Evaluating the use of an Unmanned Aerial Vehicle (UAV)-based active AirCore system to quantify methane emissions from dairy cows

Innovative drone-based methodology for quantifying methane emissions from landfills

An accurate measurement of anthropogenic methane emissions is essential for improving the representation of greenhouse gas inventories and for mitigating the effects of climate change. Often, theoretical models overestimate actual emission values, while field measurements tend to be costly and/or labour-intensive. Landfills represent an important emission sector, necessitating continued investment in innovation and technology to limit fugitive emissions, particularly of methane. This study presents a novel method based on a mass balance approach to estimate fugitive methane emissions from landfills and has been tested at a solid waste landfill in Italy. Measurements were acquired using a drone equipped with a sensor, completed in just a few minutes and processed directly in the field. Results from two tests conducted a month apart are provided, each consisting of two downwind flights at the site. Emission rates varied from 320 ± 280 mg m-2h-1 to 578 ± 385 mg m-2h-1. The data was subsequently compared with the results obtained using the flux chamber method during the second test, highlighting values that were 2 to 4 times higher than those from the ground-based method. The findings of this study highlight the potential of UAV-based methodologies for measuring methane emissions compared to traditional methods. The speed of execution and processing is indeed crucial to providing accurate data and optimising both timings and flight models during an investigation.

Feed additives for methane mitigation: Recommendations for testing enteric methane-mitigating feed additives in ruminant studies

There is a need for rigorous and scientifically-based testing standards for existing and new enteric methane mitigation technologies, including antimethanogenic feed additives (AMFA). The current review provides guidelines for conducting and analyzing data from experiments with ruminants intended to test the antimethanogenic and production effects of feed additives. Recommendations include study design and statistical analysis of the data, dietary effects, associative effect of AMFA with other mitigation strategies, appropriate methods for measuring methane emissions, production and physiological responses to AMFA, and their effects on animal health and product quality. Animal experiments should be planned based on clear hypotheses, and experimental designs must be chosen to best answer the scientific questions asked, with pre-experimental power analysis and robust post-experimental statistical analyses being important requisites. Long-term studies for evaluating AMFA are currently lacking and are highly needed. Experimental conditions should be representative of the production system of interest, so results and conclusions are applicable and practical. Methane-mitigating effects of AMFA may be combined with other mitigation strategies to explore additivity and synergism, as well as trade-offs, including relevant manure emissions, and these need to be studied in appropriately designed experiments. Methane emissions can be successfully measured, and efficacy of AMFA determined, using respiration chambers, the sulfur hexafluoride method, and the GreenFeed system. Other techniques, such as hood and face masks, can also be used in short-term studies, ensuring they do not significantly affect feed intake, feeding behavior, and animal production. For the success of an AMFA, it is critically important that representative animal production data are collected, analyzed, and reported. In addition, evaluating the effects of AMFA on nutrient digestibility, animal physiology, animal health and reproduction, product quality, and how AMFA interact with nutrient composition of the diet is necessary and should be conducted at various stages of the evaluation process. The authors emphasize that enteric methane mitigation claims should not be made until the efficacy of AMFA is confirmed in animal studies designed and conducted considering the guidelines provided herein.

Methane emissions at pressure-regulating stations in China: A comparative analysis of various quantitative methods

As a key component of the distribution system, metering and pressure-regulating (M&R) stations provide an opportunity for effective mitigation of methane emissions. Given that these stations are readily accessible, above-ground facilities, routine methane emission monitoring can identify issues and with repair or upgrades this can lead to reduced methane emissions. This practice has become an important measure for addressing climate change. China aims to actively promote the monitoring, evaluation, and inventory improvement of distribution systems. In this study, methane detection and analysis were conducted at 11 pressure regulation-metering stations operated by a company in North China and Central China. Methane emissions were estimated using the dynamic flux chamber method, the inverse Gaussian plume modeling method (OTM33A), and the stochastic Lagrangian (LS) inverse modeling method. The results of the dynamic flux chamber method indicate that flanges, instrumentation and meters, and connectors are the primary sources of methane emissions at pressure-regulating stations, with emission factors of 0.0034 kg/h (CI: 0.0030-0.0038 kg/h), 0.0022 kg/h (CI: 0.0018-0.0025 kg/h), and 7.1e-04 (CI: 6.3e-04-8.0e-04) kg/h, respectively. The correlation between detected concentration and emission rate was weak. The cumulative fluxes calculated by the three methods were 0.109 kg/h (CI: 0.094-0.125 kg/h), 0.143 kg/h (CI: 0.015-0.337 kg/h), and 0.125 kg/h (CI: 0.035-0.218 kg/h). The results of controlled tests and field measurements suggest that the LS method offers a more reliable and physically accurate analysis of methane fluxes for pressure regulating-metering stations with limited measurement distances (typically less than 30 m) and small continuous emissions. The emission factor for the pressure-regulating stations in this study is 0.013 kg/h (CI: 0.008-0.023 kg/h) using the bootstrap Monte Carlo method. This research provides guidance for operators to implement fixed monitoring, which is crucial for building emission inventories and ensuring production safety.

High-resolution satellite estimates of coal mine methane emissions from local to regional scales in Shanxi, China

Coal mines are significant anthropogenic sources of methane emissions, detectable and traceable from high spatial resolution satellites. Nevertheless, estimating local or regional-scale coal mine methane emission intensities based on high-resolution satellite observations remains challenging. In this study, we devise a novel interpolation algorithm based on high-resolution satellite observations (including Gaofen5-01A/02, Ziyuan-1 02D, PRISMA, GHGSat-C1 to C5, EnMAP, and EMIT) and conduct assessments of annual mean coal mine methane emissions in Shanxi Province, China, one of the world's largest coal-producing regions, spanning the period 2019 to 2023 across various scales: point-source, local, and regional. We use high-resolution satellite observations to perform interpolation-based estimations of methane emissions from three typical coal-mining areas. This approach, known as IPLTSO (Interpolation based on Satellite Observations), provides spatially explicit maps of methane emission intensities in these areas, thereby providing a novel local-scale coal mine methane emission inventory derived from high-resolution top-down observations. For regional-scale estimation and mapping, we utilize high-resolution satellite data to complement and substitute facility-level emission inventories for interpolation (IPLTSO+GCMT, Interpolation based on Satellite Observations and Global Coal Mine Tracker). We evaluate our IPLTSO and IPLTSO+GCMT estimation with emission inventories, top-down methane emission estimates from TROPOMI observations, and TROPOMI's methane concentration enhancements. The results suggest a notable right-skewed distribution of methane emission flux rates from coal mine point sources. Our IPLTSO+GCMT estimates the annual average coal mine methane emission in Shanxi Province from 2019 to 2023 at 8.9 ± 0.5 Tg/yr, marginally surpassing top-down inversion results from TROPOMI (8.5 ± 0.6 Tg/yr in 2019 and 8.6 ± 0.6 Tg/yr in 2020). Furthermore, the spatial patterns of methane emission intensity delineated by IPLTSO+GCMT and IPLTSO closely mirror those observed in TROPOMI's methane enhancements. Our comparative assessment underscores the superior performance and substantial potential of the developed interpolation algorithm based on high-resolution satellite observations for multi-scale estimation of coal mine methane emissions.

First detection of industrial hydrogen emissions using high precision mobile measurements in ambient air

Projections towards 2050 of the global hydrogen (H2) demand indicate an eight-fold increase in present-day hydrogen consumption. Leakage during production, transport, and consumption therefore presents a large potential for increases in the atmospheric hydrogen burden. Although not a greenhouse gas itself, hydrogen has important indirect climate effects, and the Global Warming Potential of H2 is estimated to be 12.8 times that of CO2. Available technologies to detect hydrogen emissions have been targeted at risk mitigation of industrial facilities, while smaller climate-relevant emissions remain undetected. The latter requires measurement capacity at the parts-per-billion level (ppb). We developed and demonstrated an effective method to detect small hydrogen emissions from industrial installations that combines active AirCore sampling with ppb-precision analysis by gas chromatography. We applied our methodology at a chemical park in the province of Groningen, the Netherlands, where several hydrogen production and storage facilities are concentrated. From a car and an unmanned aerial vehicle, we detected and quantified for the first time small but persistent industrial emissions from leakage and purging across the hydrogen value chain, which include electrolysers, a hydrogen fuelling station, and chemical production plants. Our emission estimates indicate current loss rates up to 4.2% of the estimated production and storage in these facilities. This is sufficiently large to urgently flag the need for monitoring and verification of H2 emissions for the purpose of understanding our climate change trajectory in the 21st century.

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.

Automatic Monitoring Methods for Greenhouse and Hazardous Gases Emitted from Ruminant Production Systems: A Review

The research on automatic monitoring methods for greenhouse gases and hazardous gas emissions is currently a focal point in the fields of environmental science and climatology. Until 2023, the amount of greenhouse gases emitted by the livestock sector accounts for about 11-17% of total global emissions, with enteric fermentation in ruminants being the main source of the gases. With the escalating problem of global climate change, accurate and effective monitoring of gas emissions has become a top priority. Presently, the determination of gas emission indices relies on specialized instrumentation such as breathing chambers, greenfeed systems, methane laser detectors, etc., each characterized by distinct principles, applicability, and accuracy levels. This paper first explains the mechanisms and effects of gas production by ruminant production systems, focusing on the monitoring methods, principles, advantages, and disadvantages of monitoring gas concentrations, and a summary of existing methods reveals their shortcomings, such as limited applicability, low accuracy, and high cost. In response to the current challenges in the field of equipment for monitoring greenhouse and hazardous gas emissions from ruminant production systems, this paper outlines future perspectives with the aim of developing more efficient, user-friendly, and cost-effective monitoring instruments.

Advances in methane emissions from agricultural sources: Part I. Accounting and mitigation

Methane is one of the major greenhouse gases (GHGs) and agriculture is recognized as its primary emitter. Methane accounting is a prerequisite for developing effective agriculture mitigation strategies. In this review, methane accounting methods and research status for various agricultural emission source including rice fields, animal enteric fermentation and livestock and poultry manure management were overview, and the influencing factors of each emission source were analyzed and discussed. At the same time, it analyzes the different research efforts involving agricultural methane accounting and makes recommendations based on the actual situation. Finally, mitigation strategies based on accounting results and actual situation are proposed. This review aims to provide basic data and reference for agriculture-oriented countries and regions to actively participate in climate action and carry out effective methane emission mitigation.

Reconciling a national methane emission inventory with in-situ measurements

Reconciling top-down and bottom-up country-level greenhouse gas emission estimates remains a key challenge in the MRV (Monitoring, Reporting, Verification) paradigm. Here we propose to independently quantify cumulative emissions from a significant number of methane (CH4) emitters at national level and derive robust constraints for the national inventory. Methane emissions in Cyprus, an insular country, stem primarily from waste and agricultural activities. We performed 24 intensive survey days of mobile measurements of CH4 from October 2020 to September 2021 at emission 'hotspots' in Cyprus accounting together for about 28 % of national CH4 emissions. The surveyed areas include a large active landfill (Koshi, 8 % of total emissions), a large closed landfill (Kotsiatis, 18 %), and a concentrated cattle farm area (Aradippou, 2 %). Emission rates for each site were estimated using repeated downwind transects and a Gaussian plume dispersion model. The calculated methane emissions from landfills of Koshi and Kotsiatis (25.9 ± 6.4 Gg yr-1) and enteric fermentation of cattle (10.4 ± 4.4 Gg yr-1) were about 129 % and 40 % larger, respectively than the bottom-up sectorial annual estimates used in the national UNFCCC inventory. The parametrization of the Gaussian plume model dominates the uncertainty in our method, with a typical 21 % uncertainty. Seasonal variations have little influence on the results. We show that using an ensemble of in situ measurements targeting representative methane emission hotspots with consistent temporal and spatial coverage can contribute to the monitoring and validation of national bottom-up emission inventories.

State of the Art and Future Perspectives of Atmospheric Chemical Sensing Using Unmanned Aerial Vehicles: A Bibliometric Analysis

In recent years, unmanned aerial vehicles (UAVs) have been increasingly used to monitor and assess air quality. The interest in the application of UAVs in monitoring air pollutants and greenhouse gases is evidenced by the recent emergence of sensors with the most diverse specifications designed for UAVs or even UAVs designed with integrated sensors. The objective of this study was to conduct a comprehensive review based on bibliometrics to identify dynamics and possible trends in scientific production on UAV-based sensors to monitor air quality. A bibliometric analysis was carried out in the VOSViewer software (version 1.6.17) from the Scopus and Web of Science reference databases in the period between 2012 and 2022. The main countries, journals, scientific organizations, researchers and co-citation networks with greater relevance for the study area were highlighted. The literature, in general, has grown rapidly and has attracted enormous attention in the last 5 years, as indicated by the increase in articles after 2017. It was possible to notice the rapid development of sensors, resulting in smaller and lighter devices, with greater sensitivity and capacity for remote work. Overall, this analysis summarizes the evolution of UAV-based sensors and their applications, providing valuable information to researchers and developers of UAV-based sensors to monitor air pollutants.