Atmospheric Methane Concentrations Research Articles

Feedbacks From Young Permafrost Carbon Remobilization to the Deglacial Methane Rise

AbstractThe abrupt warming events punctuating the Termination 1 (about 11.7–18 ka Before Present, BP) were marked by sharp rises in the concentration of atmospheric methane (CH4). The role of permafrost organic carbon (OC) in these rises is still debated, with studies based on top‐down measurements of radiocarbon (14C) content of CH4 trapped in ice cores suggesting minimum contributions from old and strongly 14C‐depleted permafrost OC. However, organic matter from permafrost can exhibit a continuum of 14C ages (contemporaneous to >50 ky). Here, we investigate the large‐scale permafrost remobilization at the Younger Dryas‐Preboreal transition (ca. 11.6 ka BP) using the sedimentary record deposited at the Lena River paleo‐outlet (Arctic Ocean) to reflect permafrost destabilization in this vast drainage basin. Terrestrial OC was isolated from sediments and characterized geochemically measuring δ13C, Δ14C, and lignin phenol molecular fossils. Results indicate massive remobilization of relatively young (about 2,600 years) permafrost OC from inland Siberia after abrupt warming triggered severe active layer deepening. Methane emissions from this young fraction of permafrost OC contributed to the deglacial CH4 rise. This study stresses that underestimating permafrost complexities may affect our comprehension of the deglacial permafrost OC‐climate feedback and helps understand how modern permafrost systems may react to rapid warming events, including enhanced CH4 emissions that would amplify anthropogenic climate change.

Inverse modeling of 2010–2022 satellite observations shows that inundation of the wet tropics drove the 2020–2022 methane surge

Atmospheric methane concentrations rose rapidly over the past decade and surged in 2020-2022 but the causes have been unclear. We find from inverse analysis of GOSAT satellite observations that emissions from the wet tropics drove the 2010-2019 increase and the subsequent 2020-2022 surge, while emissions from northern mid-latitudes decreased. The 2020-2022 surge is principally contributed by emissions in Equatorial Asia (43%) and Africa (30%). Wetlands are the major drivers of the 2020-2022 emission increases in Africa and Equatorial Asia because of tropical inundation associated with La Niña conditions, consistent with trends in the GRACE terrestrial water storage data. In contrast, emissions from major anthropogenic emitters such as the United States, Russia, and China are relatively flat over 2010-2022. Concentrations of tropospheric OH (the main methane sink) show no long-term trend over 2010-2022 but a decrease over 2020-2022 that contributed to the methane surge.

Multiple-model based simulation of urban atmospheric methane concentration and the attributions to its seasonal variations: A case study in Hangzhou megacity, China

Cities are treated as global methane (CH4) emission hotspots and the monitoring of atmospheric CH4 concentration in cities is necessary to evaluate anthropogenic CH4 emissions. However, the continuous and in-situ observation sites within cities are still sparsely distributed in the largest CH4 emitter as of China, and although obvious seasonal variations of atmospheric CH4 concentrations have been observed in cities worldwide, questions regarding the drivers for their temporal variations still have not been well addressed. Therefore, to quantify the contributions to seasonal variations of atmospheric CH4 concentrations, year-round CH4 concentration observations from 1st December 2020 to 30th November 2021 were conducted in Hangzhou megacity, China, and three models were chosen to simulate urban atmospheric CH4 concentration and partition its drivers including machine learning based Random Forest (RF) model, atmospheric transport processes based numerical model (WRF-STILT), and regression analysis based Multiple Linear Regression (MLR) model. The findings are as follows: (1) the atmospheric CH4 concentration showed obvious seasonal variations and were different with previous observations in other cities, the seasonality were 5.8 ppb, 21.1 ppb, and 50.1 ppb between spring-winter, summer-winter and autumn-winter, respectively, where the CH4 background contributed by −8.1 ppb, −44.6 ppb, and −1.0 ppb, respectively, and the CH4 enhancements contributed by 13.9 ppb, 65.7 ppb, and 51.1 ppb. (2) The RF model showed the highest accuracy in simulating CH4 concentrations, followed by MLR model and WRF-STILT model. (3) We further partition contributions from different factors, results showed the largest contribution was from temperature-induced increase in microbial process based CH4 emissions including waste treatment and wetland, which ranged from 38.1 to 76.3 ppb when comparing different seasons with winter. The second largest contribution was from seasonal boundary layer height (BLH) variations, which ranged from −13.4 to −6.3 ppb. And the temperature induced seasonal CH4 emission and enhancement variations were overwhelming BLH changes and other meteorological parameters.

Postglacial ocean methane release from the northern South China Sea

Methane release into seawater and the atmosphere as a result of cold seepage activity is a sensitive indicator of global environmental change during the last glacial period. However, the link between postglacial ocean methane escape and possible increases in atmospheric methane concentration remains unclear, which hinders the evaluation of methane impact on climatic variability. In this study, lipid biomarkers were analyzed on samples from the Shenhu area in the South China Sea to reconstruct the marine gas hydrate variability since the last glacial time. The reconstructed methane release during the cool Last Glacial Maximum and Younger Dryas agreed well with previous findings. Interestingly, our results identified a pronounced methane outgassing at the end of Heinrich 1, which was likely triggered by the abrupt increase in intermediate water temperature. The occurrence of this gas hydrate dissociation before the drastic rise in atmospheric methane during Bølling-Allerød implied that the change in the gas hydrate reservoir could likely indirectly contribute to the initiation of the warming event, though more global evidence needs to be provided in the future.

A novel sampling technique for monitoring atmospheric methane concentrations: A case study with livestock sources

The substantial increase in the presence of greenhouse gases (GHGs) in the atmosphere has led to the development of several sampling techniques to quantify and characterize the sources of high global warming potential gas emissions. In this context, we developed a new method to estimate the time-averaged concentration of atmospheric methane that employs a long hose to collect a sample of gas by diffusion through one of its ends. We performed numerical simulations to illustrate the basis of our method and to determine the numerical factors required to estimate the time-averaged concentration of methane. This novel technique for estimating the mean gas concentration was then validated with two sets of experiments, where the source of methane was ruminant enteric fermentation measured in a respiration chamber. We compared the time-averaged methane concentration obtained with our methodology for periods (T) ranging from 1 to 4 days with those measured using the sensor of a respiration chamber. We found that the accuracy of the estimates improved as T increased from an error of 20 % for T = 1 to an error smaller than 10 % for T ≥ 2 days. In additional tests, and as suggested by numerical simulations, we confirmed that measuring and employing the methane concentration in the furthest half of the collector leads to a more precise estimation of atmospheric concentration than when the concentration of the entire collector is considered. This work demonstrates that the new methodology for air sampling, in conjunction with numerical analysis, is a viable alternative for quantifying atmospheric methane concentrations. In addition, the simple design of the devices showed remarkable benefits in terms of both the cost and simplicity for implementing large-scale individual sampling. We discuss its potential application to other GHGs.

Monitoring Methane Concentrations with High Spatial Resolution over China by Using Random Forest Model

Atmospheric methane is one of the major greenhouse gases with a drastic impact on climate change. This study developed a random forest model to obtain a daily 5 km resolution atmospheric methane concentration dataset with full spatial coverage (100%) from 2019 to 2021 in mainland China, thereby filling the gap in the methane product data from the Tropospheric Monitoring Instrument (TROPOMI). The coefficients of determination for a sample-based and spatial-based cross-validation are 0.97 and 0.93, respectively. The average deviation of the seamless methane product reconstructed by the random forest model is less than 1%, validated with the measured methane concentration data from the Total Carbon Column Observing Network sites. Methane concentrations in China show a distribution of high in the east and south and low in the west and north. The high-concentration areas include Central China, the Sichuan Basin, the Pearl River Delta, and the Yangtze River Delta. In terms of time scale, the methane concentration has evident seasonal variation, as it is low in spring (average 1852 ppb) and winter (average 1881 ppb) and high in summer (average 1885 ppb) and autumn (average 1886 ppb). This is mainly due to the significant increase in emissions from rice cultivation and wetlands during the summer and autumn. During the COVID-19 pandemic, the methane concentration decreases significantly and then starts to return to normal around 70 days after the Lunar New Year, indicating that the seamless methane product can potentially detect anomalous changes in methane concentration.

Unveiling the impact of soil methane sink on atmospheric methane concentrations in 2020.

In 2020, anthropogenic methane (CH4) emissions decreased due to COVID-19 containment policies, but there was a substantial increase in the concentration of atmospheric CH4. Previous research suggested that this abnormal increase was linked to higher wetland CH4 emissions and a decrease in the atmospheric CH4 sink. However, the impact of changes in the soil CH4 sink remained unknown. To address this, we utilized a process-based model to quantify alterations in the soil CH4 sink of terrestrial ecosystems between 2019 and 2020. By implementing the model with various datasets, we consistently observed an increase in the global soil CH4 sink, reaching up to 0.35 ± 0.06 Tg in 2020 compared to 2019. This increase was primarily attributed to warmer soil temperatures in northern high latitudes. Our results emphasize the importance of considering the CH4 sink in terrestrial ecosystems, as neglecting this component can lead to an underestimation of both emission increases and reductions in atmospheric CH4 sink capacity. Furthermore, these findings highlight the potential role of increased soil warmth in terrestrial ecosystems in slowing the growth of CH4 concentrations in the atmosphere.

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.