High peatland methane emissions following permafrost thaw: enhanced acetoclastic methanogenesis during early successional stages

Permafrost thaw in northern peatlands often leads to increased methane (CH4) emissions, but gaps remain in our understanding of the underlying controls responsible for increased emissions and the duration for which they persist. We assessed how shifting ecological conditions affect microbial communities, and the magnitude and stable isotopic signature (δ13C) of CH4 emissions along a thermokarst bog transect in boreal western Canada. Thermokarst bogs develop following permafrost thaw when dry, elevated peat plateaus collapse and become saturated and dominated by Sphagnum mosses. We differentiated between a young and a mature thermokarst bog stage (~30 and years ~200 since thaw, respectively). The young bog located along the thermokarst edge, was wetter, warmer and dominated by hydrophilic vegetation compared to the mature bog. Using 16S rRNA gene high throughput sequencing, we show that microbial communities were distinct near the surface and converged with depth, but lesser differences remained down to the lowest depth (160 cm). Microbial community analysis and δ13C data from CH4 surface emissions and dissolved gas depth profiles show that hydrogenotrophic methanogenesis was the dominant pathway at both sites. However, the young bog was found to have isotopically heavier δ13C-CH4 in both dissolved gases profiles and surface CH4 emissions, suggesting that acetoclastic methanogenesis was relatively more enhanced throughout the young bog peat profile. Furthermore, young bog CH4 emissions were three times greater than the mature bog. Our study suggests that interactions between ecological conditions and methanogenic communities enhance CH4 emissions in young thermokarst bogs, but these favorable conditions only persist for the initial decades after permafrost thaw.

Quantification of CH₄ emissions from waste disposal sites near the city of Madrid using ground- and space-based observations of COCCON, TROPOMI and IASI

The objective of this study is to derive methane (CH4) emissions from three landfills, which are found to be the most significant CH4 sources in the metropolitan area of Madrid in Spain. We derive CH4 emissions from the CH4 enhancements observed by spaceborne and ground-based instruments. We apply satellite-based measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) together with measurements from the ground-based COllaborative Carbon Column Observing Network (COCCON) instruments. In 2018, a 2-week field campaign for measuring the atmospheric concentrations of greenhouse gases was performed in Madrid in the framework of Monitoring of the Greenhouse Gases Concentrations in Madrid (MEGEI-MAD) project. Five COCCON instruments were deployed at different locations around the Madrid city center, enabling the observation of total column-averaged CH4 mixing ratios (XCH4). Considering the prevalent wind regimes, we calculate the wind-assigned XCH4 anomalies for two opposite wind directions. Pronounced bipolar plumes are found when applying the method to NO2, which implies that our method of wind-assigned anomaly is suitable to estimate enhancements of trace gases at the urban level from satellite-based measurements. For quantifying the CH4 emissions, the wind-assigned plume method is applied to the TROPOMI XCH4 and to the lower tropospheric CH4 / dry-air column ratio (TXCH4) of the combined TROPOMI+IASI product. As CH4 emission strength we estimate 7.4 × 1025 ± 6.4 × 1024 molec. s−1 from the TROPOMI XCH4 data and 7.1 × 1025 ± 1.0 × 1025 molec. s−1 from the TROPOMI+IASI merged TXCH4 data. We use COCCON observations to estimate the local source strength as an independent method. COCCON observations indicate a weaker CH4 emission strength of 3.7 × 1025 molec. s−1 from a local source (the Valdemingómez waste plant) based on observations from a single day. This strength is lower than the one derived from the satellite observations, and it is a plausible result. This is because the analysis of the satellite data refers to a larger area, covering further emission sources in the study region, whereas the signal observed by COCCON is generated by a nearby local source. All emission rates estimated from the different observations are significantly larger than the emission rates provided via the official Spanish Register of Emissions and Pollutant Sources.

Quantification of methane emissions from hotspots and during COVID-19 using a global atmospheric inversion

Concentrations of atmospheric methane (CH4), the second most important greenhouse gas, continue to grow. In recent years this growth rate has increased further (2020: +14.7 ppb), the cause of which remains largely unknown. Here, we demonstrate a high-resolution (~80 km), short-window (24-hour) 4D-Var global inversion system based on the ECMWF Integrated Forecasting System (IFS) and newly available satellite observations. The largest national disagreement found between prior (63.1 Tg yr−1) and posterior (59.8 Tg yr−1) CH4 emissions is from China, mainly attributed to the energy sector. Emissions estimated form our global system agree well with previous basin-wide regional studies and point source specific studies. Emission events (leaks/blowouts) >10 t hr−1 were detected, but without accurate prior uncertainty information, were not well quantified. Our results suggest that global anthropogenic CH4 emissions for 2020 were 5.7 Tg yr−1 (+1.6 %) higher than for 2019, mainly attributed to the energy and agricultural sectors. Regionally, the largest 2020 increases were seen from China (+2.6 Tg yr−1, 4.3 %), with smaller increases from India (+0.8 Tg yr−1, 2.2 %) and Indonesia (+0.3 Tg yr−1, 2.6 %). Results show the rise in emissions, and subsequent atmospheric growth, would have occurred with or without the COVID-19 slowdown. During the onset of the global slowdown (March–April, 2020) energy sector CH4 emissions from China increased; however, during later months (May–June, 2020) emissions decreased below expected pre-slowdown levels. The accumulated impact of the slowdown on CH4 emissions from March–June 2020 is found to be small. Changes in atmospheric chemistry, not investigated here, may have contributed to the observed growth in 2020. Future work aims to develop the global IFS inversion system and to extend the 4D-Var window-length using a hybrid ensemble-variational method.

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.

Emission Determination by Three Remote Sensing Methods in Two Release Trials

Concentrations of greenhouse gases such as carbon dioxide (CO2), nitrous dioxide (N2O) and methane (CH4) in the atmosphere are rising continuously. The first step to reduce emissions from landfills is to gain better knowledge about the quantities emitted. There are several ways to quantify CH4 emissions at landfills. Comprehensive quality analyses of individual methods for emission rate quantification at landfills are few to date. In the present paper, the authors conducted two field trials with three different remote sensing methods to gain more knowledge about the possibilities and challenges in quantification of CH4 emissions from landfills. One release trial was conducted with released N2O as tracer and CH4 for quality assessment of the methods. In the second trial, the N2O tracer was released on a landfill to gain experience under field conditions. The well-established inverse dispersion modelling method (IDMM) was used based on concentration data of TDLAS (Tunable Diode Laser Absorption Spectroscopy)-instruments and on concentration data of a partly drone based Fourier-Transformation-Infrared-Spectroscopy (FTIR)-instrument. Additionally, a tracer-method with N2O-tracer and FTIR measurements was conducted. In both trials, IDMM based on TDLAS data and FTIR data provided the best results for high emission rates (15% deviation) and low emission rates (47% deviation). However, both methods have advantages, depending on the field of application. IDMM based on TDLAS measurements is the best choice for long-term measurements over several hours with constant wind conditions (8% deviation). The IDMM based on drone based FTIR measurements is the means of choice for measurements under changing wind conditions and where no linear measurement distances are possible.

Measuring Livestock CH4 Emissions with the Laser Methane Detector: A Review

The handheld, portable laser methane detector (LMD) was developed to detect gas leaks in industry from a safe distance. Since 2009, it has also been used to measure the methane (CH4) concentration in the breath of cattle, sheep, and goats to quantify their CH4 emissions. As there is no consensus on a uniform measurement and data-analysis protocol with the LMD, this article discusses important aspects of the measurement, the data analysis, and the applications of the LMD based on the literature. These aspects, such as the distance to the animal or the activity of the animals, should be fixed for all measurements of an experiment, and if this is not possible, they should at least be documented and considered as fixed effects in the statistical analysis. Important steps in data processing are thorough quality control and reduction in records to a single point measurement or “phenotype” for later analysis. The LMD can be used to rank animals according to their CH4 breath concentration and to compare average CH4 production at the group level. This makes it suitable for genetic and nutritional studies and for characterising different breeds and husbandry systems. The limitations are the lower accuracy compared to other methods, as only CH4 concentration and not flux can be measured, and the high amount of work required for the measurement. However, due to its flexibility and non-invasiveness, the LMD can be an alternative in environments where other methods are not suitable or a complement to other methods. It would improve the applicability of the LMD method if there were a common protocol for measurement and data analysis developed jointly by a group of researchers.

Effects of the Autumn Incorporation of Rice Straw and Application of Lime Nitrogen on Methane and Nitrous Oxide Emissions and Rice Growth of a High-Yielding Paddy Field in a Cool-Temperate Region in Japan

The effects of autumn plowing and lime nitrogen application on rice straw decomposition, CH4 and N2O emission and rice growth in the following year in a high-yielding rice cultivated paddy field were evaluated for two years. The experimental plots were set up, combining different times of rice straw (750 g m−2) incorporation into the soil by plowing (autumn or the following spring), with and without lime nitrogen application in autumn (5 g-N m−2). Autumn plowing promoted the decomposition of rice straw, but the application of lime nitrogen did not show a consistent trend. The soil pH was high (7.3) at the studied site, and the alkaline effect of lime nitrogen may not have been significant. As with straw decomposition, CH4 emissions were suppressed by autumn plowing, and no effect from the lime nitrogen application was observed. It was also suggested that the straw decomposition period may be shorter and the CH4 emissions may be higher in high-yielding cultivars that require a longer ripening period than in normal cultivars. The effect of both treatments on N2O emission was not clear. Both the autumn plowing of rice straw and lime nitrogen application were effective in promoting rice growth and increasing rice yield.