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  1. Reconciling Basin-Scale Top-Down and Bottom-Up Methane Emission Measurements for Onshore Oil and Gas Development: Cooperative Research and Development Final Report, CRADA Number CRD-14-572

    The overall objective of the Research Partnership to Secure Energy for America (RPSEA)-funded research project is to develop independent estimates of methane emissions using top-down and bottom-up measurement approaches and then to compare the estimates, including consideration of uncertainty. Such approaches will be applied at two scales: basin and facility. At facility scale, multiple methods will be used to measure methane emissions of the whole facility (controlled dual tracer and single tracer releases, aircraft-based mass balance and Gaussian back-trajectory), which are considered top-down approaches. The bottom-up approach will sum emissions from identified point sources measured using appropriate source-level measurement techniquesmore » (e.g., high-flow meters). At basin scale, the top-down estimate will come from boundary layer airborne measurements upwind and downwind of the basin, using a regional mass balance model plus approaches to separate atmospheric methane emissions attributed to the oil and gas sector. The bottom-up estimate will result from statistical modeling (also known as scaling up) of measurements made at selected facilities, with gaps filled through measurements and other estimates based on other studies. The relative comparison of the bottom-up and top-down estimates made at both scales will help improve understanding of the accuracy of the tested measurement and modeling approaches. The subject of this CRADA is NREL's contribution to the overall project. This project resulted from winning a competitive solicitation no. RPSEA RFP2012UN001, proposal no. 12122-95, which is the basis for the overall project. This Joint Work Statement (JWS) details the contributions of NREL and Colorado School of Mines (CSM) in performance of the CRADA effort.« less
  2. Projected Growth in Small-Scale, Fossil-Fueled Distributed Generation: Potential Implications for the U.S. Greenhouse Gas Inventory

    The generation capacity of small-scale (less than one megawatt) fossil-fueled electricity in the United States is anticipated to grow by threefold to twenty-fold from 2015 to 2040. However, in adherence with internationally agreed upon carbon accounting methods, the Environmental Protection Agency's (EPA's) U.S. Greenhouse Inventory (GHGI) does not currently attribute greenhouse gases (GHGs) from these small-scale distributed generation sources to the electric power sector and instead accounts for these emissions in the sector that uses the distributed generation (e.g., the commercial sector). In addition, no other federal electric-sector GHG emission data product produced by the EPA or the U.S. Energymore » Information Administration (EIA) can attribute these emissions to electricity. We reviewed the technical documentation for eight federal electric-sector GHG emission data products, interviewed the data product owners, collected their GHG emission estimates, and analyzed projections for growth in fossil-fueled distributed generation. We show that, by 2040, these small-scale generators could account for at least about 1%- 5% of total CO2 emissions from the U.S. electric power sector. If these emissions fall outside the electric power sector, the United States may not be able to completely and accurately track changes in electricity-related CO2 emissions, which could impact how the country sets GHG reduction targets and allocates mitigation resources. Because small-scale, fossil-fueled distributed generation is expected to grow in other countries as well, the results of this work also have implications for global carbon accounting.« less
  3. Air Quality Considerations for Biofuels: Development of Preliminary Estimates of Permitted Potential Emissions for the Bioenergy Supply Chain

    This presentation was made to the USDA Agricultural Air Quality Task Force on Air Quality Considerations for Biofuels.
  4. Global carbon intensity of crude oil production

    Producing, transporting, and refining crude oil into fuels such as gasoline and diesel accounts for ~15 to 40% of the 'well-to-wheels' life-cycle greenhouse gas (GHG) emissions of transport fuels (1). Reducing emissions from petroleum production is of particular importance, as current transport fleets are almost entirely dependent on liquid petroleum products, and many uses of petroleum have limited prospects for near-term substitution (e.g., air travel). Better understanding of crude oil GHG emissions can help to quantify the benefits of alternative fuels and identify the most cost-effective opportunities for oil-sector emissions reductions (2). Yet, while regulations are beginning to address petroleummore » sector GHG emissions (3-5), and private investors are beginning to consider climate-related risk in oil investments (6), such efforts have generally struggled with methodological and data challenges. First, no single method exists for measuring the carbon intensity (CI) of oils. Second, there is a lack of comprehensive geographically rich datasets that would allow evaluation and monitoring of life-cycle emissions from oils. We have previously worked to address the first challenge by developing open-source oil-sector CI modeling tools [OPGEE (7, 8), supplementary materials (SM) 1.1]. Here, we address the second challenge by using these tools to model well-to-refinery CI of all major active oil fields globally - and to identify major drivers of these emissions.« less
  5. Spatially Explicit Modeling of Criteria Air Pollutants from Agricultural and Forestry Feedstock Production

    The Feedstock Production Emissions to Air Model (FPEAM) is a spatially explicit supply chain model that quantifies criteria air pollutants and precursors generated by biomass production, harvest and transportation to biorefineries. FPEAM was most recently used for the 2016 Billion Ton Study (BTS) published by the U.S. Department of Energy Bioenergy Technologies Office. This presentation describes several use cases for the model, and provides an overview of recent restructuring that increases FPEAM's usability and functionality. FPEAM currently estimates emissions of volatile organic compounds, particulate matter, nitrogen oxides, sulfur oxides, and carbon monoxide from on-farm equipment operation, on-farm fugitive dust, on-farmmore » chemical (fertilizer, herbicide and insecticide) application, and biomass transportation. These calculations are implemented within FPEAM as modules, which independently perform calculations for each emissions category. This structure allows users to include or exclude emissions categories according to the goal and scope of their analysis. Each module's input data defines the activities taking place with information including fuel combusted, chemicals applied, and equipment used. Users can replace or augment the default input data to quantify additional pollutant types, emissions categories, farming practices and biomass types. Emissions from biomass transportation and on-farm equipment operation are by default calculated by the U.S. Environmental Protection Agency's Motor Vehicle Emission Simulator (MOVES) and NONROAD model, respectively. These models - which are computationally intensive but provide detailed results - can optionally be replaced with user-provided emissions factors for fuel combustion and vehicle operation. This approach reduces FPEAM's runtime and allows users to run sensitivity analyses around biomass production and transportation scenarios. Other enhancements currently under development are the use of farm-to-biorefinery route information to increase the spatial resolution of transportation emissions, and a module to connect FPEAM and the Intervention Model for Air Pollution (InMAP), which will enable the quantification of human health impacts.« less
  6. Understanding Potential Air Emissions from a Cellulosic Biorefinery Producing Renewable Diesel Blendstock.

    The Energy Independence and Security Act of 2007, through the Renewable Fuel Standard (RFS), mandates increased use of biofuels, including cellulosic biofuels. The RFS is expected to spur the development of advanced biofuel technologies (e.g., new and innovative biofuel conversion pathways) as well as the construction of biorefineries (refineries that produce biofuels) using these technologies. To develop sustainable cellulosic biofuels, one of the goals of the Bioenergy Technologies Office (BETO) at the Department of Energy is to minimize air pollutants from the entire biofuel supply chain, as stated in their 2014 Multi-Year Program Plan (2014). Although biofuels in general havemore » been found to have lower life cycle greenhouse gas (GHG) emissions compared to petroleum fuels on an energy basis, biomass feedstock production, harvesting, transportation, processing and conversion are expected to emit a wide range of other air pollutants (e.g., criteria air pollutants, hazardous air pollutants), which could affect the environmental benefits of biofuels when displacing petroleum fuels. While it is important for policy makers, air quality planners and regulators, biofuel developers, and investors to understand the potential implications on air quality from a growing biofuel industry, there is a general lack of information and knowledge about the type, fate and magnitude of potential air pollutant emissions from the production of cellulosic biofuels due to the nascent stage of this emerging industry. This analysis assesses potential air pollutant emissions from a hypothetical biorefinery, selected by BETO for further research and development, which uses a biological conversion process of sugars to hydrocarbons to produce infrastructural-compatible renewable diesel blendstock from cellulosic biomass.« less
  7. Systematic Review of Life Cycle Greenhouse Gas Emissions from Geothermal Electricity

    The primary goal of this work was to assess the magnitude and variability of published life cycle greenhouse gas (GHG) emission estimates for three types of geothermal electricity generation technologies: enhanced geothermal systems (EGS) binary, hydrothermal (HT) flash, and HT binary. These technologies were chosen to align the results of this report with technologies modeled in National Renewable Energy Laboratory's (NREL's) Regional Energy Deployment Systems (ReEDs) model. Although we did gather and screen life cycle assessment (LCA) literature on hybrid systems, dry steam, and two geothermal heating technologies, we did not analyze published GHG emission estimates for these technologies. Inmore » our systematic literature review of the LCA literature, we screened studies in two stages based on a variety of criteria adapted from NREL's Life Cycle Assessment (LCA) Harmonization study (Heath and Mann 2012). Of the more than 180 geothermal studies identified, only 29 successfully passed both screening stages and only 26 of these included estimates of life cycle GHG emissions. We found that the median estimate of life cycle GHG emissions (in grams of carbon dioxide equivalent per kilowatt-hour generated [g CO2eq/kWh]) reported by these studies are 32.0, 47.0, and 11.3 for EGS binary, HT flash, and HT binary, respectively (Figure ES-1). We also found that the total life cycle GHG emissions are dominated by different stages of the life cycle for different technologies. For example, the GHG emissions from HT flash plants are dominated by the operations phase owing to the flash cycle being open loop whereby carbon dioxide entrained in the geothermal fluids is released to the atmosphere. This is in contrast to binary plants (using either EGS or HT resources), whose GHG emissions predominantly originate in the construction phase, owing to its closed-loop process design. Finally, by comparing this review's literature-derived range of HT flash GHG emissions to data from currently operating geothermal plants, we found that emissions from operational plants exhibit more variability and the median of emissions from operational plants is twice the median of operational emissions reported by LCAs. Further investigation is warranted to better understand the cause of differences between published LCAs and estimates from operational plants and to develop LCA analytical approaches that can yield estimates closer to actual emissions.« less
  8. Assessing Potential Air Pollutant Emissions from Agricultural Feedstock Production using MOVES

    Biomass feedstock production is expected to grow as demand for biofuels and bioenergy increases. The change in air pollutant emissions that may result from large-scale biomass supply has implications for local air quality and human health. We developed spatially explicit emissions inventories for corn grain and six cellulosic feedstocks through the extension of the National Renewable Energy Laboratory's Feedstock Production Emissions to Air Model (FPEAM). These inventories include emissions of seven pollutants (nitrogen oxides, ammonia, volatile organic compounds, particulate matter, sulfur oxides, and carbon monoxide) generated from biomass establishment, maintenance, harvest, transportation, and biofuel preprocessing activities. By integrating the EPA'smore » MOtor Vehicle Emissions Simulator (MOVES) into FPEAM, we created a scalable framework to execute county-level runs of the MOVES-Onroad model for representative counties (i.e., those counties with the largest amount of cellulosic feedstock production in each state) on a national scale. We used these results to estimate emissions from the on-road transportation of biomass and combined them with county-level runs of the MOVES-Nonroad model to estimate emissions from agricultural equipment. We also incorporated documented emission factors to estimate emissions from chemical application and the operation of drying equipment for feedstock processing, and used methods developed by the EPA and the California Air Resources Board to estimate fugitive dust emissions. The model developed here could be applied to custom equipment budgets and is extensible to accommodate additional feedstocks and pollutants. Future work will also extend this model to analyze spatial boundaries beyond the county-scale (e.g., regional or sub-county levels).« less
  9. Methane Leaks from Natural Gas Systems Follow Extreme Distributions

    Future energy systems may rely on natural gas as a low-cost fuel to support variable renewable power. However, leaking natural gas causes climate damage because methane (CH 4) has a high global warming potential. In this study, we use extreme-value theory to explore the distribution of natural gas leak sizes. By analyzing ~15,000 measurements from 18 prior studies, we show that all available natural gas leakage datasets are statistically heavy-tailed, and that gas leaks are more extremely distributed than other natural and social phenomena. A unifying result is that the largest 5% of leaks typically contribute over 50% of themore » total leakage volume. While prior studies used lognormal model distributions, we show that lognormal functions poorly represent tail behavior. Our results suggest that published uncertainty ranges of CH 4 emissions are too narrow, and that larger sample sizes are required in future studies to achieve targeted confidence intervals. Additionally, we find that cross-study aggregation of datasets to increase sample size is not recommended due to apparent deviation between sampled populations. Finally, understanding the nature of leak distributions can improve emission estimates, better illustrate their uncertainty, allow prioritization of source categories, and improve sampling design. Also, these data can be used for more effective design of leak detection technologies.« less
  10. Gathering pipeline methane emissions in Fayetteville shale pipelines and scoping guidelines for future pipeline measurement campaigns

    Gathering pipelines, which transport gas from well pads to downstream processing, are a sector of the natural gas supply chain for which little measured methane emissions data are available. This study performed leak detection and measurement on 96 km of gathering pipeline and the associated 56 pigging facilities and 39 block valves. The study found one underground leak accounting for 83% (4.0 kg CH 4/hr) of total measured emissions. Methane emissions for the 4684 km of gathering pipeline in the study area were estimated at 402 kg CH 4/hr [95 to 1065 kg CH 4/hr, 95% CI], or 1% [0.2%more » to 2.6%] of all methane emissions measured during a prior aircraft study of the same area. Emissions estimated by this study fall within the uncertainty range of emissions estimated using emission factors from EPA's 2015 Greenhouse Inventory and study activity estimates. While EPA's current inventory is based upon emission factors from distribution mains measured in the 1990s, this study indicates that using emission factors from more recent distribution studies could significantly underestimate emissions from gathering pipelines. To guide broader studies of pipeline emissions, we also estimate the fraction of the pipeline length within a basin that must be measured to constrain uncertainty of pipeline emissions estimates to within 1% of total basin emissions. The study provides both substantial insight into the mix of emission sources and guidance for future gathering pipeline studies, but since measurements were made in a single basin, the results are not sufficiently representative to provide methane emission factors at the regional or national level.« less

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