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We investigate the ability of current and scheduled satellite measurements of atmospheric methane in the shortwave infrared to measure methane emissions from the global scale down to point sources. We classify satellite devices as area flux mappers and point source imagers with similar functionality. These high-precision devices with 0. 1-to-ten km pixel size are intended to record total methane emissions on a regional to global scale. Point source imagers are fine-pixel cameras that can determine individual point sources by analyzing the plumes. The GHGSat constellation and several hyperspectral and multispectral land imaging devices are among the latest point source imagers, with detection thresholds in the 100–10000 kg h-1 range. Through increased geographic coverage and frequent return times, the newest generation of Carbon Mapper point source imagers would achieve high observing system completeness for point sources.
Source link: https://doi.org/10.5194/acp-2022-246
Coal mining accounts for over 12% of global total anthropogenic methane emissions. Methane emissions in Europe are concentrated in Upper Silesian Basin, Poland, where large amounts of CH4 are emitted to the atmosphere by ventilation shafts of underground hard coal mines, is one of Europe's most hot spots for methane emissions. Five individual ventilation shafts in the USCB were downwind of five separate ventilation shafts in the United StatesCB, during the Carbon Dioxide and CH4 mission 1. 0 launch in May – June 2018. We flew a recently installed active AirCore system aboard unmanned aerial vehicle to obtain CH4 and CO2 mole fractions 150-300 meters downwind of five individual ventilation shafts. We found a strong correlation between the quantified and hourly inventory data-averaged CH4 emissions, which would allow regional estimates of CH4 emissions to be generated by increasingscaling individual hourly inventory data of all shafts.
Source link: https://doi.org/10.5194/acp-2021-1061
Prior and posterior CH4 emissions were the most significant national difference between prior and subsequent CH4 emissions, mainly due to the energy sector. Our global system's emissions estimates are in good agreement with those of previous regional surveys and point source-specific reports. Global anthropogenic CH4 emissions for the first six months of 2020 were, on average, 470 Gg per month higher than for 2019, mainly due to the energy and agricultural sectors, according to our results. During the onset of the global slowdown energy sector's CH4 emissions from China, the first year-on-year positive trend in emissions, however, emissions dropped below predicted levels in later months. The cumulative effect of the COVID-19 downturn in CH4 emissions from March–June 2020 might be small relative to the long-term positive trend in emissions, according to the first 6 months of 2019/20.
Source link: https://doi.org/10.5194/acp-22-5961-2022
The most significant national difference found between prior and posterior CH4 emissions is from China, mainly due to the energy sector. According to previous basin-wide regional studies and point source specific reports, emission estimates from our global system align well with prior basin-wide regional studies and point source specific studies. Our findings indicate that global anthropogenic CH4 emissions for 2020 were 5. 8 Tg yr1 higher than those of 2019, mainly due to the energy and agricultural industries. The rise in gases, as well as subsequent atmospheric expansion, may have occurred with or without the COVID-19 stagnation. During the first months of the global slowdown, CH4 emissions from China increased; however, later months, emissions fell below predicted pre-slowdown limits. The cumulative effect of the slowdown on CH4 emissions from March to June 2020 has been modest, according to the authors. Future work aims to produce the global IFS inversion device and extend the 4D-Var window-length using a hybrid ensemble-variation technique.
Source link: https://doi.org/10.5194/acp-2021-1056
The Upper Silesian Coal Basin in southern Poland, one of Europe's biggest sources of anthropogenic methane emissions due to its abundant coal mining industry, the Upper Silesian Coal Basin in southern Poland is one of the world's largest sources of anthropogenic methane emissions. Here, we provide you with a CH4 emission estimate for coal mine ventilation plants in the United StatesCB. We report on six case studies for which we delayed emissions by investigating the mismatch between the measured downwind rises and simulations based on trajectory calculations and particles released outside of the ventilation shafts using the Lagrangian particle dispersion model FLEXPART. Three co-deployed wind lidars were led by wind fields estimated by WRF under assimilation of vertical wind profile measurements of three co-deployed wind lidars. We find that, depending on the catchment area of the downwind measurements, our ad hoc network can solve individual or groups of ventilation plants, but that checking the emissions averaging kernels is necessary to find correlated estimates.
Source link: https://doi.org/10.5194/acp-22-5859-2022
The Upper Silesian Coal Basin in southern Poland is one of Europe's most significant sources for anthropogenic methane emissions due to its abundant coal mining industry, the Upper Silesian Coal Basin. Here's a case study on CH4 emission estimates for coal mine ventilation plants in the United StatesCB. In approx. , the EM27/SUN was delivered in the four cardinal directions around the USCB. In approx. Using the Lagrangian particle dispersion program FLEXPART, we report on six case studies for which we delayed emissions by determining the mismatch between the measured downwind revisions and simulations based on trajectory modeling, releasing particles out of the ventilation shafts. Wind fields estimated by WRF under assimilation of vertical wind profile measurements of three co-deployed wind lidars was driving the latter.
Source link: https://doi.org/10.5194/acp-2021-978
Sensor results were evaluated using single blind certified gas samples and by comparison with a continuously operated quantum cascade tunable infrared laser differential absorption spectrometer system. These concentration thresholds were used to determine dispersion models for the sensors, and dispersion modeling was used to estimate concentrations that would have to be detected to find continuous and intermittent emission rates of 5–10 kg/hr from oil and gas production sites within 50 to 100 meters of the sensors. These findings show that several commercially available sensing systems are suitable for long-term methane monitoring in remote oil and gas production areas.
Source link: https://doi.org/10.5194/amt-2022-24
The center is constructed on an integrated Methane Inversion optimal estimation process and ready for use on Amazon Web Services. On the 0. 25° 0. 3125° grid, users choose a location and period of interest, and the IMI delivers an analytical solution for the Bayesian optimal emission estimates of emissions, including error statistics, website content, and visualization code for inspection of results. In addition, users can also configure their inversions to infer emissions for irregular areas of concern, swap in their own prior emission inventories, and modify inversion parameters. Once the Jacobian matrix for the analytical inversion has been established, Inversion ensembles can be made at minimal additional cost. Before performing the inversion, a preview function helps users to determine the TROPOMI information for their area and time period of interest.
Source link: https://doi.org/10.5194/gmd-2022-45
Methane emissions in China and the contributions from various industries are characterized by an inverse analysis of forensic methane measurements by the 2019 TROPOMI satellite data of atmospheric methane. The inversion uses the national sector-resolved anthropogenic emission inventory compiled by the Chinese government to the United Nations Framework Convention on Climate Change as a predecessor estimate and thus serves as a definitive assessment of the inventory. Tg a-1, China's highest estimate for total human emissions is 65. 0 Tg a-1, where parentheses indicate a wide variety. It is also higher than previous inverse studies that used the less granular GOSAT satellite observations and were conducted at coarser resolution, which were also higher than previous inverse studies that used the less costly GOSAT satellite imagery and were performed at coarser resolution. We are in particular better at separating coal and rice emissions. Our elevated livestock emissions are attributed in large part to northern China, where GOSAT has no sensitivity. Our higher waste emissions are one of at least part a rapid rise in wastewater treatment in China. The UNFCCC's report indicates that unaccounted super-emissions facilities would contribute to the underestimate of oil emissions. The majority of China's greenhouse gas emissions result from transport, in part because of low emission standards from manufacturing and in part because 42 percent of the gas is imported. Our estimate of emissions per unit of domestic gas production in China indicates a low life-cycle loss rate of 1. 7 percent, which would imply net climate gains from China's upcoming coal-to-gas transition. This small loss rate is nonetheless somewhat misleading considering China's high gas imports, including from Turkmenistan, where emission per unit of gas production is extremely high.
Source link: https://doi.org/10.5194/acp-2022-303
The residual methane emissions test has been established as the most suitable phenotype for determining the hazardous capacity of ruminant livestock due to the trait's freedom from animal development but also high correlation with daily methane emissions. Therefore, the aim of this research was to investigate the relationship between the rumen microbiota and RME in a population of finishing beef cattle. Individual animals using the GreenFeed Emissions Monitoring system for 21 days were estimated with Methane emissions for 21 days, over a median feed intake measurement period of 91 days. Following which a 30% difference in all expressions of methane emissions was noted between high and low RME ranked cattle, a difference in both high and low RME ranked animals was calculated for 282 crossbred finishing beef cattle. In low RME animals, an elevated prevalence of the genus Methanosphaera and Methanobrevibacter RO clade was observed within the rumen methanogen group.
Source link: https://doi.org/10.3389/fmicb.2022.855565
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