THE EFFECT OF BIODIESEL UTILIZATION ON

CHAPTER III THE EFFECT OF BIODIESEL UTILIZATION ON

TRANSPORTATION SECTOR EMISSION IN JAKARTA Introduction Biodiesel has been proven as an environmental friendly alternative diesel fuel. Biodiesel is essentially sulphur free. Engines fueled by biodiesel emit significantly fewer particulates, hydrocarbons and less carbon monoxide than the conventional diesel fuel. Many studies on the performances and emission of compression ignition engines, fueled with pure biodiesel and blends with diesel oil, have been performed and reported in the literature. Although based on different engine architectures that may influence the final results, all the tests showed a slight reduction in the performances e.g. 5 decrease in the power over the entire speed range and a slight increase in the fuel consumption. The presence of oxygen in biodiesel led to more complete combustion processes, resulting in lower emissions of CO, particulate and visible smoke. However, an increase in NO x emissions has been reported, due to higher temperatures in the combustion chamber. The investigations of different blends of biodiesel fuel on exhaust emissions and health effect have been done by numerous studies conducted worldwide. They provided both proponents and opponents of alternatives with arguments for their causes. Those studies, however, mostly used US and European countries raw material based biodiesel such as soybean, rapeseed, sunflower and canola oil. Meanwhile, similar specific studies on the Indonesian raw material based biodiesel such as palm oil were rarely found. One comprehensive study on air quality was performed by Syahril et al. [4]. They proposed interventions that will have a direct impact in reducing Jakarta’s air pollution problem. They include: • A reduction in sulphur content in fuel • A switch in fuel type, i.e. from gasoline or diesel to CNG, LNG, and biodiesel • Implementation of emission standards for new-type vehicles • Introduction of catalytic converters for taxis 31 • Improvements in IM programs for public vehicles • Implementation of IM programs for passenger cars • Development of public transport, i.e. Improvements to rail-based transport and the implementation of bus rapid transit In their study, Syahril and his co-workers investigated the impact of PM 10 , NO 2 and SO 2 ambient and suggested four policies that should be implemented in 2015; i the new vehicle emissions standard, ii catalytic converters for taxis, iii public transportation management and iv the combination of these three policies. The study however did not include a policy to switch to the new fuel that has lower sulphur content such as biodiesel. Table 7 shows the reduction in health costs associated with implementation of the four abatement policies as the result of Syahril’s study. Table 7. Reduction in health costs caused by the abatement policies No. Proposed Action Plan Jakarta North East South West Central Total 1 New Vehicle Emission Standard 46,463 10.61 82,257 6.72 68,496 6.18 90,416 6.42 23,421 13.75 311,053 28.80 7.15 2 Catalytic Converter for taxis 3,363 0.77 6,064 0.50 5,139 0.46 6,698 0.48 1,675 0.98 22,940 2.12 0.53 3 Public Transportation Management 29,519 6.74 52,688 4.31 44,229 3.99 58,043 4.12 14,800 8.69 199,279 18.45 4.58 4 Combined Policy 63,491 14.49 113,416 9.27 94,674 8.55 125,143 8.88 32,353 18.99 429,077 39.73 9.87 Note : • Unit in million Rupiah and percentages. • For total Jakarta, the unit for the figures in the bracket is million USD USD 1 = IDR 10,800 The percentage numbers are reduction in health cost that might be saved due to the implementation of an abatement policy compared to the based condition; i.e. No abatement policy. The figures demonstrate that the new vehicle emissions standard policy is more effective than the policies of installing catalytic converters in taxis and improving public transportation management in reducing the health costs associated with PM 10 , NO 2 and SO 2 . Most health cost reductions were estimated to occur in west Jakarta. However, in terms of percentage health cost 32 reductions, central Jakarta should benefit most from the implementation of these policies. The result also showed that the total health cost reduction that can be achieved in 2015 by implementing all three policies should be approximately 429 billion Rupiah 40 million US. This reduction would be equal to approximately 13 percent of the Jakarta government’s total revenue in 2000. By implementing a new vehicle emissions standard such as utilization of biodiesel in Indonesia, this may improve the air quality level in major cities especially in Jakarta. Thus, the objective of this study is to assess the effect of biodiesel utilization in transportation sector on the air pollution level in Jakarta. The targeted emission are carbon monoxide CO, nitrogen oxides NOx, hydrocarbons HC, sulphur dioxides SO 2 and particulate matter PM from vehicle sources. The scenarios used are the utilization of B10 in 2010, B20 in 2015, B30 in 2020 and B50 in 2025 as an automotive diesel fuel substitution in Jakarta. The calculation was performed based on the emission measurement data collected from the studies by Wirawan et al. [29, 37]. Materials and Methods The study was performed through quantitative methodology based on the measurement and secondary data collection and qualitative methodology. The data collection was carried out by surveying literatures related to the energy used in the transportation sector in Jakarta and its emission effect. Those data were inputted into the model that was specifically developed to estimate the effect of biodiesel utilization on the reduction of emission from transportation sector in Jakarta. The targeted emission pollutants are: • Carbon monoxide CO • Nitrogen oxides NO x • Hydrocarbon HC • Sulphur dioxides SO 2 , and • Particulate matters PM Figure 15 shows the methods to conduct the study. 33 Figure 15. Flowchart of study to estimate the Effect of Biodiesel Utilization on Transportation Sector Emission in Jakarta Result and Discussion Projection of RGDP and population of Jakarta The population in DKI Jakarta Province was increasing from 8.4 million in 2000 to 9.04 million in 2005 or an average growth of 1.5 per year. Based on the year of 2000-price constant, the growth rate of RGDP was estimated to around 5 per year. In 2000, RGDP reached 227.9 trillion rupiah and it increased to 295.3 trillion rupiah in 2005. The highest growth of RGDP was in 2005 with the value of 6 per year [38]. The growth of population and RGDP of Jakarta for the period of the year of 2000 until 2005 is shown on Figure 16. Number of Vehicles BPS Jakarta Effective Operation yr ITB, 2000 Specific fuel consumption ltkm Source: BPPT – KFA, 1992 Milleage kmyr BPPT – KFA, 1992 Fuel Consumption UPMS III kl Source: PT. PERTAMINA Base Data 2005 Emission Factor gkm BPPT – KFA, 1992 Emission Factor gkm Wirawan et. al. , 2008 Emission Factor gkm : - Per type of emission CO, HC, NOx, SOx, PM - Per type of vehicle private, bus, truck - Per type of fuel gasoline, diesel, biodiesel RGDP, Number and growth of population, etc Source: BPS, 2006 No. of Vehicle projection 2005 - 2025 Fuel consumption 2005 Match No Fuel demand kl 2005 - 2025 Milleage kmyr Ok Study output BAU Pollutant per type of emission gryr 2005 - 2025 Effect of Biodiesel to the emission reduction B10, B20, B30, B50 34 150 175 200 225 250 275 300 2000 2001 2002 2003 2004 2005 RG DP T rillio n Ru p ia h 8000 8200 8400 8600 8800 9000 9200 P opul at ion T housa nds RGDP constant 2000 Population Figure 16. RGDP and population in Jakarta Assuming that family planning program has been performed successfully, the growth of population should decrease gradually from 1.5 per year in 2005 to 0.85 per year in 2025. Further assumption is that urbanization process has been forcing the expansion of urban district development to the suburb areas of Jakarta such as Bogor, Depok, Tangerang and Bekasi. Therefore, Jakarta’s population may be projected to increase from 9.04 million in 2005 become 11.21 million in the year of 2025 [39]. In the case of RGDP, its value was expected to increase from 295.3 trillion rupiah in year 2005 to be 965.0 trillion rupiah in year 2025 based on year 2000 constant price or increase in average 6.1 per year. That RGDP growth was estimated based on the assumption of projected national growth rate of 6 to 6.5 set by the central government. Therefore, per capita income was also expected to rise from 32.65 million rupiah in year 2005 to be 86.05 million rupiah in year 2025 or an average increase of 4.5 per year. Detailed projected growth of population and RGDP from 2000 until 2025 is shown in Figure 17. Figure 17. Projection of population and RGDP in Jakarta 100 200 300 400 500 600 700 800 900 1000 2000 2005 2010 2015 2020 2025 T ri ll io n R u p ia h 8 9 10 11 12 13 P o p u la ti o n M il li o n RGDP Population 35 ฀Fuel consumption for transportation sector in Jakarta Jakarta is highly dependent on oil fuel type of energy, especially for its transportation sector. In the year of 2005, the fuel consumption in Jakarta including gasoline, diesel fuel and kerosene reached to about 68 of the total energy consumption. Since data on fuel consumption for the transportation sector in Jakarta were not specifically available, therefore the consumption was estimated based on the data of fuel sold by PERTAMINA Marketing Unit III UPMS III. The data that covered three provinces including DKI Jakarta, West Java and Banten are shown on Figure 18. The number of vehicles and their growth in Jakarta for the year of 2000 up to 2005 are shown in Figure 19. 1000 2000 3000 4000 5000 6000 7000 Gasoline Kerosene ADO IDO MFO M illio n lit e r Electricity Sector Industrial Sector Household Transportation Figure 18. Fuel sold by UPMS III in the year of 2005 Figure 19. Number of vehicles in Jakarta 2001 – 2005 1.000.000 2.000.000 3.000.000 4.000.000 5.000.000 2001 2002 2003 2004 2005 N um ber of v ehi c les Motor Cycle Passenger car Truck Bus 36 The ratio between Regional Growth Domestic Product RGDP per capita of Jakarta and RGDP per capita in other areas in UPMS III Banten and West Java were taken from statistic data issued by BPS DKI Jakarta [38]. RGDP per capita of Jakarta in the year of 2005 was 48.25 million rupiah, whereas for Banten and West Java it was about 9.45 million rupiah. Fuel consumption in Jakarta was assumed to be equal to the ratio of Jakarta’s RGDP to the total of RGDP of UPMS III region, which is 83 from the fuel consumption recorded in UPMS III data. The fuel consumption by type of fuel in Jakarta for the year of 2005 is shown in Figure 20. Figure 20. Estimated fuel consumption for transportation sector in Jakarta The growth of vehicle population Prediction of the future number of vehicles in this study was performed using four types of calculation model based on regression analysis approach. Such an approach depends on the vehicle type and it has been applied in the BPPT- KFA study [40]. Each model depends on the RGDP, growth rate of the value of goods, the correction factor of the growth of each type of vehicle. The correction factors are shown on the following equations [40]: Model for Car 1 1 t t t KORC RVC RVC + = − 1 Where: CCA GC t BCA ACA KORC t t × + − × − × = −1 1 exp 2 RVC : Registered cars per capita KORC : Corrected growth factor for cars Year 2005 Total: 7.612.302 kl Kerosene 1,09 Gasoline 65,15 Industrial Diesel Oil 0,18 Automotive Diesel Oil 33,58 37 t : time GC : RGDPcapita growth ACA : Factor to adjust the starting value of growth BCA : Speed factor to reach final growth CCA : Multiplier for final growth Model for Bus 1 1 t t t KORB RVB RVB + = − 3 Where: CBU GB t BBU ABU KORB t t × + − × − × = −1 1 exp 4 RVB : Registered buses per capita KORB : Corrected growth factor for buses t : time GB : RGDPcapita growth ABU : Factor to adjust the starting value of growth BBU : Speed factor to reach final growth CBU : Multiplier for final growth Model for Truck 1 1 t t t KORT TRU TRU + = − 5 Where: CTR GVTT t BTR ATR KORT t t × + − × − × = −1 1 exp 6 TRU : Registered trucks per capita KORT : Corrected growth factor for trucks t : time GVTT : Growth rate of value of goods per capita ATR : Factor to adjust the starting value of growth BTR : Speed factor to reach final growth CTR : Multiplier for final growth Model for Motor Cycles 1 1 t t t KORM RVM RVM + = − 7 Where: 38 CMO GM t BMO AMO KORM t t × + − × − × = −1 1 exp 8 RVM : Registered Motor Cycles per capita KORM : Corrected growth factor for Motor Cycles t : time GM : RGDPcapita growth AMO : Factor to adjust the starting value of growth BMO : Speed factor to reach final growth CMO : Multiplier for final growth The growth of RGDP, population and value of goods are parameters that will influence to the growth of vehicle number projection. The growth rates of passenger car, bus and motorcycle are likely influenced by the growth of RGDP and population, while the growth rate of truck is influenced more by the growth rates of value of goods and population. The growth of RGDP and population are presented in Figure 16 and 17. The annual growth rate of value of goods from 2006 until 2025 was assumed at a value of 2.06. Therefore, input data of the growth of vehicle number projection parameters can be listed as shown in Table 8. Table 8. The list of the growth of vehicle number projection input data Parameter Unit 2000 2001 2002 2003 2004 2005 Value of good product Billion Rupiah 79,769 90,063 101,684 107,047 126,256 140,686 RGDP Billion Rupiah 227,856 238,656 250,331 263,624 278,525 295,270 Population Thousand 8,386 8,516 8,649 8,784 8,921 9,042 Passenger car unit 1,065,121 1,130,496 1,195,871 1,529,824 1,645,306 1,766,801 Bus unit 252,447 253,648 254,849 315,652 316,396 316,502 Truck unit 328,665 347,443 366,221 464,748 488,517 499,581 Motorcycle unit 1,369,078 1,813,136 2,257,194 3,316,900 3,940,700 4,647,435 Note: Value of gross output of large and medium scale manufacturing [38] Based on 2000 – 2005 historical data shown in Table 8 and using the regression analysis method, the other parameters in the vehicle number projection equations can then be determined as shown in Table 9. 39 Table 9. Parameters for vehicle number projection Type of vehicle Parameter Value Passenger car ACA 0.10 BCA 0.25 CCA 0.90 Bus ABU 0.05 BBU 0.78 CBU 0.45 Truck ATR 0.10 BTR 0.34 CTR 0.18 Motorcycle AMO 0.36 BMO 0.19 CMO 0.16 Figure 21 shows the projection of vehicles population by type in Jakarta until 2025 2.000 4.000 6.000 8.000 10.000 12.000 14.000 16.000 2000 2005 2010 2015 2020 2025 Year V ehi c le num ber T hous and Motor Cycle Passenger car Truck Bus Figure 21. Projection of vehicle number in Jakarta Passenger cars and motor cycles are the type of vehicles that have highest growth rate with the value of 6.24 and 5.94 per year respectively, whereas bus and truck are only 3.44 and 1.67 growth. Motor Cycles was expected to grow from 4.647 million in 2005 to 14.745 million units in 2025. Passenger cars increased from 1.767 million in 2005 to become 5.933 million in 2025. The growth rate of buses and trucks were relatively small with the value of 317 thousand and 500 thousand vehicle sin the year of 2005 and they are expected to increase to 623 thousands and 696 thousand vehicles respectively in 2025. 40 Projection of fuel consumption for each type of vehicle The calculation of the projected fuel consumption for each type of vehicle started by collecting the data of fuel consumption at base case fuel consumption 2000 – 2005, specific fuel consumption, and mileage per year of each type of vehicle. A survey of the fuel consumption, for each type of vehicle in Jakarta and Surabaya areas, was conducted by interviewing 150 respondents for private cars, 90 respondents for public transporter vehicles and 60 respondents for goods transporter vehicles for each area has been carried out by RPC, 2006 [41]. BPPT- KFA study [12] also performed a survey to the fuel consumption, milleage and specific fuel consumption for each type of vehicles. The studies showed that not all registered vehicles are operated everyday. The study performed by ITB, 2001 [42] showed that three wheels vehicles has a highest effective operation per year 82, whereas the big trucks has a lowest effective operation with the value of 23. Specific Fuel Consumption kmL, milleage kmyr and yearly effective operation data used in this study is shown on Table 10 and the estimated projection of fuel demand for Business As Usual BAU case is shown in Figure 22. The figure shows that gasoline consumption keep its domination during the analysis period, especially the gasoline for passenger car. Whereas the automotive diesel oil mainly is used for passenger cars and trucks. Table 10. Specific fuel consumption, mileage and yearly effective operation Vehicle type Milleage kmyr Specific Fuel Consumption kmlt Yearly Effective Operation Passenger Car Private Gasoline 15,379 8.48 55 ADO 20,429 8.80 41 Public Gasoline 101,307 9.19 55 ADO 113,400 16.00 41 Bus Small and medium bus Gasoline 39,979 8.81 60 ADO 39,338 8.45 60 Big bus ADO 42,985 5.92 29 Truck Small Truck Gasoline 20,563 12.33 41 ADO 19,380 9.40 41 Medium Truck ADO 69,800 6.60 27 Big Truck ADO 121,176 6.32 23 Motor Cycle Gasoline 20,706 37.59 64 Source: Adapted from RPC, 2006 [41], ITB, 2001 [42] and BPPT-KFA, 1992 [43], 41 0,0 2,5 5,0 7,5 10,0 12,5 15,0 17,5 20,0 22,5 2000 2005 2010 2015 2020 2025 Year E ner gy C onsu m pt ion M il li on kl Bus Diesel Truck Diesel Passenger car Diesel Bus Gasoline Truck Gasoline Passenger car Gasoline Motor Cycle Gasoline Figure 22. Projection of fuel demand for transportation sector in Jakarta BAUnon biodiesel Scenario Projection of emission BAU scenario The number of emission load can be estimated based on the type of fuel, yearly milleage and emission coefficient for each type of vehicle. The emission coefficient for gasoline and pure ADO was taken from the study performed by BPPT-KFA, 1992 [43], whereas the emission coefficient for ADO-biodiesel blends fuel was taken from the study performed by Wirawan et al. [29, 33]. Direct measurement of emission coefficient was only performed on passenger car [29], where the emission coefficient for the other type of vehicle ADO-biodiesel cases were assumed proportional to the ratio of passenger car ADO case. 42 The emission coefficient estimation determination method is shown in appendix 1 and the estimated emission coefficient result is shown on Table 11. The total emission for each type of emission as the calculation result is illustrated in Figure 23. CO is the main pollutant emitted from vehicles, followed by NO x and HC. Whereas the emission of SO 2 and PM Particulate Matter are relatively smaller but the effect to the human health is significant. Table 11. Emission coefficient for each type of vehicles Type of vehicle Type of fuel Emission Factor gkm CO NOx HC SO 2 PM Passenger car Gasoline 4.373 3.939 0.448 0.060 0.000 ADO 0.876 1.167 0.121 0.860 0.176 B10 0.831 1.107 0.106 0.774 0.108 B20 0.790 1.140 0.060 0.688 0.095 B30 0.710 1.080 0.050 0.602 0.090 B50 0.660 1.030 0.040 0.430 0.070 Big truck ADO 0.427 6.236 0.290 1.280 0.264 B10 0.405 5.916 0.255 1.152 0.162 B20 0.385 6.092 0.144 1.024 0.143 B30 0.346 5.771 0.120 0.896 0.135 B50 0.322 5.504 0.096 0.640 0.105 Big bus ADO 0.437 9.632 0.290 1.290 0.274 B10 0.415 9.137 0.255 1.161 0.168 B20 0.394 9.410 0.144 1.032 0.148 B30 0.354 8.914 0.120 0.903 0.140 B50 0.329 8.502 0.096 0.645 0.109 Small bus and truck Gasoline 6.601 4.927 0.786 0.090 0.000 ADO 0.905 1.371 0.465 1.020 0.235 B10 0.858 1.300 0.409 0.918 0.144 B20 0.816 1.339 0.230 0.816 0.127 B30 0.733 1.269 0.192 0.714 0.120 B50 0.682 1.210 0.154 0.510 0.093 Motor Cycle Gasoline 3.267 0.123 0.733 0.030 0.059 Sources: Wirawan et. al., 2008 [29], Wirawan et. al., 2005 [33], BPPT-KFA, 1992 [43] Figure 23. Projection of emission BAU scenario 200 400 600 800 1000 1200 1400 2000 2005 2010 2015 2020 2025 E m is s ion Thous a nd t on y r CO NOx HC SO2 PM 43 Projection of emission B10, B20, B30 and B50 scenarios The utilization of biodiesel is an effort to reduce the emission emitted from the vehicles. This study assumed that B10 would be completely used in Jakarta by the year 2010, B20 in 2015, B30 in 2020 and B50 in 2025. The projection of emission result shows that there are different value between B10, B20, B30 and B50 biodiesel case with BAU non-biodiesel case. More significant emission reduction showed by SO 2 and PM emission for 2010 and 2025 as can be seen on Table 12. Table 12. Comparison of emission value of BAU and biodiesel scenario Emission 1000 tonyr BAU B10 2010 B50 2025 B10 Decrease BAU B50 Decrease CO 591,64 590,71 0,16 1152,50 1145,06 0,65 NO x 243,80 241,80 0,82 517,05 509,48 1,47 HC 108,87 108,11 0,70 202,47 196,59 2,91 SO 2 26,09 24,01 7,95 46,68 30,01 35,69 PM 10,73 9,05 15,62 18,79 14,43 23,21 Generally, the result shows that the utilization of biodiesel will reduce the exhaust gas emission consistently with the increasing of biodiesel content in blending composition but depend on the characteristic each type of emission. Significant emission reduction occurred for SO 2 and PM emission. If all ADO in Jakarta substituted by B10 in 2010 scenario, the SO 2 will decrease around 7.90 2,070 ton and particle around 15.62 1,680 ton. If the B50 is used in 2025, the SO 2 emission will decrease around 35.69 16,660 ton and particle around 23.21 4,360 ton. As a comparison, Syahril et al. [4] concluded that the load of SO 2 and PM emission in 1998 are 5,774 ton and 6,156 ton respectively. The value of reduction coefficient emission because of biodiesel utilization used in this study is taken by a simple method based on the coefficient emission measured on passenger car. More measurements of coefficient emission on each type of vehicles small bustruck and big bustruck should be done specifically if results that are more accurate were desired. 44 Conclusion The study on the effect of biodiesel utilization on transportation sector emission in Jakarta has been performed and come up with the following conclusion: 1. Generally the result shows that the utilization of biodiesel will reduce the exhaust gas emission consistently with the increasing of biodiesel content in blending composition. 2. The rate of emission decrease depends on the characteristic of each type of emission. Significant emission reduction mainly showed by SO 2 and PM emission. If all ADO in Jakarta subtituted by B10 in 2010 scenario, will reduce the SO 2 around 7.90 2,070 ton and particle around 15.62 1,680 ton. When the B50 used in 2025 will reduce the emission of SO 2 around 35.69 16,660 ton and particle around 23.21 4,360 ton. As comparison, the load of SO 2 and PM emission in 1998 according to Syahril et al. 2002 [4] are 5,774 ton and 6,156 ton respectively.

CHAPTER IV BIODIESEL BLENDING SCENARIO