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Since at least the 1980s, many farmers in northwest India have switched to mechanized combine harvesting to boost efficiency. This harvesting technique leaves abundant crop residue on the fields, which farmers typically burn to prepare their fields for subsequent planting. A key question is to what extent the large quantity of smoke emitted by these fires contributes to the already severe pollution in Delhi and across other parts of the heavily populated Indo-Gangetic Plain located downwind of the fires. Using a combination of observed and modeled variables, including surface measurements of PM2.5, we quantify the magnitude of the influence of agricultural fire emissions on surface air pollution in Delhi. With surface measurements, we first derive the signal of regional PM2.5 enhancements (i.e. the pollution above an anthropogenic baseline) during each post-monsoon burning season for 2012-2016. We next use the Stochastic Time-Inverted Lagrangian Transport model (STILT) to simulate surface PM2.5 using five fire emission inventories. We reproduce up to 25% of the weekly variability in total observed PM2.5 using STILT. Depending on year and emission inventory, our method attributes 7.0%-78% of the maximum observed PM2.5 enhancements in Delhi to fires. The large range in these attribution estimates points to the uncertainties in fire emission parameterizations, especially in regions where thick smoke may interfere with hotspots of fire radiative power. Although our model can generally reproduce the largest PM2.5 enhancements in Delhi air quality for 1-3 consecutive days each fire season, it fails to capture many smaller daily enhancements, which we attribute to the challenge of detecting small fires in the satellite retrieval. By quantifying the influence of upwind agricultural fire emissions on Delhi air pollution, our work underscores the potential health benefits of changes in farming practices to reduce fires.

Solid waste management represents one of the largest anthropogenic methane emission sources. However, precise quantification of landfill and composting emissions remains difficult due to variety of site-specific factors that contribute to landfill gas generation and effective capture. Remote sensing is an avenue to quantify process-level emissions from waste management facilities. The California Methane Survey flew the Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) over 270 landfills and 166 organic waste facilities repeatedly during 2016-2018 to quantify their contribution to the statewide methane budget. We use representative methane retrievals from this campaign to present three specific findings where remote sensing enabled better landfill and composting methane monitoring: (1) Quantification of strong point source emissions from the active face landfills that are difficult to capture by in situ monitoring or landfill models, (2) emissions that result from changes in landfill infrastructure (design, construction, and operations), and (3) unexpected large emissions from two organic waste management methods (composting and digesting) that were originally intended to help mitigate solid waste emissions. Our results show that remotely-sensed emission estimates reveal processes that are difficult to capture in biogas generation models. Furthermore, we find that airborne remote sensing provides an effective avenue to study the temporally changing dynamics of landfills. This capability will be further improved with future spaceborne imaging spectrometers set to launch in the 2020s.

The rapid growth of liquefied natural gas (LNG) exports underscores the importance of CO2 monitoring for LNG export terminals. We present a method for measuring LNG terminal CO2 emissions using remote sensing imaging spectroscopy. The method is first validated using 47 power plant emission events with in situ measured data, then applied to 22 emission events in Sabine Pass and Cameron. The power plant data set shows a robust correlation between our estimates and in situ data, with R2 0.9146 and the average error −2%. At Sabine Pass, eight point sources are identified with emission rates from 219.69 ± 54.95 to 1,083.22 ± 308.06 t/hr. At Cameron, three point sources are identified with emission rates from 91.64 ± 25.81 to 265.61 ± 67.80 t/hr. The liquefaction carbon intensity estimates also align with past study ranges. This illustrates that remote sensing can validate environmental reporting and CO2 inventories for industrial facilities. Plain Language Summary The natural gas (NG) system is an important source of carbon dioxide (CO2) emissions. Rising U.S. NG production and international energy demand led to a rapid growth of liquefied natural gas (LNG) exports. This makes it increasingly important to assess the CO2 emissions along the LNG supply chain, especially during gas liquefaction at LNG export terminals. However, existing inventories only provide annual/monthly data for some major LNG terminals from operators, which lack measurement‐based in situ validation. Here we introduce a top‐down CO2 measuring method using remote sensing imaging spectroscopy, which can provide an independent third‐party data source above the detection threshold with uniform measuring technology across all infrastructure. Additionally, the independent measurements from this method would help evaluate the magnitude and variation of existing emission inventories. When combined with remote sensing methane detection, it can further monitor the carbon emissions more efficiently along the NG supply chain. This could be achieved by retrieving atmospheric CO2 and CH4 simultaneously from the same remote sensing campaign. This study also shows the mapping and quantification capability of imaging spectroscopy on the plumes with emission rate of 100–3,000 t CO2/hr, implying a broader application potential in CO2 top‐down detection. Key Points Twenty‐Two CO2 emission events from Sabine Pass and Cameron liquefied natural gas (LNG) terminals are found and quantified by airborne imaging spectrometers Imaging spectrometers are capable of measuring power plant CO2 emissions with relatively high agreement with in situ measured data Compared to power plant, LNG terminal CO2 plumes are smaller in spatial extent, with lower emission rates and more background noise

Offshore oil and natural gas platforms are responsible for about 30% of global oil and natural gas production. Despite the large share of global production there are few studies that have directly measured atmospheric methane emanating from these platforms. This study maps CH 4 emissions from shallow water offshore oil and gas platforms with an imaging spectrometer by employing a method to capture the sun glint reflection from the water directly surrounding the target areas. We show how remote sensing with imaging spectrometers and glint targeting can be used to efficiently observe offshore infrastructure, quantify methane emissions, and attribute those emissions to specific infrastructure types. In 2021, the Global Airborne Observatory platform, which is an aircraft equipped with a visible shortwave infrared imaging spectrometer, surveyed over 150 offshore platforms and surrounding infrastructure in US federal and state waters in the Gulf of Mexico representing ∼8% of active shallow water infrastructure there. We find that CH 4 emissions from the measured platforms exhibit highly skewed super emitter behavior. We find that these emissions mostly come from tanks and vent booms or stacks. We also find that the persistence and the loss rate from shallow water offshore infrastructure tends to be much higher than for typical onshore production.

We use satellite observations of atmospheric methane from the TROPOMI instrument to estimate total annual methane emissions for 2019–2023 from four large Southeast US landfills with gas collection and control systems. The emissions are on average 6× higher than the values reported by the landfills to the US Greenhouse Gas Reporting Program (GHGRP) which are used by the US Environmental Protection Agency for its national Greenhouse Gas Inventory (GHGI). We find increasing emissions over the 2019–2023 period whereas the GHGRP reports a decrease. The GHGRP requires gas-collecting landfills to estimate their annual emissions either with a recovery-first model (estimating emissions as a function of methane recovered) or a generation-first model (estimating emissions from a first-order decay applied to waste-in-place). All four landfills choose to use the recovery-first model, which yields emissions that are one-quarter of those from the generation-first model and decreasing over 2019–2023, in contrast with the TROPOMI observations. Our TROPOMI estimates for two of the landfills agree with the generation-first model, with increasing emissions over 2019–2023 due to increasing waste-in-place or decreasing methane recovery, and are still higher than the generation-first model for the other two landfills. Further examination of the GHGRP emissions from all reporting landfills in the US shows that the 19% decrease in landfill emissions reported by the GHGI over 2005–2022 reflects an increasing preference for the recovery-first model by the reporting landfills, rather than an actual emission decrease. The generation-first model would imply an increase in landfill emissions over 2013–2022, and this is more consistent with atmospheric observations.

Methane (CH 4 ) emissions from the oil and natural gas (O&G) supply chain have been demonstrated to be one of the largest anthropogenic greenhouse gas emission sources ripe for mitigation to limit near-term climate warming. In recent years, exploration and production (E&P) operators have made public commitments to reducing their greenhouse gas emission intensity, yet little empirical information has been made available in the public domain to allow an accurate comparison of their emissions performance. In this study, we utilize a series of aircraft surveys of large CH 4 point source emissions (∼10 1 –10 4 kg CH 4 hr −1 ) related to O&G production in the Permian Basin to enable comparison of company-level production-sector emission intensities. We calculate gas and total energy production normalized emission intensities for several of the largest E&P operators in the Permian Basin accounting for ∼85% of production within the flight region. We find differences of more than an order of magnitude in emission intensity across operators, with nearly half demonstrating a ⩾50% improvement in performance from 2019 to 2021. With the availability of such publicly attributed emissions data anticipated to increase in the future, we provide methodological insights and cautions to developing operator metrics from future empirical datasets.

Methane super-emitter events (>100 kg methane hr−1) are prevalent across the oil and gas supply chain and are being targeted for methane mitigation policies due to their climate impacts; however, few studies have evaluated the air quality impacts and direct safety and public health risks. Here, we evaluate seven upstream oil and gas methane super-emitter events to examine the safety (explosivity) risks from methane and the short-term, noncancer health risks from benzene and other key co-emitted non-methane volatile organic compounds (NMVOCs). We used airborne instrument and satellite-measured methane emissions rates to estimate hourly air concentrations of methane using US EPA’s American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD), a regulatory-grade dispersion model, and applied estimated speciated NMVOC-methane molar ratios to calculate hourly air concentrations of NMVOCs. We assessed when and where hourly modeled methane and NMVOC air concentrations exceeded national safety (0.5% methane) and state-based health (8 ppb and 53 ppb benzene) benchmarks. Large methane super-emitters (>2900 kg hr−1) had safety benchmark exceedances as far as 270 m from the source, indicating that safety risks are greatest for facility workers and nearby communities. Health benchmark exceedances were the greatest and most frequent close to the source (<300 m), but, in contrast to the safety risks, reached beyond one kilometer (1.1–19 km) for modeled methane super-emitters (210–15 800 kg hr−1), posing health risks to residents and sensitive populations. We also found that smaller methane super-emitters may pose outsized health risks: our second lowest methane emission case (539 kg hr−1) yielded the highest benzene air concentration (28 000 ppb), farthest 8 ppb benzene exceedance distance (19 km), and highest frequency of health benchmark exceedances between 1–5 km (2.6%). Our study demonstrates that policies and early detection efforts that control methane super-emitters should prioritize factors beyond methane emissions rate magnitude, such as gas composition, to provide the strongest co-benefits for public health and safety.

The Global Fuel Exploitation Inventory (GFEI) is a global 0.1° x 0.1° resolution gridded inventory of methane emissions from oil, gas, and coal exploitation. Here, we present GFEI v3 with updated national emissions to 2020 using reports submitted to the United Nations Framework Convention on Climate Change (UNFCCC), leading to new global emissions of 23, 20, and 31 Tg a.sup.-1 for oil, gas, and coal, respectively. We also use new geospatial information from the Oil and Gas Infrastructure Mapping database (OGIM v1) for spatial distribution of global oil-gas methane emissions. We use coal mine locations from the Global Energy Monitor's Global Coal Mine Tracker (GCMT), combined with our own estimates for mine-level methane emissions, to distribute national emissions between coal mine locations. Our mine-level methane emission estimates use country specific emission factors for top producing countries supplemented with modeled emission factors based on coal mine depth and grade. We see the greatest change in the spatial distribution of emissions in GFEI v3 compared to v2 in China due to the use of GCMT for coal mine locations. Large point source plumes (super-emitters) observed by the NASA EMIT instrument are co-located with infrastructure identified in GFEI v3, but the magnitude of the measured emissions is poorly correlated with the gridded emissions in GFEI. This may reflect missing or misrepresented sources in GFEI v3 but also the sporadic nature of the super-emitter measurements used here. By aligning GFEI v3 with national UNFCCC reports and using state-of-the-science geospatial information, the inventory can be confronted with satellite observations of atmospheric methane through inverse modeling to evaluate and improve the UNFCCC reports. We plan to continue updating GFEI to align with reported national emissions and new geospatial information, including assessment of GFEI spatial accuracy through comparison to super-emitter detections. GFEI v3 emission grids by sector and subsector are available at

Wetland methane (CH4) emissions comprise about one-third of the global CH4 source. The latitudinal distribution and climate sensitivity of wetland CH4 fluxes are the key determinants of the global CH4-climate feedback. However, large differences exist between bottom-up estimates, informed by ground-based flux measurements, and top-down estimates derived from spaceborne total column CH4. Despite the extensive coverage of satellite CH4 concentration observations, challenges remain with using top-down estimates to test bottom-up models, mainly because of the uncertainties in the satellite retrievals, the model representation errors, the variable prior emissions, and the confounding role of the posterior error covariance structures. Here, we use satellite-based top-down CH4 flux estimates (2010–2012) to test and refine 42 bottom-up estimates of wetland emissions that use a range of hypothesized wetland extents and process controls. Our comparison between bottom-up models and satellite-based fluxes innovatively accounts for cross-correlations and spatial uncertainties typically found in top-down inverse estimates, such that only the information from satellite observations and the atmospheric transport model is kept as a constraint. We present a satellite-constrained wetland CH4 ensemble product derived from assembling the highest-performance bottom-up models, which estimates global wetland CH4 emissions of 148 (117–189, 5th–95th percentile) Tg CH4 yr−1. We find that tropical wetland emissions contribute 72% (63%–85%) to the global wetland total. We also find that a lower-than-expected temperature sensitivity agrees better with atmospheric CH4 measurements. Overall, our approach demonstrates the potential for using satellites to quantitatively refine bottom-up wetland CH4 emission estimates, their latitudinal distributions, and their sensitivity to climate.

Anthropogenic carbon dioxide (CO2) emissions dominate uncertainties in the global carbon budget. Global inventories, such as the National Greenhouse Gas Inventories, have latencies of 12–24 months and may not keep pace with rapidly changing infrastructure, particularly in the developing world. Our work reveals that airborne and satellite imaging spectrometers provide 3–30 m spatial resolution and accurate quantification of CO2 emissions at the facility scale. Examples from 17 coal and gas fired power plants across the United States demonstrate robust correlation and 21% agreement on average between our remotely sensed estimates and simultaneous in situ measured emissions. We highlight four examples of coal‐fired power plants in India, Poland, and South Korea, where we quantify significant carbon dioxide emissions from power plants where limited public emissions data exist. Leveraging previous work on methane (CH4) plume detection, we present a strategy to exploit joint CO2 and CH4 plume imaging to quantify carbon emissions across widely distributed industrial infrastructure, including facilities that co‐emit CO2 and CH4. We show an example of a coal operation, where we attribute 25% of greenhouse gas emissions to coal extraction (CH4) and the remaining 75% to energy generation (CO2). Satellite spectrometers could track high emitting coal‐fired power plants that collectively contribute to 60% or more of global coal CO2 emissions. Multiple revisits and coordinated targeting of these high emitting facilities by multiple spaceborne instruments will be key to reducing uncertainties in global anthropogenic CO2 emissions and supporting emissions mitigation strategies. Plain Language Summary Carbon dioxide (CO2) emissions from power plants represents one of the largest sources of greenhouse gases from humans. Keeping track of CO2 emissions from all global power plants is difficult, as good emission data can depend on a country's emission reporting protocols. Remote sensing with imaging spectrometer instruments offers a new capability to do top‐down monitoring. These instruments provide high spatial resolution CO2 plume maps which can be used to quantify emissions. In this study, we show examples where we quantified and validated CO2 emissions at 21 global gas and coal fired power plants using airborne and satellite imaging spectrometers. With repeated targeting by satellites, we estimate that we could constrain 60% of all global power plant emissions. This capability is key to reducing uncertainties in global anthropogenic CO2 emission budgets and supporting emissions mitigation strategies. Key Points CO2 emissions are quantified and validated at 21 power plants using airborne and satellite imaging spectrometers With sufficient targeting, satellites could constrain at least 60% of global coal power plant CO2 emissions Imaging spectrometers are capable of joint CO2 and CH4 monitoring, enabling quantification of supply chain emissions