Gas Concentration Monitoring Research Articles

Assessment of a Low-Cost Portable Device for Gas Concentration Monitoring in Livestock Housing

The increasing regulatory pressure to monitor and reduce GHG emissions and air pollutants requires cost-effective methods for their surveillance. The most common techniques used for scientific investigations into gas concentration monitoring in barns are accurate but expensive and require complex maintenance. This research study analyzed the potential use of low-cost portable measurement devices for the measurement of ammonia (NH3) and carbon dioxide (CO2) concentrations in an open dairy barn. A comparison between the gas concentrations acquired at different heights from the floor by using portable devices and those acquired by a photoacoustic infrared multigas spectroscope (i.e., reference measurement) in the same sampling locations was carried out to determine the precision of the low-cost portable devices. The performances of the low-cost portable devices were statistically analyzed by application of the one-way analysis of variance, correlation analysis, and regression analysis. The results showed a significant difference between the gas concentration values at various heights from the floor for both NH3 and CO2. The correlations between the concentrations acquired by the low-cost portable devices and the INNOVA were statistically significant (r = 0.83; p < 0.001) for gas concentrations monitored at 0.4 m from the floor. Compared with the reference measurement device, the low-cost devices were effective at the monitoring of NH3 concentrations at 0.40 m from the floor; however, they underestimated the concentrations in the barn at increasing heights from the floor, and the device was not adequate for CO2 concentrations. In detail, the relative measurement error of the low-cost devices compared to the INNOVA was reduced close to the floor during NH3 concentration measurements. Within these limitations, this device may be useful for monitoring the NH3 concentration in the barn and assessing variations in the NH3 concentrations mainly related to the animal occupied zone. Further efforts are needed in this field of research to identify a low-cost device that can simplify emission estimation from open dairy barns.

Carbon Monoxide (CO) and Particulate Matter (PM2.5) Concentration at Central Kalimantan, Indonesia

Peatland forest fires cause high concentrations of gases and particles in the air, especially carbon monoxide (CO) and PM2.5 which are very dangerous for health. Continuous monitoring of gas and particulate concentrations must be carried out during dry and rainy seasons so that the impact of hazards can be immediately identified and actions can be taken to overcome them. Monitoring equipment was placed from south to north at fire-prone locations to see the effect of wind and vegetation on the CO and PM2,5 concentrations. The concentration of Carbon Monoxide (CO) and Particulate Matter (PM2.5) at Central Kalimantan in the dry season is higher than in the rainy season. In the dry season of 2019, CO concentration in four locations was between 7104.1 – 22,127.5 µg/m3, while in 2021, it was the rainy season, so the CO concentration in six locations was lower, ranging from 38.7 – 1371.5 µg/m3. In the dry season of 2019, PM2,5 concentration in four locations was 196.8 – 360.5 µg/m3, while in 2021, at six locations was 5.0 – 40.0 µg/m3. The high concentration of CO and PM2.5 in the dry season needs to be anticipated with a serious handling program to prevent forest and peat fires

Analysis of the Horizontal Distribution of Sampling Points for Gas Concentrations Monitoring in an Open-Sided Dairy Barn.

In the livestock sector, the monitoring of gas concentrations plays a relevant role in the implementation of mitigation strategies, as well as the improvement in the indoor air quality for both workers and animals in barns. In the present study, measurements of gas concentrations (NH3, CH4, and CO2) at different locations were carried out in an open dairy barn using a commercial photo-acoustic spectroscopy system. Measurement variability for different datasets was computed to contribute to the evaluation of the sampling strategy in the measurement campaign. The results showed that the position of sampling locations (SLs) significantly influenced (p < 0.001) the variability in the gas distribution. Specifically, the SLs located on the perimeter of the building had the highest variability. The number of SLs utilised for determining the mean value of gas concentration in the barn had a significant influence on NH3 (p < 0.001), CO2 (p < 0.001), and CH4 (p < 0.001) in both the central zone and the perimeter one. At least three SLs were necessary to obtain a mean value of gas concentration that reduced the variability to under the 10% in the central zone and 16% in the perimeter one. Moreover, the NH3 concentrations obtained as the mean value of the gas measurements at two SLs with a 10 m distance had a lower variability compared to those recorded at two SLs located at 5 m.

An emergency task recommendation model of long-distance oil and gas pipeline based on knowledge graph convolution network

When long-distance oil and gas pipeline accidents take place, a set of requirements and constraints ought to be considered, due to the various environment, multiple accident types and hazard-bearing bodies. Compliance with the response tasks directly influences the subsequent accident development. Furthermore, attribute to geographic location or communication limitations, the accident information may be delayed or unclear. Knowledge graphs are an advantageous representation of the fusion of complex relationships. On this basis, this study aims to establish an emergency task recommendation model that can adapt to several accident information description levels. Combined with the proposed emergency case framework, a knowledge graph of long-distance oil and gas pipeline emergency cases is established for the first time. Realize embedding learning by aggregating neighbor node and relationships. The accuracy of the emergency task recommendation is improved by introducing a variety of relations in the case-feature graph. Apply this model to the 11.22 Qingdao Oil Pipeline Explosion incident. Tasks such as culvert and well boundary detection, combustible gas concentration monitoring, and response upgrading are supplemented. Compare with the accident investigation report conclusions, the recommended tasks help to prevent the explosion. The proposed model not only has a case information recording framework but is also not bound by the input format of structured cases. It solves the issue of ambiguous case feature input in the task recommendation and has good practical application value.

A low-cost calibration method for the infrared sensor array for quantitative analysis of natural gas

Infrared-based measurement technology is a promising approach to natural gas analysis, and high-precision calibration models for infrared sensor arrays require sufficient calibration data, which is costly. Therefore, we propose a calibration-transfer-based low-cost calibration method for infrared gas sensor arrays. We construct four identical infrared sensor arrays, one as the master array and three as slave arrays. A dedicated multi-layer perceptual neural network undertakes the data space mapping between the slave array and the master array. Thus, the slave sensor arrays can reuse the existing high-precision calibration model of the master array, reducing the calibration cost. The root mean squared errors of methane, ethane, and propane of the calibrated slave array are 2.87%(detection range: 0%–100%), 1.56%(detection range: 0%–24%), and 1.54%(detection range: 0%–12%), respectively, which are close to the accuracy level of the master array. The results show that our approach outperforms related algorithms in infrared sensor array calibration. In addition, experimental results on actual natural gas samples validate the method. The proposed low-cost and high-precision calibration method provides a basis for the generalization of infrared sensor arrays for mixed gas concentration monitoring. Furthermore, the reported scheme can be extended to quantitative analysis of other gas mixtures.

On Data-Driven Self-Calibration for IoT-Based Gas Concentration Monitoring Systems

In this article, data-driven self-calibration algorithms for the Internet of Things-based gas concentration monitoring systems embedded with low-cost gas sensors are designed. The measurement errors are assumed to be caused by imperfect compensation for the variation of sensor component behavior. Specifically, the calibration procedure for the nondispersive infrared <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CO_{2}$ </tex-math></inline-formula> sensors is developed, for which the temperature dependency is the most dominant drift source. For a single sensor, the hidden Markov model is used to characterize the statistical relationship between different quantities introduced by the physical model that builds on the Beer–Lambert law. For the calibration in the Internet of Things-based system, sensors first transmit their belief functions of the true gas concentration level to the cloud. Then, the cloud fusion center computes a fused belief function according to certain rules. This belief function is then used as reference for calibrating the sensors. To deal with the case where belief functions highly conflict with each other, a Wasserstein distance-based weighted average belief function fusion approach is first proposed as a networked calibration algorithm. To achieve more long-term stable calibration results, the networked calibration problem is further formulated as a partially observed Markov decision process (MDP) problem, and the calibration strategies are derived in a sequential manner. Correspondingly, the deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network approach is applied as a computationally efficient method to solve the proposed MDP problem. The performance of practical designs of the proposed self-calibration algorithms is finally illustrated in numerical experiments utilizing real data.

Addressed Combined Fiber-Optic Sensors as Key Element of Multisensor Greenhouse Gas Monitoring Systems.

The design and usage of the addressed combined fiber-optic sensors (ACFOSs) and the multisensory control systems of the greenhouse gas concentration on their basis are investigated herein. The main development trend of the combined fiber-optic sensors (CFOSs), which consists of the fiber Bragg grating (FBG) and the Fabry–Perot resonator (FPR), which are successively formed at the optical fiber end, is highlighted. The use of the addressed fiber Bragg structures (AFBSs) instead of the FBG in the CFOSs not only leads to the significant cheapening of the sensor system due to microwave photonics interrogating methods, but also increasing its metrological characteristics. The structural scheme of the multisensory gas concentration monitoring system is suggested. The suggested scheme allows detecting four types of greenhouse gases (CO2, NO2, CH4 and Ox) depending on the material and thickness of the polymer film, which is the FPR sensitive element. The usage of the Karhunen–Loève transform (KLT), which allows separating each component contribution to the reflected spectrum according to its efficiency, is proposed. In the future, this allows determining the gas concentration at the AFBS address frequencies. The estimations show that the ACFOS design in the multisensory system allows measuring the environment temperature in the range of −60…+300 °C with an accuracy of 0.1–0.01 °C, and the gas concentration in the range of 10…90% with an accuracy of 0.1–0.5%.

Electrochemical Hydrogen Sensor Assembly for Monitoring High‐Concentration Hydrogen

The demand for hydrogen energy is rapidly increasing because of its clean, abundant, and efficient nature. To utilize hydrogen gas as a fuel for fuel‐cell vehicles, several types of hydrogen sensors are needed to address cost and safety issues. Precise monitoring of hydrogen gas concentration is critical in optimizing vehicle fuel‐cell efficiency. Herein, a fully packaged hydrogen sensor to continuously measure a hydrogen concentration of 20–100% with environmental variations is fabricated, including temperature (from −40 to 105 °C), humidity (from 10 to 98 RH%), and pressure (from 30 to 350 kPa). A signal‐processing circuit is designed to reduce the error rate, and a sensor chip and printed circuit board are assembled in a module using a packaging process with robust materials for harsh vehicle environments. The result herein suggests direct application of the sensor to monitor hydrogen concentration in the hydrogen recirculation unit in a fuel‐cell vehicle.

Terahertz sensing with high sensitivity and substance identification capability using a novel High-quality resonance supported by a thin structured silicon film

We propose and numerically investigate a novel gas sensing structure with large sensitivity and the capability of substance selectivity, as well as high compactness based on the terahertz spectroscopic technique, by using a novel high-quality resonance, which is designed to match the absorption characteristic frequency of the target chemical. The resonance is achieved by employing the concept of bound state in the continuum (BIC), supported by a simple structure of silicon slab perforated by an interdigitated slot array with alternating slot width. An example of the detection of a toxic gas of HCN (hydrogen cyanide) was demonstrated and it is shown that a HCN concentration as low as 2 ppm in air under the atmospheric pressure of 1 atm can be detected with a slab thickness less than 100 μm. The selectivity of this assembled structure is also confirmed by introducing non-target Hydrogen bromide (HBr) gas, with no transmission drop at the original resonance. It refers to the conclusion that this structure shows great potential for practical applications for both gas identification and concentration monitoring.

Open Satellite Data for Global Greenhouse Gas Monitoring

Open satellite concentration data for the main greenhouse gases (CO2, CH4, N2O) are considered in terms of their possible use for local, regional, and global monitoring. The main data characteristics are provided. The satellite products most suitable for global monitoring of greenhouse gas concentrations are specified. The disadvantages of existing satellite data are analyzed.

Research on multi-functional intelligent ventilator based on UC/OS-III operating system for gas concentration detection

In order to change the traditional gas concentration inspection and exhaust device, the system through the gas concentration to control the rotation of the fan, to achieve real-time gas concentration monitoring, alarm control and GPS global positioning. In this study, STM32F4ZGT6 with ARM Coretex-M4 as the core controller is combined with embedded system design, and MDK is used to develop software programming. Through the ADC MQ-9 carbon monoxide sensor detection module and infrared NDIR carbon dioxide sensor detection module of the output of the analog conversion to digital quantity acquisition and processing, when the gas concentration exceeds the pre-set threshold, triggering a buzzer alarm, timer and the energy conservation and exhaust fan work, implementation is beyond the rapid circulation of the gas and gas concentration inside and outside the display of the time.Through bluetooth and mobile phone connection, real-time check of gas concentration, using the upper computer to control the operation mode of exhaust fan and change the gas concentration threshold. Modular programming is adopted in this study to improve the maintainability and facilitate debugging and modification in the later stage of the study. Through practical verification, the system can realize the detection of carbon monoxide and carbon dioxide concentration, real-time alarm monitoring, GPS positioning and change exhaust fan working mode and other functions, with real time, practicality and reliability.

Versatile soil gas concentration and isotope monitoring: optimization and integration of novel soil gas probes with online trace gas detection

Abstract. Gas concentrations and isotopic signatures can unveil microbial metabolisms and their responses to environmental changes in soil. Currently, few methods measure in situ soil trace gases such as the products of nitrogen and carbon cycling or volatile organic compounds (VOCs) that constrain microbial biochemical processes like nitrification, methanogenesis, respiration, and microbial communication. Versatile trace gas sampling systems that integrate soil probes with sensitive trace gas analyzers could fill this gap with in situ soil gas measurements that resolve spatial (centimeters) and temporal (minutes) patterns. We developed a system that integrates new porous and hydrophobic sintered polytetrafluoroethylene (sPTFE) diffusive soil gas probes that non-disruptively collect soil gas samples with a transfer system to direct gas from multiple probes to one or more central gas analyzer(s) such as laser and mass spectrometers. Here, we demonstrate the feasibility and versatility of this automated multiprobe system for soil gas measurements of isotopic ratios of nitrous oxide (δ18O, δ15N, and the 15N site preference of N2O), methane, carbon dioxide (δ13C), and VOCs. First, we used an inert silica matrix to challenge probe measurements under controlled gas conditions. By changing and controlling system flow parameters, including the probe flow rate, we optimized recovery of representative soil gas samples while reducing sampling artifacts on subsurface concentrations. Second, we used this system to provide a real-time window into the impact of environmental manipulation of irrigation and soil redox conditions on in situ N2O and VOC concentrations. Moreover, to reveal the dynamics in the stable isotope ratios of N2O (i.e., 14N14N16O, 14N15N16O, 15N14N16O, and 14N14N18O), we developed a new high-precision laser spectrometer with a reduced sample volume demand. Our integrated system – a tunable infrared laser direct absorption spectrometry (TILDAS) in parallel with Vocus proton transfer reaction mass spectrometry (PTR-MS), in line with sPTFE soil gas probes – successfully quantified isotopic signatures for N2O, CO2, and VOCs in real time as responses to changes in the dry–wetting cycle and redox conditions. Broadening the collection of trace gases that can be monitored in the subsurface is critical for monitoring biogeochemical cycles, ecosystem health, and management practices at scales relevant to the soil system.

A Robust Miniaturized Gas Sensor for H2 and CO2 Detection Based on the 3ω Method.

Gas concentration monitoring is essential in industrial or life science areas in order to address safety-relevant or process-related questions. Many of the sensors used in this context are based on the principle of thermal conductivity. The -method is a very accurate method to determine the thermal properties of materials. It has its origin in the thermal characterization of thin solid films. To date, there have been very few scientific investigations using this method to determine the thermal properties of gases and to apply it to gas measurement technology. In this article, we use two exemplary gases ( and ) for a systematical investigation of this method in the context of gas analysis. To perform our experiments, we use a robust, reliable sensing element that is already well established in vacuum measurement technology. This helix-shaped thin wire of tungsten exhibits high robustness against chemical and mechanical influences. Our setup features a compact measurement environment, where sensor operation and data acquisition are integrated into a single device. The experimental results show a good agreement with a simplified analytical model and FEM simulations. The sensor exhibits a lower detection limit of 0.62% in the case of , and only 0.062% in case the of at an excitation frequency of 1. This is one of the lowest values reported in literature for thermal conductivity sensors.

FA-LSTM: A Novel Toxic Gas Concentration Prediction Model in Pollutant Environment

Real-time monitoring and accurate prediction of toxic gas concentration in the future are of great significance for emergency capability assessment and rescue work. At present, the method of gas concentration prediction based on artificial intelligence still has problems of low accuracy, slow convergence speed and equal feature importance. This paper proposes a feature-aware LSTM model to predict pollutant gas concentration. First of all, we design a set of multi-component toxic gas monitoring equipment that applies in pollution environment, which can at the same time monitor CO, NO₂, NH₃, HCN, H₂S and SO₂, six common pollutants; To accurate estimate the toxic gas concentration, we combine the collected the gas data and the environmental parameters and regard them as the input features, and then we obtain toxic gas data based on the sampling policy and the environmental data as our data-set. Finally, we train a FA-LSTM gas concentration prediction model on these data-set. We test the proposed model and compared with ARIMA, ETS and BP network on the same test set. Experimental results show that the proposed model outperforms traditional concentration prediction model. Also, it is better than other state-of-the-art models in predicting accuracy.

Air harmful gas concentration monitoring method based on particle filter algorithm

In order to solve the problem of poor accuracy of traditional air harmful gas concentration monitoring, an air harmful gas concentration monitoring method based on particle filter algorithm is proposed. The state space model of air harmful gas distribution is constructed, and the posterior probability density function is obtained by pushover estimation. The gas concentration to be measured is obtained by particle filter. According to the transition frequency, the second harmonic signal characteristics of harmful air gases are described by Lambert Beer's law. The Lorentz linear function is used to obtain the light intensity change after gas concentration modulation, obtain the relationship between the second harmonic and gas concentration, and realise the monitoring of harmful gas concentration in the air. The experimental results show that this method can improve the monitoring efficiency and accuracy of air harmful gas concentration, and the maximum monitoring accuracy is 98%.

Liquefied Petroleum Gas Concentration Monitoring System with Alarm and Cloud-Based Logging

Liquefied Petroleum Gas Concentration Monitoring System with Alarm and Cloud-Based Logging

Network of Radon Gas Concentration Monitoring of Research and Development Centre – BMKG for Earthquake Precursor Research in Indonesia

The geographical position of Indonesia, which is flanked by several subduction zones and the presence of active faults in the sea and land make Indonesian territory prone to earthquakes and tsunamis which can result in many deaths and damaged. There are several efforts we can do to minimize the occurrence of earthquakes, including developing earthquake resistant buildings, increasing the ability/capacity of the community, and predicting earthquakes or better known as earthquake precursors. The BMKG Research Centre began conducting research on earthquake prediction using several methods, including the Radon monitoring method. Monitoring of Radon gas concentrations for earthquake precursors has several advantages, including the presence of radioactive gas which is abundant in ground water that has a half-life of 3.2 days. Radon is the result of decay of uranium 278U which is abundant in the earth’s crust rock so that when rock friction occurs, the Radon gas can be detached. Based on the results of Radon monitoring at Tadulako and Palolo stations - Southeast Sulawesi province, there was a change in the pattern of radon gas concentration and water level rising up and down drastically and a gradual decrease in ground water temperature before the earthquake on 28 September 2018. In addition to Central Sulawesi, since 2012 the Centre for Research and Development of BMKG has been conducting research to monitor radon gas concentrations in the DI Yogyakarta region precisely in Piyungan and Pundong districts with the aim of monitoring radon gas concentrations in the Opak fault. In 2021, the BMKG Research and Development Centre added a new radon gas monitoring network in the active fault areas of Cimandiri and Lembang in the West Java province. There are 1 station in the Cimandiri fault segment and 2 stations in the Lembang fault section. It is hoped that in the future the results of monitoring can reduce the impact caused by the earthquake disaster in Indonesia.

Gas Concentration Mapping Using Sample Pooling With a Single Detector

Detection of hazardous gases may be lethal in minute doses, requiring a sensitive detection system. A timely warning, preferably before the gas exceeds a critical threshold, depends on the capability to map the gas concentration inside a complex structure. This requires constant spatial monitoring of gas concentrations at several locations. This letter presents a generic pooling approach for spatial gas concentration mapping using a single detector. It is based on gas sampling from several locations simultaneously at predetermined mixing ratios. The gas concentration at each sampling point is computed using a repetitious scheme based on the sensor readings and the known mixing ratios.

Ultrasensitive measurement of gas refractive index based on cascaded Mach–Zehnder interferometers and Vernier effect

An ultrasensitive gas refractive index (RI) sensor based on fiber Mach–Zehnder interferometers (MZIs) and Vernier effect is proposed and demonstrated. The sensor consists of two cascaded fiber MZIs, one of which serves as a reference unit and is fabricated by fusion splicing a section of symmetrical side-hole fiber in between two short pieces of multi-mode fiber (MMF); the other acts as a sensing unit, which is composed of a section of tapered single-mode fiber sandwiched between two sections of MMF. The two MZIs are designed to have similar free spectral range for generating optical Vernier effect. Because the two MZIs have different responses to external RI, the RI-related Vernier effect can be obtained and the RI sensitivity can be improved, while there is no temperature sensitivity enhancement because the two MZIs have the same response to temperature. Thus the structure has low temperature crosstalk when obtaining high RI sensitivity. Experimental results show that the gas RI sensor has an ultrahigh sensitivity of 4.2 × 104 nm RIU−1 (RI unit). At the same time, the temperature cross-sensitivity is only 2.2 × 10−6 RIU °C−1. Therefore, the proposed sensor has potential applications for modern gas concentration monitoring in a large dynamic range.

Design of low-power distributed gas concentration monitoring system based on LoRa

Design of low-power distributed gas concentration monitoring system based on LoRa