Atmospheric Methane Concentrations Research Articles

The novel regulator HdrR controls the transcription of the heterodisulfide reductase operon hdrBCA in Methanosarcina barkeri.

Methanogenic archaea play a key role in the global carbon cycle because these microorganisms remineralize organic compounds in various anaerobic environments. The microorganism Methanosarcina barkeri is a metabolically versatile methanogen, which can utilize acetate, methanol, and H2/CO2 to synthesize methane. However, the regulatory mechanisms underlying methanogenesis for different substrates remain unknown. In this study, RNA-seq analysis was used to investigate M. barkeri growth and gene transcription under different substrate regimes. According to the results, M. barkeri showed the best growth under methanol, followed by H2/CO2 and acetate, and these findings corresponded well with the observed variations in genes transcription abundance for different substrates. In addition, we identified a novel regulator, MSBRM_RS03855 (designated as HdrR), which specifically activates the transcription of the heterodisulfide reductase hdrBCA operon in M. barkeri. HdrR was able to bind to the hdrBCA operon promoter to regulate transcription. Furthermore, the structural model analyses revealed a helix-turn-helix domain, which is likely involved in DNA binding. Taken together, HdrR serves as a model to reveal how certain regulatory factors control the expression of key enzymes in the methanogenic pathway.IMPORTANCEThe microorganism Methanosarcina barkeri has a pivotal role in the global carbon cycle and contributes to global temperature homeostasis. The consequences of biological methanogenesis are far-reaching, including impacts on atmospheric methane and CO2 concentrations, agriculture, energy production, waste treatment, and human health. As such, reducing methane emissions is crucial to meeting set climate goals. The methanogenic activity of certain microorganisms can be drastically reduced by inhibiting the transcription of the hdrBCA operon, which encodes heterodisulfide reductases. Here, we provide novel insight into the mechanisms regulating hdrBCA operon transcription in the model methanogen M. barkeri. The results clarified that HdrR serves as a regulator of heterodisulfide reductase hdrBCA operon transcription during methanogenesis, which expands our understanding of the unique regulatory mechanisms that govern methanogenesis. The findings presented in this study can further our understanding of how genetic regulation can effectively reduce the methane emissions caused by methanogens.

Opinion: A research roadmap for exploring atmospheric methane removal via iron salt aerosol

Abstract. The escalating climate crisis requires rapid action to reduce the concentrations of atmospheric greenhouse gases and lower global surface temperatures. Methane will play a critical role in near-term warming due to its high radiative forcing and short atmospheric lifetime. Methane emissions have accelerated in recent years, and there is significant risk and uncertainty associated with the future growth in natural emissions. The largest natural sink of methane occurs through oxidation reactions with atmospheric hydroxyl and chlorine radicals. Enhanced atmospheric oxidation could be a potential approach to remove atmospheric methane. One method proposes the addition of iron salt aerosol (ISA) to the atmosphere, mimicking a natural process proposed to occur when mineral dust mixes with chloride from sea spray to form iron chlorides, which are photolyzed by sunlight to produce chlorine radicals. Under the right conditions, lofting ISA into the atmosphere could potentially reduce atmospheric methane concentrations and lower global surface temperatures. Recognizing that potential atmospheric methane removal must only be considered an additive measure – in addition to, not replacing, crucial anthropogenic greenhouse gas emission reductions and carbon dioxide removal – roadmaps can be a valuable tool to organize and streamline interdisciplinary and multifaceted research to efficiently move towards understanding whether an approach may be viable and socially acceptable or if it is nonviable and further research should be deprioritized. Here we present a 5-year research roadmap to explore whether ISA enhancement of the chlorine radical sink could be a viable and socially acceptable atmospheric methane removal approach.

Unraveling the dynamics of atmospheric methane: the impact of anthropogenic and natural emissions

The reduction in methane concentration is crucial for achieving the goals of the Paris agreement. However, its annual growth rate is unstable, and understanding the reasons for changes in methane growth is essential for climate policy-making. Currently, there is considerable uncertainty regarding its attribution. Here, we utilize multi-source data and optimal fingerprinting methods to detect the contributions of several key drivers to the methane trend and interannual variability. We find that the methane growth trend is primarily influenced by anthropogenic emissions, while interannual variability is predominantly determined by wetland and biomass burning emissions. This result underscores the central role of anthropogenic emissions in methane dynamics, providing confidence in the effectiveness of human efforts to control methane atmospheric concentrations through emission reductions. It also helps alleviate concerns about the recent surge in atmospheric methane concentration, as it may be a short-term peak caused by increased wetland emissions rather than a long-term change.

Unveiling the drivers of atmospheric methane variability in Iran: A 20-year exploration using spatiotemporal modeling and machine learning

Understanding the factors controlling spatial and temporal variability of atmospheric methane concentration (XCH4) is crucial for mitigating its impacts and implementing emission reduction strategies. This study comprehensively investigates XCH4 and its driving factors (environmental, meteorological, and anthropogenic activity) across Iran over 20 years, from 2003 to 2022. It combines multi-source satellite observations, advanced spatiotemporal modeling techniques, correlation analysis, and machine learning algorithms. The spatiotemporal analysis showed notable spatial variation, with high XCH4 levels in central, southern, and eastern Iran and lower levels in the northwest and north. Moreover, distinct seasonal cycles emerged, with maximum XCH4 occurring during summer (August-September) and minimum levels in spring (April-May). Correlation analysis and variable importance assessment were developed to elucidate the key drivers governing XCH4 dynamics. Correlation analysis revealed that vegetation cover, precipitation, and soil moisture were negatively correlated with XCH4, while temperature indices showed a positive correlation, exhibiting the highest correlation in time dispersion and quantity among the studied variables. The Permutation Importance technique, used with a Random Forest classifier, a machine learning-based approach that considers the role of all variables together, showed that land surface temperature, wind speed, soil moisture, and vegetation cover are the dominant controls, with their importance ranked respectively. Surprisingly, anthropogenic emissions played a relatively minor role in shaping XCH4 distributions at the regional scale. These findings highlight the significant influence of meteorological variables and ecosystem processes on XCH4 modulation, revealing intricate Earth system feedbacks that inform targeted mitigation strategies and predictive models for curbing greenhouse gas emissions and mitigating climate change impacts.

Highly sensitive mid-infrared methane remote sensor using a deep neural network filter.

A novel mid-infrared methane remote sensor integrated on a movable platform based on a 3.291-µm interband cascade laser (ICL) and wavelength modulation spectroscopy (WMS) is proposed. A transmitting-receiving coaxial, visualized optical layout is employed to minimize laser energy loss. Using a hollow retro-reflector remotely deployed as a cooperative target, the atmospheric average methane concentration over a 100-meter optical range is measured with high sensitivity. A deep neural network (DNN) filter is used for second harmonic (2f) signal denoising to compensate for the performance shortcomings of conventional filtering. Allan deviation analysis indicated that after applying the DNN filter, the limit of detection (LOD) of methane was 86.62 ppb with an average time of 1 s, decreasing to 12.03 ppb with an average time of 229 s, which is a significant promotion compared to similar work reported. The high sensitivity and stability of the proposed sensor are shown through a 24-hour continuous monitoring experiment of atmospheric methane conducted outdoors, providing a new solution for high-sensitivity remote sensing of atmospheric methane.

Increased methane emissions from oil and gas following the Soviet Union’s collapse

Global atmospheric methane concentrations rose by 10 to 15 ppb/y in the 1980s before abruptly slowing to 2 to 8 ppb/y in the early 1990s. This period in the 1990s is known as the "methane slowdown" and has been attributed in part to the collapse of the former Soviet Union (USSR) in December 1991, which may have decreased the methane emissions from oil and gas operations. Here, we develop a methane plume detection system based on probabilistic deep learning and human-labeled training data. We use this method to detect methane plumes from Landsat 5 satellite observations over Turkmenistan from 1986 to 2011. We focus on Turkmenistan because economic data suggest it could account for half of the decline in oil and gas emissions from the former USSR. We find an increase in both the frequency of methane plume detections and the magnitude of methane emissions following the collapse of the USSR. We estimate a national loss rate from oil and gas infrastructure in Turkmenistan of more than 10% at times, which suggests the socioeconomic turmoil led to a lack of oversight and widespread infrastructure failure in the oil and gas sector. Our finding of increased oil and gas methane emissions from Turkmenistan following the USSR's collapse casts doubt on the long-standing hypothesis regarding the methane slowdown, begging the question: "what drove the 1992 methane slowdown?"

Improved Gaussian regression model for retrieving ground methane levels by considering vertical profile features

Atmospheric methane is one of the major greenhouse gases and has a great impact on climate change. To obtain the polluted levels of atmospheric methane in the ground-level range, this study used satellite observations and vertical profile features derived by atmospheric chemistry model to estimate the ground methane concentrations in first. Then, the improved daily ground-level atmospheric methane concentration dataset with full spatial coverage (100%) and 5-km resolution in mainland China from 2019 to 2021 were retrieved by station-based observations and gaussian regression model. The overall estimated deviation between the estimated ground methane concentrations and the WDCGG station-based measurements is less than 10 ppbv. The R by ten-fold cross-validation is 0.93, and the R2 is 0.87. The distribution of the ground-level methane concentrations in the Chinese region is characterized by high in the east and south, and low in the west and north. On the time scale, ground-level methane concentration in the Chinese region is higher in winter and lower in summer. Meanwhile, the spatial and temporal distribution and changes of ground-level methane in local areas have been analyzed using Shandong Province as an example. The results have a potential to detect changes in the distribution of methane concentration.

Tropical vegetation productivity and atmospheric methane over the last 40,000 years from model simulations and stalagmites in Sulawesi, Indonesia

AbstractRecent research has shown the potential of speleothem δ13C to record a range of environmental processes. Here, we report on 230Th-dated stalagmite δ13C records for southwest Sulawesi, Indonesia, over the last 40,000 yr to investigate the relationship between tropical vegetation productivity and atmospheric methane concentrations. We demonstrate that the Sulawesi stalagmite δ13C record is driven by changes in vegetation productivity and soil respiration and explore the link between soil respiration and tropical methane emissions using HadCM3 and the Sheffield Dynamic Global Vegetation Model. The model indicates that changes in soil respiration are primarily driven by changes in temperature and CO2, in line with our interpretation of stalagmite δ13C. In turn, modelled methane emissions are driven by soil respiration, providing a mechanism that links methane to stalagmite δ13C. This relationship is particularly strong during the last glaciation, indicating a key role for the tropics in controlling atmospheric methane when emissions from high-latitude boreal wetlands were suppressed. With further investigation, the link between δ13C in stalagmites and tropical methane could provide a low-latitude proxy complementary to polar ice core records to improve our understanding of the glacial–interglacial methane budget.

Neighbouring effect of land use changes and fire emissions on atmospheric CO2 and CH4 over suburban region of India (Shadnagar)

The present study investigated the effects of land use/land cover (LU/LC) changes on atmospheric carbon dioxide (CO2) and methane (CH4) concentrations over the sub-urban region of India (Shadnagar) using continuous decadal CO2 and CH4in-situ data measured by the greenhouse gas analyser (GGA). Data was collected from 2013 to 2022 at a 1 Hz frequency. Analysis of the current study indicates that during pre-monsoon, the seasonal maximum of CO2 was 409.91 ± 9.26 ppm (μ ± 1σ), while the minimum during monsoon was about 401.64 ± 7.13 ppm. Post-monsoon has a high seasonal mean CH4 concentration of 2.08 ± 0.06 ppm, while monsoon has a low seasonal mean CH4 concentration of 1.88 ± 0.03 ppm. The primary classes, such as forest, crop, and built-up, were considered to estimate the effect of LU/LC changes on atmospheric CO2 and CH4 concentrations. Between 2005 and 2021, the study's results show that the built-up area at radii of 10 km, 20 km, and 50 km increased by 0.17 %, 0.10 %, and 0.4 %, respectively. While other LU/LC categories declined by 30 %, agriculture areas increased by 30 % on average. As a result, the CO2 and CH4 concentrations at the study site are increased by 6 % (26 ppm) and 6.5 % (140 ppb), respectively. The present study utilised the fire-based carbon emissions data from the Global Fire Emissions Database (GFED) to understand the impact on atmospheric CO2 and CH4. Analysis of the present work investigated the influence of transported airmass on CO2 and CH4 during the pre-monsoon and post-monsoon seasons using the HYSPLIT trajectories and found emissions were from the northwest, southeast, and northeast of the study site. Further, in-situ CO2 and CH4 records are compared against the MIROC4-ACTM simulation, and strong agreement was found with bias of 1.80 ppm and 0.98 ppb for CO2 and CH4, respectively.

Aircraft-based mass balance estimate of methane emissions from offshore gas facilities in the southern North Sea

Abstract. Atmospheric methane (CH4) concentrations have more than doubled since the beginning of the industrial age, making CH4 the second most important anthropogenic greenhouse gas after carbon dioxide (CO2). The oil and gas sector represents one of the major anthropogenic CH4 emitters as it is estimated to account for 22 % of global anthropogenic CH4 emissions. An airborne field campaign was conducted in April–May 2019 to study CH4 emissions from offshore gas facilities in the southern North Sea with the aim of deriving emission estimates using a top-down (measurement-led) approach. We present CH4 fluxes for six UK and five Dutch offshore platforms or platform complexes using the well-established mass balance flux method. We identify specific gas production emissions and emission processes (venting and fugitive or flaring and combustion) using observations of co-emitted ethane (C2H6) and CO2. We compare our top-down estimated fluxes with a ship-based top-down study in the Dutch sector and with bottom-up estimates from a globally gridded annual inventory, UK national annual point-source inventories, and operator-based reporting for individual Dutch facilities. In this study, we find that all the inventories, except for the operator-based facility-level reporting, underestimate measured emissions, with the largest discrepancy observed with the globally gridded inventory. Individual facility reporting, as available for Dutch sites for the specific survey date, shows better agreement with our measurement-based estimates. For all the sampled Dutch installations together, we find that our estimated flux of (122.9 ± 36.8) kg h−1 deviates by a factor of 0.64 (0.33–12) from reported values (192.8 kg h−1). Comparisons with aircraft observations in two other offshore regions (the Norwegian Sea and the Gulf of Mexico) show that measured, absolute facility-level emission rates agree with the general distribution found in other offshore basins despite different production types (oil, gas) and gas production rates, which vary by 2 orders of magnitude. Therefore, mitigation is warranted equally across geographies.

SPATIAL VARIABILITY OF METHANE EMISSIONS FROM SOILS OF WET FORESTS: A BRIEF REVIEW

SPATIAL VARIABILITY OF METHANE EMISSIONS FROM SOILS OF WET FORESTS: A BRIEF REVIEW

Sub‐Diurnal Methane Variations on Mars Driven by Barometric Pumping and Planetary Boundary Layer Evolution

AbstractIn recent years, the Tunable Laser Spectrometer within the Sample Analysis at Mars (TLS‐SAM) instrument on board the Mars Science Laboratory (MSL) Curiosity rover has detected methane variations in the atmosphere at Gale crater. Methane concentrations appear to fluctuate seasonally as well as sub‐diurnally, which is difficult to reconcile with an as‐yet‐unknown transport mechanism delivering the gas from underground to the atmosphere. To potentially explain the fluctuations, we consider barometrically induced transport of methane from an underground source to the surface, modulated by temperature‐dependent adsorption. The subsurface fractured‐rock seepage model is coupled to a simplified 1‐D atmospheric mixing model to provide insights on the pattern of atmospheric methane concentrations in response to transient surface methane emissions, as well as to predict sub‐diurnal variation in methane abundance for the northern summer period, which is a candidate time frame for a MSL Curiosity sampling campaign. Our analysis suggests that there is a lower limit to the subsurface fracture density that can produce the observed methane patterns, below which the atmospheric methane variations would be out of phase with the observations. The best‐performing model scenarios indicate a significant, short‐lived methane pulse just prior to sunrise, the detection of which by TLS‐SAM would be a potential indicator of the contribution of barometric pumping to Mars' atmospheric methane variations.

Evaluation of atmospheric methane concentrations and growth trends in selected locations of Rivers State, Nigeria

The study addresses the increasing atmospheric methane mixing ratios in Rivers State, Nigeria, a pressing issue linked to global warming. The objective was to analyze the growth rate and sources of methane emissions in the region. Using a quantitative approach, ground-level methane measurements were collected from seven different locations between September 2021 and June 2022, complemented by data from the Tropospheric Monitoring Instrument satellite spanning January 2019 to June 2022. The methodology combined descriptive, comparative, and regression analyses with plume chemistry modeling using the U.S. EPA Atmospheric Dispersion Modeling System software. Results indicated significant spatial variance in methane concentrations, ranging from 9.76 ppm in Ogbogu to 21.5 ppm in Choba, with notable temporal fluctuations. Regression analysis revealed a correlation between atmospheric conditions and methane levels, while plume chemistry identified a landfill near Choba market as a major emission source, raising concerns about tropospheric ozone pollution and health risks. The study highlights the need for effective waste management in Rivers State and emphasizes the importance of continuous monitoring to address the challenges posed by methane emissions and tropospheric ozone pollution.

Source apportionment of methane emissions from the Upper Silesian Coal Basin using isotopic signatures

Abstract. Anthropogenic emissions are the primary source of the increase in atmospheric methane (CH4) levels. However, estimates of anthropogenic CH4 emissions still show large uncertainties at global and regional scales. Differences in CH4 isotopic source signatures δ13C and δ2H can help to constrain different source contributions (e.g., fossil, waste, agriculture). The Upper Silesian Coal Basin (USCB) represents one of the largest European CH4 emission regions, with more than 500 Gg CH4 yr−1 released from more than 50 coal mine ventilation shafts, landfills, and wastewater treatment plants. During the CoMet (Carbon Dioxide and Methane Mission) campaign in June 2018 methane observations were conducted from a variety of platforms including aircraft and cars to quantify these emissions. Besides the continuous sampling of atmospheric methane concentration, numerous air samples were taken from inside and around the ventilation shafts (1–2 km distance) and aboard the High Altitude and Long Range Research Aircraft (HALO) and DLR Cessna Caravan aircraft, and they were analyzed in the laboratory for the isotopic composition of CH4. The airborne samples downwind of the USCB contained methane from the entire region and thus enabled determining the mean signature of the USCB accurately. This mean isotopic signature of methane emissions was -50.9±0.7 ‰ for δ13C and -226±9 ‰ for δ2H. This is in the range of previous USCB studies based on samples taken within the mines for δ13C but more depleted in δ2H than reported before. Signatures of methane enhancements sampled upwind of the mines and in the free troposphere clearly showed the influence of biogenic sources. We determined the source signatures of individual coal mine ventilation shafts using ground-based samples. These signatures displayed a considerable range between different mines and also varied for individual shafts from day to day. Different layers of the USCB coal contain thermogenic methane, isotopically similar to natural gas, and methane formed through biogenic carbonate reduction. The signatures vary depending on what layer of coal is mined at the time of sampling. Mean shaft signatures range from −60 ‰ to −42 ‰ for δ13C and from −200 ‰ to −160 ‰ for δ2H. A gradient in the signatures of subregions of the USCB is reflected both in the aircraft data and in the ground samples, with emissions from the southwest being most depleted in δ2H and emissions from the south being most depleted in δ13C, which is probably associated with the structural and lithostratigraphic history of the USCB and generation and migration processes of methane in the coal. The average signature of -49.8±5.7 ‰ in δ13C and -184±32 ‰ in δ2H from the ventilation shafts clearly differs from the USCB regional signature in δ2H. This makes a source attribution using δ2H signatures possible, which would not be possible with only the δ13C isotopic signatures. We assume that the USCB plume mainly contains fossil coal mine methane and biogenic methane from waste treatment, because the USCB is a highly industrialized region with few other possible methane sources. Assuming a biogenic methane signature between and −320 ‰ and −280 ‰ for δ2H, the biogenic methane emissions from the USCB account for 15 %–50 % of total emissions. The uncertainty range shows the need of comprehensive and extensive sampling from all possible source sectors for source apportionment. The share of anthropogenic–biogenic emissions of 0.4 %–14 % from this densely populated industrial region is underestimated in commonly used emission inventories. Generally, this study demonstrates the importance of δ2H-CH4 observations for methane source apportionment in regions with a mix of thermogenic and biogenic sources and, especially in our case, where the δ13C signature of the coal mine gas has a large variability.

MATHEMATICAL MODELS OF METHANE CONSUMPTION BY SOILS: A REVIEW

MATHEMATICAL MODELS OF METHANE CONSUMPTION BY SOILS: A REVIEW

Spatio-temporal variability of Atmospheric CO2 and CH4 concentrations over Antarctica using Ground and Space-based measurements

The present study reports on the variability of atmospheric carbon dioxide (CO2) and methane (CH4) concentrations over Antarctica using data collected during the 2015–2016 and 2016–2017 as part of the Indian Scientific Expedition to Antarctica (ISEA). During the austral summer (January–March), this study examined the spatio-temporal variability in atmospheric CO2 and CH4 concentrations at the Indian Antarctic Stations (IAS). An average atmospheric CO2 concentration was observed to be 396.25 ± 4.20 ppm at Bharati station during 2015-16 austral summer period, whereas CO2 (CH4) concentrations were 398.7 ± 0.46 ppm (1780 ± 2 ppb) and 399.1 ± 0.41 ppm (1780 ± 3 ppb) at Bharati and Maitri stations, respectively during 2016–17. During the study period, the Orbiting Carbon Observatory-2 (OCO-2) based column averaged CO2 concentration shows similar values against records. At Bharati and Maitri, the diurnal amplitudes of CO2 (CH4) are varied from 0.11 ppm (0.37 ppb) to 8.90 ppm (11.44 ppb) and 0.54 ppm (1.96 ppb) to 7.30 ppm (16 ppb), respectively. In the austral summer, dominant winds at the IAS are from the east, northeast, and southeast.

Hotspot Detection and Estimation of Methane Emissions from Landfill Final Cover

The main objectives of this study were to identify methane hotspots through spatial distribution tests of the surface methane concentration above a landfill final cover and to investigate the effects of rainfall, atmospheric pressure, ground temperature, and ambient methane concentration on methane emissions. A portable laser methane detector was used to measure the spatial distribution of methane concentrations. The methane concentration distribution showed a distinct spatial variability. The maximum methane concentration reached 3225 ppm, while 73.0% of the methane concentration values were below 10.0 ppm. Several meteorological factors were found to be associated with the variation in methane emissions. Rainfall limited gas transport in the cover, resulting in more significant methane hotspots. Atmospheric pressure was negatively correlated with methane emission. The ambient methane concentration and methane flux had a significant positive linear correlation. Based on a linear correlation equation, the spatial distribution of methane concentrations in the landfill could be converted into a methane emission distribution. The estimated average value for methane emissions in the test area was approximately 4.3 g m−2 d−1. This study provides an experimental basis for locating methane hotspots and assessing methane emissions in landfill final covers, and proposes supplementary means for detecting geomembrane damage in landfill covers.

Short-term variation in Mars atmospheric methane concentrations driven by barometric pumping

Measurements of atmospheric methane by the Curiosity rover's SAM-TLS instrument are providing evidence of seasonality and diurnal variation in concentration. Given methane's short atmospheric lifetime relative to geological timescales, its presence implies a replenishing source, and the observed variation demands the proposition of a modulation mechanism. This paper focuses on the modulation mechanism on a diurnal scale, extending earlier modeling of seasonal variation. Our modeling shows that barometric pumping driven by both diurnal and seasonal variation of atmospheric pressure, along with possible adsorption and desorption of methane in the shallow subsurface driven by temperature and pressure change, can explain the variation in methane concentration. In the model, an active, continuous, steady-state deep source of methane is assumed, and carbon dioxide serves as the carrier gas for drawing methane and possible trace gaseous constituents into the atmosphere.

Methane Emissions in Boreal Forest Fire Regions: Assessment of Five Biomass-Burning Emission Inventories Based on Carbon Sensing Satellites

Greenhouse gases such as CH4 generated by forest fires have a significant impact on atmospheric methane concentrations and terrestrial vegetation methane budgets. Verification in conjunction with “top-down” satellite remote sensing observation has become a vital way to verify biomass-burning emission inventories and accurately assess greenhouse gases while looking into the limitations in reliability and quantification of existing “bottom-up” biomass-burning emission inventories. Therefore, we considered boreal forest fire regions as an example while combining five biomass-burning emission inventories and CH4 indicators of atmospheric concentration satellite observation data. By introducing numerical comparison, correlation analysis and trend consistency analysis methods, we explained the lag effect between emissions and atmospheric concentration changes and evaluated a more reliable emission inventory using time series similarity measurement methods. The results indicated that total methane emissions from five biomass-burning emission inventories differed by a factor of 2.9 in our study area, ranging from 2.02 to 5.84 Tg for methane. The time trends of the five inventories showed good consistency, with the Quick Fire Emissions Dataset version 2.5 (QFED2.5) having a higher correlation coefficient (above 0.8) with the other four datasets. By comparing the consistency between the inventories and satellite data, a lagging effect was found to be present between the changes in atmospheric concentration and gas emissions caused by forest fires on a seasonal scale. After eliminating lagging effects and combining time series similarity measures, the QFED2.5 (Euclidean distance = 0.14) was found to have the highest similarity to satellite data. In contrast, Global Fire Emissions Database version 4.1 with small fires (GFED4.1s) and Global Fire Assimilation System version 1.2 (GFAS1.2) had larger Euclidean distances of 0.52 and 0.4, respectively, which meant that they had lower similarity to satellite data. Therefore, QFED2.5 was found to be more reliable while having higher application accuracy compared to the other four datasets in our study area. This study further provided a better understanding of the key role of forest fire emissions in atmospheric CH4 concentrations and offered reference for selecting appropriate biomass burning emission inventory datasets for bottom-up inventory estimation studies.

Trends in atmospheric methane concentrations since 1990 were driven and modified by anthropogenic emissions

The atmospheric methane trend is not fully understood. Here we investigate the role of the main sink, the main natural source, and anthropogenic emissions on the methane growth rate over the last three decades using numerical models and emission inventories. We find that the long-term trend is driven by increased anthropogenic methane emissions, while wetland emissions show large variability and can modify the trend. The anthropogenic influence on hydroxyl radical, through nitrogen oxides and carbon monoxide emissions, has modified the trend over the last decades and contributed to the atmospheric methane stabilization from 2000 to 2007. The hydroxyl radical increase prior to this stabilization period might have contributed to the decline in the isotopic ratio after 2007 due to the time dependent isotopic response of hydroxyl radical. Emission reductions due to COVID-19 restrictions via the influence on hydroxyl radical, possibly contributed to approximately two thirds of the increase in methane growth from 2019 to 2020.