Improving Characterization of Anthropogenic Methane Emissions in the United States (2018) / Chapter Skim
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Its recovery via natural gas or petroleum wells and from landfills provides an economic benefit. Monitoring of methane emissions is a safety need for petroleum and natural gas exploration, recovery, and transport, as well as for coal mines and landfill sites.
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MEASUREMENT APPROACHES Measurements and emission estimates are made along a spectrum of spatial and temporal scales, from instantaneous measurements of methane emissions from individual emission sources to global assessments of annual methane emissions (Figure S.3; Chapter 3)
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Global monthly mean methane concentrations (red) from 1983 to 2017, with the running average (black)
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, the resulting top-down estimates of regional- to global-scale emissions are also referred to as "inverse models" or "inversions." The distinction between top-down and bottom-up techniques is not always clearly defined. At intermediate spatial scales (e.g., total emissions from a large complex facility such as a natural gas processing plant, an animal feeding operation, or a large regional landfill)
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. The Committee's goal for this report is to summarize the current state of understanding of methane emission sources and the measurement approaches, evaluate opportunities for methodological and inventory development improvements, and inform future research agendas of various U.S.
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anthropogenic methane emissions are enteric fermentation in domestic ruminant animals (such as cattle) , and petroleum and natural gas systems (Figure S.5)
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MEETING THE CHALLENGES OF CHARACTERIZING METHANE EMISSIONS Recent advances in atmospheric observations and facility-scale measurements make it possible to significantly improve estimates of anthropogenic methane emissions. Top-down and bottom-up approaches provide complementary information about methane emissions (Table S.1)
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SOURCE: EPA, 2017b. TABLE S.1 Comparison of Top-Down and Bottom-Up Approaches to Estimating Methane Emissions Top-Down Data Bottom-Up Data and Inventories Spatial scale Facility to global Individual site level to global Temporal scale Instantaneous to annual or multiyear Generally reported as annual averages averages for inventory purposes from limited time duration data collected at various temporal scales Source attribution Involves use of models and Calculated from source-specific activity assumptions as well as molecular and data isotopic tracers Potential for Measurements reflect all sources that May not account for all sources in a given missing sources contribute to observed atmospheric region concentrations 8
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Long-term observations, together with atmospheric models that estimate methane emissions, are critical for detecting large-scale trends in methane emissions. Adequate observational coverage in space and over time is required to constrain emission estimates using inverse modeling at global, national, or regional scales.
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I M P R O V I N G C H A R A C T E R I Z AT I O N O F A N T H R O P O G E N I C M E T H A N E E M I S S I O N S FIGURE S.6 Schematic summarizing the four major recommendations for improving characterization of anthropogenic methane emissions in the United States.
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At all scales, uncertainties in top-down methane emission estimates arise due to uncertainties in atmospheric transport models. A major source of variability among different top-down estimates is modeled atmospheric transport, which can change the emission source apportionment among regions.
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The publicly available GHGI is used by diverse communities for a variety of scientific and policy purposes. As the most frequently updated comprehensive inventory of anthropogenic methane emissions in the United States, the GHGI has increasingly been used for purposes for which it was not initially designed, including comparisons with top-down estimates of methane emissions in specific regions at specific times, thus attempting to merge and compare information collected at different spatial and temporal scales.
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To be most useful, a gridded inventory should be consistent with the GHGI for integrated total emissions per source and have sufficient documentation to allow the scientific and policy communities interested in regional methane emissions to adapt the inventory to meet their needs. The spatial and temporal resolution should be at as fine a scale as possible (e.g., 0.1° × 0.1° or finer spatial resolution and monthly or finer temporal resolution)
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For example, for petroleum and natural gas, many of the emission factors that are used in developing methane emission inventories rely on a comprehensive study conducted by the Gas Research Institute and the EPA in the 1990s. For manure management, most of the equations in the IPCC (2006)
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for each uncertainty value is also provided, with the descriptions and assigned source categories given for the CLs.
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Examples of the former include sources in certain natural gas end-use sectors such as residential and commercial operations, electric power plants, refineries, and high-emitting sources. Identifying and quantifying unaccounted-for emissions through multiscale measurement campaigns is critical to addressing uncertainties in emission inventories and to improve understanding of ethane emissions m in general.
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methane inventory methodologies. Research for Improving Characterization of Anthropogenic Methane Emissions Improvements in the accuracy and precision of methane emission estimates will be maximized through the complementary use of both top-down and bottom-up measurements and linkage to field-validated process-based models at appropriate scales.
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Coordinated, contemporaneous top-down and bottom-up measurement campaigns, conducted in a variety of source regions for anthropogenic methane emissions, are crucial for identifying knowledge gaps and prioritizing emission inventory improvements. Careful evaluation of such data for use in national methane inventories is necessary to ensure representativeness of annual average assessments.
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for which the guiding goal should be (1) better quantification and attribution of methane emission rates and trends over time and (2)
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