Changes In Gas Concentrations Research Articles

The role of gas emissions (He, Rn, and CO2) from fault zones in understanding fault and seismic activity

Active fault zones are critical pathways for the migration of deep fluids to the Earth’s surface, carrying gases such as He, Rn, and CO2 that provide evidence for the physical and chemical dynamics of the Earth’s interior. This review examines the geochemical characteristics of fault zone gases and their implications for understanding fault activity and seismic events. Fault zones with high activity levels exhibit significant gas release, and variations in soil and hot spring gas concentrations can serve as indicators of seismic activity. Changes in gas concentrations and isotopic ratios, particularly before and after earthquakes, reflect the dynamic interplay between deep-sourced and shallow-sourced fluids. Seismic-induced stress alterations enhance gas release along fault zones, leading to observable anomalies that can aid in earthquake monitoring and prediction. The study underscores the importance of isotope tracing in deciphering fluid sources, migration pathways, and the evolution of fault zones, providing valuable information for assessing tectonic activity and mitigating seismic risks.

Optimization of cleaning process in semiconductor gas delivery system by computational fluid dynamics simulation

Purging specialty gas delivery systems in semiconductor industries are essential for ensuring worker safety and complying with stringent environmental regulations. The cleaning process impacts production costs due to the required resources and time. This study introduced and evaluated various purging methods, combining vacuum, vacuum with purge, pressurization, and pressure-gradient purge steps. Considering the density and viscosity of various specialty gases used in semiconductor manufacturing, such as gaseous NH3, HCl, WF6, and SiCl4, were selected as the representative specialty gases for the system. Considering the utilization of the semiconductor industries, ultra-high purity nitrogen was selected to clean all components of the gas delivery system, including gas channels, filters, pressure regulator and diaphragm valves. Efficiency was measured by monitoring residual gas concentration changes during different purge-step configurations, the number of purge configuration, and step durations. The dynamic behavior of specialty gases was analyzed based on geometric factors and chemical properties. Findings indicated that cleaning dead-end zones was particularly challenging and impacting overall system efficiency. Frequent purge with a short step time proved more effective in removing residual chemicals than the present conventional vacuum purge process with a continuous N2 supply. Employing a pressure gradient between the inlet and outlet enhanced cleaning efficiency. These insights provide valuable guidelines for optimizing purging processes to reduce cleaning time and resource consumption in semiconductor manufacturing.

Novel Gas Supply System for Multi-Chamber Tri-Gas Cell Culture: Low Gas Consumption and Wide Concentration Range

Gas plays a crucial role in cell culture as cells require a specific gas environment to maintain their growth, reproduction, and function. Here, we propose a gas supply system for tri-gas multi-channel cell incubators to meet the specific needs of various cells. The system utilizes a circulating gas supply method powered by air pumps for each chamber. Gas inflow from the cylinder is independently controlled by Mass Flow Controllers (MFCs), and a quantitative step-by-step adjustment control strategy is employed to calculate the volume of different gases being introduced. Through mixing simulations and experiments, we identified the SV static mixer with an L/D ratio of 2.5 as the optimal choice. To evaluate the concentration accuracy and gas consumption of the gas system, we conduct gas mixing and distribution experiments under different conditions. The results show that the system could achieve a concentration range of 0–100% for O2 with an accuracy of ±0.5%, and a concentration range of 0–10% for CO2 with an accuracy of ±0.1%. The daily gas consumption during cultivation is 3570 mL of N2, 330 mL of CO2, and 115 mL of O2, significantly lower than conventional incubators. Overall, our system can effectively manage dynamic gas concentration changes, particularly in high O2 concentration environments. It offers advantages such as low gas consumption, a wide concentration range, and high accuracy compared to existing incubators.

The potential link between gas diffusion and embolism spread in angiosperm xylem: Evidence from flow-centrifuge experiments and modelling.

Understanding xylem embolism formation is challenging due to dynamic changes and multiphase interactions in conduits. Here, we hypothesise that embolism spread involves gas diffusion in xylem, and is affected by time. We measured hydraulic conductivity (Kh) in flow-centrifuge experiments over 1 h at a given pressure and temperature for stem samples of three angiosperm species. Temporal changes in Kh at 5, 22, and 35°C, and at various pressures were compared to modelled gas concentration changes in a recently embolised vessel in the centre of a centrifuge sample. Temporal changes in Kh were logarithmic and species-specific. Maximum relative increases of Kh between 6% and 40% happened at 22°C for low centrifugal speed (<3250 RPM), while maximum decreases between 41% and 61% occurred at higher speeds. These reductions in Kh were experimentally shown to be associated with a temporal increase of embolism at the centre of centrifuge samples, which was likely associated with gas concentration increases in recently embolized vessels. Although embolism is mostly pressure-driven, our experimental and modelled data indicate that time, conduit characteristics, and temperature are involved due to their potential role in gas diffusion. Gas diffusion, however, does not seem to cover the entire process of embolism spread.

Investigating similarities and differences of the penultimate and last glacial terminations with a coupled ice sheet–climate model

Abstract. Glacial terminations are marked by a re-organisation of the different components of the climate system. In particular, rapid ice sheet disintegration leads to multiple complex feedback loops that are still poorly understood. To further investigate this aspect, we use here a fully coupled Northern Hemisphere ice sheet–climate model to perform numerical experiments of the last two glacial terminations. We show that even if the first-order climate trajectory is similar for the two terminations, the difference in terms of solar insolation leads to important changes for the ice sheet–climate system. Warmer temperatures during the penultimate termination are compatible with higher sea level during the last interglacial period with respect to the Holocene. We simulate a last interglacial Greenland contribution to sea level rise of about 2 m of sea level equivalent. We also simulate warmer subsurface Southern Ocean, compatible with an additional contribution from the Antarctic ice sheet. In addition, even without considering freshwater flux to the ocean resulting from ice sheet melting, the two terminations display different Atlantic overturning circulation sensitivity, this circulation being more prone to collapses during the penultimate termination. Finally, with additional sensitivity experiments we show that, for the two terminations, the Northern Hemisphere insolation is the main driver for the ice sheet retreat even if vegetation changes have also to be taken into account to simulate the full deglaciation. Conversely, even though it impacts the temperature, greenhouse gas concentration change alone does not explain the amplitude of ice sheet retreat and only modulates its timing.

Development of a Sensitive Colorimetric Indicator for Detecting Beef Spoilage in Smart Packaging.

This study aimed to fabricate and characterize a novel colorimetric indicator designed to detect ammonia (NH3) and monitor meat freshness. The sensing platform was constructed using electrospun nanofibers made from polylactic acid (PLA), which were then impregnated with anthocyanins as a natural pH-sensitive dye, extracted from red cabbage. This research involved investigating the relationship between the various concentrations of anthocyanins and the colorimetric platform's efficiency when exposed to ammonia vapor. Scanning electron microscope (SEM) results were used to examine the morphology and structure of the nanofiber mats before and after the dip-coating process. The study also delved into the selectivity of the indicator when exposed to various volatile organic compounds (VOCs) and their stability under extreme humidity levels. Furthermore, the platform's sensitivity was evaluated as it encountered ammonia (NH3) in concentrations ranging from 1 to 100 ppm, with varying dye concentrations. The developed indicator demonstrated an exceptional detection limit of 1 ppm of MH3 within just 30 min, making it highly sensitive to subtle changes in gas concentration. The indicator proved effective in assessing meat freshness by detecting spoilage levels in beef over time. It reliably identified spoilage after 10 h and 7 days, corresponding to bacterial growth thresholds (107 CFU/mL), both at room temperature and in refrigerated environments, respectively. With its simple visual detection mechanism, the platform offered a straightforward and user-friendly solution for consumers and industry professionals alike to monitor packaged beef freshness, enhancing food safety and quality assurance.

Local summer temperature changes over the past 440 ka revealed by the total air content in the Antarctic EPICA Dome C ice core

Abstract. Seasonal temperature reconstructions from ice cores are missing over glacial–interglacial timescales, preventing a good understanding of the driving factors of Antarctic past climate changes. Here the total air content (TAC) record from the Antarctic EPICA Dome C (EDC) ice core is analyzed over the last 440 ka (thousand years). While the water isotopic record, a tracer for annual mean surface temperature, exhibits a dominant ∼100 kyr cyclicity, the TAC record is associated with a dominant ∼40 kyr cyclicity. Our results show that the TAC record is anti-correlated with the mean insolation over the local astronomical summer half-year. They also show for the first time that it is highly anti-correlated with local summer temperature simulated with an Earth system model of intermediate complexity. We propose that (1) the local summer insolation controls the local summer temperature; (2) the latter, through the development of temperature gradients at the near-surface of the ice sheet (&lt;2 m), is affecting the surface snow structure; and (3) those snow structure changes propagating down to the bottom of the firn through densification are eventually controlling the pore volume at the bubble close-off and consequently the TAC. Hence, our results suggest that the EDC TAC record could be used as a proxy for local summer temperature changes. Also, our new simulations show that the mean insolation over the local astronomical summer half-year is the primary driver of Antarctic summer surface temperature variations, while changes in atmospheric greenhouse gas (GHG) concentrations and Northern Hemisphere (NH) ice sheet configurations play a more important role in Antarctic annual surface temperature changes.

Methane (CH4) Detection System Using The TGS2611 Sensor and MQ-4 Sensor

Abstract This research focused on cow dung digesters as measurement samples, utilizing the TGS2611 and MQ-4 sensors, both known for their affordability. The objective was to develop a low-cost gas sensor system for monitoring methane gas concentrations through a straightforward web-based data acquisition platform. The DHT11 sensor was employed to measure air temperature and humidity. The electronic sensor circuit in this research utilized a voltage divider circuit. Testing the performance of the low-cost gas sensor involved integrating it with an anaerobic digestion system. For both the TGS2611 and MQ-4 sensors, the graph illustrating the relationship between sensor voltage (volts) and methane gas concentration (ppm) formed a logarithmic or non-linear pattern. The measurement range for the system was around 4700 ppm to 6300 ppm for TGS2611 and 4000 ppm to 5100 ppm for MQ-4. The sensor sensitivity was determined as 0.5433 Volts/ppm for TGS2611 and 0.5639 Volt/ppm for MQ-4. The TGS2611 sensor exhibited a higher response to changes in methane gas concentration, while the MQ-4 sensor successfully detected methane gas within its designated range. Interestingly, the TGS2611 sensor accurately detected methane gas concentrations above its specified range. The gas sampling procedure involved automatic gas sampling in a vacuum chamber for measurement, a strategy potentially enhancing sensor robustness. This research successfully amalgamated sensor advantages, circuit control, and data analysis to realize an economical yet reliable monitoring system.

Estimating errors in vehicle secondary aerosol production factors due to oxidation flow reactor response time

Abstract. Oxidation flow reactors used in secondary aerosol research do not immediately respond to changes in the inlet concentration of precursor gases because of their broad transfer functions. This is an issue when measuring the vehicular secondary aerosol formation in transient driving cycles because the secondary aerosol measured at the oxidation flow reactor outlet does not correspond to the rapid changes in the exhaust flow rate. Since the secondary aerosol production factor is determined by multiplying the secondary aerosol mass by the exhaust flow rate, the misalignment between the two leads to incorrect production factors. This study evaluates the extent of the error in production factors due to oxidation flow reactor transfer functions using synthetic and semi-synthetic exhaust emission data. It was found that the transfer-function-related error could be eliminated when only the total production factor of the full cycle was measured using constant-volume sampling. For shorter segments within a driving cycle, a narrower transfer function led to a smaller error. Even with a narrow transfer function, the oxidation flow reactor could report production factors that were more than 10 times higher than the reference production factors if the segment duration was too short.

Research on Gas Multi-indicator Warning Method of Coal Mine Working Face Based on MOA-Transformer.

The gas emission from the coal mine working face is influenced by multiple factors, resulting in the real-time value, fluctuation, and trend changes of gas concentration being relatively independent and interrelated. This paper establishes a gas multi-indicator warning method that can comprehensively warn the status of real-time value, fluctuation, and trend changes of gas concentration from the working face. This paper proposes six basic indicators and includes two main research contents: intelligent threshold partition and gas multi-indicator warning. First, this paper proposes an intelligent threshold partition algorithm based on GF-KMeans (genetic fixed-centered K-means), which combines a genetic algorithm (GA) and an FC-KMeans (fixed-centered K-means) algorithm to dynamically partition the threshold range corresponding to the gas warning level. The GA solves the local optimal problem in the traditional K-Means algorithm, enhancing its stability and predictability. The FC-KMeans algorithm achieves a more precise control in the initial clustering center selection. Second, this paper researches a gas multi-indicator warning method based on a multihead optimal attention (MOA)-Transformer. By using the multihead optimization attention mechanism to represent classification features and utilizing Transformer's encoder structure to classify gas warning. The experimental result shows that the accuracy of the MOA-Transformer method is 86.17%, which is 3.45% higher than that of the Transformer method. The precision of the MOA-Transformer method is 88.78%, which is 3.75% higher than that of the Transformer method. The recall of the MOA-Transformer method is 85.23%, which is 4.70% higher than that of the Transformer method. The macro-F1 of the MOA-Transformer method is 86.96%, which is 4.39% higher than that of the Transformer method. The results fully demonstrate the superiority of the MOA-Transformer method in gas warning tasks.

9 Use of new technology to measure methane emissions

Abstract Over recent decades, the impact of human-induced climate change and the role of ruminant agriculture has been of growing concern for both the public and the scientific community. This has led to rapid advancements in technology needed to measure methane emissions accurately and precisely. Primarily, these advancements have been focused on measurements of enteric methane, with the proliferation of GreenFeed emission measurement systems (GEM; C-Lock, Inc; Rapid City, SD) being utilized by animal scientists across the world. The GEM relies on spot measurements collected in a portable head-box, either in a freestall or on a trailer. The GEM uses bait feed to entice animals to place their head into the head-box to collect measurements. Use of respiration chambers or head-boxes are still considered the “gold standard”, but the GEM allows for quantification in production environments and with greater number of animals. While enteric emissions are the primary source of methane from ruminant agriculture, manure methane emissions account for 15.8% of direct emissions from cattle in the U.S. This emission source is a significant contributor to confined feeding operations’ greenhouse gas emissions (GHG), where consolidation and changes in manure handling practices have occurred over the last several decades. From 2010 to 2020, manure methane emissions were a larger contributor to climate warming than enteric methane (90.8 MMT CO2-we vs. 89.2 MMT CO2-we, respectively) when calculated using the new GWP* metric. However, few advancements have been made in quantifying emissions from manure, with the cost-effective static chamber methodology still widely used. This technique allows for the determination of temporal gas concentration change in a known volume of air with a PVC cylinder, inserted 5-10 cm in the soil and posteriorly closed with a PVC lid to generate “known volume” headspace. However, this method does have known limitations on spatial and temporal flux calculations. These limitations include: the PVC chamber may alter the soil microenvironment thereby altering soil GHG flux estimates; they have potential for technician bias; and are short term monitoring, limiting interpretations to that snapshot in time and making comparisons across experiments difficult. Differing measuring techniques, such as eddy covariance and dual-comb, that more accurately and precisely monitor the soil-atmosphere interface may help better understand gas fluxes from livestock systems. Key Word: cattle, enteric, manure, methane

Synthesis and Characterization of Palladium/Silver Modified Reduced Graphene Oxide–Nanocomposite Platform for Electrochemical Sensors

AbstractMetal nanoparticles incorporated into carbon nanostructures have tremendous applications in the field of nanosensors and other technologies. Herein, a nanohybrid of reduced graphene oxide modified with silver and/or palladium nanoparticles (Ag/Pd/rGO) was prepared by impregnation of Pd and Ag into the rGO platform and deposited on a glass carbon electrode. Palladium and silver nanoparticles improve the stability and durability of the sensor platform by preventing the agglomeration of graphene oxide sheets and providing chemical stability against environmental factors. This ensures long‐term reliability and repeatability of the sensor performance. The morphologies and microstructures of the as‐prepared nanohybrid were characterized by X‐ray photoelectron spectroscopy, scanning electron microscopy, transmission electron microscopy, X‐ray diffraction, UV–visible and infrared spectroscopy. The average crystallite size of PdAgNPs was found to be between 12 and 15 nm and demonstrated good dispersion across reduced graphene oxide sheets with high‐loading capacities. Thorough investigation of the electrochemical properties of the nanomaterial surfaces through cyclic voltammetry (CV) experiments, revealed distinct redox peaks for Ag+/Ag0 and Pd2+/Pd0 processes and demonstrated the excellent electrochemical properties of the prepared nanocomposites. The rGO‐PdAgNPs‐GCE had a diffusion coefficient value of 1.14×10−14 cm2 s−1 which is acceptable for electrochemical sensors as it is indicative of a fast response time and enhanced sensitivity to changes in gas concentration. The results obtained through thorough characterizations contributes to the novelty of the research by offering a deeper understanding of the material properties and the potential of the nanohybrid material for a broad application scope.

Gas control technology for coal and gas outburst mines based on new sealing materials

In order to solve the problem of gas overlimit in corner corners of coal and gas prominent mines, through the combination of air leakage mechanism in the goaf, near-field fissure expansion and rich area division, blocking material development and optimization, performance measurement of blocking materials and on-site test, we started to study the causes of gas concentration in corner corners, analysis of roof collapse and transparency in corners and performance test of blocking materials, and optimized the blocking materials by combining laboratory test and engineering test. Considering the thickness of the sealing film, the attenuation ratio of the sealing film thickness, the gelation time, and the gelation viscosity under different ratios, we designed a multi factor orthogonal experiment to optimize the optimal ratio suitable for the engineering site. Factors affecting blocking effectiveness, such as gel water retention and gel flame resistance, were also tested. The sealing scheme was implemented in the 2109 working face of a coal and gas outburst mine in Gansu, China. Through on-site monitoring of the changes in temperature, gas concentration, and air leakage at each monitoring point before and after the use of sealing materials, the analysis of the detection results shows that the temperature changes at each monitoring point after the use of sealing materials do not exceed 0.2°C; The change in oxygen concentration is less than 0.27 ​%; The gas concentration has decreased by more than 60 ​%, with a decrease of 71.32 ​% in the gas concentration in the upper corner. The air leakage has decreased by more than 53 ​%, and the proportion of decrease in air leakage at the upper corner is as high as 56.83 ​%. This air leakage control technology has remarkable blocking effect, meets the requirements of corner near-field fissure blocking material, and is easy to prepare, inexpensive, non-toxic, tasteless and green, providing a successful experience for the treatment of similar coal and gas outburst mines that can be referenced.

Design and evaluation of a low-cost sensor node for near-background methane measurement

Abstract. We developed a low-cost methane sensing node incorporating two metal oxide (MOx) sensors (Figaro Engineering TGS2611-E00 and TGS2600), humidity and temperature sensing, data storage, and telemetry. We deployed the prototype sensor alongside a reference methane analyzer at two sites: one outdoors and one indoors. We collected data at each site for several months across a range of environmental conditions (particularly temperature and humidity) and methane levels. We explored calibration models to investigate the performance of our system and its suitability for methane background monitoring and enhancement detection, first selecting a linear regression to fit a sensor baseline response and then fitting methane response by the sensor deviation from baseline. We achieved moderate accuracy in a 2 to 10 ppm methane range compared to data from the reference analyzer (RMSE &lt; 0.6 ppm), but we found that the sensor response varied over time, possibly as a result of changes in non-targeted gas concentrations. We suggest that this cross sensitivity may be responsible for mixed results in previous studies. We discuss the implications of our results for the use of these and similar inexpensive MOx sensors for methane monitoring in the 2 to 10 ppm range.

Desorption Characteristics of CH4-C2H6 Mixed Gas in Heavy Hydrocarbon-Rich Coal Seams.

The gas desorption characteristics of coal are closely related to the gas content of the coal seam. The gas in heavy hydrocarbon-rich coal seams contains CH4 and C2H6 heavy hydrocarbons. However, most current research on the gas desorption characteristics of coal seams focuses on CH4 analysis, ignoring the influence of the C2H6 heavy hydrocarbon gas. To accurately determine the gas content of a heavy hydrocarbon-rich coal seam, methods based on CH4 analysis are inadequate and the desorption characteristics of CH4-C2H6 mixed gas must be clarified. This work experimentally and theoretically studies the desorption characteristics of single-component gas and CH4-C2H6 mixed gas from coal samples. The results show that increasing the adsorption-equilibrium pressure was found to increase the desorption quantity and desorption speed of single-component gas and increase the desorption quantity, desorption ratio, and diffusion coefficient of mixed gas. Under the same adsorption-equilibrium pressure, the desorption quantity and rate of single-component CH4 gas exceeded those of C2H6. The quantity and speed of mixed gas desorption increased with rising CH4 concentration and decreased with rising C2H6 concentration. The change in the mixed gas concentration during desorption reflects the distribution characteristics of light hydrocarbon components on the outer surface and heavy hydrocarbon components on the inner surface of coal. From the desorption characteristics of mixed gas, desorption models of mixed gas were obtained at different concentrations, laying a theoretical foundation for accurate determinations of gas contents in heavy hydrocarbon-rich coal seams.

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.

Glacial inception through rapid ice area increase driven by albedo and vegetation feedbacks

Abstract. We present transient simulations of the last glacial inception using the Earth system model CLIMBER-X with dynamic vegetation, interactive ice sheets, and visco-elastic solid Earth responses. The simulations are initialized at the middle of the Eemian interglacial (125 kiloyears before present, ka) and run until 100 ka, driven by prescribed changes in Earth's orbital parameters and greenhouse gas concentrations from ice core data. CLIMBER-X simulates a rapid increase in Northern Hemisphere ice sheet area through MIS5d, with ice sheets expanding over northern North America and Scandinavia, in broad agreement with proxy reconstructions. While most of the increase in ice sheet area occurs over a relatively short period between 119 and 117 ka, the larger part of the increase in ice volume occurs afterwards with an almost constant ice sheet extent. We show that the vegetation feedback plays a fundamental role in controlling the ice sheet expansion during the last glacial inception. In particular, with prescribed present-day vegetation the model simulates a global sea level drop of only ∼ 20 m, compared with the ∼ 35 m decrease in sea level with dynamic vegetation response. The ice sheet and carbon cycle feedbacks play only a minor role during the ice sheet expansion phase prior to ∼ 115 ka but are important in limiting the deglaciation during the following phase characterized by increasing summer insolation. The model results are sensitive to climate model biases and to the parameterization of snow albedo, while they show only a weak dependence on changes in the ice sheet model resolution and the acceleration factor used to speed up the climate component. Overall, our simulations confirm and refine previous results showing that climate–vegetation–cryosphere feedbacks play a fundamental role in the transition from interglacial to glacial states characterizing Quaternary glacial cycles.

A spatio-temporal temperature prediction model for coal spontaneous combustion based on back propagation neural network

Coal spontaneous combustion (CSC) occurs with the change of temperature and gas concentration which is useful for early warning. To explore the intrinsic correlation between temperature evolution and gas distribution, a Back Propagation Neural Network (BPNN) model of coal temperature - gas composition - spatial distance was constructed, which is based on the gas characteristics and spatial distribution of coal temperature in large-scale coal spontaneous combustion experiments. The results showed that the O2 concentration reached the lowest value (8.058%) at 45 cm on Day 52 with the time. CO and CO2 also formed concentration peaks at 45 cm, and the gas distribution continuously moved towards the inlet position. The hydrocarbon gas components showed periodic increases, with values below 100 ppm. When the supply air time increased, the temperature rise rate in the initial stage of oxidation was slow but relatively uniform. Affected by ventilation conditions and coal density, two high-temperature zones were observed at 45 cm and 85 cm in the experimental furnace. With the inclusion of spatial distance parameters in coal temperature prediction, the BPNN model exhibits accurate predictive capability, with a total error of 0.94997. This can provide a method for predicting temperature changes in coal mine.

Effects of biomass burning on CO, HCN, C2H6, C2H2 and H2CO during long-term FTIR measurements in Hefei, China

High-resolution solar absorption spectra were continuously collected by a ground-based Fourier transform infrared (FTIR) spectrometer to retrieve the total column of carbon monoxide (CO), hydrogen cyanide (HCN), ethane (C2H6), acetylene (C2H2), and formaldehyde (H2CO). The time series and variation characteristics of these gases were analyzed. The biomass combustion process is identified by using the correlations between the monthly mean deviations of HCN, C2H6, C2H2 and H2CO versus CO and satellite fire point data. The months with high correlation coefficients (R &gt; 0.8) and peaks of fire point number are considered to be with biomass combustion occurrence. The emissions of HCN, C2H6, C2H2 and H2CO in Anhui were estimated using the enhancement ratios of gases to CO in these months when biomass combustion was the main driving factor of gas concentration change. The study proved the ability of FTIR system in inferring the period during biomass combustion and estimating emissions of the trace gases concerning biomass combustion.

The Discrete Taxonomic Classification of Soils Subjected to Diverse Treatment Modalities and Varied Fertility Grades Utilizing Machine Olfaction

Soil classification stands as a pivotal aspect in the domain of agricultural practices and environmental research, wielding substantial influence over decisions related to real-time soil management and precision agriculture. Nevertheless, traditional methods of assessing soil conditions, primarily grounded in labor-intensive chemical analyses, confront formidable challenges marked by substantial resource demands and spatial coverage limitations. This study introduced a machine olfaction methodology crafted to emulate the capabilities of the human olfactory system, providing a cost-effective alternative. In the initial phase, volatile gases produced during soil pyrolysis were propelled into a sensor array comprising 10 distinct gas sensors to monitor changes in gas concentration. Following the transmission of response data, nine eigenvalues were derived from the response curve of each sensor. Given the disparate sample counts for the two distinct classification criteria, this computational procedure yields two distinct eigenspaces, characterized by dimensions of 112 or 114 soil samples, each multiplied by 10 sensors and nine eigenvalues. The determination of the optimal feature space was guided by the “overall feature information” derived from mutual information. Ultimately, the inclusion of random forest (RF), multi-layer perceptron (MLP), and multi-layer perceptron combined with random forest (MLP-RF) models was employed to classify soils under four treatments (tillage and straw management) and three fertility grades. The assessment of model performance involved metrics such as overall accuracy (OA) and the Kappa coefficient. The findings revealed that the optimal classification model, MLP-RF, achieved impeccable performance with an OA of 100.00% in classifying soils under both criteria, which showed almost perfect agreement with the actual results. The approach proposed in this study provided near-real-time data on the condition of the soil and opened up new possibilities for advancing precision agriculture management.