Transportation Research Part D 17 (2012) 457–465

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Transportation Research Part D

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Impacts of biodiesel blending on freight emissions in the MidwesternUnited States

Matt Johnston a,b,c, Erica Bickford a,b,⇑, Tracey Holloway a,b, Chris Dresser d, Teresa M. Adams b

a Center for Sustainability and the Global Environment, Nelson Institute for Environmental Studies, University of Wisconsin—Madison, Madison, WI 53726, USAb National Center for Freight and Infrastructure Research and Education, University of Wisconsin—Madison, Madison, WI 53726, USAc Institute on the Environment, University of Minnesota, St. Paul, MN 55108, USAd US Environmental Protection Agency, Office of Transportation and Air Quality, Ann Arbor MI, USA

a r t i c l e i n f o

Keywords:BiodieselEmissions inventoryFreightGreenhouse gases

1361-9209/$ - see front matter � 2012 Elsevier Ltdhttp://dx.doi.org/10.1016/j.trd.2012.05.001

⇑ Corresponding author at: Center for SustainWisconsin–Madison, Madison, WI 53726, USA.

E-mail address: ebickford@wisc.edu (E. Bickford)

a b s t r a c t

We use a combination of petroleum–diesel models, datasets and tools along with biodie-sel-specific corrections to create a roadway-level emissions inventory capable of evaluat-ing spatial, temporal and scale aspects of fuel distribution options for the MidwesternUS. Specifically, we compare the emissions of a year-round ‘‘low-blend’’ biodiesel imple-mentation scenario, already under consideration in a variety of states, with a more strate-gic summer-only, interstate-only ‘‘high-blend’’ scenario. Our results indicate that spatialand seasonal distribution decisions do affect the overall emissions impacts of any biodieseldeployment, even those at low-blend levels. However, we also finds that changes in emis-sions due to biodiesel are considerably smaller than those anticipated from improvementsto engine and control technologies.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

The Renewable Fuel Standard Program (RFS2) Final Rule of the 2007 Energy Independence and Security Act (EISA) re-quires that the US produce ‘‘no less than’’ 1 billion gallons of biomass-based diesel fuel by 2012, with the target growingan unspecified amount by 2022 (US Environmental Protection Agency, 2010b). To date, there has been limited evaluationof how to best utilize this expanding supply of biodiesel. How, where, and when these new biodiesel volumes are consumedmay affect potential air quality and human health benefits, as well as the development of fuel distribution infrastructure.Here we have developed a roadway-by-roadway emissions inventory for the Midwestern US to assess the impacts biodieselwill have on the freight sector—the largest consumer of diesel fuel in the US (US Department of Energy, 2010b).

Although diesel fuel comprises only 24% of on-road fuel consumption, diesel vehicles account for 42% of on-road mobilesource nitrogen oxide (NOX) emissions and 72% of on-road fine particulate matter (PM2.5) emissions (US Department ofTransportation, 2007; US Environmental Protection Agency, 2007). Both NOX and PM2.5 are regulated by the US Environmen-tal Protection Agency (EPA) for their direct effects on human health and, in the case of NOX, contribution to another regulatedand harmful air pollutant, ozone (O3). These pollutants cause decreased lung function, irritation of respiratory conditionsincluding asthma, heart attacks and increased mortality.

While technical aspects of biodiesel have been well studied, emissions research has largely focused on individual engineand vehicle tests. Here, we focus on the potential emissions impacts of strategic deployment at large scales, which requires afleet-level analysis of biodiesel emissions. Biodiesel meta-analyses from the US EPA and National Renewable Energy

. All rights reserved.

ability and the Global Environment, Nelson Institute for Environmental Studies, University of

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Laboratory (NREL) were the only suitable data for this type of analysis, the former of which was utilized in this study (USEnvironmental Protection Agency, 2002a; Lindhjem and Pollack, 2003).

2. Materials and methods

To evaluate the emission impacts of fuel switching and other policy options, we built a ‘‘bottom-up’’ roadway-by-road-way emissions inventory for freight trucking in the Midwestern US, hereafter referred to as the Wisconsin Inventory ofFreight Emissions (WIFE). We evaluate two different options for distributing the RFS biodiesel volume targets. A ‘‘low-blend’’biodiesel implementation scenario would be made up of a consistent year-round blend of mostly petroleum diesel mixedwith a small percentage of biodiesel, standardized across the region. Low-blend mandates ranging between B2 (2% biodiesel,98% petroleum diesel) and B20 are already under consideration (and in a few cases implemented) in a variety of states,including: Minnesota (B2–B20), Washington (B2–B5), Oregon (B2–B5), New Mexico (B5), and Pennsylvania (B2–B20) (USDepartment of Energy, 2010a). The ‘‘high-blend’’ scenario we evaluate consists of a summer-only mandate, with distributionconfined to interstate highways. The benefits of this type of high-blend scenario (B30–B100) include a greater use of biodie-sel in summer months (to reduce risk of gelling in cold weather) and/or concentrating biodiesel fueling stations in high-traf-fic thoroughfares to help reduce O3 and particulates in regions out of attainment with the EPA’s National Ambient Air QualityStandards.

The focus on summer versus year-round blending bears relevance to the characteristics of biodiesel, as well as to thechemistry of the summer atmosphere in the Upper Midwest. While PM2.5 is a year-round problem, O3 in our study areais only a summer pollutant. Currently, 83 Midwest counties are out of attainment with federal air quality standards forPM2.5 and/or O3 (US Environmental Protection Agency, 2010a). Ozone is formed chemically through the combination ofNOX and volatile organic compounds (VOCs) in the presence of sunlight.

To develop our baseline WIFE inventory, annual average daily freight truck traffic from the US Federal Highway Admin-istration’s (FHWA) Freight Analysis Framework 2.2 (FAF) (US Department of Transportation, 2006) is combined with the FAFroadway network to calculate VMT for freight trucks on every major freight road in our study region. Our analysis regionincludes Illinois, Indiana, Iowa, Kansas, Kentucky, Michigan, Minnesota, Missouri, Ohio, and Wisconsin – which comprisethe Mid-America Freight Coalition. Detailed emission factors based on speed, seasons, road-types and years were calculatedusing MOBILE6.2 (US Environmental Protection Agency, 2002b) and then adjusted to represent various biodiesel blends.Roadway-specific VMT and emissions data were then combined in a geographic information system (GIS) framework result-ing in the new spatial emissions inventory.

As a basic check on emission estimates, we compare with the EPA National Emissions Inventory (NEI) for 2002. For lateranalyses of biodiesel, we calculate 2009 and 2018 WIFE estimates, using corresponding specifications in MOBILE6.2 (FAFactivity estimates are held constant to clarify the impact of fuel-switching alone). Although we report results on a12 km � 12 km grid, actual estimates are roadway-specific and could be adapted to any desired data resolution.

2.1. FAF Data and Heavy-Duty Diesel Vehicle (HDDV) VMT per Road

The Freight Analysis Framework 2.2 (FAF) is a multi-commodity, multi-modal freight database and analysis tool devel-oped by Oak Ridge National Laboratory’s Center for Transportation Analysis and the Federal Highway Administration(FHWA). Included in the FAF is a spatial, GIS roadway network derived from the National Highway Planning Network.The FAF also includes roadway attributes (including freight activity along road segments), length of road segments and roadspeeds.

The FAF’s annual average freight truck activity estimates are based on major freight commodity flows, designed to informtransportation analysis rather than emissions. Thus, while FAF represents the only freely available, roadway-by-roadwayfreight activity dataset containing the necessary attributes needed to spatially quantify freight vehicle emissions, thereare some important characteristics affecting its application to emissions: the FAF only includes truck trips greater than 50miles; the data set lacks details on vehicle and fuel types; empty truck trips are not included; and reported road speed datarepresents speed at maximum congestion, not average or free-flow speeds. Based on the literature, we assume that 98% oftotal freight VMT is attributable to HDDVs (limitation 2) and that 25% of truck miles are empty (limitation 3).

For use in the WIFE, we calculate HDDV truck VMT data (HTVdaily) from the FAF data (FAFdaily) using the followingequation:

HTVdaily ¼ FAFdaily � ET � DIESELfraction � RL ð1Þ

where empty VMT trips (ET) are assumed to be 25% (1.33), the percentage of diesel vehicles (DIESELfraction) equals 98%, andthe FAF road lengths (RL) are in miles.

2.2. Emission factors and adjustments

The MOBILE6.2 EPA Mobile Source Emission Factor Model estimates gram-per-mile emissions of VOCs, NOX, primary PM,and a range of other criteria pollutants and air toxics from cars, trucks, and motorcycles under a variety of fuel types,

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operating conditions and calendar years ranging from 1952 to 2050. Although the EPA recently released MOVES, the EPAonly approved the use of MOVES in creating State Implementation Plans (SIPs) in March 2010. We followed EPA guidelinesfor using MOBILE6.2 to prepare seasonal and annual emissions inventories, adjusting more than two dozen input parametersthat resulted in 1392 unique MOBILE6.2 runs (US Environmental Protection Agency, 2004).

To estimate biodiesel emission factors for VOCs, CO, and NOX, biodiesel adjustments factors from the EPA were applied tothe MOBILE6.2 diesel emission curves (US Environmental Protection Agency, 2002a). In the case of PM, the biodiesel adjust-ment factors were only applied to exhaust emissions (brake/tire emissions were unmodified). With the exception of PM,emissions calculated here are for tailpipe emissions only and do not incorporate evaporative emissions.

2.3. Calculating blend targets

Because the RFS biodiesel production targets are national targets, we assumed an equal distribution of the 1 billiongallons of biodiesel across all fifty states. In practice, the 10 Midwestern states at the heart of this study would likelyend up with a greater share of the production target due to their proximity to feedstock crops and biofuel refineries.

To calculate the summer-only, interstate-only (high) blend scenario, we use FHWA data to estimate that 39% of diesel fuelis consumed on interstates and 24.3% of diesel is consumed in the summer months of June, July and August (US Departmentof Energy, 2009; US Department of Transportation, 2008). Eq. (2) is used to estimate the 2010 summer-only, interstate-onlyhigh-blend ratio (HBR) by dividing the national RFS biodiesel production target (BPT) by US diesel consumption (DC) mul-tiplied by the fraction consumed on interstates (INTERSTATEfraction) and the fraction consumed in summer (SUMMERfraction)months

HBR ¼ BPT=ðDC � INTERSTATEfraction � SUMMERfractionÞ ð2Þ

Using this approach, a national RFS production target of 1 billion gallons would allow 29.0% of diesel fuel to bereplaced by biodiesel in Class 5 and Class 6 vehicles on interstates during summer months – which we round to aB30 ‘‘high-blend’’ (30% biodiesel, 70% petroleum). The year-round (low) blend B3 scenario – or 3% biodiesel, 97% petro-leum diesel – was calculated in an analogous manner, based on total, on-road fuel consumption (36.7 billion gallons). Toaccount for future increases stipulated in the RFS by 2022, we include an additional B100 high-blend scenario (equiva-lent to a 3.5 billion gallons per year RFS), which in turn would yield a corresponding year-round B10 low-blend followingEq. (2).

3. Results

3.1. Diesel emissions inventory

The EPA’s NEI is created using different data scales and different methodology from our WIFE estimate, but it provides avaluable benchmark despite not being an ‘‘apples to apples’’ point of comparison. The NEI includes estimated activity fromall heavy-duty diesel trucks, versus the FAF/WIFE limitation to trips greater than 50 miles, a difference reflected in aggregateVMT in Table 1.

Fig. 1 compares VMT at the county-scale resolution of the NEI (Fig. 1a) with our 12 km � 12 km aggregated WIFE VMT(Fig. 1b). NEI VMT exceeds WIFE VMT due to the NEI’s inclusion of local deliveries (truck trips <50 miles) and non-freightHDDVs (municipal vehicles), as well as the NEI’s population-dependent emissions allocation algorithm. To create the NEI,the EPA disaggregates estimated state HDDV emissions based on road length (not road activity) and population, an approachthat likely underestimates activity on heavily-traveled interstate roads in rural areas. Beyond VMT, WIFE differs from NEI inits spatial distribution of activity data, and in travel speed assumptions. These methodological differences yield region-wideemissions differences between 12%, for NOX, and 57%, for VOCs (Table 1), with spatial differences following patterns similarto those of VMT.

Table 1Comparison of emissions inventories.

Pollutant 2002 NEI 2002 WIFE Difference (%)

VMT (millions mi.) 53,677 31,598 �41NOX (tons) 902,421 797,446 �12PM 2.5 (tons) 24,766 13,926 �44PM 10 (tons) 28,400 16,060 �43VOCs (tons) 48,411 20,925 �57CO (tons) 244,719 151,915 �38NH3 (tons) 1655 1223 �26SO2 (tons) 18,424 15,856 �14

(b)

(a)

2

2

Fig. 1. VMT comparison between 2002 NEI (a) showing the EPA’s county-level VMT distribution, and WIFE (b) showing the 12 km gridded VMT distributionbased on the 2002 FAF.

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3.2. Impacts from biodiesel blending

Emissions impacts from our region-wide biodiesel blending analysis are presented in Fig. 2. The figures show results bypollutant for the B3 year-round low-blend scenario, and the B30 summer-only, interstate-only high-blend scenario com-pared against baseline diesel emissions for 2009 (left) and 2018 (center). We also show results for a higher RFS target in

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2018 (right) that assume a B10 year-round low-blend scenario, and a B100 summer-only, interstate-only high-blendscenario.

Fig. 2a demonstrates that NOX is the only pollutant expected to increase with the introduction of biodiesel into the na-tional fuel supply, at less than 3% in all cases examined. Changes in NOX emissions range from a 0.3% (low-blend, 2009 andlow-target, 2018) to a 1.0% (high-target, 2018) increase for year-round distribution, and a 0.7% (high-blend, 2009 and low-target 2018) to a 2.5% (high-target, 2018) increase for summer-only, interstate-only distribution. These changes are smalland it has been suggested that newer vehicles and engine technologies might eliminate the expected NOX increase all to-gether (Szybist et al., 2005; Yanowitz and McCormick, 2009).

Fig. 2. Annual emissions changes in the Midwest resulting from biodiesel distribution. Notes: Blue bars represent baseline diesel emissions, orange barsrepresent emissions from RFS mandated biodiesel production distributed as a universal year-round ‘low’ blend, and green bars represent emissions fromRFS mandated biodiesel production distributed as a summer-only, interstate-only ‘high’ blend. For the 1 billion gallon RFS scenarios, the ‘low’ blend(orange) is B3 (3% biodiesel, 97% petroleum diesel) and the ‘high’ blend (green) is B30. For the 3.5 billion gallon RFS scenarios, the ‘low’ blend is B10 and the‘high’ blend is B100. Error bars reflect uncertainty in biodiesel emission factors.

Fig. 2 (continued)

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PM2.5 emissions (Fig. 2b) are projected to decrease with all biodiesel blends and production targets. Low-blend assump-tions yield reduction of 1–2% for the current RFS and reductions of 4% for the high-target, 2018 case. High-blend scenariosresult in reductions of 3–4% for the current RFS and close to 8% for the high-target, 2018 case. Similarly, VOC (Fig. 2c) and CO(Fig. 2d) emissions improvements are greatest when biodiesel is introduced into the fuel system as a summer-only, inter-state-only high-blend.

4. Discussion

Based on the results in Fig. 2, there is not a clear ‘‘winner’’ between high- and low-blending options when consideringregional emission changes. Of interest is the net impact of these emissions on health-relevant pollutant concentrations, espe-cially ground-level O3 and PM2.5 (both primary and secondary). Increased NOX emissions would be expected to have the mostimpact on non-urban O3 concentrations. The decreases in PM2.5 would be expected to have the largest impact on primary PMnear roadways. The decreases in VOCs would be expected to have the greatest benefit on O3 in urban areas where O3 pro-

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duction is typically limited by the availability of VOCs. Although the percentage impacts of biodiesel on reducing PM2.5, VOCsand CO are much greater than the percentage impacts on NOX increases, the contribution of HDDV emissions to NOX aremuch higher. The net impact of these changes on ambient concentrations and health impacts requires a more detailedassessment, and evaluation with a regional air quality model.

A comparison of baseline petroleum diesel emissions between 2009 and 2018 in Fig. 2a–d shows that MOBILE6.2 esti-mates a 78% reduction in CO, a 75% reduction in NOX and PM2.5, and a 33% reduction in VOCs over the 9-year intervaldue to improvements in vehicle emissions control technologies. These engine technology-driven reductions dwarf eventhe most aggressive distributions of biodiesel that max out at 8–16% (and include a 2.5% increase in NOX emissions).

Although biodiesel blends do not appear to be a major lever for reducing emissions, these emission changes – especiallyfor NOX and PM2.5 – could be important co-benefits of biodiesel policies motivated by energy independence, carbon reduc-tion, or other goals.

Beyond health-relevant air quality, biodiesel and other biofuels are being investigated as an option to reduce greenhousegas (GHG) emissions. Assuming that biomass-based diesel emits 50% of the GHGs associated with petroleum diesel, per theRFS2 (US Environmental Protection Agency, 2010b) the production targets used in this study would translate to 1.2% and

Fig. 3. Percent change in criteria pollutants by biodiesel blend. Notes: Solid lines represent EPA maximum likelihood curves, while dotted lines are high- andlow-bounds calculated from the 90th and 10th percentile of the original B20 and B100 test data (represented by black hash marks). The yellow diamondrepresents a comparison to the EPA best fit (solid line) from Yanowitz and McCormick (2009) that added 15 new studies to the EPA test data for B20, andremoved older two-stroke engine tests to better reflect the current ‘fleet’ makeup.

Fig. 3 (continued)

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3.5% annual reductions in GHG emissions. By comparison, GHG emissions from HDDVs have seen 3.1% annualized growthsince 1990.

Our results are highly dependent on the EPA’s biodiesel adjustment factors, used to scale petroleum diesel emission fac-tors from MOBILE6.2. The adjustment factors were calculated in a meta-analysis of 39 biodiesel emission studies, but real-world changes in emissions can vary significantly due to age and size of vehicle, displacement of the engine, travel speed andoperating conditions, emission control technologies, choice of lipid feedstock used to produce the biodiesel, and other fac-tors. This variation is illustrated by the individual test results (shown as black hash marks) used in the EPA meta-analysis inFig. 3a–d. Though it is widely cited that VOC, CO, and PM emissions will decrease and NOX emissions will slightly increasewith the use of biodiesel (Granda et al., 2007; Swanson et al., 2007), tests from the EPA’s biodiesel emission database illus-trate that the results can vary widely and even change sign for all four pollutants depending on testing conditions.

5. Conclusions

This study introduces the new WIFE inventory, which was used to evaluate the emissions impacts of biodiesel deploy-ment policies. Choices on where, when, and how to increase biodiesel use can change the magnitude of emissions, with de-

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creases up to 8% for PM2.5, up to 16% for VOCs, and up to 12% for CO, along with increases up to 2.5% increase for NOX. Asexpected, the ‘‘high’’ seasonal blend results in the greatest change in magnitude of emissions, both on an annual averageand during the months in which blending occurs. However, these interstate-only high-blend scenarios concentrate changein urban areas and rural areas with major arterial roads – two areas with significantly different ground-level air quality is-sues. This analysis shows that biodiesel may help to address emissions of both health-relevant and climate-relevant pollu-tants, but only if the seasonal and spatial distribution of the fuel is strategically implemented and the local–global tradeoffsare given full consideration.

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