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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,


K. Divya

P.G Student, Department of Civil Engineering,


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

Estimation and Analysis of Various Pollutants in Mixed Traffic Conditions–A Comparative Study


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.

R. Srinivasa Rao, Sanmithra Swargam and K. Divya


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



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...)



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.



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)



Figure 7 CO (BSIII and BSIV) (Spatially Resolved)

Figure 8 1, 3 Butadiene (BSIII and BSIV) (Spatially Resolved)



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,



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.

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