A METHOD FOR DEFINING STREETS AS SOURCES OF CO2 EMISSION AND THEIR CLASSIFICATION IN THE CITY

OF NI

Mladen Tomi, Predrag ivkovi, Gradimir Ili, Mia Vuki, Jelena Milisavljevi, Petar eki

University of Ni, Faculty of Mechanical Engineering

Aleksandra Medvedeva 14, 18000 Ni

Abstract: Traffic is one of the main sources of CO2 in Serbia, as well as in the world. The subject

of this study is to determine city streets as sources of CO2 emission in south Serbian city of Ni. In

order to make these possible, measurements were carried out of frequency of traffic at critical

intersections in the city, measurement of the concentration of CO2, as well as monitoring of a

direction of the vehicle at the crossroad. Based on these data, the connection has been established

between the traffic flow at crossroads, and traffic flow on the roads, between monitored crossroads,

enabling the definition of CO2 emissions on the road. Also, this makes it possible to perform

classification of streets according to their annual emissivity.

Keywords: COPERT, CO2, emission, traffic

1. INTRODUCTION Impact of pollutants, as a consequence of a traffic activity (CO, NOX, CO2, SOX, VOC) has been

very well documented [1,2]. Recently, since recognizing the problem of global warming, caused by

GHG emissions, more attention has been focused on CO2 emissions. Vehicles are representing one

of the greatest emitters all pollutants, as well as of carbon dioxide. As estimated, in the overall

balance of CO2 vehicles participate with 10%, and in Europe with 20% of anthropogenic emissions

[1]. Countries with rapid urbanization, such as India and China [3,4] are becoming increasingly

dependent on automobile transport, which becomes a major air pollutant in urban areas [3,5].

Although the measurement of CO2 concentration itself is not difficult, a closer determination of

traffic sources is very complicated considering their stochastic nature [4]. For this reason, the

different methods for modelling emissions from traffic were suggested. Such estimates are of great

importance for more efficient management of air quality. An emission from traffic depends on

many parameters: vehicle type, engine size, age, fuel used, cruising; all these parameters affects the

emissions. Examining the emissions of CO2 during the study, the authors have chosen macro

approach or top bottom approach to determine CO2 emission from a vehicle, and based on

obtained emission data, and determined traffic frequency a micro approach or bottom up

15th Symposium on Thermal Science and Engineering of Serbia

Sokobanja, Serbia, October 1821, 2011

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approach to determine CO2 concentration on a specific location. Such data, when they are

sufficiently determined, can be used for building a national inventory of emissions. [2,6,7]. In

recent years there have been significant efforts in determining emissions in the function of engine

types, engine size and the speed at which the vehicle is moving [7,8,9,10]. Based on this study,

various models for prediction of gaseous emissions were developed [1,2,11,12]. COPERT,

computer program used in this paper, [13] utilizes the macroscopic approach. Emission assessment

was done on the basis of data measured on the major intersections in the City, as well as from the

data obtained from the Republic of Serbia Ministry of Interior Affairs, PU Ni (in further text

referred as MUP).

2. METHODOLOGY

Traffic monitoring was done on the main crossroads in the City. Selected locations are main street

intersections, or characteristic locations on main streets, fig. 1:

Bulevar Nemanjia Sremska ulica, (Boulevard),

Bulevar Dr Zorana inia Zetska ulica, (City Hospital),

Stefana Prvovenanog Vodova, (Theater),

Bulevar Nikole Tesle Pantelejska, (Jagodin Mala),

Obliiev Venac Duanova, (Marger).

Figure 1. Measuring locations in the City of Ni

Quantification of traffic intensity was done by counting vehicles that pass through the crossroad,

where logging is performed every 5 min for a period from 5 a.m., until 1 a.m. next day. This period

was chosen as the period when public transport is active. The vehicles were divided into two

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categories: passenger cars, and buses with trucks. The category of passenger cars implies scooters,

motorcycles, cars and light trucks, while buses and trucks imply buses and heavy vehicles. The fact,

that the measurement was done in autumn, makes the number of scooters and motorcycles

negligible. While traffic monitoring, CO2 concentration measurements were done. MUP, provided

data on vehicle fleet in the City. City fleet data were imputed into a software package COPERT,

from which overall CO2 traffic emissions were calculated. By averaging output per vehicle and

kilometre, an averaged emission of CO2 per vehicle and km was obtained. These results were

compared with the measured traffic intensity. The results were used for determining traffic induced

CO2 emissions. In order to establish the accuracy of the traffic frequency, crossroad of Jagodin

Mala was monitored during entire week in order to determine the measured data statistical stability.

3. EMISSION CALCULATION

City of Ni fleet composition data used in this paper were obtained from MUP. Based on these data,

one can notice that passenger cars occupy by far the largest share of the fleet. Vehicles with petrol

engines are the most common, making almost 2/3 of the fleet. The average age of vehicles is about

14 years. This data were used for the rate of CO2 emission estimation. The values are calculated

using the COPERT methodology [11]. Basically, COPERT was used as a tool for assessing

emissions from traffic at the national level. COPERT operation is based on the analysis of large

amounts of data from several European vehicles testing facilities. This is very reliable methodology,

since the actual high-level agreement of data obtained [12]. Contribution to the total emission EC in

[kgkm-1s-1] for each sub-category of vehicles is calculated from the formula

.1

n

iii

tEFnum

EC (1)

In (1), numi represents traffic flow of i-th category in vehicles per time interval t. Emission factor

EFi in [kgkm-1] is representative for each category. If we adopt a particular vehicle velocity of 23

[kmh-1] as a constant, then emission factor can be calculated as:

cubuaEFi 2 . (2)

Equation (2), from which we calculate the emission factor, is generally approximated by a second

degree polynomial function of average vehicle speed, or a graph [1, 8, 9, 12].

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Some authors suggest more complicated formulas for emission calculation as a function of average

velocity [1]:

3232

u

f

u

euducubuakEFi , (3)

On the other hand, some authors obtain emission factor as a function of acceleration, and velocity

and acceleration product [8, 10, 14-16]. Due its simplicity, eg 2 was used in the paper.

Althrough EFi is not a constant, it is assumed that for an average vehicle speed, it is approximately

a constant.

3.1. Fleet composition

As it has been stated earlier in the paper, MUP data were used for the analysis. Vehicles were

divided into following categories: Personal vehicles, Buses, Trucks, and Motorcycles.

Personal vehicles By its number, passenger cars, by far, represent most of the fleet, with number of

60720 a 91.12% of the fleet. Before inputting the data into the COPERT, statistical averaging was

carried out while processing the data obtained from the MUP, in order to adjust them for use in

COPERT. For the average mileage per year in the City, an amount of 3500 km per year was

adopted, with uncertainty of 10%. The speed was adopted by monitoring the driving habits. In

average, driver crosses 10 km per day in the city.

Buses In the City of Ni total of 477 buses has been registered. However, during the emission

calculation, only the buses from the public transport company, where used into the account.

According to [17], in the year 2009., in the public transport 124 buses were engaged, and they have

made 8609250 kilometres during a one year period. This makes public transport buses far more

influential, and then other busses registered in the city.

Trucks During the traffic frequency counting, light trucks, were counted among passenger cars.

Authors have adopted for a boundary between light and heavy trucks, an engine size of 2000 cm3.

All transport vehicles from the MUP data, which engine is larger than 2000 cm3, were assorted

among heavy trucks. For an annual mileage 2000 km were adopted on the city territory, with an

uncertainty of 10%.

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Figure 2. Composition of the Fleet in Ni.

Motorcycles During the time period when the monitoring was done, the number of motorcycles on

the streets was negligible. Also, motorcycle number itself is negligible in relation to motor vehicles.

Figure 3. Structure of personal vehicles per fuel type.

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Figure 4. Structure of personal vehicles per engine size in cm3.

Figure 5. Structure of personal vehicles per age in years.

3.2. Traffic monitoring validation

The results of traffic frequency monitoring at the crossroad at Jagodin Mala, have been shown in

the tables 2 and 3 with daily variations. It could be concluded from the results in the table, that the

total volume of passenger vehicles during a working day, does not vary more than 3.46%, in

comparison with the average. Also in the graphics in the fig. 6 one can see the similarity of curves

representing the frequency of traffic through the day. On the graph in two peaks could be noted: in

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the period between 8 a.m. and 9 a.m., and between 3 p.m. and 4 p.m. Those periods correspond to

the daily morning and evening rush-hours.

For the buses and trucks traffic, the biggest difference in total traffic frequency, in comparison with

the average traffic frequency, is higher and amounts 8.13%, but it is still in an acceptable limits

(table 3). Picks in the fig. 7 occur almost at the same position as in the fig. 6.

Table 1 Daily traffic fluctuation at Jagodin Mala crossroad

Date No. of personal vehicles No. of buses and trucks 29.10. 38721 2839 30.10 37182 2951 01.12. 40428 3421 02.12. 38408 3380

average 38685 3148 1338 (3.45%) 256 (8.13%)

3.3. Emission coefficient

COPERT analysis has shown that average personal vehicle CO2 emission is 0.330 [kgkm-1], which

is consistent with [8,18,19], as it has been presented in the table 1. For buses and trucks were

obtained coefficients respectively 2.45 and 0.66 [kgkm-1]. Due to specific features of the fleet in the

City of Ni, in comparison with western Europian fleets (age of fleet), the advantage has been given

to local emission coefficient, and error was estimated as a difference between obtained and average

data.

Table 2 Personal vehicle emission coefficient deviation

Authors Results kgkm-1

Current research 0.330 Samaras 0.227

Joumard et al. 0.300 Average emission 0.285

0.046 (13.65%)

4. RESULTS AND DISCUSION Based on traffic frequency data at major intersections, it is possible to determine approximate

number of vehicles on the section between monitored crossroads. As it could be seen on figures 6

and 7, two picks occur in daily traffic variation, at 9 h, and 16 h, which correspond to daily rush

hours. An annual emission for jth main road can be estimated as:

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2

1, 365

ijidayij LEFnumE (4)

The City of Ni has 266 km of streets and local roads [20]. The length of monitored street sections

is 4.35 km long, which makes 1.63% of overall streets in the city. The traffic in this street section

contributes with 15.90% in the total annual emission in the city. Results for the sections have been

presented in table 4 for personal vehicle subcategory, and in table 5 for buses and trucks.

Table 3 Number of vehicles in the City of Ni and their average annual mileage on the City territory per vehicle

Vehicle Type No. of

Vehicles Annual Mileage in the City

[km] Annual CO2 emission

[t] Personal Vehicle

58049 3500 67034.28

Trucks 2286 2000 2996.1 Buses 124 69500 20775 Sum 60459 - 90805.38.1

Figure 6. Daily variation of personal vehicles traffic on observed sections.

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Figure 7. Daily variation of personal vehicles CO2 emission on observed sections.

The rest of 261.65 km contributes with 84.10% or 76397.00 t of CO2 (tables 4 and 5). Based upon

this fact, it is possible to divide streets in the city, into two categories. The first category occupies

monitored street sections, with annual emissivity of 3318.46 t of CO2 per km. The second category,

which is complementary to the first one, has the emissivity of 291.87 t of CO2 per km of street.

Table 4 Personal vehicles CO2 emission estimation on observed street sections

Street section Section length

[km] CO2 emission [kgkm-1day-1]

CO2 emission [t year-1]

Boulevard 7 Juli 1,63 5900,7 3510,62 7 Juli Theater 0,38 6258,8 868,1

Boulevard C. Hospital 1,00 6051,6 2208,8 Theater C. Hospital 0,95 4839,8 1766,5

J. Mala 7 Juli 0,39 6822,4 2490,2 Sum 4,35 - 10844,2

1,63% - 16,11%

Table 5 Busses and trucks CO2 emission estimation on observed street sections

Street Section Length [km]

CO2 Emission[kgkm-1day-1]

CO2 Emission [t year-1]

Boulevard 7 Juli 1,63 2456 1461,2 7 Jul Theater 0,38 2835 393,2

Boulevard C. Hospital 1 676,9 247,07 Theater C. Hospital 0,95 3132,9 1086,3

J. Mala 7 Juli 0,39 2835,2 403,6 Sum 4,35 - 3591,1

1,63% - 15.11%

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Table 6 Specific annual emission for road categories

Length [km]

Annual emissivity [t km-1]

I category 4.35 3318,46 II category 261.65 291,87

5. ERROR ESTIMATION

On the basis of traffic deviation data, emission deviation data, and eq. 4, it is possible to calculate

emission deviation. Deviations have been presented in the table for the observed sections, and for

total annual emission. The analysis of the method conducted by [21] has shown that depending on

the method can vary up to 36%. The main reason for high value of uncertainty is the uncertainty of

emission data on the first place, but also uncertainty of average mileage in the city.

inumEFiiLiEi EFLLnumEFnum i (5)

Table 7 Obtained deviations for observed section and total emission

Emission deviation

Vehicle type

Observed section

Total emission

Personal vehicles 17.06% 23.64% Buses 20.54% 13.94% Trucks 24.11%

6. CONCLUSIONS In the present paper, a method for determining street emission has been presented. During the study,

6 major cross roads in the city were monitored. Monitoring of cross roads has enabled

determination of traffic on street section between those sections. In order to calculate a specific

emission of the city fleet, the fleet composition data were obtained from the Ministry of Interior.

According to this data, specific vehicle emission, and total annual emission was calculated using the

COPERT methodology. This has enabled to assign to each subcategory, personal vehicles and buses

and trucks an emission value, and therefore to calculate annual emissions. The results have shown

that on observed sections, the emission per km is 11 times higher, in comparison to the rest of the

city. Based upon this fact division was made on two categories: main streets, and side streets. On

the end in the paper an error analysis was given. Although high, the error is still in a reasonable

boundary. There is still a need for further research however, since there are still many gaps in

emission data. Closer determining of emission data for categories, and annual mileage would

additionally raise the quality of input data, and therefore output data.

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AKNOWLEGAMENT

Authors would like to thank Ministry of Interior - Republic of Serbia for providing data of the fleet

composition in the City of Ni.

NOMENCLATURE E - total emission in [t] EC - emission contribution in [kgkm-1s-1] EF - emission coefficient in [kgkm-1] i - coordinate for vehicle category j - coordinate for section L - mileage, section length in [km] num - vehicle number [-] u - average vehicle speed in [kmh-1] - error - deviation

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