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ArticleNovember 16, 2016

Gridded National Inventory of U.S. Methane Emissions
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Environmental Science & Technology

Cite this: Environ. Sci. Technol. 2016, 50, 23, 13123–13133

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https://doi.org/10.1021/acs.est.6b02878

Published November 16, 2016

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Abstract

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We present a gridded inventory of US anthropogenic methane emissions with 0.1° × 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 US Environmental Protection Agency (EPA) Inventory of US Greenhouse Gas Emissions and Sinks (GHGI) for 2012. The EPA inventory is available only as national totals for different source types. We use a wide range of databases at the state, county, local, and point source level to disaggregate the inventory and allocate the spatial and temporal distribution of emissions for individual source types. Results show large differences with the EDGAR v4.2 global gridded inventory commonly used as a priori estimate in inversions of atmospheric methane observations. We derive grid-dependent error statistics for individual source types from comparison with the Environmental Defense Fund (EDF) regional inventory for Northeast Texas. These error statistics are independently verified by comparison with the California Greenhouse Gas Emissions Measurement (CALGEM) grid-resolved emission inventory. Our gridded, time-resolved inventory provides an improved basis for inversion of atmospheric methane observations to estimate US methane emissions and interpret the results in terms of the underlying processes.

Subjects

Introduction

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Under the United Nations Framework Convention on Climate Change (UNFCCC), individual countries must report their national anthropogenic greenhouse gas emissions calculated using comparable methods. (1) The Intergovernmental Panel on Climate Change (IPCC) (2) provides three different methods or “tiers” for calculating emissions. All are bottom-up approaches in which emissions from individual source types are generally calculated as the product of activity data and emission factors. Increasing tiers are more detailed and require more country-specific data. In the United States, the Environmental Protection Agency (EPA) produces an annual Inventory of US Greenhouse Gas Emissions and Sinks (GHGI) (3) for reporting to the UNFCCC. The GHGI uses detailed information on activity data and emission factors, generally following IPCC Tier 2 and 3 methods. It provides detailed sectoral breakdown of emissions but only reports national totals for most source types. Here we present a spatially disaggregated version of the GHGI at 0.1° × 0.1° spatial resolution and monthly temporal resolution, including detailed information and error characterization for individual emission types. Our goal is to enable the use of the GHGI as an a priori estimate for inversions of atmospheric methane that may guide improvements in the inventory.

Table 1 gives the GHGI estimates for 2012 with methodology updated in 2016 (3) and including contributions from different source types. Total US anthropogenic emission is 29.0 Tg a^–1, including major contributions from natural gas systems (24%), enteric fermentation (23%), landfills (20%), coal mining (9%), manure management (9%), and petroleum (or equivalently oil) systems (8%). The inventory includes forest fire emissions but no other natural sources. The main natural source of methane is thought to be wetlands, accounting for 8.5 ± 5 Tg A^–1 in the contiguous US (CONUS). (4) Annual anthropogenic emissions from 1990 to 2014 computed by EPA (3) with a consistent method (revised each year to include updated information) show no significant trend and little interannual variability, with US totals staying in the range 28.6-31.2 Tg a^–1 and contributions from individual source types varying by only a few percent.

Table 1. Inventories of US Anthropogenic Methane Emissions (Gg a^–1)^a

source type	EPA GHGI (2012)	EDGAR v4.2 (2008)
agriculture
enteric fermentation	6670 (5936–7871)	6720
manure management	2548 (2089–3058)	2200
rice cultivation	476 (395–557)	418
field burning of agricultural residues	11 (7–15)	38
natural gas systems	6906 (5594–8978)	4758
production	4442
processing	890
transmission and storage	1116
distribution	457
waste
landfills	5691 (3528–9333)	5230
municipal	5098
industrial	593
wastewater treatment	601 (367–613)	887
domestic	368
industrial	232
composting	77 (39–116)	83
coal mines
coal mining	2658 (2339–3057)	4140
underground	2159
surface	499
abandoned coal mines	249 (204–309)
petroleum systems	2335 (1775–5814)	1032
other
forest fires	443 (62–1214)	17
stationary combustion	265 (156–676)	424
mobile combustion	86 (76–101)	104
petrochemical production	3 (1–4)	24
ferroalloy production	1 (1–1)	1
total	29020 (26698–36565)	26075

Column two shows the EPA inventory of US Greenhouse Gas Emissions and Sinks (GHGI) for 2012 as updated in 2016. (3) 95% confidence intervals are in parentheses as provided by EPA, sometimes only for broad source categories. Column three shows the US component of the global EDGAR v4.2 inventory for 2008. (7) The gridded version of the EPA GHGI developed in this work includes separate files for all entries in this table.

Application of atmospheric methane observations to estimate emissions usually involves inversion of an atmospheric transport model, with consideration of a priori information from an emission inventory to regularize the results and achieve a Bayesian optimal estimate of emissions. (5, 6) The inversion optimizes emissions on a grid, and the inventory used as a priori information must be available on that grid. In the absence of a gridded version of the GHGI, previous inverse studies for the US have relied on the global EDGAR inventory (7) which provides annual emissions at 0.1° × 0.1° resolution. EDGAR uses IPCC Tier 1 methods with international data sets, and only includes a limited breakdown by source type. National totals in EDGAR are generally consistent with EPA, as shown in Table 1, but we will see that there are large errors in spatial allocation that affect inverse analyses and their interpretation. Our gridded version of the GHGI not only provides a better a priori estimate but also a better basis for interpreting inversion results and hence improving our understanding of the underlying processes.

Methods

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We disaggregate the 2012 national emissions reported by the 2016 version of the GHGI (3) into a gridded 0.1° × 0.1° monthly inventory. The gridded inventory is consistent with the EPA national emission totals for each source type (each entry in Table 1) and distributes these emissions based on information at the state, county, subcounty, and point source levels. In this manner, our inventory is a gridded representation of the national GHGI. Similar disaggregation has been done for national methane inventories in Switzerland, (8, 9) Australia, (10) and the United Kingdom. (11) We limit our domain to the CONUS, which accounts for over 98% of total US emissions on the basis of our state-level estimates. We use the 2012 emissions from the 2014 EPA GHGI published in 2016, which includes detailed descriptions of the methods used to calculate the national emissions. (3) The 2014 GHGI includes updates to the petroleum and natural gas emissions to reflect new studies. (3, 12) We focus on the year 2012 as the latest year for which all spatial activity data are available. Updating our gridded inventory to newer iterations (the GHGI is updated annually) and later years will be straightforward as new activity data are released.

We start from the most detailed spatial information directly available from the GHGI. This information varies by source type. Livestock emissions are available for each state, whereas waste and petroleum systems emissions are only available as national totals. Separate from the national inventory, EPA also collects methane emission and supporting data from large facilities under the Greenhouse Gas Reporting Program (GHGRP). (13) Facilities with emissions greater than 25 Gg CO₂ equivalent a^–1 (corresponding to 0.11 tons h^–1 for a pure methane source) and subject to the applicable regulatory requirements must report to the GHGRP. Some emissions reported to the GHGRP are directly measured (e.g., underground coal mines), while others are calculated on the basis of facility-level activity data (e.g., landfills). Where possible, we use facility-level emissions from the GHGRP but those sometimes need to be adjusted, as discussed below, to be consistent with the national inventory.

Agriculture

Emissions from agriculture include enteric fermentation, manure management, rice cultivation, and field burning of agricultural residues. EPA provides annual state-level enteric fermentation and manure management emissions for different animal types, taking into account varying practices across the country. We estimate county-level emissions by using livestock numbers for 14 different animal types (including different types of cattle) from the 2012 US Department of Agriculture Census of Agriculture for each animal type. (14) County-level emissions are allocated to the 0.1° × 0.1° grid using 9 different livestock occurrence probability maps (again distinguishing between different types of cattle) from USDA based on landtype. (15) Emissions from enteric fermentation are assumed to have no intra-annual variability. Emissions from manure management vary with temperature as given by (16)

(1)where f is a monthly scaling factor, A = 64 kJ mol^–1 is the activation energy, R is the ideal gas constant, T_m is the monthly average surface skin (radiant) temperature, and T_o = 303 K. (16) Monthly emissions are calculated by scaling annual emissions with normalized monthly f-fields using 0.625° × 0.5° monthly average surface skin temperature fields from the NASA MERRA-2 meteorological data. (17) Livestock emissions also vary subanually as a function of varying herd size, and management practices but those effects are not included in our inventory.

Annual state-level emissions from rice cultivation are obtained from EPA and allocated to counties using acreage harvested from the USDA Census. (14) Emissions for each county are allocated to the 0.1° × 0.1° grid based on crop maps with 30 m resolution from the USDA Cropland Data Layer product. (18) Annual emissions are then distributed over individual months using normalized mean 2001–2010 heterotrophic respiration rates from the 1° × 1° monthly Carbon Data-Model Framework (CARDAMOM) terrestrial C cycle analysis. (19)

Emissions from field burning of agricultural residues of five individual crops (corn, rice, soybeans, sugar cane, and wheat) are allocated to a 2003–2007 monthly climatology of agricultural fires. (20)

Natural Gas Systems

This source type includes emissions from natural gas production, processing, transmission, and distribution. It does not include emissions from abandoned wells. (21) Emissions from natural gas production are available from EPA for each of the six National Energy Modeling System (NEMS) regions defined by the US Energy Information Administration (EIA). (22) The GHGI attributes emissions to different activities (e.g., vessel blowdowns, well workovers, liquid unloading) and equipment (e.g., pneumatic devices). Detailed maps of these activities and equipment are not available. Therefore, we rely on monthly well data obtained from DrillingInfo. (23) Separate DrillingInfo data are available for the number of gas producing wells, nonassociated gas wells (gas-to-oil ratio over 100 mcf gas per barrel), coalbed methane wells, and coalbed methane well water production. We also distinguish conventional and unconventional wells as some emissions are specific to hydraulic fracturing. A well is flagged as unconventional if the drilling direction is horizontal as given by DrillingInfo or if the reservoir type is coalbed, low permeability, or shale. (3) For each NEMS region, we allocate emissions using the DrillingInfo-based maps best representative of the spatial distribution of the considered activity or equipment. State-level condensate production from EIA (24) is combined with nonassociated gas well maps to allocate emissions from condensate tank vents. Three gas-producing states (Illinois, Indiana, and Tennessee) do not have active wells in the DrillingInfo database and amount to less than 1% of national active gas wells. (25) For these states we use state-level data on the number of natural gas wells (25) to calculate state emissions and then use county-level gas production (26) and finally three different well databases to grid emissions. (27-29) For offshore emissions, the 2011 Gulfwide Offshore Activity Data System (GOADS) platform-level emission database is used for the Gulf of Mexico (3, 30) and DrillingInfo is used outside of the Gulf of Mexico, scaling total emissions to the national emission from the GHGI. As no national spatial data are available for gathering processes (only a subset report to the GHGRP (31)), emissions from these processes are included in the production sector and gridded in the same way.

Emissions from gas processing are only available as national totals in the GHGI. We allocate emissions to processing plants by combining the GHGRP data (13) with the EIA database for these plants. (32) The GHGRP covers 85% of the processed gas flow from the EIA database. For the remaining plants in the EIA database, emissions are estimated by multiplying their gas flow with the average ratio of methane emissions to gas flow of the GHGRP plants. Subsequently, emissions from all plants are scaled to match the national GHGI number; the scaling is required because the GHGRP does not include all emitting processes occurring at the plants and has different emission estimates per process. (31) Thus, we only use the GHGRP to allocate emissions in a relative sense with emission magnitudes constrained by the GHGI. The EIA database only provides postal codes for the processing plants and not coordinates; we determine non-GHGRP plant coordinates from the Rextag Strategies US Natural Gas Pipeline and Infrastructure Wall Map. (33) If there is no match with the GHGRP or Rextag data, emissions from the plant in the EIA database are spread out over the associated postal code area. (34)

EPA provides national emissions for different parts of the transmission sector. Most important are transmission compressor stations, for which we use a similar mapping as for processing plants. The GHGRP data for individual compressor stations are complemented with the EIA database for nonreporting compressor stations. (35) Emissions for nonreporting compressor stations are estimated based on their throughput, (35) using the average ratio of throughput to methane emission from the GHGRP data. Emissions are then scaled to the national total so our results are not affected by potential underestimates in the GHGRP emissions. (36) Similarly, a database of storage stations (37) is combined with the GHGRP using total field capacity to predict emissions. Locations are based on the GHGRP, supplemented by gas storage field locations georeferenced from the Rextag Strategies US Natural Gas Pipeline and Infrastructure Wall Map, (33) and DrillingInfo. (23) Similar approaches are also used for liquid natural gas (LNG) storage (38) and LNG import terminals. (39) Emissions from pipeline leaks and transmission meter and regulator stations are allocated to the network of interstate and intrastate pipelines. (40) Emissions from farm taps are allocated to pipelines intersecting with agricultural land. (18) Emissions related to storage at wells are mapped to all nonassociated gas wells. (23)

Emissions from different parts of the distribution network are available from EPA as national estimates. State-level emissions from distribution pipeline leaks are calculated using state data on pipeline miles and services from the Pipeline and Hazardous Materials Safety Administration (PHMSA) of which the sum is used for the national GHGI. (41) This takes into account different materials (e.g., cast iron, plastic) with different emission factors. Emissions from distribution meter and regulator stations are divided among states using state-level aggregated GHGRP information (no finer spatial information is available). Other distribution emissions are partitioned between the states based on leaked gas volume data from EIA. (42) Within states, emissions are mapped to 0.1° × 0.1° population data from the 2010 US Census. (43)

Waste

Waste emissions include landfills, wastewater treatment, and composting, for which EPA provides national totals following the categories in Table 1. We allocate emissions from landfills based on a combination of data from the GHGRP (1231 municipal landfills, 175 industrial landfills), the Landfill Methane Outreach Program (LMOP, municipal landfills only), (44) and the Facility Registration Service (FRS). (45) GHGRP landfills are assigned their reported emissions. 900 of 2049 LMOP landfills do not report emissions to the GHGRP. For those we estimate emissions from GHGRP-reporting landfills with similar attributes (presence of a collection system, flares). Some landfills report landfill gas production through the LMOP. For the other landfills, waste in place is used as estimation metric combined with a decay factor for landfills that closed before 2012. (46) For landfills without any data (108), we assign the median emissions from the landfills for which information was available. Finally, we use landfills with known coordinates from the FRS that are not present in the GHGRP or LMOP data sets. We decide whether a landfill is municipal or industrial based on keyword descriptors in the databases. The 722 municipal FRS landfills are assigned the median emission derived from above, after which all non-GHGRP emissions are scaled to match the national emission estimate. For industrial landfills, the national estimate minus the GHGRP emissions is uniformly allocated across the 2309 industrial landfills from the FRS.

Emissions from wastewater treatment are reported as municipal or industrial in the GHGI. Facilities that report to the GHGRP account for 84% of the national industrial wastewater treatment emissions. To allocate the remaining industrial emissions as well as the municipal emissions, we use facility-level wastewater flow data from the Clean Watersheds Needs Survey. (47) Industrial wastewater treatment emissions are mapped to treated industrial flow, emissions from municipal septic systems to decentralized municipal flow, and centralized municipal systems emissions to centralized municipal flow.

State-level emissions from composting are calculated using the tonnage of municipal solid waste composted or, if composting data are not available, from the correlated tonnage recycled. (48) Within states, emissions are allocated to locations from the US Composting Council, (49) BioCycle composter database, (50) and composting entries in the FRS. (45) If there are fewer than three facilities found in a state, we allocate based on gridded population instead. (43)

Coal Mines

We allocate coal mining emissions using state-level emission estimates produced for the GHGI (M. Coté, Ruby Canyon Engineering, unpublished data) for underground mines and surface mines. These estimates account for methane recovered or destroyed, as well as postmining emissions (methane released during coal handling and processing). We use the locations and production of all active surface and underground coal mines from EIA. (51) A large number of underground mines report their annual methane emissions to GHGRP. We estimate emissions from nonreporting underground mines based on their share of the state total coal production combined with the state-level emissions, weighted by the basin-level in situ methane content of the coal for states that have mines in multiple basins. (3) Subsequently, we scale the emissions from nonreporting mines so that the total national emissions (including the GHGRP mines) match the GHGI. For surface mines, no GHGRP data are available. Emissions are allocated using the EPA state-level data as given above, combined with EPA basin-level emission factors and EIA mine-level production data. Similarly, postmining emissions are allocated to all mines based on their production and basin-specific emission factors.

The GHGI also includes emissions from abandoned coal mines. We start from the Abandoned Coal Mine Methane Opportunities Database (ACMMOD) (52) and add recently closed coal mines plus county-level estimates of mine closures before 1972 not included in ACMMOD (unpublished data produced for EPA by Ruby Canyon Engineering). For all closed mines, estimates of closure dates, status (venting, sealed, flooded), and estimates of emissions when the mine was active are available or estimated from county-level averages, allowing the estimation of present-day emissions based on decline equations used in the GHGI. (53) ACMMOD only includes mine locations on the county level. Precise locations of approximately one-third of the abandoned mines are found in the Full Mine Info data set. (54) The remaining emissions are allocated on the county level.

Petroleum Systems

The GHGI includes national emissions from different activities and equipment related to petroleum production, refining, and transport. We use monthly well data for several production quantities from DrillingInfo (23) to spatially allocate these emissions. These include total, heavy, and light oil production, with the cutoff between the last two at an American Petroleum Institute (API) gravity of 20. EPA estimates some emissions separately for heavy and light oil production. Furthermore, we created maps of oil wells (defined as wells with a produced gas-to-oil ratio under 100 mcf per barrel, wells with a higher ratio are classified as nonassociated gas wells), stripper wells (producing fewer than 10 barrels per day), and total and unconventional oil well completions. Similar to the allocation of natural gas production emissions, states without active wells in DrillingInfo are represented using state-specific data sets and amount to less than 1% of national production. (55) For the other states, the national-level emissions from each activity and device are allocated using the DrillingInfo maps. For example, emissions from well drilling are mapped to well completions, while emissions from heavy crude oil wellheads are mapped to heavy oil wells. As for natural gas systems, offshore emissions are based on the GOADS database for the Gulf of Mexico and DrillingInfo elsewhere. Emissions from petroleum refining are allocated to GHGRP facilities based on their reported emissions. National emissions from petroleum transportation are divided between the wells, offshore platforms, and refineries.

Other

Other refers to a number of smaller sources listed in Table 1. National forest fire emissions from the GHGI are distributed on a daily basis at 0.1° × 0.1° resolution using the Quick Fire Emissions Data set (QFED v2.4) for 2012. (56) Stationary combustion emissions from electricity generation are calculated by multiplying plant-level heat inputs from the Acid Rain Program with fuel type specific emission factors. (57) Additional stationary combustion emissions from the industrial, commercial, and residential sectors are based on state-level consumption of different types of fuel (coal, fuel oil, natural gas, and wood) as reported by EIA. (58) Within states, residential and commercial emissions are allocated based on population while industrial emissions are allocated based on combustion emissions reported to the GHGRP. National on-road mobile combustion emissions for individual vehicle types in the GHGI are allocated spatially by first calculating state-level vehicle miles traveled (VMT) for six types of roads: urban and rural for each of primary, secondary, and other (minor) roads (59) and attributing those to individual vehicle types. (59) These state totals are then mapped to the different road networks taken from the National Transportation Atlas (60) and US Census products. (61) Combustion emissions from rail transport are allocated over the US railroad network. (61) Emissions from agricultural equipment are uniformly spread out across all agricultural land. (18) Emissions from mining-related vehicles are allocated to active mines. (54) Construction and “other” mobile combustion emissions are mapped based on population. National emissions from ferroalloy production and from the petrochemical industry are divided over the facilities reporting to the GHGRP based on their total reported emissions.

Results and Discussion

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Figure 1 shows the distribution of annual emissions on the 0.1° × 0.1° grid for the six general emission categories of the Methods section. Emissions from agriculture are broadly distributed across livestock farming areas. Hotspots are mostly from concentrated dairy cattle or hog populations such as in Iowa, North Carolina, and California. Rice cultivation contributes hotspots in northern California and along the lower Mississippi River. Emissions from natural gas systems are high in production fields, for example in Pennsylvania (Marcellus shale) and in Texas, with a maximum at Four Corners as found in top-down studies. (62) Waste emissions (dominated by landfills; Table 1) roughly map to population, with hotspots from large landfills and wastewater facilities. Coal mining emissions are concentrated in Appalachia. Petroleum systems emissions peak over the Bakken region in North Dakota and western Texas where natural gas emissions are low. Other emissions mostly feature forest fire hotspots in the West and stationary combustion emissions in populated areas.

Figure 1. Contiguous US (CONUS) methane emissions from different source categories. Total annual US emissions from the 2016 EPA GHGI for 2012 are disaggregated here on a 0.1° × 0.1° grid. “Other” refers to the ensemble of minor sources in Table 1. (An equivalent figure for EDGAR v4.2 is shown by in Turner et al. (70)).

Total CONUS emissions for 2012 are 28.7 Tg a^–1, slightly lower than the 29.0 Tg a^–1 national total reported in Table 1 because of contributions from Alaska, Hawaii, and outside territories. Several sources vary monthly in our inventory including manure management, natural gas and petroleum production, stationary combustion, and forest fires (daily). Monthly emissions vary from 73 Gg per day in December to 89 Gg per day in July. Most of this monthly variation arises from manure management, which varies nationally from 2.4 Gg per day in January to 16.8 Gg per day in July. Eq 1 is for liquid storage systems but is applied here to all manure management systems, which may overestimate the seasonal variation. (63, 64) For rice emissions, we assume a constant methane to CO₂ emission ratio from heterotrophic respiration, which may underestimate the seasonal variation as the ratio has been found to increase with temperature in wetlands and aquatic ecosystems. (65) On the other hand, some seasonal factors are not considered in our inventory due to lack of data such as livestock numbers, feed, and gas/petroleum distribution. Transient elevated emissions from oil/gas systems (the so-called “super-emitters” (66)) are also not resolved.

Figure 2 compares the distribution of total methane emissions in our gridded EPA inventory for 2012 to the EDGAR v4.2 inventory for 2008, the latest year of full release. (7) A fast track version of EDGAR (v4.2 FT2010 (7)) has come out since but, based on visual inspection, the spatial emissions patterns in EDGAR v4.2 are of higher quality and most inverse studies have used EDGAR v4.2. There are large differences in spatial patterns between the Gridded EPA inventory and EDGAR v4.2, particularly for oil/gas systems and manure management. Emissions in the gridded EPA inventory are much higher over oil/gas production areas and lower over distribution (populated) areas. The two inventories show no significant correlation at their native 0.1° × 0.1° resolution (r = 0.06). The correlation increases to r = 0.42 at 0.5° × 0.5° resolution and r = 0.63 at 1.0° × 1.0° resolution.

Figure 2. Total methane emissions in the gridded EPA inventory for 2012 (top), EDGAR v4.2 for 2008 (middle), and difference between the two (bottom).

Previous inverse studies for US methane emissions using EDGAR as a priori estimate have all found the need for a large upward correction of emissions in the South-Central US. (67-70)Figure 3 shows the distributions of livestock, oil/gas systems, and waste emissions for that region in the gridded EPA and EDGAR v4.2 inventories. The EDGAR v4.2 inventory places the oil/gas emissions in urban areas and completely misses areas of production. The oil/gas emissions in EDGAR v4.2 are strongly correlated with waste emissions because both are largely distributed following population. An inversion using EDGAR v4.2 as a priori estimate would not be able to separate the two and might wrongly attribute a source in oil/gas production regions to livestock. This stresses the importance of using a high-quality a priori inventory in inverse analyses, both to regularize the solution and to enable interpretation of results. Whereas different source types show spatial correlation in the EDGAR v4.2 inventory because of mapping to common databases, there is no such correlation between source types in our gridded EPA inventory even at 1° × 1° resolution. This separation between individual source types holds promise for interpreting results from inverse analyses.

Figure 3. Emissions from livestock, oil/gas systems, and waste over the South-Central US in the gridded EPA inventory for 2012 and the EDGAR v4.2 inventory for 2008.

Error characterization is necessary for a gridded emission inventory to serve as a priori estimate in Bayesian inversions and to interpret results from the inversions. Error characterization is not available for the EDGAR v4.2 inventory and inversions have typically assumed 30–100% uniform error based on expert judgment, or used the inversion to estimate the error in the a priori. (71) The GHGI includes detailed error characterization on its national totals for individual source types, based on propagation of uncertainties in the construction of the bottom-up estimates (Table 1). Errors in our 0.1° × 0.1° gridded inventory may be larger because of local uncertainties in activity data and emission factors, including the precise localization of emissions. For the same reason, averaging our inventory over coarser grids (by adding contributions from 0.1° × 0.1° grid cells) could reduce the error. This scale dependence is important to describe because inversions may seek to optimize emissions at different spatial resolutions depending on the information content of the atmospheric observations.

Here we derive scale-dependent error statistics for our gridded EPA inventory by comparison to a detailed bottom-up emission inventory compiled by Environmental Defense Fund (EDF) for the ∼300 × 300 km² Barnett Shale region in Northeast Texas by Lyon et al. (72) and subsequently updated with top-down constraints by Zavala-Araiza et al. (73) The EDF inventory was constructed largely independently from the GHGI. It is based on an extensive field campaign in the region in September–October 2013 including measurements of individual facilities as well as regional surveys. (12) The Barnett Shale region is of particular interest as a comparison standard because it includes diverse sources: the largest oil/gas field in the CONUS (30 000 active wells), major livestock operations, and the metropolitan area of Dallas/Fort Worth. The EDF inventory incorporates considerable local information that goes beyond the databases used in constructing our inventory, and including for example precise locations of dairy farms, gas gathering stations, and landfills. (72) Emissions are reported on a 4 × 4 km² grid (approximately 0.04° × 0.04°) with detailed breakdown by source types and statistical sampling of “super-emitter” facilities with anomalously large emissions.

Figure 4 shows emissions from livestock, natural gas, waste, and petroleum in the Zavala-Araiza EDF Barnett Shale inventory and compares to our gridded EPA inventory. Emission totals for the domain are shown in Table 2. There is a large difference in the magnitude of the source from oil/gas production, at least in part because Zavala-Araiza et al. find a larger frequency of superemitters than assumed in the GHGI emission factors. Despite this difference in magnitude there is a strong spatial correlation on the 0.1° × 0.1° grid (r = 0.78), implying that correction to the gridded EPA distribution in an inversion of atmospheric data could be reliably attributed to the oil/gas production source type, smoothing temporally over superemitters. The spatial correlation coefficient of the livestock source between the gridded EPA and EDF inventories is only 0.37 at 0.1° × 0.1° resolution but increases to 0.88 at 0.5° × 0.5° resolution. The gridded EPA inventory misses the exact locations of farms but this error is smoothed out on the county scale.

Table 2. Regional Methane Emissions (Gg a^–1)^a

	Barnett Shale region				California
source	EDF (Lyon)	EDF (Zavala-Araiza)	this work	r	CALGEM	this work	r
oil/gas production	330	436	327	0.78	171	264	0.90
gas processing	49	65	62	0.24	12	7	0.25
gas transmission	16	2	8	0.20	22	24	0.69
gas distribution	10	9	16	0.87	131	39	0.98
livestock	104	102	122	0.37	721	885	0.46
landfills	105	99	92	0.76	316	507	0.86
wastewater	7	7	12	0.21	91	45	0.53
sum	621	720	640	0.68	1463	1772	0.66

Anthropogenic emissions from the Barnett Shale region in Northeast Texas (Figure 4) and from the state of California (Figure 6). Regional totals by source type from our gridded version of the gridded EPA inventory for 2012 (this work) are compared to the original bottom-up (Lyon) EDF inventory for the Barnett Shale in October 2013, (72) the updated (Zavala-Araiza) EDF inventory including top-down information, (73) and the CALGEM inventory for California in 2008 (livestock/waste) (63, 74) and 2010 (oil/gas). (75) Also shown are spatial correlation coefficients r on the 0.1° × 0.1° grid for the Barnett Shale (73) and 0.2° × 0.2° for California.

Figure 4. Methane emissions in the Barnett Shale region of Northeast Texas. Values for the four main categories are shown for our gridded EPA inventory and for the EDF inventory (73) at 0.1° × 0.1° resolution. The original EDF inventory is at 4 × 4 km² and is regridded here to 0.1° × 0.1° for comparison with our inventory. The location of the Barnett Shale region is shown inset. Emission totals for the region are given in Table 2.

We take the Zavala-Araiza EDF Barnett Shale inventory as our best approximation of emissions in the region in order to derive scale-dependent error statistics for different source types that can be used in an inversion of atmospheric concentration data. We assume for this purpose that the total error probability density function (pdf) for each source type in a given grid cell is Gaussian and includes a displacement error due to imprecise localization. Our error model is given by

(2)Here, σ(x) is the Gaussian error standard deviation for the grid cell centered at location x and for a given source type, α is a base relative error standard deviation assuming no displacement error, E(x′) is the 2-D field of emissions for that source type over all grid cells, and β is a length scale for the displacement error. α and β are assumed to be uniform for a given source type. We find optimal values for α and β by minimizing a least-squares cost function J(α, β) for the difference between our estimated error standard deviation and the absolute difference between the gridded EPA and EDF emissions:

(3)where the summation is over all grid cells of the Barnett Shale domain in Figure 4. Optimization of α and β is done for the different source types of Figure 4 (also separating waste as landfills and wastewater) and for grid resolutions L from 0.1° to 0.5° to determine the scale dependence of the error. 0.5° is the coarsest scale that can be usefully constrained from the Barnett Shale inventory, but from there we can extrapolate to the national scale using the GHGI error estimates. For this purpose we take the average of the upper and lower confidence intervals for the given source type in Table 1 as representing the relative error standard deviation α_N on the national scale. We then fit our results for α(L) and β(L) to exponential forms of L, with asymptote α_N for α. This yields

(4)

(5)Here L₀ = 0.1° is the native resolution of our inventory, and k_α and k_β (in units of inverse degrees) are smoothing coefficients that express the scale dependence of the error. The fit is subject to the condition α₀ ≥ 0; if the base error standard deviation derived from the Barnett Shale inventory is smaller than α_N then we assume that α is scale-independent and equal to α_N.

Figure 5 shows the base relative error standard deviation α and displacement length scale β as a function of grid resolution L for the different source types active in the Barnett Shale. Values for all coefficients in eqs 4 and 5 are given in Table 3. Base error standard deviations (α) for different source types at 0.1° × 0.1° grid resolution are all above 50%. Errors for livestock, natural gas systems, and wastewater are scale-dependent and decrease when coarser grid resolutions are used. Errors for petroleum systems and landfills are defined by the national estimates, which are relatively large, and are thus scale-independent. The displacement error measured by β is usually very small, less than 0.1°, in part because it is isotropic (there is no a priori information on the direction of displacement error). Because of its Gaussian form, it emphasizes the effect of neighboring misplacements; it would not capture the error from a distant misplacement or from a completely missing source.

Figure 5. Relative error standard deviations for methane emissions from individual source types and their scale dependences. The figure shows the error parameters α and β used in eq 2 to calculate the absolute error standard deviations for a given L × L grid cell and source type as a function of the grid resolution L. The native grid resolution of the inventory is 0.1° × 0.1°, and averaging over coarser scales decreases errors for individual source types as described by exponential decay functions (eqs 4 and 5). The asymptotes for the base error standard deviations are the national values α_N shown as tick marks on the right side of the left panel. Values for all error parameters are given in Table 3.

Table 3. Error Parameters for the Gridded EPA Emission Inventory

source	α₀	k_α	α_N	β₀	k_β
livestock	0.89	3.1	0.12	0
natural gas systems	0.28	4.2	0.25	0.09	3.9
landfills	0		0.51	0.08	2.0
wastewater treatment	0.78	1.4	0.21	0.06	6.9
petroleum systems	0		0.87	0.04	197

Error parameters for use in eqs 4 and 5 to compute the base relative error standard deviation α(L) and displacement error length scale β(L) for different source types at different grid resolutions L × L. The resulting values of α(L) and β(L) should be used in eq 2 to estimate the error standard deviation for a given source type and grid cell. Units are degrees for L and β₀, and inverse degrees for k_α and k_β. α₀ and α_N are dimensionless. The livestock error estimate is to be applied to the sum of enteric fermentation and manure management emissions.

We recommend that users of our emission inventory at a given grid resolution L apply the error parameters in Table 3 nationally to derive α(L) and β(L) from eqs 4 and 5, and from there use eq 2 to derive the absolute error standard deviation σ for individual source types and grid cells. For source types not constrained by the Barnett Shale inventory, we assume here that the base error standard deviation at 0.1° × 0.1° resolution is 2.5 times the national value from Table 1, based on the median scale dependence for the sources in the Barnett Shale. We use median values of the other error parameters in Table 3 and cap α at 1.0. Error variances for the different source types present in a grid cell can be added in quadrature to derive the error variance for the total emission in that grid cell. A simple variogram analysis (76) of the difference between the EDF and EPA inventories shows no spatial error correlation, either for total emissions or for individual source types, suggesting that the a priori error covariance matrix needed for a Bayesian inversion can be assumed diagonal. A previous study comparing a disaggregated national inventory for Switzerland to EDGAR v4.2 did find significant spatial error correlations. (8)

Our error model is a first attempt to quantify grid-dependent errors for use in inversions of atmospheric concentration data, and in that it significantly improves on previous bottom-up inventories. It has however a number of weaknesses. First, the Barnett Shale region may not be representative nationally and offers no error characterization for some sources (in particular coal mining). Second, inverse analyses of atmospheric observations (67, 70, 77) suggest that EPA underestimates on the national scale maybe be larger than estimated from α_N, although these inverse analyses have their own errors. Third, the assumed Gaussian form for the error pdf is convenient for analytical inversions (6) but is not optimal. It does not exclude unphysical negative solutions and it does not capture the “fat tail” of the pdf contributed by superemitters. (66, 72) A log-normal error pdf would solve the positivity problem and allow a better description of the fat tail. Fourth, some spatial error correlation would be expected even though it cannot be detected in our simple variogram analysis for the Barnett Shale; more advanced variogram analyses and better data sets might enable detection. (8, 76) Fifth, we do not consider temporal error correlations because inversions typically focus on optimizing the spatial distribution while assuming the temporal variation to be known (and relatively weak in our case). This may not be appropriate for some applications, in particular when optimizing emissions from seasonally varying sources. (19)

The state of California has developed its own methane emission inventory in support of its policy objective to reduce greenhouse gas emissions to 1990 levels by 2020. (78) Similarly to our work here, this California inventory has been disaggregated by Zhao et al., (74) Jeong et al., (63) and Jeong et al. (75) to produce the gridded 0.1° × 0.1 ° California Greenhouse Gas Emissions Measurement (CALGEM) inventory (calgem.lbl.gov/emissions). The CALGEM grid is offset by 0.05° from ours, so we can only compare them at 0.2° × 0.2° and even then with some unresolvable remapping error. Table 2 compares total California emissions for individual source types, including spatial correlation coefficients. Total state emissions are close (1772 Gg a^–1 in our work and 1463 Gg a^–1 in CALGEM). There is more difference in individual source types but most source types show strong correlations between the two inventories, suggesting that they could be effectively constrained in an inverse analysis of atmospheric observations. Figure 6 compares the spatial distributions of emissions in the two inventories, including our (Barnett-based) estimated error standard deviation on the 0.2° × 0.2° grid, and adding error from different source types in quadrature for a given grid cell. We find that 51% of CALGEM emissions are from cells that have emission magnitudes within one standard deviation of our gridded EPA emissions. The largest differences are from livestock emissions, as CALGEM uses more local data to distribute these emissions within the large California counties.

Figure 6. Methane emissions in California in 2012 from all sources in Table 2. The top panels show results from our gridded EPA inventory and from the CALGEM inventory on their native 0.1° × 0.1° grids. The bottom left panel shows the difference and the bottom right panel shows the error standard deviation in our gridded EPA inventory as computed with the method described in the text. The sign of the error standard deviation in the figure is the same as the EPA-CALGEM difference to facilitate visual comparison. Differences and error standard deviations are shown on a 0.2° × 0.2° grid to account for the 0.05° offset between the EPA and CALGEM grids.

In summary, we have constructed a gridded version of the EPA GHGI published in 2016 for US anthropogenic methane emissions with monthly 0.1° × 0.1° resolution including detailed information on different source types. Our inventory includes error characterization for different source types and spatial scales, as required for application as a priori estimate in inverse analyses. Our inventory is for 2012 emissions but can easily be updated to later years as activity data become available. Monthly gridded emission fields for all emission subcategories in Table 1 are publicly available at www.epa.gov/ghgemissions/gridded-2012-methane-emissions.

Author Information

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Corresponding Author
- Joannes D. Maasakkers - School of Engineering and Applied Sciences, Harvard University, Pierce Hall, 29 Oxford Street, Cambridge, Massachusetts 02138, United States; Email: [email protected]
Authors
- Daniel J. Jacob - School of Engineering and Applied Sciences, Harvard University, Pierce Hall, 29 Oxford Street, Cambridge, Massachusetts 02138, United States
- Melissa P. Sulprizio - School of Engineering and Applied Sciences, Harvard University, Pierce Hall, 29 Oxford Street, Cambridge, Massachusetts 02138, United States
- Alexander J. Turner - School of Engineering and Applied Sciences, Harvard University, Pierce Hall, 29 Oxford Street, Cambridge, Massachusetts 02138, United States
- Melissa Weitz - Climate Change Division, Environmental Protection Agency, Washington, District of Columbia 20460, United States
- Tom Wirth - Climate Change Division, Environmental Protection Agency, Washington, District of Columbia 20460, United States
- Cate Hight - Climate Change Division, Environmental Protection Agency, Washington, District of Columbia 20460, United States
- Mark DeFigueiredo - Climate Change Division, Environmental Protection Agency, Washington, District of Columbia 20460, United States
- Mausami Desai - Climate Change Division, Environmental Protection Agency, Washington, District of Columbia 20460, United States
- Rachel Schmeltz - Climate Change Division, Environmental Protection Agency, Washington, District of Columbia 20460, United States
- Leif Hockstad - Climate Change Division, Environmental Protection Agency, Washington, District of Columbia 20460, United States
- Anthony A. Bloom - Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91109, United States
- Kevin W. Bowman - Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91109, United States
- Seongeun Jeong - Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- Marc L. Fischer - Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
Notes
The authors declare no competing financial interest.

Acknowledgment

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This research was funded by the NASA Carbon Monitoring System (CMS). A.J. Turner was supported by a Department of Energy (DOE) Computational Science Graduate Fellowship (CSGF). Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA. Work by M.L. Fischer and S. Jeong at LBNL was supported by the NASA CMS program (NNH13ZDA001N) and the California Energy Commission Natural Gas Research Program under U.S. Department of Energy Contract No. DE-AC02-05CH11231. We thank D.R. Lyon, D. Zavala-Araiza, and S.P. Hamburg for providing the EDF methane emissions over the Barnett Shale. We thank the anonymous reviewers for their thorough comments.

References

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Kang, M.; Kanno, C. M.; Reid, M. C.; Zhang, X.; Mauzerall, D. L.; Celia, M. A.; Chen, Y.; Onstott, T. C. Direct measurements of methane emissions from abandoned oil and gas wells in Pennsylvania Proc. Natl. Acad. Sci. U. S. A. 2014, 111, 18173– 18177 DOI: 10.1073/pnas.1408315111
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Direct measurements of methane emissions from abandoned oil and gas wells in Pennsylvania
Kang, Mary; Kanno, Cynthia M.; Reid, Matthew C.; Zhang, Xin; Mauzerall, Denise L.; Celia, Michael A.; Chen, Yuheng; Onstott, Tullis C.
Proceedings of the National Academy of Sciences of the United States of America (2014), 111 (51), 18173-18177CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
Abandoned oil and gas wells provide a potential pathway for subsurface migration and emissions of CH4 and other fluids to the atm. Little is known about CH4 fluxes from millions of abandoned wells in the USA. Direct measurements of CH4 fluxes from abandoned oil and gas wells in Pennsylvania using static flux chambers are reported. A total of 42 and 52 direct measurements were made at wells and sites near the wells (controls) in forested, wetland, grassland, and river areas in Jul.-Oct., 2013, and Jan. 2014, resp. Mean CH4 flow rates at these well locations were 0.27 kg/day-well; the mean CH4 flow rate at control sites was 4.5 × 10-6 kg/day-site. Three of 19 measured wells were high emitters with CH4 flow rates 3 orders of magnitude larger than the median flow rate (1.3 × 10-3 kg/day-well). Assuming the mean flow rate is representative of all abandoned wells in Pennsylvania, CH4 emissions were scaled to be 4-7% of estd. total anthropogenic CH4 emissions in Pennsylvania. The presence of ethane, propane, and n-butane, along with CH4 isotopic compn., indicated emitted CH4 was predominantly of thermo-genic origin. Measurements showed CH4 emissions from abandoned oil and gas wells can be significant. Research required to quantify these emissions nationally should be undertaken so they can be accurately described and included in greenhouse gas emission inventories.
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Methane Emissions from United States Natural Gas Gathering and Processing
Marchese, Anthony J.; Vaughn, Timothy L.; Zimmerle, Daniel J.; Martinez, David M.; Williams, Laurie L.; Robinson, Allen L.; Mitchell, Austin L.; Subramanian, R.; Tkacik, Daniel S.; Roscioli, Joseph R.; Herndon, Scott C.
Environmental Science & Technology (2015), 49 (17), 10718-10727CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
Facility-level CH4 emission measurements from 114 natural gas collection and 16 processing facilities in 13 US states were combined with facility counts from state and national databases in a Monte Carlo simulation to est. CH4 emissions from US natural gas collecting and processing operations. Total annual CH4 emissions of 2421 (+245/-237) Gg were estd. for all US collecting and processing operations, which represent a CH4 loss rate of 0.47% (±0.05%) when normalized by 2012 CH4 prodn. More than 90% of those emissions were attributed to normal operation at collecting (1697 +189/-185 Gg) and processing facilities (506 +55/-52 Gg); the balance was attributed to pipelines and processing facility routine maintenance and upsets. Median CH4 emissions est. for processing facilities was a factor of 1.7 lower than the 2012 USEPA Greenhouse Gas Inventory (GHGI) est., the difference was due largely to fewer reciprocating compressors; and a factor of 3.0 higher than that reported in the EPA Greenhouse Gas Reporting Program. Since collecting operations are currently embedded within the EPA GHGI prodn. segment, direct comparison to results is complicated. However, results suggested CH4 emissions from collection were substantially higher than the current EPA GHGI est., and were equiv. to 30% of the total net CH4 emissions in natural gas system GHGI. Since emissions from most collecting facilities are not reported under the current rule and not all source categories are reported for processing facilities, total CH4 emissions from collecting and processing reported under the EPA GHGRP (180 Gg) represents only 14% of that tabulated in the EPA GHGI, and 7% of that predicted by this study.
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Zimmerle, D. J.; Williams, L. L.; Vaughn, T. L.; Quinn, C.; Subramanian, R.; Duggan, G. P.; Willson, B.; Opsomer, J. D.; Marchese, A. J.; Martinez, D. M.; Robinson, A. L. Methane Emissions from the Natural Gas Transmission and Storage System in the United States Environ. Sci. Technol. 2015) 49, 9374– 9383 DOI: 10.1021/acs.est.5b01669
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Methane Emissions from the Natural Gas Transmission and Storage System in the United States
Zimmerle, Daniel J.; Williams, Laurie L.; Vaughn, Timothy L.; Quinn, Casey; Subramanian, R.; Duggan, Gerald P.; Willson, Bryan; Opsomer, Jean D.; Marchese, Anthony J.; Martinez, David M.; Robinson, Allen L.
Environmental Science & Technology (2015), 49 (15), 9374-9383CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
Recent growth in natural gas prodn. and utilization offers potential climate benefits which depend on CH4 (primary natural gas component and greenhouse gas) life cycle emissions. This work estd. CH4 emissions from transmission and storage (T&S) sector of the US natural gas industry using data collected in 2012, including 2292 on-site measurements, addnl. emissions data from 677 facilities, and activity data from 922 facilities. The largest emission sources were fugitive emissions from compressor-related equipment and super-emitter facilities. Total CH4 emissions estd. from the T&S sector was 1503 (1220-1950) Gg/yr (95% confidence interval) vs. the 2012 USEPA Greenhouse Gas Inventory (GHGI) est. of 2071 (1680-2690) Gg/yr. While the overlap in confidence intervals indicated the difference is not statistically significant, this is due to several significant, but offsetting, factors. Factors which reduce the study est. include: a lower estd. facility count, a shift away from engines toward lower-emitting turbine and elec. compressor drivers, and redns. in use of gas-driven pneumatic devices. Factors which increase the study est. relative to the GHGI include: updated emission rates in certain emission categories and explicit treatment of skewed emissions at component and facility levels. For T&S stations required to report to the EPA Greenhouse Gas Reporting Program (GHGRP), this study estd. total emissions to be 260% (215-330%) of reportable emissions for these stations, primarily due to inclusion of emission sources not reported under GHGRP rules, updated emission factors, and super-emitter emissions.
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Kort, E. A.; Frankenberg, C.; Costigan, K. R.; Lindenmaier, R.; Dubey, M. K.; Wunch, D. Four corners: The largest US methane anomaly viewed from space Geophys. Res. Lett. 2014) 41, 6898– 6903 DOI: 10.1002/2014GL061503
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Four corners: The largest US methane anomaly viewed from space
Kort, Eric A.; Frankenberg, Christian; Costigan, Keeley R.; Lindenmaier, Rodica; Dubey, Manvendra K.; Wunch, Debra
Geophysical Research Letters (2014), 41 (19), 6898-6903CODEN: GPRLAJ; ISSN:1944-8007. (Wiley-Blackwell)
Methane (CH4) is a potent greenhouse gas and ozone precursor. Quantifying methane emissions is crit. for projecting and mitigating changes to climate and air quality. Here we present CH4 observations made from space combined with Earth-based remote sensing column measurements. Results indicate the largest anomalous CH4 levels viewable from space over the conterminous U.S. are located at the Four Corners region in the Southwest U.S. Emissions exceeding inventory ests., totaling 0.59 Tg CH4/yr [0.50-0.67; 2σ], are necessary to bring high-resoln. simulations and observations into agreement. This underestimated source approaches 10% of the EPA est. of total U.S. CH4 emissions from natural gas. The persistence of this CH4 signal from 2003 onward indicates that the source is likely from established gas, coal, and coalbed methane mining and processing. This work demonstrates that space-based observations can identify anomalous CH4 emission source regions and quantify their emissions with the use of a transport model.
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Jeong, S.; Zhao, C.; Andrews, A. E.; Sweeney, C.; Bianco, L.; Wilczak, J. M.; Fischer, M. L. Seasonal variations in CH₄ emissions from central California Geophys. Res. Lett. 2012, 117, D11306 DOI: 10.1029/2011JD016896
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Greenhouse gas emissions from dairy manure management: a review of field-based studies
Owen Justine J; Silver Whendee L
Global change biology (2015), 21 (2), 550-65 ISSN:.
Livestock manure management accounts for almost 10% of greenhouse gas emissions from agriculture globally, and contributes an equal proportion to the US methane emission inventory. Current emissions inventories use emissions factors determined from small-scale laboratory experiments that have not been compared to field-scale measurements. We compiled published data on field-scale measurements of greenhouse gas emissions from working and research dairies and compared these to rates predicted by the IPCC Tier 2 modeling approach. Anaerobic lagoons were the largest source of methane (368 ± 193 kg CH4 hd(-1) yr(-1)), more than three times that from enteric fermentation (~120 kg CH4 hd(-1) yr(-1)). Corrals and solid manure piles were large sources of nitrous oxide (1.5 ± 0.8 and 1.1 ± 0.7 kg N2O hd(-1) yr(-1), respectively). Nitrous oxide emissions from anaerobic lagoons (0.9 ± 0.5 kg N2O hd(-1) yr(-1)) and barns (10 ± 6 kg N2O hd(-1) yr(-1)) were unexpectedly large. Modeled methane emissions underestimated field measurement means for most manure management practices. Modeled nitrous oxide emissions underestimated field measurement means for anaerobic lagoons and manure piles, but overestimated emissions from slurry storage. Revised emissions factors nearly doubled slurry CH4 emissions for Europe and increased N2O emissions from solid piles and lagoons in the United States by an order of magnitude. Our results suggest that current greenhouse gas emission factors generally underestimate emissions from dairy manure and highlight liquid manure systems as promising target areas for greenhouse gas mitigation.
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Yvon-Durocher, G.; Allen, A. P.; Bastviken, D.; Conrad, R.; Gudasz, C.; St-Pierre, A.; Thanh-Duc, N.; Del Giorgio, P. A. Methane fluxes show consistent temperature dependence across microbial to ecosystem scales Nature 2014, 507, 488– 491 DOI: 10.1038/nature13164
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Methane fluxes show consistent temperature dependence across microbial to ecosystem scales
Yvon-Durocher, Gabriel; Allen, Andrew P.; Bastviken, David; Conrad, Ralf; Gudasz, Cristian; St-Pierre, Annick; Nguyen, Thanh-Duc; del Giorgio, Paul A.
Nature (London, United Kingdom) (2014), 507 (7493), 488-491CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
Methane (CH4) is an important greenhouse gas because it has 25 times the global warming potential of carbon dioxide (CO2) by mass over a century. Recent calcns. suggest that atm. CH4 emissions were responsible for approx. 20% of Earth's warming since pre-industrial times. Understanding how CH4 emissions from ecosystems will respond to expected increases in global temp. is therefore fundamental to predicting whether the carbon cycle will mitigate or accelerate climate change. Methanogenesis is the terminal step in the remineralization of org. matter and is carried out by strictly anaerobic Archaea. Like most other forms of metab., methanogenesis is temp.-dependent. However, it is not yet known how this physiol. response combines with other biotic processes (for example, methanotrophy, substrate supply, microbial community compn.) and abiotic processes (for example, water-table depth) to det. the temp. dependence of ecosystem-level CH4 emissions. It is also not known whether CH4 emissions at the ecosystem level have a fundamentally different temp. dependence than other key fluxes in the carbon cycle, such as photosynthesis and respiration. Here we use meta-analyses to show that seasonal variations in CH4 emissions from a wide range of ecosystems exhibit an av. temp. dependence similar to that of CH4 prodn. derived from pure cultures of methanogens and anaerobic microbial communities. This av. temp. dependence (0.96 eV (eV)), which corresponds to a 57-fold increase between 0 and 30°, is considerably higher than previously obsd. for respiration (approx. 0.65 eV) and photosynthesis (approx. 0.3 eV). As a result, we show that both the emission of CH4 and the ratio of CH4 to CO2 emissions increase markedly with seasonal increases in temp. Our findings suggest that global warming may have a large impact on the relative contributions of CO2 and CH4 to total greenhouse gas emissions from aquatic ecosystems, terrestrial wetlands and rice paddies.
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Zavala-Araiza, D.; Lyon, D.; Alvarez, R. A.; Palacios, V.; Harriss, R.; Lan, X.; Talbot, R.; Hamburg, S. P. Toward a Functional Definition of Methane Super-Emitters: Application to Natural Gas Production Sites Environ. Sci. Technol. 2015, 49, 8167– 8174 DOI: 10.1021/acs.est.5b00133
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Toward a Functional Definition of Methane Super-Emitters: Application to Natural Gas Production Sites
Zavala-Araiza, Daniel; Lyon, David; Alvarez, Ramon A.; Palacios, Virginia; Harriss, Robert; Lan, Xin; Talbot, Robert; Hamburg, Steven P.
Environmental Science & Technology (2015), 49 (13), 8167-8174CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
Natural gas prodn. site emissions are characterized by skewed distributions, where a small percentage of sites, commonly labeled super-emitters, account for a majority of emissions. A better characterization of super-emitters is needed to operationalize ways to identify them and reduce emissions. This work designed a conceptual framework to functionally define super-emitting sites as those with the highest proportional loss rates (Ch4 emitted vs. CH4 produced). Using this concept, total CH4 emissions from Barnett Shale natural gas prodn. sites (Texas) were estd.; super-emitting sites functionally accounted for approx. 3/4 of total emissions. The potential to reduce emissions from these sites is discussed under the assumption that sites with high proportional loss rates have excess emissions resulting from abnormal or otherwise avoidable operating conditions, e.g., malfunctioning equipment. Since the population of functionally super-emitting sites is not expected to be static over time, continuous monitoring will be necessary to identify them and improve their operation. This work suggested that achieving and maintaining uniformly low emissions across the entire population of prodn. sites will require mitigation steps at a large fraction of sites.
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PMID: 26148555.
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Miller, S. M.; Wofsy, S. C.; Michalak, A. M.; Kort, E. A.; Andrews, A. E.; Biraud, S. C.; Dlugokencky, E. J.; Eluszkiewicz, J.; Fischer, M. L.; Janssens-Maenhout, G.; Miller, B. R.; Miller, J. B.; Montzka, S. A.; Nehrkorn, T.; Sweeney, C. Anthropogenic emissions of methane in the United States Proc. Natl. Acad. Sci. U. S. A. 2013, 110, 20018– 20022 DOI: 10.1073/pnas.1314392110
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Anthropogenic emissions of methane in the United States
Miller, Scot M.; Wofsy, Steven C.; Michalak, Anna M.; Kort, Eric A.; Andrews, Arlyn E.; Biraud, Sebastien C.; Dlugokencky, Edward J.; Eluszkiewicz, Janusz; Fischer, Marc L.; Janssens-Maenhout, Greet; Miller, Ben R.; Miller, John B.; Montzka, Stephen A.; Nehrkorn, Thomas; Sweeney, Colm
Proceedings of the National Academy of Sciences of the United States of America (2013), 110 (50), 20018-20022,S20018/1-S20018/11CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
This work quant. estd. the spatial distribution of anthropogenic CH4 sources in the US by combining comprehensive atm. CH4 observations, extensive spatial datasets, and a high resoln. atm. transport model. Results showed current USEPA and Emissions Database for Global Atm. Research (EDGAR) inventories underestimated national CH4 emissions a factor of ∼1.5 and ∼1.7, resp. Results indicated emissions due to ruminants and manure are up to twice the magnitude of existing inventories. Discrepancies in CH4 source ests. are particularly pronounced in the south-central US where total emissions are ∼2.7 times greater than in most inventories and account for 24 ± 3% of national emissions. Spatial patterns of emission fluxes and obsd. CH4/C3H8 correlations indicated fossil fuel extn. and refining are major contributors (45 ± 13%) in the south-central US. This suggested regional CH4 emissions due to fossil fuel extn. and processing could be 4.9 ± 2.6 times larger than in EDGAR, the most comprehensive global CH4 inventory. Results cast doubt on a recent USEPA decision to down-scale its est. of national natural gas emissions by 25-30%. It was concluded that CH4 emissions assocd. with animal husbandry and fossil fuel industries have larger greenhouse gas impacts than indicated by existing inventories.
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Wecht, K. J.; Jacob, D. J.; Frankenberg, C.; Jiang, Z.; Blake, D. R. Mapping of North American methane emissions with high spatial resolution by inversion of SCIAMACHY satellite data J. Geophys. Res-Atmos. 2014, 119, 7741– 7756 DOI: 10.1002/2014JD021551
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Mapping of North American methane emissions with high spatial resolution by inversion of SCIAMACHY satellite data
Wecht, Kevin J.; Jacob, Daniel J.; Frankenberg, Christian; Jiang, Zhe; Blake, Donald R.
Journal of Geophysical Research: Atmospheres (2014), 119 (12), 7741-7756CODEN: JGRDE3; ISSN:2169-8996. (Wiley-Blackwell)
We est. methane emissions from North America with high spatial resoln. by inversion of Scanning Imaging Absorption Spectrometer for Atm. Chartog. (SCIAMACHY) satellite observations using the Goddard Earth Observing System Chem. (GEOS-Chem) chem. transport model and its adjoint. The inversion focuses on summer 2004 when data from the Intercontinental Chem. Transport Expt.-North America (INTEX-A) aircraft campaign over the eastern U.S. are available to validate the SCIAMACHY retrievals and evaluate the inversion. From the INTEX-A data we identify and correct a water vapor-dependent bias in the SCIAMACHY data. We conduct an initial inversion of emissions on the horizontal grid of GEOS-Chem (1/2° × 2/3°) to identify correction tendencies relative to the Emission Database for Global Atm. Research (EDGAR) v4.2 emission inventory used as a priori. We then cluster these grid cells with a hierarchical algorithm to ext. the max. information from the SCIAMACHY observations. A 1000 cluster ensemble can be adequately constrained, providing ∼100 km resoln. across North America. Anal. of results indicates that the Hudson Bay Lowland wetlands source is 2.1 Tg a-1, lower than the a priori but consistent with other recent ests. Anthropogenic U.S. emissions are 30.1 ± 1.3 Tg a-1, compared to 25.8 Tg a-1 and 28.3 Tg a-1 in the EDGAR v4.2 and Environmental Protection Agency (EPA) inventories, resp. We find that U.S. livestock emissions are 40% greater than in these two inventories. No such discrepancy is apparent for overall U.S. oil and gas emissions, although this may reflect some compensation between overestimate of emissions from storage/distribution and underestimate from prodn. We find that U.S. livestock emissions are 70% greater than the oil and gas emissions, in contrast to the EDGAR v4.2 and EPA inventories where these two sources are of comparable magnitude.
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Alexe, M.; Bergamaschi, P.; Segers, A.; Detmers, R.; Butz, A.; Hasekamp, O.; Guerlet, S.; Parker, R.; Boesch, H.; Frankenberg, C. Inverse modelling of CH 4 emissions for 2010–2011 using different satellite retrieval products from GOSAT and SCIAMACHY Atmos. Chem. Phys. 2015, 15, 113– 133 DOI: 10.5194/acp-15-113-2015
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Turner, A. J.; Jacob, D. J.; Wecht, K. J.; Maasakkers, J. D.; Lundgren, E.; Andrews, A. E.; Biraud, S. C.; Boesch, H.; Bowman, K. W.; Deutscher, N. M.; Dubey, M. K.; Griffith, D. W. T.; Hase, F.; Kuze, A.; Notholt, J.; Ohyama, H.; Parker, R.; Payne, V. H.; Sussmann, R.; Sweeney, C.; Velazco, V. A.; Warneke, T.; Wennberg, P. O.; Wunch, D. Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data Atmos. Chem. Phys. 2015, 15, 7049– 7069 DOI: 10.5194/acp-15-7049-2015
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Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data
Turner, A. J.; Jacob, D. J.; Wecht, K. J.; Maasakkers, J. D.; Lundgren, E.; Andrews, A. E.; Biraud, S. C.; Boesch, H.; Bowman, K. W.; Deutscher, N. M.; Dubey, M. K.; Griffith, D. W. T.; Hase, F.; Kuze, A.; Notholt, J.; Ohyama, H.; Parker, R.; Payne, V. H.; Sussmann, R.; Sweeney, C.; Velazco, V. A.; Warneke, T.; Wennberg, P. O.; Wunch, D.
Atmospheric Chemistry and Physics (2015), 15 (12), 7049-7069CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)
We use 2009-2011 space-borne methane observations from the Greenhouse Gases Observing SATellite (GOSAT) to est. global and North American methane emissions with 4° × 5° and up to 50 km × 50 km spatial resoln., resp. GEOS-Chem and GOSAT data are first evaluated with atm. methane observations from surface and tower networks (NOAA/ESRL, TCCON) and aircraft (NOAA/ESRL, HIPPO), using the GEOS-Chem chem. transport model as a platform to facilitate comparison of GOSAT with in situ data. This identifies a high-latitude bias between the GOSAT data and GEOS-Chem that we correct via quadratic regression. Our global adjoint-based inversion yields a total methane source of 539 Tg a-1 with some important regional corrections to the EDGARv4.2 inventory used as a prior. Results serve as dynamic boundary conditions for an anal. inversion of North American methane emissions using radial basis functions to achieve high resoln. of large sources and provide error characterization. We infer a US anthropogenic methane source of 40.2-42.7 Tg a-1, as compared to 24.9-27.0 Tg a-1 in the EDGAR and EPA bottom-up inventories, and 30.0-44.5 Tg a-1 in recent inverse studies. Our est. is supported by independent surface and aircraft data and by previous inverse studies for California. We find that the emissions are highest in the southern-central US, the Central Valley of California, and Florida wetlands; large isolated point sources such as the US Four Corners also contribute. Using prior information on source locations, we attribute 29-44 % of US anthropogenic methane emissions to livestock, 22-31 % to oil/gas, 20 % to landfills/wastewater, and 11-15 % to coal. Wetlands contribute an addnl. 9.0-10.1 Tg a-1.
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Ganesan, A.; Rigby, M.; Zammit-Mangion, A.; Manning, A.; Prinn, R.; Fraser, P.; Harth, C.; Kim, K.-R.; Krummel, P.; Li, S. Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods Atmos. Chem. Phys. 2014, 14, 3855– 3864 DOI: 10.5194/acp-14-3855-2014
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Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods
Ganesan, A. L.; Rigby, M.; Zammit-Mangion, A.; Manning, A. J.; Prinn, R. G.; Fraser, P. J.; Harth, C. M.; Kim, K.-R.; Krummel, P. B.; Li, S.; Muhle, J.; O'Doherty, S. J.; Park, S.; Salameh, P. K.; Steele, L. P.; Weiss, R. F.
Atmospheric Chemistry and Physics (2014), 14 (8), 3855-3864, 10 pp.CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)
We present a hierarchical Bayesian method for atm. trace gas inversions. This method is used to est. emissions of trace gases as well as "hyper-parameters" that characterize the probability d. functions (PDFs) of the a priori emissions and model-measurement covariances. By exploring the space of "uncertainties in uncertainties", we show that the hierarchical method results in a more complete estn. of emissions and their uncertainties than traditional Bayesian inversions, which rely heavily on expert judgment. We present an anal. that shows the effect of including hyper-parameters, which are themselves informed by the data, and show that this method can serve to reduce the effect of errors in assumptions made about the a priori emissions and model-measurement uncertainties. We then apply this method to the estn. of sulfur hexafluoride (SF6) emissions over 2012 for the regions surrounding four Advanced Global Atm. Gases Expt. (AGAGE) stations. We find that improper accounting of model representation uncertainties, in particular, can lead to the derivation of emissions and assocd. uncertainties that are unrealistic and show that those derived using the hierarchical method are likely to be more representative of the true uncertainties in the system. We demonstrate through this SF6 case study that this method is less sensitive to outliers in the data and to subjective assumptions about a priori emissions and model-measurement uncertainties than traditional methods.
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Lyon, D. R.; Zavala-Araiza, D.; Alvarez, R. A.; Harriss, R.; Palacios, V.; Lan, X.; Talbot, R.; Lavoie, T.; Shepson, P.; Yacovitch, T. I. Constructing a spatially resolved methane emission inventory for the Barnett Shale Region Environ. Sci. Technol. 2015, 49, 8147– 8157 DOI: 10.1021/es506359c
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Constructing a Spatially Resolved Methane Emission Inventory for the Barnett Shale Region
Lyon, David R.; Zavala-Araiza, Daniel; Alvarez, Ramon A.; Harriss, Robert; Palacios, Virginia; Lan, Xin; Talbot, Robert; Lavoie, Tegan; Shepson, Paul; Yacovitch, Tara I.; Herndon, Scott C.; Marchese, Anthony J.; Zimmerle, Daniel; Robinson, Allen L.; Hamburg, Steven P.
Environmental Science & Technology (2015), 49 (13), 8147-8157CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
CH4 emissions from the oil and gas industry (O&G) and other sources in the Barnett Shale region (Texas) were estd. by developing a spatially resolved emission inventory. In total, 18 source categories were estd. using multiple datasets, including empirical measurements at regional O&G sites and a national study of collecting/processing facilities. Spatially referenced activity data were compiled from federal and state databases and combined with O&G facility emission factors calcd. by Monte Carlo simulations which accounted for high emission sites representing the very upper portion, or fat-tail, of obsd. emissions distributions. Total CH4 emissions in the 25-county Barnett Shale region in Oct. 2013 were estd. to be 72,300 (63,400-82,400) kg CH4/h. O&G emissions were estd. to be 46,200 (40,000-54,100) kg CH4/h; 19% of emissions from fat-tail sites represented <2% of sites. Estd. O&G emissions in the Barnett Shale region were higher than alternative inventories based on the USEPA Greenhouse Gas Inventory, EPA Greenhouse Gas Reporting Program, and Emissions Database for Global Atm. Research by factors of 1.5, 2.7, and 4.3, resp. Collecting compressor sites, accounting for 40% of O&G emissions in this inventory, had the largest difference from emission ests. based on EPA data sources. This inventory higher O&G emissions est. was due primarily to its more comprehensive activity factors and inclusion of fat-tail sites.
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Zavala-Araiza, D.; Lyon, D. R.; Alvarez, R. A.; Davis, K. J.; Harriss, R.; Herndon, S. C.; Karion, A.; Kort, E. A.; Lamb, B. K.; Lan, X. Reconciling divergent estimates of oil and gas methane emissions Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 15597– 15602 DOI: 10.1073/pnas.1522126112
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Reconciling divergent estimates of oil and gas methane emissions
Zavala-Araiza, Daniel; Lyon, David R.; Alvarez, Ramon A.; Davis, Kenneth J.; Harriss, Robert; Herndon, Scott C.; Karion, Anna; Kort, Eric Adam; Lamb, Brian K.; Lan, Xin; Marchese, Anthony J.; Pacala, Stephen W.; Robinson, Allen L.; Shepson, Paul B.; Sweeney, Colm; Talbot, Robert; Townsend-Small, Amy; Yacovitch, Tara I.; Zimmerle, Daniel J.; Hamburg, Steven P.
Proceedings of the National Academy of Sciences of the United States of America (2015), 112 (51), 15597-15602CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
Published ests. of methane emissions from atm. data (top-down approaches) exceed those from source-based inventories (bottom-up approaches), leading to conflicting claims about the climate implications of fuel switching from coal or petroleum to natural gas. Based on data from a coordinated campaign in the Barnett Shale oil and gas-producing region of Texas, we find that top-down and bottom-up ests. of both total and fossil methane emissions agree within statistical confidence intervals (relative differences are 10% for fossil methane and 0.1% for total methane). We reduced uncertainty in top-down ests. by using repeated mass balance measurements, as well as ethane as a fingerprint for source attribution. Similarly, our bottom-up est. incorporates a more complete count of facilities than past inventories, which omitted a significant no. of major sources, and more effectively accounts for the influence of large emission sources using a statistical estimator that integrates observations from multiple ground-based measurement datasets. Two percent of oil and gas facilities in the Barnett accounts for half of methane emissions at any given time, and high-emitting facilities appear to be spatiotemporally variable. Measured oil and gas methane emissions are 90% larger than ests. based on the US Environmental Protection Agency's Greenhouse Gas Inventory and correspond to 1.5% of natural gas prodn. This rate of methane loss increases the 20-y climate impacts of natural gas consumed in the region by roughly 50%.
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Zhao, C.; Andrews, A. E.; Bianco, L.; Eluszkiewicz, J.; Hirsch, A.; MacDonald, C.; Nehrkorn, T.; Fischer, M. L. Atmospheric inverse estimates of methane emissions from Central California J. Geophys. Res. 2009, 114, D16302 DOI: 10.1029/2008JD011671
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Jeong, S.; Millstein, D.; Fischer, M. L. Spatially Explicit Methane Emissions from Petroleum Production and the Natural Gas System in California Environ. Sci. Technol. 2014, 48, 5982– 5990 DOI: 10.1021/es4046692
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Spatially Explicit Methane Emissions from Petroleum Production and the Natural Gas System in California
Jeong, Seongeun; Millstein, Dev; Fischer, Marc L.
Environmental Science & Technology (2014), 48 (10), 5982-5990CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
An updated, spatially-resolved CH4 emissions inventory based on USEPA emission factors and publically available activity data for 2010 California petroleum and natural gas prodn., processing, transmission, and distribution, is presented. Compared to official California bottom-up inventories, initial ests. are 3-7 times higher for petroleum and natural gas prodn. sectors, but similar for natural gas transmission and distribution sectors. Evidence from published top-down atm. measurement campaigns in southern California supported initial emission ests. from prodn. and processing, but indicated emission ests. from transmission and distribution are low by a factor of ∼2. To provide emission maps with more accurate total emissions, spatially-resolved inventory was scaled by sector-specific results from a southern California aircraft measurement campaign to all of California. Assuming uncertainties were detd. by uncertainties estd. in the top-down study, estd. state total CH4 emissions are 541 ± 144 Gg/yr (vs. 210.7 Gg/yr in the State official inventory), where the majority of reported uncertainty was derived from transmission and distribution. Uncertainties relative to the mean for a given region were likely larger than that for the State total, emphasizing the need for addnl. measurements in under-sampled regions.
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Science (Washington, DC, United States) (2014), 343 (6172), 733-735CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
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Zhen Qu, Daniel J. Jacob, A. Anthony Bloom, John R. Worden, Robert J. Parker, Hartmut Boesch. Inverse modeling of 2010–2022 satellite observations shows that inundation of the wet tropics drove the 2020–2022 methane surge. Proceedings of the National Academy of Sciences 2024, 121 (40) https://doi.org/10.1073/pnas.2402730121
Stuart N. Riddick, Mercy Mbua, Abhinav Anand, Elijah Kiplimo, Arthur Santos, Aashish Upreti, Daniel J. Zimmerle. Estimating Total Methane Emissions from the Denver-Julesburg Basin Using Bottom-Up Approaches. Gases 2024, 4 (3) , 236-252. https://doi.org/10.3390/gases4030014
James D. East, Daniel J. Jacob, Nicholas Balasus, A. Anthony Bloom, Lori Bruhwiler, Zichong Chen, Jed O. Kaplan, Loretta J. Mickley, Todd A. Mooring, Elise Penn, Benjamin Poulter, Melissa P. Sulprizio, John R. Worden, Robert M. Yantosca, Zhen Zhang. Interpreting the Seasonality of Atmospheric Methane. Geophysical Research Letters 2024, 51 (10) https://doi.org/10.1029/2024GL108494
Stuart N. Riddick, Mercy Mbua, Arthur Santos, Wendy Hartzell, Daniel J. Zimmerle. Potential Underestimate in Reported Bottom-up Methane Emissions from Oil and Gas Operations in the Delaware Basin. Atmosphere 2024, 15 (2) , 202. https://doi.org/10.3390/atmos15020202
Griffin J. Mead, Daniel I. Herman, Fabrizio R. Giorgetta, Nathan A. Malarich, Esther Baumann, Brian R. Washburn, Nathan R. Newbury, Ian Coddington, Kevin C. Cossel. Apportionment and Inventory Optimization of Agriculture and Energy Sector Methane Emissions Using Multi‐Month Trace Gas Measurements in Northern Colorado. Geophysical Research Letters 2024, 51 (2) https://doi.org/10.1029/2023GL105973
Kai Qin, Wei Hu, Qin He, Fan Lu, Jason Blake Cohen. Individual coal mine methane emissions constrained by eddy covariance measurements: low bias and missing sources. Atmospheric Chemistry and Physics 2024, 24 (5) , 3009-3028. https://doi.org/10.5194/acp-24-3009-2024
Hannah Nesser, Daniel J. Jacob, Joannes D. Maasakkers, Alba Lorente, Zichong Chen, Xiao Lu, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Margaux Winter, Shuang Ma, A. Anthony Bloom, John R. Worden, Robert N. Stavins, Cynthia A. Randles. High-resolution US methane emissions inferred from an inversion of 2019 TROPOMI satellite data: contributions from individual states, urban areas, and landfills. Atmospheric Chemistry and Physics 2024, 24 (8) , 5069-5091. https://doi.org/10.5194/acp-24-5069-2024
Ana Maria Roxana Petrescu, Glen P. Peters, Richard Engelen, Sander Houweling, Dominik Brunner, Aki Tsuruta, Bradley Matthews, Prabir K. Patra, Dmitry Belikov, Rona L. Thompson, Lena Höglund-Isaksson, Wenxin Zhang, Arjo J. Segers, Giuseppe Etiope, Giancarlo Ciotoli, Philippe Peylin, Frédéric Chevallier, Tuula Aalto, Robbie M. Andrew, David Bastviken, Antoine Berchet, Grégoire Broquet, Giulia Conchedda, Stijn N. C. Dellaert, Hugo Denier van der Gon, Johannes Gütschow, Jean-Matthieu Haussaire, Ronny Lauerwald, Tiina Markkanen, Jacob C. A. van Peet, Isabelle Pison, Pierre Regnier, Espen Solum, Marko Scholze, Maria Tenkanen, Francesco N. Tubiello, Guido R. van der Werf, John R. Worden. Comparison of observation- and inventory-based methane emissions for eight large global emitters. Earth System Science Data 2024, 16 (9) , 4325-4350. https://doi.org/10.5194/essd-16-4325-2024
Colby W Reavis, Michele L Reba, Daniel D Shults, Benjamin R K Runkle. Assessing the methane mitigation potential of innovative management in US rice production. Environmental Research Letters 2023, 18 (12) , 124020. https://doi.org/10.1088/1748-9326/ad0925
Richard G. Derwent, Peter G. Simmonds, Simon O'Doherty, Alistair J. Manning, T. Gerard Spain. High-frequency, continuous hydrogen observations at Mace Head, Ireland from 1994 to 2022: Baselines, pollution events and ‘missing’ sources. Atmospheric Environment 2023, 312 , 120029. https://doi.org/10.1016/j.atmosenv.2023.120029
Jinling Guo, Junlian Gao, Kejia Yan, Bo Zhang. Unintended mitigation benefits of China's coal de-capacity policies on methane emissions. Energy Policy 2023, 181 , 113718. https://doi.org/10.1016/j.enpol.2023.113718
John R. Worden, Sudhanshu Pandey, Yuzhong Zhang, Daniel H. Cusworth, Zhen Qu, A. Anthony Bloom, Shuang Ma, Joannes D. Maasakkers, Brendan Byrne, Riley Duren, David Crisp, Deborah Gordon, Daniel J. Jacob. Verifying Methane Inventories and Trends With Atmospheric Methane Data. AGU Advances 2023, 4 (4) https://doi.org/10.1029/2023AV000871
Leyang Feng, Sakineh Tavakkoli, Sarah M. Jordaan, Arlyn E. Andrews, Joshua S. Benmergui, Darryn W. Waugh, Mingyang Zhang, Dylan C. Gaeta, Scot M. Miller. Inter‐Annual Variability in Atmospheric Transport Complicates Estimation of US Methane Emissions Trends. Geophysical Research Letters 2023, 50 (14) https://doi.org/10.1029/2022GL100366
Tian Xia, Sachraa G. Borjigin, Julia Raneses, Craig A. Stroud, Stuart A. Batterman. Mobile Measurements of Atmospheric Methane at Eight Large Landfills: An Assessment of Temporal and Spatial Variability. Atmosphere 2023, 14 (6) , 906. https://doi.org/10.3390/atmos14060906
Beata Bukosa, Jenny A. Fisher, Nicholas M. Deutscher, Dylan B. A. Jones. A Coupled CH4, CO and CO2 Simulation for Improved Chemical Source Modeling. Atmosphere 2023, 14 (5) , 764. https://doi.org/10.3390/atmos14050764
Vineet Yadav, Kristal Verhulst, Riley Duren, Andrew Thorpe, Jooil Kim, Ralph Keeling, Ray Weiss, Dan Cusworth, Marikate Mountain, Charles Miller, James Whetstone. A declining trend of methane emissions in the Los Angeles basin from 2015 to 2020. Environmental Research Letters 2023, 18 (3) , 034004. https://doi.org/10.1088/1748-9326/acb6a9
Diane Mayerfeld. The Limits of Grass. 2023, 157-175. https://doi.org/10.1007/978-3-031-09023-3_7
Xueying Yu, Dylan B. Millet, Daven K. Henze, Alexander J. Turner, Alba Lorente Delgado, A. Anthony Bloom, Jianxiong Sheng. A high-resolution satellite-based map of global methane emissions reveals missing wetland, fossil fuel, and monsoon sources. Atmospheric Chemistry and Physics 2023, 23 (5) , 3325-3346. https://doi.org/10.5194/acp-23-3325-2023
Zachary Barkley, Kenneth Davis, Natasha Miles, Scott Richardson, Aijun Deng, Benjamin Hmiel, David Lyon, Thomas Lauvaux. Quantification of oil and gas methane emissions in the Delaware and Marcellus basins using a network of continuous tower-based measurements. Atmospheric Chemistry and Physics 2023, 23 (11) , 6127-6144. https://doi.org/10.5194/acp-23-6127-2023
Megan E. McCabe, Ilana B. Pollack, Emily V. Fischer, Kathryn M. Steinmann, Dana R. Caulton. Technical note: Isolating methane emissions from animal feeding operations in an interfering location. Atmospheric Chemistry and Physics 2023, 23 (13) , 7479-7494. https://doi.org/10.5194/acp-23-7479-2023
Daniel J. Varon, Daniel J. Jacob, Benjamin Hmiel, Ritesh Gautam, David R. Lyon, Mark Omara, Melissa Sulprizio, Lu Shen, Drew Pendergrass, Hannah Nesser, Zhen Qu, Zachary R. Barkley, Natasha L. Miles, Scott J. Richardson, Kenneth J. Davis, Sudhanshu Pandey, Xiao Lu, Alba Lorente, Tobias Borsdorff, Joannes D. Maasakkers, Ilse Aben. Continuous weekly monitoring of methane emissions from the Permian Basin by inversion of TROPOMI satellite observations. Atmospheric Chemistry and Physics 2023, 23 (13) , 7503-7520. https://doi.org/10.5194/acp-23-7503-2023
Mark Omara, Ritesh Gautam, Madeleine A. O'Brien, Anthony Himmelberger, Alex Franco, Kelsey Meisenhelder, Grace Hauser, David R. Lyon, Apisada Chulakadabba, Christopher Chan Miller, Jonathan Franklin, Steven C. Wofsy, Steven P. Hamburg. Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at high resolution. Earth System Science Data 2023, 15 (8) , 3761-3790. https://doi.org/10.5194/essd-15-3761-2023
Jagat S. H. Bisht, Prabir K. Patra, Masayuki Takigawa, Takashi Sekiya, Yugo Kanaya, Naoko Saitoh, Kazuyuki Miyazaki. Estimation of CH 4 emission based on an advanced 4D-LETKF assimilation system. Geoscientific Model Development 2023, 16 (6) , 1823-1838. https://doi.org/10.5194/gmd-16-1823-2023
Daniel H. Cusworth, Andrew K. Thorpe, Alana K. Ayasse, David Stepp, Joseph Heckler, Gregory P. Asner, Charles E. Miller, Vineet Yadav, John W. Chapman, Michael L. Eastwood, Robert O. Green, Benjamin Hmiel, David R. Lyon, Riley M. Duren. Strong methane point sources contribute a disproportionate fraction of total emissions across multiple basins in the United States. Proceedings of the National Academy of Sciences 2022, 119 (38) https://doi.org/10.1073/pnas.2202338119
Sihong Zhu, Liang Feng, Yi Liu, Jing Wang, Dongxu Yang. Decadal Methane Emission Trend Inferred from Proxy GOSAT XCH4 Retrievals: Impacts of Transport Model Spatial Resolution. Advances in Atmospheric Sciences 2022, 39 (8) , 1343-1359. https://doi.org/10.1007/s00376-022-1434-6
David T. Allen, Felipe J. Cardoso-Saldaña, Yosuke Kimura, Qining Chen, Zhanhong Xiang, Daniel Zimmerle, Clay Bell, Chris Lute, Jerry Duggan, Matthew Harrison. A Methane Emission Estimation Tool (MEET) for predictions of emissions from upstream oil and gas well sites with fine scale temporal and spatial resolution: Model structure and applications. Science of The Total Environment 2022, 829 , 154277. https://doi.org/10.1016/j.scitotenv.2022.154277
Daniel Zimmerle, Gerald Duggan, Timothy Vaughn, Clay Bell, Christopher Lute, Kristine Bennett, Yosuke Kimura, Felipe J. Cardoso-Saldaña, David T. Allen. Modeling air emissions from complex facilities at detailed temporal and spatial resolution: The Methane Emission Estimation Tool (MEET). Science of The Total Environment 2022, 824 , 153653. https://doi.org/10.1016/j.scitotenv.2022.153653
George C Hurtt, Arlyn Andrews, Kevin Bowman, Molly E Brown, Abhishek Chatterjee, Vanessa Escobar, Lola Fatoyinbo, Peter Griffith, Maddie Guy, Sean P Healey, Daniel J Jacob, Robert Kennedy, Steven Lohrenz, Megan E McGroddy, Valeria Morales, Thomas Nehrkorn, Lesley Ott, Sassan Saatchi, Edil Sepulveda Carlo, Shawn P Serbin, Hanqin Tian. The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps. Environmental Research Letters 2022, 17 (6) , 063010. https://doi.org/10.1088/1748-9326/ac7407
Pankaj Sadavarte, Sudhanshu Pandey, Joannes D. Maasakkers, Hugo Denier van der Gon, Sander Houweling, Ilse Aben. A high-resolution gridded inventory of coal mine methane emissions for India and Australia. Elementa: Science of the Anthropocene 2022, 10 (1) https://doi.org/10.1525/elementa.2021.00056
Joannes D. Maasakkers, Mark Omara, Ritesh Gautam, Alba Lorente, Sudhanshu Pandey, Paul Tol, Tobias Borsdorff, Sander Houweling, Ilse Aben. Reconstructing and quantifying methane emissions from the full duration of a 38-day natural gas well blowout using space-based observations. Remote Sensing of Environment 2022, 270 , 112755. https://doi.org/10.1016/j.rse.2021.112755
Ryan P. Scott, Tyler A. Scott, Robert A. Greer. Who owns the pipes? Utility ownership, infrastructure conditions, and methane emissions in United States natural gas distribution. Review of Policy Research 2022, 39 (2) , 170-198. https://doi.org/10.1111/ropr.12463
Luke Bergmann, Luis Fernando Chaves, Carolyn R. Betz, Serena Stein, Brian Wiedenfeld, Ann Wolf, Robert G. Wallace. Mapping Agricultural Lands: From Conventional to Regenerative. Land 2022, 11 (3) , 437. https://doi.org/10.3390/land11030437
Joseph R. Pitt, Israel Lopez-Coto, Kristian D. Hajny, Jay Tomlin, Robert Kaeser, Thilina Jayarathne, Brian H. Stirm, Cody R. Floerchinger, Christopher P. Loughner, Conor K. Gately, Lucy R. Hutyra, Kevin R. Gurney, Geoffrey S. Roest, Jianming Liang, Sharon Gourdji, Anna Karion, James R. Whetstone, Paul B. Shepson. New York City greenhouse gas emissions estimated with inverse modeling of aircraft measurements. Elementa: Science of the Anthropocene 2022, 10 (1) https://doi.org/10.1525/elementa.2021.00082
Benjamin Poulter, Ana Bastos, Josep G. Canadell, Philippe Ciais, Deborah Huntzinger, Richard A. Houghton, Werner Kurz, A.M. Roxana Petrescu, Julia Pongratz, Stephen Sitch, Sebastiaan Luyssaert. Bottom-up approaches for estimating terrestrial GHG budgets: Bookkeeping, process-based modeling, and data-driven methods. 2022, 59-85. https://doi.org/10.1016/B978-0-12-814952-2.00010-1
Genevieve Plant, Eric A. Kort, Lee T. Murray, Joannes D. Maasakkers, Ilse Aben. Evaluating urban methane emissions from space using TROPOMI methane and carbon monoxide observations. Remote Sensing of Environment 2022, 268 , 112756. https://doi.org/10.1016/j.rse.2021.112756
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Yan Wang, Nawa Raj Baral, Minliang Yang, Corinne D. Scown. Co-Processing Agricultural Residues and Wet Organic Waste Can Produce Lower-Cost Carbon-Negative Fuels and Bioplastics. SSRN Electronic Journal 2022, 15 https://doi.org/10.2139/ssrn.4167270
Bing Wang, Zhongxun Li, Rui Shi, Yuzheng Zhang, Yao Yao. Exploring Win-Wins from Trade-Offs? Co-Benefits of Coalbed Methane Utilization for the Environment, Economy and Safety. Energy Engineering 2022, 119 (6) , 2469-2487. https://doi.org/10.32604/ee.2022.021171
Zichong Chen, Daniel J. Jacob, Hannah Nesser, Melissa P. Sulprizio, Alba Lorente, Daniel J. Varon, Xiao Lu, Lu Shen, Zhen Qu, Elise Penn, Xueying Yu. Methane emissions from China: a high-resolution inversion of TROPOMI satellite observations. Atmospheric Chemistry and Physics 2022, 22 (16) , 10809-10826. https://doi.org/10.5194/acp-22-10809-2022
Lu Shen, Ritesh Gautam, Mark Omara, Daniel Zavala-Araiza, Joannes D. Maasakkers, Tia R. Scarpelli, Alba Lorente, David Lyon, Jianxiong Sheng, Daniel J. Varon, Hannah Nesser, Zhen Qu, Xiao Lu, Melissa P. Sulprizio, Steven P. Hamburg, Daniel J. Jacob. Satellite quantification of oil and natural gas methane emissions in the US and Canada including contributions from individual basins. Atmospheric Chemistry and Physics 2022, 22 (17) , 11203-11215. https://doi.org/10.5194/acp-22-11203-2022
Haiyue Tan, Lin Zhang, Xiao Lu, Yuanhong Zhao, Bo Yao, Robert J. Parker, Hartmut Boesch. An integrated analysis of contemporary methane emissions and concentration trends over China using in situ and satellite observations and model simulations. Atmospheric Chemistry and Physics 2022, 22 (2) , 1229-1249. https://doi.org/10.5194/acp-22-1229-2022
Tia R. Scarpelli, Daniel J. Jacob, Shayna Grossman, Xiao Lu, Zhen Qu, Melissa P. Sulprizio, Yuzhong Zhang, Frances Reuland, Deborah Gordon, John R. Worden. Updated Global Fuel Exploitation Inventory (GFEI) for methane emissions from the oil, gas, and coal sectors: evaluation with inversions of atmospheric methane observations. Atmospheric Chemistry and Physics 2022, 22 (5) , 3235-3249. https://doi.org/10.5194/acp-22-3235-2022
Xiao Lu, Daniel J. Jacob, Haolin Wang, Joannes D. Maasakkers, Yuzhong Zhang, Tia R. Scarpelli, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Hannah Nesser, A. Anthony Bloom, Shuang Ma, John R. Worden, Shaojia Fan, Robert J. Parker, Hartmut Boesch, Ritesh Gautam, Deborah Gordon, Michael D. Moran, Frances Reuland, Claudia A. Octaviano Villasana, Arlyn Andrews. Methane emissions in the United States, Canada, and Mexico: evaluation of national methane emission inventories and 2010–2017 sectoral trends by inverse analysis of in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) atmospheric observations. Atmospheric Chemistry and Physics 2022, 22 (1) , 395-418. https://doi.org/10.5194/acp-22-395-2022
Joe McNorton, Nicolas Bousserez, Anna Agustí-Panareda, Gianpaolo Balsamo, Luca Cantarello, Richard Engelen, Vincent Huijnen, Antje Inness, Zak Kipling, Mark Parrington, Roberto Ribas. Quantification of methane emissions from hotspots and during COVID-19 using a global atmospheric inversion. Atmospheric Chemistry and Physics 2022, 22 (9) , 5961-5981. https://doi.org/10.5194/acp-22-5961-2022
John R. Worden, Daniel H. Cusworth, Zhen Qu, Yi Yin, Yuzhong Zhang, A. Anthony Bloom, Shuang Ma, Brendan K. Byrne, Tia Scarpelli, Joannes D. Maasakkers, David Crisp, Riley Duren, Daniel J. Jacob. The 2019 methane budget and uncertainties at 1° resolution and each country through Bayesian integration Of GOSAT total column methane data and a priori inventory estimates. Atmospheric Chemistry and Physics 2022, 22 (10) , 6811-6841. https://doi.org/10.5194/acp-22-6811-2022
Daniel J. Jacob, Daniel J. Varon, Daniel H. Cusworth, Philip E. Dennison, Christian Frankenberg, Ritesh Gautam, Luis Guanter, John Kelley, Jason McKeever, Lesley E. Ott, Benjamin Poulter, Zhen Qu, Andrew K. Thorpe, John R. Worden, Riley M. Duren. Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane. Atmospheric Chemistry and Physics 2022, 22 (14) , 9617-9646. https://doi.org/10.5194/acp-22-9617-2022
Rory A. Barton-Grimley, Amin R. Nehrir, Susan A. Kooi, James E. Collins, David B. Harper, Anthony Notari, Joseph Lee, Joshua P. DiGangi, Yonghoon Choi, Kenneth J. Davis. Evaluation of the High Altitude Lidar Observatory (HALO) methane retrievals during the summer 2019 ACT-America campaign. Atmospheric Measurement Techniques 2022, 15 (15) , 4623-4650. https://doi.org/10.5194/amt-15-4623-2022
Vanessa C. Monteiro, Natasha L. Miles, Scott J. Richardson, Zachary Barkley, Bernd J. Haupt, David Lyon, Benjamin Hmiel, Kenneth J. Davis. Methane, carbon dioxide, hydrogen sulfide, and isotopic ratios of methane observations from the Permian Basin tower network. Earth System Science Data 2022, 14 (5) , 2401-2417. https://doi.org/10.5194/essd-14-2401-2022
Daniel J. Varon, Daniel J. Jacob, Melissa Sulprizio, Lucas A. Estrada, William B. Downs, Lu Shen, Sarah E. Hancock, Hannah Nesser, Zhen Qu, Elise Penn, Zichong Chen, Xiao Lu, Alba Lorente, Ashutosh Tewari, Cynthia A. Randles. Integrated Methane Inversion (IMI 1.0): a user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations. Geoscientific Model Development 2022, 15 (14) , 5787-5805. https://doi.org/10.5194/gmd-15-5787-2022
Sajjan Heerah, Isis Frausto‐Vicencio, Seongeun Jeong, Alison R. Marklein, Yifan Ding, Aaron G. Meyer, Harrison A. Parker, Marc L. Fischer, Jonathan E. Franklin, Francesca M. Hopkins, Manvendra Dubey. Dairy Methane Emissions in California's San Joaquin Valley Inferred With Ground‐Based Remote Sensing Observations in the Summer and Winter. Journal of Geophysical Research: Atmospheres 2021, 126 (24) https://doi.org/10.1029/2021JD034785
John C. Lin, Ryan Bares, Benjamin Fasoli, Maria Garcia, Erik Crosman, Seth Lyman. Declining methane emissions and steady, high leakage rates observed over multiple years in a western US oil/gas production basin. Scientific Reports 2021, 11 (1) https://doi.org/10.1038/s41598-021-01721-5
Daniel H. Cusworth, A. Anthony Bloom, Shuang Ma, Charles E. Miller, Kevin Bowman, Yi Yin, Joannes D. Maasakkers, Yuzhong Zhang, Tia R. Scarpelli, Zhen Qu, Daniel J. Jacob, John R. Worden. A Bayesian framework for deriving sector-based methane emissions from top-down fluxes. Communications Earth & Environment 2021, 2 (1) https://doi.org/10.1038/s43247-021-00312-6
Shuo Sun, Linwei Ma, Zheng Li. Methane Emission Estimation of Oil and Gas Sector: A Review of Measurement Technologies, Data Analysis Methods and Uncertainty Estimation. Sustainability 2021, 13 (24) , 13895. https://doi.org/10.3390/su132413895
Maryann R. Sargent, Cody Floerchinger, Kathryn McKain, John Budney, Elaine W. Gottlieb, Lucy R. Hutyra, Joseph Rudek, Steven C. Wofsy. Majority of US urban natural gas emissions unaccounted for in inventories. Proceedings of the National Academy of Sciences 2021, 118 (44) https://doi.org/10.1073/pnas.2105804118
Jianxiong Sheng, Rachel Tunnicliffe, Anita L Ganesan, Joannes D Maasakkers, Lu Shen, Ronald G Prinn, Shaojie Song, Yuzhong Zhang, Tia Scarpelli, A Anthony Bloom, Matthew Rigby, Alistair J Manning, Robert J Parker, Hartmut Boesch, Xin Lan, Bo Zhang, Minghao Zhuang, Xi Lu. Sustained methane emissions from China after 2012 despite declining coal production and rice-cultivated area. Environmental Research Letters 2021, 16 (10) , 104018. https://doi.org/10.1088/1748-9326/ac24d1
Mengyao Liu, Ronald van der A, Michiel van Weele, Henk Eskes, Xiao Lu, Pepijn Veefkind, Jos de Laat, Hao Kong, Jingxu Wang, Jiyunting Sun, Jieying Ding, Yuanhong Zhao, Hongjian Weng. A New Divergence Method to Quantify Methane Emissions Using Observations of Sentinel‐5P TROPOMI. Geophysical Research Letters 2021, 48 (18) https://doi.org/10.1029/2021GL094151
Sang-Ryong Lee, Yunseo Cho, Hyuck K. Ju, Eunjeong Kim. Theoretical Methane Emission Estimation from Volatile Fatty Acids in Bovine Rumen Fluid. Applied Sciences 2021, 11 (16) , 7730. https://doi.org/10.3390/app11167730
Itziar Irakulis-Loitxate, Luis Guanter, Yin-Nian Liu, Daniel J. Varon, Joannes D. Maasakkers, Yuzhong Zhang, Apisada Chulakadabba, Steven C. Wofsy, Andrew K. Thorpe, Riley M. Duren, Christian Frankenberg, David R. Lyon, Benjamin Hmiel, Daniel H. Cusworth, Yongguang Zhang, Karl Segl, Javier Gorroño, Elena Sánchez-García, Melissa P. Sulprizio, Kaiqin Cao, Haijian Zhu, Jian Liang, Xun Li, Ilse Aben, Daniel J. Jacob. Satellite-based survey of extreme methane emissions in the Permian basin. Science Advances 2021, 7 (27) https://doi.org/10.1126/sciadv.abf4507
Y. Wei, R. Shrestha, S. Pal, T. Gerken, S. Feng, J. McNelis, D. Singh, M. M. Thornton, A. G. Boyer, M. A. Shook, G. Chen, B. C. Baier, Z. R. Barkley, J. D. Barrick, J. R. Bennett, E. V. Browell, J. F. Campbell, L. J. Campbell, Y. Choi, J. Collins, J. Dobler, M. Eckl, A. Fiehn, A. Fried, J. P. Digangi, R. Barton‐Grimley, H. Halliday, T. Klausner, S. Kooi, J. Kostinek, T. Lauvaux, B. Lin, M. J. McGill, B. Meadows, N. L. Miles, A. R. Nehrir, J. B. Nowak, M. Obland, C. O’Dell, R. M. P. Fao, S. J. Richardson, D. Richter, A. Roiger, C. Sweeney, J. Walega, P. Weibring, C. A. Williams, M. M. Yang, Y. Zhou, K. J. Davis. Atmospheric Carbon and Transport – America (ACT‐America) Data Sets: Description, Management, and Delivery. Earth and Space Science 2021, 8 (7) https://doi.org/10.1029/2020EA001634
Lu Shen, Daniel Zavala-Araiza, Ritesh Gautam, Mark Omara, Tia Scarpelli, Jianxiong Sheng, Melissa P. Sulprizio, Jiawei Zhuang, Yuzhong Zhang, Zhen Qu, Xiao Lu, Steven P. Hamburg, Daniel J. Jacob. Unravelling a large methane emission discrepancy in Mexico using satellite observations. Remote Sensing of Environment 2021, 260 , 112461. https://doi.org/10.1016/j.rse.2021.112461
Youngseok Hwang, Stephan Schlüter, Tanupriya Choudhury, Jung-Sup Um. Comparative Evaluation of Top-Down GOSAT XCO2 vs. Bottom-Up National Reports in the European Countries. Sustainability 2021, 13 (12) , 6700. https://doi.org/10.3390/su13126700
Z. R. Barkley, K. J. Davis, S. Feng, Y. Y. Cui, A. Fried, P. Weibring, D. Richter, J. G. Walega, S. M. Miller, M. Eckl, A. Roiger, A. Fiehn, J. Kostinek. Analysis of Oil and Gas Ethane and Methane Emissions in the Southcentral and Eastern United States Using Four Seasons of Continuous Aircraft Ethane Measurements. Journal of Geophysical Research: Atmospheres 2021, 126 (10) https://doi.org/10.1029/2020JD034194
Shoma Yamanouchi, Kimberly Strong, Orfeo Colebatch, Stephanie Conway, Dylan B A Jones, Erik Lutsch, Sébastien Roche. Atmospheric trace gas trends obtained from FTIR column measurements in Toronto, Canada from 2002-2019. Environmental Research Communications 2021, 3 (5) , 051002. https://doi.org/10.1088/2515-7620/abfa65
Cody Floerchinger, Paul B. Shepson, Kristian Hajny, Bruce C. Daube, Brian H. Stirm, Colm Sweeney, Steven C. Wofsy. Relative flux measurements of biogenic and natural gas-derived methane for seven U.S. cities. Elementa: Science of the Anthropocene 2021, 9 (1) https://doi.org/10.1525/elementa.2021.000119
Taylor S. Jones, Jonathan E. Franklin, Jia Chen, Florian Dietrich, Kristian D. Hajny, Johannes C. Paetzold, Adrian Wenzel, Conor Gately, Elaine Gottlieb, Harrison Parker, Manvendra Dubey, Frank Hase, Paul B. Shepson, Levi H. Mielke, Steven C. Wofsy. Assessing urban methane emissions using column-observing portable Fourier transform infrared (FTIR) spectrometers and a novel Bayesian inversion framework. Atmospheric Chemistry and Physics 2021, 21 (17) , 13131-13147. https://doi.org/10.5194/acp-21-13131-2021
Zhen Qu, Daniel J. Jacob, Lu Shen, Xiao Lu, Yuzhong Zhang, Tia R. Scarpelli, Hannah Nesser, Melissa P. Sulprizio, Joannes D. Maasakkers, A. Anthony Bloom, John R. Worden, Robert J. Parker, Alba L. Delgado. Global distribution of methane emissions: a comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments. Atmospheric Chemistry and Physics 2021, 21 (18) , 14159-14175. https://doi.org/10.5194/acp-21-14159-2021
Sven Krautwurst, Konstantin Gerilowski, Jakob Borchardt, Norman Wildmann, Michał Gałkowski, Justyna Swolkień, Julia Marshall, Alina Fiehn, Anke Roiger, Thomas Ruhtz, Christoph Gerbig, Jaroslaw Necki, John P. Burrows, Andreas Fix, Heinrich Bovensmann. Quantification of CH4 coal mining emissions in Upper Silesia by passive airborne remote sensing observations with the Methane Airborne MAPper (MAMAP) instrument during the CO2 and Methane (CoMet) campaign. Atmospheric Chemistry and Physics 2021, 21 (23) , 17345-17371. https://doi.org/10.5194/acp-21-17345-2021
Sabour Baray, Daniel J. Jacob, Joannes D. Maasakkers, Jian-Xiong Sheng, Melissa P. Sulprizio, Dylan B. A. Jones, A. Anthony Bloom, Robert McLaren. Estimating 2010–2015 anthropogenic and natural methane emissions in Canada using ECCC surface and GOSAT satellite observations. Atmospheric Chemistry and Physics 2021, 21 (23) , 18101-18121. https://doi.org/10.5194/acp-21-18101-2021
Yuzhong Zhang, Daniel J. Jacob, Xiao Lu, Joannes D. Maasakkers, Tia R. Scarpelli, Jian-Xiong Sheng, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Jinfeng Chang, A. Anthony Bloom, Shuang Ma, John Worden, Robert J. Parker, Hartmut Boesch. Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations. Atmospheric Chemistry and Physics 2021, 21 (5) , 3643-3666. https://doi.org/10.5194/acp-21-3643-2021
Joannes D. Maasakkers, Daniel J. Jacob, Melissa P. Sulprizio, Tia R. Scarpelli, Hannah Nesser, Jianxiong Sheng, Yuzhong Zhang, Xiao Lu, A. Anthony Bloom, Kevin W. Bowman, John R. Worden, Robert J. Parker. 2010–2015 North American methane emissions, sectoral contributions, and trends: a high-resolution inversion of GOSAT observations of atmospheric methane. Atmospheric Chemistry and Physics 2021, 21 (6) , 4339-4356. https://doi.org/10.5194/acp-21-4339-2021
Xiao Lu, Daniel J. Jacob, Yuzhong Zhang, Joannes D. Maasakkers, Melissa P. Sulprizio, Lu Shen, Zhen Qu, Tia R. Scarpelli, Hannah Nesser, Robert M. Yantosca, Jianxiong Sheng, Arlyn Andrews, Robert J. Parker, Hartmut Boesch, A. Anthony Bloom, Shuang Ma. Global methane budget and trend, 2010–2017: complementarity of inverse analyses using in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) observations. Atmospheric Chemistry and Physics 2021, 21 (6) , 4637-4657. https://doi.org/10.5194/acp-21-4637-2021

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Cite this: Environ. Sci. Technol. 2016, 50, 23, 13123–13133

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Abstract
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Figure 1
Figure 1. Contiguous US (CONUS) methane emissions from different source categories. Total annual US emissions from the 2016 EPA GHGI for 2012 are disaggregated here on a 0.1° × 0.1° grid. “Other” refers to the ensemble of minor sources in Table 1. (An equivalent figure for EDGAR v4.2 is shown by in Turner et al. (70)).
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Figure 2
Figure 2. Total methane emissions in the gridded EPA inventory for 2012 (top), EDGAR v4.2 for 2008 (middle), and difference between the two (bottom).
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Figure 3
Figure 3. Emissions from livestock, oil/gas systems, and waste over the South-Central US in the gridded EPA inventory for 2012 and the EDGAR v4.2 inventory for 2008.
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Figure 4
Figure 4. Methane emissions in the Barnett Shale region of Northeast Texas. Values for the four main categories are shown for our gridded EPA inventory and for the EDF inventory (73) at 0.1° × 0.1° resolution. The original EDF inventory is at 4 × 4 km² and is regridded here to 0.1° × 0.1° for comparison with our inventory. The location of the Barnett Shale region is shown inset. Emission totals for the region are given in Table 2.
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Figure 5
Figure 5. Relative error standard deviations for methane emissions from individual source types and their scale dependences. The figure shows the error parameters α and β used in eq 2 to calculate the absolute error standard deviations for a given L × L grid cell and source type as a function of the grid resolution L. The native grid resolution of the inventory is 0.1° × 0.1°, and averaging over coarser scales decreases errors for individual source types as described by exponential decay functions (eqs 4 and 5). The asymptotes for the base error standard deviations are the national values α_N shown as tick marks on the right side of the left panel. Values for all error parameters are given in Table 3.
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Figure 6
Figure 6. Methane emissions in California in 2012 from all sources in Table 2. The top panels show results from our gridded EPA inventory and from the CALGEM inventory on their native 0.1° × 0.1° grids. The bottom left panel shows the difference and the bottom right panel shows the error standard deviation in our gridded EPA inventory as computed with the method described in the text. The sign of the error standard deviation in the figure is the same as the EPA-CALGEM difference to facilitate visual comparison. Differences and error standard deviations are shown on a 0.2° × 0.2° grid to account for the 0.05° offset between the EPA and CALGEM grids.
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  Remote sensing-based estimates of annual and seasonal emissions from crop residue burning in the contiguous United States
  McCarty, Jessica L.
  Journal of the Air & Waste Management Association (2011), 61 (1), 22-34CODEN: JAWAFC; ISSN:1096-2247. (Air & Waste Management Association)
  Crop residue burning is an extensive agricultural practice in the contiguous United States (CONUS). This anal. presents the results of a remote sensing-based study of crop residue burning emissions in the CONUS for the time period 2003-2007 for the atm. species of carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), PM2.5 (particulate matter [PM] ≤ 2.5 μm in aerodynamic diam.), and PM10 (PM ≤ 10 μm in aerodynamic diam.). Cropland burned area and assocd. crop types were derived from Moderate Resoln. Imaging Spectroradiometer (MODIS) products. Emission factors, fuel load, and combustion completeness ests. were derived from the scientific literature, governmental reports, and expert knowledge. Emissions were calcd. using the bottom-up approach in which emissions are the product of burned area, fuel load, and combustion completeness for each specific crop type. On av., annual crop residue burning in the CONUS emitted 6.1 TG of CO2, 8.9 Gg of CH4, 232.4 Gg of CO, 10.6 Gg of NO2, 4.4 Gg of SO2, 20.9 Gg of PM2.5, and 28.5 Gg of PM10. These emissions remained fairly consistent, with an av. interannual variability of crop residue burning emissions of ±10%. The states with the highest emissions were Arkansas, California, Florida, Idaho, Texas, and Washington. Most emissions were clustered in the southeastern United States, the Great Plains, and the Pacific Northwest. Air quality and carbon emissions were concd. in the spring, summer, and fall, with an exception because of winter harvesting of sugarcane in Florida, Louisiana, and Texas. Sugarcane, wheat, and rice residues accounted for ∼70% of all crop residue burning and assocd. emissions. Ests. of CO and CH4 from agricultural waste burning by the U.S. Environmental Protection Agency were 73 and 78% higher than the CO and CH4 emission ests. from this anal., resp. This anal. also showed that crop residue burning emissions are a minor source of CH4 emissions ( < 1%) compared with the CH4 emissions from other agricultural sources, specifically enteric fermn., manure management, and rice cultivation.
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30. 30
  US Bureau of Ocean Energy Management.2011 Gulfwide EmissionInventory, (2011. boem.gov/2011-Gulfwide-Emission-Inventory/.
  There is no corresponding record for this reference.
31. 31
  Marchese, A. J.; Vaughn, T. L.; Zimmerle, D. J.; Martinez, D. M.; Williams, L. L.; Robinson, A. L.; Mitchell, A. L.; Subramanian, R.; Tkacik, D. S.; Roscioli, J. R. Methane emissions from United States natural gas gathering and processing Environ. Sci. Technol. 2015) 49, 10718 DOI: 10.1021/acs.est.5b02275
  31
  Methane Emissions from United States Natural Gas Gathering and Processing
  Marchese, Anthony J.; Vaughn, Timothy L.; Zimmerle, Daniel J.; Martinez, David M.; Williams, Laurie L.; Robinson, Allen L.; Mitchell, Austin L.; Subramanian, R.; Tkacik, Daniel S.; Roscioli, Joseph R.; Herndon, Scott C.
  Environmental Science & Technology (2015), 49 (17), 10718-10727CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
  Facility-level CH4 emission measurements from 114 natural gas collection and 16 processing facilities in 13 US states were combined with facility counts from state and national databases in a Monte Carlo simulation to est. CH4 emissions from US natural gas collecting and processing operations. Total annual CH4 emissions of 2421 (+245/-237) Gg were estd. for all US collecting and processing operations, which represent a CH4 loss rate of 0.47% (±0.05%) when normalized by 2012 CH4 prodn. More than 90% of those emissions were attributed to normal operation at collecting (1697 +189/-185 Gg) and processing facilities (506 +55/-52 Gg); the balance was attributed to pipelines and processing facility routine maintenance and upsets. Median CH4 emissions est. for processing facilities was a factor of 1.7 lower than the 2012 USEPA Greenhouse Gas Inventory (GHGI) est., the difference was due largely to fewer reciprocating compressors; and a factor of 3.0 higher than that reported in the EPA Greenhouse Gas Reporting Program. Since collecting operations are currently embedded within the EPA GHGI prodn. segment, direct comparison to results is complicated. However, results suggested CH4 emissions from collection were substantially higher than the current EPA GHGI est., and were equiv. to 30% of the total net CH4 emissions in natural gas system GHGI. Since emissions from most collecting facilities are not reported under the current rule and not all source categories are reported for processing facilities, total CH4 emissions from collecting and processing reported under the EPA GHGRP (180 Gg) represents only 14% of that tabulated in the EPA GHGI, and 7% of that predicted by this study.
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32. 32
  EIA.EIA-757 Natural Gas Processing Plant Survey, (2013. eia.gov/cfapps/ngqs/ngqs.cfm.
  There is no corresponding record for this reference.
33. 33
  Natural Gas Pipeline and Infrastructure Wall Map; Rextag Strategies, 2008.
  There is no corresponding record for this reference.
34. 34
  US Census Bureau. Cartographic BoundaryShapefiles: ZIPCode Tabulation Areas, (2013. census.gov/geo/maps-data/data/cbf/cbf_zcta.html.
  There is no corresponding record for this reference.
35. 35
  EIA, Office of Oil and Gas. The CrucialLink Between NaturalGas Production and Its Transportation to Market, (2006. http://www.eia.gov/pub/oil_gas/natural_gas/feature_articles/2006/ngprocess/ngprocess.pdf.
  There is no corresponding record for this reference.
36. 36
  Zimmerle, D. J.; Williams, L. L.; Vaughn, T. L.; Quinn, C.; Subramanian, R.; Duggan, G. P.; Willson, B.; Opsomer, J. D.; Marchese, A. J.; Martinez, D. M.; Robinson, A. L. Methane Emissions from the Natural Gas Transmission and Storage System in the United States Environ. Sci. Technol. 2015) 49, 9374– 9383 DOI: 10.1021/acs.est.5b01669
  36
  Methane Emissions from the Natural Gas Transmission and Storage System in the United States
  Zimmerle, Daniel J.; Williams, Laurie L.; Vaughn, Timothy L.; Quinn, Casey; Subramanian, R.; Duggan, Gerald P.; Willson, Bryan; Opsomer, Jean D.; Marchese, Anthony J.; Martinez, David M.; Robinson, Allen L.
  Environmental Science & Technology (2015), 49 (15), 9374-9383CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
  Recent growth in natural gas prodn. and utilization offers potential climate benefits which depend on CH4 (primary natural gas component and greenhouse gas) life cycle emissions. This work estd. CH4 emissions from transmission and storage (T&S) sector of the US natural gas industry using data collected in 2012, including 2292 on-site measurements, addnl. emissions data from 677 facilities, and activity data from 922 facilities. The largest emission sources were fugitive emissions from compressor-related equipment and super-emitter facilities. Total CH4 emissions estd. from the T&S sector was 1503 (1220-1950) Gg/yr (95% confidence interval) vs. the 2012 USEPA Greenhouse Gas Inventory (GHGI) est. of 2071 (1680-2690) Gg/yr. While the overlap in confidence intervals indicated the difference is not statistically significant, this is due to several significant, but offsetting, factors. Factors which reduce the study est. include: a lower estd. facility count, a shift away from engines toward lower-emitting turbine and elec. compressor drivers, and redns. in use of gas-driven pneumatic devices. Factors which increase the study est. relative to the GHGI include: updated emission rates in certain emission categories and explicit treatment of skewed emissions at component and facility levels. For T&S stations required to report to the EPA Greenhouse Gas Reporting Program (GHGRP), this study estd. total emissions to be 260% (215-330%) of reportable emissions for these stations, primarily due to inclusion of emission sources not reported under GHGRP rules, updated emission factors, and super-emitter emissions.
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37. 37
  EIA. Natural Gas Underground Storage Facilities. EIA-191,Monthly Underground Gas Storage Report, (2014. eia.gov/maps/map_data/NaturalGas_UndergroundStorage_US_EIA.zip.
  There is no corresponding record for this reference.
38. 38
  EIA. LNG Peaking Shaving and Import Facilities, (2008. eia.gov/pub/oil_gas/natural_gas/analysis_publications/ngpipeline/lngpeakshaving_map.html.
  There is no corresponding record for this reference.
39. 39
  EIA; Federal Energy Regulatory Commission; US Dept. of Transportation. Liquefied Natural Gas Import/Export Terminals, 2013. eia.gov/maps/map_data/Lng_ImportExportTerminals_US_EIA.zip.
  There is no corresponding record for this reference.
40. 40
  EIA Natural Gas Interstate and Intrastate Pipelines.Collectedby EIA from FERC and other external sources, (2012. eia.gov/maps/map_data/NaturalGas_InterIntrastate_Pipelines_US_EIA.zip.
  There is no corresponding record for this reference.
41. 41
  PHMSA, Office of Pipeline Safety. GasDistribution AnnualData, (2012. phmsa.dot.gov/pipeline/library/data-stats.
  There is no corresponding record for this reference.
42. 42
  EIA, EIA-176 Natural Gas Deliveries. Availableat: eia.gov/cfapps/ngqs/ngqs.cfm, 2013.
  There is no corresponding record for this reference.
43. 43
  US Census Bureau. US Census TIGER/Line Population and HousingUnit Counts, (2010. census.gov/geo/maps-data/data/tiger-data.html.
  There is no corresponding record for this reference.
44. 44
  EPA. Landfill Methane Outreach Program:Landfill-level data, (2015. epa.gov/lmop/projects-candidates/index.html#map-area.
  There is no corresponding record for this reference.
45. 45
  EPA. Facility Registry Service, (2015. epa.gov/enviro/geospatial-data-download-service.
  There is no corresponding record for this reference.
46. 46
  RTI International. Solid Waste Emissions Inventory Support, Review Draft; EPA, 2004.
  There is no corresponding record for this reference.
47. 47
  EPA. Clean WatershedsNeeds Survey, (2008. epa.gov/cwns.
  There is no corresponding record for this reference.
48. 48
  Shin, D. Generation and Disposition of Municipal Solid Waste (MSW) in the United States: A National Survey. Master of Science Thesis, Columbia University, New York, 2014; http://www.seas.columbia.edu/earth/wtert/sofos/Dolly_Shin_Thesis.pdf.
  There is no corresponding record for this reference.
49. 49
  US Composting Council. Compost LocatorMap, (2015. compostingcouncil.org/wp/compostmap.php.
  There is no corresponding record for this reference.
50. 50
  BioCycle. Find A Composter database, (2015. findacomposter.com.
  There is no corresponding record for this reference.
51. 51
  EIA. Coal Production and Preparation Report.Review Draft, (2013. eia.gov/maps/layer_info-m.cfm.
  There is no corresponding record for this reference.
52. 52
  EPA. Abandoned Coal Mine Methane OpportunitiesDatabase, (2008. https://www.epa.gov/sites/production/files/2016-03/documents/amm_opportunities_database.pdf.
  There is no corresponding record for this reference.
53. 53
  EPA. Methane Emissions from AbandonedCoal Mines in theUS: Emission Inventory Methodology and 1990–2002 EmissionsEstimates, (2004. https://nepis.epa.gov/Exe/ZyPDF.cgi/600004JM.PDF?Dockey=600004JM.PDF.
  There is no corresponding record for this reference.
  Addendum to Methane Emissions from Abandoned Coal Mines in the US: Emission Inventory Methodology and 1990–2002 Emissions Estimates, 2007. https://www.epa.gov/sites/production/files/2016-03/documents/abandoned_mine_variables_by_basin.pdf.
  There is no corresponding record for this reference.
54. 54
  US Department of Labor Mine Safety and Health Administration. Full MineInfo dataset, (2015. http://developer.dol.gov/health-and-safety/full-mine-info-mines.
  There is no corresponding record for this reference.
55. 55
  EIA.Crude Oil Production, (2004. eia.gov/dnav/pet/pet_crd_crpdn_adc_mbbl_m.htm.
  There is no corresponding record for this reference.
56. 56
  Darmenov, A.; da Silva, A. The quick fire emissions dataset (QFED)-documentation of versions 2.1, 2.2 and 2.4 NASA Technical Report Series on Global Modeling and Data Assimilation, NASA TM-2013–104606 2013) 32, 183
  There is no corresponding record for this reference.
57. 57
  EPA. Air Markets Program Data, (2012. ampd.epa.gov/ampd/.
  There is no corresponding record for this reference.
58. 58
  EIA. State Energy Data System, (2012. eia.gov/state/seds/.
  There is no corresponding record for this reference.
59. 59
  Federal Highway Administration. HighwayPolicy Information:Highway Statistics 2013. https://www.fhwa.dot.gov/policyinformation/statistics/2013/.
  There is no corresponding record for this reference.
60. 60
  US Department of Transportation. NationalTransportationAtlas Database, (2015. http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/2015/index.html.
  There is no corresponding record for this reference.
61. 61
  US Census Bureau. TIGER/Line Shapefiles, (2012. census.gov/geo/maps-data/data/tiger-line.html.
  There is no corresponding record for this reference.
62. 62
  Kort, E. A.; Frankenberg, C.; Costigan, K. R.; Lindenmaier, R.; Dubey, M. K.; Wunch, D. Four corners: The largest US methane anomaly viewed from space Geophys. Res. Lett. 2014) 41, 6898– 6903 DOI: 10.1002/2014GL061503
  62
  Four corners: The largest US methane anomaly viewed from space
  Kort, Eric A.; Frankenberg, Christian; Costigan, Keeley R.; Lindenmaier, Rodica; Dubey, Manvendra K.; Wunch, Debra
  Geophysical Research Letters (2014), 41 (19), 6898-6903CODEN: GPRLAJ; ISSN:1944-8007. (Wiley-Blackwell)
  Methane (CH4) is a potent greenhouse gas and ozone precursor. Quantifying methane emissions is crit. for projecting and mitigating changes to climate and air quality. Here we present CH4 observations made from space combined with Earth-based remote sensing column measurements. Results indicate the largest anomalous CH4 levels viewable from space over the conterminous U.S. are located at the Four Corners region in the Southwest U.S. Emissions exceeding inventory ests., totaling 0.59 Tg CH4/yr [0.50-0.67; 2σ], are necessary to bring high-resoln. simulations and observations into agreement. This underestimated source approaches 10% of the EPA est. of total U.S. CH4 emissions from natural gas. The persistence of this CH4 signal from 2003 onward indicates that the source is likely from established gas, coal, and coalbed methane mining and processing. This work demonstrates that space-based observations can identify anomalous CH4 emission source regions and quantify their emissions with the use of a transport model.
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63. 63
  Jeong, S.; Zhao, C.; Andrews, A. E.; Sweeney, C.; Bianco, L.; Wilczak, J. M.; Fischer, M. L. Seasonal variations in CH₄ emissions from central California Geophys. Res. Lett. 2012, 117, D11306 DOI: 10.1029/2011JD016896
  There is no corresponding record for this reference.
64. 64
  Owen, J. J.; Silver, W. L. Greenhouse gas emissions from dairy manure management: a review of field-based studies Glob. Change Biol. 2015, 21, 550– 565 DOI: 10.1111/gcb.12687
  64
  Greenhouse gas emissions from dairy manure management: a review of field-based studies
  Owen Justine J; Silver Whendee L
  Global change biology (2015), 21 (2), 550-65 ISSN:.
  Livestock manure management accounts for almost 10% of greenhouse gas emissions from agriculture globally, and contributes an equal proportion to the US methane emission inventory. Current emissions inventories use emissions factors determined from small-scale laboratory experiments that have not been compared to field-scale measurements. We compiled published data on field-scale measurements of greenhouse gas emissions from working and research dairies and compared these to rates predicted by the IPCC Tier 2 modeling approach. Anaerobic lagoons were the largest source of methane (368 ± 193 kg CH4 hd(-1) yr(-1)), more than three times that from enteric fermentation (~120 kg CH4 hd(-1) yr(-1)). Corrals and solid manure piles were large sources of nitrous oxide (1.5 ± 0.8 and 1.1 ± 0.7 kg N2O hd(-1) yr(-1), respectively). Nitrous oxide emissions from anaerobic lagoons (0.9 ± 0.5 kg N2O hd(-1) yr(-1)) and barns (10 ± 6 kg N2O hd(-1) yr(-1)) were unexpectedly large. Modeled methane emissions underestimated field measurement means for most manure management practices. Modeled nitrous oxide emissions underestimated field measurement means for anaerobic lagoons and manure piles, but overestimated emissions from slurry storage. Revised emissions factors nearly doubled slurry CH4 emissions for Europe and increased N2O emissions from solid piles and lagoons in the United States by an order of magnitude. Our results suggest that current greenhouse gas emission factors generally underestimate emissions from dairy manure and highlight liquid manure systems as promising target areas for greenhouse gas mitigation.
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65. 65
  Yvon-Durocher, G.; Allen, A. P.; Bastviken, D.; Conrad, R.; Gudasz, C.; St-Pierre, A.; Thanh-Duc, N.; Del Giorgio, P. A. Methane fluxes show consistent temperature dependence across microbial to ecosystem scales Nature 2014, 507, 488– 491 DOI: 10.1038/nature13164
  65
  Methane fluxes show consistent temperature dependence across microbial to ecosystem scales
  Yvon-Durocher, Gabriel; Allen, Andrew P.; Bastviken, David; Conrad, Ralf; Gudasz, Cristian; St-Pierre, Annick; Nguyen, Thanh-Duc; del Giorgio, Paul A.
  Nature (London, United Kingdom) (2014), 507 (7493), 488-491CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
  Methane (CH4) is an important greenhouse gas because it has 25 times the global warming potential of carbon dioxide (CO2) by mass over a century. Recent calcns. suggest that atm. CH4 emissions were responsible for approx. 20% of Earth's warming since pre-industrial times. Understanding how CH4 emissions from ecosystems will respond to expected increases in global temp. is therefore fundamental to predicting whether the carbon cycle will mitigate or accelerate climate change. Methanogenesis is the terminal step in the remineralization of org. matter and is carried out by strictly anaerobic Archaea. Like most other forms of metab., methanogenesis is temp.-dependent. However, it is not yet known how this physiol. response combines with other biotic processes (for example, methanotrophy, substrate supply, microbial community compn.) and abiotic processes (for example, water-table depth) to det. the temp. dependence of ecosystem-level CH4 emissions. It is also not known whether CH4 emissions at the ecosystem level have a fundamentally different temp. dependence than other key fluxes in the carbon cycle, such as photosynthesis and respiration. Here we use meta-analyses to show that seasonal variations in CH4 emissions from a wide range of ecosystems exhibit an av. temp. dependence similar to that of CH4 prodn. derived from pure cultures of methanogens and anaerobic microbial communities. This av. temp. dependence (0.96 eV (eV)), which corresponds to a 57-fold increase between 0 and 30°, is considerably higher than previously obsd. for respiration (approx. 0.65 eV) and photosynthesis (approx. 0.3 eV). As a result, we show that both the emission of CH4 and the ratio of CH4 to CO2 emissions increase markedly with seasonal increases in temp. Our findings suggest that global warming may have a large impact on the relative contributions of CO2 and CH4 to total greenhouse gas emissions from aquatic ecosystems, terrestrial wetlands and rice paddies.
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66. 66
  Zavala-Araiza, D.; Lyon, D.; Alvarez, R. A.; Palacios, V.; Harriss, R.; Lan, X.; Talbot, R.; Hamburg, S. P. Toward a Functional Definition of Methane Super-Emitters: Application to Natural Gas Production Sites Environ. Sci. Technol. 2015, 49, 8167– 8174 DOI: 10.1021/acs.est.5b00133
  66
  Toward a Functional Definition of Methane Super-Emitters: Application to Natural Gas Production Sites
  Zavala-Araiza, Daniel; Lyon, David; Alvarez, Ramon A.; Palacios, Virginia; Harriss, Robert; Lan, Xin; Talbot, Robert; Hamburg, Steven P.
  Environmental Science & Technology (2015), 49 (13), 8167-8174CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
  Natural gas prodn. site emissions are characterized by skewed distributions, where a small percentage of sites, commonly labeled super-emitters, account for a majority of emissions. A better characterization of super-emitters is needed to operationalize ways to identify them and reduce emissions. This work designed a conceptual framework to functionally define super-emitting sites as those with the highest proportional loss rates (Ch4 emitted vs. CH4 produced). Using this concept, total CH4 emissions from Barnett Shale natural gas prodn. sites (Texas) were estd.; super-emitting sites functionally accounted for approx. 3/4 of total emissions. The potential to reduce emissions from these sites is discussed under the assumption that sites with high proportional loss rates have excess emissions resulting from abnormal or otherwise avoidable operating conditions, e.g., malfunctioning equipment. Since the population of functionally super-emitting sites is not expected to be static over time, continuous monitoring will be necessary to identify them and improve their operation. This work suggested that achieving and maintaining uniformly low emissions across the entire population of prodn. sites will require mitigation steps at a large fraction of sites.
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  PMID: 26148555.
67. 67
  Miller, S. M.; Wofsy, S. C.; Michalak, A. M.; Kort, E. A.; Andrews, A. E.; Biraud, S. C.; Dlugokencky, E. J.; Eluszkiewicz, J.; Fischer, M. L.; Janssens-Maenhout, G.; Miller, B. R.; Miller, J. B.; Montzka, S. A.; Nehrkorn, T.; Sweeney, C. Anthropogenic emissions of methane in the United States Proc. Natl. Acad. Sci. U. S. A. 2013, 110, 20018– 20022 DOI: 10.1073/pnas.1314392110
  67
  Anthropogenic emissions of methane in the United States
  Miller, Scot M.; Wofsy, Steven C.; Michalak, Anna M.; Kort, Eric A.; Andrews, Arlyn E.; Biraud, Sebastien C.; Dlugokencky, Edward J.; Eluszkiewicz, Janusz; Fischer, Marc L.; Janssens-Maenhout, Greet; Miller, Ben R.; Miller, John B.; Montzka, Stephen A.; Nehrkorn, Thomas; Sweeney, Colm
  Proceedings of the National Academy of Sciences of the United States of America (2013), 110 (50), 20018-20022,S20018/1-S20018/11CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
  This work quant. estd. the spatial distribution of anthropogenic CH4 sources in the US by combining comprehensive atm. CH4 observations, extensive spatial datasets, and a high resoln. atm. transport model. Results showed current USEPA and Emissions Database for Global Atm. Research (EDGAR) inventories underestimated national CH4 emissions a factor of ∼1.5 and ∼1.7, resp. Results indicated emissions due to ruminants and manure are up to twice the magnitude of existing inventories. Discrepancies in CH4 source ests. are particularly pronounced in the south-central US where total emissions are ∼2.7 times greater than in most inventories and account for 24 ± 3% of national emissions. Spatial patterns of emission fluxes and obsd. CH4/C3H8 correlations indicated fossil fuel extn. and refining are major contributors (45 ± 13%) in the south-central US. This suggested regional CH4 emissions due to fossil fuel extn. and processing could be 4.9 ± 2.6 times larger than in EDGAR, the most comprehensive global CH4 inventory. Results cast doubt on a recent USEPA decision to down-scale its est. of national natural gas emissions by 25-30%. It was concluded that CH4 emissions assocd. with animal husbandry and fossil fuel industries have larger greenhouse gas impacts than indicated by existing inventories.
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68. 68
  Wecht, K. J.; Jacob, D. J.; Frankenberg, C.; Jiang, Z.; Blake, D. R. Mapping of North American methane emissions with high spatial resolution by inversion of SCIAMACHY satellite data J. Geophys. Res-Atmos. 2014, 119, 7741– 7756 DOI: 10.1002/2014JD021551
  68
  Mapping of North American methane emissions with high spatial resolution by inversion of SCIAMACHY satellite data
  Wecht, Kevin J.; Jacob, Daniel J.; Frankenberg, Christian; Jiang, Zhe; Blake, Donald R.
  Journal of Geophysical Research: Atmospheres (2014), 119 (12), 7741-7756CODEN: JGRDE3; ISSN:2169-8996. (Wiley-Blackwell)
  We est. methane emissions from North America with high spatial resoln. by inversion of Scanning Imaging Absorption Spectrometer for Atm. Chartog. (SCIAMACHY) satellite observations using the Goddard Earth Observing System Chem. (GEOS-Chem) chem. transport model and its adjoint. The inversion focuses on summer 2004 when data from the Intercontinental Chem. Transport Expt.-North America (INTEX-A) aircraft campaign over the eastern U.S. are available to validate the SCIAMACHY retrievals and evaluate the inversion. From the INTEX-A data we identify and correct a water vapor-dependent bias in the SCIAMACHY data. We conduct an initial inversion of emissions on the horizontal grid of GEOS-Chem (1/2° × 2/3°) to identify correction tendencies relative to the Emission Database for Global Atm. Research (EDGAR) v4.2 emission inventory used as a priori. We then cluster these grid cells with a hierarchical algorithm to ext. the max. information from the SCIAMACHY observations. A 1000 cluster ensemble can be adequately constrained, providing ∼100 km resoln. across North America. Anal. of results indicates that the Hudson Bay Lowland wetlands source is 2.1 Tg a-1, lower than the a priori but consistent with other recent ests. Anthropogenic U.S. emissions are 30.1 ± 1.3 Tg a-1, compared to 25.8 Tg a-1 and 28.3 Tg a-1 in the EDGAR v4.2 and Environmental Protection Agency (EPA) inventories, resp. We find that U.S. livestock emissions are 40% greater than in these two inventories. No such discrepancy is apparent for overall U.S. oil and gas emissions, although this may reflect some compensation between overestimate of emissions from storage/distribution and underestimate from prodn. We find that U.S. livestock emissions are 70% greater than the oil and gas emissions, in contrast to the EDGAR v4.2 and EPA inventories where these two sources are of comparable magnitude.
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69. 69
  Alexe, M.; Bergamaschi, P.; Segers, A.; Detmers, R.; Butz, A.; Hasekamp, O.; Guerlet, S.; Parker, R.; Boesch, H.; Frankenberg, C. Inverse modelling of CH 4 emissions for 2010–2011 using different satellite retrieval products from GOSAT and SCIAMACHY Atmos. Chem. Phys. 2015, 15, 113– 133 DOI: 10.5194/acp-15-113-2015
  There is no corresponding record for this reference.
70. 70
  Turner, A. J.; Jacob, D. J.; Wecht, K. J.; Maasakkers, J. D.; Lundgren, E.; Andrews, A. E.; Biraud, S. C.; Boesch, H.; Bowman, K. W.; Deutscher, N. M.; Dubey, M. K.; Griffith, D. W. T.; Hase, F.; Kuze, A.; Notholt, J.; Ohyama, H.; Parker, R.; Payne, V. H.; Sussmann, R.; Sweeney, C.; Velazco, V. A.; Warneke, T.; Wennberg, P. O.; Wunch, D. Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data Atmos. Chem. Phys. 2015, 15, 7049– 7069 DOI: 10.5194/acp-15-7049-2015
  70
  Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data
  Turner, A. J.; Jacob, D. J.; Wecht, K. J.; Maasakkers, J. D.; Lundgren, E.; Andrews, A. E.; Biraud, S. C.; Boesch, H.; Bowman, K. W.; Deutscher, N. M.; Dubey, M. K.; Griffith, D. W. T.; Hase, F.; Kuze, A.; Notholt, J.; Ohyama, H.; Parker, R.; Payne, V. H.; Sussmann, R.; Sweeney, C.; Velazco, V. A.; Warneke, T.; Wennberg, P. O.; Wunch, D.
  Atmospheric Chemistry and Physics (2015), 15 (12), 7049-7069CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)
  We use 2009-2011 space-borne methane observations from the Greenhouse Gases Observing SATellite (GOSAT) to est. global and North American methane emissions with 4° × 5° and up to 50 km × 50 km spatial resoln., resp. GEOS-Chem and GOSAT data are first evaluated with atm. methane observations from surface and tower networks (NOAA/ESRL, TCCON) and aircraft (NOAA/ESRL, HIPPO), using the GEOS-Chem chem. transport model as a platform to facilitate comparison of GOSAT with in situ data. This identifies a high-latitude bias between the GOSAT data and GEOS-Chem that we correct via quadratic regression. Our global adjoint-based inversion yields a total methane source of 539 Tg a-1 with some important regional corrections to the EDGARv4.2 inventory used as a prior. Results serve as dynamic boundary conditions for an anal. inversion of North American methane emissions using radial basis functions to achieve high resoln. of large sources and provide error characterization. We infer a US anthropogenic methane source of 40.2-42.7 Tg a-1, as compared to 24.9-27.0 Tg a-1 in the EDGAR and EPA bottom-up inventories, and 30.0-44.5 Tg a-1 in recent inverse studies. Our est. is supported by independent surface and aircraft data and by previous inverse studies for California. We find that the emissions are highest in the southern-central US, the Central Valley of California, and Florida wetlands; large isolated point sources such as the US Four Corners also contribute. Using prior information on source locations, we attribute 29-44 % of US anthropogenic methane emissions to livestock, 22-31 % to oil/gas, 20 % to landfills/wastewater, and 11-15 % to coal. Wetlands contribute an addnl. 9.0-10.1 Tg a-1.
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71. 71
  Ganesan, A.; Rigby, M.; Zammit-Mangion, A.; Manning, A.; Prinn, R.; Fraser, P.; Harth, C.; Kim, K.-R.; Krummel, P.; Li, S. Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods Atmos. Chem. Phys. 2014, 14, 3855– 3864 DOI: 10.5194/acp-14-3855-2014
  71
  Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods
  Ganesan, A. L.; Rigby, M.; Zammit-Mangion, A.; Manning, A. J.; Prinn, R. G.; Fraser, P. J.; Harth, C. M.; Kim, K.-R.; Krummel, P. B.; Li, S.; Muhle, J.; O'Doherty, S. J.; Park, S.; Salameh, P. K.; Steele, L. P.; Weiss, R. F.
  Atmospheric Chemistry and Physics (2014), 14 (8), 3855-3864, 10 pp.CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)
  We present a hierarchical Bayesian method for atm. trace gas inversions. This method is used to est. emissions of trace gases as well as "hyper-parameters" that characterize the probability d. functions (PDFs) of the a priori emissions and model-measurement covariances. By exploring the space of "uncertainties in uncertainties", we show that the hierarchical method results in a more complete estn. of emissions and their uncertainties than traditional Bayesian inversions, which rely heavily on expert judgment. We present an anal. that shows the effect of including hyper-parameters, which are themselves informed by the data, and show that this method can serve to reduce the effect of errors in assumptions made about the a priori emissions and model-measurement uncertainties. We then apply this method to the estn. of sulfur hexafluoride (SF6) emissions over 2012 for the regions surrounding four Advanced Global Atm. Gases Expt. (AGAGE) stations. We find that improper accounting of model representation uncertainties, in particular, can lead to the derivation of emissions and assocd. uncertainties that are unrealistic and show that those derived using the hierarchical method are likely to be more representative of the true uncertainties in the system. We demonstrate through this SF6 case study that this method is less sensitive to outliers in the data and to subjective assumptions about a priori emissions and model-measurement uncertainties than traditional methods.
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72. 72
  Lyon, D. R.; Zavala-Araiza, D.; Alvarez, R. A.; Harriss, R.; Palacios, V.; Lan, X.; Talbot, R.; Lavoie, T.; Shepson, P.; Yacovitch, T. I. Constructing a spatially resolved methane emission inventory for the Barnett Shale Region Environ. Sci. Technol. 2015, 49, 8147– 8157 DOI: 10.1021/es506359c
  72
  Constructing a Spatially Resolved Methane Emission Inventory for the Barnett Shale Region
  Lyon, David R.; Zavala-Araiza, Daniel; Alvarez, Ramon A.; Harriss, Robert; Palacios, Virginia; Lan, Xin; Talbot, Robert; Lavoie, Tegan; Shepson, Paul; Yacovitch, Tara I.; Herndon, Scott C.; Marchese, Anthony J.; Zimmerle, Daniel; Robinson, Allen L.; Hamburg, Steven P.
  Environmental Science & Technology (2015), 49 (13), 8147-8157CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
  CH4 emissions from the oil and gas industry (O&G) and other sources in the Barnett Shale region (Texas) were estd. by developing a spatially resolved emission inventory. In total, 18 source categories were estd. using multiple datasets, including empirical measurements at regional O&G sites and a national study of collecting/processing facilities. Spatially referenced activity data were compiled from federal and state databases and combined with O&G facility emission factors calcd. by Monte Carlo simulations which accounted for high emission sites representing the very upper portion, or fat-tail, of obsd. emissions distributions. Total CH4 emissions in the 25-county Barnett Shale region in Oct. 2013 were estd. to be 72,300 (63,400-82,400) kg CH4/h. O&G emissions were estd. to be 46,200 (40,000-54,100) kg CH4/h; 19% of emissions from fat-tail sites represented <2% of sites. Estd. O&G emissions in the Barnett Shale region were higher than alternative inventories based on the USEPA Greenhouse Gas Inventory, EPA Greenhouse Gas Reporting Program, and Emissions Database for Global Atm. Research by factors of 1.5, 2.7, and 4.3, resp. Collecting compressor sites, accounting for 40% of O&G emissions in this inventory, had the largest difference from emission ests. based on EPA data sources. This inventory higher O&G emissions est. was due primarily to its more comprehensive activity factors and inclusion of fat-tail sites.
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73. 73
  Zavala-Araiza, D.; Lyon, D. R.; Alvarez, R. A.; Davis, K. J.; Harriss, R.; Herndon, S. C.; Karion, A.; Kort, E. A.; Lamb, B. K.; Lan, X. Reconciling divergent estimates of oil and gas methane emissions Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 15597– 15602 DOI: 10.1073/pnas.1522126112
  73
  Reconciling divergent estimates of oil and gas methane emissions
  Zavala-Araiza, Daniel; Lyon, David R.; Alvarez, Ramon A.; Davis, Kenneth J.; Harriss, Robert; Herndon, Scott C.; Karion, Anna; Kort, Eric Adam; Lamb, Brian K.; Lan, Xin; Marchese, Anthony J.; Pacala, Stephen W.; Robinson, Allen L.; Shepson, Paul B.; Sweeney, Colm; Talbot, Robert; Townsend-Small, Amy; Yacovitch, Tara I.; Zimmerle, Daniel J.; Hamburg, Steven P.
  Proceedings of the National Academy of Sciences of the United States of America (2015), 112 (51), 15597-15602CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
  Published ests. of methane emissions from atm. data (top-down approaches) exceed those from source-based inventories (bottom-up approaches), leading to conflicting claims about the climate implications of fuel switching from coal or petroleum to natural gas. Based on data from a coordinated campaign in the Barnett Shale oil and gas-producing region of Texas, we find that top-down and bottom-up ests. of both total and fossil methane emissions agree within statistical confidence intervals (relative differences are 10% for fossil methane and 0.1% for total methane). We reduced uncertainty in top-down ests. by using repeated mass balance measurements, as well as ethane as a fingerprint for source attribution. Similarly, our bottom-up est. incorporates a more complete count of facilities than past inventories, which omitted a significant no. of major sources, and more effectively accounts for the influence of large emission sources using a statistical estimator that integrates observations from multiple ground-based measurement datasets. Two percent of oil and gas facilities in the Barnett accounts for half of methane emissions at any given time, and high-emitting facilities appear to be spatiotemporally variable. Measured oil and gas methane emissions are 90% larger than ests. based on the US Environmental Protection Agency's Greenhouse Gas Inventory and correspond to 1.5% of natural gas prodn. This rate of methane loss increases the 20-y climate impacts of natural gas consumed in the region by roughly 50%.
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74. 74
  Zhao, C.; Andrews, A. E.; Bianco, L.; Eluszkiewicz, J.; Hirsch, A.; MacDonald, C.; Nehrkorn, T.; Fischer, M. L. Atmospheric inverse estimates of methane emissions from Central California J. Geophys. Res. 2009, 114, D16302 DOI: 10.1029/2008JD011671
  There is no corresponding record for this reference.
75. 75
  Jeong, S.; Millstein, D.; Fischer, M. L. Spatially Explicit Methane Emissions from Petroleum Production and the Natural Gas System in California Environ. Sci. Technol. 2014, 48, 5982– 5990 DOI: 10.1021/es4046692
  75
  Spatially Explicit Methane Emissions from Petroleum Production and the Natural Gas System in California
  Jeong, Seongeun; Millstein, Dev; Fischer, Marc L.
  Environmental Science & Technology (2014), 48 (10), 5982-5990CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
  An updated, spatially-resolved CH4 emissions inventory based on USEPA emission factors and publically available activity data for 2010 California petroleum and natural gas prodn., processing, transmission, and distribution, is presented. Compared to official California bottom-up inventories, initial ests. are 3-7 times higher for petroleum and natural gas prodn. sectors, but similar for natural gas transmission and distribution sectors. Evidence from published top-down atm. measurement campaigns in southern California supported initial emission ests. from prodn. and processing, but indicated emission ests. from transmission and distribution are low by a factor of ∼2. To provide emission maps with more accurate total emissions, spatially-resolved inventory was scaled by sector-specific results from a southern California aircraft measurement campaign to all of California. Assuming uncertainties were detd. by uncertainties estd. in the top-down study, estd. state total CH4 emissions are 541 ± 144 Gg/yr (vs. 210.7 Gg/yr in the State official inventory), where the majority of reported uncertainty was derived from transmission and distribution. Uncertainties relative to the mean for a given region were likely larger than that for the State total, emphasizing the need for addnl. measurements in under-sampled regions.
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76. 76
  Kitanidis, P. Introduction to Geostatistics: Applications in Hydrogeology; Cambridge University Press, 1997.
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77. 77
  Brandt, A.; Heath, G.; Kort, E.; O’sullivan, F.; Pétron, G.; Jordaan, S.; Tans, P.; Wilcox, J.; Gopstein, A.; Arent, D. Methane leaks from North American natural gas systems Science 2014, 343, 733– 735 DOI: 10.1126/science.1247045
  77
  Methane leaks from North American natural gas systems
  Brandt, A. R.; Heath, G. A.; Kort, E. A.; O'Sullivan, F.; Petron, G.; Jodraan, S. M.; Tans, P.; Wilcox, J.; Gopstein, A. M.; Arent, D.; Wofsy, S.; Brown, N. J.; Bradley, R.; Stucky, G. D.; Eardley, D.; Harriss, R.
  Science (Washington, DC, United States) (2014), 343 (6172), 733-735CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
  There is no expanded citation for this reference.
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78. 78
  California Air Resources Board. FirstUpdate to the ClimateChange Scoping Plan, (2014. arb.ca.gov/cc/scopingplan/2013_update/first_update_climate_change_scoping_plan.pdf.
  There is no corresponding record for this reference.

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Gridded National Inventory of U.S. Methane Emissions
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