author
Anil Singh, Niraj Sharma and Sunil Gulia Environmental Science Division, CSIR-Central Road Research Institute (CRRI), New Delhi-110025, India E-mail: [email protected] E-mail: [email protected] E-mail: [email protected]
Abstract: CALINE 4 highway dispersion model is extensively used all over the world including India for prediction of vehicular pollution along highway corridors. A few studies have reported its application and suitability under complex/hilly terrain. In the present paper, an attempt has been made to evaluate the performance of CALINE 4 model in hilly terrain for Indian traffic and meteorological conditions. CALINE 4 model predictions have been compared between flat and hilly terrains along two road corridors in Solan District in the state of Himachal Pradesh (India). The statistical indicators of the model performance such as, index of agreement (d), fractional bias (FB), and normalised mean square error (NMSE) reveal that the model performed satisfactorily in the flat terrain. However, model fell short in explaining the complexity of terrain and performed unsatisfactorily in hilly terrain conditions.
Keywords: CALINE 4 model; hilly terrain; vehicular emission; emission load; performance evaluation.
Reference to this paper should be made as follows: Dhyani, R., Singh, A., Sharma, N. and Gulia, S. (2013) ‘Performance evaluation of CALINE 4 model in a hilly terrain – a case study of highway corridors in Himachal Pradesh (India)’, Int. J. Environment and Pollution, Vol. 52, Nos. 3/4, pp.244–262.
Biographical notes: Rajni Dhyani is a PhD Scholar in Academy of Scientific and Innovative Research (AcSIR) at the Environmental Science Division, CSIR-Central Road Research Institute, New Delhi, India. She has nearly five years of experience on various R&D and consultancy projects related to in environment impact assessment of roads, buildings, and metro rail corridor projects, air pollution studies and air pollutant dispersion modelling. She has publications in peer reviewed journals and international conferences.
Performance evaluation of CALINE 4 model in a hilly terrain 245
Anil Singh is Chief Scientist and Head of Environmental Science Division at the CSIR-Central Road Research Institute, New Delhi. He has 30 years of professional experience in air quality, environmental aspects of roads and road traffic, vehicular emission, transport sector GHG emission inventory, EIA of roads/highways, etc. He has several publications in various national and international journals.
Niraj Sharma is a Principal Scientist in Environmental Science Division at the CSIR-Central Road Research Institute, New Delhi. He has 25 years of research experience in area of EIA of roads, highways, metro rail corridor and building projects. Air pollution studies, emission gains from metro rail projects, ambient and exhausts emission modelling. He has several publications in various national and international journals.
Sunil Gulia is a Senior Project Fellow in Environmental Science Division at the CSIR-Central Road Research Institute, New Delhi. He is pursuing his PhD at IIT Delhi, in area of urban air quality modelling and management. He has five years of research experience in environment impact assessment of building and highway projects, urban air quality management, air quality modelling of point, line and area sources, air quality models. He has publications in peer reviewed journals and international conferences.
1 Introduction
Transportation sector, especially the road transportation sector, is one of the key components of economic growth of a country. Road transportation sector in India has registered a sustainable growth rate of 9.9% over the last decade. The total number of registered motor vehicles has increased from about 21.4 million in 1991 to about 142 million in 2011 (MoRTH, 2012). The rapidly growing vehicle fleet, distance travelled by each vehicle type and change in land use pattern are some of the primary causes of air pollution and consequently deteriorating air quality (Mayer, 1999).
In determining the levels of pollutants and managing the deteriorating air quality, air pollution dispersion models have emerged as a potential tool over air quality monitoring. Modelling can provide greater spatial detail and can estimate the pollutant concentration at many receptor locations for a multitude of averaging periods. Air dispersion models could be validated using air pollution monitoring data for a particular terrain or for given meteorological conditions (OEHHA, 2012). Various dispersion models are available for abatement and management of pollution caused by motor vehicles. The line source emission modelling is one such important tool for control and management of vehicular exhaust emissions (VEE) in urban environment and provide information on current and future air quality levels (Nagendra and Khare, 2002). These models are used to simulate dispersion of vehicular pollutants near road sides and are useful in prediction of pollutant concentrations along the roads/ highways.
Most of the line source dispersion models are designed and developed to predict pollutant concentrations in flat terrain. They are not suitable to manage orography-induced effects, such as pollutant confinements into valleys (Nanni et al., 1996). Hilly terrains strongly influence the local wind flow field and cause significant changes in the dispersion characteristics (Kim and Lee, 1998). The study of the atmospheric pollution due to the traffic movement in a mountain valley involves understanding of a lot of
246 R. Dhyani et al.
physical processes and problems related to the peculiarity of both meteorological and dispersive characteristics in such complex topography (Castelli et al., 2007).
From the modelling perspective, vehicular pollution modelling in complex terrain causes difficulty primarily due to spatial and temporal in-homogeneity of the atmospheric fields and inaccuracies in the models structures (Podnar et al., 2002). Modelling roadway emissions is developing in parallel with other fields of atmospheric dispersion modelling (NIWA, 2004). However, over the years, many researchers have developed and used line source air dispersion models in complex terrain. Nanni et al. (1996) have reported satisfactory performance of SPRAY (a Lagrangian model) particle model to reproduce pollutant dispersion from a highway at the bottom of deep alpine valley. National Institute of Water and Atmospheric Research (NIWA) has observed that development of non-steady state air dispersion modelling for complex terrain or street canyon has become popular in recent years and has reported that various advanced roadway models are available for complex terrains such as Operational Street Pollution Model (OSPM) (Fu et al., 2000); SPRAY (Nanni et al., 1996); Hybrid Roadway Model (HYROAD) (Ireson and Carr, 2000); and Lagrangian wall model (LWM) (Cope et al., 2005; NIWA, 2004). Goyal et al. (2006) determined the concentration distribution patterns of the pollutants by using California Line Source (CALINE 3) and IIT Line Source (IITLS) models for three criteria pollutants viz. sulphur dioxide (SO2), suspended particulate matter (SPM) and oxides of nitrogen (NOx) in the hilly regions of Gangtok City. The results indicated that both the models performed satisfactorily in the study area. Statistical analysis showed that IITLS model performed better than the CALINE 3 because of its ability to incorporate calm winds. However, they did not consider the influence of complex terrain features in the study. American Meteorological Society/Environmental Protection Agency regulatory model (AERMOD) is a near field steady state Gaussian plume model based on boundary layer theorem and similarities theory and includes treatment of both simple flat and complex terrains. It is able to model multiple sources of different types including point, area and volume sources (roadways) (US Environmental Protection Agency, 2005). AERMOD incorporates a simple method to approximate flows over complex terrain (Holmes and Morawska, 2006). Castelli et al. (2007) used the prognostic modelling system RMS (RAMS-MIRS-SPRAY) for the pollutant dispersion of emission from major traffic routes in Susa (Italy) and Maurienne (France) valleys under the ALPANP project. The CALINE 4 model (Benson, 1984), latest in CALINE series of models, is most widely used vehicular pollution dispersion model to predict air pollution concentrations along the highway under rural (i.e., open) and semi-urban conditions. CALINE 4 model is used to estimate the vehicular pollutant concentration at source-receptor distance of tens to hundreds of meters (Pournazeri et al., 2013). CALINE 4 offers several advantages over other models and has been used in various Indian cities to predict vehicular pollutant concentrations along the roads/highway corridors (Jain et al., 2006; Majumdar et al., 2010). It has been reported that CALINE 4 under predicts the CO concentration and exhibited moderate correlation between observed and predicted concentrations (Nirjar et al., 2002; Goyal et al., 2006; Sharma et al., 2011). In complex topographic situations where the bluff or canyon options are not applicable, use of CALINE 4 is restricted to small areas which can be reasonably expected to experience horizontal homogenous wind flows (Benson, 1984). CALINE 4 suffers from the inherent limitations of the Gaussian equations to urban dispersion modelling over short distances and within complex environments (Holmes and Morawska, 2006). However, the
Performance evaluation of CALINE 4 model in a hilly terrain 247
performance and evaluation of CALINE 4 has not been explored for complex terrain (hilly terrain) under Indian meteorological and heterogeneous traffic conditions.
In the present study, two different roads/highway corridors have been selected in the Solan District of Himachal Pradesh (Figure 1). The selected sites have different topographical features (i.e., flat and hilly terrain) to evaluate the performance of the CALINE 4 model. In addition, the vehicular emission loads have been estimated for CO, HC, NOx and PM (kg/day).
Figure 1 Study areas (see online version for colours)
2 Methodologies
2.1 Site characteristics
The study area lies in Solan District which is one of the major industrial centres of Himachal Pradesh. There are nearly 5012 small and large industrial units in the district which is highest in any district of the state (Department of Industry, 2012). Due to rapid industrialisation and urbanisation in the area, the numbers of motor vehicles have increased over the years. Two sites have been selected for the study in the Solan District representing different terrain/topographical features. There is 90 km distance between the two sites. One site is the section of NH-21 at Kiratpur, Himachal Pradesh (generally referred as Kullu-Manali Road) which represents elevated flat terrain conditions with a length of 1.2 km. The second site is part of NH-88 corridor at Darlaghat (Shimla-Bilaspur-Manali State Highway) represented by hilly terrain characteristics [1,025 m above mean sea level (MSL)] with a length of 1.2 km. The road corridors are single carriageway, having width of 16 m (10 m + 3 m at both sites) at both Darlaghat and Kiratpur.
248 R. Dhyani et al.
Table 1 Age profile of vehicles based on fuel station survey
2W
3W
4W
Truc
ks
LCV
Buse
s Ye
ar
2S
(age
%)
No.
of
vehi
cle
4S
(age
%)
No.
of
vehi
cle
(A
ge %
) N
o. o
f ve
hicl
e
Petr
ol
(age
%)
No
.of
vehi
cle
Die
sel
(age
%)
No.
of
vehi
cle
(A
ge %
)N
o. o
f ve
hicl
e
(Age
%)
No.
of
vehi
cle
(A
ge %
) N
o. o
f ve
hicl
e
≤ 19
95
3 30
0
0
0 0
0
0 0
0
8 61
3
3 44
0 0
1996
–200
0 13
15
0 0
0
0 0
0
0 0
0
20
1,46
5
6 88
0 0
2001
–200
5 29
32
6 18
64
3
11
4
51
2,63
2 24
43
8
22
1,70
1
9 13
5
17
164
≥ 20
06
55
594
81
2,65
9
87
25
51
2,
632
74
1,32
0
52
3,88
6
84
1,24
1
84
782
Tota
l 10
0 1,
101
100
3,30
3
100
28
10
0 5,
262
100
1,75
4
100
7,66
5
100
1,50
5
100
947
Sour
ce:
CR
RI (
2012
)
Performance evaluation of CALINE 4 model in a hilly terrain 249
2.2 Traffic data
The 24-hr classified traffic volume data on both the road corridors was collected by manual counting method (CRRI, 2012). The observed diurnal pattern of traffic flow has been shown in Figure 2 (Kiratpur) and Figure 3 (Darlaghat). In addition, to obtain the directional information on age profile (vintage) of vehicles (petrol and diesel), fuel station surveys were carried out at different fuel dispensing stations along both the corridors (Table 1) using a set of questionnaire which included information on registration number, vintage (year of registration), fuel type, engine technology of the vehicle, etc. The age structure of vehicle fleet was determined assuming that the vehicles at the fuel station(s) have the similar age structure and distribution of vehicles as that of vehicle fleet plying on the road corridor. Since the traffic fleet (type of vehicle, fuel, engine technology, etc.) could vary even within different cities in a state choosing site specific age profile of vehicles for a model validation (as in the present case) study is more rational as compared to the use of regional or national average age profiles. Past studies have also used similar kind of methodology to determine the age profile of vehicles plying on the road (URTRAP, 2002; Sharma and Gangopadhyay, 2007; Sharma et al., 2013). The information, such as percentage of two-stroke (2S) and four-stroke (4S) vehicles in two wheeler and percentage of petrol and diesel driven vehicles in four wheeler (4W, i.e., car) categories has been used to estimate weighted/composite emission factors (WEF or CEF) as an input in the CALINE 4 model as well as for the estimation of emission load (gathered through the fuel station survey statistics). The percentage share of 2S and 4S vehicles in two wheeler category was found to be nearly 25% and 75% respectively at both the locations. Whereas, for 4W (cars) nearly 75% of the cars were found to be petrol driven.
Figure 2 Traffic flow characteristics along Kiratpur corridor (NH-21)
250 R. Dhyani et al.
Figure 3 Traffic flow characteristics along Darlaghat corridor (NH-88)
2.3 Meteorological data
The on-site micro-meteorological data (wind speed, wind direction, temperature and relative humidity) was measured at each of the locations in the study area and summarised in Table 2 (CRRI, 2012). The hourly mixing height values were obtained from the Indian Meteorological Department (IMD) (Attri et al., 2008). The meteorological conditions at Kiratpur were observed to be more dispersive in nature as compared to Darlaghat (Table 2). The predominant wind direction and wind speed are shown in the form of wind rose diagram for both the sites as Figure 4. Further, hourly wind angle (with respect to the measurement locations) has been estimated between predominant wind direction and road alignment at both the sites. In addition, hourly P-G stability class has been estimated by using well referred methodology (Turner, 1994) (Table 3).
Table 2 Comparative analysis of meteorological parameters at study sites
Meteorological parameters
Temperature (°C)
Wind speed (m/s) Wind direction Relative humidity (%)
Kiratpur Darlaghat Kiratpur Darlaghat Kiratpur Darlaghat Kiratpur Darlaghat
Max. 27 19.1 2 0.36 NE* NE* 95 79.53
Min. 9 6.3 0 0 33 30.29
Avg. 15 11.8 0.8 0.16 75.5 53.5
Std. 6.3 4.5 0.5 0.11 22.6 16.8
Note: *Predominant wind direction.
Performance evaluation of CALINE 4 model in a hilly terrain 251
Figure 4 Wind rose diagrams for, (a) Darlaghat (b) Kiratpur (see online version for colours)
(a) (b)
Table 3 Hourly value of wind angle and stability class for both sites
Hour of the day
Darlaghat site
Kiratpur site
Wind angle (degree) P-G stability class Wind angle (degree) P-G stability class
00–01 7 F 26 F 01–02 6 F 3 F 02–03 12 F 33 F 03–04 2 F 8 F 04–05 8 F 32 F 05–06 45 E 24 E 06–07 23 B 40 D 07–08 75 B 36 B 08–09 88 A 12 B 09–10 63 A 76 B 10–11 33 A 35 B 11–12 85 A 44 A 12–13 27 A 30 A 13–14 6 A 89 A 14–15 19 A 50 A 15–16 80 B 75 B 16–17 53 B 51 B 17–18 86 D 86 D 18–19 56 D 50 D 19–20 33 E 81 F 20–21 16 E 42 F 21–22 17 E 25 F 22–23 7 F 9 F 23–24 4 F 7 F
252 R. Dhyani et al.
Table 4 Emission factors for different vehicle types and vintage*
Type Year CO HC NOx PM
(gm/km) (gm/km) (gm/km) (gm/km)
2W(2S) 1991–1995 6 3.68 0.02 0.073 (Scooters) 1996–2000 5.1 2.46 0.01 0.07 (> 80 CC) 2001–2005 3.435 1.905 0.03 0.065 > 2006 0.16 0.86 0.02 0.057 2W (4S) 1991–1995 3.12 0.78 0.23 0.01 (Motor cycles) 1996–2000 1.58 0.74 0.3 0.015 (< 100 CC) 2001–2005 1.65 0.61 0.27 0.035 > 2006 0.72 0.52 0.15 0.013 ≥ 2001 1.37 2.53 0.2 0.045 ≥ 2005 1.15 1.63 0.16 0.043 3W(2S) 1991–1995 4.75 0.84 0.95 0.008 Passenger cars 1996–2000 4.825 0.58 0.645 0.0195 (Petrol) 2001–2005 1.3 0.24 0.2 0.004 2006–2010 3.01 0.19 0.12 0.006 Passenger cars 1996–2000 0.87 0.22 0.45 0.145 (Diesel) 2001–2005 (BS-I) 0.72 0.14 0.84 0.19 (< 1,600 CC) 2001–2005 (BS-II) 0.3 0.26 0.49 0.06 > 2006 0.06 0.08 0.28 0.015 LCV 1991–1995 3.07 2.28 3.03 0.998 (< 3,000 CC) 1996–2000 3 1.28 2.48 0.655 > 2001 3.66 1.35 2.12 0.475 Bus (diesel)** 1991–1995 5.5 1.78 19 3 (> 6,000 CC) 1996–2000 4.5 1.21 16.8 1.6 2001–2005 3.6 0.87 12 0.56 2006–2010 3.2 0.87 11 0.24 HCV diesel truck*** 1991–2000 19.3 2.63 13.84 1.965 (> 6,000 CC) Post 2000 6 0.37 9.3 1.24
Notes: *Compiled; **due to incomplete emission factor (CO, HC, NOx and PM) for diesel buses in ARAI report, emission factor for buses has been taken from CPCB (2000); and ***CO emission factor appears to be on higher side. It could not be reconciled. However, used for emission estimation calculation.
Source: ARAI (2008)
2.4 Emission load estimation
The total number of vehicles (estimated by the traffic volume surveys) was segregated into different categories of vehicles such as 2W (two wheeler), 3W (three wheeler), 4W (car), buses, trucks (HGV), etc. The vehicles then apportioned and grouped into different years (based on the year of registration) according to the age profile (vintage)
Performance evaluation of CALINE 4 model in a hilly terrain 253
obtained from the fuel station surveys. Respective emission factors (Table 4) (ARAI, 2008) and deterioration factors (CPCB, 2000) were used to estimate the total emission loads of various pollutants viz., CO, HC, NOx, PM and CO2. Emission factors used in India are not speed dependent and based on Indian driving cycle on a chassis dynamometer, representing typical driving condition in India (CMVR, 1989). These emission factors are available for different categories of in-use vehicles and are based on vintage, engine technology and fuel quality/type. Several model performance evaluation studies have been carried out in recent past using these emission factors (Khare et al., 2012; Sharma et al., 2013). Table 5 Input parameters used in CALINE 4 model
S. no. Parameters Values/units Source 1 Traffic data (24-hour) 24 hourly Manual count 2 Weighted emission factor (WEF) g/mile
a Kiratpur [based on ARAI emission factors (ARAI, 2008)]
Table 4 Calculated from equation (2)
b Darlaghat [based on ARAI emission factors (ARAI, 2008)]
Table 4 Calculated from equation (2)
3 Terrain type
a Kiratpur Semi-urban flat terrain Physically observed
b Darlaghat Semi-urban hilly terrain
Physically observed
4 Road geometry
a Mixing zone width (carriage width + 3 on both sides)
• Kiratpur 16 metres Physically measured
• Darlaghat 16 metres Physically measured
b Road alignment
• Kiratpur Straight Google map
• Darlaghat Straight Google map
c Road type • Kiratpur At-grade Physical observed
• Darlaghat At-grade Physical observed
5 Meteorological data CRRI (2012) 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) d Stability class 1, 2, 3, 4, 5, 6 or 7 Pasquill (P-G) stability
class 6 Background CO concentration µg/m3 CPCB 7 Monitored CO concentrations µg/m3 CRRI (2012)
254 R. Dhyani et al.
The vehicular emission loads (kg/day) are estimated by using the following equation: 6( ) ( ) ( ) ( ) ..365.10i i ijEj t V t VKT t Ef t Dfijky −=∑ (1)
where Ej(t) is the total emission of pollutant type j in year t (kg/day), Vi(t) is the vehicle population on street in year t, VKTi(t) is the average annual vehicle kilometer travelled by a vehicle type i in year t, EFij(t) is the emission factor of pollutant type j of vehicle type i in year (t) in g/km and Dfijky deterioration factor of pollutant j of vehicle type i and vintage ky in year y (dimensionless).
2.5 CALINE 4 model description
CALINE 4 model is the fourth generation simple line source Gaussian plume dispersion model. It predicts the concentrations of carbon monoxide (CO), nitrogen dioxide (NO2) and suspended particulates (PM10/PM2.5) near roadways. It employs a mixing zone concept to characterise 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 200m under given traffic and meteorological conditions. 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 concentration of pollutants (ppm or µg/m3) and pre-identified receptor locations along the road corridors. In the present study the dispersion modelling for CO along the highway corridors has been carried out. The summary of various input parameters used in the CALINE 4 model along with their sources has been given in Table 5.
2.6 Model setup and run
The CALINE 4 model has been used to predict CO concentrations under two different topographical conditions (road corridor) under prevailing traffic and meteorological conditions. The CO being the indicator pollutant for vehicular activities was chosen for the present study. The CALINE 4 model was run using emission factors specified by ARAI (Table 4) for Indian vehicles. The WEF, input parameter for CALINE 4, is a function of vehicle emission factor (vehicle category, type, fuel type, vintage, etc.) and vehicle activity (traffic volume). The equation for calculation of WEF is as follows:
( ) ( ) ( ). ( , , ) . WEF j ky N jky EF i y ky Total No of Vehicles⎡ ⎤= ⎣ ⎦∑ ∑ (2)
where WEF is weighted emission factor (g/km), N(j, ky) is number of vehicles of a particular type j and vintage ky in year y, EF(i, j, ky) is emission factor for pollutant i for the vehicle type j and vintage ky in year y (g/km). In the present study, hourly weighted emission factors have been calculated and used to evaluate model performance at both the sites (Table 6). Further, weighted emission factors have been converted from g/km to g/mile as per CALINE 4 model requirement.
The prediction of CO concentrations was made to find out 1-hour (standard case) average CO concentrations. Under standard case (1-hr), the model has been setup and
Performance evaluation of CALINE 4 model in a hilly terrain 255
run based on observed wind speed and direction for predictions of CO concentrations at pre-identified receptor points. Table 6 Hourly WEF value for both sites
Hour of the day
WEF (gm/miles)
Darlaghat Kiratpur 00–01 10.8 11.0 01–02 12.0 11.0 02–03 16.8 12.1 03–04 16.6 12.1 04–05 14.5 14.9 05–06 14.7 13 06–07 11.9 13.4 07–08 11.7 11.4 08–09 11.5 9.5 09–10 8.6 8.4 10–11 7.7 8.6 11–12 7.7 8.1 12–13 7.4 7.6 13–14 8.1 7.7 14–15 7.1 7.7 15–16 7.1 7.1 16–17 7.7 7.3 17–18 7.9 7.1 18–19 7.7 7.9 19–20 6.5 7.7 20–21 8.9 9.0 21–22 9.8 8.9 22–23 12.1 9.0 23–24 11.3 9.7
3 Results and discussion
The higher traffic flows were observed during 0900–2000 hours (69% of total traffic volume) with highest hourly traffic volume of 7.6% between 1700–1800 hrs at Kiratpur corridor (Figure 2). Whereas at Darlaghat (Nauni-Shimla) corridor it was observed during 0700–2000 hours (69% of total traffic volume) with highest hourly traffic flow of 8.8% during 0800–0900 hours (Figure 3). The road corridors selected are part of the National Highways (NH-21 and NH-88) which are passing through the Solan District, one of the most industrialised districts of the state of Himachal Pradesh with nearly 5,102 registered small, medium and large scale industries. Being a major to and fro route, heavy goods/commercial vehicles (HCV or LCV) dominates the total traffic volume along the corridor. The share of commercial vehicles (HCV + LCV) was
256 R. Dhyani et al.
observed to be nearly 30% and 43% along Kiratpur and Darlaghat road corridors respectively (Figures 5 and 6).
Figure 5 Distributions of vehicle types along Kiratpur road corridor of NH-21
Figure 6 Distributions of vehicle types along Darlaghat road corridor of NH-88
3.1 Emission load estimation
The pollutant emission load from different categories of vehicles along the study corridors have been calculated by using equation (1). The emission loads have been estimated for CO, HC, NOx and PM for both the sites. The share of CO emissions was observed to be highest at 52% of the total estimated emission load followed by NOx, HC and PM at both the sites. Trucks have contributed nearly 80% and 82% of the total vehicular emissions at Kiratpur and Darlaghat, respectively (Table 7 and Table 8).
Performance evaluation of CALINE 4 model in a hilly terrain 257
Table 7 Vehicular emission load at Kiratpur
Types of vehicle
Number of vehicle
Emission load (Kg/day)
CO HC NOx PM Total
2W-2S 2,627 (8%) 7.10 5.13 0.07 0.21 12.5 (3.5%) 2W-4S 7,880 (24%) 8.85 5.22 1.69 0.17 15.9 (4.5%) 3W 176 (1%) 0.32 0.46 0.04 0.01 0.8 (0.2%) 4W-petrol 5,831 (18%) 13.46 1.27 0.93 0.03 15.7 (4.4%) 4W-diesel 1,944 (6%) 0.49 0.18 0.82 0.14 1.6 (0.5%) LCV 1,976 (6%) 7.86 2.71 4.28 1.08 15.9 (4.5%) Buses (diesel) 1,545 (5%) 5.49 0.19 2.43 0.06 8.2 (2.3) Trucks (diesel) 10,379 (32%) 137.68 10.20 109.29 24.02 281.2 (79.9%)
Total 32,356 (100%) 181.2 (52%) 25.4 (7%) 119.6 (34%) 25.7(7) 351.9 (100)
Table 8 Vehicular emission load at Darlaghat
Types of vehicle
Number of vehicle
Emission load (Kg/day)
CO HC NOx PM Total
2W-2S 1,101 (5%) 2.98 2.15 0.03 0.09 5.2 (2.1%) 2W-4S 3,303 (15%) 3.71 2.19 0.71 0.07 6.7 (2.6%) 3W 28 (0.13%) 0.05 0.07 0.01 0.00 0.1 (0.1%) 4W-petrol 5,262 (24%) 12.15 1.14 0.84 0.03 14.2 (5.6%) 4W-diesel 1,754 (8%) 0.44 0.17 0.74 0.13 1.5 (0.6%) LCV 1,505 (7%) 5.99 2.07 3.26 0.82 12.1 (4.8%) Buses 947 (4%) 3.37 0.12 1.49 0.04 5.0 (2.0%) Trucks 7,665 (36%) 101.68 7.53 80.72 17.74 207.7 (81.7%)
Total 21,565 (100%) 130.4 (52%) 15.4 (6%) 87.8 (35%) 18.9 (7%) 252.5 (100%)
3.2 Performance evaluation of CALINE 4 model
Pollutant transport and dispersion in complex topography are usually difficult to model reasonably. In regions of complex terrain (hilly areas) winds fields can be highly variable on much shorter distance. Mean wind flows and turbulence are significantly modified by the complex topography and secondary circulations. These conditions are of great importance in determining the effectiveness of the dispersion of road traffic pollutant, and cannot be treated with simplified models (Castelli et al., 2007). In such cases, the Gaussian-based line source dispersion models are inadequate in properly reproducing wind and turbulence fields and interactions between pollutant and orography (Nanni et al., 1996). In the present study, the site at Darlaghat is surrounded by hills on one side and nearly flat terrain on the other. The details of each road link of Darlaghat site (complex terrain) along with height from the MSL are given in Table 9. It has been observed that site characteristics are complex with variation in road gradient from nearly 20 m to –7 m (from reference point of 1,442 MSL) in 1km stretch of the road (considered for modelling purpose) (Figure 1). However, the study area at Kiratpur is flat terrain and does not have such variation in road gradient.
258 R. Dhyani et al.
Table 9 Site characteristic of Darlaghat (complex terrain)
Link name
X-coordinate latitude
Y-coordinate longitude
Altitude (above MSL)
Difference in altitude from reference point (meter)
R1 683,753.12 3,457,377.82 1,455 13
R2 683,803.8 3,457,315.95 1,457 14
R3 683,951.24 3,457,245.44 1,462 20
R4 684,074.18 3,457,043.03 1,459 17
R5 684,187.39 3,457,020.07 1,456 14
R6 684,249.6 3,457,005.81 1,452 10
R7 684,419.00 3,457,094.08 1,442* 0
R8 684,588.2 3,457,167.15 1,445 3
R9 684,895.07 3,457,285.24 1,439 –3
R10 684,968.62 3,457,269.01 1,435 –7
R11 685,014.73 3,457,123.89 1,442 0
Note: *Reference point.
The diurnal variation of measured and predicted CO concentrations at pre-identified receptor locations along both the study corridors have been shown in Figure 7 and Figure 8. It has been observed that model predicted satisfactorily at Kiratpur (flat terrain) as compared to Darlaghat (complex terrain). CALINE 4 model is unable to consider the variation in altitude (Table 9) present at a site. The model only takes single value of altitude as input data for the whole site irrespective of the considerable variation in road gradient, which is quite common in road corridors in hilly terrain.
Figure 7 Diurnal pattern of measured and predicted CO concentration at Kiratpur
Performance evaluation of CALINE 4 model in a hilly terrain 259
Figure 8 Diurnal pattern of measured and predicted CO concentration at Darlaghat
The performance of model predictions has been tested for both flat terrain (Kiratpur) and hilly terrain (Darlaghat) by comparing the value of statistical descriptors, i.e., index of agreement (d), fractional bias (FB) and normal mean square error (NMSE). The performance of model can be deemed acceptable if: NMSE ≤ 0.5; –0.5 ≤ FB ≤ 0.5 (Kumar et al., 2006) and 0.5 ≤ d ≤ 1.0 (Moriasi et al., 2007). For Kiratpur, the values of d, FB and NMSE were calculated to be 0.68, 0.22 and 0.048 respectively indicating satisfactory performance of CALINE 4. However, the values of d, FB and NMSE were calculated to be 0.22, –1.11 and 1.78, respectively for Darlaghat, exhibiting unsatisfactorily performance of CALINE 4. The correlation between observed and predicted CO concentration is found to be satisfactorily at Kiratpur (r2 = 0.33) as compared to Darlaghat (r2 = 0.092) (Table 10). Table 10 Statistical parameters for model results
Parameters Kiratpur Darlaghat Correlation coefficient (r2) 0.332 0.092 Index of agreement (d) 0.68 0.221 Fractional bias (FB) 0.217 –1.11 Normalised mean square error (NMSE) 0.048 1.78
The poor performance of model in hilly terrain might be due to inability of the model to tackle the temporal and spatial changes in meteorology (common nature of mountainous terrain), etc. Benson et al. (1985) have reported that CALINE 4 application in complex terrain is restricted to receptors immediately adjacent to the right of way. Also, simple computational algorithms like Gaussian or hybrid models for complex terrain used in CALINE 4 model can be successful only in certain idealised terrain conditions (Nanni et al., 1996). Moreover, in hilly terrain, high engine load condition (gradient) induces more fuel consumption resulting in higher emissions as compared to normal (plain)
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terrain conditions (Pierson et al., 1996; Pandian et al., 2009). The vehicle emission factors available in India do not account for change in vehicle speed and engine load conditions, which is a common feature of vehicles plying in hilly terrain. Non-availability of emission factors considering variation in speed and engine load conditions is one of the reasons for unsatisfactory performance of model at Darlaghat.
4 Conclusions
Diesel powered vehicles such as trucks, buses and LCV were found to be major contributors of CO and NOx emission at both the locations. The higher share of commercial vehicle (trucks/LCV) activities is indicative of higher industrial growth in the area. The performance of CALINE 4 model has been evaluated for predicting CO concentrations under two different types of terrain along highway corridors in Himachal Pradesh. At Kiratpur (flat terrain), the value of index of agreement and NMSE indicates satisfactory performance of the model. However, values of FB indicate that model under predicted the concentration. On the other hand, the model performance was observed to be unsatisfactory for hilly/complex terrain conditions (Darlaghat) as evident by weak strength of various statistical descriptors. However, the CALINE 4 model predicted CO concentrations reasonably along semi-urban highway corridor (flat terrain) for given Indian meteorological and heterogeneous traffic conditions. However, along complex/hilly terrain conditions the model performance was observed to be limited. Comprehensive studies need to be carried out to evaluate the performance of CALINE 4 model in different terrain types under Indian meteorological and traffic conditions. Further, the model performance in hilly terrain (for example Darlaghat site) could be improved if emission factors are available satisfying variable vehicle speed and gradient conditions. Nevertheless, comparatively accurate prediction capabilities and user-friendly nature of CALINE 4 model could be effectively used as a tool for vehicular pollution management for urban road corridors in Indian cities.
Acknowledgements
The authors are thankful to Director, CSIR-CRRI for kindly permitting to publish the present paper. Authors are thankful to two anonymous reviewers for their critical comments and useful observations that have improved the quality of the manuscript. Rajni Dhyani is thankful to CSIR for providing financial assistance through CSIR Senior Research Fellowship.
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