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A Spatially and Temporally Explicit Life Cycle Inventory of Air Pollutants from Gasoline and Ethanol in the United States
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A Spatially and Temporally Explicit Life Cycle Inventory of Air Pollutants from Gasoline and Ethanol in the United States
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† Department of Civil Engineering, University of Minnesota, Minneapolis, Minnesota, United States
‡ Department of Bioproducts and Biosystems Engineering, University of Minnesota, St. Paul, Minnesota, United States
*E-mail: [email protected]
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Environmental Science & Technology

Cite this: Environ. Sci. Technol. 2012, 46, 20, 11408–11417
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https://doi.org/10.1021/es3010514
Published August 20, 2012

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Abstract

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The environmental health impacts of transportation depend in part on where and when emissions occur during fuel production and combustion. Here we describe spatially and temporally explicit life cycle inventories (LCI) of air pollutants from gasoline, ethanol derived from corn grain, and ethanol from corn stover. Previous modeling for the U.S. by Argonne National Laboratory (GREET: Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation) suggested that life cycle emissions are generally higher for ethanol from corn grain or corn stover than for gasoline. Our results show that for ethanol, emissions are concentrated in the Midwestern “Corn Belt”. We find that life cycle emissions from ethanol exhibit different temporal patterns than from gasoline, reflecting seasonal aspects of farming activities. Enhanced chemical speciation beyond current GREET model capabilities is also described. Life cycle fine particulate matter emissions are higher for ethanol from corn grain than for ethanol from corn stover; for black carbon, the reverse holds. Overall, our results add to existing state-of-the-science transportation fuel LCI by providing spatial and temporal disaggregation and enhanced chemical speciation, thereby offering greater understanding of the impacts of transportation fuels on human health and opening the door to advanced air dispersion modeling of fuel life cycles.

Copyright © 2012 American Chemical Society

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Introduction

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On-road transportation accounts for approximately 20% of United States energy consumption. (1) Associated tailpipe emissions alone account for 40–60% of ground-level ozone (O3) precursors, 6% of fine particulate matter (PM2.5), (2) and 22% of greenhouse gases (GHGs) (3) emitted. Upstream processes involved in fuel production also contribute to overall environmental impacts. Life cycle assessment (LCA) has been used extensively to quantify the combined effects of fuel production and use, (4, 5) but descriptions of where and when emissions occur are typically not reported in life cycle inventories (LCI). Such information is generally not relevant for long-lived GHGs or for fossil fuel depletion, which together have received overwhelming attention among extant LCAs of transportation fuels. For many non-GHG pollutants, knowledge of spatial and temporal aspects of emissions is critical for understanding life cycle impacts; such information has been identified as a priority for inclusion in future analyses. (6-8)
Many extant LCAs incorporate spatial and temporal information to some degree (e.g., spatially explicit treatment of a single process within the life cycle, (9-16) country, region, or state-specific impact factors, (17-27) disaggregation by urban versus rural locations, (28) county-specific information for the whole life cycle, (1, 29) or spatial and temporal information for aggregated groups of processes.) (30, 31) Here we add process-specific spatial and temporal information to an existing attributional life cycle inventory (LCI) so as to reveal patterns in the geographic distribution and intra-annual timing of emissions. We focus on transportation fuels in the U.S. and analyze three fuels pathways: gasoline, ethanol from corn grain, and cellulosic ethanol from corn stover. One goal of our work is to set the stage for future air quality modeling in the preparation of advanced life cycle impact assessments (LCIA). For example, our approach uses existing chemical speciation factors (32) to describe pollutant emissions by chemical group. Another goal is to explore effects of model spatial resolution on the apparent distribution between urban and rural emissions.

Materials and Methods

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The approach presented here builds on the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model, version 1.8d1, from Argonne National Laboratory. (33) GREET models the energy use and air emissions of pollutants from activities that occur during fuel production and use. GREET, as configured for this analysis, is an attributional life cycle model, meaning that it includes emissions in fuel supply chains, but not those caused indirectly by market-mediated effects. Additional information about GREET is available elsewhere. (33)
GREET models five groups of air pollutant emissions: oxides of nitrogen (NOx), nonmethane volatile organic compounds (VOCs), primary particulate matter less than 2.5 and 10 micrometers in diameter (PM2.5 and PM10, respectively), and sulfur oxides (SOx). Following prior work, (29, 40) we also include a sixth pollutant, ammonia (NH3). PM2.5 and PM10 inhalation can cause human mortality (34, 35) and can decrease visibility. Particulate matter can be directly emitted (“primary PM2.5”) or can form in the atmosphere (“secondary PM2.5”). VOCs can cause respiratory symptoms, (36) and NOx can increase mortality risk. (37) VOCs and NOx are precursors for ground-level ozone, which has also been linked to human mortality. (38, 39) VOCs, ammonia, SOx, and NOx can each contribute to secondary PM2.5. SOx and NOx can cause acid rain.
The number of fuels and fuel-processes one could study is large; here, we focus on a small number of fuels that are societally relevant, widely studied in previous research, and representative of a range of fuel types. Specifically, we present emission estimates for the production and use of gasoline, ethanol from corn grain through dry milling (“corn ethanol”), and ethanol from corn stover through cellulosic fermentation (“stover cellulosic ethanol”). Stover cellulosic ethanol is not currently produced on a large scale; we assume processes for the farming and refining of stover cellulosic ethanol to be colocated with the corresponding processes for corn grain ethanol. We present results using a functional unit of a vehicle-mile traveled.
We focus on adding to an existing life cycle inventory rather than refining its existing data. Accordingly, we use default GREET settings with the following exceptions: we assume that (1) corn ethanol plants use 100% natural gas process heat; (2) the ethanol produced is 100% ethanol without denaturant; however, because tailpipe emissions depend on blend level, we use tailpipe emission factors for E10 (i.e., a mixture of 90% gasoline, 10% ethanol) for the ethanol fuels (results for E85 [85% ethanol, 15% gasoline] can be found in the Supporting Information (SI) S1); (3) gasoline production is 100% conventional (i.e., not reformulated) gasoline; and (4) crude oil production is 100% conventional crude (most oil sands production occurs outside of our spatial modeling domain and is therefore excluded from our spatial analyses). Vehicle energy-efficiency and emissions are the same for all fuels, except SOx emissions, which are lower for ethanol vehicles. Efficiencies and emissions factors reported here are for year 2010.
Transportation fuels used in the U.S. are delivered by a global supply chain, and their associated pollution likewise occurs worldwide. GREET includes hundreds of unit processes involved in fuel production and use, and reports life cycle emissions as global totals. Here, we focus on the fraction of life cycle emissions that occurs in the continental U.S. and its neighboring waters. To estimate GREET emissions inside that boundary, we multiply individual GREET unit process emissions by the ratio of domestic production of their products (refs 41−43, excluding Alaska and Hawaii) to total consumption. We use the same factor to adjust upstream emissions for each process. For ocean tanker emissions, we consider emissions within the continental United States’ Exclusive Economic Zone (within 200 nautical miles from shore) to be within the spatial modeling domain. We assume that ocean tankers acquire 50% of their fuel outside of the spatial modeling domain. Figure 1 shows emissions allocated within and outside of the spatial modeling domain. Most emissions are domestic. The median value in Figure 1 is 94% domestic and 6% international. SI S2 provides similar details for all processes; as in Figure 1, in all cases, most emissions are domestic. Figure 2 shows domestic emissions for select processes; further details are in SI S1.

Figure 1

Figure 1. Amounts of emissions inside (domestic) and outside (international) the spatial modeling domain in units of grams emitted per vehicle mile traveled. Numeric labels indicate percent of life cycle emissions that are international. Our results are for domestic emissions only.

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Spatial Disaggregation

In this attributional approach, emissions are assumed to come from existing infrastructure. The fraction of emissions allocated to each facility is assumed to be equal to the fraction of total U.S. production at that facility. For example, a refinery that currently produces 5% of the gasoline in the U.S. would have 5% of gasoline production emissions allocated to it. We obtain information on the locations and average U.S. production of coal mines, (44) crude oil and natural gas extraction, (45) natural gas processing, (46) petroleum and natural gas pipelines, (2) fertilizer production, (47) sulfuric acid production, (48) pesticide production, (2) biorefineries, (49) petroleum refineries, (50) corn and soy farm locations, (51) and vehicle use, (-52, 53) and use it to spatially distribute the emissions by life cycle stage to a user-defined grid or within geographical polygons (e.g., states, counties, regions, etc.). (54) A 2010 calendar year industry is modeled using available data from between 2002 and 2011 (see SI S2 for more detail). To accomplish this spatial (and later temporal) assignment of emissions, GREET data and equations are rewritten as a program in the Python language to allow process-specific emissions tracking for at least 97% of emissions for each fuel. We present results for a 12-km grid resolution, with comparisons to 4- and 36-km resolutions in SI S2. References for the spatial data used for each process, and the year each data set was collected, are in SI S2. For processes that use electricity, we allocate the electrical generation emissions to generators from the U.S. EPA’s eGRID database (55) located in the same North American Reliability Corporation (NERC) region as the end-use process. We allocate emissions from transportation via truck, rail, and barge using a combination of geographic network analysis (54) and linear optimization (56) techniques. The method used is described in more detail in SI S2. Pipeline and ocean tanker emissions are allocated to existing emission locations. (2) Coproducts, such as distiller’s grain with solubles (DGS) production for corn ethanol and excess electricity production for stover cellulosic ethanol, are treated in GREET using a system expansion approach and are assumed to displace emissions from competing products (i.e., soy production or electricity generation). We allocate negative emissions resulting from displacement using the same methodology as for positive emissions. Each emission source is additionally classified as ground-level or elevated; elevated pollutant releases are assigned average values for U.S. electricity generating units: height, 23 m; diameter, 3 m; temperature , 456 K; exit velocity, 1.8 m s–1. (2) Refer to SI S1 for the height classification of each process. Process-specific spatial and temporal information is summarized in Figure 3 for six of the more than 400 processes; additional details are in SI S3.

Figure 2

Figure 2. Fuel life cycle emissions, disaggregated by process. Plots exclude international emissions. For visual clarity, processes with emissions too small to display individually are lumped into an “Other” category; details regarding “Other” emissions can be found in SI S1. When a coproduct of a fuel production process displaces a competing product, the emissions from the life cycle of the displaced product are treated as negative emissions. For life cycles with negative emissions (e.g., stover cellulosic ethanol in plot (a)), net emissions are indicated by a small triangle on the left side of that bar. Abbreviations: trans. = transportation, ferm. = fermentation, gen. = generation, prod. = production.

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Figure 3

Figure 3. An overview of the spatial and temporal disaggregation for six of the highest emitting processes in the life cycles of the fuels discussed here. (Over 400 processes are described for each life cycle; see SI S1 and S3 for processes not shown here.) The bar charts show emissions by pollutant in grams per vehicle mile traveled; each pollutant has its own bar length scale, and scales are consistent among panels. The maps show the points or areas where the process occurs, with either the grid cell color intensity or the area of the circle proportional to the fraction of total activity occurring at each location. Line plots indicate the relative amount of emissions by month of the year and hour of the weekday. For fertilizer nitrification, temporal profiles vary by location; the average values are plotted for each month. “Stover cellulosic” = stover cellulosic ethanol.

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Temporal Disaggregation

The time at which air pollutant emissions occur can be an important determinant of their ultimate impacts. For instance, conversion rates of VOCs and NOx into ground-level ozone are greater during hot summer days than at other times. In contrast, emissions of PM2.5 and its precursors may result in higher concentrations during winter than during other times because dilution rates tend to be lower in winter. (57, 58) We link each of the processes shown in SI S1 with process-specific temporal profiles (2, 59) to allocate emissions by month, day-of-week, and hour-of-day, with different allocations for weekdays and weekends. Figure 3 shows temporal profiles for several processes.

Chemical Disaggregation

The species categories that GREET outputs (VOCs, NOx, SOx, PM2.5, and PM10) are aggregates of many individual chemicals or pollutant types. Such broad categories can limit the accuracy of an impact analysis. For instance, the combustion of gasoline and ethanol both produce VOCs, but they produce different types of VOCs, with varying toxicity, reactivity, and ozone-production potential. The combustion of gasoline and ethanol both produce PM2.5 and its precursors, but certain types of PM2.5 cause atmospheric cooling (e.g., sulfate aerosols) while others cause atmospheric warming (e.g., black carbon aerosols). (60) To develop the inventory presented here, we link each of the processes shown in SI S1 with chemical speciation profiles (32) to disaggregate the emissions into 34 chemical species groups according to the Carbon Bond 2005 (CB05 (61)) chemical mechanism. CB05 speciates emissions into the following groups:
•

VOCs: acetaldehyde, higher aldehydes, benzene, methane, ethene, ethane, ethanol, formaldehyde, internal olefins, isoprene, methanol, olefins, paraffins, sesquiterpenes, terpenes, toluene, xylene, nonreactive VOCs, nonvolatile VOCs, and unknown/other VOCs

•

PM2.5: black carbon, nitrate particulates, organic particulates, sulfate particulates, and unclassified PM2.5

•

NOx: NO and NO2

•

SOx: SO2 and SO4,g.

PM10 and NH3 are included without speciation.

Results

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Figure 1 shows fractions of emissions occurring outside of the spatial modeling domain for each fuel. (These and all other results show emissions of air pollutants, which may or may not be directly correlated with changes in ambient pollution levels.) As mentioned above, emissions that occur outside of the spatial modeling domain (contiguous U.S. and surrounding waters), are excluded from results below. Excluding international emissions has the largest effect on the gasoline life cycle. This result is because we model 96% of emissions from transportation of crude oil by ocean tanker as occurring outside the spatial modeling domain. International tanker emissions account for 34% of SOx, 16% of NOx, 14% of PM2.5, and 8% of PM10 gasoline life cycle emissions. However, these ocean tanker emissions occur over the open ocean where human exposure is very low, (26) so the exclusion of these emissions will likely not noticeably affect estimates of total human exposure. Other notable spatial modeling domain exclusions include 28% of sulfuric acid production for fertilizer, (62) accounting for 12% of corn ethanol and 20% of stover cellulosic ethanol SOx life cycle emissions, and 56% of crude oil extraction, (63) accounting for 6% of NOx emissions from the gasoline life cycle. All other processes either occur completely within the modeling domain or comprise a negligible fraction of the life cycle total emissions.
Figure 4 shows 12 km resolution gridded emissions for the gasoline, corn ethanol, and stover cellulosic ethanol fuels. In general, gasoline emissions tend to be correlated with vehicle use and so are distributed in or near urban centers. Ethanol emissions tend also to be correlated with ethanol production and so are concentrated in the Midwest “Corn Belt”. The area along the Kentucky/Virginia border extending into West Virginia experiences a reduction in PM2.5 emissions (shown in blue in Figure 4) owing to reduced coal mining activity caused by excess electricity generation in biorefineries. Excess electricity is sold to the electrical grid and assumed to offset electricity produced elsewhere.

Figure 4

Figure 4. Annual total life cycle emissions for three fuels. For ease of viewing, each linear color scale contains a discontinuity at the 99th percentile of emissions.

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Figure 5 shows emissions contributions by region of the U.S. For all three fuels, the greatest intensity of emissions per land area occurs in the Northeast for VOCs, owing to the large portion of total vehicle miles traveled per land area occurring there. See SI S2 for the fraction of vehicle miles traveled in each region. The Midwest receives a large amount of emissions for both ethanol fuels, owing to ethanol fermentation plants and ammonia emissions from fertilizer nitrification. For the Midwest, emissions are lower for gasoline than for ethanol, with the exception that SOx emissions are negative (i.e., reduced) for stover cellulosic ethanol owing to excess electricity generation at fermentation plants. SOx emissions in the Southeast for corn ethanol are mainly attributable to Florida-based sulfuric acid production for phosphate fertilizer. The Southwest and West regions generally do not receive large proportions of pollutant emissions for any of the three fuels (exception: SOx emissions for gasoline).
Emissions are spatially disaggregated by allocating them to cells in a raster grid. As has been previously noted, (15) we observe that the resolution of the grid used can influence the apparent locations of emission. SI Figures S2-1 and S2-2 show the dependence of emissions allocated to urban areas on the spatial scale of the grid used for allocation and compares the results to the urban emissions given by GREET. (28) Overall, increasing grid resolution leads to an increased allocation of emissions to urban areas, suggesting that a coarse grid tends to artificially dilute urban emissions to the surrounding rural areas. See S2 for further discussion.
Figure 6 shows temporal profiles for fuels and pollutants discussed here. Pollutant emissions from the gasoline life cycle do not vary appreciably by month. For the corn ethanol life cycle, however, there is a spike in NH3 and NOx emissions in the spring, corresponding to fertilizer application. This pattern is also true to a lesser extent for stover cellulosic ethanol. All fuels show a slight decrease in emissions on weekends. Weekday emissions commonly show a bimodal distribution around the morning and evening rush hours owing to increased vehicle tailpipe emissions at those times. For fuels and pollutants where farming activities are a major contributor, however, the weekday and weekend emissions are unimodally distributed around the daylight farming hours.

Figure 5

Figure 5. Contributions of U.S. regions to total life cycle emissions for three fuels (micrograms emitted per vehicle-mile traveled per square kilometer land area). Dashed lines show U.S. average emissions. Refer to SI S2 for a version of this figure with units of g mi–1. Key: Northeast , Midwest , Southeast , Southwest , West .

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Figure 6

Figure 6. Temporal profiles of life cycle emissions (mg emitted per vehicle-mile traveled) by month of year, day of week, and hour of day (weekday). Results by hour of day (weekend) are in SI S2.

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Our chemical speciation reveals the following. Although stover ethanol emits the lowest total amount of PM2.5 of all three fuels, it emits more black carbon (15 mg mi–1), a species of fine particulate matter that contributes to atmospheric warming, than either of the other two fuels (gasoline: 4.5 mg mi–1, corn ethanol: 10). Stover cellulosic ethanol also emits the lowest amount of sulfate aerosols (−1.3 mg mi–1; gasoline: 1.9, corn ethanol: 3.8), which cause atmospheric cooling. Emissions of ethanol are 30 000–40 000 times higher for the ethanol fuels than for gasoline; ethanol in the atmosphere may be oxidized to form acetaldehyde (a carcinogen). However, emissions of benzene (another carcinogen (64)) are higher for gasoline than for the ethanol fuels (the relative amounts by fuel depend on the ethanol feedstock and the blend level of the final fuel). Full results for chemical speciation, including for 85% ethanol blends (E85), are in SI S1.

Discussion

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We have presented a spatially and temporally explicit life cycle inventory for transportation fuels. Prior life cycle inventories were typically presented at global, national, or regional levels, which is sufficient for understanding global processes such as climate change and fossil fuel depletion, but is insufficient for the analysis of local processes such as air pollution. The spatially and temporally explicit LCI presented here not only provides the level of detail necessary to perform detailed LCIA of air pollutant emissions, it also gives information on spatial and temporal trends that can be useful in policy making and regulation. For instance, in the U.S., implementation plans for coming into compliance with air quality standards are generally determined by individual states. Spatially and temporally explicit LCA can help state-level policy makers identify potential sources of air pollutant emissions within their jurisdictions and create appropriate regulations. Such information is not delivered by conventional LCA approaches.
The framework presented here outputs gridded, time-resolved emissions files, which is an important step toward photochemical dispersion modeling. For example, U.S. production and consumption of stover cellulosic ethanol would increase emissions of NOx, NH3, and PM2.5 in the Midwest, but would decrease SOx emissions in the same region. All four of these species influence ambient concentrations of PM2.5, it is unclear a priori whether the net change in PM2.5 concentrations will be positive or negative in this region. Photochemical dispersion modeling can help answer this question.
Our choice of spatial modeling domain (continental U.S.) by definition restricts our study. However, we do not see this aspect as a major limitation because most emissions occur within our boundary, and because most of the excluded emissions occur over the open ocean, for which human exposures are much lower than for emissions on land. (26) This same domain has been used in prior regulatory analysis. (65) At present, lack of computational power and accurate input data make it impractical to extend the system boundaries to the entire world. For future work, a nested approach (detailed spatial treatment of emissions within the modeling domain; coarser resolution outside of the domain) could prove useful. A further limitation of this study is that owing to limited data availability, the spatial data used here represent a range of years (2002–2011; see SI S2 for details). Finally, uncertainty and variability in the spatial, temporal, and chemical speciation data used here contribute to the overall uncertainty in the life cycle inventory, but this information is generally unknown or unreported.
Our work considers supply chain emissions of existing production. GREET, and by extension the framework presented here, performs an exclusively attributional (static) LCA for non-GHG air pollutants. We do not include indirect (market-mediated) effects. An example of an indirect effect would be if corn ethanol production in the U.S. increases global grain prices, causing emissions from the burning of tropical rainforest to grow crops. A consequential (dynamic) LCA, which we do not do, would aim to capture indirect effects. We assume here that all processes occur at existing production locations; in reality, new production may cause new facilities to open, or existing facilities to close or change locations. We assume that cellulosic biorefineries for stover cellulosic ethanol are colocated with existing corn ethanol biorefineries; in reality, the difficulty of transporting corn stover may cause biorefineries to be smaller and closer to cornfields.
We have focused here on the air pollutant implications of the choice between ethanol and gasoline as a transportation fuel. In general, methods presented here can provide insight into any spatially or temporally inhomogeneous environmental impact categories, such as water quality and availability, soil properties, or wildlife habitats. They can also be expanded to study specific processes that affect those impact categories, such as agriculture and food production, building construction, or electricity generation; the possible applications are only limited by the availability of data.

Supporting Information

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Process-specific, chemically speciated, and state and urban area specific emissions data (S1a–c, Excel format), supplemental text and figures, and process-specific maps (S2 and S3a–c, pdf format) for gasoline, corn ethanol, and stover cellulosic ethanol. This material is available free of charge via the Internet at http://pubs.acs.org.

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Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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  • Corresponding Author
    • Jason D. Hill - Department of Bioproducts and Biosystems Engineering, University of Minnesota, St. Paul, Minnesota, United States;  Email: [email protected]
  • Authors
    • Christopher W. Tessum - Department of Civil Engineering, University of Minnesota, Minneapolis, Minnesota, United States
    • Julian D. Marshall - Department of Civil Engineering, University of Minnesota, Minneapolis, Minnesota, United States
  • Notes
    The authors declare no competing financial interest.

Acknowledgment

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We acknowledge the University of Minnesota Institute on the Environment: Initiative for Renewable Energy and the Environment Grant No. Rl-0026-09 and U.S. Department of Energy Award No. DE-EE0004397 for funding, the Minnesota Supercomputing Institute for computational resources, and Tom Nickerson for assistance with data acquisition and processing.

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    Geyer, R.; Stoms, D. M.; Lindner, J. P.; Davis, F. W.; Wittstock, B. Coupling GIS and LCA for biodiversity assessments of land use, part 1: Inventory modeling Int. J. Life Cycle Assess. 2010, 15, 454– 67
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    Coupling GIS and LCA for biodiversity assessments of land use. Part 1: Inventory modeling
    Geyer, Roland; Stoms, David M.; Lindner, Jan P.; Davis, Frank W.; Wittstock, Bastian
    International Journal of Life Cycle Assessment (2010), 15 (5), 454-467CODEN: IJLCFF; ISSN:0948-3349. (Springer)
    Purpose: Geospatial details about land use are necessary to assess its potential impacts on biodiversity. Geog. information systems (GIS) are adept at modeling land use in a spatially explicit manner, while life cycle assessment (LCA) does not conventionally utilize geospatial information. This study presents a proof-of-concept approach for coupling GIS and LCA for biodiversity assessments of land use and applies it to a case study of ethanol prodn. from agricultural crops in California. Materials and methods: GIS modeling was used to generate crop prodn. scenarios for corn and sugar beets that met a range of ethanol prodn. targets. The selected study area was a four-county region in the southern San Joaquin Valley of California, USA. The resulting land use maps were translated into maps of habitat types. From these maps, vectors were created that contained the total areas for each habitat type in the study region. These habitat compns. are treated as elementary input flows and used to calc. different biodiversity impact indicators in a second paper (Geyer et al., submitted). Results and discussion: Ten ethanol prodn. scenarios were developed with GIS modeling. Current land use is added as baseline scenario. The parcels selected for corn and sugar beet prodn. were generally in different locations. Moreover, corn and sugar beets are classified as different habitat types. Consequently, the scenarios differed in both the habitat types converted and in the habitat types expanded. Importantly, land use increased nonlinearly with increasing ethanol prodn. targets. The GIS modeling for this study used spatial data that are commonly available in most developed countries and only required functions that are provided in virtually any com. or open-source GIS software package. Conclusions: This study has demonstrated that GIS-based inventory modeling of land use allows important refinements in LCA theory and practice. Using GIS, land use can be modeled as a geospatial and nonlinear function of output. For each spatially explicit process, land use can be expressed within the conventional structure of LCA methodol. as a set of elementary input flows of habitat types.
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  14. 14
    Zhang, X.; Izaurralde, R. C.; Manowitz, D.; West, T. O.; Post, W. M.; Thomson, A. M.; Bandaru, V. P.; Nichols, J.; Williams, J. R. An integrative modeling framework to evaluate the productivity and sustainability of biofuel crop production systems Global Change Biol. Bioenergy 2010, 2, 258– 77
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    Mutel, C. L.; Pfister, S.; Hellweg, S. GIS-based regionalized life cycle assessment: How big is small enough? Methodology and case study of electricity generation. Environ. Sci. Technol. 2012, 46, 1096– 103.
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    GIS-Based Regionalized Life Cycle Assessment: How Big Is Small Enough? Methodology and Case Study of Electricity Generation
    Mutel, Christopher L.; Pfister, Stephan; Hellweg, Stefanie
    Environmental Science & Technology (2012), 46 (2), 1096-1103CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    New methodol. is described for performing regionalized life cycle assessment and systematically choosing the spatial scale of regionalized impact assessment methods. Std. matrix-based calcns. are extended to include matrixes that describe the mapping from inventory to impact assessment spatial supports. Uncertainty in inventory spatial data is modeled using a discrete spatial distribution function, which in a case study is derived from empirical data. The minimization of global spatial autocorrelation is used to choose the optimal spatial scale of impact assessment methods. These techniques are demonstrated on electricity prodn. in the United States, using regionalized impact assessment methods for air emissions and freshwater consumption. Case study results show important differences between site-generic and regionalized calcns., and provide specific guidance for future improvements of inventory data sets and impact assessment methods.
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  16. 16
    Millet, D. B..; Apel, E.; Henze, D. K.; Hill, J.; Marshall, J. D.; Singh, H. B.; Tessum, C. W. Natural and anthropogenic ethanol sources in North America and potential atmospheric impacts of ethanol fuel use. Environ. Sci. Technol. 2012, 46, 8484– 92.
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    16
    Natural and Anthropogenic Ethanol Sources in North America and Potential Atmospheric Impacts of Ethanol Fuel Use
    Millet, Dylan B.; Apel, Eric; Henze, Daven K.; Hill, Jason; Marshall, Julian D.; Singh, Hanwant B.; Tessum, Christopher W.
    Environmental Science & Technology (2012), 46 (15), 8484-8492CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    An ensemble of aircraft measurements in conjunction with the GEOS-Chem chem. transport model were used to constrain present-day North American ethanol sources and gauge potential long-range impacts of increased ethanol fuel use. Current ethanol emissions are underestimated by 50% in western North America and overestimated by a factor of 2 in the east. The best est. for year 2005 North American ethanol emissions is 670 Gg C/yr, with 440 Gg C/yr from the continental US. These optimized source ests. were used to assess 2 scenarios for increased ethanol fuel use in the US: one assumed a complete transition from gasoline to E85 fuel; the other was tied to biofuel requirements of the US Energy Independence and Security Act (EISA). For both scenarios, increased ethanol emissions led to higher atm. acetaldehyde concns. (up to 14% in winter for the all-E85 scenario and 2% for the EISA scenario) and an assocd. shift in reactive N partitioning reflected by an increase in the peroxyacetyl nitrate (PAN):NOy ratio. The largest relative impacts occurred in fall, winter, and spring due to large natural ethanol and other org. compd. emissions in summer. Projected changes in atm. PAN reflected a balance between an increased supply of peroxyacetyl radicals from acetaldehyde oxidn., and lower NOx emissions for E85- vs. gasoline-fueled vehicles. The net effect was a general PAN increase in fall through spring, and a weak decrease over the southeastern US and the Atlantic Ocean in summer. Predicted NOx concns. decreased in surface air over North America (as much 5% in the all-E85 scenario); downwind from North America, this effect was counter-acted by higher NOx export efficiency driven by increased PAN prodn. and transport. From an NOx export from North America viewpoint, increased PAN formation assocd. with E85 fuel use acted to offset the assocd. lower NOx emissions.
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  17. 17
    Posch, M.; Seppälä, J.; Hettelingh, J.-P.; Johansson, M.; Margni, M.; Jolliet, O. The role of atmospheric dispersion models and ecosystem sensitivity in the determination of characterisation factors for acidifying and eutrophying emissions in LCIA Int. J. Life Cycle Assess. 2008, 13, 477– 86
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    17
    The role of atmospheric dispersion models and ecosystem sensitivity in the determination of characterisation factors for acidifying and eutrophying emmissions in LCIA
    Posch, Maximilian; Seppaelae, Jyri; Hettelingh, Jean-Paul; Johansson, Matti; Margni, Manuele; Jolliet, Olivier
    International Journal of Life Cycle Assessment (2008), 13 (6), 477-486CODEN: IJLCFF; ISSN:0948-3349. (Springer)
    The methodol. choices and framework to assess environmental impacts in life cycle assessment are still under discussion. Despite intensive developments worldwide, few attempts have been made hitherto to systematically present the role of different factors of characterization models in life cycle impact assessment (LCIA). The aim of this study is to show how European av. and country-dependent characterization factors for acidifying and eutrophying emissions differ when using (a) acidifying and eutrophying potentials alone, (b) depositions from an atm. dispersion model or (c) crit. loads in conjunction with those depositions. Furthermore, in the latter case, the contributions of emissions, an atm. transport model and crit. loads to changes in characterization factors of NO2 are studied. In addn., the new characterization factors based on the accumulated exceedance (AE) method are presented using updated emissions, a new atm. transport model and the latest crit. loads. In this study, characterization factors for acidifying and eutrophying emissions are calcd. by three different methods. In the 'no fate' (NF) methods, acidifying and eutrophying potentials alone are considered as characterization factors. In the 'only above terrestrial environment' (OT) approach, characterization factors are based on the deposition of the acidifying or eutrophying substances to terrestrial land surfaces. The third method is the so-called AE method in which crit. loads are used in conjunction with depositions. The results of the methods are compared both at the European and the country level using weighted mean, weighted std. deviation, min. and max. values. To illustrate the sensitivity of the AE method, changes in European emissions, employed atm. dispersion model and the crit. loads database are conducted step-by-step, and the differences between the results are analyzed. For European av. characterization factors, the three characterization methods of acidification produce results in which the contributions of NH3, NO2 and SO2 to the acidification indicator do not differ much within each method when 1 kg of each acidifying substance is emitted. However, the NF methods cannot describe any spatial aspects of environmental problems. Both OT and AE methods show that the spatial aspects play an important role in the characterization factors. The AE method results in greater differentiations between country-dependent characterization factors than does the OT method. In addn., the results of the AE and OT methods differ from each other for individual countries. A major shortcoming of the OT approach is that it does not consider the sensitivity of the ecosystems onto which the pollutants are deposited, whereas the AE approach does. In the case of the AE method, a new atm. dispersion model, new information on emissions and crit. loads have a different influence on the characterization factors, depending on the country. The results of statistics show that the change in the atm. dispersion model has a greatest influence on the results, since ecosystem-specific depositions are taken into account for the first time. The simple NF methods can be used in a first approxn. to assess the impacts of acidification and terrestrial eutrophication in cases where we do not know where the emissions occur. The OT approach is a more advanced method compared with the NF method, but its capability to describe spatial aspects is limited. The AE factors are truly impact-oriented characterization factors and the information used here represents the current best knowledge about the assessment practice of acidification and terrestrial eutrophication in Europe. The key message of this study is that there is no shortcut to achieving advanced characterization of acidification and terrestrial eutrophication: an advanced methodol. cannot develop without atm. dispersion models and information on ecosystem sensitivity.
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    Pennington, D. W.; Margni, M.; Ammann, C.; Jolliet, O. Multimedia fate and human intake modeling: Spatial versus nonspatial insights for chemical emissions in western Europe Environ. Sci. Technol. 2005, 39, 1119– 28
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    Raugei, M.; Ulgiati, S. A novel approach to the problem of geographic allocation of environmental impact in life cycle assessment and material flow analysis Ecol. Indic. 2009, 9, 1257– 64
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    A novel approach to the problem of geographic allocation of environmental impact in Life Cycle Assessment and Material Flow Analysis
    Raugei, Marco; Ulgiati, Sergio
    Ecological Indicators (2009), 9 (6), 1257-1264CODEN: EICNBG; ISSN:1470-160X. (Elsevier Ltd.)
    Life Cycle Assessment and Material Flow Anal., in spite of their invaluable contribution to the investigation of the environmental performance of human-dominated processes, still fall short of properly addressing the issue of the geog. distribution of the potential environmental impacts, which often has wide-reaching environmental as well as political implications. An innovative allocation method based on matrix algebra is introduced here, in order to allow to split the calcd. environmental impact indicators into fractions thereof which are geog. attributed to the different world regions. This is done on the basis of: (i) where the analyzed process takes place and (ii) where the directly and indirectly required fossil and nuclear fuels are sourced from (including those for electricity prodn.). The method has been successfully tested on primary aluminum prodn., as a first case study.
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    Shah, V. P.; Ries, R. J. A Characterization model with spatial and temporal resolution for life cycle impact assessment of photochemical precursors in the United States Int. J. Life Cycle Assess. 2009, 14, 313– 27
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    20
    A characterization model with spatial and temporal resolution for life cycle impact assessment of photochemical precursors in the United States
    Shah, Viral P.; Ries, Robert J.
    International Journal of Life Cycle Assessment (2009), 14 (4), 313-327CODEN: IJLCFF; ISSN:0948-3349. (Springer)
    Traditional life cycle impact assessment methodologies have used aggregated characterization factors, neglecting spatial and temporal variations in regional impacts like photochem. oxidant formation. This increases the uncertainty of the LCA results and diminishes the ease of decision-making. This study compares four common impact assessment methods, CML2001, Eco-indicator 99, TRACI, and EDIP2003, on their underlying models, spatial and temporal resoln., and the level at which photochem. oxidant impacts are calcd. A new characterization model is proposed that incorporates spatial and temporal differentiation. A photochem. air quality modeling system (CAMx-MM5-SMOKE) is used to simulate the process of formation, transformation, transport, and removal of photochem. pollutants. Monthly characterization factors for individual US states are calcd. at three levels along the cause-effect chain, namely, fate level, human and ecosystem exposure level, and human effect level. The results indicate that a spatial variability of one order of magnitude and a temporal variability of two orders of magnitude exist in both the fate level and human exposure and effect level characterization factors for NOx. The summer time characterization factors for NOx are higher than the winter time factors. However, for anthropogenic VOC, the summer time factors are lower than the winter time in almost half of the states. This is due to the higher emission rates of biogenic VOCs in the summer. The ecosystem exposure factors for NOx and VOC do not follow a regular pattern and show a spatial variation of about three orders of magnitude. They do not show strong correlation with the human exposure factors. Sensitivity anal. has shown that the effect of meteorol. and emission inputs is limited to a factor of three, which is several times smaller than the variation seen in the factors. Uncertainties are introduced in the characterization of photochem. precursors due to a failure to consider the spatial and temporal variations. Seasonal variations in photochem. activity influence the characterization factors more than the location of emissions. The human and ecosystem exposures occur through different mechanisms, and impacts calcd. at the fate level based only on ozone concn. are not a good indicator for ecosystem impacts. Spatial and temporal differentiation account for fate and transport of the pollutant, and the exposure of and effect on the sensitive human population or ecosystem. Adequate resoln. for seasonal and regional processes, like photochem. oxidant formation, is important to reduce the uncertainty in impact assessment and improve decision-making power. An emphasis on incorporating some form of spatial and temporal information within std. LCI databases and using adequately resolved characterization factors will greatly increase the fidelity of a std. LCA.
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    Hauschild, M.; Potting, J. Spatial Differentiation in Life Cycle Impact Assessmen–the EDIP2003 Methodology, 2005. www2.mst.dk/udgiv/publications/2005/87-7614-579-4/pdf/87-7614-580-8.pdf (accessed March 2012).
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    Norris, G. A. Impact characterization in the tool for the reduction and assessment of chemical and other environmental impacts: Methods for acidification, eutrophication, and ozone formation J. Ind. Ecol. 2002, 6, 79– 101
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    Wegener Sleeswijk, A.; Heijungs, R. GLOBOX: A spatially differentiated global fate, intake and effect model for toxicity assessment in LCA Sci. Total Environ. 2010, 408, 2817– 32
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    Querini, F.; Morel, S.; Boch, V.; Rousseaux, P. USEtox relevance as an impact indicator for automotive fuels. application on diesel fuel, gasoline and hard coal electricity Int. J. Life Cycle Assess. 2011, 16, 829– 40
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    USEtox relevance as an impact indicator for automotive fuels: Application on diesel fuel, gasoline and hard coal electricity
    Querini, Florent; Morel, Stephane; Boch, Valerie; Rousseaux, Patrick
    International Journal of Life Cycle Assessment (2011), 16 (8), 829-840CODEN: IJLCFF; ISSN:0948-3349. (Springer)
    Purpose: In order to provide more sustainable fuels and address the depletion of oil as a feedstock, the automotive industry must adapt to a growing market share of alternative fuels. The environmental impacts of the automotive industry to date would suggest that these alternatives will be more environmentally friendly than petroleum-based fuels. This is nonetheless an assumption that cannot be confirmed without a systematic life cycle assessment (LCA). This article explores the feasibility of USEtox to provide information needed for automotive-fuel LCA. Materials and methods: USEtox is tested on three energy pathways: gasoline, diesel fuel and hard coal electricity. The studied emissions are mainly volatile org. compds. (VOCs) and heavy metals. USEtox being dependent on the physicochem. and toxic properties of the studied species, a speciation of all VOCs emitted was performed. Also, since USEtox allows a distinction between rural and urban emissions, a geog. information system was developed to distinguish these emissions. Finally, because crude oil comes from various countries, characterization factors were calcd. for new regional compartments. Results and discussion Human health issues are caused by aldehydes and heavy metals while ecotoxicity is caused by polycyclic arom. hydrocarbons, aldehydes and heavy metals. For org. compds., a clear distinction is obsd. between urban and rural emissions while inorg. mechanisms are independent of this distinction. Among the three energy pathways, urban diesel is the more impacting. Conclusions: USEtox can be used for the assessment of automotive fuels, though it only addresses specific aspects of human health and ecotoxicity. The LCA practitioner must keep in mind that USEtox has to be used in conjunction with other indicators, such as ReCiPe or CML, to comprehensively cover the toxic and ecotoxic impacts of fuels. The level of anal. is dependent on the accuracy of the inventory, aldehydes and PAH playing a crucial role. Inorg. impacts are highly uncertain, contrary to org. compds. The distinction between rural and urban emissions allows a better assessment of internal combustion engine-powered cars compared with elec. and hybrid cars, which is esp. useful for the automotive industry now that these technologies are clearly being developed.
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    Lloyd, S.; Ries, R. Spatial and temporal life cycle assessment: ozone formation potential from natural gas use in a typical residential building in Pittsburgh, USA. In Dynamics of Regions and Networks in Industrial Ecosystems; Ruth, M.; Davidsdottir, B., Eds.; The MIT Press, 2009.
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    Humbert, S.; Marshall, J. D.; Shaked, S.; Spadaro, J. V.; Nishioka, Y.; Preiss, P.; McKone, T. E.; Horvath, A.; Jolliet, O. Intake fraction for particulate matter: Recommendations for life cycle impact assessment Environ. Sci. Technol. 2011, 45, 4808– 16
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    Intake Fraction for Particulate Matter: Recommendations for Life Cycle Impact Assessment
    Humbert, Sebastien; Marshall, Julian D.; Shaked, Shanna; Spadaro, Joseph V.; Nishioka, Yurika; Preiss, Philipp; McKone, Thomas E.; Horvath, Arpad; Jolliet, Olivier
    Environmental Science & Technology (2011), 45 (11), 4808-4816CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    A review concerning the impact of particulate matter (PM) as a significant contributor to death and disease worldwide, summarizing the work of an international expert group on integrating human PM exposure into life cycle impact assessment (LCIA) within the United Nations Environmental Program/TETAC Life Cycle Initiative, is given. Literature-derived intake fraction values (inhaled emissions fraction), based on emission release height and archetypal environment (indoor vs. outdoor; urban, rural, or remote locations) were assessed. Recommended intake fraction values are provided for primary PM10-2.5 (coarse particles), primary PM2.5 (fine particles), and secondary PM2.5 from SO2, NOx, and NH3, to provide as complete and consistent an archetype framework as possible given the current understanding of each pollutant. Presented values facilitate incorporating regional impacts into LCIA for human health adverse effects from PM. Topics discussed include: methods (general framework, intake fraction, factors affecting intake fraction [regionalization, emission height, archetypes addressing regionalization and emission height, PM types], archetype parameterization [indoor,urban, rural, remote, emissions- and population-weighted arithmetic av.], comparing available models and data [primary PM2.5; primary PM10, primary PM10-2.5, secondary PM], intake fraction value summary [urban, rural, remote, emission release height, weighted arithmetic av.]); results and discussion (uncertainty and the importance of spatial differentiation, future research needs); and supporting information (archetype parametrization [breathing rate, indoor environment and world city parameterization, parameterization by continent], characterizing PM emissions [total emission by source type and emission height, fraction of PM10 <2.5 μm], intake fraction [regressions, values, recommended fractions], variability and uncertainty).
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    Manneh, R.; Margni, M.; Deschenes, L. Spatial variability of intake fractions for Canadian emission scenarios: A comparison between three resolution scales Environ. Sci. Technol. 2010, 44, 4217– 24
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    Huo, H.; Wu, Y.; Wang, M. Total versus urban: Well-to-wheels assessment of criteria pollutant emissions from various vehicle/fuel systems Atmos. Environ. 2009, 43, 1796– 804
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    Hill, J.; Polasky, S.; Nelson, E.; Tilman, D.; Huo, H.; Ludwig, L.; Neumann, J.; Zheng, H.; Bonta, D. Climate change and health costs of air emissions from biofuels and gasoline Proc. Natl. Acad. Sci. U.S.A. 2009, 106, 2077– 82
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    Cook, R.; Phillips, S.; Houyoux, M.; Dolwick, P.; Mason, R.; Yanca, C.; Zawacki, M.; Davidson, K.; Michaels, H.; Harvey, C.; Somers, J.; Luecken, D. Air quality impacts of increased use of ethanol under the United States’ energy independence and security act Atmos. Environ. 2010, 45, 7714– 24
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    Tsao, C.-C.; Campbell, J. E.; Mena-Carrasco, M.; Spak, S. N.; Carmichael, G. R.; Chen, Y. Increased estimates of air-pollution emissions from Brazilian sugar-cane ethanol Nat. Clim. Change 2011, 2, 53– 7
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    Simon, H.; Beck, L.; Bhave, P. V.; Divita, F.; Hsu, Y.; Luecken, D.; Mobley, J. D.; Pouliot, G. A.; Reff, A.; Sarwar, G.; Strum, M. The development and uses of EPA’s SPECIATE database Atmos. Pollut. Res. 2010, 1, 196– 206
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    The development and uses of EPA's SPECIATE database
    Simon, Heather; Beck, Lee; Bhave, Prakash V.; Divita, Frank; Hsu, Ying; Luecken, Deborah; David Mobley, J.; Pouliot, George A.; Reff, Adam; Sarwar, Golam; Strum, Madeleine
    Atmospheric Pollution Research (2010), 1 (), 196-206CODEN: APRTCD; ISSN:1309-1042. (Dokuz Eylul University, Dep. Environmental Engineering)
    SPECIATE is the U.S. Environmental Protection Agency's (EPA) repository of volatile org. compds. (VOCs) and particulate matter (PM) speciation profiles of air pollution sources. These source profiles can be used to (1) provide input to chem. mass balance (CMB) receptor models; (2) verify profiles derived from ambient measurements by multivariate receptor models (e.g., factor anal. and pos. matrix factorization); (3) interpret ambient measurement data; and (4) create speciated emission inventories for regional haze, climate, and photochem. air quality modeling. This paper describes the SPECIATE v4.2 database, provides specific examples of its use, and makes recommendations for future improvements.
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    UChicago Argonne LLC. The Greenhouse Gases,Regulated Emissions,and Energy Use in Transportation Model v1.8d1, (2010. http://greet.es.anl.gov/ (accessed November 2010).
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    Pope, C. A., III; Dockery, D. W. Health effects of fine particulate air pollution: Lines that connect J. Air Waste Manage. Assoc. 2006) 56, 709– 42
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    Health effects of fine particulate air pollution: lines that connect
    Pope, C. Arden, III; Dockery, Douglas W.
    Journal of the Air & Waste Management Association (2006), 56 (6), 709-742CODEN: JAWAFC; ISSN:1096-2247. (Air & Waste Management Association)
    A review. Efforts to understand and mitigate the health effects of participate matter (PM) air pollution have a rich and interesting history. This review focuses on six substantial lines of research that have been pursued since 1997 that have helped elucidate our understanding about the effects of PM on human health. There has been substantial progress in the evaluation of PM health effects at different time-scales of exposure and in the exploration of the shape of the concn.-response function. There has also been emerging evidence of PM-related cardiovascular health effects and growing knowledge regarding interconnected general pathophysiol. pathways that link PM exposure with cardiopulmonary morbidity and mortality. Despite important gaps in scientific knowledge and continued reasons for some skepticism, a comprehensive evaluation of the research findings provides persuasive evidence that exposure to fine particulate air pollution has adverse effects on cardiopulmonary health. Although much of this research has been motivated by environmental public health policy, these results have important scientific, medical, and public health implications that are broader than debates over legally mandated air quality stds.
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    Pope, C. A., III; Burnett, R. T.; Thun, M. J.; Calle, E. E.; Krewski, D.; Ito, K.; Thurston, G. D. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution JAMA, J. Am. Med. Assoc. 2002, 287, 1132– 41
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    Curtis, L.; Rea, W.; Smith-Willis, P.; Fenyves, E.; Pan, Y. Adverse health effects of outdoor air pollutants Environ. Int. 2006, 32, 815– 30
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    Chiusolo, M.; Cadum, E.; Stafoggia, M.; Galassi, C.; Berti, G.; Faustini, A.; Bisanti, L.; Vigotti, M. A.; Dess, M. P.; Cernigliaro, A.; Mallone, S.; Pacelli, B.; Minerba, S.; Simonato, L.; Forastiere, F. Short term effects of nitrogen dioxide on mortality and susceptibility factors in ten italian cities: The EpiAir study Environ. Health Perspect. 2011, 119, 1233– 38
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    Bell, M. L.; McDermott, A.; Zeger, S. L.; Samet, J. M.; Dominici, F. Ozone and short-term mortality in 95 U.S. urban communities, 1987–2000 JAMA, J. Am. Med. Assoc. 2004, 292, 2372– 8
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    Jerrett, M.; Burnett, R. T.; Pope, C. A., III; Ito, K.; Thurston, G.; Krewski, D.; Shi, Y.; Calle, E.; Thun, M. Long-term ozone exposure and mortality New Engl. J. Med. 2009, 360, 1085– 95
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  • Abstract

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    Figure 1

    Figure 1. Amounts of emissions inside (domestic) and outside (international) the spatial modeling domain in units of grams emitted per vehicle mile traveled. Numeric labels indicate percent of life cycle emissions that are international. Our results are for domestic emissions only.

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    Figure 2

    Figure 2. Fuel life cycle emissions, disaggregated by process. Plots exclude international emissions. For visual clarity, processes with emissions too small to display individually are lumped into an “Other” category; details regarding “Other” emissions can be found in SI S1. When a coproduct of a fuel production process displaces a competing product, the emissions from the life cycle of the displaced product are treated as negative emissions. For life cycles with negative emissions (e.g., stover cellulosic ethanol in plot (a)), net emissions are indicated by a small triangle on the left side of that bar. Abbreviations: trans. = transportation, ferm. = fermentation, gen. = generation, prod. = production.

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    Figure 3

    Figure 3. An overview of the spatial and temporal disaggregation for six of the highest emitting processes in the life cycles of the fuels discussed here. (Over 400 processes are described for each life cycle; see SI S1 and S3 for processes not shown here.) The bar charts show emissions by pollutant in grams per vehicle mile traveled; each pollutant has its own bar length scale, and scales are consistent among panels. The maps show the points or areas where the process occurs, with either the grid cell color intensity or the area of the circle proportional to the fraction of total activity occurring at each location. Line plots indicate the relative amount of emissions by month of the year and hour of the weekday. For fertilizer nitrification, temporal profiles vary by location; the average values are plotted for each month. “Stover cellulosic” = stover cellulosic ethanol.

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    Figure 4

    Figure 4. Annual total life cycle emissions for three fuels. For ease of viewing, each linear color scale contains a discontinuity at the 99th percentile of emissions.

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    Figure 5

    Figure 5. Contributions of U.S. regions to total life cycle emissions for three fuels (micrograms emitted per vehicle-mile traveled per square kilometer land area). Dashed lines show U.S. average emissions. Refer to SI S2 for a version of this figure with units of g mi–1. Key: Northeast , Midwest , Southeast , Southwest , West .

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    Figure 6

    Figure 6. Temporal profiles of life cycle emissions (mg emitted per vehicle-mile traveled) by month of year, day of week, and hour of day (weekday). Results by hour of day (weekend) are in SI S2.

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      Ethanol Can Contribute to Energy and Environmental Goals
      Farrell, Alexander E.; Plevin, Richard J.; Turner, Brian T.; Jones, Andrew D.; O'Hare, Michael; Kammen, Daniel M.
      Science (Washington, DC, United States) (2006), 311 (5760), 506-508CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
      A review. To study the potential effects of increased biofuel use, we evaluated six representative analyses of fuel ethanol. Studies that reported neg. net energy incorrectly ignored coproducts and used some obsolete data. All studies indicated that current corn ethanol technologies are much less petroleum-intensive than gasoline but have greenhouse gas emissions similar to those of gasoline. However, many important environmental effects of biofuel prodn. are poorly understood. New metrics that measure specific resource inputs are developed, but further research into environmental metrics is needed. Nonetheless, it is already clear that large-scale use of ethanol for fuel will almost certainly require cellulosic technol.
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    5. 5
      Hill, J.; Nelson, E.; Tilman, D.; Polasky, S.; Tiffany, D. Environmental, economic, and energetic costs and benefits of biodiesel and ethanol biofuels Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 11206– 10
      There is no corresponding record for this reference.
    6. 6
      Reap, J.; Roman, F.; Duncan, S.; Bras, B. A survey of unresolved problems in life cycle assessment: Part 2: Impact assessment and interpretation Int. J. Life Cycle Assess. 2008, 13, 374– 88
      There is no corresponding record for this reference.
    7. 7
      McKone, T. E.; Nazaroff, W. W.; Berck, P.; Auffhammer, M.; Lipman, T.; Torn, M. S.; Masanet, E.; Lobscheid, A.; Santero, N.; Mishra, U.; Barrett, A.; Bomberg, M.; Fingerman, K.; Scown, C.; Strogen, B.; Horvath, A. Grand Challenges for Life-Cycle Assessment of Biofuels Environ. Sci. Technol. 2011, 45, 1751– 6
      There is no corresponding record for this reference.
    8. 8
      National Research Council. Renewable Fuel Standard:Potential Economic and Environmental Effectsof U.S. Biofuel Policy, (2011. http://www.nap.edu/catalog.php?record_id=13105 (accessed March 2012).
      There is no corresponding record for this reference.
    9. 9
      Fargione, J.; Hill, J.; Tilman, D.; Polasky, S.; Hawthorne, P. Land clearing and the biofuel carbon debt Science 2008) 319, 1235– 8
      9
      Land Clearing and the Biofuel Carbon Debt
      Fargione, Joseph; Hill, Jason; Tilman, David; Polasky, Stephen; Hawthorne, Peter
      Science (Washington, DC, United States) (2008), 319 (5867), 1235-1238CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
      Increasing energy use, climate change, and CO2 emissions from fossil fuels make switching to low-C fuels a high priority. Biofuels are a potential low-C energy source, but whether biofuels offer C savings depends on how they are produced. Converting rain forest, peatland, savanna, or grassland to produce food crop-based biofuels in Brazil, southeast Asia, and the US creates a biofuel C debt by releasing 17-420 times more CO2 than the annual greenhouse gas (GHG) redns. these biofuels would provide by displacing fossil fuels. In contrast, biofuels made from waste biomass or from biomass grown on degraded and abandoned agricultural lands planted with perennials incur little or no C debt and can offer immediate, sustained GHG advantages.
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    10. 10
      Searchinger, T.; Heimlich, R.; Houghton, R. A.; Dong, F.; Elobeid, A.; Fabiosa, J.; Tokgoz, S.; Hayes, D.; Yu, T.-H. Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land use change Science 2008, 319, 1238– 40
      There is no corresponding record for this reference.
    11. 11
      Hertel, T. W.; Golub, A. A.; Jones, A. D.; O’Hare, M.; Plevin, R. J.; Kammen, D. M. Effects of US maize ethanol on global land use and greenhouse gas emissions: Estimating market-mediated responses BioScience 2010, 60, 223– 31
      There is no corresponding record for this reference.
    12. 12
      Fingerman, K. R.; Torn, M. S.; O’Hare, M. H.; Kammen, D. M. Accounting for the water impacts of ethanol production Environ. Res. Lett. 2010, 5, 014020
      There is no corresponding record for this reference.
    13. 13
      Geyer, R.; Stoms, D. M.; Lindner, J. P.; Davis, F. W.; Wittstock, B. Coupling GIS and LCA for biodiversity assessments of land use, part 1: Inventory modeling Int. J. Life Cycle Assess. 2010, 15, 454– 67
      13
      Coupling GIS and LCA for biodiversity assessments of land use. Part 1: Inventory modeling
      Geyer, Roland; Stoms, David M.; Lindner, Jan P.; Davis, Frank W.; Wittstock, Bastian
      International Journal of Life Cycle Assessment (2010), 15 (5), 454-467CODEN: IJLCFF; ISSN:0948-3349. (Springer)
      Purpose: Geospatial details about land use are necessary to assess its potential impacts on biodiversity. Geog. information systems (GIS) are adept at modeling land use in a spatially explicit manner, while life cycle assessment (LCA) does not conventionally utilize geospatial information. This study presents a proof-of-concept approach for coupling GIS and LCA for biodiversity assessments of land use and applies it to a case study of ethanol prodn. from agricultural crops in California. Materials and methods: GIS modeling was used to generate crop prodn. scenarios for corn and sugar beets that met a range of ethanol prodn. targets. The selected study area was a four-county region in the southern San Joaquin Valley of California, USA. The resulting land use maps were translated into maps of habitat types. From these maps, vectors were created that contained the total areas for each habitat type in the study region. These habitat compns. are treated as elementary input flows and used to calc. different biodiversity impact indicators in a second paper (Geyer et al., submitted). Results and discussion: Ten ethanol prodn. scenarios were developed with GIS modeling. Current land use is added as baseline scenario. The parcels selected for corn and sugar beet prodn. were generally in different locations. Moreover, corn and sugar beets are classified as different habitat types. Consequently, the scenarios differed in both the habitat types converted and in the habitat types expanded. Importantly, land use increased nonlinearly with increasing ethanol prodn. targets. The GIS modeling for this study used spatial data that are commonly available in most developed countries and only required functions that are provided in virtually any com. or open-source GIS software package. Conclusions: This study has demonstrated that GIS-based inventory modeling of land use allows important refinements in LCA theory and practice. Using GIS, land use can be modeled as a geospatial and nonlinear function of output. For each spatially explicit process, land use can be expressed within the conventional structure of LCA methodol. as a set of elementary input flows of habitat types.
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    14. 14
      Zhang, X.; Izaurralde, R. C.; Manowitz, D.; West, T. O.; Post, W. M.; Thomson, A. M.; Bandaru, V. P.; Nichols, J.; Williams, J. R. An integrative modeling framework to evaluate the productivity and sustainability of biofuel crop production systems Global Change Biol. Bioenergy 2010, 2, 258– 77
      There is no corresponding record for this reference.
    15. 15
      Mutel, C. L.; Pfister, S.; Hellweg, S. GIS-based regionalized life cycle assessment: How big is small enough? Methodology and case study of electricity generation. Environ. Sci. Technol. 2012, 46, 1096– 103.
      15
      GIS-Based Regionalized Life Cycle Assessment: How Big Is Small Enough? Methodology and Case Study of Electricity Generation
      Mutel, Christopher L.; Pfister, Stephan; Hellweg, Stefanie
      Environmental Science & Technology (2012), 46 (2), 1096-1103CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      New methodol. is described for performing regionalized life cycle assessment and systematically choosing the spatial scale of regionalized impact assessment methods. Std. matrix-based calcns. are extended to include matrixes that describe the mapping from inventory to impact assessment spatial supports. Uncertainty in inventory spatial data is modeled using a discrete spatial distribution function, which in a case study is derived from empirical data. The minimization of global spatial autocorrelation is used to choose the optimal spatial scale of impact assessment methods. These techniques are demonstrated on electricity prodn. in the United States, using regionalized impact assessment methods for air emissions and freshwater consumption. Case study results show important differences between site-generic and regionalized calcns., and provide specific guidance for future improvements of inventory data sets and impact assessment methods.
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    16. 16
      Millet, D. B..; Apel, E.; Henze, D. K.; Hill, J.; Marshall, J. D.; Singh, H. B.; Tessum, C. W. Natural and anthropogenic ethanol sources in North America and potential atmospheric impacts of ethanol fuel use. Environ. Sci. Technol. 2012, 46, 8484– 92.
      16
      Natural and Anthropogenic Ethanol Sources in North America and Potential Atmospheric Impacts of Ethanol Fuel Use
      Millet, Dylan B.; Apel, Eric; Henze, Daven K.; Hill, Jason; Marshall, Julian D.; Singh, Hanwant B.; Tessum, Christopher W.
      Environmental Science & Technology (2012), 46 (15), 8484-8492CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      An ensemble of aircraft measurements in conjunction with the GEOS-Chem chem. transport model were used to constrain present-day North American ethanol sources and gauge potential long-range impacts of increased ethanol fuel use. Current ethanol emissions are underestimated by 50% in western North America and overestimated by a factor of 2 in the east. The best est. for year 2005 North American ethanol emissions is 670 Gg C/yr, with 440 Gg C/yr from the continental US. These optimized source ests. were used to assess 2 scenarios for increased ethanol fuel use in the US: one assumed a complete transition from gasoline to E85 fuel; the other was tied to biofuel requirements of the US Energy Independence and Security Act (EISA). For both scenarios, increased ethanol emissions led to higher atm. acetaldehyde concns. (up to 14% in winter for the all-E85 scenario and 2% for the EISA scenario) and an assocd. shift in reactive N partitioning reflected by an increase in the peroxyacetyl nitrate (PAN):NOy ratio. The largest relative impacts occurred in fall, winter, and spring due to large natural ethanol and other org. compd. emissions in summer. Projected changes in atm. PAN reflected a balance between an increased supply of peroxyacetyl radicals from acetaldehyde oxidn., and lower NOx emissions for E85- vs. gasoline-fueled vehicles. The net effect was a general PAN increase in fall through spring, and a weak decrease over the southeastern US and the Atlantic Ocean in summer. Predicted NOx concns. decreased in surface air over North America (as much 5% in the all-E85 scenario); downwind from North America, this effect was counter-acted by higher NOx export efficiency driven by increased PAN prodn. and transport. From an NOx export from North America viewpoint, increased PAN formation assocd. with E85 fuel use acted to offset the assocd. lower NOx emissions.
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    17. 17
      Posch, M.; Seppälä, J.; Hettelingh, J.-P.; Johansson, M.; Margni, M.; Jolliet, O. The role of atmospheric dispersion models and ecosystem sensitivity in the determination of characterisation factors for acidifying and eutrophying emissions in LCIA Int. J. Life Cycle Assess. 2008, 13, 477– 86
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      The role of atmospheric dispersion models and ecosystem sensitivity in the determination of characterisation factors for acidifying and eutrophying emmissions in LCIA
      Posch, Maximilian; Seppaelae, Jyri; Hettelingh, Jean-Paul; Johansson, Matti; Margni, Manuele; Jolliet, Olivier
      International Journal of Life Cycle Assessment (2008), 13 (6), 477-486CODEN: IJLCFF; ISSN:0948-3349. (Springer)
      The methodol. choices and framework to assess environmental impacts in life cycle assessment are still under discussion. Despite intensive developments worldwide, few attempts have been made hitherto to systematically present the role of different factors of characterization models in life cycle impact assessment (LCIA). The aim of this study is to show how European av. and country-dependent characterization factors for acidifying and eutrophying emissions differ when using (a) acidifying and eutrophying potentials alone, (b) depositions from an atm. dispersion model or (c) crit. loads in conjunction with those depositions. Furthermore, in the latter case, the contributions of emissions, an atm. transport model and crit. loads to changes in characterization factors of NO2 are studied. In addn., the new characterization factors based on the accumulated exceedance (AE) method are presented using updated emissions, a new atm. transport model and the latest crit. loads. In this study, characterization factors for acidifying and eutrophying emissions are calcd. by three different methods. In the 'no fate' (NF) methods, acidifying and eutrophying potentials alone are considered as characterization factors. In the 'only above terrestrial environment' (OT) approach, characterization factors are based on the deposition of the acidifying or eutrophying substances to terrestrial land surfaces. The third method is the so-called AE method in which crit. loads are used in conjunction with depositions. The results of the methods are compared both at the European and the country level using weighted mean, weighted std. deviation, min. and max. values. To illustrate the sensitivity of the AE method, changes in European emissions, employed atm. dispersion model and the crit. loads database are conducted step-by-step, and the differences between the results are analyzed. For European av. characterization factors, the three characterization methods of acidification produce results in which the contributions of NH3, NO2 and SO2 to the acidification indicator do not differ much within each method when 1 kg of each acidifying substance is emitted. However, the NF methods cannot describe any spatial aspects of environmental problems. Both OT and AE methods show that the spatial aspects play an important role in the characterization factors. The AE method results in greater differentiations between country-dependent characterization factors than does the OT method. In addn., the results of the AE and OT methods differ from each other for individual countries. A major shortcoming of the OT approach is that it does not consider the sensitivity of the ecosystems onto which the pollutants are deposited, whereas the AE approach does. In the case of the AE method, a new atm. dispersion model, new information on emissions and crit. loads have a different influence on the characterization factors, depending on the country. The results of statistics show that the change in the atm. dispersion model has a greatest influence on the results, since ecosystem-specific depositions are taken into account for the first time. The simple NF methods can be used in a first approxn. to assess the impacts of acidification and terrestrial eutrophication in cases where we do not know where the emissions occur. The OT approach is a more advanced method compared with the NF method, but its capability to describe spatial aspects is limited. The AE factors are truly impact-oriented characterization factors and the information used here represents the current best knowledge about the assessment practice of acidification and terrestrial eutrophication in Europe. The key message of this study is that there is no shortcut to achieving advanced characterization of acidification and terrestrial eutrophication: an advanced methodol. cannot develop without atm. dispersion models and information on ecosystem sensitivity.
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    18. 18
      Pennington, D. W.; Margni, M.; Ammann, C.; Jolliet, O. Multimedia fate and human intake modeling: Spatial versus nonspatial insights for chemical emissions in western Europe Environ. Sci. Technol. 2005, 39, 1119– 28
      There is no corresponding record for this reference.
    19. 19
      Raugei, M.; Ulgiati, S. A novel approach to the problem of geographic allocation of environmental impact in life cycle assessment and material flow analysis Ecol. Indic. 2009, 9, 1257– 64
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      A novel approach to the problem of geographic allocation of environmental impact in Life Cycle Assessment and Material Flow Analysis
      Raugei, Marco; Ulgiati, Sergio
      Ecological Indicators (2009), 9 (6), 1257-1264CODEN: EICNBG; ISSN:1470-160X. (Elsevier Ltd.)
      Life Cycle Assessment and Material Flow Anal., in spite of their invaluable contribution to the investigation of the environmental performance of human-dominated processes, still fall short of properly addressing the issue of the geog. distribution of the potential environmental impacts, which often has wide-reaching environmental as well as political implications. An innovative allocation method based on matrix algebra is introduced here, in order to allow to split the calcd. environmental impact indicators into fractions thereof which are geog. attributed to the different world regions. This is done on the basis of: (i) where the analyzed process takes place and (ii) where the directly and indirectly required fossil and nuclear fuels are sourced from (including those for electricity prodn.). The method has been successfully tested on primary aluminum prodn., as a first case study.
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    20. 20
      Shah, V. P.; Ries, R. J. A Characterization model with spatial and temporal resolution for life cycle impact assessment of photochemical precursors in the United States Int. J. Life Cycle Assess. 2009, 14, 313– 27
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      A characterization model with spatial and temporal resolution for life cycle impact assessment of photochemical precursors in the United States
      Shah, Viral P.; Ries, Robert J.
      International Journal of Life Cycle Assessment (2009), 14 (4), 313-327CODEN: IJLCFF; ISSN:0948-3349. (Springer)
      Traditional life cycle impact assessment methodologies have used aggregated characterization factors, neglecting spatial and temporal variations in regional impacts like photochem. oxidant formation. This increases the uncertainty of the LCA results and diminishes the ease of decision-making. This study compares four common impact assessment methods, CML2001, Eco-indicator 99, TRACI, and EDIP2003, on their underlying models, spatial and temporal resoln., and the level at which photochem. oxidant impacts are calcd. A new characterization model is proposed that incorporates spatial and temporal differentiation. A photochem. air quality modeling system (CAMx-MM5-SMOKE) is used to simulate the process of formation, transformation, transport, and removal of photochem. pollutants. Monthly characterization factors for individual US states are calcd. at three levels along the cause-effect chain, namely, fate level, human and ecosystem exposure level, and human effect level. The results indicate that a spatial variability of one order of magnitude and a temporal variability of two orders of magnitude exist in both the fate level and human exposure and effect level characterization factors for NOx. The summer time characterization factors for NOx are higher than the winter time factors. However, for anthropogenic VOC, the summer time factors are lower than the winter time in almost half of the states. This is due to the higher emission rates of biogenic VOCs in the summer. The ecosystem exposure factors for NOx and VOC do not follow a regular pattern and show a spatial variation of about three orders of magnitude. They do not show strong correlation with the human exposure factors. Sensitivity anal. has shown that the effect of meteorol. and emission inputs is limited to a factor of three, which is several times smaller than the variation seen in the factors. Uncertainties are introduced in the characterization of photochem. precursors due to a failure to consider the spatial and temporal variations. Seasonal variations in photochem. activity influence the characterization factors more than the location of emissions. The human and ecosystem exposures occur through different mechanisms, and impacts calcd. at the fate level based only on ozone concn. are not a good indicator for ecosystem impacts. Spatial and temporal differentiation account for fate and transport of the pollutant, and the exposure of and effect on the sensitive human population or ecosystem. Adequate resoln. for seasonal and regional processes, like photochem. oxidant formation, is important to reduce the uncertainty in impact assessment and improve decision-making power. An emphasis on incorporating some form of spatial and temporal information within std. LCI databases and using adequately resolved characterization factors will greatly increase the fidelity of a std. LCA.
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    21. 21
      Hauschild, M.; Potting, J. Spatial Differentiation in Life Cycle Impact Assessmen–the EDIP2003 Methodology, 2005. www2.mst.dk/udgiv/publications/2005/87-7614-579-4/pdf/87-7614-580-8.pdf (accessed March 2012).
      There is no corresponding record for this reference.
    22. 22
      Norris, G. A. Impact characterization in the tool for the reduction and assessment of chemical and other environmental impacts: Methods for acidification, eutrophication, and ozone formation J. Ind. Ecol. 2002, 6, 79– 101
      There is no corresponding record for this reference.
    23. 23
      Wegener Sleeswijk, A.; Heijungs, R. GLOBOX: A spatially differentiated global fate, intake and effect model for toxicity assessment in LCA Sci. Total Environ. 2010, 408, 2817– 32
      There is no corresponding record for this reference.
    24. 24
      Querini, F.; Morel, S.; Boch, V.; Rousseaux, P. USEtox relevance as an impact indicator for automotive fuels. application on diesel fuel, gasoline and hard coal electricity Int. J. Life Cycle Assess. 2011, 16, 829– 40
      24
      USEtox relevance as an impact indicator for automotive fuels: Application on diesel fuel, gasoline and hard coal electricity
      Querini, Florent; Morel, Stephane; Boch, Valerie; Rousseaux, Patrick
      International Journal of Life Cycle Assessment (2011), 16 (8), 829-840CODEN: IJLCFF; ISSN:0948-3349. (Springer)
      Purpose: In order to provide more sustainable fuels and address the depletion of oil as a feedstock, the automotive industry must adapt to a growing market share of alternative fuels. The environmental impacts of the automotive industry to date would suggest that these alternatives will be more environmentally friendly than petroleum-based fuels. This is nonetheless an assumption that cannot be confirmed without a systematic life cycle assessment (LCA). This article explores the feasibility of USEtox to provide information needed for automotive-fuel LCA. Materials and methods: USEtox is tested on three energy pathways: gasoline, diesel fuel and hard coal electricity. The studied emissions are mainly volatile org. compds. (VOCs) and heavy metals. USEtox being dependent on the physicochem. and toxic properties of the studied species, a speciation of all VOCs emitted was performed. Also, since USEtox allows a distinction between rural and urban emissions, a geog. information system was developed to distinguish these emissions. Finally, because crude oil comes from various countries, characterization factors were calcd. for new regional compartments. Results and discussion Human health issues are caused by aldehydes and heavy metals while ecotoxicity is caused by polycyclic arom. hydrocarbons, aldehydes and heavy metals. For org. compds., a clear distinction is obsd. between urban and rural emissions while inorg. mechanisms are independent of this distinction. Among the three energy pathways, urban diesel is the more impacting. Conclusions: USEtox can be used for the assessment of automotive fuels, though it only addresses specific aspects of human health and ecotoxicity. The LCA practitioner must keep in mind that USEtox has to be used in conjunction with other indicators, such as ReCiPe or CML, to comprehensively cover the toxic and ecotoxic impacts of fuels. The level of anal. is dependent on the accuracy of the inventory, aldehydes and PAH playing a crucial role. Inorg. impacts are highly uncertain, contrary to org. compds. The distinction between rural and urban emissions allows a better assessment of internal combustion engine-powered cars compared with elec. and hybrid cars, which is esp. useful for the automotive industry now that these technologies are clearly being developed.
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    25. 25
      Lloyd, S.; Ries, R. Spatial and temporal life cycle assessment: ozone formation potential from natural gas use in a typical residential building in Pittsburgh, USA. In Dynamics of Regions and Networks in Industrial Ecosystems; Ruth, M.; Davidsdottir, B., Eds.; The MIT Press, 2009.
      There is no corresponding record for this reference.
    26. 26
      Humbert, S.; Marshall, J. D.; Shaked, S.; Spadaro, J. V.; Nishioka, Y.; Preiss, P.; McKone, T. E.; Horvath, A.; Jolliet, O. Intake fraction for particulate matter: Recommendations for life cycle impact assessment Environ. Sci. Technol. 2011, 45, 4808– 16
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      Intake Fraction for Particulate Matter: Recommendations for Life Cycle Impact Assessment
      Humbert, Sebastien; Marshall, Julian D.; Shaked, Shanna; Spadaro, Joseph V.; Nishioka, Yurika; Preiss, Philipp; McKone, Thomas E.; Horvath, Arpad; Jolliet, Olivier
      Environmental Science & Technology (2011), 45 (11), 4808-4816CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      A review concerning the impact of particulate matter (PM) as a significant contributor to death and disease worldwide, summarizing the work of an international expert group on integrating human PM exposure into life cycle impact assessment (LCIA) within the United Nations Environmental Program/TETAC Life Cycle Initiative, is given. Literature-derived intake fraction values (inhaled emissions fraction), based on emission release height and archetypal environment (indoor vs. outdoor; urban, rural, or remote locations) were assessed. Recommended intake fraction values are provided for primary PM10-2.5 (coarse particles), primary PM2.5 (fine particles), and secondary PM2.5 from SO2, NOx, and NH3, to provide as complete and consistent an archetype framework as possible given the current understanding of each pollutant. Presented values facilitate incorporating regional impacts into LCIA for human health adverse effects from PM. Topics discussed include: methods (general framework, intake fraction, factors affecting intake fraction [regionalization, emission height, archetypes addressing regionalization and emission height, PM types], archetype parameterization [indoor,urban, rural, remote, emissions- and population-weighted arithmetic av.], comparing available models and data [primary PM2.5; primary PM10, primary PM10-2.5, secondary PM], intake fraction value summary [urban, rural, remote, emission release height, weighted arithmetic av.]); results and discussion (uncertainty and the importance of spatial differentiation, future research needs); and supporting information (archetype parametrization [breathing rate, indoor environment and world city parameterization, parameterization by continent], characterizing PM emissions [total emission by source type and emission height, fraction of PM10 <2.5 μm], intake fraction [regressions, values, recommended fractions], variability and uncertainty).
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    27. 27
      Manneh, R.; Margni, M.; Deschenes, L. Spatial variability of intake fractions for Canadian emission scenarios: A comparison between three resolution scales Environ. Sci. Technol. 2010, 44, 4217– 24
      There is no corresponding record for this reference.
    28. 28
      Huo, H.; Wu, Y.; Wang, M. Total versus urban: Well-to-wheels assessment of criteria pollutant emissions from various vehicle/fuel systems Atmos. Environ. 2009, 43, 1796– 804
      There is no corresponding record for this reference.
    29. 29
      Hill, J.; Polasky, S.; Nelson, E.; Tilman, D.; Huo, H.; Ludwig, L.; Neumann, J.; Zheng, H.; Bonta, D. Climate change and health costs of air emissions from biofuels and gasoline Proc. Natl. Acad. Sci. U.S.A. 2009, 106, 2077– 82
      There is no corresponding record for this reference.
    30. 30
      Cook, R.; Phillips, S.; Houyoux, M.; Dolwick, P.; Mason, R.; Yanca, C.; Zawacki, M.; Davidson, K.; Michaels, H.; Harvey, C.; Somers, J.; Luecken, D. Air quality impacts of increased use of ethanol under the United States’ energy independence and security act Atmos. Environ. 2010, 45, 7714– 24
      There is no corresponding record for this reference.
    31. 31
      Tsao, C.-C.; Campbell, J. E.; Mena-Carrasco, M.; Spak, S. N.; Carmichael, G. R.; Chen, Y. Increased estimates of air-pollution emissions from Brazilian sugar-cane ethanol Nat. Clim. Change 2011, 2, 53– 7
      There is no corresponding record for this reference.
    32. 32
      Simon, H.; Beck, L.; Bhave, P. V.; Divita, F.; Hsu, Y.; Luecken, D.; Mobley, J. D.; Pouliot, G. A.; Reff, A.; Sarwar, G.; Strum, M. The development and uses of EPA’s SPECIATE database Atmos. Pollut. Res. 2010, 1, 196– 206
      32
      The development and uses of EPA's SPECIATE database
      Simon, Heather; Beck, Lee; Bhave, Prakash V.; Divita, Frank; Hsu, Ying; Luecken, Deborah; David Mobley, J.; Pouliot, George A.; Reff, Adam; Sarwar, Golam; Strum, Madeleine
      Atmospheric Pollution Research (2010), 1 (), 196-206CODEN: APRTCD; ISSN:1309-1042. (Dokuz Eylul University, Dep. Environmental Engineering)
      SPECIATE is the U.S. Environmental Protection Agency's (EPA) repository of volatile org. compds. (VOCs) and particulate matter (PM) speciation profiles of air pollution sources. These source profiles can be used to (1) provide input to chem. mass balance (CMB) receptor models; (2) verify profiles derived from ambient measurements by multivariate receptor models (e.g., factor anal. and pos. matrix factorization); (3) interpret ambient measurement data; and (4) create speciated emission inventories for regional haze, climate, and photochem. air quality modeling. This paper describes the SPECIATE v4.2 database, provides specific examples of its use, and makes recommendations for future improvements.
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    Supporting Information

    Process-specific, chemically speciated, and state and urban area specific emissions data (S1a–c, Excel format), supplemental text and figures, and process-specific maps (S2 and S3a–c, pdf format) for gasoline, corn ethanol, and stover cellulosic ethanol. This material is available free of charge via the Internet at http://pubs.acs.org.


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