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A comparative life cycle assessment examining soybean and petroleum-based lubricants is compiled using Monte. Carlo analysis to assess system variabil...
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Environ. Sci. Technol. 2007, 41, 4143-4149

A Comparative Life Cycle Assessment of Petroleum and Soybean-Based Lubricants S H E L I E A . M I L L E R , * ,† A M Y E . L A N D I S , ‡ THOMAS L. THEIS,‡ AND RONALD A. REICH§ School of the Environment, Environmental Engineering and Science, Clemson University, Clemson, South Carolina, Institute for Environmental Science and Policy, University of Illinois at Chicago, 2121 W. Taylor Street, Chicago, Illinois 60612, and Alcoa Technical Center, Alcoa, Pennsylvania

A comparative life cycle assessment examining soybean and petroleum-based lubricants is compiled using Monte Carlo analysis to assess system variability. Experimental data obtained from an aluminum manufacturing facility indicate significantly less soybean lubricant is required to achieve similar or superior performance. With improved performance and a lower use rate, a transition to soybean oil results in lower aggregate impacts of acidification, smog formation, and human health from criteria pollutants. Regardless of quantity consumed, soybean-based lubricants exhibit significant climate change and fossil fuel use benefits; however, eutrophication impacts are much greater due to nonpoint nutrient emissions. Fundamental tradeoffs in the carbon and nitrogen cycles are addressed in the analysis, demonstrating that a transition to soybean oil may result in climate change benefits at the expense of regional water quality.

Introduction Commodities derived entirely or partially from biomass, known as bio-based products or biocommodities, have been proposed as substitutes for petroleum in applications ranging from transportation fuels to plastics. While many studies have focused on transportation fuels, potential exists for successful implementation of biocommodities in niche markets, such as lubricants, plastics, and specialty chemicals (1-4). Biobased lubricants are currently used as substitutes for hydraulic fluids and once-through operations such as cutting and stamping. They have yet to appear in continuousloop operations such as aluminum rolling; however, experimental evidence presented in this paper suggests that such potential exists. Over two billion gallons (∼7.5 billion liters) of petroleumbased lubricants are produced annually in the United States (5). Of these, over 900 million gallons (∼3 billion liters) are used for industrial purposes, with approximately 100 million gallons (∼380 million liters) dedicated to metalworking operations including aluminum rolling (6). The U.S. agricultural sector produces approximately two and a half billion gallons of vegetable oils annually, with 2% of these stocks currently used in nonfood applications (7, 8). Biolubricants are increasing in popularity due to superior technical properties and environmental concerns associated with * Corresponding author phone: 864-572-2889; fax:864-656-0672; e-mail: [email protected]. † Clemson University. ‡ University of Illinois at Chicago. § Alcoa Technical Center. 10.1021/es062727e CCC: $37.00 Published on Web 04/26/2007

 2007 American Chemical Society

petroleum lubricants (9, 10). Recent chemical modifications improve the oxidative stability of vegetable oils, demonstrating potential to compete with mineral oils in longer-term applications (6, 11-13). It is often assumed that biobased products are environmentally preferable to petroleum products due to their renewable nature. To determine the validity of this assumption, a comparative life cycle assessment (LCA) allows a quantitative comparison of the energy and material flows throughout the stages of each product, from creation to disposal or reuse (14). A significant body of work is available on the life cycles of biobased transportation fuels such as ethanol and biodiesel, and various databases catalog the environmental impacts of agriculture (15-19). One study focusing on rapeseed oil for use in hydraulic applications shows greenhouse gas emissions reductions for biolubricants but increased impacts of eutrophication, smog production, and energy use (20). Agricultural systems exhibit considerable variability and uncertainty in emission profiles because of differences in geography, climate, and agricultural practices. The use of average data to characterize agricultural systems may not represent emissions occurring during “extreme” years (such as rainy or drought years), and the subsequent environmental impacts. This paper incorporates data variability to provide a more comprehensive system description. The use of variable data allows the LCA practitioner to compare alternative products using average values and also best and worst case scenarios given probability estimates. The purpose of this paper is to conduct a comparative LCA for biolubricants and mineral oils, using Monte Carlo analysis (MCA) to incorporate data variability into the assessment. Unlike most metalworking operations using biobased lubricants, rolling processes recycle lubricants in a continuous loop. Experimental data from aluminum rolling is used to determine lubricant behavior during the process, and convert to an appropriate functional unit. Use phase data are not often incorporated into LCAs of products in the development stage due to lack of data, which results in a cradle-to-gate analysis that may overlook important inventory flows. By incorporating experimental data within the analysis, the results are more relevant to actual applications. Although this paper focuses on aluminum rolling as an example, the reported data for the stages of soybean agriculture and processing are applicable to a range of soybean-based products.

Materials and Methods Boundaries. The boundaries for the life cycles of the mineral oil and soybean lubricants modeled in this paper are presented in Figure 1. All primary and secondary inputs related to upstream manufacturing are included; however, the contributions from the manufacture of capital equipment are assumed to be negligible. It is assumed that nonmodified soybean oil is used in the process due to its acceptable performance in this application. When nonmodified soybean oil can be used, it is preferable for economic considerations, since additional processing increases the cost. Applications using chemically modified or heat-treated soybean oil require incorporation of additional inventory data as appropriate, which will increase the energy consumed along the life cycle. Aluminum Rolling Trials. The performance of soybean lubricant was tested in an aluminum rolling manufacturing facility (21). These experiments indicated superior performance by the soybean oil and improved surface quality of the metal. Soybean oil achieved greater reductions in metal VOL. 41, NO. 11, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 1. Flow diagrams of soybean and mineral oil life cycles, depicting relevant material and energy flows documented in the inventory. Transportation between processes is also included. thickness at lower temperatures than the traditional mineral oil, indicating that approximately 75% less lubricant can be used in an emulsion to achieve similar or improved results. A summary of the experimental data obtained at the aluminum rolling trial can be found in the Supporting Information. The lubricant system consists of oil-in-water emulsions with additive packages that are assumed to be similar for both types of lubricants. Other emissions associated with the use phase, such as energy use by the mill, are the same regardless of lubricant and are excluded from this study. Inventory Analysis. Inventory data on agricultural operations are available from many sources; however, values of mass and energy flows for agricultural operations can differ substantially, depending on annual fluctuations in crop yield, weather patterns, and agricultural operations. Evaluation of three databases and their underlying assumptions was conducted to establish an inventory for this study, and is described in detail in a prior publication (22). From the findings of this study, the Greenhouse Gases, Regulated Emissions, and Energy use in transporation (GREET) model is the basic framework for the inventory data in this study, because of its transparency and adaptability to user assumptions (23). Data gaps and disparities identified in the earlier study are addressed in this study. These include incorporation of nonpoint nitrogen emissions, variability in VOC emissions from hexane extraction, and upstream emissions from soybean-specific agricultural chemicals. From the analysis of available data, it is evident that significant variability exists in agricultural inventories. Variability was simulated via Monte Carlo analysis (MCA), a tool that repeatedly and randomly selects values from probability distributions assigned to system parameters. In addition to providing transparency and adaptability to user assumptions, GREET Version 1.6 contains variability information for energy generation processes (24). Modifications to the GREET model as well as determination of variability distributions for individual parameters are described in the Supporting Information. GREET 1.6 catalogues energy use and emissions for each stage of the agricultural process, including variability estimates. Detailed descriptions of the inventory calculations can be found in the GREET manual (23). While GREET provides valuable information pertaining to the agricultural sector, it was originally created for analysis of transportation fuels, thus modifications are needed for this analysis. These include a separate nitrogen characterization model, which supplies aqueous nitrate, N2O, NOx, and NH3 emissions. A complete description of the nitrogen distribution model is detailed in an earlier paper (25). Agricultural chemical manufacturing data for fertilizer production and application, lime, and crop-specific pesticides are also added (26). VOC 4144

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emissions resulting from the hexane extraction of soybean oil are modified from the original GREET values using a distribution resulting from a best-fit regression of 10 collected industry sources (27). Although other methods of oil extraction exist, hexane extraction is currently the most prominent and economically viable, so it is the extraction method used in this analysis. Carbon sequestration and end-of-life releases are also incorporated into the analysis. A credit of 2.44 kg CO2/kg soybean oil is included in the farming inventory, which is based on the assumption that 66% by mass of processed soybean oil derives from atmospheric carbon (16). Both the Energy Information Administration (EIA) and International Panel on Climate Change (IPCC) assume that 50% of the carbon in lubricants is sequestered at the end-of-life in a solid state and 50% is released as emissions (28). Filter material containing waste lubricants is generally sent to sealed landfills where it remains in a solid carbon state indefinitely, accounting for the end-of-life sequestration assumption. This study assumes that all end-of-life carbon emissions are CO2, although a study is currently underway to measure the respective VOC emissions of soybean and mineral oils during aluminum rolling. A sensitivity analysis on the final impact data shows this assumption does not affect the overall trends of the analysis. The end-of-life emissions for mineral oils are based on a lubricant carbon content equivalent to 1.95 kg CO2/kg mineral oil, with uncertainty bounds of -1%/+6%, as defined by the EIA (28). Allocation. Many industrial processes generate more than one product. Each product is responsible for a portion of the emissions generated during the process, although it is often unclear exactly how the inventory should be divided among the components in LCA. The choice of allocation scheme can be an important factor in LCA, and can significantly impact the outcome (14). Allocation is usually conducted on a mass, energy, or market basis. In this study, all allocation is conducted on a mass basis at the process level, and is described in detail in the Supporting Information. Although market-based allocation is also a reasonable alternative, the interdependence of corn-soybean agriculture presents uncertainties in the allocation scheme. Allocation of emissions on an energy basis is rejected for this study since the ultimate function of the product is not related to energy purposes. In LCA, products are compared on the basis of a functional unit that appropriately compares the performance of the products. The functional unit for the analysis is area of aluminum rolled. The relative performance of soybean and mineral oil must be determined to complete the analysis. From the experimental results described in the Supporting Information, 75% less soybean-based lubricant can be used to achieve metal production similar to mineral oils.

Impact Assessment. Several software tools are available to perform life cycle impact assessments (LCIA) (29-34). These vary by the inclusion of different impact categories, characterization factors, and the use of normative factors such as weighting the importance of impacts to normalize an analysis (34, 35). LCIA uses characterization factors to determine the amount of impact a given amount of emission will have. The Tool for the Reduction and Assessment of Chemical and other Environmental Impacts (TRACI) is used in this analysis because of location-specific characterization factors for U.S. data as well as its midpoint approach to impact assessment. TRACI provides characterization factors for 12 impact categories: ozone depletion, global warming, acidification, eutrophication, photochemical smog formation, ecotoxicity, human health criteria air pollutants, human health cancer, human health noncancer, fossil fuel use, land use, and water use (33). The method for measuring each environmental impact is different, and is discussed in detail in the literature (33, 36). Due to constraints of data availability and consistency, the only impacts calculated in this analysis are not compound-specific. Impacts pertaining to aquatic and human health depend greatly on emissions of individual chemicals and are not calculated to avoid biasing the results of the assessment due to lack of uniformity in the data. Reported human health impacts pertain only to those resulting from exposure to the criteria pollutants and do not indicate the acute or chronic health issues from toxic or carcinogenic substances. This may be a significant issue from a worker exposure issue, and should be addressed in the future when compound-specific data is available. Ozone depletion potential is also not considered since neither inventory tracks specific chemical information on ozone-depleting substances. Characterization factors for climate change are sitegeneric, meaning the location of the emission does not affect the magnitude of the impact value. Other environmental impacts vary depending on the location of relevant emissions. Using TRACI’s site-specific characterization factors, better impact assessments can be made than with generalized factors (34). Information for determining location-specific impact factors can be found in the Supporting Information.

Results and Discussion Inventory Results. MCA allows calculation of probability distributions for each inventory flow. Figure 2 presents the inventory data for mineral oil and soybean lubricants on a mass of emission/mass of lubricant basis. The contributions of each stage of the life cycle to total process emissions are shown with variability bars indicating the values within a 10-90% probability range, with the median value of the total indicated above each emission. Median fossil energy consumption for soybean oil is 5.27 MJ/kg oil. Oil processing is responsible for 55% of energy consumption, followed by farming operations (28%), and transportation (11%). Upstream processes make up the remainder. Oil processing is the most energy intensive stage because of the natural gas consumed drying the beans and generating steam for the extraction process. Internal energy for soybean oil is not included since it derives from solar energy. In contrast, the life cycle of mineral oil consumes 44.78 MJ/kg oil of fossil energy, almost ten times that of soybean oil. The majority (91%) is from fossil energy embodied within the product. The internal energy of the mineral oil is included because it derives from a source of fossil energy; soybean oil does not. Of the remaining 4.24 MJ/kg oil consumed throughout the manufacture of the lubricant, 51% occurs during the refinery stage, 42% during crude oil extraction, and 7% during transportation.

As seen in Figure 2a, crude oil extraction is responsible for the majority of air emissions in mineral oil production, especially for methane, where fugitive emissions from oil extraction and transportation to the refinery are abundant. Life cycle emissions for soybean oil are distributed among various processes. Use of farming equipment and transportation are major contributors to NOx, SOx, and CO2 emissions. Soybean oil processing is the dominant contributor to VOC emissions, due to process emissions during hexane extraction, which separates oil from meal. On-field processes make up the majority of N2O emissions, and contribute substantially to NOx. These emissions are the byproducts of denitrification and nitrification reactions of fertilizer and mineralized soil nitrogen. In addition, on-field processes include CO2 sequestration, which results in net negative CO2 emissions due to the assumption that 50% of the carbon is re-released to the atmosphere while 50% stays in a stable solid state. Even if it is assumed that the carbon is re-released in its entirety at the end of life, the carbon emissions are offset by the initial carbon sequestration, resulting in only the CO2 emitted by fossil fuel combustion throughout the life cycle. System Variability and Error. The variability bars depicted in Figure 2 demonstrate the 10-90% range of possible values for these data. In the mineral oil inventory, aggregate PM10 and SOx emissions vary by over 100% between the 10th and 90th percentiles. Inventory components from particular life cycle stages also demonstrate significant variability, but these do not greatly affect the cumulative variability of the life cycle. For instance, refinery emissions from noncombustion processes (which include fugitive emissions, blowdown systems, thermal and catalytic cracking, and catalyst regeneration) possess variability ranges over several orders of magnitude for VOC, CO, PM10, NOx, and SOx emissions; however, these do not greatly impact the aggregate inventory for these compounds since they have relatively small values when compared to combustion processes. Certain soybean oil components demonstrate significantly greater variability. For soybean oil, aggregate VOC and N2O emissions vary by over 300% over the 10-90% range due to process variability in soybean oil extraction, and variability in N2O emissions from on-field processes. Three sources of variability are contained within inventory data: operational variability (e.g., differences in operational practices such as operating machinery more efficiently); natural variability (e.g., nutrient cycles in agricultural systems, composition of crude oil, methane released from oil fields), and systematic variability within the model (e.g., different researchers compiling data and assigning probability distributions to values and the use of different models to compile data). The large variability in nitrogen compounds in the soybean inventory is an artifact of the natural variability in agricultural systems, whereas the variability associated with SOx emissions in the mineral oil inventory is largely due to operational variability. Systematic variability differences appear in this inventory from the combination of GREET and nutrient models, as well as other supplemental data that were added. Systematic variability also encompasses uncertainty in inventory ranges and inventory flows that are not assigned variability distributions in this model due to limitations in data availability. These include CH4 emissions from crude oil recovery, CO2 sequestration during on-field processes, and end-of-life CO2 emissions. While we have tried to limit the systematic variability wherever possible, it is an inevitable result of combining multiple data sources. Operational and natural variability cannot be reduced in this analysis, since they are a result of actual variation occurring within the system. Inventory Comparisons. Figure 3 presents life cycle inventory results on a mass of emission per area of metal basis. If performance data is neglected and comparison is VOL. 41, NO. 11, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 2. Contributions of each life cycle stage to total emissions, with variability bars showing the 10-90% probability range. Indicated values represent the median of the distribution. The life cycle stages of mineral oils are crude oil recovery, refining combustion emissions, refining noncombustion emissions (refining NC), and transportation to production facility. For soybean oil, the life cycle stages represented are farming equipment, fertilizer production and transportation, other (upstream production and transportation of lime and pesticides), onfield emissions, transportation to processing plant, soybean processing, and transportation to production facility. conducted using similar use rates, soybean oil is responsible for greater life cycle emissions for VOC, NOx, N2O, NO3-, and total P, and for significantly fewer emissions of CO2 and CH4, as well as significantly less fossil energy consumption. Due to the intensive participation of agriculture in the nitrogen cycle, soybean oil has notably higher NOx, N2O, and aqueous nutrient emissions than mineral oils on a mass basis. Hexane extraction, which dominates the VOC emissions for soybean oil, is responsible for significantly greater VOC emissions than mineral oil. The lower CO2 and CH4 emissions for soybean oil are due to the sequestration of carbon during soybean farming and the methane releases during crude oil extraction. The comparative emissions for CO, PM10, and SOx have similar values for soybean and mineral oils, and the variability ranges associated with these emissions overlap. Experimental data suggest that approximately 75% less soybean oil will be required in the rolling process, resulting 4146

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in lower life cycle emissions proportional to the reduction in the total amount of oil consumed. Interestingly, the total net carbon sequestration is lower for the improved performance case, since there is less oil consumed throughout the process and, therefore, less potential for sequestration. LCA cautions against direct emissions comparisons, since the data can be misleading. The emissions must be first translated into appropriate impact metrics for proper comparison. Impact Results. An LCIA is compiled using the inventory data for the life cycles of soybean and mineral oil and locationspecific characterization factors from TRACI. Table 1 reports the impact results for producing 100 000 m2 of rolled aluminum, indicating the median and the 10-90% probability range. For easier comparison among the data, the results have been normalized to the impacts associated with mineral oil,

FIGURE 3. Comparative life cycle inventory results for soybean and mineral oils, showing the 10-90% probability range per 100 m2 of aluminum production. Two soybean cases are shown: no change in performance, as well as the expected 75% reduction in the amount of oil used to produce of a similar quantity of aluminum. All values are reported as kg/100 m2 rolled aluminum, except for CO2 and NO3emissions, which are in Mg/100 m2.

FIGURE 4. LCIA results normalized to mineral oil.

TABLE 1. Impact Results for Mineral Oil and Four Use-Phase Soybean Oil Scenarios, for 100 000 m2 Aluminuma impact units reduction in lubricant used during the use phase acidification eutrophication human health smog formation global warming fossil fuel use a

H+

mineral oil

m2

moles equivalents/100 000 kg N equivalents/100 000 m2 DALYs/ 100 000 m2 kg NOx equivalents/100 000 m2 kg CO2 equivalents/100 000 m2 GJ/ 100 000 m2

437 (374-525) 0.26 (0.23-0.30) 1.14 (0.92-1.43) 8.45 (7.59-9.59) 5731 (5577-5900) 182 (179-184)

soybean oil mass basis

soybean oil performance basis, generated from experimental data

no change

76% (65-86%)

753 (540-1026) 154 (52.7-291) 1.38 (1.14-1.67) 15.5 (10.8-21.0) -2343 (-2924, -1583) 20.7 (20.1-21.4)

166 (96-285) 27.9 (10.7-73.3) 0.31 (0.19-0.50) 3.42 (1.95-5.88) -538 (-889, -288) 4.92 (3.11-7.61)

Median value is shown with values in parenthesis representing the 10-90% range

as seen in Figure 4. In this figure, variability bars are included only for soybean oil data, since normalized results for mineral oil have a value of 1 by definition. The variability associated with normalized soybean oil scenarios depicts the variability of the impact categories relative to mineral oil. Soybean oil shows negative impact profiles in the climate change category. This is caused by sequestration of carbon dioxide from the atmosphere, and results in net improvement instead of an impact. As discussed earlier, if the assumption of net carbon sequestration is rejected, the climate change potential for soybean oil will still be significantly less than mineral oil due to soybeans’ participation in photosynthesis. In addition,

fossil fuel consumption for soybean oil is less than 10% of mineral oil. Eutrophication is several orders of magnitude greater for soybean lubricants regardless of production improvements. It should be noted that the eutrophication impact of mineral oil is nearly negligible, which makes calculating relative values difficult. The data indicate that a significant eutrophication impact occurs with soybean oil, but not with mineral oil. This result is not surprising, given the participation of biomass in the nitrogen cycle, and direct emissions of NO3- and phosphorus into watersheds. A sensitivity analysis of the results was performed and can be found in the Supporting Information. VOL. 41, NO. 11, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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Tradeoff Analysis. The results show that soybean oil lubricants result in significant climate change and fossil fuel use benefits, but have increased impacts in eutrophication potential. Fossil fuel combustion is a controlling factor determining the environmental impacts of most products and processes due to the quantity of emissions resulting from combustion. The inventory emissions for the criteria pollutants are directly related to fossil fuel combustion. In general, products that have greater fossil fuel consumption throughout their life cycle will generate more emissions and cause the most deleterious environmental impacts. When combustion of fossil fuels is greater, the resultant emissions and subsequent impacts are also greater. Bio-based products are one of the exceptions to this linkage between fossil fuels and the emissions inventory. Agricultural systems rely largely on natural fluxes of carbon and nitrogen to produce biomass. While combustion processes are an integral part of modern agricultural systems, natural cycles that extract carbon and nitrogen from the air can contribute greatly to the overall fluxes within the system. As this analysis shows, the more biomass used within a system that has net carbon sequestration, the greater the climate change benefit. The more soybean oil that is used, the more carbon sequestration and subsequent climate change benefit can be realized. Conversely, as consumption of biomass increases, the flux of nitrogen into the environment also intensifies. Nitrogen contributes to many impacts in its reactive forms, with emissions of NOx, N2O, and NO3- primary factors in eutrophication, acidification, and smog formation, and contributors to climate change and human health impacts (37). The carbon and nitrogen cycles in agriculture are inextricably linked. As atmospheric carbon uptake increases in soybeans, so does biological nitrogen fixation in order to reach the typical 4:1-6:1 ratio for soybeans (38). As shown in Figure 4, there is an inverse relationship between climate change benefits and eutrophication impacts. Essentially, the substitution of soybean oils for mineral oils results in a fundamental tradeoff between the impacts of carbon and those of nitrogen. Increased biomass substitution may assist in ameliorating the global issue of climate change, but may occur at the detriment of regional impacts such as eutrophication and hypoxia. Consideration of increased nitrogen flux and the subsequent environmental impacts should be an important factor in decisions concerning widespread adoption of biobased products. A correlation between the aggregate amount of carbon sequestered and the nitrogen compounds emitted can be determined. Based on the assumptions of this analysis, the ratio of nitrogen compounds emitted in soybean fields during on-field processes to the rate of CO2 sequestration via photosynthesis is: 14 g NO3-/kg CO2 sequestered (10-90% range 4.4-27.0); 0.33 g N2O/kg CO2 sequestered (0.13-0.59); 0.51 g NOx/kg CO2 sequestered (0.13-0.59). For the complete inventory data specific to soybean lubricants and the relative amount used in rolling including all process emissions and sequestered carbon released at the end of life, the ratio of nitrogen compounds emitted for carbon dioxide sequestered is 36 g NO3-/kg CO2 sequestered (13.2-76.3); 0.61 g N2O/kg CO2 sequestered (0.37-1.69); 3.42 g NOx/kg CO2 sequestered (2.46-4.52). These ratios are only applicable to study data. Durable goods that maintain carbon in a solid state at the end of life may result in net carbon sequestration. If the carbon stored during photosynthesis is released into the atmosphere completely via combustion or biodegradation, there is no net sequestration. Instead, there is a net release of carbon from fossil sources used to produce the biomass. In these cases, there is no carbon sequestration, and the ratio of the amount of reactive nitrogen released to the 4148

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amount of displaced fossil carbon becomes much greater. Instead of creating climate change benefits, combustion products derived from biomass merely slow the flux of fossil carbon into the atmosphere, while accelerating the flux of reactive nitrogen. Nutrient fluxes are often neglected in agricultural LCAs because of their variability and difficulty integrating them into life cycle inventories (16). However, as the results of this analysis indicate, inclusion of this information is essential to understand and evaluate the tradeoffs that arise for biobased products. Eutrophication impacts are significantly greater and climate change is lower for soybean-based lubricants, but the extent of the remainder of impacts for soybean oil is largely dependent on the amount of oil used in the process.

Acknowledgments Support for this research was provided by the National Science Foundation’s PREMISE (DMI no. 225912 and DMI no. 400277) and IGERT (DGE no. 9720779) programs, the U.S. EPA’s TSE program (RD no. 83152101), and Alcoa, Inc. We thank Dr. Susan Powers at Clarkson University, Dr. Thomas Seager at Purdue University, and Dr. Michael Wang at Argonne National Laboratory for their constructive feedback and insightful conversations. We thank Jane Bare at the U.S. Environmental Protection Agency for providing the characterization factors used in this study.

Supporting Information Available A summary of the experimental data obtained at the aluminum rolling trial, modifications to the GREET model as well as determination of variability distributions for individual parameters are described, a description of the allocation conducted on a mass basis at the process level, information for determining location-specific impact factors, and a sensitivity analysis of the results. This material is available free of charge via the Internet at http://pubs.acs.org.

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Received for review November 14, 2006. Accepted March 14, 2007. ES062727E

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