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Article

Well-to-Wheels Approach for the Environmental Impact Assessment of Road Freight Services

1
CIRCE Institute, University of Zaragoza, Campus Río Ebro, 50018 Zaragoza, Spain
2
Department of Mechanical Engineering, CIRCE Institute, University of Zaragoza, 50018 Zaragoza, Spain
3
Faculty of Human Ecology, Universiti Putra Malaysia, Serdang 43400, Malaysia
*
Author to whom correspondence should be addressed.
Sustainability 2018, 10(12), 4487; https://doi.org/10.3390/su10124487
Received: 23 October 2018 / Revised: 15 November 2018 / Accepted: 24 November 2018 / Published: 28 November 2018
(This article belongs to the Special Issue Sustainable Road Transportation Planning)

Abstract

:
The diffuse nature of road transport and the heterogeneity of heavy vehicles have hindered the implementation of emissions accounting systems. Even though there are emission factors in well-known databases, these factors have commonly been designed in industrialized countries, which might have geography, type of roads, and operating conditions different to other countries. This paper proposes a method for the energy consumption and emissions estimation based on vehicle operating conditions in regions with different topology, such as Colombia, Malaysia, and Spain, as case studies. Moreover, the environmental impacts of fuel production in each country are calculated. The diesel consumption on mountainous roads for a full loaded rigid truck in Colombia was 45 L/100 km, compared to averages between 22–26 L/100 km from other sources usually applied. In contrast, the diesel consumption for an articulated truck on a hilly road in Spain from both the proposed method and generic databases coincided in 31 L/100 km. The vehicle speed, load, and road gradient also generated large variations up to 145% in the air pollutants’ estimation. This study contributes to the need for more research about emission factors and tools that facilitate and reduce uncertainty in the environmental accounting in freight companies in different geographies.

1. Introduction

Despite the fact that sustainable development has been considered a topic of interest since the 1980s [1,2], the transport sector has been one of the last sectors to develop relevant initiatives aimed at optimizing its operations from the environmental perspective. The road freight sector has been more concerned with minimizing the time and costs of its activity with the application of vehicle routing models since the 1950s [3], or developing their own mathematical algorithms for the optimization of their activity based on the specific variables on the types of products, clients, type of fleet, geographical situation, etc. [4]. These improvements have led, without it being its main objective, to fuel savings per ton-kilometer (tkm) transported and reductions of their environmental impacts. However, taking Europe as an example, while all sectors reduced a quarter of their greenhouse gas (GHG) emissions between 1990 and 2009, the transport sector increased almost a third of its emissions in the same period [5]. This was mainly due to the increase in the domestic truck traffic, which despite representing only 3% of the vehicle fleet in the European Union (EU), produces almost a quarter of the total CO2 emissions generated by the road transport sector [6]. The CO2 emissions produced by road freight increased by 36% between 1990 and 2010 [7]. This increase stopped in 2010 due to the European economic crisis that began between 2008 and 2009. However, some projections have suggested that under this perspective, without the intervention of governments, GHG emissions produced by trucks will be, with respect to the 1990 levels, around 35% higher for the years, 2030 and 2050 [8].
The energy consumption and GHG emissions of the sector are not being reduced as expected, mainly due to the difficulties in implementing energy efficiency measures in the road freight subsector and the difficulty of finding alternatives to diesel fuel [9]. In contrast to the industrial sector where real-time emissions can be easily measured and thus different emission regulation and trading mechanisms can be established [10], in the transport sector, due to its diffuse nature, the establishment of these mechanisms has been hampered. Policy planning for traffic emissions has commonly consisted of sub-national or municipal strategies, such as the implementation of low-emission zones or the improvement of traffic flow to reduce the concentration of air pollutants in specific areas, but these types of measures, instead of reducing them, are dispersing these air pollutants over a bigger geographical area [11]. Measures to effectively reduce the environmental impacts in this sector have consisted in limiting, through regulations, the emissions of air pollutants, such as carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOC), and particulate matter (PM), of newly manufactured vehicles prior to entering the market [12]. The existing regulations for the control of air polluting emissions around the world are based almost generally on the European standards [13], US Environmental Protection Agency (EPA) [14], or Japanese regulations [15], from which most countries directly take the established limits for the specific standard or reference the limitation values of different standards [16,17]. Truck manufacturers have concentrated efforts into meeting these demanding standards by modifying engines and installing devices for the post-treatment of exhaust gases, such as exhaust gas recirculation valves and particulate filters, in order to reduce NOx and PM emissions, respectively. However, these efforts have negatively affected the fuel efficiency [18,19,20]. In recent years, some manufacturers have shown technological improvements to meet the Euro VI standards by using selective catalytic reduction systems that use urea to reduce NOx emissions without significantly affecting the fuel consumption, obtaining a diesel consumption of up to 28 L/100 km for articulated trucks under optimal road and driving conditions during efficiency tests [21,22].
Although countries, such as Japan, the United States, Canada, and China [8,23], have incorporated legislative measures for the control of CO2 emissions, the EU has yet to establish limits for these emissions from road freight heavy vehicles. In the EU, among the first initiatives to tackle GHG emissions was the Communication 2014/285 [8] “Strategy to reduce fuel consumption and CO2 emissions in heavy vehicles”. In addition, the series of documents that accompanied this communication highlighted the absence of GHG control measures as well as common and internationally recognized standards for their measurement. Recently, a surveillance system for heavy vehicles in the EU was proposed through a regulation that is expected to be published in 2019, which requires manufacturers to provide accurate data on the CO2 emissions of their new trucks in order to collect information that will allow the future establishment of limits to GHG truck emissions in a suitable and uniform way [23]. Despite the absence of measures to control CO2 emissions during vehicle operation, the transport sector, particularly, freight transport, has voluntarily adopted standards to account for these emissions and energy consumption. Freight companies have been prompted by other sectors that have demanded a reduction in their contribution to the carbon footprint of their products, goods, or services. In Europe, the carbon footprint calculation has become popular since the entry into force of Directive 2003/87/EC [24], which requires mandatory GHG reporting by companies in the energy and industrial subsectors with the highest energy use. Companies in other diffuse sectors, who were not included until 2009 in the Emissions Trading Scheme [25], began to take an interest in their carbon footprint due to the benefits that it could bring to their organization and their products, such as higher market value, an increase in brand value, corporate image improvement, reduced insurance fees as well as an improvement in their credit ratings [26]. Among these initiatives for fuel consumption and emissions accounting, they can be referenced to the related general tools for life cycle analysis, such as SimaPro [27], GaBi [28], Gemis [29]; specific tools for the transport sector include Tremove [30], COPERT [31], MOVES [32], GREET [33], the decarbonization prediction model [34], EcoTransIT [35], the GHG Protocol tool for mobile combustion [36], the World Ports Climate Initiative (WPCI) Carbon Footprinting Calculator [37], the Third Party Road Freight CO2 emissions pilot model [38], the Network for Transport Measures (NTM) basic Freight Calculator [39], Planung Transport Verkehr (PTV) Map & Guide [40], Logistics Emissions Calculator (LogEC) [41], Versit+ [42]; and databases, reports, or methodologies, such as the European standard (EN) 16258 [43], the GHG Protocol Corporate Standard [44], the European Association for Forwarding, Transport, Logistics and Customs Services (CLECAT) guide [45], Odette [46], Panteia and Duoinlog [47], the Intergovernmental Panel on Climate Change (IPCC) [48], Ecoinvent [49], IDEMAT [50], the Handbook of Emission Factors for Road Transport (HBEFA) [51], Lipasto [52], the GHG Reporting Database [53], the Department for Environment, Food and Rural Affairs (DEFRA) GHG conversion factors [54], the European Monitoring and Evaluation Programme and the Environmental European Agency (EMEP/EEA) emission inventory guidebook [55], the Joint Research Centre (JRC) technical reports [56], and the Netherlands Organisation for applied scientific research (TNO) reports [57].
Most of the aforementioned tools are currently based on the standard EN-16258 [43] “Methodology for the calculation and declaration of energy consumption and greenhouse gases emissions in transport services (transport of goods and passengers)”. This standard is the first international standard to harmonize and standardize the procedures for the calculation and reporting of emissions and energy for the transport sector. This standard has been fully accepted among the European transport companies [58] and represents a possible basis for future international standardization initiatives [59]. The EN-16258 [43] focuses the reporting of energy consumption and GHG emissions on the fuel life cycle analysis (Well-to-Wheels, WTW). The WTW analysis includes the generated impacts by the energy use in the vehicle (Tank-to-Wheels, TTW), plus the impacts of the extraction of the raw materials, transportation, transformation, and distribution of the fuel to the service station (Well-to-Tank, WTT). However, limiting the emissions accounting just to the GHGs narrows the analysis only to the climate change impact, leaving aside other environmental impacts associated with road freight services. Although accounting the energy consumption during the vehicle operation can calculate the fuel-dependent pollutants other than CO2, such as emissions of sulfur dioxide (SO2) and heavy metals contained in the fuel, the emissions of CO, NOx, methane (CH4), nitrous oxide (N2O), ammonia (NH3), PM, and non-methane volatile organic compounds (NMVOC) do not depend proportionally on fuel consumption, but on other factors, such as the emission control technology and operating conditions. The emissions estimation through the mentioned tools for road transport commonly base the results on factors that only consider operation parameters, such as vehicle size, emission control technology, and some also look at the load factor, except for tools [31,32] that consider other important factors, such as the vehicle speed and the road gradients. However, the main limitation using even the most comprehensive available tools is the impossibility of considering the variations in speed and road gradients in the different slopes during a specific freight service. Therefore, it is necessary to use emission factors and a methodology that provides accurate results for each assessed case. Until there is a procedure for measuring pollutant emissions for diffuse sectors, its estimation is the only way to establish the objectives for its reduction and measure its degree of achievement. The emission factors and the fuel consumption calculation models that are incorporated in the different tools must be susceptible to improvements and updates based on experimentally obtained information.
This work aimed to advance research along that line. On the one hand, a method was proposed for the calculation of fuel consumption and TTW emissions during a road freight service. The method was based on the proposed procedure in the EMEP/EEA air pollutant emission inventory guidebook of 2016 [55] and incorporated variables related to the characteristics of the route and the service itself that were identified from the study of three experiments. These adjustments allowed us to estimate more accurately fuel consumption to the actual measured figures, and consequently reduce the error of the proportional emissions. On the other hand, the emission factors were adjusted based on the inventory analysis for fuels other than 100% diesel fossil fuel, which affects the reliability of the estimation of the WTW emissions [60]. From the results of the emission inventories for the WTT and TTW phases obtained with the new calculation method, an environmental impact assessment was performed for the three cases under study through the ReCiPe 2016 [61] method with the tool for life cycle analysis SimaPro 8.5.0 [27] and the Ecoinvent v3.4 [49] database. Given that the production of fuels can generate impacts in categories other than those mainly affected by emissions from the operation of the vehicles, such as climate change, acidification, eutrophication, ozone depletion, and energy consumption [62,63], the 18 impact categories of the assessment method were considered.

2. Materials and Methods

In 2017, the EEA published the “EMEP/EEA air pollutant emission inventory guidebook 2016” [55], which details the calculation methods and factors for fuel consumption and emissions with three levels of precision (tiers) for the vehicle operation phase or TTW analysis. These emission factors compiled in the EMEP/EEA [55] guide were based on previous initiatives, such as Artemis [64], Corinair [65], the Methodologies to Estimate Emissions from Transport (MEET) project [66], and HBEFA emission factors [51].
The calculation model proposed by EMEP/EEA [55] offers three approaches with different consumption and emission factors, depending on the data availability. Tier 1 provides emission factors based on the amount (kg) of fuel; Tier 2 provides emissions and fuel consumption factors per km traveled for different vehicle types and emission control technologies; and Tier 3 establishes a series of equations and factors to incorporate, in addition to the variables included in the Tier 2 method, others, such as the speed, the load factor, and the road gradient.
It should be noted that the Tier 2 and Tier 3 emission factors available for freight vehicles are only for diesel and gasoline engines, considering the correction factors for biodiesel and bioethanol mixtures, respectively. The considered emissions by the EMEP/EEA [55] guide can be classified into four groups:
  • Group 1: Pollutants not directly dependent on fuel consumption, such as CO, NOx, CH4, N2O, NH3, NMVOC, and PM2.5.
  • Group 2: Estimated emissions based on the fuel, lube oil, and urea consumption, such as CO2, SO2, and heavy metals.
  • Group 3: Emissions of polycyclic aromatic hydrocarbons (PAHs), persistent organic pollutants (POPs), and dioxins and furans.
  • Group 4: NMVOC emissions, such as alkanes, cycloalkanes, alkenes, alkynes, aldehydes, and aromatics, calculated as a fraction of the total NMVOC.
In addition to these pollutants derived from fuel, lube oil, and urea consumption, emissions from tires, brakes, and road surface abrasion were considered.
The proposed method for the energy consumption and emissions estimation for a specific road freight service basically seeks to individually analyze different sections of the route considering changes in the operating parameters, such as average speed and road gradient, as determined through the Google Earth Pro software [67]. The accounting of emissions was elaborated based on the Tier 3 factors, equations, and coefficients from the EMEP/EEA [55] guide.
The calculation of each of these pollutants was performed for each section of the route, which was divided considering significant changes in the circulation area (urban or interurban), number of lanes, traffic density, average speed, topology, or trend in the road gradient. The energy consumption, emission factors, and calculation equations for each group of pollutants are detailed in the following subsections.

2.1. Pollutants of Group 1

For the estimation of CO, NOx, VOC, and PM2.5 emissions and energy consumption, the Tier 3 method was used. For the CH4, N2O, and NH3 emissions, the Tier 2 emission factors were used, which are available by vehicle type, control emissions technology, and type of road circulation.
The Tier 3 method provides the coefficients for each type of vehicle and the emission control technology (conventional, Euro I–VI), load factor (0%, 50%, and 100%), and the road gradient (0%, ±2%, ±4%, and ±6%), based on the speed in each route section to be applied in Equation (1) [55]:
E C   o r   E F = α × V 2 + β × V + γ + δ V ε × V 2 + ζ × V + η × ( 1 R F )
where EC is the Energy Consumption (MJ/km); EF is the emission factor of CO, PM2.5, NOx, and VOC (g/km); V is the vehicle speed (km/h); and RF a reduction factor. Coefficients (α, β, γ, δ, ε, ζ, η) and the RF are obtained from the 1.A.3.b.i-iv Road transport hot EFs Annex 2017, attached in the EMEP/EEA report [55].
Based on the obtained results using the EMEP/EEA Equation (1) and the coefficients for 0% and 100% load factors, it is possible to calculate the energy consumption or emission factor for a partial load using the Equation (2), where the specific load factor, LF, is from 0 to 1.
( E C   o r   E F ) L F = ( ( E C   o r   E F ) e m p t y + ( E C   o r   E F ) f u l l ( E C   o r   E F ) e m p t y ) × L F
For the estimation of CH4, N2O, NO2, and NH3 gases, the emission factors per km were directly applied from Table A1, Table A2, Table A3 and Table A4. For the effect of biodiesel blends on emissions, the average emissions of CO, PM2.5, NOx, and VOC were determined by the application of the variation rates presented in Table A5, which are only recommended for Euro III or earlier vehicles, since for more modern vehicles, the variation is more difficult to predict due to the implementation of exhaust gas treatment systems [55].

2.2. Pollutants of Group 2

2.2.1. Pollutants from fuel consumption

For the estimation of CO2 and SO2 emissions, the fuel characteristics were considered, such as the content of sulfur (s), carbon (c), hydrogen (h), oxygen (o), and nitrogen (n). From these characteristics in mass terms, for the CO2 calculation, Equations (3)–(5) were applied [55]:
r H : C = 11.916 h c
r O : C = 0.7507 o c
E C O 2 = 44011 × E C m 12011 + 1008   r H : C + 16000   r O : C
where E C O 2 are the CO2 emissions (kg) and E C m is the energy consumption (kg). If biofuel blends are used, the calculation of CO2 emissions should be made by only considering the fraction of fossil diesel.
For the SO2 calculation, Equations (6) and (7) were applied [55]:
S m = s × 10 6 .
where s is the sulfur content in parts per million (ppm).
E S O 2 = 2 × S m × E C m
where E S O 2 are the SO2 emissions (g), and S m is the sulfur content (g/g).
The emission factors of heavy metals in ppm contained in the consumed fuel by heavy vehicles are presented in Table A6.

2.2.2. Pollutants from lube oil consumption

The consumption of engine lube oil for heavy diesel trucks is on average 1.56 kg/10,000 km with an average CO2 emissions of 0.486 g/km [55]. From this consumption, the content of heavy metals in the lube oil used by heavy vehicles are presented in Table A6.

2.2.3. Pollutants from urea consumption

In Euro V and Euro VI heavy-duty diesel engines, urea is used as a catalyst to reduce NOx emissions. The urea consumption generates CO2 emissions, whose average emission factor is 0.26 kg CO2/L or 0.238 kg CO2/kg of urea solution [55]. The urea solution has an urea content of 32.5% [68]. If the amount of consumed urea is unknown, this consumption is assumed to be 6% or 3.5% of the diesel consumption in Euro V or Euro VI heavy vehicles, respectively [55].

2.3. Other Pollutants

For the estimation of pollutants included in Group 3, the emission factors for diesel heavy vehicles for PAHs and POPs are presented in Table A7, and those for the polychlorinated dibenzo dioxins (PCDDs), polychlorinated dibenzo furans (PCDFs), and polychlorinated biphenyls (PCBs) are presented in Table A8.
For the pollutants included in Group 4, the fractions of alkanes, cycloalkanes, alkenes, alkynes, aldehydes, and aromatics, corresponding to 96.71% of the total NMVOC, are presented in Table A9. The residual amount, that is, 3.29% of the total NMVOC, were considered HAPs.
The abrasion of brakes, tires, and the road surface generates particulate matter (PM>10, PM2.5–10, and PM<2.5) that contains metal and non-metal particles that are released into the air, water, and soil. The emission of particles from tire and brake abrasion was calculated based on the weight, number of axes, and speed in each section of the route; hence, this method was considered as Tier 2+.
For the calculation of the total particulate matter (TPM) of the tire abrasion of heavy vehicles, Equations (8) and (9) were applied [55]:
L C F N = 1.41 + ( 1.38 × L F )
T P M N = N a x e s 2 × L C F N × 0.0107 × S C F N
where L F C N is the load correction factor for tire abrasion; N a x e s is the number of axes; T P M N is the total PM emission of tire abrasion (g/km); and S C F N is the speed correction factor for tire abrasion. If V < 40 km/h, then S C F N = 1.39 ; if 40 km/h ≤ V ≤ 90 km/h, then S C F N = ( 0.00974 × V ) + 1.78 ; and if V > 90 km/h, then S C F N = 0.902 .
For the calculation of the TPM of brake abrasion in heavy vehicles, Equations (10) and (11) were applied [55]:
L C F F = 1 + ( 0.79 × L F )
T P M F = 3.13 × L C F F × 0.0075 × S C F F
where L C F F is the load correction factor for brake abrasion; T P M F is the total PM emissions of brake abrasion (g/km); and S C F F is the speed (V) correction factor for brake abrasion. If V < 40 km/h, then S C F F = 1.67 ; if 40 km/h ≤ V ≤ 95 km/h, then S C F F = ( 0.0270 × V ) + 2.75 ; and if V > 95 km/h, then S C F F = 0.185 .
For the calculation of the TPM from road surface abrasion by heavy vehicles, an average factor of 0.076 g/km was used [55]. From the TPM emissions calculated for each origin, it is necessary to specify what type of particles are contained in this total by the fractions shown in Table A10. Additionally, in the TPM emissions of tires and brakes, there is a content of different metal and non-metal elements and PAHs, which are presented in Table A11. Of the TPM produced by brake abrasion, 100% is released into the air, while of the total produced by tire abrasion, 14% goes into air, 43% into water, and 43% into soil [27,49].

3. Results

In order to analyze the opportunities opened by the previous method for its adjustment as well as to identify variables related to the characteristics of the route and the service to be incorporated that would improve the reliability of the estimations, information collected during three freight transport services was applied. These experiences took place at three different times throughout 2017 in Colombia, Malaysia, and Spain for different types of vehicles, fuels, roads, and operating conditions.

3.1. Tank-to-Wheels Analysis for Case Studies

The analyzed service in Colombia consisted in the transport of 10 t of goods for a one-way trip from the city of Pereira to Quibdo in a rigid truck with a pre-Euro standard and gross vehicle weight (GVW) of 16 t. The complete route consisted of 260 km and was completed in approximately 8.6 h. The actual consumption of B10 diesel showed an average value of 58 L/100 km. The analyzed service in Malaysia transported 2 t of goods, considering the 774-km round trip between Kuala Lumpur and Kulim on practically flat terrain at sea level. In this case, the vehicle was a 16-t rigid truck with a Euro I standard transporting a load of 2.5 t in an average time of 6.4 h. The load consisted of 2 t of low density goods and 0.5 t corresponding to metal racks that return empty to Kuala Lumpur. The actual consumption of B7 diesel showed an average of 26 L/100 km. The analyzed freight service in Spain was performed between the cities of Zaragoza and Almusafes, mainly by highway on hilly terrain of a 694 km round trip. A load of 15 t was transported on an articulated truck with a gross weight of 40 t with Euro VI technology. The cargo consisted of 10 t of goods and 5 t of metal racks, which returned empty to Zaragoza. The approximate time for the journey was 4.5 h. From the refueled liters of diesel, an average consumption of 31 L/100 km was calculated for the round trip.
The estimated energy consumption by Tier 2 factors from the EMEP/EEA [55] guide were, for the Colombia case, taking an average of 9.3 MJ/km for the assessed 16 t rigid truck, was 25.7 L/100 km using B10 diesel (density of 0.858 kg/L and energy content of 41.86 MJ/kg [69]). This estimated figure was below half the actual consumption reported by the company. For the Malaysia case, a consumption of 21.9 L/100 km was estimated using B7 diesel (density 0.8314 kg/L and energy content 42.94 MJ/kg [70,71]). This estimated figure was not very far from the actual consumption since the journey was performed by motorways in slightly rugged terrain. For the Spanish case, according to the Tier 2 factors, trucks with GVW > 32 t and Euro I–VI technology consumed on average 10.72 MJ/km. In this sense, considering the use of B5 diesel (density 0.839 kg/L and energy content 42.63 MJ/kg [72,73]), an average consumption of 29.9 L/100 km was obtained, which was close to the average consumption figure reported by the company.
Figure 1 shows the elevation profiles for each analyzed route. The journeys were performed on roads with many ascending and descending slopes and different average speeds, the main reasons for the uncertainty in the estimated figures through the Tier 2 factors, which only consider the type of vehicle and the emissions control technology. The methods found in the literature reported independent equations or factors for ascending sections and other equations for descending sections, thus to apply them only to the gradient between the origin and the end was considered, resulting in the analysis of a route with a constant gradient, that is, a non-rugged road, therefore underestimating the actual consumption. This was verified by estimating the fuel consumption through the Tier 3 method for the whole routes. For the Colombian case, according to the elevation profile in Figure 1, the average gradients for the ascending and descending sections were 3.2% and −3.3%, respectively, thus the Tier 3 coefficients for 4% and −4% gradients were applied in Equation (1). Then, considering an average speed of 30 km/h (260 km in 8.6 h), a fuel consumption of 27.01 MJ/km for ascending sections and 3.80 MJ/km for descending sections were estimated. According to the meters of increase and loss of elevation of the route, it was obtained that the truck was ascending for 44% of the 260 km and descending for 56%. Hence, an average fuel consumption of 39.2 L/100 km was obtained; a figure closer to the actual consumption than that calculated with the Tier 2 factors. For the Malaysia case, the average speed of 60 km/h and the coefficients for gradients of 0% were used for Equation (1) as the average gradients of the route were 0.8% and −0.8%. From Equation (1), the fully loaded truck would consume 8.22 MJ/km and an empty truck would use 6.46 MJ/km. From Equation (2), for partial loads, it was obtained that the energy consumption for a load factor of 25% was 6.90 MJ/km. Therefore, the average diesel consumption would be 19.3 L/100 km, lower than that obtained through the Tier 2 factors. For the case in Spain, the calculation for the whole round trip was performed considering an average speed of 77.3 km/h and gradients of ±2%, given that the average gradients were 1.1% and −1.4%. The results for the energy consumption were calculated for the load factor of 60% for the outward and 20% for the return journeys with Equation (2), obtaining fuel consumptions of 34.1 L/100 km and 26.9 L/100 km, respectively. For the round trip, the average consumption would be 30.5 L/100 km; a close figure, but slightly lower than the actual average consumption.
The estimated fuel consumption figures by the Tier 3 factors for the whole routes in each studied case were closer to the actual consumptions than the estimated figures by the Tier 2 factors. However, there was still uncertainty because of the omission of high gradients in some sections of the routes, especially in the mountainous route in Colombia. Additionally, despite the almost flat route with an average gradient of ±0.8% in Malaysia, this route had some mountainous sections, with one 5 km section with an average gradient of 5% from 228 km until the entrance of a tunnel where an altitude of 341 m above sea level (m.a.s.l) was reached. In the elevation profile of the Colombia route, it was found that particularly between 41 km and 130 km, there were greater gradients than 6%; there was even a 12 km section with an average gradient of 13.3% and another 31 km section with an average gradient of 8.5%.
Therefore, to increase the accuracy of the estimations, it would be necessary to apply the Tier 3 equations for each one of the hundreds of slopes, thus in a simplified way, where each route was divided into sections with similar characteristics. For the Colombian case, the route was divided into 33 sections, most of them because of the presence of 16 small towns along the way and the absence of variant or bypass roads and the changes in the slope gradients (Table A12). Similarly, for the Malaysian case, the route was divided into 33 sections for each journey, mainly due to changes in the slope gradients (Table A13). For the Spanish case, as the speed during the route had many variations given the hilly terrain, the route was divided into 29 sections for each journey (Table A14).
As the speed depends on factors, such as the length of the section with a certain gradient, the deflection, radius, and frequency of the curves, the rolling surface, the percentages of non-overflow zones, congestion, and climatic factors, among others, it was necessary to adopt an average speed for each section. According to the manual of capacity and level of service of the National Institute of Roads of Colombia (INVIAS) [74] and the HCM-2000 (highway capacity manual) [75], the road type for most of the analyzed route in Colombia (with only one lane for each direction) would have a service level E, the narrowest unpaved stretches would have a service level F, while the motorways a service level B [76]. The speed used for estimating the consumption and emissions was the value corrected by a factor obtained from the total theoretical time of the route compared to the average real time. Based on the calculations with the data from Table A12, an average speed of 36.4 km/h was obtained for the interurban route. The average and single speeds in each section coincided in most cases with those established for the service levels defined by INVIAS [74], which indicates that for a type E road in a hilly terrain, the speed would be between 34 and 43 km/h, and between 26 and 33 km/h in mountainous terrain.
The application of the Tier 3 method by sections for the route in Colombia obtained an average fuel consumption of 44.7 L/100 km. For the Malaysian case, fuel consumptions of 22.14 L/100 km for the outward journey and of 21 L/100 km for the return journey were obtained, with an average for both journeys of 21.6 L/100 km. For the Spanish case, fuel consumptions of 35.7 L/100 km for the outward journey and of 26.6 L/100 km for the return journey were obtained. The average consumption for the round trip was 31.2 L/100 km.
Given that the estimated fuel consumption by the proposed method was more reliable than the figures calculated by other sources and by the Tier 2 and Tier 3 methods for complete journeys, the Tier 3 method by sections was used to estimate the regulated gas emissions (Group 1 pollutants) and emissions from brake and tire abrasion. Likewise, the dependent emissions on fuel consumption (Group 2 pollutants) were calculated from the estimated fuel consumption. The results of the inventory of the vehicles’ operation, calculated for the 33 sections of the Colombian route, the 66 sections of the Malaysian route, and the 58 sections of the Spanish route, are represented in Table 1, Table 2, Table 3 and Table 4.
The urea consumption in the Euro VI truck generated additional emissions of about 2.18 g of CO2 per km in the Spanish case. The lube oil emissions and the pollutants included in Group 3 for each case study, calculated based on factors per km traveled, were equivalent to those presented in Table A6, Table A7 and Table A8. The emissions included in Group 4 were calculated based on the corresponding fraction of total NMVOC presented in Table A9. Additionally, the content of metal and non-metal particles and PAHs in the total particles emitted by the tire and brake abrasion was calculated by the fractions presented in Table A11.

3.2. Well-to-Tank Analysis for Case Studies

For the calculation of the environmental impact of the production, storage, and transportation of fuels, the inventory was prepared for the consumed fuel in each studied case, which were refined locally from local or imported feedstocks.
The B10 diesel placed at the service station in Pereira, Colombia, was composed of 10% v/v from biodiesel (fatty acid methyl esters, FAME) from locally grown oil palm, while 90% v/v was conventional diesel (sulfur ≈ 500 ppm), also from locally extracted petroleum. Both fuels were refined locally in Barrancabermeja, Colombia. For the production of palm biodiesel in the refinery located in Barrancabermeja, an average of 200 km was considered for the transport of the palm fruit from plantations to the extraction plant and 50 km for the transport of the crude palm oil from these extraction plants to the refinery [77]. The transportation of B10 diesel by pipeline with a distance of approximately 500 km to regional storage and 20 km by tanker truck to the service station in Pereira were considered [78]. The proportion in kg of the two types of fuel was calculated considering the densities for fossil diesel of 0.856 kg/L and 0.875 kg/L for palm oil biodiesel [69].
Diesel in Malaysia is composed of 7% v/v FAME fromlocally grown palm oil, while 93% is conventional diesel (sulfur ≈ 330 ppm) [70] from petroleum from different parts of the world, but mainly from locally extracted petroleum [79], specifically from production platforms in the South China Sea about 200 km off the east coast of the Peninsular Malaysia, property of the main supplier of ExxonMobil Exploration and Production Malaysia Inc. (EMEPMI) [79]. The B7 diesel is transported to the Petron South City service station by tanker truck from the Klang Valley Distribution Terminal (≈19 km), which receives the fuel through pipelines from the Petron Port Dickson terminal (≈80 km), adjacent to the Petron refinery [79]. In the case of palm biodiesel, an average 79 km distance was assumed for the transport of the palm fruit from the crops to the extraction plants [80], 41 km from these plants to the refineries [81], and 25 km from these refineries to the B7 diesel storage and distribution terminals. The proportion of the two types of fuel was calculated considering the densities for the fossil diesel of 0.828 kg/L and 0.875 kg/L for the palm oil biodiesel [71].
The conventional diesel (diesel A) in Spain follows the European regulation, EN 590, with a maximum content of 7% v/v of FAME and maximum 10 ppm of sulfur [82]. Spanish legislation does not establish an exact or minimum proportion of FAME in each liter of diesel; therefore, this content can be between 0 and 7%. In 2015, with the introduction of Royal Decree 1085 to promote biofuels, annual targets were set to meet the goal of 10% of renewable energy in transport in 2020. This established a minimum biofuel proportion of 5% for 2017, 6% for 2018, 7% for 2019, and 8.5% for 2020 [83]. However, the requirement of these biofuel minimums does not imply that in each liter of diesel these percentages are met, since these minimums are accounted for per company. Therefore, a company (producer, distributor, or consumer) could introduce less biodiesel to each liter of diesel if in exchange, for example, the company distributes or uses biodiesel B20 or B30. Hence, it was assumed for this analysis that the diesel contained 5% FAME. This biodiesel in Spain in 2016 was produced mostly locally (75%), while the rest was imported from Italy (7.9%), Germany (5.9%), the Netherlands (3.1%), and others (8.1%) [84]. The feedstocks for the production of FAME were mainly crude palm oil (72.4%), rapeseed oil (15.5%), and soybean oil (10.3%), grown in Southeast Asia, Europe, and South America, respectively [84]. The crude petroleum for diesel production was imported with 99.6% [85] of diverse origins that vary month to month, hence the imports of the past years were considered to determine the main supplier countries. During the five years prior to 2016, the crude was imported mainly from Nigeria (14.9%), Mexico (14.0%), Saudi Arabia (12.9%), and Russia (11.2%) [85]. The fossil diesel was locally refined and transported through pipelines to the fuel storage center in Zaragoza and then by tanker truck to the service station. The proportion of these two types of fuels in the inventory was calculated considering the densities of 0.837 kg/L for the fossil diesel and of 0.892 kg/L for the biodiesel [73]. We also considered the transportation of raw materials from each of the exporting countries in the different transport modes. For the production of ultra-low sulfur diesel (ULSD) in Spain, an additional energy expenditure of approximately 6.5% was considered [86] to reduce the sulfur content to 10 ppm from the 50 ppm low sulfur diesel available in the Ecoinvent v3.4 database. For this low sulfur diesel, an additional 6% of energy was used to reduce the sulfur content of conventional diesel of 350 ppm [49].
As the refining of the two types of fuel are not modeled for specific countries in the databases for life cycle analysis and, considering that the refining activities available for Switzerland (CH), Europe (RER), or the rest of the world (RoW) do not represent the life cycle of the production of Colombian and Malaysian fuels because they contain crude oil imported from many parts of the world, the corresponding inventories were created for the countries in each studied case. The fuel distribution activities considered transport by different modes, including the construction activities of the transport and storage infrastructure in each country, using the main materials and energy produced in the respective country. In this way, the life cycle inventory for the WTT analysis for one kg of fuel placed at the service station is presented in Table 5.

3.3. Environmental Impact Assessment

From the inventories of data obtained for the TTW and WTT analyses, the characterization results of the WTW analysis were obtained for the 18 environmental impact categories shown in Table 6. The extended version of Table 6, including the WTT and TTW analyses results, is presented in Table A15.
In Figure 2, the contribution shares of the WTT and TTW phases in the total WTW results in Table 6 for each impact category are presented. The impact categories of ionizing radiation, freshwater eutrophication, land use, mineral resource scarcity, fossil resource scarcity, and water consumption were excluded from Figure 2, since of the total WTW impacts, 100% is because of the WTT phase. That is, the vehicle operation emissions do not affect these environmental impact categories.
In Figure 3, the contribution shares of each of the emission sources considered in the vehicle operation for each impact category are presented.

4. Discussion

The application of the proposed method for the accounting of TTW emissions for road freight services in the three different locations demonstrates the increase in the accuracy and reliability of this type of calculation through route sections, especially in roads on rugged terrains and with high gradients as in the assessed case in Colombia. In this analyzed service in Colombia, the use of Tier 2 fuel consumption and emission factors from databases or generic tools, developed with data for standard conditions in European or North American countries, generated very high uncertainties when they were applied in different geographies. The diesel consumption on mountainous roads for a fully loaded rigid truck in Colombia was 45 L/100 km, compared to averages between 22–26 L/100 km that are usually applied from other sources. In contrast, the diesel consumption for an articulated truck on a hilly road in Spain from both the proposed method and generic databases coincided with an average of 31 L/100 km. These figures coincided with the average European road type, with traffic at a speed limit of 90 km/h, which is why this journey obtained a similar fuel consumption to the average calculated for the factors in Tier 2. The estimated figure through the Tier 3 method by sections was the closest to the average for this route, which can vary up to 5%, according to the company’s technical director. It is noteworthy that the average consumption for trucks in Europe varies only approximately 10% between a flat and a mountainous road [87] due to the developed infrastructure with several tunnels and viaducts, something nonexistent for the analyzed route in Colombia between two capital cities. In this route, trucks must travel the Western mountain range on narrow roads with fog, sharp curves, high gradients, and unpaved or damaged sections due to landslides. For this reason, the distance of the analyzed route could be done in approximately 4 h in Europe, while the Pereira–Quibdo route takes more than 8 h. In the case of Malaysia, the fuel consumption figures obtained by both the Tier 2 and Tier 3 methods by sections were very similar, but both were also below the actual average for the route. This uncertainty could be produced mainly because none of the methods can account for the extra consumption in dense traffic conditions in urban areas, a variable that affected the analyzed service given that the truck must cross Kuala Lumpur City from south to north through streets with very dense traffic.
The results through the Tier 3 method by sections, despite being closer to the actual consumption than the results obtained with the Tier 2 and Tier 3 method for the entire journey, might have omitted the additional consumption that is generated in the gear shift on each of the slopes in each section, plus many of these slopes have gradients above the average gradient of each analyzed section. On the other hand, the method does not consider the altitude, the age of the truck, or the driving style, factors that significantly influence the actual fuel consumption [6,88,89,90,91]. It is estimated that an efficient driving style can save on average between 5 and 12% [92], and even up to 25% of fuel [93].
For the assessed case in Spain, due to the similarity in fuel consumption estimations from both the Tier 2 and Tier 3 methods, it could be thought that the use of Tier 2 factors for the estimation of emissions from vehicle operation could be acceptable. However, there are emissions that greatly vary depending on operating parameters, such as air pollutants and tire and brake abrasion emissions. This was demonstrated by the proposed calculation method by sections, where for the loaded truck for the outward journey, a consumption of 35.7 L/100 km was obtained compared to the return of 26.6 L/100 km, which indicates that the quantity of released polluting gases can also vary significantly in each journey. Analyzing the variation rate in the generation of air pollutants obtained by the Tier 3 method by sections compared to the Tier 2 method, very significant changes of up to 145% can be observed in Figure 4.
Although the obtained figures from the three studied cases should not be compared, basically because in one of them only the outward journey was considered, and in the other cases, the type of vehicles and load factors were different, important conclusions can be obtained from the analysis of the contribution shares of each phase of the WTW analysis. In general, the environmental impact of the TTW phase was an order of magnitude higher than that of the WTT, so the models used to estimate the corresponding emissions must be as reliable as possible if they are to be used as planning tools or for the environmental reporting of freight transport services.
In particular, for all three cases, the TTW phase mainly affected the impact categories related to human health and ecosystems, while the WTT phase affected the categories related to resource availability. The contribution of each phase to the impact categories was mainly influenced by the type of road and by the emission control technology of the vehicle. This can be observed in Spain where the route was carried out by motorway and with a Euro VI vehicle, hence the TTW phase had greater responsibility in seven of the 18 impact categories, compared to the case in Colombia where this phase had responsibility in nine impact categories and with very high contributions, especially in the categories of ozone and particulate matter formation and in human toxicity. Additionally, in the Colombian case, the TTW contribution share was about 50% in marine ecotoxicity impacts, in contrast to shares of 18% and 25% for the Malaysian and Spanish cases, respectively. The high contribution in marine ecotoxicity due to the vehicle operation in the Colombian case was because of the high copper emissions from brake abrasion, which was influenced by the high load factor and low average speed on the mountainous road, generating around three times more brake abrasion particles than those generated in the Malaysian and Spanish cases.
It can also be observed that in each case, there were different contributions in the climate change category, which was not influenced by vehicle technology, but was affected by the fuel production process in each country. In the case of Spain, the WTT phase for B5 diesel at the station generated 0.74 kg CO2 eq/kg, compared to 0.32 kg CO2 eq/kg of B10 diesel in Colombia and 0.43 kg CO2 eq/kg of B7 diesel in Malaysia; which means GHG emissions of 17.3, 7.5, and 10 g CO2 eq/MJ, respectively. This is because the ULSD in Spain needs higher energy expenditure than the conventional diesel production used in Colombia. Additionally, the electricity used in Colombia is 76% produced by hydroelectric power [94], contributing less CO2 to the process. Furthermore, the petroleum used in Spain is transported from different continents, compared to the local petroleum used in Colombia. In addition, there is a very important factor that increases the amount of CO2 to each kg of B5 diesel in Spain, which is related to the production of palm oil biodiesel. In the case of Colombia, the 10% of palm oil biodiesel in the fuel generated a reduction of CO2 emissions in this WTT phase. However, the 5% of biodiesel incorporated in the fuel in Spain increased the CO2 emissions by more than 30%. This is because the palm oil production in Malaysia and, mainly in Indonesia, has been grown in tropical forests and peatlands, whose preparation for cultivation releases large amounts of CO2 [95,96]. In consequence, the total GHG, including combustion emissions, were 88.5, 75.6, and 78.5 g CO2 eq/MJ of B5, B10, and B7 diesel in the respective countries. Despite this environmental impact of fuel in Spain, the use of Euro VI vehicles and ULSD achieved relatively low impacts against cases, such as Colombia, in the categories related to human health. For example, WTW emissions for the categories of fine particulate matter formation, ozone formation, and terrestrial acidification per km, in the case of Spain, were 1.26 g PM2.5 eq, 5.80 g NOx eq, and 3.08 g SO2 eq, respectively, when compared to 15.8 g PM2.5 eq, 16.1 g NOx eq, and 7.38 g SO2 eq in the studied case in Colombia, which shows the importance of fuel and vehicle emissions regulations.
The application of the Tier 3 method for the estimation of emission factors is considered a reliable method since for diesel heavy vehicles, the data has been based on a sufficiently large set of experimental data [55]. However, in addition to the experiments that have been conducted under European driving conditions, these factors did not consider the error caused by the mileage age of the vehicles and the cold-start overemissions, therefore, increasing the uncertainty for all estimated gases, especially for CO and VOC emissions. To verify the uncertainty of the obtained estimations, basically, a soft verification method was used by comparing the estimations with the results from the Tier 2 factors and other GHG calculation tools. A ground truth verification method was applied only for the fuel consumption figures due to the impossibility of applying on-board measurements since two of the tested vehicles had pre-Euro and Euro I technology, which lacked on-board diagnostic (OBD-II) connectors for the use of instrumentation for the collection of operation data, consumption, and emissions during the journeys, being practical and necessary for the theoretical estimation by the proposed method for companies that have these types of old technology vehicles. Another parameter that can change the uncertainty in the estimations is the average speed caused by dense traffic, heavy meteorological conditions, or incidents on the road, such as landslides or accidents. This parameter more significantly affected freight transport on single lane roads on mountainous topology than on motorways. In this sense, a sensitivity analysis of the freight transport in the Colombian case was conducted for three scenarios: A fast service where the journey time was reduced by half an hour in optimal road conditions and low traffic; a slightly delayed service, which lasted half an hour more due to the traffic; and a very delayed service that could take up to two hours more due to heavy rain conditions. In the fast service scenario, due to the reduction of 6–7% in the journey time, the fuel consumption and the respective fuel-dependent emissions were reduced by 3.4%, while the emissions of air pollutant gases were reduced between 2.4% and 9%. In the slightly delayed service, the 6–7% increased time produced an increase of 3.9% in fuel consumption and fuel-dependent emissions, and between 3% and 11% of the air pollutant emissions. In the 2-h delay scenario, the 23% increased time produced an increase of 17% in the fuel consumption and fuel-dependent emissions, and between 12% and 47% of the air pollutant emissions. These assessed scenarios show that variation in the journey time and, consequently, the average speed does not cause a direct proportional variation in the fuel consumption and emissions. This is mainly because the fuel consumption and emissions are more affected by other parameters, such as the road section gradients. For this reason, to increase the accuracy of the results, it is important to elaborate new estimations by applying the proposed method every time a freight service parameter changes, since extrapolating previously calculated average emission factors could increase the uncertainty in the results.

5. Conclusions

The proposed emissions estimation method in this paper, unlike most similar methods where only the type of vehicle and the emission control technology are considered, also considers the load factor, gradients, and speed for the different slopes that can be found on a specific route. Through this method, companies, in addition to being able to estimate GHG emissions for journeys in which fuel consumption is unknown, can estimate other emissions, such as air pollutant gases and particles from the abrasion of tires, brakes, and road surface. From these estimations, companies can calculate the carbon footprint of the transported products as well as analyze the different environmental impacts related to the operation of the vehicle and the corresponding fuel used for a specific route. From these analyses, measures can be taken to reduce the impacts of the operation, such as load limits, speed, fleet modernization, or change of type and fuel supplier. It is worth mentioning that this estimation method by sections of the route, supported by information of open source software for the elaboration of elevation profiles, is mainly useful for small and medium companies with limited resources to establish environmental accounting through tools or commercial equipment, which are commonly not adapted to the actual conditions of their operation.
The results of the case studies showed the high uncertainties by using fuel consumption and emission factors from databases developed in a different region to where the freight service took place. Specifically, the diesel consumption on mountainous roads in Colombia was 45 L/100 km, compared to averages between 22–26 L/100 km from generic factors from European sources. In contrast, the diesel consumption for an articulated truck on a hilly motorway in Spain from both the proposed method and generic factors coincided at 31 L/100 km, because this route has similar characteristics to the average European road type and driving conditions. However, even for the Spanish route, the estimated air pollutant emissions by the proposed method differed up to 127% when compared to the generic factors, since the vehicle speed, load, and road gradient have a relevant impact in these emissions. Similarly, for the Colombian route, the variation in air pollutant estimations was up to 145%, while the Malaysian route was up to 40%. The differences in the obtained results in each country were basically because of the topology and characteristics of the roads and the emission control technologies of vehicles. In general, the consideration of air pollutants and tire, brake, and road surface emissions and specific parameters of the operation revealed the importance of emission sources other than diesel combustion and different impact categories to climate change. Specifically, we can highlight the impact generated by the vehicle operation in the terrestrial and marine ecotoxicity categories, where the main reason is the released copper particles by the brake abrasion. The results also showed that the fuel consumption and emissions, depending on the type and condition of the road, could increase more than double when compared to a motorway in good condition. It can be seen that the more rugged the terrain, the greater the variation of non-dependent gases on fuel consumption considering the different operating parameters when compared to the average Tier 2 emission factors, which were established according to the type of vehicle and the emission control technology. In this sense, the investment made in the construction of infrastructure, despite generating environmental impacts, can generate reductions in vehicle operation that would compensate for the impacts in some of the assessed categories. Therefore, it is interesting to include infrastructure construction processes in the life cycle analysis associated with transport services.
In conclusion, the relevance of the different emission sources that must be taken into account was demonstrated, being necessary to apply estimation methods for specific sections of the route, given that the quantity of pollutant emissions is extremely influenced by the speed, load factors, and road gradients. These factors are decisive when evaluating the environmental impacts associated with a specific transport service and the various strategies that are intended to be implemented to improve the sustainability of the sector in different territories.

Author Contributions

All authors participated equally to the research design, development of the theoretical framework, methodological choices and the analysis. All authors revised and approved the manuscript., J.L.O.-T. performed the formal analysis, investigation, methodology and writing; E.L.-S. provided direction, guidance and a critical revision of the manuscript; A.H.H. contributed for case studies data collection and critical revision of the manuscript.

Funding

This work was based on the Ph.D. thesis by Jose Luis Osorio granted by Colciencias call 646/2014. Part of this study was developed thanks to the support of the CIRCE Research Institute and the aids for Research Groups of the Aragon Government (T46_17R).

Acknowledgments

The authors would like to thank all companies involved in the experimental part of the paper and people who collaborated with all required information; special acknowledgments to Ramon Pascual, Conrado Lopez and Sanath Kumaran.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Emission factors for the TTW calculation method. The following tables show condensed and organized data for heavy vehicles obtained from the EMEP/EEA guide [55].
Table A1. CH4 emission factors for heavy vehicles (mg/km) (Own elaboration based on data from EMEP/EEA [55]).
Table A1. CH4 emission factors for heavy vehicles (mg/km) (Own elaboration based on data from EMEP/EEA [55]).
Type of VehicleType of Road
UrbanRuralMotorway
Gross vehicle weight < 16 t852320
Gross vehicle weight > 16 t1758070
Table A2. Reduction rates for CH4 emissions for diesel heavy vehicles (%) (Own elaboration based on data from EMEP/EEA [55]).
Table A2. Reduction rates for CH4 emissions for diesel heavy vehicles (%) (Own elaboration based on data from EMEP/EEA [55]).
Euro StandardType of Road
UrbanRuralMotorway
Euro II36137
Euro III4479
Euro IV979394
Euro V and earlier979394
Table A3. N2O emission factors for diesel heavy vehicles by type of road (mg/km) (Own elaboration based on data from EMEP/EEA [55]).
Table A3. N2O emission factors for diesel heavy vehicles by type of road (mg/km) (Own elaboration based on data from EMEP/EEA [55]).
Euro StandardRigid Truck
7.5–12 t
Rigid and Articulated
12–28 t
Rigid and Articulated
28–34 t
Articulated > 34 t
UrbanRuralMotorwayUrbanRuralMotorwayUrbanRuralMotorwayUrbanRuralMotorway
Pre-Euro303030303030303030303030
Euro I6531197171410181511
Euro II5531196171410181510
Euro III332554886997
Euro IV67.25.811.213.811.417.424.417.41923.419.2
Euro V1519.817.229.840.233.645.661.651.64966.655.8
Euro VI18.5191537392956.559.544.5616448
Table A4. Fraction of NO2 in NOx emissions and NH3 emission factors for diesel heavy vehicles. (Own elaboration based on data from EMEP/EEA [55]).
Table A4. Fraction of NO2 in NOx emissions and NH3 emission factors for diesel heavy vehicles. (Own elaboration based on data from EMEP/EEA [55]).
Euro StandardNO2 in NOxNH3
%(mg/km)
Pre-Euro113
Euro I–Euro II11
Euro III14
Euro IV10
Euro V1211
Euro VI89
Euro III + CRT *35
* Continuously Regenerating Trap
Table A5. Variation rate of emissions for diesel heavy vehicles with biodiesel blends (Own elaboration based on data from EMEP/EEA [55]).
Table A5. Variation rate of emissions for diesel heavy vehicles with biodiesel blends (Own elaboration based on data from EMEP/EEA [55]).
PollutantBiodiesel Blend
B10B20B100
CO−5%−9%−20%
PM−10%−15%−47%
NOx3%3.5%9%
VOC−10%−15%−17%
Table A6. Emission factors of heavy metals in fuel and lube oil in diesel heavy vehicles in (Own elaboration based on data from EMEP/EEA [55]).
Table A6. Emission factors of heavy metals in fuel and lube oil in diesel heavy vehicles in (Own elaboration based on data from EMEP/EEA [55]).
SourceLeadCadmiumCopperChromiumNickelSeleniumZincMercuryArsenic
Fuel0.00050.000050.00570.00850.00020.00010.0180.00530.0001
Oil0.03324.5677819.231.894.54450.200
Table A7. Emission factors for PAHs and POPs (Own elaboration based on EMEP/EEA [55]).
Table A7. Emission factors for PAHs and POPs (Own elaboration based on EMEP/EEA [55]).
Pollutantμg/kmPollutantμg/km
Indeno(1,2,3-cd) pyrene1.40Benzo(j)fluoranthene13.07
Benzo(k)fluoranthene6.09Benzo(a)anthracene2.39
Benzo(b)fluoranthene5.45Fluorene39.99
Benzo(ghi)perylene0.77Chrysene16.24
Fluoranthene21.39Phenanthrene23.00
Benzo(a)pyrene0.90Napthalene56.00
Pyrene31.59Anthracene8.65
Perylene0.20Coronene0.15
Benzo(b)fluorene10.58Dibenzo(ah)anthracene0.34
Benzo(e)pyrene2.04Perylene0.20
Triphenylene0.96Benzo(b)fluorene10.58
Table A8. Emission factors for PCDDs, PCDFs, and PCBs (Own elaboration based on data from EMEP/EEA [55]).
Table A8. Emission factors for PCDDs, PCDFs, and PCBs (Own elaboration based on data from EMEP/EEA [55]).
Euro StandardPollutant (pg/km)
PCDDPCDFPCB
Pre-Euro253810.9
Euro I253812.6
Euro II253812.6
Euro III253812.6
Euro IV253812.6
Euro V0.310.450.15
Euro VI0.160.240.08
Table A9. Pollutants included in Group 4; fraction of the total NMVOC (Own elaboration based on data from EMEP/EEA [55]).
Table A9. Pollutants included in Group 4; fraction of the total NMVOC (Own elaboration based on data from EMEP/EEA [55]).
Sub-groupPollutant% WeightSub-groupPollutant% Weight
AlkanesEthane0.03AldehydesFormaldehyde8.4
Propane0.1Acetaldehyde4.57
Butane0.15Acrolein1.77
Isobutane0.14Benzaldehyde1.37
Pentane0.06Crotonaldehyde1.48
Heptane0.3Metacrotine0.86
2-methylhexane0.63Butyraldehyde0.88
2-methylheptane0.21Isobutanaldehyde0.59
3-methylhexane0.35Propionaldehyde1.25
Dean1.79Hexanal1.42
3-methylheptane0.27I-valeraldehyde0.09
Alcanes c > 1327.5Valeraldehyde0.4
O-tolualdehyde0.8
M-tolualdehyde0.59
AromaticsToluene0.01
M.p-xylene0.98
O-xylene0.4
AlkenesEthylene7.011.2.3-trimethylbenzene0.3
Propylene1.321.2.4-trimethylbenzene0.86
Isobutene1.71.3.5-trimethylbenzene0.45
1,3-butadiene3.3Styrene0.56
Benzene0.07
Aromatic c91.17
Aromatics c > 1320.37
CycloalkanesAll1.16AlkynesAcetylene1.05
Table A10. Fraction of PM>10, PM2.5–10, and PM<2.5 content in the TPM by source (Own elaboration based on data from EMEP/EEA [55]).
Table A10. Fraction of PM>10, PM2.5–10, and PM<2.5 content in the TPM by source (Own elaboration based on data from EMEP/EEA [55]).
TiresBrakesRoad Surface
PM>1040%2%50%
PM2.5–1018%59%23%
PM<2.542%39%27%
TPM100%100%100%
Table A11. Content of PAHs and chemical elements in ppm of the TPM from tire and brake abrasion (Own elaboration based on data from EMEP/EEA [55]).
Table A11. Content of PAHs and chemical elements in ppm of the TPM from tire and brake abrasion (Own elaboration based on data from EMEP/EEA [55]).
ElementTiresBrakesElementTiresBrakes
Benzo(a)pyrene3.90.74Magnesium cation (Mg2+)16644,570
Benzo(b)fluoranthene00.42Manganese (Mn)512460
Benzo(k)fluoranthene00.62Molybdenum (Mo)2.810,000
Sodium (Na+)6457740
Silver (Ag)0.10Ammonium cation (NH4+)19030
Aluminum (Al)3242050Nickel (Ni)29.9327
Arsenic (As)3.867.5Nitrate (NO3)15001600
Barium (Ba)12538,520Lead (Pb)1766072
Bromine (Br)2040Rubidium (Rb)050
Calcium (Ca)892770Sulfur (S)110012,800
Cadmium (Cd)4.722.4Antimony (Sb)210,000
Chlorine (Cl)5201500Selenium (Se)2020
Chloride (Cl-)6001500Silicon (Si)180067,900
Cobalt (Co)12.86.4Sulfate (SO4)250033,400
Chromium (Cr)23.82311Tin (Sn)07000
Copper (Cu)17451,112Strontium (Sr)14.4520
Iron (Fe)1712209,667Titanium (Ti)3783600
Potassium (K)280523.5Vanadium (V)1660
Lithium (Li)1.355.6Zinc (Zn)74348676

Appendix B

The following tables show the parameters for each of the route sections for the estimation of fuel consumption and emissions for the studied cases. The presented data were obtained from our own calculations through the software, Google Earth Pro [67] and Google Maps [97].
Table A12. Data for each section of the Pereira–Quibdo route, Colombia.
Table A12. Data for each section of the Pereira–Quibdo route, Colombia.
Section NumberCirculation AreaAltitude Start (m)Altitude End (m)Distance (km)GradientAverage Gradient∆ Elevation (m)Time
(min)
IncreaseLoss
1Urban138714404.71.1%3.2%−3.5%10754.520
2Urban144014091.7−1.8%5.9%−4.8%27.754.87
3Interurban140912579−1.7%6.3%−6.7%27942815
4Interurban125711968.9−0.7%2.0%−2.8%83.514310
5Interurban119689810.7−2.8%1.1%−4.0%31.333116
6Urban89890060.0%1.3%−1.2%40.237.612
7Interurban900154430.72.1%7.2%−7.4%160596045
8Urban154415300.93−1.5%9.6%−14.7%49.980.83
9Interurban15301985123.8%13.3%−10.8%102257520
10Interurban1985146711.6−4.5%7.5%−11.3%35887618
11Urban146715142.12.2%9.0%−8.8%12274.76
12Interurban151436231−3.7%8.5%−10.1%1222237065
13Urban3623521.4−0.7%5.8%−4.8%46.856.23
14Interurban35227810.9−0.7%6.5%−6.2%32840124
15Interurban2782643.5−0.4%13.6%−14.0%2462609
16Interurban26412526.3−0.5%4.6%−4.6%63777340
17Urban1251191−0.6%1.3%−1.6%5.6810.83
18Interurban1199213−0.2%3.3%−2.8%19622322
19Urban92980.51.2%3.2%−2.0%12.552
20Interurban98924.74−0.1%3.0%−3.2%75.582.38
21Urban92783−0.5%2.0%−1.6%21.132.68
22Interurban781016.540.4%3.0%−2.6%10383.910
23Urban1011053.950.1%2.3%−1.6%40.435.39
24Interurban105657.15−0.6%1.9%−2.3%58.498.29
25Urban65740.91.0%8.2%−3.1%26.817.32
26Interurban74726.60.0%2.7%−2.2%82.578.58
27Urban72651.87−0.4%2.7%−2.7%1833.23
28Interurban655712.4−0.1%2.7%−2.6%16417616
29Urban57571.50.0%4.1%−3.0%29.1294
30Interurban574412.5−0.1%1.8%−2.0%11813515
31Urban44550.81.4%5.9%−6.1%32.219.92
32Interurban555560.0%1.7%−1.8%54.450.88
33Urban55416.6−0.2%2.4%−2.6%80.387.620
Table A13. Data for each section of the Kuala Lumpur–Kulim one-way route, Malaysia.
Table A13. Data for each section of the Kuala Lumpur–Kulim one-way route, Malaysia.
Section NumberCirculation AreaAltitude Start (m)Altitude End (m)Distance (km)GradientAverage Gradient∆ Elevation (m)Time
(min)
IncreaseLoss
1Urban394710.8%2.0%−1.4%14.36.783
2Urban477927.60.1%2.3%−2.4%38435250
3Interurban791652.14.1%9.9%−5.3%13042.93
4Interurban165486.5−1.8%5.7%−5.5%1292475
5Interurban482036.6−0.1%2.7%−2.8%58160623
6Interurban209316.90.4%3.1%−3.0%31224211
7Interurban933728.8−0.2%1.9%−2.0%29234817
8Interurban37748.10.5%4.5%−4.0%1981615
9Interurban747450.60.0%2.1%−2.3%69569631
10Interurban741535.81.4%4.4%−3.7%16585.24
11Interurban153525−2.0%2.0%−3.2%24.51253
12Interurban521713.333.6%6.2%−2.9%15232.63
13Interurban171513.85−3.1%1.3%−4.5%17.51373
14Interurban51674.20.4%1.6%−1.4%41.425.63
15Interurban67673.60.0%3.8%−4.0%71.471.83
16Interurban676424.10.0%1.5%−1.5%20620915
17Interurban643445.734.9%6.4%−2.2%31736.75
18Interurban3443341−1.0%0.0%−1.0%0101
19Interurban334487.2−4.0%4.2%−5.9%703565
20Interurban488821.50.2%3.4%−3.3%40136114
21Interurban88703−0.6%6.7%−5.7%87.51063
22Interurban70927.7−0.2%1.2%−1.3%17223317
23Interurban959.40.0%1.8%−1.8%84.388.26
24Interurban5918.70.0%0.9%−1.0%10610214
25Interurban984.70.0%4.5%−4.0%1031043
26Interurban838.6−0.1%1.7%−1.6%70.375.36
27Interurban38210.0%0.9%−1.0%12111617
28Urban854−0.1%1.4%−1.7%31.233.74
29Urban572.80.1%1.2%−1.7%2018.36
30Interurban73216.60.2%1.8%−1.5%15713316
31Interurban32251.3−0.5%1.5%−1.8%11.317.32
32Interurban25270.850.2%7.2%−4.4%28.826.72
33Interurban27284.80.0%1.6%−1.7%4140.86
Table A14. Data for each section of the Zaragoza–Almusafes one-way route, Spain.
Table A14. Data for each section of the Zaragoza–Almusafes one-way route, Spain.
Section NumberCirculation AreaAltitude Start (m)Altitude End (m)Distance (km)GradientAverage Gradient∆ Elevation (m)Time
(min)
IncreaseLoss
1Semiurban2131919−0.2%1.4%−1.9%82.31058
2Interurban1912869.61.0%2.9%−2.0%188936
3Interurban2863206.20.5%1.4%−0.9%6025.34
4Interurban3203624.70.9%3.8%−3.3%109684.5
5Interurban36254817.21.1%1.7%−1.3%27185.210
6Interurban548711171.0%2.0%−1.5%24579.610
7Interurban71193445.6%7.5%−4.1%25129.13
8Interurban9348666−1.1%4.5%−4.8%1091784
9Interurban8668693.70.1%2.4%−3.5%56.553.32
10Interurban869105418.21.0%2.0%−1.3%2647911
11Interurban10548879.4−1.8%2.6%−3.8%81.62495
12Interurban8879182.41.3%2.4%−1.9%44.813.81.5
13Interurban9189231.80.3%5.5%−3.2%39.534.31.2
14Interurban9238972.9−0.9%0.4%−1.4%2.97301.5
15Interurban89798518.70.5%1.2%−0.9%15668.610
16Interurban9859788−0.1%0.8%−1.0%34.641.14.2
17Interurban97898740.2%3.0%−2.0%53.845.12.2
18Interurban98797431.40.0%0.8%−0.8%15216716
19Interurban9749907.40.2%3.2%−3.7%1391234
20Interurban990100511.5%4.0%−4.0%6256.40.7
21Interurban1005117691.9%1.5%−1.8%547.685
22Interurban1176100049.2−0.4%2.0%−2.1%49166826
23Interurban10002254.2−1.8%2.4%−2.9%354133228
24Interurban22244.50.0%1.8%−1.6%4642.93
25Interurban24521.42.0%2.7%−1.4%33.25.851
26Interurban527312.80.2%1.1%−0.9%1241027
27Interurban7395100.2%2.2%−1.9%1371166
28Interurban9510113.70.0%1.4%−1.3%1331278
29Interurban1012710.2−0.7%1.4%−1.8%671416
Table A15. Characterized WTT and TTW analysis results per km in each case study; ReCiPe 2016 midpoints.
Table A15. Characterized WTT and TTW analysis results per km in each case study; ReCiPe 2016 midpoints.
ColombiaMalaysiaSpain
Impact categoryUnitWTTTTWWTTTTWWTTTTW
Global warmingkg CO2 eq/km1.57 × 10−11.09 × 1007.71 × 10−25.29 × 10−11.93 × 10−17.97 × 10−1
Stratospheric ozone depletionkg CFC11 eq/km5.41 × 10−78.33 × 10−81.47 × 10−78.10 × 10−84.10 × 10−77.81 × 10−8
Ionizing radiationkBq Co-60 eq/km1.50 × 10−305.59 × 10−306.94 × 10−30
Fine particulate matter formationkg PM2.5 eq/km2.31 × 10−42.64 × 10−31.07 × 10−49.52 × 10−43.50 × 10−49.08 × 10−4
Ozone formation, Human healthkg NOx eq/km4.91 × 10−41.55 × 10−21.85 × 10−45.47 × 10−35.22 × 10−45.27 × 10−3
Ozone formation, Ecosystemskg NOx eq/km5.34 × 10−41.56 × 10−21.95 × 10−45.47 × 10−35.78 × 10−45.28 × 10−3
Terrestrial acidificationkg SO2 eq/km6.99 × 10−46.68 × 10−32.67 × 10−42.33 × 10−39.44 × 10−42.14 × 10−3
Freshwater eutrophicationkg P eq/km4.61 × 10−506.16 × 10−601.48 × 10−50
Marine eutrophicationkg N eq/km2.55 × 10−52.56 × 10−94.92 × 10−51.24 × 10−91.10 × 10−43.35 × 10−9
Terrestrial ecotoxicitykg 1,4-DCB/km2.68 × 10−14.61 × 1001.11 × 10−11.87 × 1001.63 × 10−11.46 × 100
Freshwater ecotoxicitykg 1,4-DCB/km2.60 × 10−36.10 × 10−55.38 × 10−42.68 × 10−51.35 × 10−34.81 × 10−5
Marine ecotoxicitykg 1,4-DCB/km2.63 × 10−32.04 × 10−33.70 × 10−38.35 × 10−42.09 × 10−36.90 × 10−4
Human carcinogenic toxicitykg 1,4-DCB/km3.39 × 10−34.88 × 10−31.01 × 10−31.37 × 10−32.63 × 10−31.19 × 10−3
Human non-carcinogenic toxicitykg 1,4-DCB/km7.02 × 10−21.88 × 10−11.32 × 10−25.52 × 10−23.86 × 10−24.56 × 10−2
Land usem2a crop eq/km3.16 × 10−202.08 × 10−207.10 × 10−20
Mineral resource scarcitykg Cu eq/km3.32 × 10−409.42 × 10−502.22 × 10−40
Fossil resource scarcitykg oil eq/km4.88 × 10−101.78 × 10−102.79 × 10−10
Water consumptionm3/km3.17 × 10−301.36 × 10−302.32 × 10−30

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Figure 1. Elevation profiles (altitude vs. distance) for the outward journeys in each studied case: (a) Colombian route; (b) Malaysian route; (c) Spanish route. Images obtained through Google Earth Pro.
Figure 1. Elevation profiles (altitude vs. distance) for the outward journeys in each studied case: (a) Colombian route; (b) Malaysian route; (c) Spanish route. Images obtained through Google Earth Pro.
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Figure 2. Contribution shares of the Well-to-Tank and Tank-to-Wheels phases in the environmental impact in each studied case: (a) Colombian route; (b) Malaysian route; (c) Spanish route.
Figure 2. Contribution shares of the Well-to-Tank and Tank-to-Wheels phases in the environmental impact in each studied case: (a) Colombian route; (b) Malaysian route; (c) Spanish route.
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Figure 3. Contribution shares of the emission sources of the Tank-to-Wheels analysis in the environmental impacts in each studied case: (a) Colombian route; (b) Malaysian route; (c) Spanish route.
Figure 3. Contribution shares of the emission sources of the Tank-to-Wheels analysis in the environmental impacts in each studied case: (a) Colombian route; (b) Malaysian route; (c) Spanish route.
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Figure 4. Variation in the quantity of air pollutant gases; Tier 3 by sections vs. Tier 2.
Figure 4. Variation in the quantity of air pollutant gases; Tier 3 by sections vs. Tier 2.
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Table 1. Total Tank-to-Wheels energy consumption for each case study.
Table 1. Total Tank-to-Wheels energy consumption for each case study.
Diesel ConsumptionTestCalculation Method
Case StudyTier 2Tier 3Tier 3
(Whole Route)(Route by Sections)
ColombiaL (B10 a diesel)15167102116
MJ5423240836634181
L/100 km58.025.739.244.7
g/km499223334384
MalaysiaL (B7 b diesel)201.5169147167
MJ7242603252425957
L/100 km26.121.919.021.6
g/km215.6181.5157.7179.3
SpainL (B5 c diesel)215.1208212216
MJ7730743875807743
L/100 km31.029.930.531.2
g/km259.5251.4256.2261.7
a diesel with 10% v/v of biodiesel; b diesel with 7% v/v of biodiesel; c diesel with 5% v/v of biodiesel
Table 2. Group 1 emissions from fuel combustion in each case study (g/km).
Table 2. Group 1 emissions from fuel combustion in each case study (g/km).
Case StudyCONOxPM<2.5 umCH4NMVOCN2ONH3
Colombia4.7217.40.5620.08430.9950.007540.003
Malaysia1.096.130.2050.07950.2760.007360.003
Spain0.08400.4230.002950.0001020.02150.007090.003
Table 3. Group 2 emissions from fuel combustion in each case study (g/km).
Table 3. Group 2 emissions from fuel combustion in each case study (g/km).
Case StudyCO2SO2LeadArsenicCadmiumCopperChromiumMercuryNickelSeleniumZinc
Colombia1.09 × 1033.83 × 10−11.92 × 10−73.83 × 10−81.92 × 10−82.19 × 10−62.99 × 10−62.03 × 10−67.67 × 10−93.83 × 10−96.90 × 10−6
Malaysia5.23 × 1021.18 × 10−18.96 × 10−81.79 × 10−88.96 × 10−91.02 × 10−62.99 × 10−69.50 × 10−73.59 × 10−91.79 × 10−93.23 × 10−6
Spain7.90 × 1025.23 × 10−31.31 × 10−72.62 × 10−81.31 × 10−81.49 × 10−62.99 × 10−61.39 × 10−65.23 × 10−92.62 × 10−97.90 × 102
Table 4. Emissions from the abrasion of tires, brakes, and road surface in each case study (g/km).
Table 4. Emissions from the abrasion of tires, brakes, and road surface in each case study (g/km).
Case StudyParticle SizeTiresBrakesRoad
ColombiaPM>10 um1.65 × 10−21.39 × 10−33.80 × 10−2
PM>2.5 um, and <10 um7.44 × 10−34.10 × 10−21.75 × 10−2
PM<2.5 um1.74 × 10−21.74 × 10−22.05 × 10−2
MalaysiaPM>10 um8.00 × 10−35.38 × 10−43.80 × 10−2
PM>2.5 um, and <10 um3.60 × 10−31.59 × 10−21.75 × 10−2
PM<2.5 um8.41 × 10−38.41 × 10−32.05 × 10−2
SpainPM>10 um2.14 × 10−23.99 × 10−43.80 × 10−2
PM>2.5 um, and <10 um9.64 × 10−31.18 × 10−21.75 × 10−2
PM<2.5 um2.25 × 10−22.25 × 10−22.05 × 10−2
Table 5. Well-to-Tank inventory for one kg of fuel at the service station in each case study.
Table 5. Well-to-Tank inventory for one kg of fuel at the service station in each case study.
InputsUnitColombiaMalaysiaSpain *
Diesel fossil {Country}, at plant akg8.98 × 10−19.26 × 10−19.47 × 10−1
Methyl ester of vegetable oil {Country} esterification of palm oil, at plant bkg1.02 × 10−17.37 × 10−25.31 × 10−2
Transport by pipeline, on land, oil products {Country} processtkm5.00 × 10−17.44 × 10−22.80 × 10−1
Infrastructure, for the regional distribution of oil product {Country} constructionunit2.48 × 10−102.48 × 10−102.48 × 10−10
Freight transport by truck {Country} all sizes, generic to EURO III, at market ctkm2.00 × 10−22.08 × 10−21.54 × 10−2
Tap water {RoW}, at marketkg6.89 × 10−46.89 × 10−46.89 × 10−4
Electricity, low voltage {Country}, at marketkWh6.70 × 10−36.70 × 10−36.70 × 10−3
Outputs
To air
Water/m3m31.03 × 10−31.03 × 10−31.03 × 10−3
To water
Water, (Country)m35.86 × 10−35.86 × 10−35.86 × 10−3
* Notes for Spain: a Ultra-low sulfur diesel; b biodiesel from a mix of vegetable oils; c transport by trucks from generic to Euro VI technology.
Table 6. Characterized Well-to-Wheels analysis results per km in each case study; ReCiPe 2016 midpoints.
Table 6. Characterized Well-to-Wheels analysis results per km in each case study; ReCiPe 2016 midpoints.
Impact CategoryUnitColombiaMalaysiaSpain
Global warmingkg CO2 eq/km1.25 × 1006.06 × 10−19.90 × 10−1
Stratospheric ozone depletionkg CFC11 eq/km6.25 × 10−72.28 × 10−74.88 × 10−7
Ionizing radiationkBq Co-60 eq/km1.50 × 10−35.49 × 10−36.94 × 10−3
Fine particulate matter formationkg PM2.5 eq/km1.58 × 10−21.06 × 10−31.26 × 10−3
Ozone formation, Human healthkg NOx eq/km1.61 × 10−25.65 × 10−35.79 × 10−3
Ozone formation, Terrestrial ecosystemskg NOx eq/km1.61 × 10−25.66 × 10−35.85 × 10−3
Terrestrial acidificationkg SO2 eq/km7.38 × 10−32.59 × 10−33.08 × 10−3
Freshwater eutrophicationkg P eq/km4.61 × 10−56.16 × 10−61.48 × 10−5
Marine eutrophicationkg N eq/km2.55 × 10−54.92 × 10−51.10 × 10−4
Terrestrial ecotoxicitykg 1,4-DCB/km4.87 × 1001.98 × 1001.62 × 100
Freshwater ecotoxicitykg 1,4-DCB/km2.66 × 10−35.65 × 10−41.40 × 10−3
Marine ecotoxicitykg 1,4-DCB/km4.67 × 10−34.54 × 10−32.78 × 10−3
Human carcinogenic toxicitykg 1,4-DCB/km8.26 × 10−32.38 × 10−33.82 × 10−3
Human non-carcinogenic toxicitykg 1,4-DCB/km2.59 × 10−16.84 × 10−28.43 × 10−2
Land usem2a crop eq/km3.16 × 10−22.08 × 10−27.10 × 10−2
Mineral resource scarcitykg Cu eq/km3.32 × 10−49.42 × 10−52.22 × 10−4
Fossil resource scarcitykg oil eq/km4.88 × 10−11.78 × 10−12.79 × 10−1
Water consumptionm3/km3.17 × 10−31.36 × 10−32.32 × 10−3

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MDPI and ACS Style

Osorio-Tejada, J.L.; Llera-Sastresa, E.; Hariza Hashim, A. Well-to-Wheels Approach for the Environmental Impact Assessment of Road Freight Services. Sustainability 2018, 10, 4487. https://doi.org/10.3390/su10124487

AMA Style

Osorio-Tejada JL, Llera-Sastresa E, Hariza Hashim A. Well-to-Wheels Approach for the Environmental Impact Assessment of Road Freight Services. Sustainability. 2018; 10(12):4487. https://doi.org/10.3390/su10124487

Chicago/Turabian Style

Osorio-Tejada, Jose Luis, Eva Llera-Sastresa, and Ahmad Hariza Hashim. 2018. "Well-to-Wheels Approach for the Environmental Impact Assessment of Road Freight Services" Sustainability 10, no. 12: 4487. https://doi.org/10.3390/su10124487

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