Search

Digestion of waste feedstocks by larvae of the black soldier fly Hermetia illucens (Diptera: Stratiomyidae) (BSF) results in proteins for animal feed and organic fertilizer with a reduced environmental footprint, but it can still have negative environmental effects through greenhouse gas (GHG) and ammonia (NH3) emissions. Both biomass conversion by BSF larvae and associated GHG and NH3 emissions can depend on substrate properties that may be optimized through microbial inoculation pre-treatments, such as bokashi fermentation. Here, we quantified the effects of bokashi fermentation of brewery's spent grains on BSF rearing metrics and associated GHG and NH3 emissions at benchtop scale. We found that bokashi fermentation increased larval biomass by 40% and shortened development time by over two days on average, compared with unfermented spent grains. In line with increased larval growth, CO2 emissions in BSF larvae treatments were 31.0 and 79.0% higher in the bokashi fermented spent grains and Gainesville substrates, respectively, compared to the unfermented spent grains. Adding BSF larvae to the spent grains increased cumulative N2O emissions up to 64.0 mg N2O kg substratedry-1 but there were essentially no N2O emissions when larvae were added to fermented spent grains. Bokashi fermentation also reduced NH3 fluxes from the volatilization of substrate nitrogen in the BSF larvae treatment by 83.7-85.8% during days 7 and 9, possibly by increasing N assimilation by larvae or by reducing the transformation of substrate NH4+ to NH3. Therefore, bokashi fermentation may be applied to improve performance of BSF larvae on a common industrial waste stream and reduce associated emissions.

California's dairy sector accounts for ∼50% of anthropogenic CH4 emissions in the state's greenhouse gas (GHG) emission inventory. Although California dairy facilities' location and herd size vary over time, atmospheric inverse modeling studies rely on decade-old facility-scale geospatial information. For the first time, we apply artificial intelligence (AI) to aerial imagery to estimate dairy CH4 emissions from California's San Joaquin Valley (SJV), a region with ∼90% of the state's dairy population. Using an AI method, we process 316,882 images to estimate the facility-scale herd size across the SJV. The AI approach predicts herd size that strongly (>95%) correlates with that made by human visual inspection, providing a low-cost alternative to the labor-intensive inventory development process. We estimate SJV's dairy enteric and manure CH4 emissions for 2018 to be 496-763 Gg/yr (mean = 624; 95% confidence) using the predicted herd size. We also apply our AI approach to estimate CH4 emission reduction from anaerobic digester deployment. We identify 162 large (90th percentile) farms and estimate a CH4 reduction potential of 83 Gg CH4/yr for these large facilities from anaerobic digester adoption. The results indicate that our AI approach can be applied to characterize the manure system (e.g., use of an anaerobic lagoon) and estimate GHG emissions for other sectors.

In this study, we present seasonal atmospheric measurements of δ13CCH4 from dairy farms in the San Joaquin Valley of California. We used δ13CCH4 to characterize emissions from enteric fermentation by measuring downwind of cattle housing (e.g., freestall barns, corrals) and from manure management areas (e.g., anaerobic manure lagoons) with a mobile platform equipped with cavity ring-down spectrometers. Across seasons, the δ13CCH4 from enteric fermentation source areas ranged from −69.7 ± 0.6 per mil (‰) to −51.6 ± 0.1‰ while the δ13CCH4 from manure lagoons ranged from −49.5 ± 0.1‰ to −40.5 ± 0.2‰. Measurements of δ13CCH4 of enteric CH4 suggest a greater than 10‰ difference between cattle production groups in accordance with diet. Isotopic signatures of CH4 were used to characterize enteric and manure CH4 from downwind plume sampling of dairies. Our findings show that δ13CCH4 measurements could improve the attribution of CH4 emissions from dairy sources at scales ranging from individual facilities to regions and help constrain the relative contributions from these different sources of emissions to the CH4 budget.

The dairy industry in the San Joaquin Valley (SJV) is one of California’s largest methane (CH4) sources. Reducing dairy emissions is a priority for the state’s climate change plans. Observations of current dairy CH4 emissions are key to monitoring actions taken toward this goal. To help support this, we present new ground-based measurements of atmospheric column-averaged CH4 mixing ratio (XCH4) gradients across a group of 600 dairies in the central SJV using EM27/SUN solar spectrometers. We used measurements from the 2019 summer and 2020 winter seasons for a top-down emission inversion based on the WRF-STILT model. Our top-down estimates of the region’s dairy emissions range from 90% to 183% of the current CALGEM inventory’s emissions of 277 Gg/yr. In contrast to the strong temperature dependence found by earlier dairy CH4 emission studies, we also find that our top-down emissions during the winter measurement days are comparable to the summer measurement days, possibly due to seasonal changes in dairy management practices and meteorological conditions. Furthermore, we find significant interday variability in our measurements and find that our emission estimates overlap with earlier top-down studies and bottom-up inventories in this region. Our study demonstrates how analysis of ground-based remotely sensed CH4 gradient observations can help improve our understanding of CH4 sources at scales relevant to mitigation policy. It also reflects the need for long-term monitoring of CH4 emissions in the region and at individual facilities to better understand their emissions.

In this study, we test the performance of a compact gas chromatograph with photoionization detector (GC-PID) and optimize the configuration to detect ambient (sub-ppb) levels of benzene, toluene, ethylbenzene, and xylene isomers (BTEX). The GC-PID system was designed to serve as a relatively inexpensive (~10 k USD) and field-deployable air toxic screening tool alternative to conventional benchtop GCs. The instrument uses ambient air as a carrier gas and consists of a Tenax-GR sorbent-based preconcentrator, a gas sample valve, two capillary columns, and a photoionization detector (PID) with a small footprint and low power requirement. The performance of the GC-PID has been evaluated in terms of system linearity and sensitivity in field conditions. The BTEX-GC system demonstrated the capacity to detect BTEX at levels as high as 500 ppb with a linear calibration range of 0-100 ppb. A detection limit lower than 1 ppb was found for all BTEX compounds with a sampling volume of 1 L. No significant drift in the instrument was observed. A time-varying calibration technique was established that requires minimal equipment for field operations and optimizes the sampling procedure for field measurements. With an analysis time of less than 15 min, the compact GC-PID is ideal for field deployment of background and polluted atmospheres for near-real time measurements of BTEX. The results highlight the application of the compact and easily deployable GC-PID for community monitoring and screening of air toxics.

Accurate and timely detection, quantification, and attribution of methane emissions from Underground Gas Storage (UGS) facilities is essential for improving confidence in greenhouse gas inventories, enabling emission mitigation by facility operators, and supporting efforts to assess facility integrity and safety. We conducted multiple airborne surveys of the 12 active UGS facilities in California between January 2016 and November 2017 using advanced remote sensing and in situ observations of near-surface atmospheric methane (CH4). These measurements where combined with wind data to derive spatially and temporally resolved methane emission estimates for California UGS facilities and key components with spatial resolutions as small as 1-3 m and revisit intervals ranging from minutes to months. The study spanned normal operations, malfunctions, and maintenance activity from multiple facilities including the active phase of the Aliso Canyon blowout incident in 2016 and subsequent return to injection operations in summer 2017. We estimate that the net annual methane emissions from the UGS sector in California averaged between 11.0 3.8 GgCH4 yr-1 (remote sensing) and 12.3 3.8 GgCH4 yr-1 (in situ). Net annual methane emissions for the 7 facilities that reported emissions in 2016 were estimated between 9.0 3.2 GgCH4 yr-1 (remote sensing) and 9.5 3.2 GgCH4 yr-1 (in situ), in both cases around 5 times higher than reported. The majority of methane emissions from UGS facilities in this study are likely dominated by anomalous activity: higher than expected compressor loss and leaking bypass isolation valves. Significant variability was observed at different time-scales: daily compressor duty-cycles and infrequent but large emissions from compressor station blow-downs. This observed variability made comparison of remote sensing and in situ observations challenging given measurements were derived largely at different times, however, improved agreement occurred when comparing simultaneous measurements. Temporal variability in emissions remains one of the most challenging aspects of UGS emissions quantification, underscoring the need for more systematic and persistent methane monitoring.

We report continuous surface observations of carbon dioxide (CO2) and methane (CH4) from the Los Angeles (LA) Megacity Carbon Project during 2015. We devised a calibration strategy, methods for selection of background air masses, calculation of urban enhancements, and a detailed algorithm for estimating uncertainties in urban-scale CO2 and CH4 measurements. These methods are essential for understanding carbon fluxes from the LA megacity and other complex urban environments globally. We estimate background mole fractions entering LA using observations from four "extra-urban" sites including two "marine" sites located south of LA in La Jolla (LJO) and offshore on San Clemente Island (SCI), one "continental" site located in Victorville (VIC), in the high desert northeast of LA, and one "continental/mid-troposphere" site located on Mount Wilson (MWO) in the San Gabriel Mountains. We find that a local marine background can be established to within ~1 ppm CO2 and ~10 ppb CH4 using these local measurement sites. Overall, atmospheric carbon dioxide and methane levels are highly variable across Los Angeles. "Urban" and "suburban" sites show moderate to large CO2 and CH4 enhancements relative to a marine background estimate. The USC (University of Southern California) site near downtown LA exhibits median hourly enhancements of ~20 ppm CO2 and ~150 ppb CH4 during 2015 as well as ~15 ppm CO2 and ~80 ppb CH4 during mid-afternoon hours (12:00-16:00 LT, local time), which is the typical period of focus for flux inversions. The estimated measurement uncertainty is typically better than 0.1 ppm CO2 and 1 ppb CH4 based on the repeated standard gas measurements from the LA sites during the last 2 years, similar to Andrews et al. (2014). The largest component of the measurement uncertainty is due to the single-point calibration method; however, the uncertainty in the background mole fraction is much larger than the measurement uncertainty. The background uncertainty for the marine background estimate is ~10 and ~15 % of the median mid-afternoon enhancement near downtown LA for CO2 and CH4, respectively. Overall, analytical and background uncertainties are small relative to the local CO2 and CH4 enhancements; however, our results suggest that reducing the uncertainty to less than 5 % of the median mid-afternoon enhancement will require detailed assessment of the impact of meteorology on background conditions.

This paper evaluates the performance of a multirotor uncrewed aircraft and AirCore system (UAAS) for measuring vertical profiles of wind velocity (speed and direction) and the mole fractions of methane (CH 4) and carbon dioxide (CO 2) , and it presents a use case that combines UAAS measurements and dispersion modeling to quantify CH 4 emissions from a dairy farm. To evaluate the atmospheric sensing performance of the UAAS, four field deployments were performed at three locations in the San Joaquin Valley of California where CH 4 hotspots were observed downwind of dairy farms. A comparison of the observations collected on board the UAAS and an 11 m meteorological tower show that the UAAS can measure wind velocity trends with a root mean squared error varying between 0.4 and 1.1 m s -1 when the wind magnitude is less than 3.5 m s -1. Findings from UAAS flight deployments and a calibration experiment also show that the UAAS can reliably resolve temporal variations in the mole fractions of CH 4 and CO 2 occurring over periods of 10 s or longer. Results from the UAAS and dispersion modeling use case further demonstrate that UAASs have great potential as low-cost tools for detecting and quantifying CH 4 emissions in near real time. [ABSTRACT FROM AUTHOR]

Urban areas are increasingly recognized as a globally important source of methane to the atmosphere; however, the location of methane sources and relative contributions of source sectors are not well known. Recent atmospheric measurements in Los Angeles, California, USA, show that more than a third of the city’s methane emissions are unaccounted for in inventories and suggest that fugitive fossil emissions are the unknown source. We made on-road measurements to quantify fine-scale structure of methane and a suite of complementary trace gases across the Los Angeles Basin in June 2013. Enhanced methane levels were observed across the basin but were unevenly distributed in space. We identified 213 methane hot spots from unknown emission sources. We made direct measurements of ethane to methane (C2H6/CH4) ratios of known methane emission sources in the region, including cattle, geologic seeps, landfills, and compressed natural gas fueling stations, and used these ratios to determine the contribution of biogenic and fossil methane sources to unknown hot spots and to local urban background air. We found that 75% of hot spots were of fossil origin, 20% were biogenic, and 5% of indeterminate source. In regionally integrated air, we observed a wider range of C2H6/CH4 values than observed previously. Fossil fuel sources accounted for 58–65% of methane emissions, with the range depending on the assumed C2H6/CH4 ratio of source end-members and model structure. These surveys demonstrated the prevalence of fugitive methane emissions across the Los Angeles urban landscape and suggested that uninventoried methane sources were widely distributed and primarily of fossil origin.

We introduce a retrieval algorithm to estimate lower tropospheric methane (CH4) concentrations from the surface to 1 km with uncertainty estimates using Hyperspectral Thermal Emission Spectrometer (HyTES) airborne radiance measurements. After resampling, retrievals have a spatial resolution of 6 × 6 m2. The total error from a single retrieval is approximately 20 %, with the uncertainties determined primarily by noise and spectral interferences from air temperature, surface emissivity, and atmospheric water vapor. We demonstrate retrievals for a HyTES flight line over storage tanks near Kern River Oil Field (KROF), Kern County, California, and find an extended plume structure in the set of observations with elevated methane concentrations (3.0 ± 0.6 to 6.0 ± 1.2 ppm), well above mean concentrations (1.8 ± 0.4 ppm) observed for this scene. With typically a 20 % estimated uncertainty, plume enhancements with more than 1 ppm are distinguishable from the background values with its uncertainty. HyTES retrievals are consistent with simultaneous airborne and ground-based in situ CH4 mole fraction measurements within the reported accuracy of approximately 0.2 ppm (or ∼8 %), due to retrieval interferences related to air temperature, emissivity, and H2O.

Abstract. Currently large uncertainties exist associated with the attribution and quantification of fugitive emissions of criteria pollutants and greenhouse gases such as methane across large regions and key economic sectors. In this study, data from the airborne Hyperspectral Thermal Emission Spectrometer (HyTES) have been used to develop robust and reliable techniques for the detection and wide-area mapping of emission plumes of methane and other atmospheric trace gas species over challenging and diverse environmental conditions with high spatial resolution that permits direct attribution to sources. HyTES is a pushbroom imaging spectrometer with high spectral resolution (256 bands from 7.5 to 12 µm), wide swath (1–2 km), and high spatial resolution (∼ 2 m at 1 km altitude) that incorporates new thermal infrared (TIR) remote sensing technologies. In this study we introduce a hybrid clutter matched filter (CMF) and plume dilation algorithm applied to HyTES observations to efficiently detect and characterize the spatial structures of individual plumes of CH4, H2S, NH3, NO2, and SO2 emitters. The sensitivity and field of regard of HyTES allows rapid and frequent airborne surveys of large areas including facilities not readily accessible from the surface. The HyTES CMF algorithm produces plume intensity images of methane and other gases from strong emission sources. The combination of high spatial resolution and multi-species imaging capability provides source attribution in complex environments. The CMF-based detection of strong emission sources over large areas is a fast and powerful tool needed to focus on more computationally intensive retrieval algorithms to quantify emissions with error estimates, and is useful for expediting mitigation efforts and addressing critical science questions.

Methane is a powerful greenhouse gas and is targeted for emissions mitigation by the US state of California and other jurisdictions worldwide.sup.1,2. Unique opportunities for mitigation are presented by point-source emitters--surface features or infrastructure components that are typically less than 10 metres in diameter and emit plumes of highly concentrated methane.sup.3. However, data on point-source emissions are sparse and typically lack sufficient spatial and temporal resolution to guide their mitigation and to accurately assess their magnitude.sup.4. Here we survey more than 272,000 infrastructure elements in California using an airborne imaging spectrometer that can rapidly map methane plumes.sup.5-7. We conduct five campaigns over several months from 2016 to 2018, spanning the oil and gas, manure-management and waste-management sectors, resulting in the detection, geolocation and quantification of emissions from 564 strong methane point sources. Our remote sensing approach enables the rapid and repeated assessment of large areas at high spatial resolution for a poorly characterized population of methane emitters that often appear intermittently and stochastically. We estimate net methane point-source emissions in California to be 0.618 teragrams per year (95 per cent confidence interval 0.523-0.725), equivalent to 34-46 per cent of the state's methane inventory.sup.8 for 2016. Methane 'super-emitter' activity occurs in every sector surveyed, with 10 per cent of point sources contributing roughly 60 per cent of point-source emissions--consistent with a study of the US Four Corners region that had a different sectoral mix.sup.9. The largest methane emitters in California are a subset of landfills, which exhibit persistent anomalous activity. Methane point-source emissions in California are dominated by landfills (41 per cent), followed by dairies (26 per cent) and the oil and gas sector (26 per cent). Our data have enabled the identification of the 0.2 per cent of California's infrastructure that is responsible for these emissions. Sharing these data with collaborating infrastructure operators has led to the mitigation of anomalous methane-emission activity.sup.10. Emission of methane from 'point sources'--small surface features or infrastructure components--is monitored with an airborne spectrometer, identifying possible targets for mitigation efforts., Author(s): Riley M. Duren [sup.1] [sup.2] , Andrew K. Thorpe [sup.1] , Kelsey T. Foster [sup.1] , Talha Rafiq [sup.3] , Francesca M. Hopkins [sup.3] , Vineet Yadav [sup.1] , [...]

© 2015 Springer Science+Business Media New York Temperate forests play an important role in the global carbon cycle, and are thought to currently be a sink for atmospheric CO2. However, we lack understanding of the drivers of forest carbon accumulation and loss, hampering our ability to predict carbon cycle responses to global change. In this study, we used CO2 flux and radiocarbon (14C) measurements to investigate the role of seasonal drivers on soil respiration. Radiocarbon measurements of CO2 evolved during incubation of fine roots and root-free soils at the beginning and end of the growing season (April and August) showed that these two soil respiration sources (fine roots vis-à-vis soils) have different mean residence times that stayed constant between seasons. Radiocarbon measurements show that root respiration was made up of carbon fixed 3–5 years prior to sampling, and that heterotrophic respiration was made up of carbon fixed 7–10 years prior. The difference in radiocarbon signature between the two sources allowed us to partition autotrophic and heterotrophic respiration sources for soil respiration measurements in the field. We observed a small but significant increase in ∆14C of soil respiration between April and August, suggesting an increase in heterotrophic respiration sources over the growing season. Using a two end-member mixing model, we estimate that 55 ± 22% of soil respiration originated from autotrophic (root) sources in April, but their contribution dropped to 38 ± 21% in August. These findings suggest that the contribution of root respiration increases at a time of high productivity and/or as a result of relatively low microbial respiration in the early spring in this old-growth coniferous forest.

This paper evaluates the performance of a multirotor uncrewed aircraft and AirCore system (UAAS) for measuring vertical profiles of wind velocity (speed and direction) and the mole fractions of methane (CH4) and carbon dioxide (CO2), and presents a use case that combines UAAS measurements and dispersion modeling to quantify CH4 emissions from a dairy farm. To evaluate the atmospheric sensing performance of the UAAS, four field deployments were performed at three locations 15 in the San Joaquin Valley of California where CH4 hotspots were observed downwind of dairy farms. A comparison of the observations collected on board the UAAS and an 11-m meteorological tower show that the UAAS can measure wind velocity trends with a root mean squared error varying between 0.4 and 1.1 m s-1 when the wind magnitude is less than 3.5 m s-1. Findings from UAAS flight deployments and a calibration experiment also show that the UAAS can reliably resolve temporal variations in the mole fractions of CH4 and CO2 occurring over 10 second periods or longer. Results from the UAAS and 20 dispersion modeling use case further demonstrate that UAAS have great potential as a low-cost tool for detecting and quantifying CH4 emissions in near real-time. [ABSTRACT FROM AUTHOR]

With global wildfires becoming more widespread and severe, tracking their emissions of greenhouse gases and air pollutants is becoming increasingly important. Wildfire emissions have primarily been characterized by in situ laboratory, and field observations at fine scales. While this approach captures the mechanisms relating emissions to combustion phase and fuel properties, their evaluation on large scale plumes has been limited. In this study, we report remote observations of total column trace gases and aerosols in the 2020 wildfire season of smoke plumes from the Sierra Nevada of California with an EM27/SUN solar Fourier transform infrared (FTIR) spectrometer. We derive total column aerosol optical depth (AOD), emission factors (EF) and modified combustion efficiency (MCE) for these fires, and evaluate relationships between them based on combustion phase at large scales. We demonstrate that the EM27/SUN effectively detects changes of CO, CO2 and CH4 in the atmospheric column at ~10 km scales that are attributed to wildfire emissions. These observations are used to derive total column EFCO of 120.5 ± 12.2 and EFCH4 of 4.3 ± 0.8 for a large smoke plume event in mixed combustion phases. These values are consistent with in situ relationships measured in similar temperate coniferous forest wildfires. FTIR derived AOD was compared to a nearby AERONET station and observed ratios of AOD to XCO were consistent with those previously observed from satellites. We also show that co-located XCO observations from the TROPOMI satellite-based instrument are 9.7% higher than our EM27/SUN observations during the wildfire period. Finally, we put wildfire CH4 emissions in context of the California state CH4 budget and estimate that 213.7 ± 49.8 Gg CH4 were emitted by large wildfires in California during 2020, about 13.6% of the total state CH4 emissions in 2019. Our novel application of an EM27/SUN solar spectrometer to quantify wildfire emission ratios at large scales follows predictive relationships that are consistent with in situ studies, offering promise for extensive monitoring from ground networks and satellite remote sensing. [ABSTRACT FROM AUTHOR]

Dairies emit roughly half of total methane (CH4) emissions in California, generating CH4 from both enteric fermentation by ruminant gut microbes and anaerobic decomposition of manure. Representation of these emission processes is essential for management and mitigation of CH4 emissions, and is typically done using standardized emission factors applied at large spatial scales (e.g., state level). However, CH4-emitting activities and management decisions vary across facilities, and current inventories do not have sufficiently high spatial resolution to capture changes at this scale. Here, we develop a spatially-explicit database of dairies in California, with information from operating permits and California-specific reports detailing herd demographics and manure management at the facility scale. We calculated manure management and enteric fermentation CH4 emissions using two previously published bottom-up approaches and a new farm-specific calculation developed in this work. We also estimate the effect of mitigation strategies - the use of mechanical separators and installation of anaerobic digesters - on CH4 emissions. We predict that implementation of digesters at the 109 dairies that are existing or planned in California will reduce manure CH4 emissions from those facilities by an average of 35%, and total state CH4 emissions by 6% (or ~ 47.3 Gg CH4/yr). In addition to serving as a planning tool for mitigation, this database is useful as a prior for atmospheric observation-based emissions estimates, attribution of emissions to a specific facility, and to validate CH4 emissions reductions from management changes. Raster files of the datasets and associated metadata are available from the Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics (ORNL DAAC; Marklein et al., 2020; https://doi.org/10.3334/ORNLDAAC/1814). [ABSTRACT FROM AUTHOR]

We report continuous surface observations of carbon dioxide (CO2) and methane (CH4) from the Los Angeles (LA) Megacity Carbon Project during 2015. We devised a calibration strategy, methods for selection of background air masses, calculation of urban enhancements, and a detailed algorithm for estimating uncertainties in urban-scale CO2 and CH4 measurements. These methods are essential for understanding carbon fluxes from the LA megacity and other complex urban environments globally. We estimate background mole fractions entering LA using observations from four extra-urban sites including two marine sites located south of LA in La Jolla (LJO) and offshore on San Clemente Island (SCI), one continental site located in Victorville (VIC), in the high desert northeast of LA, and one continental/mid-troposphere site located on Mount Wilson (MWO) in the San Gabriel Mountains. We find that a local marine background can be established to within  ∼  1 ppm CO2 and  ∼  10 ppb CH4 using these local measurement sites. Overall, atmospheric carbon dioxide and methane levels are highly variable across Los Angeles. Urban and suburban sites show moderate to large CO2 and CH4 enhancements relative to a marine background estimate. The USC (University of Southern California) site near downtown LA exhibits median hourly enhancements of  ∼  20 ppm CO2 and  ∼  150 ppb CH4 during 2015 as well as  ∼  15 ppm CO2 and  ∼  80 ppb CH4 during mid-afternoon hours (12:00–16:00 LT, local time), which is the typical period of focus for flux inversions. The estimated measurement uncertainty is typically better than 0.1 ppm CO2 and 1 ppb CH4 based on the repeated standard gas measurements from the LA sites during the last 2 years, similar to Andrews et al. (2014). The largest component of the measurement uncertainty is due to the single-point calibration method; however, the uncertainty in the background mole fraction is much larger than the measurement uncertainty. The background uncertainty for the marine background estimate is  ∼  10 and  ∼  15 % of the median mid-afternoon enhancement near downtown LA for CO2 and CH4, respectively. Overall, analytical and background uncertainties are small relative to the local CO2 and CH4 enhancements; however, our results suggest that reducing the uncertainty to less than 5 % of the median mid-afternoon enhancement will require detailed assessment of the impact of meteorology on background conditions. [ABSTRACT FROM AUTHOR]

We introduce a retrieval algorithm to estimate lower tropospheric methane (CH4) concentrations from the surface to 1 km with uncertainty estimates using Hyperspectral Thermal Emission Spectrometer (HyTES) airborne radiance measurements. After resampling, retrievals have a spatial resolution of 6 × 6 m2. The total error from a single retrieval is approximately 20 %, with the uncertainties determined primarily by noise and spectral interferences from air temperature, surface emissivity, and atmospheric water vapor. We demonstrate retrievals for a HyTES flight line over storage tanks near Kern River Oil Field (KROF), Kern County, California, and find an extended plume structure in the set of observations with elevated methane concentrations (3.0 ± 0.6 to 6.0 ± 1.2 ppm), well above mean concentrations (1.8 ± 0.4 ppm) observed for this scene. With typically a 20 % estimated uncertainty, plume enhancements with more than 1 ppm are distinguishable from the background values with its uncertainty. HyTES retrievals are consistent with simultaneous airborne and ground-based in situ CH4 mole fraction measurements within the reported accuracy of approximately 0.2 ppm (or  ∼ 8 %), due to retrieval interferences related to air temperature, emissivity, and H2O. [ABSTRACT FROM AUTHOR]