Methane Detection Research Articles

Chemiresistors Based on Hybrid Nanostructures Obtained from Graphene and Conducting Polymers with Potential Use in Breath Methane Detection Associated with Irritable Bowel Syndrome.

This paper describes the process of producing chemiresistors based on hybrid nanostructures obtained from graphene and conducting polymers. The technology of graphene presumed the following: dispersion and support stabilization based on the chemical vapor deposition technique; transfer of the graphene to the substrate by spin-coating of polymethyl methacrylate; and thermal treatment and electrochemical delamination. For the process at T = 950 °C, a better settlement of the grains was noticed, with the formation of layers predominantly characterized by peaks and not by depressions. The technology for obtaining hybrid nanostructures from graphene and conducting polymers was drop-casting, with solutions of Poly(3-hexylthiophene (P3HT) and Poly[(9,9-dioctylfluorenyl-2,7-diyl)-co-bithiophene] (F8T2). In the case of F8T2, compared to P3HT, a 10 times larger dimension of grain size and about 7 times larger distances between the peak clusters were noticed. To generate chemiresistors from graphene-polymer structures, an ink-jet printer was used, and the metallization was made with commercial copper ink for printed electronics, leading to a structure of a resistor with an active surface of about 1 cm2. Experimental calibration curves were plotted for both sensing structures, for a domain of CH4 of up to 1000 ppm concentration in air. A linearity of the curve for the low concentration of CH4 was noticed for the graphene structure with F8T2, presenting a sensitivity of about 6 times higher compared with the graphene structure with P3HT, which makes the sensing structure of graphene with F8T2 more feasible and reliable for the medical application of irritable bowel syndrome evaluation.

Integrated microcontroller mq sensors for monitoring biogas: Advancements in methane and hydrogen sulfide detection

Recent technological advances in microcontroller systems enable novel biogas monitoring capabilities. This study investigates microcontroller-based quantification of methane and hydrogen sulfide concentrations in biogas derived from anaerobic digestion. Anaerobic digesters were fed either 100% cow dung substrates or a 50:50 mixture of cow dung with municipal solid waste (MSW). Methane levels were monitored using an MQ-4 sensor, hydrogen sulfide via an MQ-136 sensor, and temperature with a K-type thermocouple, all integrated with an ATmega 2560 microcontroller system. The 100% cow dung digester produced biogas with maximum methane concentrations of 3488 ppm at 21 days, indicating improved methane production compared to the 50:50 mixture of cow dung with MSW. Hydrogen sulfide reached 195 ppm and 192 ppm for the 100% cow dung and mixed digesters. Mesophilic temperature conditions were maintained throughout the digestion process. Real-time quantification of biogas composition demonstrates the capabilities of microcontroller-based anaerobic digester monitoring to provide precise methane and hydrogen sulfide measurements.

A survey of methane point source emissions from coal mines in Shanxi province of China using AHSI on board Gaofen-5B

Abstract. Satellite-based detection of methane (CH4) point sources is crucial in identifying and mitigating anthropogenic emissions of CH4, a potent greenhouse gas. Previous studies have indicated the presence of CH4 point source emissions from coal mines in Shanxi, China, which is an important source region with large CH4 emissions, but a comprehensive survey has remained elusive. This study aims to conduct a survey of CH4 point sources over Shanxi's coal mines based on observations of the Advanced Hyperspectral Imager (AHSI) on board the Gaofen-5B satellite (GF-5B/AHSI) between 2021 and 2023. The spectral shift in centre wavelength and change in full width at half-maximum (FWHM) from the nominal design values are estimated for all spectral channels, which are used as inputs for retrieving the enhancement of the column-averaged dry-air mole fraction of CH4 (ΔXCH4) using a matched-filter-based algorithm. Our results show that the spectral calibration on GF-5B/AHSI reduced estimation biases of the emission flux rate by up to 5.0 %. We applied the flood-fill algorithm to automatically extract emission plumes from ΔXCH4 maps. We adopted the integrated mass enhancement (IME) model to estimate the emission flux rate values from each CH4 point source. Consequently, we detected CH4 point sources in 32 coal mines with 93 plume events in Shanxi province. The estimated emission flux rate ranges from 761.78 ± 185.00 to 12 729.12 ± 4658.13 kg h−1. Our results show that wind speed is the dominant source of uncertainty contributing about 84.84 % to the total uncertainty in emission flux rate estimation. Interestingly, we found a number of false positive detections due to solar panels that are widely spread in Shanxi. This study also evaluates the accuracy of wind fields in ECMWF ERA5 reanalysis by comparing them with a ground-based meteorological station. We found a large discrepancy, especially in wind direction, suggesting that incorporating local meteorological measurements into the study CH4 point source are important to achieve high accuracy. The study demonstrates that GF-5B/AHSI possesses capabilities for monitoring large CH4 point sources over complex surface characteristics in Shanxi.

Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer

Curbing methane emissions is among the most effective actions that can be taken to slow down global warming. However, monitoring emissions remains challenging, as detection methods have a limited quantification completeness due to trade-offs that have to be made between coverage, resolution, and detection accuracy. Here we show that deep learning can overcome the trade-off in terms of spectral resolution that comes with multi-spectral satellite data, resulting in a methane detection tool with global coverage and high temporal and spatial resolution. We compare our detections with airborne methane measurement campaigns, which suggests that our method can detect methane point sources in Sentinel-2 data down to plumes of 0.01 km2, corresponding to 200 to 300 kg CH4 h−1 sources. Our model shows an order of magnitude improvement over the state-of-the-art, providing a significant step towards the automated, high resolution detection of methane emissions at a global scale, every few days.

Report on Landsat 8 and Sentinel-2B observations of the Nord Stream 2 pipeline methane leak

Abstract. In late September 2022, explosions of the Nord Stream pipelines caused what could be the largest anthropogenic methane leak ever recorded. We report on Landsat 8 (L8) and Sentinel-2B (S-2B) observations of the sea-foam patch produced by the Nord Stream 2 (NS2) leak located close to Bornholm island, acquired on 29 and 30 September, respectively. Usually, reflected sunlight over sea is insufficient for these Earth imagers to observe any methane signal in nadir-viewing geometry. However, the NS2 foam patch observed here is bright enough to possibly allow the detection of methane above it. We apply the multi-band single-pass (MBSP) method to infer methane enhancement above the NS2 foam patch and then use the integrated mass enhancement (IME) method in a Monte Carlo ensemble approach to estimate methane leak rates and their uncertainties. This very specific NS2 observation case challenges some of MBSP and IME implicit assumptions and thus calls for customized calibrations: (1) for MBSP, we perform an empirical calibration of sea-foam albedo spectral dependence by using sea-foam observations in ship trails, and (2) for IME, we yield a tailored effective wind speed calibration that accounts for a partial plume observation, as methane enhancement may only be seen above the NS2 sea-foam patch. Our comprehensive uncertainty analysis yields large methane leak rate uncertainty ranges that include zero for single overpasses and, assuming they are independent, a best estimate of 502 ± 464 t h−1 for the combined averaged L8 and S-2B emission rate. Within all our Monte Carlo ensembles, positive methane leak rates have higher probabilities (80 %–88 %) than negative ones (12 %–20 %), thus indicating that L8 and S-2B likely captured a methane-related signal. Overall, we see our work both as a nuanced analysis of L8 and S-2B contributions to quantifying the NS2 leak emissions and as a methodological cautionary tale that builds insight into MBSP and IME sensitivities.

Experimental Study on Enhanced Methane Detection Using an MEMS-Pyroelectric Sensor Integrated with a Wavelet Algorithm.

An optical sensing approach that balances portability with cost efficiency has been designed for the reliable monitoring of fugitive methane (CH4) emissions. Employing a LiTaO3-based pyroelectric detector integrated with micro-electro-mechanical systems and a broad infrared source, the developed gas sensor adeptly measured CH4 concentrations with a low limit of detection of about 5.6 ppmv and showed rapid response times with t90 consistently under 3 s. Notably, the novelty of our method lies in its precise control and reduction of CH4 levels, enhanced by wavelet denoising. This technique, optimized through meticulous grid search, effectively mitigated noise interference noticeable at CH4 levels below 10 ppmv. Postdenoising, nonlinear regression analyses based on the modified Beer-Lambert equation returned R2 values of 0.985 and 0.982 for the training and validation sets, respectively. In conclusion, this gas sensor has been shown to be able to meet the requirements for early warning of CH4 leakage on the surface in various carbon capture, utilization, and storage projects such as enhanced oil or gas recovery projects using CO2 injection.

Co3O4-Based Materials as Potential Catalysts for Methane Detection in Catalytic Gas Sensors.

The present work deals with the development of Co3O4-based catalysts for potential application in catalytic gas sensors for methane (CH4) detection. Among the transition-metal oxide catalysts, Co3O4 exhibits the highest activity in catalytic combustion. Doping Co3O4 with another metal can further improve its catalytic performance. Despite their promising properties, Co3O4 materials have rarely been tested for use in catalytic gas sensors. In our study, the influence of catalyst morphology and Ni doping on the catalytic activity and thermal stability of Co3O4-based catalysts was analyzed by differential calorimetry by measuring the thermal response to 1% CH4. The morphology of two Co3O4 catalysts and two NixCo3-xO4 with a Ni:Co molar ratio of 1:2 and 1:5 was studied using scanning transmission electron microscopy and energy dispersive X-ray analysis. The catalysts were synthesized by (co)precipitation with KOH solution. The investigations showed that Ni doping can improve the catalytic activity of Co3O4 catalysts. The thermal response of Ni-doped catalysts was increased by more than 20% at 400 °C and 450 °C compared to one of the studied Co3O4 oxides. However, the thermal response of the other Co3O4 was even higher than that of NixCo3-xO4 catalysts (8% at 400 °C). Furthermore, the modification of Co3O4 with Ni simultaneously brings stability problems at higher operating temperatures (≥400 °C) due to the observed inhomogeneous Ni distribution in the structure of NixCo3-xO4. In particular, the NixCo3-xO4 with high Ni content (Ni:Co ratio 1:2) showed apparent NiO separation and thus a strong decrease in thermal response of 8% after 24 h of heat treatment at 400 °C. The reaction of the Co3O4 catalysts remained quite stable. Therefore, controlling the structure and morphology of Co3O4 achieved more promising results, demonstrating its applicability as a catalyst for gas sensing.

Methane Retrieval Algorithms Based on Satellite: A Review

As the second most predominant greenhouse gas, methane-targeted emission mitigation holds the potential to decelerate the pace of global warming. Satellite remote sensing is an important monitoring tool, and we review developments in the satellite detection of methane. This paper provides an overview of the various types of satellites, including the various instrument parameters, and describes the different types of satellite retrieval algorithms. In addition, the currently popular methane point source quantification method is presented. Based on existing research, we delineate the classification of methane remote sensing satellites into two overarching categories: area flux mappers and point source imagers. Area flux mappers primarily concentrate on the assessment of global or large-scale methane concentrations, with a further subclassification into active remote sensing satellites (e.g., MERLIN) and passive remote sensing satellites (e.g., TROPOMI, GOSAT), contingent upon the remote sensing methodology employed. Such satellites are mainly based on physical models and the carbon dioxide proxy method for the retrieval of methane. Point source imagers, in contrast, can detect methane point source plumes using their ultra-high spatial resolution. Subcategories within this classification include multispectral imagers (e.g., Sentinel-2, Landsat-8) and hyperspectral imagers (e.g., PRISMA, GF-5), contingent upon their spectral resolution disparities. Area flux mappers are mostly distinguished by their use of physical algorithms, while point source imagers are dominated by data-driven methods. Furthermore, methane plume emissions can be accurately quantified through the utilization of an integrated mass enhancement model. Finally, a prediction of the future trajectory of methane remote sensing satellites is presented, in consideration of the current landscape. This paper aims to provide basic theoretical support for subsequent scientific research.

Simultaneous measurement of hydrogen and methane concentrations with temperature self-calibration based on a SPR sensor with an anchor-shaped photonic crystal fiber

Hydrogen and methane are flammable and explosive gases, and their simultaneous measurement holds significant importance in applications of industrial safety and energy transmission. A high-sensitivity multi-parameter sensor based on the surface plasmon resonance (SPR) within an anchor-shaped photonic crystal fiber (AS-PCF) have been proposed and analyzed in detail, which can simultaneously measure hydrogen and methane concentrations while providing temperature self-compensation. To achieve simultaneous detection of hydrogen and methane, an open ring channel is introduced in both the x and y directions of the PCF. Each of these channels is coated with a layer of gold film and gas-sensitive films for hydrogen and methane, respectively. The designed AS-PCF structure is optimized to increase the reactive surface area between the gas and the gas-sensitive films, simultaneously enhancing the energy leakage of core modes to improve sensor performance. In addition, temperature sensitivity is augmented by filling PCF air holes with ethanol and enhancing the asymmetry of the fiber structure. The changes of gas concentration and environmental temperature are derived by demodulating the peak wavelength shifts in the loss and interference spectra. Simulation analysis demonstrates that the sensor can achieve average sensitivities of −0.23 nm/% for hydrogen and −4.84 nm/% for methane within the gas concentration range of 0.5 % to 3 %. Meanwhile, the temperature sensitivity can reach 0.58 nm/°C within the temperature range of 10 °C to 30 °C. This sensor demonstrates excellent linearity and high sensitivity, making it a promising tool for multi-gas measurements in various applications.

Highly sensitive mid-infrared methane remote sensor using a deep neural network filter.

A novel mid-infrared methane remote sensor integrated on a movable platform based on a 3.291-µm interband cascade laser (ICL) and wavelength modulation spectroscopy (WMS) is proposed. A transmitting-receiving coaxial, visualized optical layout is employed to minimize laser energy loss. Using a hollow retro-reflector remotely deployed as a cooperative target, the atmospheric average methane concentration over a 100-meter optical range is measured with high sensitivity. A deep neural network (DNN) filter is used for second harmonic (2f) signal denoising to compensate for the performance shortcomings of conventional filtering. Allan deviation analysis indicated that after applying the DNN filter, the limit of detection (LOD) of methane was 86.62 ppb with an average time of 1 s, decreasing to 12.03 ppb with an average time of 229 s, which is a significant promotion compared to similar work reported. The high sensitivity and stability of the proposed sensor are shown through a 24-hour continuous monitoring experiment of atmospheric methane conducted outdoors, providing a new solution for high-sensitivity remote sensing of atmospheric methane.

Exploiting the Matched Filter to Improve the Detection of Methane Plumes with Sentinel-2 Data

Existing research indicates that detecting near-surface methane point sources using Sentinel-2 satellite imagery can offer crucial data support for mitigating climate change. However, current retrieval methods necessitate the identification of reference images unaffected by methane, which presents certain limitations. This study introduces the use of a matched filter, developing a novel methane detection algorithm for Sentinel-2 imagery. Compared to existing algorithms, this algorithm does not require selecting methane-free images from historical imagery in methane-sensitive bands, but estimates the background spectral information across the entire scene to extract methane gas signals. We tested the algorithm using simulated Sentinel-2 datasets. The results indicated that the newly proposed algorithm effectively reduced artifacts and noise. It was then validated in a known methane emission point source event and a controlled release experiment for its ability to quantify point source emission rates. The average estimated difference between the new algorithm and other algorithms was about 34%. Compared to the actual measured values in the controlled release experiment, the average estimated values ranged from −48% to 42% of the measurements. These estimates had a detection limit ranging from approximately 1.4 to 1.7 t/h and an average error percentage of 19%, with no instances of false positives reported. Finally, in a real case scenario, we demonstrated the algorithm’s ability to precisely locate the source position and identify, as well as quantify, methane point source emissions.

Methane gas sensor based on direct absorption spectroscopy and the laser self-heating effect.

In this paper, a methane detection sensor based on direct absorption spectroscopy and the self-heating effect of lasers is proposed, which abandons the traditional method of relying on a thermoelectric cooler (TEC) to ensure stable gas concentration detection. The sensor can achieve stable concentration measurement in the temperature range of -10∘ to 40°C without the need for a TEC, which greatly simplifies the structure of the sensor and reduces the cost. The results of gas concentration calibration experiments show that the sensor has a good linear correlation (R 2=0.9993). Long-term continuous detection experiments show that the sensor maintains a relative detection error between -2.667% and 4.3% over the full test temperature range. In addition, signal-to-noise ratio analysis experiments further determine that the minimum detection limit of the sensor for methane gas is 27.33p p m⋅m (1σ). Given its advantages of simple structure, low cost, high accuracy, and stability, this methane detection sensor is well suited for natural gas leakage monitoring in home environments and can also be widely used in industrial safety detection and environmental monitoring applications. This technology provides a cost-effective solution for domestic and industrial methane detection.

Possible influence of Martian surface mineralogy on the detectability of atmospheric trace gases - mid-infrared simulation results

The paper focuses on the influence of the optical properties of Martian surface minerals on remotely detected gaseous components of the Martian atmosphere, when the spectrometer receives a combined signal from the Martian soil and atmosphere. Our considerations are primarily concerned with the detectability of methane, but the problem may also apply to other trace gases. Detections of methane in the Martian atmosphere have been reported from Mars Express (orbiting Mars), the Curiosity rover on the Martian surface, and from Earth. Its presence in the Martian atmosphere is being questioned today. The reason for these doubts is that both spectrometers onboard ExoMars Trace Gas Orbiter have not yet detected any methane in the Martian atmosphere using the very sensitive solar occultation method. The solar occultation method is unable to probe the lowest layers of the atmosphere at mid-latitudes, and so, its presence in this part of the atmosphere is assumed to be due to its possible source in the ground, as suggested by some works.This paper considers whether the spectral characteristics of the soil may hinder the remote detection of methane. One of the examples discussed in the article relates to the possible observation of methane over mineralogical surfaces that may be the source of this gas. The examples of other surface mineralogical compositions are also discussed. The series of numerical simulations carried out in the region of the strong methane absorption band and the examples where the optical properties of the surface change the shape and contrast of this absorption band are shown. The codes used provide estimates of the spectral reflectance/emittance and total radiance of the Martian surface and atmosphere in the mid-infrared spectral region. The surface covered by dust was described by the reflectance and emittance calculated from n,k using Mie and Hapke theories or known from laboratory measurements. The different concentrations of atmospheric trace gases were taken into account.

High-precision monitoring and point location optimization method of natural gas micro-leakage in the well site of salt cavern gas storage

The operation equipment in the well site of salt cavern gas storage is complicated. The potential leakage sources, such as wellhead, valve group and pipelines, are widely distributed, so the probability of micro-leakage events in the well site is very large under the long-term gas injection and production conditions. Aiming at the characteristics of well site layout and the existing problems of present gas detectors, such as low accuracy, multiple points, environmental influencing factors and poor stability, a high-precision monitoring and point location optimization method for natural gas micro-leakage at the well site was proposed. Firstly, an opposite-beam laser methane detector was selected after compared with different combustible gas monitoring methods, and used to monitor the gas micro-leakage at the key zones of well site which have the most probable leakage in real time. Then, a micro-leakage monitoring probability simulation model was established and the gas leakage diffusion law in the well site under different conditions were studied, such as different wind directions, wind speeds, gas leakage points and leakage rates. Finally, a high-precision monitoring method and a point location optimization scheme were proposed under the constraint conditions of economy and monitoring probability after compared with different layout paths. The detecting precision of leak gas concentration can be up to 1.0 ppm m after the improvement of the model. The real-time monitoring and effective early warning of micro-leakage in the well site of salt cavern gas storage can be realized, which can effectively reduce the risk of leakage event after the failure of gas storage leak proofness and ensure the long-term safety monitoring and efficient operation for salt cavern gas storage.

Research on low power consumption of wireless MEMS methane sensor

Aiming at the problem that the high power consumption of traditional methane sensors does not realize the comprehensive detection of coal mines underground, based on MEMS methane detection technology, wireless methane sensing low-power research is proposed. First of all, based on the analysis of power correlation, we select the modularized design with low power, which includes power supply, sensing element driver, acquisition, and processing modules. We study the change of sensitive resistance, response time, and power consumption of the MEMS methane sensing element and sensor in different operating modes. Meanwhile, experimental tests are carried out. The experimental result shows that under the premise of ensuring the timeliness and stability of methane gas detection, the power consumption of the wireless MEMS methane sensor in one minute is only 2.4% of the traditional catalytic methane sensing element.

Circuit design of a high scanning frequence and fast response laser methane remote sensor

To solve the problem of slow detection of laser methane sensors based on tunable diode laser absorption spectroscopy (TDLAS), a methane detection system with a high detection rate was proposed. Focusing on the hardware circuit design of the system which consists of a laser driver circuit, laser temperature control circuit, optical amplifier and filter circuit and main control circuit based on FPGA+ARM, this circuit is designed to achieve a laser scanning frequency of 50 kHz. A voltage-controlled current source is designed to drive the laser diode (LD). A TEC controller is designed to keep the operating temperature of the laser diode stable. Since the photocurrent can be very weak, to ensure the quality of signals from the photodiode (PD), a signal collection circuit consisting of the transimpedance amplifier, gain-controlled amplifier and low-pass filter was designed. An experiment is carried out to test system measurement capabilities. The results show the circuit can reach a response time of 2.5 ms while ensuring a measurement error of less than 4.3%.

Slow-Light-Enhanced Polarization Division Multiplexing Infrared Absorption Spectroscopy for On-Chip Wideband Multigas Detection in a 1D Photonic Crystal Waveguide.

Slow-light photonic crystal waveguide (PCW) gas sensors based on infrared absorption spectroscopy play a pivotal role in enhancing the on-chip interaction between light and gas molecules, thereby significantly boosting sensor sensitivity. However, two-dimensional (2D) PCWs are limited by their narrow mode bandwidth and susceptibility to polarization, which restricts their ability for multigas measurement. Due to quasi-TE and quasi-TM mode guiding characteristics in one-dimensional (1D) PCW, a novel slow-light-enhanced polarization division multiplexing infrared absorption spectroscopy was proposed for on-chip wideband multigas detection. The optimized 1D PCW gas sensor experimentally shows an impressive slow-light mode bandwidth exceeding 100 nm (TM, 1500-1550 nm; TE, 1610-1660 nm) with a group index ranging from 4 to 25 for the two polarizations. The achieved bandwidth in the 1D PCW is 2-3 times that of the reported quasi-TE polarized 2D PCWs. By targeting the absorption lines of different gas species, multigas detection can be realized by modulating the lasers and demodulating the absorption signals at different frequencies. As an example, we performed dual-gas measurements with the 1D PCW sensor operating in TE mode at 1.65 μm for methane (CH4) detection and in TM mode at 1.53 μm for acetylene (C2H2) detection. The 1 mm long sensor achieved a remarkable limit of detection (LoD) of 0.055% for CH4 with an averaging time of 17.6 s, while for C2H2, the LoD was 0.18%. This polarization multiplexing sensor shows great potential for on-chip gas measurement because of the slow-light enhancement in the light-gas interaction effect as well as the large slow-light bandwidth for multigas detection.

Calibration of a Low-Cost Methane Sensor Using Machine Learning.

In order to combat greenhouse gas emissions, the sources of these emissions must be understood. Environmental monitoring using low-cost wireless devices is one method of measuring emissions in crucial but remote settings, such as peatlands. The Figaro NGM2611-E13 is a low-cost methane detection module based around the TGS2611-E00 sensor. The manufacturer provides sensitivity characteristics for methane concentrations above 300 ppm, but lower concentrations are typical in outdoor settings. This study investigates the potential to calibrate these sensors for lower methane concentrations using machine learning. Models of varying complexity, accounting for temperature and humidity variations, were trained on over 50,000 calibration datapoints, spanning 0-200 ppm methane, 5-30 °C and 40-80% relative humidity. Interaction terms were shown to improve model performance. The final selected model achieved a root-mean-square error of 5.1 ppm and an R2 of 0.997, demonstrating the potential for the NGM2611-E13 sensor to measure methane concentrations below 200 ppm.

Offshore methane detection and quantification from space using sun glint measurements with the GHGSat constellation

Abstract. ​​​​​​​The ability to detect and quantify methane emissions from offshore platforms is of considerable interest in providing actionable feedback to industrial operators. While satellites offer a distinctive advantage for remote sensing of offshore platforms which may otherwise be difficult to reach, offshore measurements of methane from satellite instruments in the shortwave infrared are challenging due to the low levels of diffuse sunlight reflected from water surfaces. Here, we use the GHGSat satellite constellation in a sun glint configuration to detect and quantify methane emissions from offshore targets around the world. We present a variety of examples of offshore methane plumes, including the largest single emission at (84 000 ± 24 000) kg h−1 observed by GHGSat from the Nord Stream 2 pipeline leak in 2022 and the smallest offshore emission measured from space at (180 ± 130) kg h−1 in the Gulf of Mexico. In addition, we provide an overview of the constellation's offshore measurement capabilities. We measure a median column precision of 2.1 % of the background methane column density and estimate a detection limit, from analytical modelling and orbital simulations, that varies between 160 and 600 kg h−1 depending on the latitude and season.

High-sensitivity methane detection based on QEPAS and H-QEPAS technologies combined with a self-designed 8.7 kHz quartz tuning fork

Methane (CH4) is a greenhouse gas as well as being flammable and explosive. In this manuscript, quartz-enhanced photoacoustic spectroscopy (QEPAS) and heterodyne QEPAS (H-QEPAS) exploring a self-designed quartz tuning fork (QTF) with resonance frequency (f0) of ∼8.7 kHz was utilized to achieve sensitive CH4 detection. Compared with the standard commercial 32.768 kHz QTF, this self-designed QTF with a low f0 and large prong gap has the merits of long energy accumulation time and low optical noise. The strongest line located at 6057.08 cm−1 in the 2v3 overtone band of CH4 was chosen as the target absorption line. A diode laser with a high output power of > 30 mW was utilized as the excitation source. Acoustic micro-resonators (AmRs) were added to the sensor architecture to amplify the intensity of acoustic waves. Compared to the bare QTF, after the addition of AmRs, a signal enhancement of 149-fold and 165-fold were obtained for QEPAS and H-QEPAS systems, respectively. The corresponding minimum detection limits (MDLs) were 711 ppb and 1.06 ppm for QEPAS and H-QEPAS sensors. Furthermore, based on Allan variance analysis the MDLs can be improved to 19 ppb and 27 ppb correspondingly. Compared to the QEPAS sensor, the H-QEPAS sensor shows significantly shorter measurement timeframes, allowing for measuring the gas concentration quickly while simultaneously obtaining f0 of QTF.