Abstract  This study updates the Petroleum Refinery Life Cycle Inventory Model (PRELIM) to provide a more complete gate-to-gate life cycle inventory and to allow for the calculation of a full suite of impact potentials commonly used in life cycle assessment (LCA) studies. Prior to this update, PRELIM provided results for energy use and greenhouse gas emissions from petroleum refineries with a level of detail suitable for most LCA studies in support of policy decisions. We updated the model to add criteria air pollutants, hazardous air pollutants, releases to water, releases to land, and managed wastes reflecting 2014 reported releases and waste management practices using data from the U.S. Environmental Protection Agency Greenhouse Gas Reporting Program, National Emissions Inventory, Discharge Monitoring Reports, and Toxic Release Inventory together with process unit capacities and fuel consumption data from the U.S. Energy Information Administration (U.S. EIA). The variability of refinery subprocess release factors is characterized using log-normal distributions with parameters set based on the distribution of release factors across facilities. The U.S. EPA Tool for the Reduction and Assessment of Chemical and Environmental Impacts life cycle impact assessment (LCIA) method is used together with the updated inventory data to provide impact potentials in the PRELIM dashboard interface. Release inventories at the subprocess level enable greater responsiveness to variable selection within PRELIM, such as refinery configuration, and allocation to specific refinery products. The updated version also provides a template to allow users to import PRELIM inventory results into the openLCA software tool as unit process data sets. Here we document and validate the model updates. Impact potentials from the national crude mix in 2014 are compared to impacts from the 2005 mix to demonstrate the impact of assay and configuration on the refining sector over time. The expanded version of PRELIM offers users a reliable, transparent, and streamlined tool for estimating the effect of changes in petroleum refineries on LCIA results in the context of policy analysis.

Abstract  The Environmental Protection Agency (EPA) is revising the greenhouse gas (GHG) emissions standards under the Clean Air Act section 202(a) for light-duty vehicles for 2023 and later model years to make the standards more stringent. On January 20, 2021, President Biden issued Executive Order 13990 “Protecting Public Health and the Environment and Restoring Science To Tackle the Climate Crisis” directing EPA to consider whether to propose suspending, revising, or rescinding the standards previously revised under the “The Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule for Model Years 2021–2026 Passenger Cars and Light Trucks,” promulgated in April 2020. EPA is revising the GHG standards to be more stringent than the SAFE rule standards in each model year from 2023 through 2026. EPA is also including temporary targeted flexibilities to address the lead time of the final standards and to incentivize the production of vehicles with zero and near-zero emissions technology. In addition, EPA is making technical amendments to clarify and streamline our regulations.

Abstract  Ethanol fuel production is growing rapidly in the rural Midwest, and this growth presents potential environmental impacts. In 2002, the U.S. Environmental Protection Agency (EPA) and the Minnesota Pollution Control Agency (MPCA) entered into enforcement actions with 12 fuel ethanol plants in Minnesota. The enforcement actions uncovered underreported emissions and resulted in consent decrees that required pollution control equipment be installed. A key component of the consent decrees was a requirement to conduct emissions tests for volatile organic compounds (VOCs) with the goal of improving the characterization and control of emissions. The conventional VOC stack test method was thought to underquantify total VOC emissions from ethanol plants. A hybrid test method was also developed that involved quantification of individual VOC species. The resulting database of total and speciated VOC emissions from 10 fuel ethanol plants is relatively small, but it is the most extensive to date and has been used to develop and gauge compliance with permit limits and to estimate health risks in Minnesota. Emissions were highly variable among facilities and emissions units. In addition to the variability, the small number of samples and the presence of many values below detection limits complicate the analysis of the data. To account for these issues, a nested bootstrap procedure on the Kaplan-Meier method was used to calculate means and upper confidence limits. In general, the fermentation scrubbers and fluid bed coolers emitted the largest mass of VOC emissions. Across most facilities and emissions units ethanol was the pollutant emitted at the highest rate. Acetaldehyde, acetic acid, and ethyl acetate were also important emissions from some units. Emissions of total VOCs, ethanol, and some other species appeared to be a function of the beer feed rate, although the relationship was not reliable enough to develop a production rate-based emissions factor.

Abstract  This report documents the development and use of the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model. The model, developed in a spreadsheet format, estimates the full fuel-cycle emissions and energy use associated with various transportation fuels for light-duty vehicles. The model calculates fuel-cycle emissions of five criteria pollutants (volatile organic compounds, Co, NOx, SOx, and particulate matter measuring 10 microns or less) and three greenhouse gases (carbon dioxide, methane, and nitrous oxide). The model also calculates the total fuel-cycle energy consumption, fossil fuel consumption, and petroleum consumption using various transportation fuels. The GREET model includes 17 fuel cycles: petroleum to conventional gasoline, reformulated gasoline, clean diesel, liquefied petroleum gas, and electricity via residual oil; natural gas to compressed natural gas, liquefied petroleum gas, methanol, hydrogen, and electricity; coal to electricity; uranium to electricity; renewable energy (hydropower, solar energy, and wind) to electricity; corn, woody biomass, and herbaceous biomass to ethanol; and landfill gases to methanol. This report presents fuel-cycle energy use and emissions for a 2000 model-year car powered by each of the fuels that are produced from the primary energy sources considered in the study.

Abstract  Ethanol made from corn now constitutes approximately 10% of the fuel used in gasoline vehicles in the U.S. The ethanol is produced in over 200 fuel ethanol refineries across the nation. We report airborne measurements downwind from Decatur, Illinois, where the third largest fuel ethanol refinery in the U.S. is located. Estimated emissions are compared with the total point source emissions in Decatur according to the 2011 National Emissions Inventory (NEI-2011), in which the fuel ethanol refinery represents 68.0% of sulfur dioxide (SO2), 50.5% of nitrogen oxides (NOx=NO+NO2), 67.2% of volatile organic compounds (VOCs), and 95.9% of ethanol emissions. Emissions of SO2 and NOx from Decatur agreed with NEI-2011, but emissions of several VOCs were underestimated by factors of 5 (total VOCs) to 30 (ethanol). By combining the NEI-2011 with fuel ethanol production numbers from the Renewable Fuels Association, we calculate emission intensities, defined as the emissions per ethanol mass produced. Emission intensities of SO2 and NOx are higher for plants that use coal as an energy source, including the refinery in Decatur. By comparing with fuel-based emission factors, we find that fuel ethanol refineries have lower NOx, similar VOC, and higher SO2 emissions than from the use of this fuel in vehicles. The VOC emissions from refining could be higher than from vehicles, if the underestimated emissions in NEI-2011 downwind from Decatur extend to other fuel ethanol refineries. Finally, chemical transformations of the emissions from Decatur were observed, including formation of new particles, nitric acid, peroxyacyl nitrates, aldehydes, ozone, and sulfate aerosol.

Abstract  The relationship between gasoline properties and vehicle particulate matter emissions was investigated, for the purpose of constructing a predictive model. Various chemical species were individually blended with an indolene base fuel, and the solid particulate number (PN) emissions from each blend were measured over the New European Driving Cycle (NEDC). The results indicated that aromatics with a high boiling point and a high double bond equivalent (DBE) value tended to produce more PN emissions. However, high boiling point components with low DBE values, such as paraffins, displayed only a minor effect on PN. Upon further analysis of the test results, it was also confirmed that low vapor pressure components correlated with high PN emissions, as might be expected based on their combustion behavior. A predictive model, termed the “PM Index,” was constructed based on the weight fraction, vapor pressure, and DBE value of each component in the fuel. It was confirmed that the PM Index could accurately predict not only the total PN trend but also total particulate matter (PM) mass, regardless of engine type or test cycle. A large number of gasoline samples were collected in various countries, and submitted for detailed hydrocarbon analysis (DHA). Using the resulting hydrocarbon speciation data, a PM Index distribution was calculated for each country from which fuel samples were acquired. Based on the range of PM Indices encountered, it was estimated that the highest PM Index fuel would produce 10 times the PM emissions of that of the lowest PM Index fuel. Therefore, it was concluded that worldwide PM emissions can be reduced not only through improvements in engine hardware, but also through improvements in fuel quality.

Abstract  With the goal of understanding environmental effects of a growing bioeconomy, the U.S. Department of Energy (DOE), national laboratories, and U.S. Forest Service research laboratories, together with academic and industry collaborators, undertook a study to estimate environmental effects of potential biomass production scenarios in the United States, with an emphasis on agricultural and forest biomass. Potential effects investigated include changes in soil organic carbon (SOC), greenhouse gas (GHG) emissions, water quality and quantity, air emissions, and biodiversity. Effects of altered land-management regimes were analyzed based on select county-level biomass-production scenarios for 2017 and 2040 taken from the 2016 Billion-Ton Report: Advancing Domestic Resources for a Thriving Bioeconomy (BT16), volume 1, which assumes that the land bases for agricultural and forestry would not change over time. The scenarios reflect constraints on biomass supply (e.g., excluded areas; implementation of management practices; and consideration of food, feed, forage, and fiber demands and exports) that intend to address sustainability concerns. Nonetheless, both beneficial and adverse environmental effects might be expected. To characterize these potential effects, this research sought to estimate where and under what modeled scenarios or conditions positive and negative environmental effects could occur nationwide. The report also includes a discussion of land-use change (LUC) (i.e., land management change) assumptions associated with the scenario transitions (but not including analysis of indirect LUC [ILUC]), analyses of climate sensitivity of feedstock productivity under a set of potential scenarios, and a qualitative environmental effects analysis of algae production under carbon dioxide (CO2) co-location scenarios. Because BT16 biomass supplies are simulated independent of a defined end use, most analyses do not include benefits from displacing fossil fuels or other products, with the exception of including a few illustrative cases on potential reductions in GHG emissions and fossil energy consumption associated with using biomass supplies for fuel, power, heat, and chemicals.

Abstract  Globally, bioethanol is the largest volume biofuel used in the transportation sector, with corn-based ethanol production occurring mostly in the US and sugarcane-based ethanol production occurring mostly in Brazil. Advances in technology and the resulting improved productivity in corn and sugarcane farming and ethanol conversion, together with biofuel policies, have contributed to the significant expansion of ethanol production in the past 20 years. These improvements have increased the energy and greenhouse gas (GHG) benefits of using bioethanol as opposed to using petroleum gasoline. This article presents results from our most recently updated simulations of energy use and GHG emissions that result from using bioethanol made from several feedstocks. The results were generated with the GREET (Greenhouse gases, Regulated Emissions, and Energy use in Transportation) model. In particular, based on a consistent and systematic model platform, we estimate life-cycle energy consumption and GHG emissions from using ethanol produced from five feedstocks: corn, sugarcane, corn stover, switchgrass and miscanthus.

We quantitatively address the impacts of a few critical factors that affect life-cycle GHG emissions from bioethanol. Even when the highly debated land use change GHG emissions are included, changing from corn to sugarcane and then to cellulosic biomass helps to significantly increase the reductions in energy use and GHG emissions from using bioethanol. Relative to petroleum gasoline, ethanol from corn, sugarcane, corn stover, switchgrass and miscanthus can reduce life-cycle GHG emissions by 19-48%, 40-62%, 90-103%, 77-97% and 101-115%, respectively. Similar trends have been found with regard to fossil energy benefits for the five bioethanol pathways.

Abstract  Switchgrass is a promising bioenergy feedstock, but industrial-scale production may lead to negative environmental effects. This study considers one such potential consequence: the life cycle monetized damages to human health from air pollution. We estimate increases in mortality from long-term exposure to fine particulate matter (PM2.5 ), which is emitted directly ("primary PMPM2.5") and forms in the atmosphere ("secondary PM2.5") from precursors of nitrogen oxides (NOx), sulfur oxides (SOx), ammonia (NH3), and volatile organic compounds (V005). Changes in atmospheric concentrations of PM2.5 (primary + secondary) from on-site production and supporting supply chain activities are considered at 2694 locations (counties in the Central and Eastern US), for two biomass yields (9 and 20 Mg ha(-1)), three nitrogen fertilizer rates (50, 100, and 150 kg ha(-1)), and two nitrogen fertilizer types (urea and urea ammonium nitrate). Results indicate that on-site processes dominate lifecycle emissions of NH3 , NOx, primary PM2.5, and V005, whereas SOx is primarily emitted in upstream supply chain processes. Total air quality impacts of switchgrass production, which are dominated by NH3 emissions from fertilizer application, range widely depending on location, from 2 to 553 $ Mg-l (mean: 45) of dry switchgrass at a biomass yield of 20 Mg ha(-1) and fertilizer application of 100 kg ha(-1) N applied as urea. Switching to urea ammonium nitrate solution lowers damages to 2 to 329 $ Mg-1 (mean: 28). This work points to human health damage from air pollution as a potentially large social cost from switchgrass production and suggests means of mitigating that impact via strategic geographical deployment and management. Furthermore, by distinguishing the origin of atmospheric emissions, this paper advances the current emerging literature on ecosystem services and disservices from agricultural and bioenergy systems.

Abstract  Grease trap waste (GTW) is a low-quality waste material with variable lipid content that is an untapped resource for producing biodiesel. Compared to conventional biodiesel feedstocks, GTW requires different and additional processing steps for biodiesel production due to its heterogeneous composition, high acidity, and high sulfur content. Life-cycle assessment (LCA) is used to quantify greenhouse gas emissions, fossil energy demand, and criteria air pollutant emissions for the GTW-biodiesel process, in which the sensitivity to lipid concentration in GTW is analyzed using Monte Carlo simulation. The life-cycle environmental performance of GTW-biodiesel is compared to that of current GTW disposal, the soybean-biodiesel process, and low-sulfur diesel (LSD). The disposal of the water and solid wastes produced from separating lipids from GTW has a high contribution to the environmental impacts; however, the impacts of these processed wastes are part of the current disposal practice for GTW and could be excluded with consequential LCA system boundaries. At lipid concentrations greater than 10%, most of the environmental metrics studied are lower than those of LSD and comparable to soybean biodiesel.

Abstract  Environmentally extended input-output (EEIO) databases have been widely used by both input-output and life cycle analysis practitioners to study the environmental effects of products and processes in different sectors and regions. More recently, efforts have been focused on developing harmonized time-series of EEIO tables at both national and global levels, allowing a better understanding of the dynamics of such impacts. Based on the USEEIO framework, we develop a novel harmonized time-series of EEIO tables for the United States covering the benchmark Make-Use Tables from 2002, 2007 and 2012, and the same set of comprehensive physical accounts as the original USEEIO. We discuss the methodology employed, possible applications, and present the evolution of national aggregated environmental indicators over these years.

Abstract  This monthly release is part of the Current Agricultural Industrial Report (CAIR) program, and covers the crush of oilseeds and production of crude oil for selected states and the U.S. as well as U.S. production and consumption of selected fats and oils for edible and inedible uses. The end-of-month stock values by oilseed are also published. The report is compiled from data from facilities regarding oilseed crushing, crude oil production, once refined oil production, rendering production, and end of month stocks for the previous calendar month.

Abstract  Agriculture is essential for feeding the large and growing world population, but it can also generate pollution that harms ecosystems and human health. Here, we explore the human health effects of air pollution caused by the production of maize-a key agricultural crop that is used for animal feed, ethanol biofuel and human consumption. We use county-level data on agricultural practices and productivity to develop a spatially explicit life-cycle-emissions inventory for maize. From this inventory, we estimate health damages, accounting for atmospheric pollution transport and chemistry, and human exposure to pollution at high spatial resolution. We show that reduced air quality resulting from maize production is associated with 4,300 premature deaths annually in the United States, with estimated damages in monetary terms of US$39 billion (range: US$14-64 billion). Increased concentrations of fine particulate matter (PM2.5) are driven by emissions of ammonia-a PM2.5 precursor-that result from nitrogen fertilizer use. Average health damages from reduced air quality are equivalent to US$121t(-1) of harvested maize grain, which is 62% of the US$195 t(-1) decadal average maize grain market price. We also estimate life-cycle greenhouse gas emissions of maize production, finding total climate change damages of US$4.9 billion (range: US$1.5-7.5 billion), or US$15 t(-1) of maize. Our results suggest potential benefits from strategic interventions in maize production, including changing the fertilizer type and application method, improving nitrogen use efficiency, switching to crops requiring less fertilizer, and geographically recating production.