1b.nagpure Et Al. UrbanClimate TrafficInducedEmissionEstimates&Trends Delhi 1

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Short Communication

Traffic induced emission estimates and trends

(20002005) in megacity Delhi

Ajay S. Nagpure a,, Ketki Sharma b, Bhola R. Gurjar c,1

a Hubert H. Humphrey School of Public Affairs, University of Minnesota, Twin Cities, USAb School of Civil and Environmental Engineering, Georgia Institute of Technology, 200 Bobby Dodd Way, Atlanta, GA 30332, USAc Department of civil Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India

a r t i c l e i n f o

Article history:

Received 3 October 2012

Revised 13 March 2013

Accepted 25 April 2013

Keywords:

Emission inventory

Transportation

India

Emission trend

Air pollution

a b s t r a c t

Transport sector emission inventory for megacity Delhi has been

developed for the period 20002005 to quantify vehicular emis-

sions and evaluate the effect of relevant policy reforms on total

emissions of various air pollutants like CO2, CO, HC, NOx, TSP,

SO2, Pb and VOCs over the years to assist in future policy formula-tions. Emission factor and vehicle utilization factor based approach

as recommended by IPCC (2006) have been used for estimating

emissions. CO level were found to increase continuously during

the study period, other pollutants like CO2, TSP, NOx and SO2declined in the initial years, which clearly seem to be the result

of stricter emission norms and compressed natural gas conversion

of public transport. The levels of NOx and TSP did not show appre-

ciable rise during the study period, which is an indicator of CNG

effectiveness as an alternative fuel. However, two-wheelers popu-

lation were found to be a major contributor towards the air pollu-

tion load.

Published by Elsevier B.V.

1. Introduction

Air quality in urban areas is gradually leading to violation of ambient air safe limits. Megacity Delhi

the National Capital Region of India is one of the most polluted cities in the world (WHO, 1992)

having transport as major source of criteria area pollutants (Gurjar et al., 2004). It has been observed

2212-0955/$ - see front matter Published by Elsevier B.V.

http://dx.doi.org/10.1016/j.uclim.2013.04.005

Corresponding author. Tel.: +1 919 513 1804, mobile: +1 919 800 8194.

E-mail addresses: [email protected], [email protected] (A.S. Nagpure), [email protected] (K. Sharma),

[email protected] , [email protected](B.R. Gurjar).1 Tel.: +91 1332 285881; fax: +91 1332 275568/273560.

Urban Climate 4 (2013) 6173

Contents lists available at SciVerse ScienceDirect

Urban Climate

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / u c l i m
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that the automobile traffic has increased at a rapid rate in the urban areas during last decades in India.

Annual growth rate of motor vehicles in India has been around 10% during the last decade. The most

disturbing fact is their rising concentration in selected urban areas. About 32% of these vehicles are

plying in metropolitan cities, which constitute 11% of the total Indian vehicle population (Singh,

2005) and responsible for degrading quality of urban atmosphere. In terms of emissions of various pol-

lutants, Delhi was among the top five SO2emitting megacities of the world in early nineties and trans-

port sector was the prime culprit for it (Garg et al., 2001). Mashelkar et al. (2002) state that the

emission range of NOxfrom transport sector is 6674% in Delhi. CPCB (Central Pollution Control Board)

(CPCB, 1995) data shows that almost 50% of the emission in Delhi is from vehicular activities, followed

by domestic, industrial, and power plants. It was found that from 199091 to 199596, annual NOxemission from gasoline consumption increased from 3.5 to 4.5 Gg, respectively, whereas it was 8 Gg

in 199091 and 12.8 Gg in 199596 from diesel (Sharma et al., 2002). According to Xie and Shah

(2000), diesel driven vehicles were the major source of NOxemission in Delhi, whereas least contribu-

tion was from two- and three-wheelers. In terms of suspended particulate matter (SPM) concentra-

tion, Delhi ranks the fourth most polluted city in the world (Gadhok, 2000).Gurjar et al. (2004) and

Reddy and Venkataraman (2002)estimated that 15% of Delhis RSPM (Respirable Suspended Particu-

late Matter) emission results from automotive traffic. Transport contributed about 19% to TSP (TotalSuspended Particulate) emission in the year 2000 and almost doubled from 15 Gg in 1990 to 28 Gg

in 2000 (Gurjar et al., 2004). According to ADB (Asian Development Bank) (ADB, 2005), diesel driven

vehicles are the major contributor of PM emission among all vehicle categories in Delhi. As a conse-

quence, incidence of respiratory diseases in Delhi is 12 times the national average, and 30% of Delhis

population suffers from respiratory disorders (Kandlikar and Ramachandran, 2000). Its poor air quality

is responsible for about 18600 premature deaths per year (TERI, 2001).

Although there are other methods (e.g., inverse modeling), as suggested by IPCC (Intergovernmen-

tal Panel on Climate Change) (IPCC, 2006) using bottom-up approach based on vehicle numbers, their

utilization factor and emission factors, present study carries out quantification of the vehicular emis-

sion loads in Delhi for the period of 20002005. The study aims to estimate air pollutant emissions

from road transport and to determine the changing trends in levels of various pollutants (e.g., CO2,CO, NOx, HC, TSP, SO2, Pb, and NH3) in Delhi due to a combination of factors (e.g., rising population

and increasing travel demand) combined with various policies and technological interventions intro-

duced during the study period. This will help in establishing effects of reforms already introduced to

assist the policy makers and modelers in developing future strategies to reduce and control urban air

pollution in Delhi, India From time to time, Government of Delhi has implemented various norms and

policies for reducing traffic emissions in megacity Delhi. In present study most of the norms and pol-

icies are considered indirectly due to the use of country specific emission factors and policies oriented

vehicle population. Following are the norms and policies, which considered for emission estimations

1. 2000: India 2000 (Euro-I equivalent) norms for all 4 wheelers Bharat Stage-II (Euro-II equivalent)

norms for Passenger Cars & MUVs.2. 2001: Bharat Stage-II (Euro-II equivalent) norms for Commercial Vehicles in Delhi & Kolkata (from

24th October, 2001).

3. 20012002: Introduction of clean fuel CNG (Compressed Natural Gas) and phasing out of old age

vehicle.

4. 2005: Bharat stage-III emission norms for all categories of 4 wheelers implemented in 11

megacities.

However, since there are certain limitations related to baseline data and methodology adopted in

this study, the present emissions estimates have further scope to be revised as and when more precise

vehicle population data and emission factors are available for Delhi.

The introduction is followed by methodology of the emission estimation based on vehicular pop-ulation, vehicle utilization factor and emission factor. In results and discussion, trends of emission

estimates are analyzed and comparison is made with air quality measurements and findings of similar

studies. There is a brief section on the limitations and scope of the present study followed by

conclusions.

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2. Methodology

Emission factors and activity-based approach recommended by IPCC (2006) have been used for the

calculation of vehicular emissions from the road transport sector in Delhi (Eq. (1)). A spreadsheet

model (Microsoft Excel 1997/2003/2007) has been used for calculating emissions from transport sec-

tor using Eq.(1).

Ei X

VehjDj Ei;j km 1

Ei= Emission of pollutant (i),Vehj = Number of vehicles per type (j),Dj= Distance travelled by per vehi-

cle in per year or vehicle utilization factor (j), Ei,j km= Emission of pollutant (i) from vehicle type (j) per

driven kilometer.

2.1 Vehicle population

Population data of different vehicle types (e.g., Bus, LCV, HCV, Texi Car, etc.) for Delhi is taken from

the Economic Survey of Delhi (20032004). CNG vehicle population data for the study period has beentaken from Khaiwal et al. (2006). It is noticeable that the number of vehicles registered in Delhi is not

the actual figure plying on Delhis roads. A large number of vehicles commute daily from nearby

towns. In addition, large number of goods vehicle pass through the city at night; none of them running

on CNG and hence contributing significantly to the rising air pollution level in Delhi. The data for

external traffic (including traffic originating from the city, terminating within the city and passing

through the city) was adapted from the Central Road Research Institute (CRRI) study for 2002 (Mashel-

kar et al., 2002) and projected for other years. The two-wheeler population was categorized into 2-

stroke (2S) and 4-strokes (4S). Since 4S two-wheelers emit less pollution load in comparison to 2S

two-wheelers, for studying the relative advantage of 4S two-wheelers over 2S two-wheelers we have

taken 8:1 ration of 2S4S two wheelers to calculate their respective numbers as per Biswas (2006).

The goods vehicles population was similarly divided into Light Commercial Vehicles (LCVs) and HeavyCommercial Vehicles (HCVs) by considering the dividing ratio given by Bose (1999). The age wise dis-

tribution of Delhis vehicle population was taken from CRRI-2002 study (Mashelkar et al., 2002). The

distribution was assumed to be constant from 2001 to 2005 and was not applied for compressed nat-

ural gas (CNG) vehicles which were assumed to be in 05 years age group since the conversion of pub-

lic transport fleet took place after 2001. In 2002 Delhi government strictly implemented phasing out

program for all commercial vehicles (e.g., Auto Rickshaw, Buses, LCVs and HCVs) and cancelled their

registration due this a significant decrease in these vehicle population have been observed between

2001 and 2002. Also because of this policy demand of new commercial vehicles was unexpectedly in-

creased between 2002 and 2003.The population of vehicles for different years considered in this study

are given in Table 1. The total population has been estimated for entire period taking into account the

external traffic with an assumption that percentage of external traffic in 2002 is constant for all theyears.

2.2 Emission factors

An emission factor is a representative value that attempts to relate the quantity of a pollutant re-

leased to the atmosphere with an activity associated with the release of that pollutant. For transport

sector, these factors are usually expressed as the weight of pollutant divided by a unit distance trav-

eled by vehicle (g/km). It was hard to find the emission factors taking into account Delhis driving con-

ditions; control measures for different vehicle categories, and a range of pollutants (CO 2, CO, HC, NOx,

TSP, SO2, Pb and VOC) from a single source. Further, several of the earlier studies had simply taken the

norms introduced as the emission factors, which result in gross under estimation keeping in mind theactual driving conditions on Delhis roads. Therefore, to make a more realistic study, emission factors

were compared from range of sources and finally compiled as given in Table 2and used those repre-

senting Indian conditions. For calculating per year emissions from transport sector, we have used

average distance traveled by different vehicles. As expressed in Eq.(1), number of vehicles types (Vehj)

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(Table 1), emission factors (Ei,j km)(Table 2) and vehicle utilization factor (Table 3; as given in CRRI re-

port byMashelkar et al. (2002); which is based on actual field experiments) were used to estimate

vehicular emissions.

Table 1

Vehicular population in Delhi.

Vehicle types (j) 2000 2001 2002 2003 2004 2005

Cars/taxi-total 1150489 1212281 1429708 1572748 1707973 1822742

Cars/taxi-CNG 0 11700 15166 15505 15876 16255

Cars/taxi-other 1150489 1197947 1411128 1553753 1688523 1802828

2 Wheelers 2370160 2407799 2675396 2832620 3013505 3216012

2 Wheelers-2 stroke 2093018 2126256 2362562 2501403 2661137 2839965

2 Wheelers-4 stroke 249560 253523 281699 298254 317299 338622

Auto-total 110488 110488 56331 95119 161417 171801

Auto-CNG 0 14000 35678 44164 56846 56846

Auto-petrol 110488 92705 11013 39022 89212 99596

Buses-total 56156 64407 47102 48813 54419 55747

Buses-CNG 0 650 6396 13809 15239 15239

Buses-diesel 56133 63501 38428 30108 33776 35104

Light commercial vehicle 116928 119259 56971 100092 107163 114364

Heavy commercial vehicle 515484 525761 251167 441258 472436 504186

Total 3456579 3551690 3790515 4183609 4542646 4847911

Table 2

Emission factors (Ei,j g km1) compiled for the study.

E.F. (g/km) CO2 CO HC NOxp TSP SO2 Pb VOC

Cars/taxis 223.6e 1.98g 0.25g 0.2g 0.03g 0.053c 0.003c 2.5d,e

Cars/taxis-CNG 178.8m 0.786g 1.55g 0.92g 0.02g 0h 0h 1.9k

2 W-2S 42a 2.2g 2.13g 0.07g 0.05a 0.023c 0.003c,l 2.13a

2 W-4S 38a 2.2g 0.7g 0.3g 0.05a 0.023c 0.002c,l 0.7a

Auto-CNG 60a 3.4g 2.07g 0.25g 0.02a 0h 0b 0.25a

Auto-gasoline 60a 4.3g 2.05g 0.11g 0.08b 0.029c 0b 2.05a

Bus-CNG 412.16m 3.6f 0.44i 9.6g 0.013j 0h 0h 1.33

Bus-diesel 515.2e 4.5g 1.21g 12g 0.56g 2h 0h 1.6e

LCV 515.2e 5.1g 0.14g 1.28g 0.2g 0.37c 0c 1.6e

HCV 515.2e 3.6g 0.8 g 6.3g 0.28g 0.037c 0c 1.6e

Where,a Iyer (2002).b Bose (1999).c UNEP (1999).d EIA USA (1994).e Gurjar et al. (2004).f Value not available from either of the sources, so value taken as 19.4% less than that for gasoline vehicles as carbon-dioxide

emissions are 19.4% less for CNG (Source:De, 2004).g Mashelkar et al. (2002).h TERI (2006).i Value not available from either of the sources, so value taken as 63.2% less than that for gasoline vehicles as carbon-dioxide

emissions are 63.2% less for CNG (Source:De, 2004).j Value not available from either of the sources, so value taken as 97.7% less than that for gasoline vehicles as carbon-dioxide

emissions are 97.7% less for CNG (Source:De, 2004).k Value not available from either of the sources, so value taken as 24% less than that for gasoline vehicles as carbon-dioxide

emissions are 24% less for CNG (Source:De, 2004).l Value not available from either of the sources, so value taken as 16.67% less than that for gasoline vehicles as carbon-dioxide

emissions are 16.67% less for CNG (Source: De, 2004).m Value not available from either of the sources, so value taken as 20% less than that for gasoline vehicles as carbon-dioxide

emissions are 20% less for CNG (Source:EIA USA, 1994).



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3. Results and discussion

Emission loads from transport sector are discussed below for the following compounds; CO2, CO,

HC, NOx, TSP, SO2, VOC and Pb.Figs. 18show that emissions of particular compounds are dominatedby specific vehicle categories. For example, two-wheelers are major sources for emissions of CO, HC,

TSP, VOC and Pb (Figs. 2, 3, 5, 7 and 8), whereas cars and goods vehicles emit most of the CO2(Fig. 1)

and buses plus goods vehicles dominate in emissions of NOx and SO2 (Figs. 4 and 6).

CO2emissions (Fig. 1) increased by about 24% from 13.36 Tg in 2000 to 16.62 Tg in 2005. From 2000

to 2002, emissions decreased by about 11% from 13.36 Tg in 2000 to 11.92 Tg in 2002 followed by ris-

ing trend till 2005. Implementations of various emission norms and phasing out of old vehicles during

this period might be responsible for decreased emissions during 2001 and 2002. Increasing vehicle

population led to rising emissions between 2002 and 2005. Among all vehicle categories in 2002 on-

wards, cars contributed highest amount of CO2 emission followed by goods vehicle, two-wheelers,

buses and autos (Fig. 1).

Emissions of CO (Fig. 2) increased by 78% from 197 Gg in 2000 to 350 Gg in 2005. Gradual increasing

trend was observed in emissions from 2000 to 2001. Increasing two wheeler population might be

responsible for the gradual increment in CO emissions during 2000 to 2005. Fig. 2clearly indicates

the effect of rising population on increasing emissions.Fig. 2shows that two wheelers (56%) predom-

inated for CO emissions among all vehicle categories followed by cars (20%), goods vehicle (14%), autos

(5%) and buses (5%).

Hydrocarbons (HC) emission (Fig. 3) increased 1.3 times from 96 Gg in 2000 to 222 Gg in 2005. High-

est increment in HC emission was observed from 107 Gg in 2001 to 177 Gg in 2002 (65%). After 2002,

HC emissions showed a constant rising trend. Increasing emission trend during study period can be

easily correlate with gradually rising vehicle population. Approximate 86% of HC (Fig. 3) was reported

only from two wheeler populations (from 2000 to 2005) in Delhi. While contributions from other vehi-

cle categories were very less, e.g., cars (4.3%), goods vehicle (4%), autos (4%) and buses (2%).

NOx emission trend, in comparison to other pollutants, showed a different scenario (Fig. 4). About

0.5% of decline was observed in NOx emission (105 Gg in 2000 to 104 Gg in2005). During 2000

2002, steep decreases were observed followed by steadily rising emission trend until 2005. Highest

increase (23%) was found during 2002 to 2003; followed by 6% annual average increase till 2005.

Goods vehicles were the predominated source of NOx emission (51%) followed by buses (36%), cars

(6%), two wheelers (6%), and autos (1%) during the study period (Fig. 4).

Total suspended particles (TSP) emissions, as illustrated in Fig. 5, increased from 8 Gg in 2000 to

10 Gg in 2005 (about 31% increment). However highest decrease (9%) was observed from 8.4 Gg

in 2001 to 7.69 Gg in 2002 followed by gradual increase till 2005. This is the time when government

launched protective measures for control the rising emissions from transport sector in megacity Delhi.

Two-wheelers were responsible for high TSP emissions (40%) during 20022005 followed by goods

vehicle (29%), buses (19%), cars (10%) and autos (2%). However, it is observed that TSP emissions fromgoods vehicles were higher before 2002. It shows the efficacy of CNG implementation and clean fuel

related initiatives taken after 2001.

Table 3

Vehicle utilization factors (km traveled per day per

vehicle type).

Vehicle type km/day

Cars/jeeps 45.1

2 Wheelers 36.4

Auto 66.5

Buses 157.2

LCV 46.9

HCV 45.5

Source:Mashelkar et al. (2002).

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Figure 1. Annual CO2 emissions from transport sector, Delhi (20002005).

Figure 2. Annual CO emission from transport sector, Delhi (20002005).

Figure 3. Annual HC emissions from transport sector, Delhi (20002005).



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Figure 4. Annual NOx emission from transport sector, Delhi (20002005).

Figure 5. Annual TSP emission from transport sector, Delhi (20002005).

Figure 6. Annual SO2 emissions from transport sector, Delhi (20002005).

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SO2 emissionsfrom transport sector in Delhi demonstrated effective results of policies and protec-

tive measures (e.g., ultra low sulphur diesel fuel) introduced by the government. Approximately 9% of

the decrease was observed from 12 Gg in 2000 to 11 Gg in 2005 (Fig. 6), with highest decrease (32%)

between 2000 and 2002. After 2002, emissions increased with annual average rate of 7.69% per year,

but were less in comparison to those of 2000. Bused contributed highest amount (45%) of SO2 followed

by goods vehicle (31%), cars (12%), two-wheelers (11%) and auto (1%) (Fig. 6). Declining trends were

observed in most of the commercial vehicle categories (e.g., Auto, Bus, Goods Vehicle) in year between

2001 and 2002. Emissions from buses decreased continuously from year 2001 to 2003; with no signif-

icant changes later on. Emission trends from cars, two-wheelers and autos followed almost constant

trend after 2002.

VOC emission, however, observed about 77% increment in Delhi from transport sector with 139 Gg

in 2000 to 247 Gg in 2005 (Fig. 7). About 38% increase was observed between 2001 and 2002 followed

by (10%) from 2002 to 2003. After 2002, emissions increased at the average rate of 8% annually. Trend

showed that implementation of policies and norms were not much effective in controlling the emis-

sions of VOC in comparison to other pollutants. Two-wheelers accounted for the highest amount of

VOC emission (56%) followed by cars (31%), good vehicle (8%), autos (2%) and buses (2%) ( Fig. 7).

No declining trend was found in the emissions from cars during the entire study period. Emission

Figure 7. Annual VOC emission from transport sector, Delhi (20002005).

Figure 8. Annual Pb emissions from transport sector, Delhi (20002005).



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trends of autos and goods vehicle were not going in definite way, while there was no significant

change from buses after 2002.

Pb emissionincreased by about 91% from 0.15 Gg in 2000 to 0.29 Gg in 2005. Approximately 4% of

decrease in Pb emission was seen in Fig. 8 followed by sharp increment (62%) between 2001 and 2002.

After 2002, Pb emission increased at 7% annual rate. This trend showed that norms and policies had no

significant impact for controlling Pb emission in Delhi. Two-wheelers were, however, the predomi-

nated source of Pb followed by cars, while other sources were negligible (Fig. 8).

As shown in Fig. 9, a comparative analysis of the emissions from 2-stroke and 4-stroke engine tech-

nologies clearly points out the need for encouraging 4-stroke two wheeler as the difference in their

relative contribution towards all the pollutants is astoundingly great due to the large number of 2S

two-wheelers as well as their higher emission factors. This implies that the pollution levels can be

brought down to safer levels in spite of the rising two-wheeler population if the 4-stroke technology

for the two-wheeler segment is promoted. The 4-stroke technology would be most effective in check-

ing the VOC and HC emissions, which is quite evident from Fig. 9. However, note that 4S two-wheelers

may increase relative emission of NOxdue to efficient burning of fuel in 4-stroke internal combustion

engines.

Above discussion implies that implementation of various policy measures in Delhi has given fruit-ful results for reducing emissions from transportation sector. For example, implementation of Euro

norms and introduction of CNG in public transport has consistently reduced emissions of almost all

pollutants between 2000 and 2002. Nevertheless, our study suggests that government should intro-

duce more efficient emission norms for two-wheelers and develop some bypass for reducing heavy-

duty vehicle traffic from Delhi to improve its ambient air quality.

3.1 Comparison with other studies

Table 4 presents our estimates along with the emission estimates for Delhi taken from other emis-

sion inventories. Almost all of these studies have used similar sources and baseline data for vehiclepopulation estimation with minor differences such as inclusion or exclusion of external vehicles. Thus,

it is assumed that sample size of vehicle population in our study is comparable with other studies.

Most of these studies have concentrated on a narrow range of pollutants for a specific year.

Gurjar et al. (2004) concentrated on a set of pollutants bit different from our study. Compared with

other studies our estimates are generally higher; the major part of which could be attributed to the

vehicle population taken for estimates. Except the Auto Fuel Policy Report, all the studies have taken

projections for the vehicle population of Delhi, which came out to be much lower than the actual fig-

ures used from the Economic Survey of Delhi. For example,Das and Parikh (2004)have taken a figure

Figure 9. Relative contribution of 2-stroke and 4-stroke two-wheelers towards total emission.

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of 3508366 for 2005, which were even less than 3551690 we took for 2001. Similarly, the values esti-

mated by Bose (1999)turned out to be on much lower side. Furthermore, our figures were higher due

to the inclusion of external traffic in carrying out our estimates, which were not accounted by most of

the studies includingGurjar et al. (2004).

Gurjar et al. (2004) took the same vehicle population as adapted from the Economic Survey of

Delhi for our study. However, the results vary in certain pollutants category like CO2 and SO2.

Although our choice of emission factors (for 2000) for CO2 and SO2 was similar to those ofGurjar

et al. (2004), our estimates turned out to be much higher due to the inclusion of deterioration

factors and external traffic. In fact, CO emissions were identical and NOx emissions were still

comparable.

The other reason was the higher emission factors used in the present study, which were compiled

from a variety of sources. The estimates ofJalihal and Reddy (2006) were much lower as they took BSII

norms, which are not representative of the actual road conditions of Delhi. Our emission estimates for

CO, HC and SO2were comparable toUNEP (1999), the values from other studies were too low. In fact,

NOx emissions of UNEP were higher reflecting their choice of higher emission factors for nitrogen

oxides.

3.2 Comparison with air quality measurements

Fig. 10 indicates the yearly average ambient air quality data, taken fromCPCB (2006) for a ma-

jor road intersection (i.e., ITO intersection) in Delhi for the period 20002005. The initial decrease

in SO2 concentrations during 20002002 is also clearly visible in Fig. 10. Nevertheless, SO2 con-

centration trends show a continuous decrease in later period, which is not consistent with our

calculations. Concentration trends of NOx hardly matched with our emission trends because of

theinclusion of all nitrogen oxides for our study and not only nitrogen dioxide role of NOx in for-

mation of ozone and conversion of NOx into nitric acid could also have caused some variations

from our trends). However, both the studies point out to the check in NOx pollution levels.The rising and falling trends for suspended particulate matter (SPM or TSP) although hardly coun-

terpart and in fact were not comparable since SPM includes particles having a diameter less

than 100 micrometer whereas in the present study we have taken into account all range of

particles.

Table 4

Emission estimates taken from other studies and present study.

Studies Year CO2 CO HC NOx TSP SO2 Pb

Auto fuel policya,b 2002 154 67 40 4.7

Jalihal and Reddy (2006)a 2002 171 74 36 5.6

Bose (1999) 2000 2775 194 82 40 60 0.1

2005 3449 192 81 50 8 0.1

UNEP (1999) 2001 339 104 210 12

2002 341 104 217 13

2003 341 104 217 13

2004 340 104 216 13

2005 341 104 218 13

Das and Parikh (2004) 2005 4665 203 76 39 5.4 3.5 0.2

Gurjar et al. (2004) 2000 8555 442 133 28 27

Our estimates 2000 13363 197 96 105 8 12 0.2

2001 13524 214 106 114 8.4 13 0.2

2002 11919 265 176 74 7.7 8.9 0.2

2003 14445 300 191 91 8.8 9.6 0.3

2004 15621 328 207 98 9.6 10 0.32005 16621 350 221 104 10 11 0.3

a Converted in Gg annum for comparison.b Mashelkar et al. (2002).



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4. Limitations and further scope of the study

Due to the unavailability of year wise detailed data related to transport, our methodology accom-

panied number of limitations. Firstly, there is no actual ratio available for 2-stroke and 4-stroke two-

wheelers and light and heavy commercial vehicles. This data has been assumed on the basis of ratio

given in available literature. In addition, the vehicle utilization factors were also assumed to be con-

stant for the five-year period, which did not take into account the rising travel demand and the effect

of starting of the Delhi metro in the year 2002. Besides this, selection of emission factors from a rangeof different sources is also a cause of uncertainty in our calculations. Some of the values were adapted

fromEIA USA (1994)in the absence of any other source, which would be an underestimation of the

condition actually existing on Delhis roads. The deterioration factors were also not available for all

the pollutants and all vehicle and fuel categories and were therefore assumed unity in that case. These

limitations set a future scope to improve the present emission estimates as and when more accurate

input data (e.g., vehicle population, age wise distribution, and emission factors) are available for

megacity Delhi. Moreover, it is also required to validate the emission trends with more ambient air

quality data monitored along the roadside locations.

5. Conclusions

A six-year (20002005) emission inventory of several air pollutants including criteria pollutants,

ozone precursors, and greenhouse gases emitted from transport sector has been prepared for megacity

Delhi. It is observed that the emission loads for almost all the pollutants showed increasing trend from

year 2000 to 2005. The emission loads for pollutants NOx, TSP and SO2 decreased between 2001 and

2002. This trend is a clear indicator of the impact of steps taken by the government to control the

emissions in the form of stricter emission norms, e.g., CNG implementation and phasing out of old

age vehicles. However, after 2002 levels for all the pollutants continued to increase slowly which

can also be attributed to the spurt in annual growth rate of vehicles registered in Delhi (6% and 10%

in the years 2003 and 2004 respectively compared to the previous years 3% and 2.75% for 2001

and 2002). This indicates that increase in the emission loads could have been much greater in the ab-sence of the pollution control measures. The constant annual emission trend of NOx, SO2and TSP can

be related to the emission norms and CNG implementation. Two-wheelers stood as the major contrib-

utor for almost all the pollutants (except for NOx, SO2and TSP). In case of NOxemissions, goods vehi-

cles and buses had a major share, which pointed out the need for bypassing of external traffic passing

Figure 10. SO2, NO2, SPM ambient air quality data, Delhi (20002005).

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through the city, as a major part of the external traffic is comprised of trucks and buses entering the

city none of which run on CNG. Similarly, goods vehicles were found to be the major contributors in

TSP emission that was quite clear from the high emission factors for these vehicles. In addition to

encouraging 4-stroke two-wheelers, the problem of increasing air pollution emissions from road traf-

fic may likely be checked by a combined effective functioning of the Delhi metro and the public trans-

port, which is being planned and executed by the government.

Acknowledgments

This study was supported by the Max Planck Partner Group for Megacities and Global Change,

which is established at IIT Roorkee, India, by the Max Planck Society, Munich, and Max Planck Institute

for Chemistry, Mainz, Germany. Authors would like to sincerely thank three anonymous reviewers

whose valuable comments and suggestions have greatly improved the manuscript.

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