Recent Advances Toward Transparent Methane Emissions Monitoring: A Review

Progress in monitoring methane emissions from landfills using drones: an overview of the last ten years

Solid waste landfills are responsible for much of the anthropogenic methane emitted from the waste sector. The quantification of fugitive CH4 emissions from a landfill is to date characterised by high uncertainty and several methodologies have been devised to estimate emission fluxes. Unmanned Aerial Vehicles (UAVs, also known as drones) are revolutionising the way CH4 emission monitoring is conceived and offer new opportunities for quantifying emission fluxes from a landfill, mainly due to recent advances in sensor miniaturisation that make these instruments lighter and more suitable to be equipped on a drone. The paper analyses publications from the period 2014-2024 that illustrate UAV-based methods that can be used for this purpose, identifying experiences in the field and the current state of research. The review has highlighted a current research status characterised by a strong experimental focus, with few tests carried out in landfills under real emission conditions (33 % of the reviewed papers). Since 2018, there has been a growing interest in open-path sensors, tested in some controlled-release experiments according to different configurations which have given promising results, but experiences are limited and there are no experiments conducted directly in landfills. In general, the UAV-based methods identified by this systematic review are characterised by unclear uncertainties. Drones are a viable alternative to traditional monitoring methods at landfills and allow data to be acquired with a spatial and temporal resolution that can hardly be achieved by other low-cost methods. However, further studies and field trials are needed to better understand methodological aspects: especially the uncertainty of each step in the quantification process need to be properly analysed and quantified more precisely.

High-Sensitivity and In Situ Multi-Component Detection of Gases Based on Multiple-Reflection-Cavity-Enhanced Raman Spectroscopy

Raman spectroscopy with the advantages of the in situ and simultaneous detection of multi-components has been widely used in the identification and quantitative detection of gas. As a type of scattering spectroscopy, the detection sensitivity of Raman spectroscopy is relatively lower, mainly due to the low signal collection efficiency. This paper presents the design and assembly of a multi-channel cavity-enhanced Raman spectroscopy system, optimizing the structure of the sample pool to reduce the loss of the laser and increase the excitation intensity of the Raman signals. Moreover, three channels are used to collect Raman signals to increase the signal collection efficiency for improving the detection sensitivity. The results showed that the limits of detection for the CH4, H2, CO2, O2, and N2 gases were calculated to be 3.1, 34.9, 17.9, 27, and 35.2 ppm, respectively. The established calibration curves showed that the correlation coefficients were all greater than 0.999, indicating an excellent linear correlation and high level of reliability. Meanwhile, under long-time integration detection, the Raman signals of CH4, H2, and CO2 could be clearly distinguished at the concentrations of 10, 10, and 50 ppm, respectively. The results indicated that the designed Raman system possesses broad application prospects in complex field environments.

The challenge of estimating global termite methane emissions

Methane is a powerful greenhouse gas, more potent than carbon dioxide, and emitted from a variety of natural sources including wetlands, permafrost, mammalian guts and termites. As increases in global temperatures continue to break records, quantifying the magnitudes of key methane sources has never been more pertinent. Over the last 40 years, the contribution of termites to the global methane budget has been subject to much debate. The most recent estimates of termite emissions range between 9 and 15 Tg CH4 year-1, approximately 4% of emissions from natural sources (excluding wetlands). However, we argue that the current approach for estimating termite contributions to the global methane budget is flawed. Key parameters, namely termite methane emissions from soil, deadwood, living tree stems, epigeal mounds and arboreal nests, are largely ignored in global estimates. This omission occurs because data are lacking and research objectives, crucially, neglect variation in termite ecology. Furthermore, inconsistencies in data collection methods prohibit the pooling of data required to compute global estimates. Here, we summarise the advances made over the last 40 years and illustrate how different aspects of termite ecology can influence the termite contribution to global methane emissions. Additionally, we highlight technological advances that may help researchers investigate termite methane emissions on a larger scale. Finally, we consider dynamic feedback mechanisms of climate warming and land-use change on termite methane emissions. We conclude that ultimately the global contribution of termites to atmospheric methane remains unknown and thus present an alternative framework for estimating their emissions. To significantly improve estimates, we outline outstanding questions to guide future research efforts.

Effects of methane emissions on multiple myeloma-related mortality rates: A World Health Organization perspective

In this research, it was aimed to evaluate effects of methane emissions on multiple myeloma related mortality rates. Two countries in Europe (Germany and Netherlands) and 1 country for each region (Turkey, USA, Brazil, Egypt, and Australia) were selected within The World Health Organization Database. Multiple myeloma mortality rates of countries between 2009 and 2019 were used as dependent variable of the research. Methane emission level and agriculture methane levels of countries were used as independent variables from The World Bank Database. Current health expenditure and healthy life expectancy were used as controlling variables. Multiple myeloma-related mortality rate was the highest in the USA, followed by Germany, Brazil, Turkey, Australia, Netherlands, and Egypt. Difference analysis results were significant (P < .05). Methane and agriculture methane emissions were the highest in the USA. Multiple myeloma mortality was positively correlated with methane emissions (R = 0.504; P < .01), agricultural methane emissions (R = 0.705; P < .01), and current health expenditure (R = 0.528; P < .01). According to year and country controlled correlation analysis results, multiple myeloma mortality (MMM) was positively correlated with methane emissions (R = 0.889; P < .01), agricultural methane emissions (R = 0.495; P < .01), and current health expenditure (R = 0.704; P < .01). Methane emission (B = 0.01; P < .05), Germany (B = 9010.81; P < .01), the USA (B = 26516.77; P < .01), and Brazil (B = 4886.14; P < .01) had significant effect on MMM. Nonagricultural methane production has an increasing effect on MMM. Therefore, by looking at the differences between agricultural methane emissions and general methane emissions, studies can be conducted that allow for more effective global comparisons.

Point Sensor Networks Struggle to Detect and Quantify Short Controlled Releases at Oil and Gas Sites

This study evaluated multiple commercially available continuous monitoring (CM) point sensor network (PSN) solutions under single-blind controlled release testing conducted at operational upstream and midstream oil and natural gas (O&G) sites. During releases, PSNs reported site-level emission rate estimates of 0 kg/h between 38 and 86% of the time. When non-zero site-level emission rate estimates were provided, no linear correlation between the release rate and the reported emission rate estimate was observed. The average, aggregated across all PSN solutions during releases, shows 5% of the mixing ratio readings at downwind sensors were greater than the site's baseline plus two standard deviations. Four of seven total PSN solutions tested during this field campaign provided site-level emission rate estimates with the site average relative error ranging from -100% to 24% for solution D, -100% to -43% for solution E, -25% for solution F (solution F was only at one site), and -99% to 430% for solution G, with an overall average of -29% across all sites and solutions. Of all the individual site-level emission rate estimates, only 11% were within ±2.5 kg/h of the study team's best estimate of site-level emissions at the time of the releases.

City-level livestock methane emissions in China from 2010 to 2020

Livestock constitute the world's largest anthropogenic source of methane (CH4), providing high-protein food to humans but also causing notable climate risks. With rapid urbanization and increasing income levels in China, the livestock sector will face even higher emission pressures, which could jeopardize China's carbon neutrality target. To formulate targeted methane reduction measures, it is crucial to estimate historical and current emissions on fine geographical scales, considering the high spatial heterogeneity and temporal variability of livestock emissions. However, there is currently a lack of time-series data on city-level livestock methane emissions in China, despite the flourishing livestock industry and large amount of meat consumed. In this study, we constructed a city-level livestock methane emission inventory with dynamic spatial-temporal emission factors considering biological, management, and environmental factors from 2010 to 2020 in China. This inventory could serve as a basic database for related research and future methane mitigation policy formulation, given the population boom and dietary changes.