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With the aim of obtaining a useful tool for stakeholders to explore, assess and use the information related to CF and WF of food commodities, we implemented a multi-step methodological framework to create an easy to use CF and WF repository of food items, which can be expanded or modified for tailored requirements using a science based approach for each step of its creation (Fig. 1).The overall methodological procedure is made of 3 steps. Step 1 is related to CF and WF data collection from literature, eligibility check and harmonization, to create the base level of the database (level 1). Step 2 is about the creation of other three informative layers with higher level of data aggregation. These might be the data of direct interests for stakeholders of the food systems. A rigorous statistical approach is proposed to evaluate the quality of analysed data and criteria for the correct use of data, based on statistical evidence, are set and applied to the data. In Step 3 the complex set of statistical evaluations, done for each informative level, is summarized into an easy to use dataset reporting values of CF and WF of food items. Thanks to its multilevel approach, the database provides a flexible tool for different purposes and levels of expertise. Each step is based on transparent procedures that allow users to replicate, to implement and to modify each level of the database.The three steps are described in details in the following paragraphs.Step 1 – CF and WF data collection, harmonization and compilation of level 1 of SEL databaseThe first step was to review the published data of CF and WF of food commodities. We revised literature data published till January 2020 including peer-reviewed papers, conference proceedings, public reports or studies where methods of data collection and handling were described, and Environmental Product Declarations (EPDs).For the collection of CF data, a significant input came from the systematic review of Clune et al.11, who reviewed 369 published studies, covering the period 2000–2015, proving 168 varieties of fresh food products based on 1718 data entries. An additional source of studies reporting both CF and WF was the Double Pyramid database 2016 built on the previous version 201414 (BCFN2016 https://www.barillacfn.com/en/publications/double-pyramid-2016/), which reports 1202 CF values from 468 sources covering 240 food items and 309 WF values from 136 data sources covering 152 food items (reference period 1998–2016). Part of CF data of this latter dataset, up to year 2014, were already revised and included in the Clune et al.11 study. To avoid double counting from these two sources, data from both sources were checked for authorship, plus the CF reported data were compared and if in disagreement the original data were checked in the paper. Data reported in the Double Pyramid database 2016 but not present in Clune et al.11, mostly referring to processed food, were checked for eligibility applying the exclusion criteria reported in Table 2 and if considered eligible they were included in the present database.Table 2 Exclusion criteria to be applied to CF and WF data collected from literature to create SEL database level 1.Full size tableA new literature search was done to integrate data not covered by the previous reviews using three online bibliographic sources SCOPUS (https://www.scopus.com/home.uri), Google Scholar (https://scholar.google.com/) and the Google search engine (https://www.google.com/), which was concluded in January 2020. To search the bibliographic sources, we used the combinations of two sets of words. The first set referred to “impacts” and included the following words: carbon footprint, water footprint, virtual water, greenhouse gases, environmental impact, life cycle, LCA, LCI, EPD. The second set referred to “products” and included words like food, beverages, fish, shellfish, crops, vegetables, fruit, meat, eggs, dairy. EPDs were updated based on data reported on the International EPD’s System database (www.environdec.com). Added studies were evaluated for exclusion criteria (Table 2).The final list of data from single studies reported in the SEL database was distributed as follow: 3349 CF data, including 1397 data of fresh food commodities already reported in Clune et al.11, 803 CF data originally reported in Double Pyramid 2016 database, which were checked for eligibility and harmonized, and 701 CF data added with this study; 938 WF data, including 288 WF data originally reported in double pyramid 2016 and 650 WF data added with this study.All the CF and WF values extracted from the collected studies were assigned a group, a typology, a sub-typology when this applied, and an item name (Table 1) and were recorded on an excel sheet including the following additional information: type of bibliographic source, full reference, publication year, system boundary at distribution, country of production, region of production, relevant notes, presence of the same value in other data collections (i.e. Clune et al.11 or Double Pyramid 2016).After data collection, CF data where further analysed and handled for the harmonization of the system boundary following the approach as reported in Clune et al.11. The system boundary considered in the SEL database is the distribution centre to consumers located in the country of origin. It hence excludes post market phase like for example cooking. The system boundaries at distribution have a wide range of specifications in the published papers. We accepted regional distribution centre (RDC), international distribution centre (IDC), European distribution centre (EDC), country ports of final destination, warehouses, wholesalers, city markets, up to retailers. For the specific case of international transport, which includes also the emissions for shipping to regional distribution centres of the hosting country, rather than excluding the studies we have created a dedicated typology “imported”, which however includes very few studies. The imported commodity is indicated in the SEL database by a capital letter “I”.If CF values collected from literature referred to the system boundary “farm gate” or “slaughterhouse”, additional post farm gate GHG emissions were added as proposed by Clune et al.11. These additional emissions also included packaging if not reported in the publication. We adopted the median value for distribution to RDC (0,09 kg CO2/kg or kg CO2/L) and packaging (0,05 kg CO2/kg or kg CO2/L) used by Clune et al.11. Data referring to slaughterhouse emissions were also taken from the same publication.To address the share of WF for packaging and transportation to the market we analysed 256 EPD’s. No significant increase of WF in downstream stages associated to packaging and distribution was found. Thus we included in the analysis all system boundaries with the exception of ‘cooking’, human excretion and waste disposal.To transform CF values from carcass or live weight to bone free meat, ratios reported in in Clune et al.11 were used, while the ratio carcass weight to bone free meat for buffalo meat (1:0.684) was estimated from the studies of Gerber et al.15, Gurunathan et al.16, Li et al.17.The final version of CF and WF data, after data handling was recorded in a sheet where, in addition to the information mentioned above for each study, we also reported additional post farm gate emissions (transport T, slaughtering S, packaging P) or meat conversion factors (cf) when applying. This complete dataset represents the level 1 information sheet of the SEL database (Fig. 1).A change in 100-year global warming potential (GWP) factors provided by the International Panel on Climate Change reports AR3 (2001), AR4 (2007) and AR5 (2013) might have introduced additional variability in the studies of LCA on which CF data of level 1 are based. The extent of such variability is difficult to quantify as it depends on the relative weight of each GHG on the total CF of the item. However, the analysis of some item groups (tomato, rice, beef meat, chicken meat), used as sample test, did not show any clear trend of CF average reduction or increase over the years (1998–2020), suggesting that differences among production processes and conditions were the dominant source of CF variability.Step 2 – Creation of derived CF and WF datasets with higher aggregation level (2, 3 and 4)This step provides footprints of food commodities with a higher level of aggregation corresponding to food items, typologies and sub-typologies (Table 1), which might be of particular interest for different kinds of stakeholders. The item represents the higher detail of aggregated footprint data of a food commodity and it is often the most desirable information for food impact analysis and dietary assessments. We propose here a methodological framework to evaluate the uncertainty associated to data used to represent food items. The methodological framework will support the users in their choice of the optimal value to represent the food item on the basis of the available data present in the database. It also would easily allow for expansion and implementation of food item values.Level 2, SEL CF ITEM & SEL WF ITEM datasetsThese two datasets (CF and WF) report a comprehensive set of descriptive statistics for the list of food items present in the database. The population of data used to attribute a value and uncertainty to a food item is made of all the CF or WF values classified with that “item entry name” in the dataset of level 1 of SEL database.The item data population is described in level 2 by the following set of information.Size: number of studies used for the analysis of item population (n).Location and central-tendency measures: in terms of mean, median, first quartile (Q1) and third quartile (Q3), including also the minimum (Min) and maximum (Max) observed values.Variability measures: Standard Deviation (SD) Coefficient of Variation (CV) as absolute and relative dispersion indexes, the Interquartile Range (IQR) and the Median Absolute Deviation (MAD) as more robust indexes of variability.Shape measures: Skewness (SK), kurtosis (KU) indexes and Shapiro-Wilk normality test (SW test).The median of the item data population was chosen to assign a value of central tendency which represents the item. The median offers the advantage of not being influenced by the presence of outliers which misrepresent the value of the mean, making it a less meaningful measure. As such, the median represents the location estimator with the highest breakdown point (equal to 0.5) and with “the maximum proportion of observations that can be contaminated (i.e., set to infinity) without forcing the estimator to result in a “false” and not-representative value18,19. With these properties, the median also represents the most appropriate measure of central tendency to describe both positively and negatively skewed distributions20.To describe the uncertainty associated to the position value (median) we used descriptive statistic data relative to dispersion and shape of item data distribution. In particular, we used skewness and kurtosis indexes, which gave us information on the existence of symmetric or skewed distributions, as well as on their ‘peakedness’ measured as relative to the weights of the tails21, thus enabling us to evaluate (for each distribution) the importance of extreme values over the entire set of data and the related level of dispersion (platykurtic versus leptokurtic distributions). We completed the shape analysis by carrying out the Shapiro-Wilk test22,23 (4 ≤ n ≤ 2000).To define the uncertainty of the item value we created an assignment method based on a combination of the three quality flags (Fig. 2).Fig. 2Method for attribution of CF (or WF) value to a food item based on data quality flags. The scheme shows the procedure applied to evaluate the level of uncertainty associated to CF or WF value of a food item and how this information is used to decide the best value that should be used to represent the item. Three quality flags related to a statistical aspect of the data population are calculated to attribute the level of uncertainty. Each flag has different level of quality, red being the worst, green the best. Flags are then combined and expert judgement is used to associate a suggestion for data use to each flag combination. If the item median value is characterized by high uncertainty it poorly represents the item and caution is needed to use this data to represent the food commodity, the users is therefore redirected to a higher level of aggregation such as the sub-typology or the typology which includes the analysed item.Full size imageFlag 1, evaluation of the ‘size’ (n) of the “item data population”

Red if n  More

Performance evaluation of tractor engineThe performance of the direct-injection turbocharger diesel engine for the Kubota M-90 tractor was evaluated at different engine loads with the use of different biodiesel blends with mineral diesel to maximize the engine efficiencies of PTO torque, BP, BMEP, and BTE, while also minimizing specific fuel consumption, gas emissions, and, finally, fossil fuel consumption. The results in Table 1 showed a significant effect of engine load percentage and fuel blend percentage and their interaction on all the studied characters.Table 1 Effects of engine load percentage and fuel blends percentage on power take-off speed, power take-off power, power take-off torque, engine speed, brake power, brake specific fuel consumption, brake thermal efficiency, fuel consumption, brake mean effective pressure, O2 percentage, CO2 percentage, CO, NO, and SO2.Full size tableEngine speedFor the effects of engine load percentage on engine speed, the results in Table 1 indicated the relationship between engine load percentage and engine speed in rpm was inversely proportional. The maximum engine speed was recorded at a loading of 0%, and the lowest speed was at a loading of 100% (Table 1). Using 100% diesel fuel (B0) gave the highest engine speed among all treatments, and the lowest speed was recorded with 100% biodiesel fuel (B100). The significant interaction between engine load, percentage, and engine speed in rpm was inversely proportional, as shown in Fig. 2a. The maximum engine speed was 2854 rpm at the loading stage of 0% using 100% diesel fuel (B0), while the minimum speed was 276 rpm at the loading stage of 100% using 100% biodiesel fuel (B100), as shown in Table 2. At all loadings, stages with increased biodiesel percentages in the blended fuel samples resulted in decreased engine speed because the heating value of biodiesel is lower than that of mineral diesel32,33,34,35.Figure 2Effects of engine load on (a) engine speed, (b) PTO torque, (c) PTO speed on PTO torque, (d) engine load on brake power, (e) engine speed on brake power, (f) engine load on fuel consumption, (g) engine speed on fuel consumption, (h) engine load on (BSFC). (i) engine speed on (BSFC), (j) engine load on BMEP, (k) engine speed on BMEP, (l) engine speed on BTE and (m) engine load on BTE.Full size imageTable 2 Interaction effects between engine load percentage and fuel blends percentage on power take-off speed, power take-off power, power take-off torque, engine speed, brake power, brake specific fuel consumption, brake thermal efficiency, fuel consumption, brake mean effective pressure, O2 percentage, CO2 percentage, CO, NO, and SO2.Full size tablePTO torqueThe results presented in Table 1 showed the significant effect of load percentage on PTO torque, where a loading stage of 75% achieved the highest PTO torque among all loading stage percentages, and the lowest value for PTO torque was obtained with a loading stage of 0%. Regarding the effects of fuel blend percentage on PTO torque, the results in Table 1 indicated that the fuel blends significantly affected PTO torque, and the highest value of this trait was achieved with B0 blend (100% diesel fuel) in comparison to the other blend percentages, while the lowest PTO torque was given with 100% biodiesel. The relationship between the torque of PTO shaft, Nm, and PTO load in percentage, and speed in rpm are shown in Fig. 2b,c, respectively. Increased PTO load resulted in decreased PTO speed and increased PTO torque until maximum torque values were reached for all blended fuel samples at a loading stage of 75% and a speed between 316 and 332 rpm, and then the torque decreased incrementally until the maximum loading stage was reached at a minimum PTO speed. Table 2 presents the results of the interactions between engine load percentage and fuel blend percentage, indicating that the maximum PTO torque was 663 Nm at a loading stage of 75% and PTO speed of 332 rpm, using 100% diesel fuel (B0), and the minimum PTO torque was 98.51 Nm at loading stage of 0% and PTO speed of 699.19, rpm using 100% diesel fuel (B0). At all loading stages, increasing biodiesel percentage in the blended fuel samples resulted in decreased PTO torque, which, due to the heating value of biodiesel, was lower than that of diesel fuel34,35,36. The values for PTO torque were close at different biodiesel percentages at the loading stage 0%, but engine performance cannot be judged at the no load stage with minimum torque, so the PTO load should be increased to see the difference between fuel types.Engine brake powerData in Table 1 showed that engine load percentage significantly affected BP, kW, such that engine load of 50% achieved the highest BP, and the lowest value for BP was obtained with 100% load. The results given in Table 1 show that fuel blend percentage was significantly affected BP, whereas the highest value for this trait was achieved with the 0% blend (100% diesel fuel) in comparison to the other blend percentage, while the lowest BP was given with 100% biodiesel. The interactions among BP, engine load, and engine speed were significant and were as presented in Fig. 2d, e. Moreover, increased engine load resulted in decreased engine speed and increasing BP until the highest value was reached at the loading stage of (50%) at engine speeds of 2034–2137 rpm for all fuel types shown in Table 2, which was due to the increased mass of burning fuel. The BP decreased until engine stop at a maximum loading stage of 100%, which was due to the effects of higher frictional force at the maximum loading stage33,34,35,37. The maximum BP was 46.2 kW at a loading stage of 50% and a speed of 2137 rpm at 100% diesel fuel (B0), while the minimum BP was 5.82 kW at a maximum loading stage of 100% and a speed of 276 rpm using 100% biodiesel (B100). At all loading stages, increased biodiesel percentages resulted in decreased BP because the calorific value of biodiesel was lower than that of diesel, as noted.Fuel consumptionData in Table 1 showed that engine load percentage affected significantly fuel consumption; 50% load achieved the highest fuel consumption, and the lowest value for fuel consumption was obtained for 0% load. The results in Table 1 showed that fuel blends percentage significantly affected fuel consumption, and the highest value of fuel consumption was recorded with the B100 blend (100% biodiesel fuel), and the lowest was given with the B0 blend of 100% diesel fuel. The significant interaction between fuel consumption, kg/h (Kilogram per hour) and each of engine load and speed are shown in Fig. 2f,g and the interaction between engine load percentage and fuel blend percentage are shown in Table 2, such that increased engine load resulted in decreased engine speed and increased fuel consumption until reaching the maximum value at a loading stage of 50% at maximum BP, which was because of the increased mass of burning fuel at this stage, and then the fuel consumption decreasing until reaching maximum loading33,34,35. The maximum fuel consumption was 18.24 kg/h at an engine speed of 2034.35 rpm using 100% biodiesel B100 at a loading stage of 50%. The minimum fuel consumption was 9.76 kg/h at an engine speed of 2692.9 rpm using 100% biodiesel B100 at a no-load stage. At loading stages between 0 and 100%, increasing biodiesel percentage resulted in increased fuel consumption, which is because the density of biodiesel was higher than that of diesel fuel.Brake specific fuel consumptionThe results in Table 1 indicated that engine load percentage significantly affected BSFC, such that the highest BSFC was achieved with an engine load of 100%, while the lowest value was obtained with an engine load of 50%. Other results shown in Table 1 indicated that increased biodiesel percentage in fuel blends produced significantly increased BSFC, and the maximum value of BSFC was given with B100 (100% biodiesel fuel); the lowest was seen with B0 percentage (100% diesel fuel). The relationship of interaction between (BSFC), (Kilogram per kilowatt hour) kg/kWh, engine load, and engine speed are shown in Fig. 2h,i, indicating that increased engine load resulted in decreased engine speed and BSFC until the minimum value was reached at a loading stage of 50% at maximum BP and fuel consumption. Then, the BSFC increased until it reached maximum value at a maximum loading stage of 100%, which was due to the highest frictional force and the lowest BP occurring at this loading stage33,34,35. The maximum (BSFC) was 1.95 kg/kWh at a loading stage of 100% and an engine speed of 276 rpm using 100% biodiesel fuel (B100); the minimum BSFC was 0.32 kg/kWh at an engine speed of 2137 rpm and a loading stage of 50% using 100% diesel fuel (B0), as shown in Table 2. At all loading stages, increased biodiesel percentages resulted in increased BSFC, except at the no loading stage. This is because the fuel consumption for biodiesel was higher than that for mineral diesel. Additionally, the calorific value of biodiesel was lower than that for diesel fuel, and the viscosity of the biodiesel was higher than that for mineral diesel, which leads to unfavorable pumping and spray characteristics36,38.Brake mean effective pressureThe results in Table 1 indicated a significant effect of engine load percentage on BMEP, such that the highest BMEP was given by an engine load of 75%, and the other side the lowest value for BMEP was obtained for an engine load of 0%. The results given in the same table indicated that increased biodiesel percentage in fuel blends significantly decreased BMEP. The maximum value of BMEP was given with the 0 blend (100% diesel fuel), and the lowest BMEP was given with 100% biodiesel fuel. The interaction between BMEP, kPa, engine load, and engine speed are shown in Fig. 2j,k. The data in Table 2 show the interaction between engine load percentage and fuel blend percentage. It can be clearly seen that increased engine load resulted in decreased engine speed and increased BMEP until the maximum value was reached at a loading stage of 75% at engine speeds between 1293 and 1355 rpm. The BMEP decreased with slight values until reaching the maximum loading stage at minimum engine speeds between 276 and 288 rpm. The maximum BMEP was 625 kPa at an engine speed of 1355 rpm, using 100% diesel fuel (B0) at a loading stage of 75%. The minimum BMEP was 92 kPa at an engine speed of 2692 rpm, using 100% biodiesel (B100) at no loading stage. At all loading stages, increased biodiesel percentage resulted in decreased BMEP, except that there was no loading stage at which the BSFC did not change with different biodiesel percentages. This is because the effect of increased engine speed resulted in a decreased time remaining for combustion and resulted in an insufficient motion of air in the cylinder. Both effects decreased the combustion efficiency and the BMEP values, as shown in Fig. 2j according to33,34,35,39.Brake thermal efficiencyThe results shown in Table 1 cleared that engine load percentage significantly affected BTE; the highest BTE was recorded with a 50% load, and the lowest one was given with a 0% load percentage. Table 1 also indicated that fuel blend percentage significantly affected BTE, and the maximum value for BTE was given with 0 blend (100% diesel fuel). The lowest value was obtained with 100% biodiesel (B100). The relationship between BTE and engine load and engine speed are shown in Figs. 2l,m. Increased engine load caused decreased engine speed and increased BTE until the maximum value was reached at a loading stage of 50%; BTE decreased until a minimum value was reached at a maximum loading stage of 100% and minimum engine speeds between 276 and 288 rpm. The maximum BTE was 26% at a speed of 2137.17 rpm using 100% diesel fuel (B0) at loading stage of 50%. The minimum BTE was 4.4% at speed of 276 rpm, using 100% biodiesel (B100) at the maximum loading stage of 100%, as shown in Table 2. For all loadings stages increased biodiesel percentage resulted in decreased BTE, except at the no loading and maximum loading stages, where the BTE did not change with different biodiesel percentages. This is because the density of waste frying oil biodiesel was higher than that of diesel fuel, while its calorific value and volatility was lower, such that the combustion characteristics of biodiesel were lower than those of diesel fuel34,35,36,40.Gas emissions qualityThe results in Table 1 showed that an engine load of 0% significantly increased O2 emissions, and fuel blends of 100% biodiesel also increased O2 emissions relative to the other treatments. The relationships between O2 emissions, biodiesel percentage, engine load, and engine speed are shown in Fig. 3a,b. Increased engine load resulted in decreased O2 emissions because of the increased engine consumption of O2 to optimize fuel combustion, while increased engine speed resulted in increased O2 emissions. The maximum O2 emissions were 15.3% at the minimum loading stage for all fuel blends, while the minimum O2 emissions were 4.3% at maximum loading stage for 100% diesel fuel, as presented in Table 2. At all loading stages, increased biodiesel percentage in the blended fuel samples resulted in increased O2 emissions, except at the no loading stage, where the oxygen content in the biodiesel was about 10 to 12% higher than that of diesel fuel34,35,41,42.Figure 3Effects of engine load on (a) engine load on O2 emissions, (b) engine speed on O2 emissions, (c) engine load on CO2 emissions, (d) engine speed on CO2 emissions, (e) engine load on CO emissions, (f) engine speed on CO emissions, (g) engine load on NO emissions, (h) engine speed on NO emissions, (i) engine load on SO2 emissions and (j) engine speed on SO2 emissions.Full size imageThe results in Table 1 showed that engine loading of 100% significantly increased CO2 and CO emissions, and the fuel blend of 100% diesel fuel (B0) increased CO2 and CO emissions relative to other treatments. The relationship between CO2 emissions, engine load, and engine speed are presented in Fig. 3c,d and Table 2. Increased engine load resulted in increased CO2 emissions until a maximum loading stage of 100% was reached, while increased engine speed resulted in decreased CO2 emissions. The maximum value for CO2 emissions was 12.3% at the maximum loading stage using 100% diesel (B0), and the minimum CO2 emissions was 4.2% at the no loading stage for all fuel blends. At loading stages of 50, 75, and 100%, increased biodiesel percentage in the blended fuel samples resulted in decreased CO2 emissions, which due to the oxygen content in the biodiesel was about 10–12%. A higher oxygen content contributes to increasing ignition quality and decrease CO2 emissions35,36,41.The relationship between CO emissions, biodiesel percentage, engine load, and engine speed are shown in Fig. 3e,f. For all tested fuel samples, increased engine load resulted in a greater increase in CO emissions, until a maximum load was reached except at 100% biodiesel (B100), which increased slightly. Increased engine speed resulted in a sharp decrease in CO emissions until the maximum speed was reached, except at B100, which decreased slightly43,44,45. The maximum CO emissions value was 369 ppm at the maximum loading stage using 100% diesel fuel (B0), while the minimum CO emissions was 69 ppm at the minimum loading stage using 100% biodiesel fuel (B100). At all loading stages, increased biodiesel percentage resulted in decreased CO emissions except that at loading stages of 25% and 50%, for which the values of CO emissions were close. This was because high oxygen content in biodiesel increases ignition quality and decreases CO emissions, so increased biodiesel percentages reduce environmental pollution36,43,44,45,46. The results in Table 2 showed that an engine load of 75% significantly increased NO emissions, and 100% biodiesel fuel (B100) increased NO emissions relative to the other treatments.The relationship between NO emissions, engine load, and engine speed are shown in Fig. 3g,h. Increased engine load resulted in decreased engine speed and increased NO emissions until the maximum value was reached at a loading stage of 75%. NO emissions decreased until reach a loading stage of 100% was reached with a minimum engine speed. The maximum NO emissions were 593 ppm at a loading stage of 75% using 100% biodiesel fuel (B100), while the minimum NO emissions were 266 ppm at the minimum loading stage using (B100) as showed in Table 1. At all loading stages, increased biodiesel percentages in the blended fuel samples resulted in increased NO emissions, except at the no loading stage, which was due to the increased burned fuel, which resulted in increased cylinder temperature. This was responsible for thermal NOx formation. Higher flame and cylinder temperatures with high oxygen content in the biodiesel led to higher NOx36,43,44,45,46. Table 2 shows that the engine load of 100% significantly increased SO2 emissions, and 100% diesel fuel increased SO2 emissions, relative to the other treatments.The relationship between SO2 emissions, diesel percentage, engine load, and engine speed are shown in Fig. 3I,j and Table 1. Increased engine load resulted in increased SO2 emissions, and increased engine speed resulted in decreased SO2 emissions. There were no SO2 emissions by using 100% biodiesel (B100). The maximum SO2 emissions was 21 ppm at maximum loading stage using 100% diesel (B0). At all loading stages increasing biodiesel percentage in the blended fuel resulted in decreasing SO2 emissions43,44,45,46. More

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