Stable isotopic signatures of methane from waste sources through atmospheric measurements

This study aimed to characterize the carbon isotopic signatures (δ13C-CH4) of several methane waste sources, predominantly in the UK, and during field campaigns in the Netherlands and Turkey. CH4 plumes emitted from waste sources were detected during mobile surveys using a cavity ring-down spectroscopy (CRDS) analyser. Air samples were collected in the plumes for subsequent isotope analysis by gas chromatography isotope ratio mass spectrometry (GC-IRMS) to characterize δ13C-CH4. The isotopic signatures were determined through a Keeling plot approach and the bivariate correlated errors and intrinsic scatter (BCES) fitting method. The δ13C-CH4 and δ2H-CH4 signatures were identified from biogas plants (−54.6 ± 5.6‰, n = 34; −314.4 ± 23‰ n = 3), landfills (−56.8 ± 2.3‰, n = 43; −268.2 ± 2.1‰, n = 2), sewage treatment plants (−51.6 ± 2.2‰, n = 15; −303.9 ± 22‰, n = 6), composting facilities (−54.7 ± 3.9‰, n = 6), a landfill leachate treatment plant (−57.1 ± 1.8‰, n = 2), one water treatment plant (−53.7 ± 0.1‰) and a waste recycling facility (−53.2 ± 0.2‰). The overall signature of 71 waste sources ranged from −64.4 to −44.3‰, with an average of −55.1 ± 4.1‰ (n = 102) for δ13C, −341 to −267‰, with an average of −300.3 ± 25‰ (n = 11) for δ2H, which can be distinguished from other source types in the UK such as gas leaks and ruminants. The study also demonstrates that δ2H-CH4 signatures, in addition to δ13C-CH4, can aid in better waste source apportionment and increase the granularity of isotope data required to improve regional modelling

Clusters in the inner spiral arms of M51: the cluster IMF and the formation history

We study the cluster population in a region of 3.2x3.2 kpc^2 in the inner spiral arms of the intergacting galaxy M51, at a distance of about 1 to 3 kpc from the nucleus, based on HST--WFPC2 images taken through five broadband and two narrowband filters. We found 877 cluster candidates and we derived their ages, initial masses and extinctions by comparing their energy distribution with the Starburst99 cluster models. We describe the 3 and 2-dimensional least-square energy fitting method that was used (3DEF, 2DEF). The lack of [OIII] emission in even the youngest clusters with strong H-alpha emission, indicates the absence of the most massive stars and suggests a mass upper limit of about 25 to 30 solar masses. The mass versus age distribution of the clusters shows a drastic decrease in the number of clusters with age, which indicates that cluster disruption is occurring on a timescale of about 10 Myr for low mass clusters. The cluster initial mass function for clusters younger than 10 Myr has an exponent of alpha = 2.0 (+- 0.05) We derived the cluster formation history from clusters with an initial mass larger than 10^4 solar masses. There is no evidence for a peak in the cluster formation rate within a factor two at about 200 to 400 Myr ago, i.e. at the time of the interaction with the companion galaxy NGC 5194.Comment: 15 pages, 15 figures. Accepted for publication by Astronomy and Astrophysic

Comparing optimized CO emission estimates using MOPITT or NOAA surface network observations

[1] This paper compares two global inversions to estimate carbon monoxide (CO) emissions for 2004. Either surface flask observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) or CO total columns from the Measurement of Pollution in the Troposphere (MOPITT) instrument are assimilated in a 4D-Var framework. Inferred emission estimates from the two inversions are consistent over the Northern Hemisphere (NH). For example, both inversions increase anthropogenic CO emissions over Europe (from 46 to 94 Tg CO/yr) and Asia (from 222 to 420 Tg CO/yr). In the Southern Hemisphere (SH), three important findings are reported. First, due to their different vertical sensitivity, the stations-only inversion increases SH biomass burning emissions by 108 Tg CO/yr more than the MOPITT-only inversion. Conversely, the MOPITT-only inversion results in SH natural emissions (mainly CO from oxidation of NMVOCs) that are 185 Tg CO/yr higher compared to the stations-only inversion. Second, MOPITT-only derived biomass burning emissions are reduced with respect to the prior which is in contrast to previous (inverse) modeling studies. Finally, MOPITT derived total emissions are significantly higher for South America and Africa compared to the stations-only inversion. This is likely due to a positive bias in the MOPITT V4 product. This bias is also apparent from validation with surface stations and ground-truth FTIR columns. Our results show that a combined inversion is promising in the NH. However, implementation of a satellite bias correction scheme is essential to combine both observational data sets in the SH

Local to regional methane emissions from the Upper Silesia Coal Basin (USCB) quantified using UAV-based atmospheric measurements

Coal mining accounts for ~ 12 % of the total anthropogenic methane emissions worldwide. The Upper Silesian Coal Basin, Poland, where large quantities of CH4 are emitted to the atmosphere via ventilation shafts of underground hard coal (anthracite) mines, is one of the hot spots of methane emissions in Europe. However, coalbed CH4 emissions into the atmosphere are poorly characterized. As part of the Carbon Dioxide and CH4 mission 1.0 (CoMet 1.0) that took place in May – June 2018, we flew a recently developed active AirCore system aboard an unmanned aerial vehicle (UAV) to obtain CH4 and CO2 mole fractions 150–300 m downwind of five individual ventilation shafts in the USCB. In addition, we also measured δ13C-CH4, δ2H-CH4, ambient temperature, pressure, relative humidity, surface wind speeds and directions. We have used 34 UAV flights and two different approaches (inverse Gaussian approach and mass balance approach) to quantify the emissions from individual shafts. The quantified emissions were compared to both annual and hourly inventory data, and were used to derive the estimates of CH4 emissions in the USCB. We found a high correlation (R2 = 0.7 – 0.9) between the quantified and hourly inventory data-based shaft-averaged CH4 emissions, which in principle would allow regional estimates of CH4 emissions to be derived by upscaling individual hourly inventory data of all shafts. Currently, such inventory data is available only for the five shafts we quantified though. As an alternative, we have developed three upscaling approaches, i.e., by scaling the E-PRTR annual inventory, the quantified shaft-averaged emission rate, and the shaft-averaged emission rate that are derived from the hourly emission inventory. These estimates are in the range of 325 – 447 kt CH4/year for the inverse Gaussian approach and 268 – 347 kt CH4/year for the mass balance approach, respectively. This study shows that the UAV-based active AirCore system can be a useful tool to quantify local to regional point source methane emissions

Stratospheric carbon isotope fractionation and tropospheric histories of CFC-11, CFC-12, and CFC-113 isotopologues

We present novel measurements of the carbon isotope composition of CFC-11 (CCl3F), CFC-12 (CCl2F2), and CFC-113 (CF2ClCFCl2), three atmospheric trace gases that are important for both stratospheric ozone depletion and global warming. These measurements were carried out on air samples collected in the stratosphere the main sink region for these gases and on air extracted from deep polar firn snow. We quantify, for the first time, the apparent isotopic fractionation, ?app(13C), for these gases as they are destroyed in the high- and mid-latitude stratosphere: ?app(CFC-12, high-latitude)?=(-20.2 4.4)? , and ?app(CFC-113, high-latitude)?=(-9.4 4.4)? , ?app(CFC-12, mid-latitude)?=(-30.3 10.7)? , and ?app(CFC-113, mid-latitude)?=(-34.4 9.8)? . Our CFC-11 measurements were not sufficient to calculate ?app(CFC-11), so we instead used previously reported photolytic fractionation for CFC-11 and CFC-12 to scale our ?app(CFC-12), resulting in ?app(CFC-11, high-latitude)?=(-7.8 1.7)? and ?app(CFC-11, mid-latitude)?=(-11.7 4.2)? . Measurements of firn air were used to construct histories of the tropospheric isotopic composition, dT(13C), for CFC-11 (1950s to 2009), CFC-12 (1950s to 2009), and CFC-113 (1970s to 2009), with dT(13C) increasing for each gas. We used ?app(high-latitude), which was derived from more data, and a constant isotopic composition of emissions, dE(13C), to model dT(13C, CFC-11), dT(13C, CFC-12), and dT(13C, CFC-113). For CFC-11 and CFC-12, modelled dT(13C) was consistent with measured dT(13C) for the entire period covered by the measurements, suggesting that no dramatic change in dE(13C, CFC-11) or dE(13C, CFC-12) has occurred since the 1950s. For CFC-113, our modelled dT(13C, CFC-113) did not agree with our measurements earlier than 1980. This discrepancy may be indicative of a change in dE(13C, CFC-113). However, this conclusion is based largely on a single sample and only just significant outside the 95?% confidence interval. Therefore more work is needed to independently verify this temporal trend in the global tropospheric 13C isotopic composition of CFC-113. Our modelling predicts increasing dT(13C, CFC-11), dT(13C, CFC-12), and dT(13C, CFC-113) into the future. We investigated the effect of recently reported new CFC-11 emissions on background dT(13C, CFC-11) by fixing model emissions after 2012 and comparing dT(13C, CFC-11) in this scenario to the model base case. The difference in dT(13C, CFC-11) between these scenarios was 1.4? in 2050. This difference is smaller than our model uncertainty envelope and would therefore require improved modelling and measurement precision as well as better quantified isotopic source compositions to detect

A multi-year methane inversion using SCIAMACHY, accounting for systematic errors using TCCON measurements

This study investigates the use of total column CH₄ (<i>X</i>CH₄) retrievals from the SCIAMACHY satellite instrument for quantifying large-scale emissions of methane. A unique data set from SCIAMACHY is available spanning almost a decade of measurements, covering a period when the global CH₄ growth rate showed a marked transition from stable to increasing mixing ratios. The TM5 4DVAR inverse modelling system has been used to infer CH₄ emissions from a combination of satellite and surface measurements for the period 2003–2010. In contrast to earlier inverse modelling studies, the SCIAMACHY retrievals have been corrected for systematic errors using the TCCON network of ground-based Fourier transform spectrometers. The aim is to further investigate the role of bias correction of satellite data in inversions. Methods for bias correction are discussed, and the sensitivity of the optimized emissions to alternative bias correction functions is quantified. It is found that the use of SCIAMACHY retrievals in TM5 4DVAR increases the estimated inter-annual variability of large-scale fluxes by 22% compared with the use of only surface observations. The difference in global methane emissions between 2-year periods before and after July 2006 is estimated at 27–35 Tg yr⁻¹. The use of SCIAMACHY retrievals causes a shift in the emissions from the extra-tropics to the tropics of 50 ± 25 Tg yr⁻¹. The large uncertainty in this value arises from the uncertainty in the bias correction functions. Using measurements from the HIPPO and BARCA aircraft campaigns, we show that systematic errors in the SCIAMACHY measurements are a main factor limiting the performance of the inversions. To further constrain tropical emissions of methane using current and future satellite missions, extended validation capabilities in the tropics are of critical importance

Street-level methane emissions of Bucharest, Romania and the dominance of urban wastewater

Atmospheric methane (CH4) continues to increase, but there are multiple anthropogenic source categories that can be targeted for cost-effective emissions reduction. Cities emit CH4 to the atmosphere from a mixture of anthropogenic CH4 sources, which include, but are not limited to, fugitive emissions from natural gas distribution systems, wastewater treatment facilities, waste-and rainwater networks, and landfills. Therefore, to target mitigation measures, it is important to locate and quantify local urban emissions to prioritize mitigation opportunities in large cities. Using mobile measurement techniques, we located street-level CH4 leak indications, measured flux rates, and determined potential source origins (using carbon and hydrogen stable isotopic composition along with ethane: CH4 ratios) of CH4 in Bucharest, Romania. We found 969 confirmed CH4 leak indication locations, where the maximum mole fraction elevation (above background) was 38.3 ppm (mean = 0.9 ppm ± 0.1 ppm s.e.; n = 2482). Individual leak indicator fluxes, derived using a previously established empirical relation, ranged up to around 15 metric tons CH4 yr-1 (mean = 0.8 metric tons yr-1 ± 0.05, s.e.; n = 969). The total estimated city emission rate is 1832 tons CH4 yr-1 (min = 1577 t yr-1 and max = 2113 t yr-1). More than half (58%–63%) of the CH4 elevations were attributed to biogenic wastewater, mostly from venting storm grates and manholes connecting to sewer pipelines. Hydrogen isotopic composition of CH4 and ethane:methane ratios were the most useful tracers of CH4 sources, due to similarities in carbon isotope ratios between wastewater gas and natural gas. The annual city-wide CH4 emission estimate of Bucharest exceeded emissions of Hamburg, Germany by 76% and Paris, France by 90%

Sixteen years of MOPITT satellite data strongly constrain Amazon CO fire emissions

Despite consensus on the overall downward trend in Amazon forest loss in the previous decade, estimates of yearly carbon emissions from deforestation still vary widely. Estimated carbon emissions are currently often based on data from local logging activity reports, changes in remotely sensed biomass as well as remote detection of fire hotspots, and burned area. Here, we use sixteen years of satellite-derived carbon monoxide (CO) columns to constrain fire CO emissions from the Amazon basin between 2003 and 2018. Through data assimilation, we produce 3-daily maps of fire CO emissions over the Amazon that we verified to be consistent with a long-term monitoring program of aircraft CO profiles over five sites in the Amazon. Our new product independently confirms a long-term decrease of 54 % in deforestation-related CO emissions over the study period. Interannual variability is large, with known anomalously dry years showing a more than fourfold increase in basin-wide fire emissions. At the level of individual Brazilian states, we find that both soil moisture anomalies and human ignitions determine fire activity, suggesting that future carbon release from fires depends on drought intensity as much as on continued forest protection. Our study shows that the atmospheric composition perspective on deforestation is a valuable additional monitoring instrument that complements existing bottom-up and remote sensing methods for land-use change. Extension of such a perspective to an operational framework is timely considering the observed increased fire intensity in the Amazon basin in 2019&ndash;2021.</p

Atmospheric methane isotopes identify inventory knowledge gaps in the Surat Basin, Australia, coal seam gas and agricultural regions

In-flight measurements of atmospheric methane (CH4(a)) and mass balance flux quantification studies can assist with verification and improvement in the UNFCCC National Inventory reported CH4 emissions. In the Surat Basin gas fields, Queensland, Australia, coal seam gas (CSG) production and cattle farming are two of the major sources of CH4 emissions into the atmosphere. Because of the rapid mixing of adjacent plumes within the convective boundary layer, spatially attributing CH4(a) mole fraction readings to one or more emission sources is difficult. The primary aims of this study were to use the CH4(a) isotopic composition (13CCH4(a)) of in-flight atmospheric air (IFAA) samples to assess where the bottom-up (BU) inventory developed specifically for the region was well characterised and to identify gaps in the BU inventory (missing sources or over- and underestimated source categories). Secondary aims were to investigate whether IFAA samples collected downwind of predominantly similar inventory sources were useable for characterising the isotopic signature of CH4 sources (13CCH4(s)) and to identify mitigation opportunities. IFAA samples were collected between 100-350m above ground level (ma.g.l.) over a 2-week period in September 2018. For each IFAA sample the 2h back-trajectory footprint area was determined using the NOAA HYSPLIT atmospheric trajectory modelling application. IFAA samples were gathered into sets, where the 2h upwind BU inventory had >50% attributable to a single predominant CH4 source (CSG, grazing cattle, or cattle feedlots). Keeling models were globally fitted to these sets using multiple regression with shared parameters (background-air CH4(b) and 13CCH4(b)). For IFAA samples collected from 250-350ma.g.l. altitude, the best-fit 13CCH4(s) signatures compare well with the ground observation: CSG 13CCH4(s) of -55.4‰ (confidence interval (CI) 95%±13.7‰) versus 13CCH4(s) of -56.7‰ to -45.6‰; grazing cattle 13CCH4(s) of -60.5‰ (CI 95%±15.6‰) versus -61.7‰ to -57.5‰. For cattle feedlots, the derived 13CCH4(s) (-69.6‰, CI 95%±22.6‰), was isotopically lighter than the ground-based study (13CCH4(s) from -65.2‰ to -60.3‰) but within agreement given the large uncertainty for this source. For IFAA samples collected between 100-200ma.g.l. the 13CCH4(s) signature for the CSG set (-65.4‰, CI 95%±13.3‰) was isotopically lighter than expected, suggesting a BU inventory knowledge gap or the need to extend the population statistics for CSG 13CCH4(s) signatures. For the 100-200ma.g.l. set collected over grazing cattle districts the 13CCH4(s) signature (-53.8‰, CI 95%±17.4‰) was heavier than expected from the BU inventory. An isotopically light set had a low 13CCH4(s) signature of -80.2‰ (CI 95%±4.7‰). A CH4 source with this low 13CCH4(s) signature has not been incorporated into existing BU inventories for the region. Possible sources include termites and CSG brine ponds. If the excess emissions are from the brine ponds, they can potentially be mitigated. It is concluded that in-flight atmospheric 13CCH4(a) measurements used in conjunction with endmember mixing modelling of CH4 sources are powerful tools for BU inventory verification

Large Methane Emissions From the Pantanal During Rising Water‐Levels Revealed by Regularly Measured Lower Troposphere CH₄ Profiles

The Pantanal region of Brazil is the largest seasonally flooded tropical grassland and, according to local chamber measurements, a substantial CH4 source. CH4 emissions from wetlands have recently become of heightened interest because global atmospheric 13CH4 data indicate they may contribute to the resumption of atmospheric CH4 growth since 2007. We have regularly measured vertical atmospheric profiles for 2 years in the center of the Pantanal with the objectives to obtain an estimate of CH4 emissions using an atmospheric approach, and provide information about flux seasonality and its relation to controlling factors. Boundary layer-free troposphere differences observed in the Pantanal are large compared to other wetlands. Total emissions based on a planetary boundary layer budgeting technique are 2.0–2.8 TgCH4 yr−1 (maximum flux ∼0.4 gCH4 m−2 d−1) while those based on a Bayesian inversion using an atmospheric transport model are ∼3.3 TgCH4 yr−1. Compared to recent estimates for Amazonia (∼41 ± 3 TgCH4 yr−1, maximum flux ∼0.3 gCH4 m−2 d−1) these emissions are not that large. Our Pantanal data suggest a clear flux seasonality with CH4 being released in large amounts just after water levels begin to rise again after minimum levels have been reached. CH4 emissions decline substantially once the maximum water level has been reached. While predictions with prognostic wetland CH4 emission models agree well with the magnitude of the fluxes, they disagree with the phasing. Our approach shows promise for detecting and understanding longer-term trends in CH4 emissions and the potential for future wetlands CH4 emissions climate feedbacks