Search Results (218)

The Sichuan Basin is a natural-gas-exploiting area with intensive agriculture activities. However, the spatial and temporal distribution of atmospheric methane concentration and the relationships with intensive agriculture and natural gas extraction activities are not well investigated. In this study, a long-term (2003–2021) dataset of column-averaged dry-air mole fraction of methane (XCH₄) over the Sichuan Basin and adjacent regions was built by integrating multi-satellite remote sensing data (SCIAMACHY, GOSAT, Sentinel-5P), which was calibrated using ground station data. The results show a strong correlation and consistency (R = 0.88) between the ground station and satellite observations. The atmospheric CH₄ concentration of the Sichuan Basin showed an overall higher level (around 20 ppb) than that of the whole of China and an increasing trend in the rates, from around 2.27 ppb to 10.44 ppb per year between 2003 and 2021. The atmospheric CH₄ concentration of the Sichuan Basin also exhibits clear seasonal changes (higher in the summer and autumn and lower in the winter and spring) with a clustered geographical distribution. Agricultural activities and natural gas extraction contribute significantly to atmospheric methane concentrations in the study area, which should be considered in carbon emission management. This study provides an effective way to investigate the spatiotemporal distribution of atmospheric CH₄ concentration and related factors at a regional scale with natural and human influences using multi-source satellite remote sensing data. Full article

This study considers methods for assessing the quality of orbital observations, quantifying drift over time, and the application of correction methods to long-term series. AIRS v6 (IR-only) satellite methane (CH₄) and carbon monoxide (CO) total column (TC) measurements were compared with NDACC ground station data from 2003 to 2022. For CH₄, negative trends were observed in the difference between satellite and ground measurements (AIRS-GR) at all 18 stations (mean drift: 1.69 × 10¹⁴ ± 0.31 × 10¹⁴ molecules/cm² per day), suggesting a shift in the orbital spectrometer parameters is probable. The application of a dynamic correction based on this drift coefficient significantly improved the correlation with satellite data for both daily means and trends at all stations. In contrast, AIRS v6 CO measurements showed a strong initial correlation (R = 0.93 for the entire dataset, and R ~ 0.8–0.95 for separate stations) without systematic drift, i.e., the trends of AIRS-GR at individual sites were oppositely directed and statistically insignificant. Therefore, the AIRS v6 CO TC satellite product does not require additional correction within this method. The developed methodology for satellite data verification and correction is supposed to be universal and applicable to other long-term orbital observations. Full article

It is a well-known fact that proper nutrition is essential for human beings to live healthy lives. For thousands of years, it has been considered that dates are one of the best nutrient providers. To have better-quality dates and to enhance the shelf life of dates, it is vital to preserve dates in optimal conditions that contribute to food security. Hence, it is crucial to know the shelf life of different types of dates. In current practice, shelf life assessment is typically based on manual visual inspection, which is subjective, error-prone, and requires considerable expertise, making it difficult to scale across large storage facilities. Traditional cold storage systems, whilst being capable of monitoring temperature and humidity, lack the intelligence to detect spoilage or predict shelf life in real-time. In this study, we present a novel IoT-based shelf life estimation system that integrates multichannel gas sensors and a lightweight machine learning model deployed on an edge device. Unlike prior approaches, our system captures the real-time emissions of spoilage-related gases (methane, nitrogen dioxide, and carbon monoxide) along with environmental data to classify the freshness of date fruits. The model achieved a classification accuracy of 91.9% and an AUC of 0.98 and was successfully deployed on an Arduino Nano 33 BLE Sense board. This solution offers a low-cost, scalable, and objective method for real-time shelf life prediction. This significantly improves reliability and reduces postharvest losses in the date supply chain. Full article

Intergovernmental Panel on Climate Change (IPCC) guidelines have been standardized and widely used to calculate methane (CH₄) emissions from paddy fields. The emission factor (EF) is a key parameter in these guidelines, and it is different for each location globally and regionally. However, limited studies have been conducted to measure locally specific EFs (EFlocal) through on-site assessments and modeling their spatial distribution effectively. This study aims to investigate the potential of multisensor satellite data to develop a spatial model of CH₄ emission estimation on rice paddy fields under different water management practices, i.e., continuous flooding (CF) and alternate wetting and drying (AWD) in Subang, West Java, Indonesia. The model employed the national EF (EFnational) and EFlocal using the IPCC guidelines. In this study, we employed the multisensor satellite data to derive the key parameters for estimating CH₄ emission, i.e., rice cultivation area, rice age, and EF. Optical high-resolution images were used to delineate the rice cultivation area, Sentinel-1 SAR imagery was used for identifying transplanting and harvesting dates for rice age estimation, and ALOS-2/PALSAR-2 was used to map the water regime for determining the scaling factor of the EF. The closed-chamber method has been used to measure the daily CH₄ flux rate on the local sites. The results revealed spatial variability in CH₄ emissions, ranging from 1–5 kg/crop/season to 20–30 kg/crop/season, depending on the water regime. Fields under CF exhibited higher CH₄ emissions than those under AWD, underscoring the critical role of water management in mitigating CH₄ emissions. This study demonstrates the feasibility of combining remote sensing data with the IPCC model to spatially estimate CH₄ emissions, providing a robust framework for sustainable rice cultivation and greenhouse gas (GHG) mitigation strategies. Full article

Unmanned aerial vehicles, also termed as unarmed aerial vehicles, are used for various purposes in and around the environment, such as delivering things, spying on opponents, identification of aerial images, extinguishing fire, spraying the agricultural fields, etc. As there are multi-functions in a single UAV model, it can be used for various purposes as per the user’s requirement. The UAVs are used for faster communication of identified information, entry through the critical atmospheres, and causing no harm to humans before entering a collapsed path. In relation to the above discussion, a UAV system is designed to classify and transmit information about the atmospheric conditions of the environment to a central controller. The UAV is equipped with advanced sensors that are capable of detecting air pollutants such as carbon monoxide (CO), carbon dioxide (CO₂), methane (CH₄), ammonia (NH₃), hydrogen sulfide (H₂S), etc. These sensors present in the UAV model monitor the quality of air, time-to-time, as the UAV navigates through different areas and transmits real-time data regarding the air quality to a central unit; this data includes detailed information on the concentrations of different pollutants. The central unit analyzes the data that are captured by the sensor and checks whether the quality of air meets the atmospheric standards. If the sensed levels of pollutants exceed the thresholds, then the system present in the UAV triggers a warning alert; this alert is communicated to local authorities and the public to take necessary precautions. The developed UAV is furnished with cameras which are used to capture real-time images of the environment and it is processed using the YOLO V3 algorithm. Here, the YOLO V3 algorithm is defined to identify the context and source of pollution, such as identifying industrial activities, traffic congestion, or natural sources like wildfires. Full article

Methane (CH₄) is a potent greenhouse gas with a significant impact on short- and medium-term climate forcing, and its atmospheric concentration has been increasing rapidly in recent decades. This study aims to analyze spatio-temporal patterns of atmospheric methane concentrations between 2019 and 2025, focusing on comparisons between regions characterized by high and low emission intensities. Level-3 XCH₄ data from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite were used, which were aggregated into seasonal and annual composites. High-emission regions, such as the Mekong Delta, Nile Delta, Eastern Uttar Pradesh and Bihar, Central Thailand, Lake Victoria Basin, and Eastern Arkansas, were contrasted with low-emission areas including Patagonia, the Mongolian Steppe, Northern Scandinavia, the Australian Outback, the Sahara Desert, and the Canadian Shield. The results show that high-emission regions exhibit substantially higher seasonal amplitude in XCH₄ concentrations, with an average seasonal variation of approximately 30.00 ppb, compared to 17.39 ppb in low-emission regions. Methane concentrations generally peaked at the end of the year (Q4) and reached their lowest levels during the first half of the year (Q1 or Q2), particularly in agriculturally dominated regions. Principal component and cluster analyses further confirmed a strong spatial differentiation between high- and low-emission regions based on both temporal trends and seasonal behavior. These findings demonstrate the potential of satellite remote sensing to monitor regional methane dynamics and highlight the need for targeted mitigation strategies in major agricultural and wetland zones. Full article

Since pre-industrial times, anthropogenic methane emissions have increased and are partly responsible for a changing global climate. Natural gas and oil extraction activities are one significant source of anthropogenic methane. While methods have been developed and refined to quantify onshore methane emissions, the ability of methods to directly quantify emissions from offshore production facilities remains largely unknown. Here, we review recent studies that have directly measured emissions from offshore production facilities and critically evaluate the suitability of these measurement strategies for emission quantification in a marine environment. The average methane emissions from production platforms measured using downwind dispersion methods were 32 kg h⁻¹ from 188 platforms; 118 kg h⁻¹ from 104 platforms using mass balance methods; 284 kg h⁻¹ from 151 platforms using aircraft remote sensing; and 19,088 kg h⁻¹ from 10 platforms using satellite remote sensing. Upon review of the methods, we suggest the unusually large emissions, or zero emissions observed could be caused by the effects of a decoupling of the marine boundary layer (MBL). Decoupling can happen when the MBL becomes too deep or when there is cloud cover and results in a stratified MBL with air layers of different depths moving at different speeds. Decoupling could cause: some aircraft remote sensing observations to be biased high (lower wind speed at the height of the plume); the mass balance measurements to be biased high (narrow plume being extrapolated too far vertically) or low (transects miss the plume); and the downwind dispersion measurements much lower than the other methods or zero (plume lofting in a decoupled section of the boundary layer). To date, there has been little research on the marine boundary layer, and guidance on when decoupling happens is not currently available. We suggest an offshore controlled release program could provide a better understanding of these results by explaining how and when stratification happens in the MBL and how this affects quantification methodologies. Full article

Supramolecular compounds are capable of encapsulating small molecules to form host–guest compounds, which can be combined with sound surface wave technology to achieve high-precision detection of specific gases. In this paper, we analyzed the adsorption ability of Cryptophane-A and Cryptophane-E, the caged supramolecular materials, at room temperature by numerical simulation using first principles. The geometrical optimization of Cryptophane-A, Cryptophane-E, and gas molecules was carried out by the Dmol³ module in Materials Studio. Through adsorption calculation of gas molecules, the change of density of states and the magnitude of adsorption energy of Cryptophane-A and E were compared and analyzed. The results show that Cryptophane-A and E are van der Waals adsorption for molecules in gas (except CO₂ and C₂H₆). The adsorption energy of Cryptophane-A is lower than that of Cryptophane-E, but it is more selective and has preferential adsorption for methane. In this paper, we also tried to calculate the adsorption of Cryptophane-A and E on two methane molecules. The result showed that the former could adsorb two methane molecules, but the adsorption energy was lower than that of one methane molecule; the latter could not adsorb two methane molecules stably. The study shows that Cryptophane-A is more suitable as a sensitive material for CH₄ detection, which provides support for the development of acoustic surface wave methane detection technology. Full article

As global atmospheric methane concentrations surge at an unprecedented rate, the identification of methane super-emitters with significant mitigation potential has become imperative. In this study, we utilize remote sensing satellite data with varying spatiotemporal coverage and resolutions to detect and quantify methane emissions. We exploit the synergistic potential of Sentinel-2, EnMAP, and GF5-02-AHSI for methane plume detection. Employing a matched filtering algorithm based on EnMAP and AHSI, we detect and extract methane plumes within emission hotspots in China and the United States, and estimate the emission flux rates of individual methane point sources using the IME model. We present methane plumes from industries such as oil and gas (O&G) and coal mining, with emission rates ranging from 1 to 40 tons per h, as observed by EnMAP and GF5-02-AHSI. For selected methane emission hotspots in China and the United States, we conduct long-term monitoring and analysis using Sentinel-2. Our findings reveal that the synergy between Sentinel-2, EnMAP, and GF5-02-AHSI enables the precise identification of methane plumes, as well as the quantification and monitoring of their corresponding sources. This methodology is readily applicable to other satellite instruments with coarse SWIR spectral bands, such as Landsat-7 and Landsat-8. The high-frequency satellite-based detection of anomalous methane point sources can facilitate timely corrective actions, contributing to the reduction in global methane emissions. This study underscores the potential of spaceborne multispectral imaging instruments, combining fine pixel resolution with rapid revisit rates, to advance the global high-frequency monitoring of large methane point sources. Full article

Methane is the second largest greenhouse gas. The detection of methane super-emitters and the quantification of their emission rates are necessary for the implementation of methane emission reduction policies to mitigate global warming. High-spectral-resolution satellites such as Gaofen-5 (GF-5), EMIT, GHGSat, and MethaneSAT have been successfully employed to detect and quantify methane point source leaks. In this study, a matched filter (MF) algorithm is improved using data from the EMIT instrument and applied to data from the Advanced Hyperspectral Imager (AHSI) onboard the Ziyuan-1 (ZY-1) satellite. Validation by comparison with EMIT′s L2 XCH₄ products shows the good performance of the improved MF algorithm, in spite of the lower spectral resolution of AHSI/ZY-1 in comparison with other point source imagers. The improved MF algorithm applied to AHSI/ZY-1 data was used to detect and quantify methane super-emitters in global methane hotspot regions. The results show that the improved MF algorithm effectively suppresses noise in retrieval results over both land and ocean surfaces, enhancing algorithm robustness. Sixteen methane plumes were detected in global hotspot regions, originating from coal mines, oil and gas fields, and landfills, with emission rates ranging from 0.57 to 78.85 t/h. The largest plume was located at an offshore oil and gas field in the Gulf of Mexico, with instantaneous emissions nearly equal to the combined total of the other 15 plumes. The findings demonstrate that AHSI, despite its lower spectral resolution, can detect sources with emission rates as small as 571 kg/h and achieve faster retrieval speeds, showing significant potential for global methane monitoring. Additionally, this study highlights the need to focus on methane emissions from marine sources, alongside terrestrial sources, to efficiently implement reduction strategies. Full article

In this paper, based on the multimode interference structure fiber and the sensitive advantages of a zeolitic imidazolate framework-8/Polydimethylsiloxane (ZIF-8/PDMS)-sensitive film in methane detection, a methane sensor based on an interferometer induced by multimode interference is designed and built with the aid of modeling. The methane-sensitive single mode fiber (MS-SMF) is obtained by coating a ZIF-8/PDMS-sensitive film around the cladding of a thin-diameter SMF. The change in methane concentration leads to a change in the cladding mode of the MS-SMF, which causes a change in interference spectrum and realizes methane concentration sensing. The factors affecting the sensitivity of the methane sensor are analyzed. Methane sensors with various parameters are fabricated and tested on a methane sensor platform for performance estimation at methane concentrations of 0–4%. The experimental results show that the sensitivity of the sensor to methane reaches 2.364 nm/% when the length of the MS-SMF is 42 mm, the thickness of the sensitive film is 1.8 µm, and the diameter of the MS-SMF is 58 µm. The limit of detection is about 338 ppm. The average response time is 30 s and the recovery time is 45 s. The temperature sensitivity of the methane sensor is approximately 0.026 nm/°C. The experimental results verify the correctness of the methane sensor model. This study provides a new design idea for optical methane sensors, showing great application potential in the field of methane detection. Full article

The quantification of methane concentrations in air is essential for the quantification of methane emissions, which in turn is necessary to determine absolute emissions and the efficacy of emission mitigation strategies. These are essential if countries are to meet climate goals. Large-scale deployment of methane analyzers across millions of emission sites is prohibitively expensive, and lower-cost instrumentation has been recently developed as an alternative. Currently, it is unclear how cheaper instrumentation will affect measurement resolution or accuracy. To test this, the Wireless Autonomous Transportable Methane Emission Reporting System (WATCH₄ERS) has been developed, comprising four commercially available sensing technologies: metal oxide (MOx,), Non-dispersion Infrared (NDIR), integrated infrared (INIR), and tunable diode laser absorption spectrometer (TDLAS). WATCHERS is the accumulated knowledge of several long-term methane measurement projects at Colorado State University’s Methane Emission Technology Evaluation Center (METEC), and this study describes the integration of these sensors into a single unit and reports initial instrument response to calibration procedures and controlled release experiments. Specifically, this paper aims to describe the development of the WATCH₄ERS unit, report initial sensor responses, and describe future research goals. Meanwhile, future work will use data gathered by multiple WATCH₄ERS units to 1. better understand the cost–benefit balance of methane sensors, and 2. identify how decreasing instrumentation costs could increase deployment coverage and therefore inform large-scale methane monitoring strategies. Both calibration and response experiments indicate the INIR has little practical use for measuring methane concentrations less than 500 ppm. The MOx sensor is shown to have a logarithmic response to methane concentration change between background and 600 ppm but it is strongly suggested that passively sampling MOx sensors cannot respond fast enough to report concentrations that change in a sub-minute time frame. The NDIR sensor reported a linear change to methane concentration between background and 600 ppm, although there was a noticeable lag in reporting changing concentration, especially at higher values, and individual peaks could be observed throughout the experiment even when the plumes were released 5 s apart. The TDLAS sensor reported all changes in concentration but remains prohibitively expensive. Our findings suggest that each sensor technology could be optimized by either operational design or deployment location to quantify methane emissions. The WATCH₄ERS units will be deployed in real-world environments to investigate the utility of each in the future. Full article

This study presents the fabrication and characterization of highly selective, room-temperature gas sensors based on ternary zinc oxide–molybdenum disulfide–titanium dioxide (ZnO-MoS₂-TiO₂) nanoheterostructures. Integrating two-dimensional (2D) MoS₂ with oxide nano materials synergistically combines their unique properties, significantly enhancing gas sensing performance. Comprehensive structural and chemical analyses, including scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), Raman spectroscopy, and Fourier transform infrared spectroscopy (FTIR), confirmed the successful synthesis and composition of the ternary nanoheterostructures. The sensors demonstrated excellent selectivity in detecting low concentrations of nitrogen dioxide (NO₂) among target gases such as ammonia (NH₃), methane (CH₄), and carbon dioxide (CO₂) at room temperature, achieving up to 58% sensitivity at 4 ppm and 6% at 0.1 ppm for NO₂. The prototypes demonstrated outstanding selectivity and a short response time of approximately 0.51 min. The impact of light-assisted enhancement was examined under 1 mW/cm² weak ultraviolet (UV), blue, yellow, and red light-emitting diode (LED) illuminations, with the blue LED proving to deliver the highest sensor responsiveness. These results position ternary ZnO-MoS₂-TiO₂ nanoheterostructures as highly sensitive and selective room-temperature NO₂ gas sensors that are suitable for applications in environmental monitoring, public health, and industrial processes. Full article

An important issue in the sustainable development of agricultural engineering today is the use of biogas plants for the production of electricity and heat from the organic waste of agricultural products and other low-quality products, which also contributes to the improvement of environmental safety. Traditional methods for assessing the apparent severity of the Roslynnytsia campaign based on statistics from the dominions proved to be ineffective. A hypothesis was proposed regarding the possibility of estimating the apparent biomass by averaging the indicators of depletion and assessing the CH₄ and CO emissions based on satellite monitoring data. The aim of this work is to create a methodology for preparing a raw material base in united territorial communities to provide them with electrical and thermal energy using biogas plants. The achievement of this goal was based on solving the following tasks: monitoring biomethane emissions in the atmosphere as a result of rotting organic waste, and monitoring carbon monoxide emissions as a result of burning agricultural waste. Experimental studies were conducted using earth satellites on sites with geometric centers in the village of Gaishin in the Pereyaslav united territorial community, the city of Ovruch in the Zhytomyr region, the Oleshkovsky Sands National Park in the Kherson region (Ukraine), and the city of Jüterbog, which is located in the state of Brandenburg and is part of the Teltow-Fläming district (Germany). The most significant results of this research involve the methodology for the preparation of the raw material base in the united territorial communities for the production of biogas, based on indirect measurements of methane and carbon dioxide emissions using the process of remote sensing. Based on the use of the proposed scientific and methodological apparatus, it was found that the location of the territory with the center in the village of Gaishin has better prospects for collecting plant raw materials for biogas production than the location of the territorial district with the center in the city of Ovruch, the emissions in which are significantly lower. From March 2020–August 2023, a higher CO concentration was recorded on average by 0.0009 mol/m², which is explained precisely by crop growing practices. In addition, as a result of the conducted studies, for the considered emissions of methane and carbon monoxide for monitoring promising raw materials, carbon monoxide has the best prospects, since methane emissions can also be caused by anthropogenic factors. Thus, in the desert (Oleshkivskie Pisky), large methane emissions were recorded throughout the year which could not be explained by crop growing practices or the livestock industry. Full article

In this article, we present experimental and simulation results of a novel high-performance cascaded directional coupler-based triplexer. The device is designed to combine the wavelengths of 1530 nm, 1653.7 nm, and 2003 nm for use in spectroscopy systems targeting the detection of ammonia, methane, and carbon dioxide gases, respectively. The triplexer’s functions focus on enhancing the coupling efficiency and selectivity, while facilitating the on-chip integration of diode lasers. The experimental results demonstrate that the coupling efficiency is 82%, 73%, and 91% for the respective wavelengths of 1530 nm, 1653.7 nm, and 2003 nm. The results highlight the triplexer’s capability as a multifunctional beam combiner and an adaptable power source, essential for advanced gas sensing techniques and integrated couplers. Full article