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International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 9, September 2017, pp. 911–920, Article ID: IJCIET_08_09_101
Available online at http://http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=8&IType=9
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication Scopus Indexed
ESTIMATION AND ANALYSIS OF VARIOUS
POLLUTANTS IN MIXED TRAFFIC
CONDITIONS – A COMPARATIVE STUDY
R. Srinivasa Rao
Assistant Professor, Department of Civil Engineering,
GMR Institute of Technology, Rajam, Andhra Pradesh, India
Sanmithra Swargam
P.G Student, Department of Civil Engineering,
GMR Institute of Technology, Rajam, Andhra Pradesh, India
K. Divya
P.G Student, Department of Civil Engineering,
GMR Institute of Technology, Rajam, Andhra Pradesh, India
ABSTRACT
The Transport sector in the Indian Megacity of Hyderabad contributes extensively
to climate change through greenhouse gases emitted by vehicles. There are
infrastructure projects like construction of flyovers, expansion of the existing roads,
laying new roads, improvement of vehicle technology etc. Nevertheless the pollution
levels are high. The area of study, HYDERABAD has 6,809,970 population (according
to 2014 census) and with a vehicular population of around 3.4 million. Nine junctions
are selected where there is a less influence of industries and more influence of
vehicular pollution. The pollutants chosen are Carbon Monoxide (CO), 1, 3 butadiene
and total polycyclic aromatic hydrocarbons (PAHs).Traffic count is done at nine road
intersections of varying land-use patterns. Suitable net emission factor for each
vehicle category and type of engine are selected. Finally a geographic information
system (GIS)-based maps have been prepared for different vehicular emissions and
comparing the pollution levels from BSIII and BSIV engines.
Keywords: BSIII, BSIV, GIS, Carbon Monoxide.
Cite this Article: R. Srinivasa Rao, Sanmithra Swargam and K. Divya, Estimation
and Analysis of Various Pollutants in Mixed Traffic Conditions–A Comparative
Study, International Journal of Civil Engineering and Technology, 8(9), 2017,
pp. 911–920.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=8&IType=9
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1. INTRODUCTION
The vehicular population in Hyderabad is 3.4 million vehicles, with every year 0.2 million
new vehicle being added. This leads to the phenomenon where the traffic is growing four
faster than the population and found that the exponential growth of vehicular fleets contribute
nearly 58% to deteriorating air quality [1]. According to Hyderabad City Development Plan
the main roads are congested with a vehicle density of 720 vehicles per kilometer. The city
has now sixteen existing elevated corridors (flyovers) and fifteen proposed road widening
projects [2]. In contrast, these road based projects in the long run will only increase the
number of vehicles compared to the total length of roads available, as per theory of induced
travel demand [3]. The theory states that road improvements encourage latent demand where
it tends to invite more new people to travel [4]. Vehicular Kilometer Travelled (VKT) is the
product of total number of vehicles on the road and the length of the road they travel. It is the
determining indicator for vehicle emission in terms of grams emitted per kilometer [5].
Hyderabad, the capital of the southern state Telangana is located on the banks of Musi River
in Deccan plateau. The city which houses a population of 6.8 million has 7.75 million
residents which make the city, the fourth populous in the country [6]. The estimation revealed
that heavy-duty vehicles accounted for more than 60 percent and 36 percent of the NOx and
PM emissions respectively. About 19 percent of total emissions were that of start emissions.
Air quality in developing countries like India has reached an alarmingly high level. Particulate
matter (PM) is a major concern in Indian cities and 60 out of 62 metropolitan cities have
exceeded World Health Organization (WHO) standards (Nesamani, K. S. (2010)) [7]. Few
models are used by air pollution control agencies in the design of new control strategies (U.S.
EPA, 1993b; CARB, 1998a). It is critical that the models reflect the true emissions as closely
as possible. By monitoring for a period of several years, the effects of emission reduction
programs (such as diesel fuel reformulation, low NOx diesel engines, inspection and
maintenance, etc.) can be determined. This information can be invaluable for the design of the
next generation of emission control strategies (Jimenez-Palacios, J. L) [8]. There was a
discrepancy between the emission standards to which motor vehicles were certified and the
actual reductions seen by the network of ambient air monitors. New car CO emissions, for
instance had been decreased by 96% while ambient concentrations had gone down by perhaps
a factor of 2. (Bishop, G. A., & Stedman, D. H) [9]. Based on the rate of N2O increase in the
stratosphere, U.S. vehicles emit about two percent of anthropogenic N2O emissions. Vehicular
N2O emissions in the U.S. contribute only 0.1 percent of the calculated temperature increase
from greenhouse gases (Dasch, J. M) [10]. In HUDA, 20 monitoring stations are installed in
polluted hot spots and background sites, measuring respiratory PM, sulphur dioxide (SO2),
and nitrogen oxides (NOx). Two main reasons for reduction in PM pollution in the early
2000s, is an ordinance to replace petrol based 3-wheelers with liquefied petroleum fuel
(LPG), and relocation, shutting down, and merging of some industries falling within the now
residential zones. Out of 68,840, 3 wheelers in Hyderabad, a total of 29,346 have been
converted to LPG based vehicles. (Guttikunda, S) [11].
This paper focuses on developing EI of on-road vehicles on a geographic information
system (GIS) platform and comparing the engine types, namely BSIII and BSIV for the city
of Hyderabad in terms of Carbon Monoxide (CO), 1-3 butadiene and Total Polycyclic
Aromatic Hydrocarbons (PAHs). Figure 1 shows the selected study area and 9 locations
within a confined Latitudes and Longitudes.
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Figure 1 Study area
2. DATA COLLECTION AND METHODOLOGY
Nine In-City traffic intersections (Figure 1) were selected at Hyderabad. Traffic composition
is obtained for 24 hours.
The vehicles observed at all the locations were classified into five types such as two-
wheeler (2W), Auto Rickshaw, Car (C), Light Commercial Vehicles (LCV) and Bus (B). The
strategic Traffic volume count was done with 15 minutes interval for a period of 24 hours.
The traffic composition observed at each location is given in Figure 2. Figure 3 is the
methodology adopted for the study [12].
Figure 2 Total No of Vehicles
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Estimation and Analysis of Various Pollutants in Mixed Traffic Conditions–A Comparative Study
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Figure 3 Methodology adopted (D. Singh et.al. 2014)
4. DATA ANALYSIS
The data is analyzed by estimating the net emission factor (for each pollutant) and for each
vehicle type (e.g., 2Ws, Auto Rickshaws etc.), the net emission factor is expressed in
mg/km/2Ws for 2Ws.
In the same manner, net emissions factors for all vehicles types plying on the roads at the
nine city junctions at Hyderabad are derived. Different types of engines have different net
emission factors. Here two types of engines, namely BSIII and BSIV are considered and
comparative study is made between both engines showing the decrease in the emission levels
between the two types.
Estimation of pollutants' emissions at all nine selected junctions was done by using
emission factors of BSIII and BSIV, and the formulae taken from CPCB [13].
Table 1 Net Emission factors for BSIII type Engines
BSIII in mg/Km in µg/Km
Vehicle Type CO 1-3 Butadiene Total PAH
Bus diesel 3.92 0.01 1.372
2 W petrol 0.829 0.005 0.792
Car petrol 1.945 0.003 0.132
diesel 0.06 0.001 0.211
Auto
Rickshaw
diesel 0.205 0.007 0.699
petrol 1.534 0 0.332
LCV diesel 3.66 0.415 8.268
Selection of Pollutants and Emission Factors
Emission Estimation (Product of data and emission factors)
Generation of Spatially resolved Pollutant Maps
Collection of Activity Data (Vehicle count from various Intersections)
Maps Digitization and Formation of Thematic Layers (Locations, Roads
etc...)
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Table 2 Net Emission factors for BSIV type Engines
BSIV in mg/Km in µg/Km
VEHICLE TYPE CO 1-3 Butadiene Total PAH
Bus diesel 2.838 0.007 0.961
2 W petrol 0.744 0.001 0
Cars petrol 1.294 0.002 0.066
diesel 0.047 0 0.105
3 W diesel 0.205 0.007 0.699
petrol 1.534 0 0.332
LCVs diesel 2.65 0.291 5.796
VKT = Traffic count (Major or minor roads) x Road length (Major or minor road)
Emission = VKT x Net Emission Factor
Where, VKT = Vehiclular Kilometer Travelled
The daily estimation of vehicular kilometer travelled (VKT) for all vehicular categories in
each junction were calculated on the basis of the road lengths at each junction. The emissions
at each junction were extracted and mapped to the database.
Estimation of CO (Carbon Monoxide) at Ambedkar Junction:
Steps:
1. Bus:
VKT 1 (Bus) = 6600 X 0.5 Km = 3300 Km
Emission1 (Bus) = 3300 Km X 3.92 mg/Km = 12,936 mg
2. 2W:
VKT 2 (2W) = 96644 X 0.5 Km = 48322 Km
Emission2 (2W) = 48322 KM X 0.829 mg/Km = 40058.738 mg
3. Car (Petrol):
VKT 3(Car) = 31824 X 0.5 Km= 15912 Km
Emission3 (Car) = 15912 Km X 1.945 mg/Km = 30,948.84 mg
4. Car (Diesel):
VKT 4(Car) = 1588 X 0.5 Km= 794 Km
Emission4 (Car) = 794 Km X 0.06 mg/Km = 637.98 mg
5. Auto Rickshaw (Petrol):
VKT 4(Auto Rickshaw) = 11265 X 0.5 Km = 5632.5 Km
Emission4 (Auto Rickshaw) = 5632.5 Km X 1.534 mg/Km = 8640.255 mg
6. Auto Rickshaw (Diesel):
VKT 4(Auto Rickshaw) = 7506 X 0.5 Km = 3753 Km
Emission4 (Auto Rickshaw) = 3753 Km X 0.205 mg/Km = 769.365 mg
7. LCV:
VKT 5(LCV) = 3293 X 0.5 Km = 1646.5 Km
Emission5 (LCV) = 1646.5 Km X 3.66 mg/Km = 6026.19 mg
Total CO at Ambedkar Junction = 100017.6 mg/24 hrs.
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Similarly, net emissions factors for other vehicle types plying on the roads at all the
junctions of Hyderabad were calculated with BSIII and BSIV emission factors.
Table 3 Derived Net Emission values for BSIII and BSIV type Engines
Pollutant CO in gm/day 1-3 Butadiene in µg/day Total PAH in µg/day
Vehicle Type BSIII BSIV BSIII BSIV BSIII BSIV
BUS 94.24 68.23 240.42 168.294 32985.624 23104.362
2 W 227.73 204.38 1373.5225 274.7045 217565.964 0
CAR 136.63 91.22 252.419 137.661 18776.3285 9365.2005
Auto Rickshaw 70.51 70.51 196.9765 196.9765 33545.9485 33545.9485
LCV 238.54 17.27 2704.7625 1896.5925 53886.69 37775.43
Total 767.65 451.61 4768.1005 2674.2285 356760.555 103790.941
Figure 4 Difference in CO Emissions (gm/day) Figure 5 Difference in 1,3 Butadiene Emissions
(gm/day)
Figure 6 Difference in PAH Emissions (µg/day)
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Figure 7 CO (BSIII and BSIV) (Spatially Resolved)
Figure 8 1, 3 Butadiene (BSIII and BSIV) (Spatially Resolved)
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Figure 9 Total PAH (BSIII and BSIV) (Spatially Resolved)
5. RESULTS AND CONCLUSIONS
The Difference in emissions from BSIII and BSIV type engines are shown in table 3. There
were 549409 2-Ws in the city of Hyderabad, which accounted for 56% of the vehicle
population. Auto Rickshaws, Buses, Cars, 2-Ws, and LCVs contributes 9, 12, 17.5, 30, and
31%, respectively, to the total vehicular emissions of CO with BSIII type engine and 15.5, 15,
20, 45 and 4%, if BSIV type engines are used. 4, 5, 5, 30, and 56.5%, respectively, to the total
vehicular emissions of 1-3 Butadienes with BSIII type engine and 7, 6, 5, 10 and 71%, if
BSIV type engines are used. 9.5, 9, 5, 61, and 15%, respectively, to the total vehicular
emissions of PAH with BSIII type engine and 32, 22, 9, 0 and 36%, if BSIV type engines are
used.2-Wsare making a lot of difference in emissions of PAH with BSIV engine when
compared to BSIII engine with 0 gm and 217566 µg contributions respectively. Total PAH
emissions accounting to 61.9% followed by LCV’s (15%). The spatial distribution of CO, 1-3
Butadiene and Total PAH emission loads and their differences in usage of engine types are
shown in maps. With the introduction of BSIV type engine over BSIII, there is lot of
reduction in the emissions of CO, 1-3 Butadiene and Total PAH by 41, 43 and 71%, making
PAH the highest difference among all.
Carbon monoxide binds to hemoglobin over 200 times more easily than oxygen does, so if
carbon monoxide is present, oxygen will not be able to find space to get into the hemoglobin.
This is because the space is occupied with carbon monoxide which is like smoking cigarettes
continuously. As a result, parts of the body will be starved of oxygen, and they will die.
Running a car engine in an enclosed space can cause carbon monoxide poisoning and as the
pollution levels in the city are becoming dense, it is important to reduce these emissions. It is
estimated by European commission that 1-3 Butadiene emissions are 663 tonnes/year by
vehicles out of 1435 tonnes/year from all sources. Occupational exposures to high levels of
pollutant mixtures containing PAHs results in symptoms such as eye irritation, nausea,
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vomiting, diarrhea and confusion. Mixtures of PAHs are also known to cause skin irritation
and inflammation. However, it is not known which components of the mixture were
responsible for these effects because there are 100 of them of which 17 are found more
dangerous. Other compounds commonly found with PAHs may be the cause of these
symptoms. Since detection of these 100 types is not possible, reducing all possible PAHs is
the need of the hour. As shown, this can be done by introducing BSIV type vehicles.
Moreover PAH contains Benzene rings, so in turn even benzene emissions are reduced.
The Department of Transport has submitted an action plan to control emissions from in-
use vehicles like All petrol Auto Rickshaws to be converted to LPG, Phasing out of heavy
goods carrier of +15 years, All petrol taxies to be converted to LPG, No fuel at gas stations
with PUC certification, Permits to be cancelled for 10+ year diesel taxis and no permits for
taxis above 10 years.
Car manufacturers can substantially reduce the test emissions through further
improvements in control systems to more accurately control the fuel-air ratio resulting in
substantially low tailpipe emissions. It has been accomplished by Honda in their recently
announced ultra-low emission vehicle (ULEV) production vehicle.
REFERENCES
[1] V. Chattopadhyaya, P Samajdar, Rawat, City action - Hyderabad: CSE conducts city
dialogue on air quality and transportation challenges, proposes agenda for action, Centre
for Science and Environment, http://www.cseindia.org/print/1792, 2010.
[2] V. Geetanath, Thruston road widening, bridges, The Hindu,
http://www.thehindu.com/news/cities/Hyderabad/article2339461.ece,2011.
[3] Cervero, R. (2003). Road expansion, urban growth, and induced travel: A path analysis.
Journal of the American Planning Association, 69(2), 145-163.
[4] Räty, P., &Leviäkangas, P. (1999). Estimating vehicle kilometers of travel using PPS
sampling method. Journal of transportation engineering, 125(1), 8-14.
[5] Noland, R. B. (2001). Relationships between highway capacity and induced vehicle travel.
Transportation Research Part A: Policy and Practice, 35(1), 47-72.
[6] https://en.wikipedia.org/wiki/Hyderabad.
[7] Nesamani, K. S. (2010). Estimation of automobile emissions and control strategies in
India. Science of the Total Environment, 408(8), 1800-1811.
[8] Jimenez-Palacios, J. L. (1998). Understanding and quantifying motor vehicle emissions
with vehicle specific power and TILDAS remote sensing. Massachusetts Institute of
Technology, Cambridge.
[9] Bishop, G. A., & Stedman, D. H. (1996). Measuring the emissions of passing cars.
Accounts of Chemical Research, 29(10), 489-495.
[10] Dasch, J. M. (1992). Nitrous oxide emissions from vehicles. Journal of the Air & Waste
Management Association, 42(1), 63-67.
[11] Guttikunda, S. (2008). Air pollution & co-benefits analysis for Hyderabad, India.
[12] Singh, D., Shukla, S. P., Sharma, M., Behera, S. N., Mohan, D., Singh, N. B., &Pandey,
G. (2014). GIS-based on-road vehicular emission inventory for Lucknow, India. Journal
of Hazardous, Toxic, and Radioactive Waste, 20(4), A4014006.
[13] Status of Pollution Generated from Road Transport in Six Mega Cities. Website:
cpcb.nic.in - March 2015.
[14] Ross, M., Goodwin, R., Watkins, R., Wenzel, T., & Wang, M. Q. (1998). Real-world
emissions from conventional passenger cars. Journal of the Air & Waste Management
Association, 48(6), 502-515.
Page 10
Estimation and Analysis of Various Pollutants in Mixed Traffic Conditions–A Comparative Study
http://www.iaeme.com/IJCIET/index.asp 920 [email protected]
[15] V.S.S.R. Gupta, R. Srinivasa Rao and K. Divya, 2017. Evaluation of Groundwater Quality using
Multivariate Statistical Techniques and GIS - A Case Study, International Journal Of Civil
Engineering & Technology (IJCIET)- Scopus Indexed.Volume:8,Issue:8,Pages:1165-1176.
[16] Ify L. Nwaogazie, Abali Happy Wilson and Terry Henshaw. Assessment of Standard
Pollutants In A Gas Flaring Region: A Case of Ogba/Egbema/Ndoni Local Government
Area In Rivers State of Nigeria, International Journal of Civil Engineering and
Technology, 7(3), 2016, pp. 07–17.
[17] K. G. V. Jayaram, V. Raghukalyan, H. Jeevitesh and Dr. K. V. Ramana, Fabrication and
Testing of Automobile Pollutants Absorbers. International Journal of Mechanical
Engineering and Technology, 8(5), 2017, pp. 300–305.
[18] T. Setianingsih, Masruri and B. Ismuyanto, Influence of Impregnation Ratio on
Physicochemistry of Patchouli Biochar Using CoCl2 Chemical Activator for Adsorption
of Drug Pollutants. International Journal of Civil Engineering and Technology, 8(5), 2017,
pp. 709–716
[19] Shilpa B. S. and Lokesh K. S., Fuel Emissions’ Correlation Assessment of Indoor
Pollutants from Different Households, International Journal of Advanced Research in
Engineering and Technology (IJARET), Volume 6, Issue 5, May (2015), pp. 24-32