CHAPTER V EMISSION DISPERSION MODEL
Introduction
The second step of Impact Pathways Analysis, out of four steps, is
calculation of pollutant concentration changes dispersion. Air pollution
regulation in many countries is based on some kind of permitted concentration of contaminants. To plan and execute air pollution control programs designed to
meet the requirements of these regulation, one must predict the ambient air concentrations which will result from any planned set of emissions. These
predictions are made by way of air pollutant concentration model or dispersion model. The perfect air pollutant concentration model would allow us to predict the
concentration that would result from any specified set of pollutant emission, for any specified meteorological condition, at any location, for any time period, with
total confident in our prediction. This chapter is a core chapter in estimating Jakarta’s pollution level. It
covers the collected previous study related to the dispersion model input data, the method to determine the emission factor, as well as the air dispersion simulation.
Air Pollutant Concentration Model
The continuous measurements values of concentration can be obtained by comput er simulation. With air quality models spatial and temporal variations of
concentration can be calculated related to emission rates, meteorological variables and parameters which describe removal and transformation processes. Those
quantities can themselves be function of space and time. The advantages of computer usage are the independence of measuring equipment, cover of a wide
and complete area and result not only for the present status but also as a tool for prediction.
Nowadays there are different approaches and models in use differing by their complexity, availability, data requirement, validation and need of computer
storage and CPU time. The choice of dispersion model depends not only on the type of information that is required but also on the availability of the input data
59 needed to run the model. Generally, there are two approaches to solve the air
quality dispersion in the model: first approach is by mathematical method and the second is empirical.
Model Selection
Emission sources are categorized into three basic types of sources, i.e., point
sources, line sources, and area sources. Point sources are single sources such as
large power plants and large industries with well known site location. The place of emission can be drawn to one point. Usually, not many point sources are available
so that each source can be treated separately and independently from others. Area sources
are many similar sources distributed over a large area more or less equally. Those kinds of sources have low stacks, for example households, small
and medium industries, and small and medium electricity generators. Emissions
released continuously along a road by vehicles are line sources. The pollutions
are produced in very low atmospheric levels. Classification of source type related to the energy sector application [51] is shown in Table 23, while the comparison
between the gaussian dispersion model using mathematical approach and MLuS model using empirical approach is shown in Table 24.
Table 23. Energy source type related to sector
Sector Point source Area source Line source
Power plants x
x Industry
x x
Househoulds x
Traffic x
x
Table 24. Comparison between gaussian and MLuS model
No Item
Gaussian MLuS
1 Source of pollutant
Point source Line source
2 Data requirement
Intensive Moderate
3 Modeling approach Mathematical
Empirical 4
Complexity Moderate to complex Simple
It can be concluded that the model with gaussian approach is not suitable for vehicle emission sources, because: i point source model is not suitable for
movable emission, ii point source is suitable to estimate the concentration with
60 the distance of receptor of more than 100 m from roadside, iii it requires
stationary condition and for long difussion time, iv it is a moderate to complex model the general Gaussian mathematical equation is shown in appendix 2.
Another model, called MluS, is a line source type which is a simple model, suitable for movable emission source, suitable to estimate the concentration near
roadside, and has been used by many projects in Europe as well as in Indonesia [12]. To select the model, the following considerations should be taken [52]: i
suitable with the study objective, ii simple and easy to operate and iii calibrated and model precission have been proven and used by another similar
project. According to the evaluation of various criterias above, the MLuS model was selected to be used in this research.
Materials and Methods • MLuS Model [12]
MLuS model calculates pollution concentration from traffic sector. The traffic sector has some specific characteristics making it different from other
emission sources. The main factors are the movement of the sources the vehicles and the height of release emission which is very close to the ground. The emission
sources follow the road. The MLuS model can be under the following preconditions:
•
Traffic density greater than 5,000 vehicles per day
•
Distance to roadways starting from 0 up to 400 m The calculation of the MLuS model requires the following traffic and
meteorological data must be known:
•
Average emission coefficient for each pollutant in gkm
•
Annual Average Daily Traffic AADT in vehicles per day
•
Average value of wind speed at source location in ms
•
Percentage of hourly average value of wind speed less than 3 ms
61
Figure 25. Basic calculation of concentration MLuS model
The equation for the concentration Kis near ground level for a pollutant i at a receptor location at a height of 1.5 meters in a distance s from the roadside is
[12]: K
i
s = K
i
x f
vi
x gs x f
u
9 Where:
K
i
: Reference concentration at ground level near roadside of the pollutant i in mgm
3
f
vi
: Function to consider the traffic data gs
: Fading function of the relative pollutant concentrations s
: Distance from the roadside in m f
u
: Function to consider the meteorological data The influence of the traffic volume on the concentration value is calculated
by equation 10.
G i
i vi
AADT AADT
e e
f ×
= 10
Where: AADT
= Annual Average Daily Traffic in vehicleday AADT
G
= Annual Average Daily Traffic for German reference case: 46,000 in vehicleday
R 1
Distance from roadside s in m Relative concentration
Fading function R: Receptor
located near roadside, 1.5 m above ground
62 e
i
= Average specific reference emission factor in gkm of the pollutant i e
i
= Average emission factor of the pollutant i coming from an independent emission model in gkm.
The fading function is based on the theory that concentration of the pollutants decreases with increasing distance to the roadside Figure 26. The
decrease is described by an exponential decay function which gives the relative concentration as a function of distance to the roadside. Therefore the
concentration calculations are based on an empirical statistical diffusion model. The mathematical relation is shown in equation 11.
gs = 1 – 0.166 ln 1+s
11
The fading function for all type of pollutants can be expressed by one single equation 11 with the assumption that all of pollutants are diluted in the same
way on their way downwind. There are two exemptions, namely for particles and NO
2
. For particles, there might be sedimentation. Since only small size particles PM
10
are considered, the sedimentation can therefore be neglected. NO
x
dilutes as other pollutants. However, the speciality is NO
x
, which consists of NO and NO
2
. Their relationship depends on the dilution. The lower the NO
x
concentration, the higher is the ratio NO
2
NO. MLuS model has already considered that condition.
The MLus model allows an estimation of the pollutant concentrations of CO, HC, NO
x
, SO
2
and PM at a cross section in a determined distance s to the road side. The trend of the fading function gs see Figure 26 shows that relative
pollutant concentration reaches the value zero after a certain distance. The mathematical way to calculate this distance is by solving the equation gs = 0:
0 = 1 – 0.166 x ln1 + s s
= exp 10.166 – 1
s = 412.27 m
This result means that at a distance of 412.27 m from the roadside, relative concentration of the considered pollutants is predicted to be zero.
63
Figure 26. Fading Function gs
In order to obtain the average relative concentration IGAV between the roadside and s
, instead of the relative concentration at a cross-section, the original fading function has to be integrated over this distance and divided by s
.
Since the fading function is the same for CO, SO
2
, HC, NO
x
and PM, the value of IGAV is used for all these pollutants. Figure 27 shows the average concentration
which is caused by squashing the fading function.
Figure 27. Average relative pollutant concentration
The function for distinguished meteorological data depends on the frequency distribution of the hourly value of the wind speed. For this model, two
cases are considered based on frequency distribution of the hourly values of the
ds s
g s
IGAV
s
1
∫
=
[ ]
{ }
1 1
1 ln
1 166
. 1
+ −
+ ×
+ ×
− ×
= s
s s
s IGAV
13
Relative concentration Average concentration
Grid border Grid border
Fading Function
12
0,1 0,2
0,3 0,4
0,5 0,6
0,7 0,8
0,9 1
50 100
150 200
250 300
350 400
450
Distance to the road side m R
el at
ive C o
n cen
tr at
io n
64 wind speed. The criterion is the percentage of the annual hours, for which the
hourly average wind speed in a height of 10 m is less or more than 3 ms. - Case I: Percentage of the hourly average values less than 3 ms 50
u f
u
6 .
3 =
14 - Case II: Percentage of the hourly average values less than 3 ms
≥ 50
u f
u
7 .
1 =
15 Where:
u is annual average wind speed at a height of 10 m above ground in ms.
Methodology
The steps to estimate the emission concentration are shown in Figure 28.
Figure 28. Model calculation flow chart
• Modeling
As explained above, the emission dispersion in this study was estimated by employing MLuS model according to the calculation steps shown in Figure 28:
1. To develop the Jakarta’s grid and map digitalization
Jakarta Map , scale : 1 : 70.000
PT. Mascot Media, 2006
Emission coefficient : gkm
By type of emission
By type of vehicle
Region area data BPS DKI, 2006
Map digitalization :
Grid 1 x 1 km2
Health cost:
Each emission
By grid
Population data: By region BPS DKI,
2006 Indonesian
pollutant dose response on health
Population distribution :
By grid Receptor
External Cost Rupiah
MLus Model Emission
dispersion :
By type of emission
Each grid
Fading Model
Calibr ation
GDP PPP Indonesian
compare with German
Dose response data
Spadaro, 2002
Wind speed ms BMG
Yes No
Emission Dispersion
µgrm3
Population
Person
Annual Average Daily Traffic AADT: 2005 - 2025
No. of vehicle projection :
By type of vehicle
By type of fuel gasoline and diesel
Vehicle number data by type BPS DKI, 2006
Scenario:
•
Base case B0, B10, B20, B50,
B100
•
Base case 2005, 2010, 2015,
2020, 2025
YOLLyr-person- μgm3
Caseyr-person- μgm3
YOLL Years of Life Lost Case
65 2. To develop the road network digitalization
3. To estimate the projection of vehicle numbers 4. To determine the emission coefficient for each type of vehicle
5. To determine the meteorological data 6. To collect the AADT and emission coefficient germany reference data
7. To calculate the concentration of dispersed emission 8. To draw the emission dispersion map
9. To calibratevalidate the model The model has been written in Turbo Pascal for Windows version 1.5
Software. Each step was designed and written in a separate program and can be linked each other in the main program as shown in the main program flowchart of
Figure 29. The output of the program is presented in the form of texts, numbers
and graphics.
Result and Discussion General Description of Jakarta
• Geographical situation
Jakarta is a lowland area with average height around 7 meters above the sea level, and situated between 6
12’ south latitude and 106 48’ east longitude.
Based on Governor Decree in 1989, No. 1227, the area of DKI Jakarta province comprises of 661.52 km
2
land area and 6,977.5 km
2
sea areas. It has more than 110 islands, which spread through out the “Kepulauan Seribu”, and has 27
riversdrainscanals used as water sources, fishery and urban businesses. The Northern part is a coastal area that extends around 35 km from west to east. This
shore is a place where 9 rivers and 2 canals run into. The Southern and Eastern part of Jakarta borders with West Java province, and the Western part are with
Banten province, and Northern part is with Sea of Java [38]. The DKI Jakarta province is divided administratively into five
municipalities and Regency, namely South Jakarta, East Jakarta, Central Jakarta, West Jakarta and North Jakarta, which the area of 145.73 km
2
, 187.75 km
2
, 48.20 km
2
, 126.15 km
2
, 141.88 km
2
respectively and “Kepulauan Seribu” Regency 11.81 km
2
.
66
Figure 29. Main program flow chart
67
• Climate
Temperature, humidity, wind velocity and rainfall are continuously measured at five observation stations in Jakarta. The variation of measurements is
influenced by altitude of each location. In general, climate condition of Jakarta is tropical zone, with the highest temperature of around 34.10
C by day and the average lowest temperature of 23.50
C by night. Meanwhile, the maximum relative humidity is about 88.00 and the minimum of 71.80 with the rainfall
intensity could reach up to 174.80 mm
2
and average wind velocity of 2.5 ms [38]. Standard qualification and Air Qualities by measured location in the year of
2006 is shown in Table 25.
Table 25. Standard qualification and air qualities by measured location in 2006 [38]
Measure location Temporary method
NO
2
ppm SO
2
ppm TSP mgm
3
Pb mgm
3
Meas Std
Meas Std
Meas Std
Meas Std
Housing Area 1. Dinas pertamanan
2. Kantor Kec. Cilincing 3. Kantor kelurahan tebet
4. Mesjid Al-Firdaus 5. IPAK Lubang Buaya
0.021 0.023
0.037 0.024
0.017 0.05
0.005 0.006
0.004 0.006
0.006 0.1
158 304
219 223
155 150
- -
- -
- 2
Industry area 0.025
0.006 347
Office area 1. Mesjid Istiqlal
2. Kuningan BPLHD 0.029
0.043 0.006
0.003 243
233 -
- Recreation area
1. Dunia Fantasi Ancol 0.022
0.006 217
- Source:
BPLHD Province DKI Jakarta - means data not available
• Population
Based on the National Socio-Economic Survey Susenas conducted in mid of 2006, the population of DKI Jakarta Province was 8.96 million. The population
density reached to 13.5 thousandkm
2
, and it became the densest region in Indonesia. Even though population growth rate was decreasing from 1.21
2000-2005 to 1.11 2000-2006 because of family planning programs, the growing influx of urbanization from other provinces makes Jakarta’s population
keep increasing [38].
68
Data Acquisition • Jakarta’s grid and map digitation
The area to be studied is divided into a numbers of grids with identical area. In this study the area of Jakarta is divided into grids with the scale of 1 x 1 km
2
based on the Jabodetabek Jakarta, Bogor, Depok, Tangerang and Bekasi area map scalled of 1: 70,000 [53]. Hence, Jakarta was divided into a 34 x 34 grid. The
calculated results were then formatted into integer numeric data form, which defined the areas. To simplify the model, the convention in Table 26 will be
applied. Table 26. Data convention for grids establishment
Numeric value District
Outside Jakarta including sea area 1
North Jakarta 2
East Jakarta 3
South Jakarta 4
West Jakarta 5
Central Jakarta
Seribu island and sea area are not included in the grids to be studied. It is assumed that no vehicle utilization occurs in that areas. Hence, Jakarta’s grids map in the
34 x 34 matrix format can be shown as in Figure 30.
Figure 30. Data grids matrices of Jakarta’s area
1 2
3 4
5 6
7 8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2
0 4 4 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0
3
0 4 4 4 1 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0
4
0 0 4 4 4 4 1 1 1 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0
5
0 4 4 4 4 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
6
0 4 4 4 4 4 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
7
0 4 4 4 4 4 4 4 4 1 4 1 1 4 4 4 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
8
0 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 0
9
0 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 1 1 1 1 1 1 2 2 1 2 2 2 0
10
0 0 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 1 1 1 1 1 2 2 2 2 2 2 0
11
0 0 0 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 2 2 1 1 1 2 2 2 2 2 2 0
12
0 0 0 0 0 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 2 2 2 2 2 2 2 2 2 2 0 0
13
0 0 0 0 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 2 2 2 2 2 2 2 2 2 2 2 0 0
14
0 0 0 0 0 4 4 4 4 4 4 4 4 4 5 5 3 5 5 5 2 2 2 2 2 2 2 2 2 2 2 2 0 0
15
0 0 0 0 4 4 4 4 4 4 4 4 3 5 5 5 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 0 0 0
16
0 0 0 0 4 4 4 4 3 3 3 3 3 5 5 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 0 0 0 0
17
0 0 0 0 0 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 0 0 0 0
18
0 0 0 0 0 0 0 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 0 0 0 0 0
19
0 0 0 0 0 0 0 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 0 0 0 0 0
20
0 0 0 0 0 0 0 0 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 0 0 0 0 0 0 0 0 0
21
0 0 0 0 0 0 0 0 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 0 0 0 0 0 0 0 0 0
22
0 0 0 0 0 0 0 0 0 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 0 0 0 0 0 0 0 0
23
0 0 0 0 0 0 0 0 0 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 0 0 0 0 0 0 0 0 0
24
0 0 0 0 0 0 0 0 0 0 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 0 0 0 0 0 0 0 0
25
0 0 0 0 0 0 0 0 0 0 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 0 0 0 0 0 0 0
26
0 0 0 0 0 0 0 0 0 0 0 3 0 0 3 3 3 3 3 3 2 2 2 2 2 2 2 0 0 0 0 0 0 0
27
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 3 3 2 2 2 2 2 2 2 2 0 0 0 0 0 0 0
28
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 3 2 2 2 2 2 2 2 2 2 0 0 0 0 0 0 0
29
0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 3 3 2 2 2 2 2 2 2 2 2 0 0 0 0 0 0 0
30
0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 3 3 0 0 2 2 2 2 2 2 0 0 0 0 0 0 0 0
31
0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 3 0 0 0 0 0 2 2 2 2 2 0 0 0 0 0 0 0 0
32
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 2 0 0 0 0 0 0 0 0
33
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0
34
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
69 Before used in the model, the calculated grids need to be validated by the
existing Jakarta’s area data from BPS [38]. Table 27 shows that the deviation between the calculated grid area to BPS data is 0.04, therefore it can be assumed
that the calculated grids result is acceptable to use in the dispersion model. Table 27. Deviation of calculated grids compare to and BPS existing data
District Area km
2
deviation Grid
BPS data
North Jakarta 142.0
142.20 -0.14
East Jakarta 188.0
187.73 0.14
South Jakarta 146.0
145.73 0.18
West Jakarta 126.0
126.15 -0.12
Central Jakarta 48.0
47.90 0.21
Total Jakarta 650.0
649.71 0.04
The numeric data shown on Figure 30 can also be represented by the color graphic as shown in Figure 31.
Figure 31. Jakarta’s map digitalization
70
• Jakarta’s population growth digitation map
Based on SUPAS’s Survey Penduduk Antar Sensus data [38], the number of population in DKI Jakarta Province in 2005 was 9.04 million. The densest
population was in East Jakarta and followed by the West Jakarta area, while the lowest densest population was in Central Jakarta area as represented in Table 28.
Tabel 28. Population per area of DKI Jakarta
District Area Population
Area Density
Population share Thousand
km
2
Popkm
2
South Jakarta
1,995.2 145.7
13,691.2 0.2207
East Jakarta
2,393.8 187.7
12,751.2 0.2648
Central Jakarta
861.5 47.9
17,986.0 0.0953
West Jakarta 2,322.2
126.2 18,408.5
0.2568
North Jakarta
1,446.7 142.2
10,173.9 0.1600
Seribu Island
22.1 11.8
1,872.3 0.0024
Total 9,041.6
661.5 13,667.9
1.0000
As it was already explained in chapter 3, the population in Jakarta is projected to increas from 9.04 million in 2005 to become 11.21 million in the year
of 2025. Table 29 presents population projection in each district of Jakarta.
Table 29. Population projection at district in DKI Jakarta
District Area 2005
2010 2015
2020 2025
South Jakarta
1,995.2 2,125.2
2,249.7 2,366.7
2,474.6
East Jakarta 2,393.8
2,549.7 2,699.1
2,839.5 2,968.9
Central Jakarta
861.5 917.6
971.4 1,022.0
1,068.5
West Jakarta
2,322.2 2,473.5
2,618.4 2,754.7
2,880.2
North Jakarta
1,446.7 1,541.0
1,631.2 1,716.1
1,794.3
Seribu Island 22.1
23.6 24.9
26.2 27.4
Total 9,041.6
9,630.5 10,194.6
10,725.3 11,213.9
The above population data were then transferred to the grid dispersion map in the graphic form as can be seen in appendix 3.
71
•
Jakarta’s road network digitalization map
The roads type in Jakarta can be categorized into four groups; i highway, ii state road network, iii province road network and iv municipality road
network. Road length data in Jakarta are shown in Table 30. Municipality road contributes up to 77 from the total road network and followed by provincial
road 20, state road 2 and highway 1.
Table 30. Road length based on district area in meter, 2006
Districts Highway
State Province
Municipality Total
South Jakarta 21,884.00
50,240.00 312,087.00
1,273,686.47 1,657,897.47
East Jakarta 37,222.00
31,458.00 334,223.01
1,057,955.16 1,460,858.17
Central Jakarta 6,380.00
13,566.76 231,134.40
628,877.01 879,958.16
West Jakarta 12,882.00
39,075.25 254,615.50
1,026,653.79 1,333,226.29
North Jakarta 34,592.00
29,440.00 194,494.00
949,755.84 1,208,281.84
112,960.00 163,779.75
1,326,553.91 4,936,928.27
6,540,221.93 Source: BPS DKI Jakarta [38]
Every grid has a different traffic density. For urban area like Jakarta, majority distance between roads are less than 400 m, therefore by grid size of 1 x
1 km
2
the roads will overlap. According to the road map and index for Jakarta and surrounding area Jabodetabek published by Gunther W. Holtorf in 20042005
[54], the road can be classified into three road grid networks as follow:
1. Road with high vehicle density. Included in this classification road are highways and main roads such as Gatot Subroto street.
2. Road with medium vehicle density. MH. Thamrin street is an axample of this type of road.
3. Road with low vehicle density. This classification road includes relatively small road and the road in the housing complex.
The grid with high vehicle density road was numbered by 9, for medium vehicle density will be numbered by 8, low vehicle density will be numbered by 7.
Where 0 means the area which is not include in the scope to be studied. Figure 32 shows the road network digitation map.
72
1 2
3 4
5 6
7 8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 1
2
7 7
9 9
7 8
7
3
7 9
8 8
9 7
7 7
7 8
8 8
8 8
8 8
7 8
4
7 7
8 9
9 7
7 7
7 7
7 7
7 7
8 8
9 7
7 8
7 8
8 8
8 7
5
8 7
7 7
9 8
9 9
9 9
7 7
7 7
7 7
7 7
9 9
9 7
9 7
7 8
7 8
7 7
8 7
6
8 7
7 7
9 7
8 7
7 7
9 9
9 9
9 9
9 9
7 7
7 9
9 9
9 8
7 8
7 7
8 7
7
8 7
7 7
9 7
7 8
8 8
9 8
8 8
8 7
8 7
8 7
7 7
9 7
7 9
9 9
7 7
8 7
8
7 8
7 8
9 7
7 7
7 8
7 9
7 7
8 7
8 7
8 8
8 8
9 7
7 8
7 9
7 7
8 7
9
8 8
8 7
9 8
8 8
8 7
7 9
7 7
8 7
8 8
8 8
7 9
7 8
8 7
8 9
8 8
8 7
10
7 7
7 9
7 7
7 8
8 8
9 8
8 8
8 8
8 7
8 8
9 8
7 8
7 7
8 9
7 7
7
11
7 7
9 7
7 7
8 7
7 9
7 7
8 7
7 8
8 7
9 7
7 8
8 7
7 8
9 7
7 7
12
9 7
7 8
7 9
9 9
7 7
8 8
8 8
7 7
9 7
7 7
8 8
8 8
9 8
8
13
9 9
9 9
9 9
8 8
7 9
7 8
8 7
8 8
8 9
8 8
8 7
7 8
7 9
7 7
14
7 9
7 8
7 8
8 7
9 7
8 8
7 7
8 7
9 7
7 7
8 7
8 7
9 7
7
15
7 7
9 7
8 7
8 7
7 9
7 8
8 7
7 8
7 9
8 8
8 8
7 8
7 9
9
16
8 8
8 9
8 8
7 8
7 8
9 8
7 8
8 8
8 9
8 7
7 8
8 8
7 7
17
7 7
9 7
7 7
8 8
7 7
9 7
7 7
7 7
8 9
8 8
8 7
7 8
8
18
8 9
8 8
8 8
8 8
8 9
9 9
9 8
7 9
8 8
7 7
7 7
19
7 9
7 7
8 8
8 7
7 8
7 8
7 9
9 9
9 9
8 8
8 8
20
7 9
7 8
7 8
8 7
8 7
8 7
8 9
8 7
7
21
9 9
7 8
7 8
8 7
7 8
8 7
8 9
7 7
7
22
9 7
8 7
8 8
8 7
8 8
7 8
9 7
7 7
7
23
7 9
9 7
8 8
7 8
7 8
7 8
7 9
8 8
24
8 8
9 9
9 9
9 7
8 7
8 7
9 7
7 7
25
7 8
7 8
7 8
7 9
9 9
9 9
9 9
9 7
7
26
8 8
7 7
8 7
7 8
7 9
7 7
9 9
27
7 7
8 7
7 8
7 7
9 7
8 7
28
7 7
8 7
8 7
8 7
9 7
8 7
29
7 7
8 8
8 7
7 8
8 9
8 7
7
30
7 8
7 8
7 8
7 9
7 8
31
7 7
8 7
8 9
8 7
32
7 7
9 7
33
7
34
Figure 32. Jakarta’s road grid data
• Annual Average Daily Traffic base case
The traffic density in each grid can be estimated based on statistical data issued by Dinas Lalu Lintas Jalan Raya DKI Jakarta. Since the data are available
for only six hour in the rush traffic condition 06.30 – 08.30, 12.00 – 14.00 and 16.00 – 18.00, therefore the following assumption will be used to determine the
average daily traffic:
•
Calculate the number of vehicles that pass in two directions.
•
Calculate those that are in both high and low speed lanes.
•
The ratio between the number of vehicles at rush hour time peak and the number of vehicle at non rush hour time non-peak is assumed to be 1.5 : 1.
•
The available data that give the total number of vehicles at rush hour 6 hours will be used as a base of calculation.
•
Therefore, the vehicle number at non-peak condition should be: 24 - 6 11,5 Total vehicle number at peakhour
73 Annual Average Daily Traffic Data were then calculated and the results are
shown Figure 33 and Table 31.
Figure 33. Hourly vehicle density
Table 31. Vehicle number at selected case, 2005 road base
No Location
Vehicles density vehicleday Passenger car
Bus Truck
Motor Cycle Total
1 Gatot Subroto street 71,466
4,023 3,333
153,228 232,050
2 Kramat Raya street 88,314
3,828 4,842
107,526 204,510
3 MH Thamrin street 70,923
3,216 1,338
81,864 157,341
4 Ahmad Yani street 33,648
2,097 3,372
113,073 152,190
5 Kebayoran Lama street 20,709
783 4,581
20,505 46,578
6 Diponegoro street 24,669
1,875 852
16,584 43,980
Note: Proceed from Dinas Lalu Lintas Jalan Raya data.
Based on vehicle density data above, the classification of high, medium and low traffic vehicle density can be assumed and they are shown in Table 32.
Table 32. Traffic density classification
Classification Vehicle number vehicleday
Low range High range
Average o High
vehicle density
175,000 250,000
212,500
o Medium
vehicle density
100,000 175,000
137,500
o Low
vehicle density
25,000 100,000
62,500
5.000 10.000
15.000 20.000
25.000 30.000
00- 01
01- 02
02- 03
03- 04
04- 05
05- 06
06- 07
07- 08
08- 09
09- 10
10- 11
11- 12
12- 13
13- 14
14- 15
15- 16
16- 17
17- 18
18- 19
19- 20
20- 21
21- 22
22- 23
23- 00
V ehi
cl es
hour
Gatot S. A. Yani
Thamrin Kramat R.
Diponegoro Kb. Lama
74 Table 33 shows the yearly data of vehicle density and average daily vehicle
density in toll road issued by BPS DKI Jakarta. The data clearly show that the highest volume was recorded at Cawang-Tomang-Pluit toll line with average
volume of 296,095 vehiclesday. Compare the number with the one obtained at Gatot Subroto street of 232,050 vehicleday, then it is concluded that the values of
annual average daily traffic from the calculation are still realistic. Table 33. Volume of vehicle passing the toll road according to BPS
Transaction gate Number of vehicles in a year
Avg.day Tariff classification
Total I
II A II B
1. Jakarta – Tangerang 1.1. Jakarta-Tangerang toll gate
77,383,004 4,905,765 3,857,023 86,145,792
236,015 1.2. Kebon Jeruk toll gate
8,867,085 949,493
65,228 9,881,806
27,073 1.3. Pondok Aren toll gate
2,610,172 12,333
20,070 2,642,575
7,235 2. Cawang - Tomang – Tangerang
2.1. Cawang-Tomang-Pluit 99,428,264 5,505,661 3,140,797
108,074,722 296,095
2.2. Cawang-Tanjung Priok-Pluit 70,752,686 2,603,984 2,209,540 75,566,210
207,030 3. Pondok Pinang - Pasar Rebo
70,775,949 2,012,330 768,895 73,557,174
201,526 Source: BPS DKI Jakarta 2006
Furthermore, the average calculated data of vehicle number as shown in Table 32 212,500 vehicleday for high volume, 137,500 for medium volume and
62,500 vehicleday should represent the data of Annual Average Daily Traffic AADT to be used in the dispersion model.
AADT 2010, 2015, 2020, 2025 case
Based on the base case 2005 AADT data, the projection AADT data for 2010, 2015, 2020 and 2025 can be estimated. The value of AADT will grow
linearly with the growth of vehicles number. The method to calculate the growth of vehicles in Jakarta until 2025 has been explained in Chapter 3.
75 The result of projected vehicles number can be summarized as shown in Table 34,
where the share of each type of vehicle is illustrated in Table 35.
Table 34. Projection of vehicle number and AADT
Unit 2005
2010 2015
2020 2025
Number of vehicles
Thousand 7,230
11,738 15,495
18,764 21,996
Growthyear
0.10 0.06
0.04 0.03
AADT projection o High
vehicle density
Vehiclesday 212,500 344,989 455,396 551,491 646,470
o Medium
vehicle density
Vehiclesday 137,500 223,228 294,668 356,847 418,304 o Low
vehicle density
Vehiclesday 62,500 101,467 133,940 162,203 190,138
Table 35. Share of each type of vehicle
Type of vehicles 2005
2010 2015
2020 2025
Motor cycle gasoline 0.6428 0.7029
0.7108 0.6962 0.6703
Passenger car gasoline 0.2150 0.1901
0.1940 0.2117 0.2374
Mediumsmall bustruck gasoline 0.0345 0.0251
0.0221 0.0211 0.0208
Passenger car diesel 0.0293 0.0259
0.0264 0.0289 0.0324
Mediumsmall bustruck diesel 0.0641 0.0456
0.0377 0.0335 0.0307
Big bus diesel 0.0101 0.0074
0.0066 0.0065 0.0065
Big truck diesel 0.0042 0.0030
0.0025 0.0022 0.0019
1.0000 1.0000 1.0000 1.0000
1.0000
•
Emission coefficient determination
Every type of vehicle has a specific emission coefficient depending on the engine technology and type of fuel. Emission coefficient will also vary with the
load, engine speed, displacement, ambient temperature, coolant temperature, ignition timing, carburator adjustment, engine condition, etc. However, in order to
evaluate the overall emission, we must settle on an average value that we can multiply by the number of cars, or kilometres driven per year, to determine the
total emission released to the area. Because of its complexity, it is possible to find the emission data with wide range difference depending upon the researcher,
variables at the time of emission measurement, etc. It must be also realized that an uncontrolled source will emit at least 10 times the amount of pollutants released
from the one that is operated properly with air pollution control equipment installed [55]. The following methods are generally used to estimate the emission
coefficient:
76 a. Direct measurement method
Direct measurement method is the method to estimate the emission coefficient by directly measuring the test vehicle. The measurement performed in
the emission laboratory, which equipped by Chassy Dynamometer, are emission sampling system and emission measurement instruments. During the test
performance, the test vehicles ran at the Chassy Dynamometer that has been set on a specific driving cycle. The driving cycle is usually selected based on the
research object traffic condition. The advantage of this method is that the measured emission coefficient will be directly recorded by the computer in gkm
unit; thus the result is expected be more accurate. However, the drawbacks of this method are that measurement time is more time consuming and the measurement
cost is very high. Moreover, there is no emission laboratory yet in Indonesia that has Chassys Dynamometer which can measure the engine capacity above 15 kW
and motor cycle [56]. As it has been reported in Chapter 2, the emission coefficients of various biodiesel-petrodiesel blending compositions released from
the passenger diesel engine car type have been measured by Wirawan et al. 2008 [29]. The measurement results are shown in Table 8.
b. Calculation method The method consists of three steps:
1. Selecting and finishing the fuel combustion equation. 2. Calculating the emission coefficient in gl unit.
3. Calculating the emission coefficient in gkm unit. Assuming the fuel is combusted with the air at 21 oxygen vv and 79
nitrogen vv and the combusted products consist of CO, H
2
O, C
3
H
6
as a hydrocarbon, CO
2
and N
2
, therefore, the following combustion equation is generally used:
CH
y
+ m[0,21O
2
+ 0,79N
2
] aCO + bH
2
O + cC
3
H
6
+ dCO
2
+ eN
2
16 The advantage of this method is that the calculation can be performed using
a simple model, but several emissions such as NO, NO
2
and PM can not be obtained.
77 c. Emission coefficient data from various studies
Some emission coefficient data are available from literature reviews of appropriate research papers and journals. It is always prudent to verify the data for
checking the validity of the source and the reasonableness of the final number. Some emission coefficients, which have been in use for years, were only rough
estimates proposed by someone many years ago to establish the order of magnitude of the particular source. Moreover, current existing emission
coefficient data are mostly for conventional fossil fuel, and literature and data on biodiesel blending case are very limited. This research used the source of emission
from pure petrodiesel fuel B0 as a base case and B10, B20, B50 and B100 as a biodiesel case. Therefore the emission coefficient of each composition biodiesel-
petrodiesel fuel must be determined. The following are comprehensive studies and data that focused on vehicle
emission used as a background of this research: Study on The Effect of Palm Biodiesel Fuel on The Performance and
Emission of The Automotive Diesel Engine performed by Wirawan et. al. 2008 [29]. The study was devoted to the performance and emission evaluation
of automotive diesel engine affected by palm biodiesel fuel utilization. The concentration of palm biodiesel used in the test was ranged from B0 pure
petrodiesel, B10, B20, B30, B50 and B100 pure biodiesel. The emission coefficient data as the result of this study can be seen in Table 6.
Study on Environmental Impact of Energy Strategies for Indonesia: Air Quality Model for the Traffic Sector, Data, Modeling and Result performed by
BPPT-KFA March 1992 [12]. This study was conducted under Indonesian- German joint project entitled “Environmental Impact of Energy Strategies for
Indonesia”. Through this study, the expected development of emission from energy utilization was estimated according to the increasing energy demand
from 1992 until the year 2023. In this study dispersion and deposition of emissions on the main Indonesian island Jawa was considered in 10-year-time
periods. All emitting sources belonged to one of the four energy consuming sectors industry, power plants, households, and traffic. The geometry of the
78 emission sources were distinguished between point sources, area sources and
line sources. The emission from transportation sector was treated as the emission from line sources and MLuS empirical model was used to estimate
the concentration emission of CO, HC, NO
x
, SO
2
and PM pollutants. Another task within the framework of the project in the year 1990 was the compilation
of the emission coefficients available in the computer model MARKAL to estimate the overall emission of the traffic sector. The basic data of the 1990
project were determined from measurements and observation carried out in Indonesia. Table 36 summarizes the final result of emission coefficients used
in BPPT-KFA study:
Table 36. Emission coefficient gkm according to BPPT – KFA study [12]
Vehicle Type CO
NO
x
HC SO
2
PM
Passenger car Gasoline
25.70 6.38
3.66 0.06
- ADO
5.15 1.89
0.99 0.86
0.36 Big Trucks
ADO 2.51
10.10 2.37
1.28 0.54
Big Buses ADO
2.57 15.60
2.36 1.29
0.56 Small Truck Bus
Gasoline 38.80
7.98 6.42
0.09 -
ADO 5.32
2.22 3.80
1.02 0.48
Motor Cycle Gasoline
19.20 0.20
5.99 0.03
0.12
Study on air quality modeling from automotive emission in Surabaya
performed by Lestari on 2001 [52]. The study was done in Surabaya for the
period 1.5 working months which were spread over 3 months period. The modeling activities were done as part of GTZ’s Sustainable Urban Transport
Project SUTP in Surabaya, in cooperation with the Surabaya City Government and the East Java Provincial Government. The model used in this
case study was the Breezway model that has been calibrated for the conditions in Indonesia. Emission coefficient used in this study was taken from US EPA,
Office of Mobile Sources, AP-42 “Air Pollution Emission Factor”. The AP-42 is the list of emission coefficient data from various sources including vehicle
sources. Lestari’s study selected the emission coefficient from vehicle sources
79 manufactured before 1990. The list of emission coefficient based on EPA’s
data used in Lestari’s study is shown in Table 37.
Table 37. Emission coefficient gkm according to Lestari, 2001 [52]
Vehicle Type CO
NO
x
HC SO
2
PM
Passenger car Gasoline
40 3.2
6 0.08
0.33 ADO
1.1 0.99
0.28 0.39
0.45 Load mobile
Gasoline 12.7
5.7 2.1
0.16 0.52
ADO 32
21 5.2
1.5 0.75
Motor cycle 2 stroke
17 0.07
10 0.02
0.2 4 stroke
20 -
- -
-
The emission coefficient of various type of vehicles were also measured by the Ministry of Environment MoE. The list is shown in Table 38.
Table 38. Emission coefficient gkm according to MoE
Vehiclefuel type CO
NO
x
HC SO
2
PM
Passenger cars Gasoline
40 2
4 0.026
0.01 ADO
2.8 3.5
0.2 0.44
0.53 Big Trucks
ADO 8.4
18 1.8
0.82 1.4
Big Buses ADO
1.1 12
1.3 0.93
1.4 Small Truck Bus
Gasoline -
- -
- -
ADO -
- -
- -
Motor Cycle Gasoline
14 0.3
5.9 0.008
0.24
Study on estimation of Emission Coefficient of Various Type of Vehicles
performed by BTMP, 2008 [56]. In that study, emission coefficient in gkm
for each type of emission was determined based on the measured data from 18 engine diesel vehicles and 54 gasoline engine vehicles test samples. Emission
and engine capacity cc of every vehicles was measured and recorded. Using a regression analysis, the formula, which explain the correlation between the
measured engine capacity x axis and emision coefficient y axis can be formed. The regression result is shown in Table 39.
80 Table 39. Regression coefficient for the correlation between the measured engine
capacity and emision coefficient
Regression result for gasoline engine vehicle Emission
Formula A
b R
2
CO y = a lnx + b
14.272 - 83.851
0.9127 SO
2
y = a lnx + b 0.0204
- 0.1195 0.9101
NO
x
y = a lnx + b 0.3888
- 2.2802 0.9097
HC y = a lnx + b
0.5475 - 3.2108
0.9102 PM
y = a lnx + b 0.0761
- 3.2108 0.8984
Regression result for diesel engine vehicle Emission
Formula A
b R
2
CO y = a x
b
0.1019 0.4393
0.7190 SO
2
y = a x
b
0.0452 0.438
0.7187 NO
x
y = a x
b
0.0258 0.4391
0.7181 HC
y = a x
b
0.0339 0.4395
0.7190 PM
y = a x
b
0.0056 0.4398
0.7241
Based on the above formula, the emission coefficient for each capacity size of vehicle can be predicted. For the case of biodiesel blend B10, B20, B50 and
B100, the prediction of emission coefficient was calculated based on the emission reduction due to the effect of biodiesel utilization formula from EPA
[57] as shown below: change = expa x vol biodiesel – 1 x 100
17 Where the value of “a” is shown in Table 40.
Table 40. The value of coefficient a according to EPA
Emission The value of coefficient a
CO -0.00122
NO
x
0.00145 HC
-0.00483 PM
-0.00111
Several assumptions were made for SO
2
and NO
x
calculations. For SO
2
emission, the estimation made based on B100. Biodiesel made from vegetable oil is assumed to have no sulphur. Therefore, SO
2
emission coefficient for the vehicles using B100 is assumed to be zero. Biodiesel in Indonesia is mostly
produced from palm oil. In contrast to the generally non palm biodiesel study, several studies on the effect of palm biodiesel utilization on the engine emission
showed a decreasing of NO
x
emission. For that reason, a modification was made
81 to the NO
x
calculation in this study. The results of emission coefficient estimation in gkm performed by BTMP in 2008 are shown in Table 41.
Table 41. Emission coefficient gkm according to BTMP, 2008 [56]
Vehicle type Fuel Type
SO
2
NO
x
HC PM
CO
Motor cycle Gasoline
0.009 0.20
0.214 0.029
5.56 Passenger car
Gasoline 0.04
0.68 0.95
0.13 24.63
Mediumsmall bustruck Gasoline
0.05 0.94
1.33 0.185
34.52 Passenger car
Diesel 1.26
0.726 0.96
0.16 2.87
B10 1.13
0.72 0.85
0.15 2.69
B20 1.01
0.71 0.77
0.14 2.52
B50 0.63
0.69 0.55
0.12 2.07
B100 0.66
0.31 0.08
1.49 Mediumsmall bustruck
Diesel 1.71
0.98 1.29
0.21 3.90
B10 1.54
0.98 1.16
0.21 3.65
B20 1.37
0.96 1.04
0.19 3.42
B50 0.85
0.94 0.74
0.16 2.81
B100 0.89
0.42 0.11
2.02 Big bus
Diesel 2.32
1.33 1.76
0.29 5.28
B10 2.08
1.32 1.57
0.28 4.95
B20 1.85
1.31 1.41
0.25 4.64
B50 1.16
1.27 1.01
0.21 4.20
B100 1.21
0.57 0.16
2.74 Big truck
Diesel 2.55
1.47 1.76
0.377 5.83
B10 2.29
1.46 1.57
0.364 5.46
B20 2.04
1.44 1.41
0.325 5.12
B50 1.28
1.40 1.01
0.273 3.81
B100 1.33
0.57 0.208
3.02
Table 42 shows the data of specific fuel consumption for each type of vehicle according to ITB, 2001 [42] and RPC, 2006 [41].
Table 42. Specific fuel consumption for each type of vehicle
Vehicle type Fuel type
l100 km kml
Motor cycle Gasoline
2.66 37.6
Passenger car Gasoline
11.79 8.5
Mediumsmall bustruck Gasoline
11.35 8.8
Passenger car Diesel
11.36 8.8
Mediumsmall bustruck Diesel
11.83 8.5
Big bus Diesel
16.89 5.9
Big truck Diesel
15.82 6.3
82 According to the specific fuel consumption values as shown in table 42, the
emission coefficient in gkm can be represented in gl unit as shown in Table 43. Table 43. Emission coefficient gl according to BTMP, 2008 [56]
Vehicle type Fuel Type
SO
2
NO
x
HC PM
CO
Motor cycle Gasoline
0.34 7.52
8.05 1.09
209.02 Passenger car
Gasoline 0.34
5.77 8.06
1.10 208.91
Mediumsmall bustruck Gasoline 0.44
8.28 11.72
1.63 304.14
Passenger car Diesel
11.09 6.39
8.45 1.41
25.26 B10
9.95 6.34
7.48 1.32
23.68 B20
8.89 6.25
6.78 1.23
22.18 B50
5.55 6.07
4.84 1.06
18.22 B100
0.00 5.81
2.73 0.70
13.12 Mediumsmall bustruck
Diesel 14.45
8.28 10.90
1.78 32.97
B10 13.02
8.28 9.81
1.78 30.85
B20 11.58
8.12 8.79
1.61 28.91
B50 7.19
7.95 6.26
1.35 23.75
B100 0.00
7.52 3.55
0.93 17.08
Big bus Diesel
13.74 7.87
10.42 1.72
31.26 B10
12.32 7.82
9.30 1.66
29.31 B20
10.95 7.76
8.35 1.48
27.47 B50
6.87 7.52
5.98 1.24
24.87 B100
0.00 7.16
3.37 0.95
16.22 Big truck
Diesel 16.12
9.29 11.13
2.38 36.85
B10 14.48
9.23 9.92
2.30 34.51
B20 12.90
9.10 8.91
2.05 32.36
B50 8.09
8.85 6.38
1.73 24.08
B100 0.00
8.41 3.60
1.31 19.09
Emission coefficient for this research
Due to the drawbacks of direct measurement time consuming, high cost, the chassy dyno can not measure engine with size higher 150 kW and motor
cycle and calculation methods some emission like NO, NO
2
and PM can not be measured, therefore the emission coefficients in this research have been
determined by collecting several emission coefficients data from previous studies. For some considerations, two following emission coefficients cases were selected
in the MLuS dispersion model: 1. Low emission coefficient case
The emission coefficient data from BTMP’s study as shown in table 41 ware selected for the low emission coefficient case. Considerations for such a
selection are: i the study has just been performed in 2008, ii the vehicle test
83 sample comprised of various engine capacity and car manufacturers with the
latest new technology and manufacture years and iii The measurement results were relatively lower compared to the one from other studies.
2. High emission coefficient data case Due to wide ranges of existing available emission coefficient data as shown
above, and considering that many cars with old engine technology are still running in some parts of Jakarta, therefore high emission coefficient case
would also be assessed. The values of this emission coefficient case was determined by taking a highest values of emission coefficient for each type of
emission from selected emission coefficient data as shown above [29, 12, 52, 56]. The values of high emission coefficient case are shown in Table 44.
Table 44. High emission coefficient case gkm
Vehicle type Fuel Type
SO
2
NO
x
HC PM
CO
Motor Cycle Gasoline
0.02 0.29
10.00 0.24
19.20 Passenger car
Gasoline 0.08
6.38 6.00
0.33 25.70
Busmedium Trucksmall Gasoline 0.16
7.98 6.42
0.52 38.80
Passenger car Diesel
1.26 3.50
0.99 0.53
5.15 B10
1.13 3.47
0.88 0.50
4.83 B20
1.01 3.42
0.79 0.46
4.52 B50
0.63 3.33
0.57 0.40
3.71 B100
0.00 3.18
0.32 0.27
2.67 Busmedium Trucksmall
Diesel 1.71
2.22 5.20
0.75 5.32
B10 1.54
2.20 4.68
0.75 4.98
B20 1.37
2.18 4.19
0.68 4.67
B50 0.85
2.13 2.98
0.57 3.83
B100 0.00
2.02 1.69
0.39 2.76
Big Bus Diesel
2.32 15.60
2.36 1.40
5.28 B10
2.08 15.48
2.11 1.35
4.95 B20
1.85 15.37
1.89 1.21
4.64 B50
1.16 14.90
1.35 1.01
3.81 B100
0.00 14.19
0.76 0.77
2.74 Big Truck
Diesel 2.55
17.70 2.37
1.40 8.40
B10 2.29
17.57 2.11
1.35 7.88
B20 2.04
17.43 1.90
1.21 7.38
B50 1.28
16.90 1.36
1.01 6.06
B100 0.00
16.10 0.77
0.77 4.36
84
• Meteorological data
As described above, the MLuS model needs two values for the meteorological conditions in the surroundings of the considered source:
- Annual average wind speed VWIND. - Percentage of the hourly average value of the wind speed less than 3 ms
PORWIND. Table 45 shows the average wind velocity recorded from five observation stations
based on BPS data.
Table 45. Average wind velocity by observation station in Jakarta, 2006 [38]
Description Observation station
Pondok Betung
Halim Perdana Kusuma
Cengkareng Jakarta
Tanjung Priok
Wind Velocity
ms 5
5 3
4 Wind
direction degree
270 270
270 45
The hourly wind speed data from 2000 until 2005 have been collected from Indonesian Bureau of Meteorology and Geophysics BMG. The data then
proceed by a wind rose graphic tools. A wind rose is a graphic tool used by meteorologists to give a succinct view of how wind speed and direction are
typically distributed at a particular location. Presented in a circular format, the wind rose shows the frequency of winds blowing from particular directions. The
length of each spoke around the circle is related to the frequency that the wind blows from a particular direction per unit time. Each concentric circle represents a
different frequency, emanating from zero at the center to increasing frequencies at the outer circles. A wind rose plot may contain additional information, in that each
spoke is broken down into color-coded bands that show wind speed ranges. Wind roses typically use 16 cardinal directions, such as north N, NNE, NE, etc.,
although they may be subdivided into as many as 32 directions.
85 The WRPLOT View is a software developed by Lakes Environmental
Software that can be used freely to draw the wind rose by applying the license to the software producer. Figure 34 shows the view of WRPLOT software.
Figure 34. The view of WRPLOT Software
Various input data format can be applied, but text file data Excel format will be the simplest one for transferring from BMG data to WRPLOT. Therefore the
hourly wind speed data from BMG must be rearranged to the WRPLOT format as shown in Figure 35.
Figure 35. WRPLOT Format
86 The Excel file then was saved as a file “Text Tab delimited”that should
be compatible with the WRPLOT View software data input. The following conversion factor has been used to convert the wind speed from
knot unit to ms unit: 1 knot = 1,85 kmjam
= 0.5139 ms The wind direction is defined by the degree in the direction of a clockwise.
Wind direction 90 degree means the wind direction to east. During the period of 2001 to 2005, hourly wind speed data at kemayoran station were calculated using
windrose software and the average wind speed for yearly and monthly data are shown in Figure 36 and Figure 37.
Figure 36. Yearly windrose for 2001 - 2005
87
Figure 37. Monthly wind rose for 2005
88 It can be concluded that although the wind direction in 2001 to 2005 were
changes fequently with average wind speed of 2.1 – 3.6 ms. The wind were found coming from north, west and east. Finally the following meteorological
wind data for Jakarta are defined as shown in Table 46. Table 46. Meteorological data
Parameter Symbol
Unit Year
Average 2001
2002 2003 2004 2005
Average wind speed VWIND
MS 2.81
2.70 2.57
2.41 2.58
2.61 Percentage of wind speed
per hour 3 ms PORWIND
56 63
68 71
67 65
Therefore case II of the function to consider the meteorological data fu would be applied in the model, and a governing equation is given by equation 15.
In this case, the annual average wind speed in a height of 10 meters above the land surface u is 2.61 ms.
Reference concentration K
i
[12]
The reference concentration K
i
is the measured concentrations for a pollutant i at a ground level near roadside under the following conditions:
- Traffic volume AADT
G
: 46,000 vehicles per day - Percentage of trucks
: 13.5 - Average speed of passenger cars
: 125 km per hour - Average speed of trucks
: 80 km per hour - Annual average wind speed of 3.6 case I meters per second respectively 1.7
case II meters per second For each wind case the annual average respectively peak pollutant
concentrations K
i
of the German reference case are shown in Table 47:
Table 47. German reference concentration K
i
source: MLuS
Pollutant Wind Case 1
Wind Case II Average
mgm
3
Peak mgm
3
Average mgm
3
Peak mgm
3
SO
2
0.011 0.034
0.049 0.184
NO 0.28
0.87 0.71
2.15 HC
0.06 0.18
0.23 0.77
PM 0.0014
0.0043 0.0059
0.0244
89
Reference average emission coefficient e [12]
The German average emission coefficient e
i
for pollutant i is shown in Table 48.
Table 48. German average emission coefficient e source: MLuS
SO
2
gkm NO
x
gkm HC gkm
PM gkm
0.270 6.248
1.36 0.033
It can be seen in Tables 47 and 48 that there is no data for CO pollutant. The reason is that CO is not considered in German case as in that country, CO is no
longer a prolem anymore. Previously, many vehicles have carburetors, but nowadays many cars have fuel injection and even catalyzers. There is not problem
with CO any more, therefore they took it out from MLuS source: Dr. Achim Lohmeyer, member of Working Committee: Pollution Control on Roads
Workgroup: Air Pollution on Roads. But in Indonesia especially for Jakarta case, CO is still relevant because many vehicles have still used old technology.
• Estimation of CO average emission coefficient e
Due to the unavailability of German’s CO average emission coefficient e, therefore some effort to estimate CO average emission coefficient e based on the
domestic existing data has been performed. The emission concentration data collected from, CO concentration which has been measured at several location in
Jakarta by Balai Teknologi Kesehatan Lingkungan in year 2002 [58] were presented in Table 49.
Table 49. Emission concentration of CO at several location
Location Traffic density
Classification CO emission concentration ugm3
Number of measurement Average
1 2
3 4
Perintis Kemerdekaan High 13,800 16,100 10,350 12,650
13,225 Pondok Indah
Medium 6,555
5,405 3,450
2,530 4,485
Salemba High
23,000 36,800 36,800 25,300 30,475
Glodok High
9,775 20,700 19,550 24,725 18,688
Gunung Sahari High
35,650 5,750
6,900 19,550 16,963
Palmerah Cileduk Medium
6,900 4,600
5,750 5,750
5,750
90 The number of vehicle for each traffic density classification for year 2005
case is presented in Table 32 and a calibration was carried out to year 2002. Due to limited availability of data, therefore only two classification traffic density data
AADT were run in high and medium density vehicle volume. Based on the total number of vehicle in year 2002 and 2005 the CO emission concentration can be
estimated and presented in Table 50. Table 50. AADT and CO emission concentration in year 2002
2005 2002
Emission Concentration of CO
μgm
3
Number of vehicles in Jakarta Million 7.23
4.07 High density AADT
Vehiclesday 212,500 119,740 19.838
Medium density AADT Vehiclesday 137,500
77,480 5.118
Low density AADT Vehiclesday
62,500 35,220
n.a
Using the equation for concentration K
i
s see equation 9 and available
data German reference AADT, CO emission coefficient as shown in Table 47, therefore emission coefficient e and reference concentration K for CO emission
can be estimated as follows:
•
Reference emission coefficient e for CO emission is 11.65 gkm
•
Reference concentration K for CO emission is 13.97 mgm
3
for wind case I and 50.02 mgm
3
for wind case II. Finally, the reference emission coefficient e and reference concentration K
for model input can be summarized as shown in Tables 51 and 52.
Table 51. Reference emission coefficient e for model input
Pollutant i Emission coefficient gkm
SO
2
0.270 NO
x
6.248 HC
1.360 PM
0.033 CO
11.650
Table 52. Reference concentration K for model input
Pollutant i Emission concentration mgm
3
Wind Case I Wind Case II
SO
2
0.011 0.049
NO
x
0.280 0.710
HC 0.060
0.230 PM
0.0014 0.0059
CO 13.970
50.020
91 The values of emission load and concentration produced from calculation
result of low emission coefficient input data case are shown in Tables 53 to 56, while the ones from high emission coefficient input data cases are shown in
Tables 57 to 60. Table 53. Total emission in thousand ton, low emission coefficient case
Emission Scenarios Total Emission thousand ton
2005 2010
2015 2020
2025 SO
2
Base 27.70
35.04 42.13
50.30 60.22
B10 27.70
31.79 38.25
45.69 54.71
B20 27.70
28.65 34.52
34.52 49.45
B50 27.70
18.94 22.96
27.55 33.11
B100 27.70
2.92 3.88
4.90 6.10
NO
x
Base 51.05
73.67 95.26
118.14 144.68
B10 51.05
73.60 95.16
118.01 144.52
B20 51.05
73.29 94.80
117.60 144.04
B50 51.05
72.85 94.26
116.95 143.25
B100 51.05
71.98 93.25
115.75 141.84
HC
Base 66.06
94.25 121.74
151.62 186.88
B10 66.06
91.66 118.64
147.92 182.44
B20 66.06
89.51 116.09
144.90 178.85
B50 66.06
83.94 109.45
137.01 169.44
B100 66.06
77.92 102.27
128.48 159.26
PM
Base 9.64
13.64 17.53
21.78 26.80
B10 9.64
13.52 17.38
21.58 26.55
B20 9.64
13.18 16.98
21.11 26.00
B50 9.64
12.63 16.33
20.35 25.09
B100 9.64
11.69 15.20
19.00 23.48
CO
Base 1,270.17
1,891.60 2,498.37
3,149.07 3,911.72
B10 1,270.17
1,886.96 2,492.84
3,142.52 3,903.92
B20 1,270.17
1,882.64 2,487.70
3,136.41 3,896.64
B50 1,270.17
1,871.20 2,474.12
3,120.34 3,877.53
B100 1,270.17
1,856.34 2,456.38
3,099.25 3,852.34
92
Table 54. Emission reduction thousand ton, low emission coefficient case
Emission Scenarios
Emission reduction compare to base case thousand ton 2005
2010 2015
2020 2025
SO
2
Base-B10 0.0
3.3 3.9
4.6 5.5
Base-B20 0.0
6.4 7.6
15.8 10.8
Base-B50 0.0
16.1 19.2
22.7 27.1
Base-B100 0.0
32.1 38.3
45.4 54.1
NO
x
Base-B10 0.0
0.1 0.1
0.1 0.2
Base-B20 0.0
0.4 0.5
0.5 0.6
Base-B50 0.0
0.8 1.0
1.2 1.4
Base-B100 0.0
1.7 2.0
2.4 2.8
HC Base-B10
0.0 2.6
3.1 3.7
4.4 Base-B20
0.0 4.7
5.7 6.7
8.0 Base-B50
0.0 10.3
12.3 14.6
17.4 Base-B100
0.0 16.3
19.5 23.1
27.6 PM
Base-B10 0.0
0.1 0.2
0.2 0.3
Base-B20 0.0
0.5 0.6
0.7 0.8
Base-B50 0.0
1.0 1.2
1.4 1.7
Base-B100 0.0
2.0 2.3
2.8 3.3
CO Base-B10
0.0 4.6
5.5 6.6
7.8 Base-B20
0.0 9.0
10.7 12.7
15.1 Base-B50
0.0 20.4
24.3 28.7
34.2 Base-B100
0.0 35.3
42.0 49.8
59.4
93
Table 55. Emission concentration mgm
3
, low emission coefficient case
SO
2
Emission concentration mgm
3
2005 2010
2015 2020
2025
Base 0.0096
0.0122 0.0144
0.0168 0.0196
B10 0.0096
0.0111 0.0131
0.0153 0.0179
B20 0.0096
0.0100 0.0118
0.0139 0.0162
B50 0.0096
0.0067 0.0080
0.0094 0.0111
B100 0.0096
0.0012 0.0016
0.0020 0.0025
NO
x
Emission concentration mgm
3
2005 2010
2015 2020
2025
Base 0.0193
0.0288 0.0372
0.0457 0.0552
B10 0.0193
0.0288 0.0372
0.0457 0.0550
B20 0.0193
0.0287 0.0371
0.0455 0.0550
B50 0.0193
0.0285 0.0369
0.0454 0.0548
B100 0.0193
0.0282 0.0366
0.0450 0.0544
HC Emission concentration mgm
3
2005 2010
2015 2020
2025
Base 0.0238
0.0351 0.0453
0.0559 0.0680
B10 0.0238
0.0343 0.0443
0.0548 0.0667
B20 0.0238
0.0336 0.0435
0.0539 0.0656
B50 0.0238
0.0318 0.0415
0.0515 0.0629
B100 0.0238
0.0299 0.0392
0.0489 0.0599
PM Emission concentration mgm
3
2005 2010
2015 2020
2025
Base 0.0036
0.0053 0.0068
0.0084 0.0102
B10 0.0036
0.0053 0.0068
0.0083 0.0101
B20 0.0036
0.0052 0.0066
0.0082 0.0100
B50 0.0036
0.0050 0.0064
0.0079 0.0097
B100 0.0036
0.0047 0.0061
0.0075 0.0092
CO Emission concentration mgm
3
2005 2010
2015 2020
2025
Base 12.0983
18.6621 24.6329
30.7766 37.7780
B10 12.0983
18.6246 24.5893
30.7261 37.7194
B20 12.0983
18.5898 24.5488
30.6791 37.6649
B50 12.0983
18.5007 24.4452
30.5594 37.5265
B100 12.0983
18.3779 24.3022
30.3931 37.3330
94 Table 56. Reduction of emission concentration compare to base scenario,
low emission coefficient case
SO
2
Concentration reduction compare to base scenario ugm3
2005 2010
2015 2020
2025
Base-B10 0.00
1.11 1.29
1.50 1.74
Base-B20 0.00
2.18 2.54
2.94 3.42
Base-B50 0.00
5.49 6.39
7.41 8.59
Base-B100 0.00
10.94 12.73
14.77 17.14
NO
x
Concentration reduction compare to base scenario mgm3 2005
2010 2015
2020 2025
Base-B10 0.00
0.02 0.02
0.03 0.19
Base-B20 0.00
0.12 0.14
0.16 0.19
Base-B50 0.00
0.27 0.31
0.36 0.42
Base-B100 0.00
0.55 0.64
0.75 0.86
HC Concentration reduction compare to base scenario ugm3
2005 2010
2015 2020
2025
Base-B10 0.00
0.81 0.95
1.11 1.29
Base-B20 0.00
1.50 1.75
2.04 2.37
Base-B50 0.00
3.28 3.81
4.43 5.14
Base-B100 0.00
5.19 6.04
7.01 8.15
PM Concentration reduction compare to base scenario ugm3
2005 2010
2015 2020
2025
Base-B10 0.00
0.03 0.04
0.05 0.06
Base-B20 0.00
0.15 0.18
0.21 0.24
Base-B50 0.00
0.34 0.39
0.46 0.53
Base-B100 0.00
0.65 0.75
0.88 1.02
CO Concentration reduction compare to base scenario ugm3
2005 2010
2015 2020
2025
Base-B10 0.00
37.48 43.59
50.52 58.56
Base-B20 0.00
72.27 84.09
97.51 113.10
Base-B50 0.00
161.44 187.65
217.21 251.52
Base-B100 0.00
284.16 330.69
383.57 445.01
95 Table 57. Total emission thousand ton, high emission coefficient case
Emission Scenarios Total Emission thousand ton
2005 2010
2015 2020
2025
SO
2
Base 30.02
38.48 46.66
55.97 67.20
B10 30.02
35.26 42.81
51.40 61.76
B20 30.02
32.06 39.01
39.01 56.37
B50 30.02
22.37 27.47
33.20 40.06
B100 30.02
6.36 8.40
10.56 13.08
NO
x
Base 308.07
427.15 555.32
708.11 899.06
B10 308.07
426.41 554.44
707.06 897.80
B20 308.07
425.51 553.33
705.70 896.14
B50 308.07
423.16 550.50
702.30 892.04
B100 308.07
419.26 545.83
696.75 885.41
HC Base
870.12 1,438.98
1,906.32 2,302.56
2,684.03 B10
870.12 1,432.68
1,899.16 2,294.44
2,674.81 B20
870.12 1,427.10
1,892.85 2,287.35
2,666.82 B50
870.12 1,412.99
1,876.85 2,269.31
2,646.44 B100
870.12 1,397.85
1,859.69 2,249.94
2,624.54 PM
Base 38.41
57.32 74.09
90.30 107.83
B10 38.41
56.92 73.57
89.62 106.97
B20 38.41
55.67 72.11
87.94 105.01
B50 38.41
53.69 69.77
85.19 101.77
B100 38.41
50.35 65.79
80.46 96.12
CO Base
2,187.93 3,486.15
4,620.40 5,675.92
6,785.84 B10
2,187.93 3,479.21
4,612.03 5,665.87
6,773.71 B20
2,187.93 3,472.73
4,604.21 5,656.46
6,762.35 B50
2,187.93 3,455.54
4,583.46 5,631.52
6,732.22 B100
2,187.93 3,433.34
4,556.68 5,599.32
6,693.36
96 Table 58. Emission reduction thousand ton, high emission coefficient case
Emission Scenarios
Emission reduction compare to base case thousand ton 2005
2010 2015
2020 2025
SO
2
Base-B10 0.0
3.2 3.8
4.6 5.4
Base-B20 0.0
6.4 7.7
17.0 10.8
Base-B50 0.0
16.1 19.2
22.8 27.1
Base-B100 0.0
32.1 38.3
45.4 54.1
NO
x
Base-B10 0.0
0.7 0.9
1.1 1.3
Base-B20 0.0
1.6 2.0
2.4 2.9
Base-B50 0.0
4.0 4.8
5.8 7.0
Base-B100 0.0
7.9 9.5
11.4 13.7
HC Base-B10
0.0 6.3
7.2 8.1
9.2 Base-B20
0.0 11.9
13.5 15.2
17.2 Base-B50
0.0 26.0
29.5 33.2
37.6 Base-B100
0.0 41.1
46.6 52.6
59.5 PM
Base-B10 0.0
0.4 0.5
0.7 0.9
Base-B20 0.0
1.7 2.0
2.4 2.8
Base-B50 0.0
3.6 4.3
5.1 6.1
Base-B100 0.0
7.0 8.3
9.8 11.7
CO Base-B10
0.0 6.9
8.4 10.1
12.1 Base-B20
0.0 13.4
16.2 19.5
23.5 Base-B50
0.0 30.6
36.9 44.4
53.6 Base-B100
0.0 52.8
63.7 76.6
92.5
97 Table 59. Emission concentration mgm
3
, high emission coefficient case
SO
2
Emission concentration mgm
3
2005 2010
2015 2020
2025
Base 0.0106
0.0136 0.0163
0.0192 0.0226
B10 0.0106
0.0125 0.0150
0.0177 0.0208
B20 0.0106
0.0120 0.0144
0.0171 0.0201
B50 0.0106
0.0081 0.0099
0.0118 0.0140
B100 0.0106
0.0027 0.0036
0.0044 0.0054
NO
x
Emission concentration mgm
3
2005 2010
2015 2020
2025
Base 0.1083
0.1534 0.1990
0.2525 0.3185
B10 0.1083
0.1531 0.1987
0.2522 0.3176
B20 0.1083
0.1528 0.1983
0.2518 0.3176
B50 0.1083
0.1521 0.1975
0.2507 0.3164
B100 0.1083
0.1508 0.1959
0.2490 0.3143
HC Emission concentration mgm
3
2005 2010
2015 2020
2025
Base 0.3793
0.6487 0.8602
1.0338 1.1950
B10 0.3793
0.6465 0.8577
1.0310 1.1919
B20 0.3793
0.6445 0.8555
1.0285 1.1892
B50 0.3793
0.6395 0.8499
1.0223 1.1823
B100 0.3793
0.6341 0.8439
1.0156 1.1748
PM Emission concentration mgm
3
2005 2010
2015 2020
2025
Base 0.0157
0.0244 0.0316
0.0382 0.0450
B10 0.0157
0.0243 0.0315
0.0381 0.0448
B20 0.0157
0.0239 0.0310
0.0375 0.0441
B50 0.0157
0.0232 0.0302
0.0366 0.0431
B100 0.0157
0.0221 0.0289
0.0350 0.0413
CO Emission concentration mgm
3
2005 2010
2015 2020
2025
Base 22.8587
37.8295 50.1543
61.1193 72.1833
B10 22.8587
37.7762 50.0916
61.0458 72.0971
B20 22.8587
37.7265 50.0331
60.9773 72.0166
B50 22.8587
37.5947 49.8781
60.7954 71.8031
B100 22.8587
37.4245 49.6778
60.5607 71.5276
98 Table 60. Reduction of emission concentration compare to base scenario,
high emission coefficient case
SO
2
Concentration reduction compare to base scenario ugm3
2005 2010
2015 2020
2025
Base-B10 0.00
1.10 1.28
1.48 1.73
Base-B20 0.00
1.59 1.84
2.11 2.45
Base-B50 0.00
5.49 6.39
7.41 8.60
Base-B100 0.00
10.94 12.73
14.77 17.14
NO
x
Concentration reduction compare to base scenario mgm3
2005 2010
2015 2020
2025
Base-B10 0.00
0.23 0.28
0.33 0.86
Base-B20 0.00
0.51 0.61
0.73 0.86
Base-B50 0.00
1.25 1.50
1.79 2.11
Base-B100 0.00
2.54 3.01
3.56 4.18
HC Concentration reduction compare to base scenario
ugm3 2005
2010 2015
2020 2025
Base-B10 0.00
2.22 2.49
2.77 3.08
Base-B20 0.00
4.21 4.72
5.24 5.81
Base-B50 0.00
9.23 10.33
11.46 12.72
Base-B100 0.00
14.60 16.34
18.14 20.14
PM Concentration reduction compare to base scenario
ugm3 2005
2010 2015
2020 2025
Base-B10 0.00
0.11 0.14
0.17 0.22
Base-B20 0.00
0.56 0.65
0.76 0.89
Base-B50 0.00
1.24 1.45
1.67 1.94
Base-B100 0.00
2.36 2.75
3.19 3.70
CO Concentration reduction compare to base scenario ugm3
2005 2010
2015 2020
2025
Base-B10 0.00
53.38 62.74
73.49 86.21
Base-B20 0.00
103.05 121.18
142.03 166.68
Base-B50 0.00
234.86 276.26
323.87 380.19
Base-B100 0.00
405.07 476.48
558.61 655.73
99 Figure 38 shows the dispersion map of SO
2
emission concentration for base case in 2005 as a simulation result of high emission coefficient case. The other
emission dispersion map results for both low and high emission coefficient cases can be seen in appendix 4.
Figure 38. The dispersion map of SO
2
emission concentration for base case
100 Table 61 shows the comparison between emission load and concentration
for low and high emission coefficient for 2010 case. Table 61. Comparison between emission load and concentration for low and
high emission coefficient for year 2010 case
Emission Scenario
Emission load thousand ton Concentration mgm
3
Low EC High EC
Low EC High EC
SO
2
Base 35.04
38.48 0.0122
0.0136 B10
31.79 35.26
0.0111 0.0125
B20 28.65
32.06 0.0100
0.0120 B50
18.94 22.37
0.0067 0.0081
B100 2.92
6.36 0.0012
0.0027
NO
x
Base 73.67
427.15 0.0288
0.1534 B10
73.60 426.41
0.0288 0.1531
B20 73.29
425.51 0.0287
0.1528 B50
72.85 423.16
0.0285 0.1521
B100 71.98
419.26 0.0282
0.1508
HC
Base 94.25
1,438.98 0.0351
0.6487 B10
91.66 1,432.68
0.0343 0.6465
B20 89.51
1,427.10 0.0336
0.6445 B50
83.94 1,412.99
0.0318 0.6395
B100 77.92
1,397.85 0.0299
0.6341
PM
Base 13.64
57.32 0.0053
0.0244 B10
13.52 56.92
0.0053 0.0243
B20 13.18
55.67 0.0052
0.0239 B50
12.63 53.69
0.0050 0.0232
B100 11.69
50.35 0.0047
0.0221
CO
Base 1,891.60
3,486.15 18.6621
37.8295 B10
1,886.96 3,479.21
18.6246 37.7762
B20 1,882.64
3,472.73 18.5898
37.7265 B50
1,871.20 3,455.54
18.5007 37.5947
B100 1,856.34
3,433.34 18.3779
37.4245
Conclusion
The base non biodiesel case results show that the emission load and concentration linearly increase with the time due to the continuously growing of
population and vehicle number. Each type of pollutants SO
2
, NO
x
, HC, PM and CO has a different growth of emission load and concentration. As shown in
Tables 53 and 55, for low emission coefficient case in 2005, where biodiesel fuel has not been utilized yet, the emission load and concentration of SO
2
, NO
x
, HC, PM and CO are 27.70 thousand ton and 0.0096 mgm
3
, 51.05 thousand ton and 0.0193 mgm
3
, 66.06 thousand ton and 0.0238 mgm
3
, 9.64 thousand ton and
101 0.0036 mgm
3
and 1,270.17 thousand ton and 12.0983 mgm
3
respectively. The emission load and concentration reduction as a result of biodiesel utilization in the
year of 2010, 2015, 2020 and 2025 can be seen in Tables 54 and 56. Generally, the result shows that the values of emission load reduction
increase considerably with the increasing content in the biodiesel blend. As it was predicted, biodiesel is effective in reducing SO
2
emission load up to more than 90 comparison between B0 and B100. Table 61 shows the comparison
between emission load and concentration for low and high emission coefficients for 2010 case. The result presents very wide range of emission load and
concentration difference between low and high emission coefficient cases. These result showed that the emission coefficient value has an important impact to the
result emission load and concentration estimation.
CHAPTER VI EXTERNAL COST ANALYSIS
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