Changes In Gas Concentrations Research Articles

LSTM-Pearson Gas Concentration Prediction Model Feature Selection and Its Applications

Gas disasters threaten the safe operation of coal mines. Improving the accuracy of gas concentration predictions can effectively prevent gas disasters and reduce disaster losses. Traditional gas concentration prediction methods poorly couple the gas concentration and its influencing factors when dealing with a great number of features and data, which results in low prediction accuracy and poor efficiency. To solve this problem, we used an innovative Pearson-LSTM prediction model, which uses the Pearson coefficient to select features of gas concentration data. It then uses long short-term memory (LSTM) that has been optimized using adaptive moment estimation (Adam) to predict a time series. In the process of model establishment, the optimal prediction model was obtained by constantly adjusting the number of network layers and batch size based on the fitting effect, performance issues, and result errors. Taking monitoring data from the 2407 working face at Yuhua Coal Mine as the sample, we compared our method with the traditional Bi-RNN and GRU machine learning methods. The results show that, compared with the Bi-RNN and GRU models, the mean square error of the Pearson-LSTM model can be reduced to 0.015 with an error range of 0.005 to 0.04, which has higher prediction accuracy. This method has excellent precision and robustness for forecasting gas concentration time series. The model was able to make predictions 15 min in advance for the 2409 working face of the Yuhua Coal Mine, and the mean square error could be lowered to 0.008, which verifies the applicability and reliability of the model and provides a reference for ensuring the safety of coal mine operations. In summary, Pearson-LSTM models have higher accuracy and robustness and can effectively predict changes in gas concentration, thus allowing for more response time for accidents, which is important for coal mine production safety.

Room temperature ethanol sensor based on ZnO nanoparticles modified by WSe2 nanosheets

Ethanol gas sensors have been widely applied in food safety, biomedicine, and fuel processing. However, most ethanol sensors need to be operated at high temperature. The development of room temperature ethanol sensors with low cost and high sensitivity is still a challenge. Here, a low-cost room temperature ethanol gas sensor is realized by connecting interdigital electrodes (IDEs) coated with ZnO/WSe2 nanocomposite film in series with a quartz crystal resonator (QCR). The change in ethanol gas concentration can be converted into a frequency shift of the quartz resonator to generate a quasi-digital signal through the sensor. Compared with pure ZnO IDEs-SQCR sensor, the sensitivity of ZnO/WSe2 IDEs-SQCR sensor (4.87 Hz/ppm) has been significantly improved in ethanol concentration range of 0–400 ppm. This is mainly attributed to the introduction of WSe2 with good conductivity, which not only enables the composite to have a special layered three-dimensional structure conducive to the adsorption and diffusion of gas molecules, but also puts the resistance of the composite in a more sensitive tuning range. The proposed sensor can be fabricated on a printed circuit board (PCB) so as to reduce the cost of mass production.

İstanbul İli Merkez İlçelerindeki NO2 ve CO emisyonlarının zamansal değişimleri

This study focused on the temporal changes of pollutant gas concentrations (NO2 and CO) in the air data from different resources, the cornerstone of living life. As a result of the analyzes carried out for a total of seventeen months (January 2020 – May 2021) with consistent data, the increase and decrease trends of two of the polluting gases in the atmosphere were revealed. Geographical studies were carried out with data from one terrestrial station (Üsküdar and Mecidiyeköy) from the Anatolian and European sides of Istanbul, the most populated city in Turkiye. Satellite-based research was also carried out with the Sentinel-5 Precursor (S5p), which the European Space Agency (ESA) created to detect and monitor the concentration of gases in the atmosphere. It has been determined that either terrestrial station-based or S5p satellite data based analyzes can detect and monitor temporal changes in air quality.

Changes in soil greenhouse gas concentrations induced by plastic film mulching in a hot pepper–radish rotation

Changes in soil greenhouse gas concentrations induced by plastic film mulching in a hot pepper–radish rotation

Comparative Analysis of the Drainage Effect Based on Improved Hydraulic Flushing Technology.

The composite structure coal seam composed of coal seams with strong and weak deformation is widely developed in the coalfields in Southwest and North China. The implementation of hydraulic flushing in soft coal stratification can effectively relieve the pressure and increase the permeability of coal seams, so it can improve the effect of underground gas drainage. Taking Xin'an Coal Mine, West Henan province, China, as an example, this paper discussed the influence of hydraulic flushing on the gas drainage effect in soft coal seam by means of field investigation, numerical simulation analysis, similar simulation, and field verification. As the same time, a fluid-solid coupling model of gas drainage based on layered hydraulic flushing technology is established, and the differences and reasons of the gas drainage effect after layered hydraulic flushing in soft and hard composite coal seams are discussed. The results show that (1) the gas concentration change of layered flushing boreholes intermittently rises rapidly, and there is no obvious law, while that of conventional flushing boreholes shows a regular attenuation trend. (2) According to the numerical simulation results, after 30 days of drainage, the effective drainage range of layered hydraulic flushing is 8.33 m3 higher than that of conventional hydraulic flushing. (3) There are not a few points where the single borehole concentration data of each row of layered flushing boreholes are in the high level. Meanwhile, the maximum concentration of all conventional flushing boreholes is less than 40%, except that one borehole can reach 70% at the beginning. (4) All of the average gas drainage concentration of single-row boreholes using layered flushing technology is greater than 20% in the vast majority of days, while that of the conventional flushing boreholes is only higher than 20% in row 5 and has only lasted for 2 days. The difference of the drainage effect is obvious.

Apohemoglobin-Haptoglobin Complex Prevents Microvascular Dysfunction in Sickle Cell Mice: Potential Therapeutic Approach for Vaso-Occlusive Crisis

Background: Sickle cell disease (SCD) is associated with episodes of vaso-occlusive events (VOE) caused by the effects of hemoglobin S (HbS) polymerization in red blood cells (RBCs) and resultant hemolysis. In the acute setting, treatment for VOE is limited to supportive care such as hydration and aggressive pain management as no specific therapies can alter the course of acute VOE. The apohemoglobin-haptoglobin (apoHb-Hp) complex is a two-protein formulation that is capable of scavenging and neutralizing the toxic effects of cell-free hemoglobin (Hb) and heme released from the hemolysis of polymerized HbS RBCs. The primary objective of this study was to evaluate the microcirculatory effects of apoHb-Hp during hypoxia-induced VOE. Methods: The effects of apoHb-hp was studied in a mouse model of SCD using sickle cell transgenic mice (HbSS-Towne's mice, and compared to mice expressing normal human Hb (HbAA-Towne's mice). Each group consisted of 11 mice, they were initially kept in normal conditions (21% O2) for an hour followed by a hypoxic state (7% O2) for another hour and finally brought back to normal conditions (21% O2) for an hour before being sacrificed. ApoHb-hp (60 mg/mL) and Hp (85 mg/mL) were injected intravenously to yield equimolar total heme and Hb binding capacity between treatments prior to hypoxia exposure. During baseline, hypoxia and normoxia systemic parameters (mean arteriole pressure and heart rate), blood gases (pO2,A, pO2,v, sO2,A, sO2,v), hematological properties (total Hb concentration, hematocrit, and concentration of lactate) and microcirculatory hemodynamics (velocity, diameter, flow and functional capillary density) were measured. After the mice were sacrificed a count of present macrophages, neutrophils and lymphocytes were quantified in the pulmonary tissue. Results: There was no notable changes in blood gas concentrations with any of the treatments. ApoHb-Hp treatment improved microcirculatory hemodynamics of HbSS mice by preventing hypoxia-induced vasoconstriction and reduction of blood flow in arterioles <40µm (P=0.818, P<0.0001), arterioles >40 µm (P>0.9999, P<0.0001) and venules 20<x<60 µm (P=0.150, P=0.330) compared to HbAA mice. Furthermore, the functional capillary density saw favorable recovery to baseline when HbSS mice were treated with apoHb-Hp seeing a 36% drop initially, 50% drop after 24 hours, 35% drop after 48 hours, and 27% drop after 72 hours. For comparison, HbSS mice that didn't receive treatment saw a 50% drop initially, 77% drop after 24 hours, 80% drop after 48 hours, and 72% drop after 72 hours when compared to baseline. Conclusion: These results suggest a role for ApoHb-Hp complex in alleviating microvascular changes occurring during hypoxia mediated acute vaso-occlusion in SCD and merits further study for development as a potential therapeutic. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Gas composition during thermochemical conversion of dry solid fuels and waste-derived slurries.

Coal has long remained a promising and widely used energy resource all over the world. Special emphasis is usually put on the research and development of environmentally friendly technologies for the use of coal and coal processing waste. The development of slurry fuels based on coal waste is one of the promising ways to use raw materials with energy potential, recover wastes, and reduce the environmental load. However, no combustion technology has yet been created for heterogeneous wastes as water-based slurries. The physical principles and parameters of the corresponding processes have not been studied adequately. In this research, the environmental combustion indicators (CO2, CO, H2, NOx, and SO2 concentrations) of slurries based on water and petrochemical, coal, and plant wastes were analyzed for the first time in a wide range of temperatures covering all the typical stages of thermochemical fuel conversion: pyrolysis (400-700°C), gasification (700-900°C), and combustion (700-1000°C). We established the key patterns and aspects of changes in gas concentrations at all the main stages during the thermal decomposition of fuels. The use of water-based fuels at the pyrolysis stage was notable for up to 96% higher concentrations of the key combustible gases (CO, H2). The temperature extrema were 50-100°C lower than those of bituminous coal. In terms of the key anthropogenic emissions (CO2, NOx, and SO2), the combustion of slurries also appeared to be 20-77% more environmentally friendly than that of coal depending on the temperature conditions and fuel composition. The maximum positive effect from adding biomass to coal-water slurries was achieved in the temperature range of 850 to 1000°C. The research findings can be used for developing the technologies for thermal recovery of waste as water slurries, in particular, by intensifying the pyrolysis, gasification, and combustion.

Establishing patterns of nitrogen application for fire safety of sunflower grain storage facilities

The issue of using nitrogen to eliminate fires in granaries is related to ventilating the grain mass with nitrogen while it is necessary to take into consideration the change in gas concentration. Therefore, the object of this research was the value of the minimum concentration of nitrogen to eliminate the combustion of sunflower grain. It has been proven that in the process of thermal destruction of sunflower grain, the composition of gaseous products of thermal destruction of sunflower grain contains more than 70 % of combustible gases. Namely: carbon monoxide, over 51 %; hydrogen, about 5.7 %; and hydrocarbons with a total concentration of 13.72 %, which provide fire hazardous properties of organic material. Therefore, it should be noted that the release of the amount of combustible gases during pyrolysis requires a reduction in the amount of oxygen in the gas-air environment to eliminate fire sites. Obviously, such a mechanism for the decomposition of sunflower grain during pyrolysis is a factor in regulating the extinguishing process, due to which the amount of nitrogen to eliminate combustion increases. Comparison of experimental studies on the composition of gaseous products of thermal destruction of sunflower grain and studies to determine the minimum fire extinguishing concentration of nitrogen, at which combustion was stopped, made it possible to justify the use of nitrogen. Based on the study's results on the elimination with nitrogen of fire sites of sunflower grain, the values of the minimum fire extinguishing concentration were revealed at extinguishing, about 33.7 % by volume. At the same time, terminating the combustion of sunflower grain occurs with a decrease in the concentration of oxygen in the gas-air environment of about 14 %. The practical significance is the fact that the results obtained for determining the minimum fire extinguishing concentration of nitrogen make it possible to establish operating conditions for granaries when eliminating fire sites

A Novel Method for Gas Disaster Prevention during the Construction Period in Coal Penetration Tunnels—A Stepwise Prediction of Gas Concentration Based on the LSTM Method

Aiming at the tunnel gas disaster can produce major safety problems such as combustion, explosion, and coal and gas outbursts. Firstly, a time series consisting of the tunnel gas concentration was used as the entry point for the article, and the gas prediction models based on multiple intelligent computational methods were established for comparison to determine the optimal network prediction model. Then, this study proposed a stepwise prediction method which is based on the optimal network prediction model for gas disaster prevention during the construction period of tunnels at the excavation workface. The length of the input step, output step, and interval step were considered by the method to investigate the effect on the predictive performance of the model. The model was extrapolated by the rolling prediction method, and the adaptive grid search method was used to determine the optimal parameter combination of stepwise prediction. Finally, a stepwise prediction of short-term gas concentration trends was achieved for each construction process at the excavation workface. As a result, the best LSTM network prediction model was preferred with an R2 value of 0.94 for the fit and MAE and RMSE values of 3.2% and 4.3%. Results based on stepwise predictions showed that single-step prediction is more accurate than multi-step predictions when a reasonable input step size was determined. Moreover, with the length of both the interval step and the output step, the model prediction accuracy showed a decreasing trend. Generally speaking, the single-step continuous and interval prediction of the gas concentration at the excavation workface can be realized by the gas stepwise prediction method, and the gas concentration value can be obtained at any time in the prediction. It can also realize the transformation from single-step point prediction to multi-step trend prediction, and obtain the accurate prediction of gas concentration change trends in the stepwise prediction range (t+1~t+5). Therefore, the important security guarantee can be provided by stepwise prediction for subsequent gas disaster safety prevention and efficient tunnel production.

Fabrication and Application of 1653.7 nm Methane Sensor

In industries such as coal mining, natural gas transportation and waste-to-energy, methane detection is an essential step. In order to reduce the risk and improve accuracy, laser sensors are used to detect methane. Aiming at the characteristics of the absorption peak of methane gas at 1653.7 nm, the 1653.7 nm distributed feedback laser was obtained from the multi quantum well materials design and ridge-wide pattern design to device packaging by using metal-organic chemical vapor epitaxial deposition, holographic exposure, and nanoimprint lithography. The laser performance achieves a side-mode suppression ratio of 54 dB, a slope efficiency of 0.372 W/A, a threshold current not greater than 12 mA, a saturated optical power greater than 20 mW, and stable optical and electrical properties. Based on this laser, for methane gas with a concentration of 0% to 3%, the loss is stable with the change of gas concentration, and the absorption sensitivity to methane is 0.20237 dB/%.

Synthesis of Good Electrical Conductivity of CoCe-BTC/PEDOT for Ultrahigh Selectivity of NO2 Detection.

Metal–organic frameworks (MOFs) have broad application prospects in the development of efficient, sensitive and single select gas sensors. However, in order to construct a chemical resistance gas sensor based on MOFs, the problem of poor conductivity of MOFs must be solved. In this work, we synthesized CoCe-BTC, which based on the organic ligands of trimesic acid (H3BTC) by the water bath method and prepared CoCe-BTC/PEDOT composite film on an interdigital electrode by the spin coating process. Compared with pure MOF material, the conductivity of CoCe-BTC/PEDOT is significantly improved. Under a dry room temperature environment and N2 as the carrier gas, the response of the sensor to NO2 is about 1.2 times that of pure PEDOT and has a shorter response time. It has great repeatability and selectivity and shows a dynamic response with the change of NO2 gas concentration (5–50 ppm).

Temporal and Spatial Variation Characteristics of Methane, Carbon Dioxide, and Nitrous Oxide Concentrations and the Influencing Factors in Small Aquaculture Pond

As an important source of greenhouse gases, the changes in greenhouse gas concentrations of aquaculture ponds are not only the basis for accurate quantification of greenhouse gases emissions but are also important for identifying their influencing factors. The spatial and temporal variation characteristics of CH4, CO2, and N2O concentrations and the influencing factors in a typical small aquaculture pond in the Yangtze River Delta were analyzed based on the headspace equilibrium-gas chromatograph method. Except in spring, the concentrations of CH4, and N2O appeared high at noon or afternoon and were influenced by water temperature. Impacted by water temperature and aquatic plant photosynthesis, the concentrations of CO2 were high in the morning when photosynthesis was weak. The concentrations of CH4 and CO2 were the highest in autumn and the lowest in winter. The mean concentrations of CH4 in autumn and winter were 176.34 nmol·L-1 and 32.75 nmol·L-1, respectively, which were mainly affected by air temperature, water temperature, and dissolved oxygen. The average CO2 concentrations in autumn and winter were 134.37 μmol·L-1 and 23.10 μmol·L-1, respectively, and were mainly affected by aquatic vegetation photosynthesis and pH. N2O concentration was the highest in summer and the lowest in winter, with mean values of 97.05 nmol·L-1 and 19.41 nmol·L-1, respectively, which were mainly affected by air temperature and water temperature. In terms of the vertical spatial variations of the three greenhouse gases, the concentration of CH4decreased with water depth in summer, and the concentration differences between the surface layer and the bottom and middle layers were 71.28 nmol·L-1 and 42.80 nmol·L-1, respectively. The concentration of CH4 increased with water depth in autumn, and the concentration difference between the bottom layer and surface layer was 163.94 nmol·L-1. The CO2 concentration increased with water depth in summer and autumn. The concentration differences between the bottom and surface concentrations were 18.69 μmol·L-1 and 29.90 μmol·L-1, respectively. N2O concentration showed no obvious change in the vertical direction. For the horizontal variations, the concentrations of CH4, CO2, and N2O in the feeding area in summer and in chicken manure in spring were approximately 1.34-1.98 times and 1.95-2.42 times those in other areas, respectively, and the concentrations of N2O and CO2 in spring and summer were approximately 1.13-1.26 times and 1.39-1.74 times those in other areas.

Reversible Switching from P- to N-Type NO2 Sensing in ZnO Rods/rGO by Changing the NO2 Concentration, Temperature, and Doping Ratio

Gas sensors based on hybrid materials of reduced graphene oxide/metal oxide semiconductors are an effective way to improve sensor performance. In this paper, rGO (reduced graphene oxide) was uniformly and compactly anchored on the surface of presynthesized ZnO rods using a facile hydrothermal synthesis. The ZnO rods and reduced graphene oxide combined to form an np-type heterojunction structure for use in a nitrogen dioxide (NO2) sensor. Compared with pure ZnO rods and rGO, the ZnO rod/rGO composites display abnormal sensing behavior in the form of reversible transitions from p- to n-type sensing induced by changes in the NO2 gas concentration (C) and temperature (T). Based on familiar phase diagrams, a binary (T–C) transition diagram was created in terms of the gas-sensing response, which can be directly used to design and control p–n transitions. In addition, we also found that as the doping ratios (c) change, the semiconductor type of the composite also changes. Finally, the mechanisms underlying the NO2-sensing transitions are described based on the dual conduction path model: electron transfer in the internal region and chemical reactions at the surface. This reversible p–n transition of ZnO/rGO materials upon NO2 sensing is interesting and holds great potential for the efficient detection of NO2.

Characteristics and source analysis of greenhouse gas concentration changes at Akedala Station in Central Asia

Mole fractions of atmospheric carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and sulfur hexafluoride (SF6) have been continuously measured since September 2009 at the Akedala Station (47°06′N, 87°58′E, 563.3 masl) in China. The station is located in the Central Asia and northwest of China, and it is the only station in that region with background conditions for long-term greenhouse gas observations. Characteristics of the mole fractions, growth rates, and influence of long-distance transport were studied considering data from September 2009 to December 2019. The greenhouse gases concentrations at Akedala Station show a trend of year-on-year growth, with CO2 concentrations ranging from 389.80 × 10−6 to 408.79 × 10−6 (molar fraction of substances, same below), CH4 concentrations ranging from 1890.07 × 10−9 to 1976.32 × 10−9, N2O concentrations ranging from 321.26 × 10−9 to 332.03 × 10−9, and SF6 concentrations ranging from 7.04 × 10−12 to 10.07 × 10−12, the growth rate of which is similar to the decadal average growth rate in the Northern Hemisphere. There exist obvious seasonal variations, with CO2 concentrations showing high in winter and low in summer and CH4 showing a distinct “W”-shaped trend while N2O and SF6 showing little difference between the four seasons. A relatively strong correlation and homology exist among the four greenhouse gases except in summer, and the analysis based on backward trajectories model shows that the Akedala Station is influenced by the airflow from northwest or southwest throughout the year. The Akedala Station is an important atmospheric background station in Central Asia, and its greenhouse gas concentration levels and variation characteristics are significantly different from those of the background stations in the monsoon region. Its degree changes are closely related to local source emissions, monsoon transport, and atmospheric photochemical processes.

Microcellular injection molding of polyether-ether-ketone

Polyether-ether-ketone (PEEK) has been widely used in aerospace field for its excellent performance. Microcellular injection molding (MIM) is a promising method to reduce product weight and save energy consumption. In this study, microcellular PEEK was prepared by injection molding. Firstly, the thermal properties of raw PEEK, injection molded PEEK, and foamed PEEK were investigated and results show that the thermal performance of the three types of PEEK were similar. Secondly, the effects of process parameters, including melt temperature, injection speed, injection volume and gas concentration, on microcellular PEEK were studied. The results show that injection volume had the most significant influence on the weight-reduction ratio and tensile strength, while the change of melt temperature, injection speed, injection volume and gas concentration could alter the internal cell structure. After optimization, two kinds of microcellular PEEK with the largest weight-reduction ratio of 17.29% and the greatest tensile strength of 74.13 MPa were obtained respectively. Thirdly, the effect of carbon fiber on microcellular PEEK was preliminarily studied. It was found that carbon fiber could promote the formation of dense cell structure. This work laid the foundation for further fabrication of high strength and light weight microcellular PEEK. • Effects of parameters on foamed PEEK are studied by single factor and orthogonal test. • Weight reduction and tensile properties are mainly dependent on injection volume. • Melt temperature, injection speed and injection volume greatly affect cell structure. • The weight-reduction ratio increases to 17.29% through optimization. • The tensile strength and tensile modulus reach 74 MPa and 2784 MPa after optimization.

A New Approach in the Field of Hydrogen Gas Sensing Using MEMS Based 3C-SiC Microcantilevers

This paper demonstrates the ability of 3CSiC microcantilevers (μCs) to monitor binary gas mixture without sensitive coating. Here, 3CSiC is chosen in particular, as the newly designed sensor will be placed in a radioactive environment. The change in gas concentration is identified using relative shifts in the cantilever’s mechanical resonance frequency (∆fr). The presented microcantilevers work on electromagnetic actuation and inductive detection. In this paper, the fabrication process, optical characterization results using laser Doppler vibrometry and test results under a gas mixture environment are demonstrated. The presented limit of detection shows the ability of 3CSiCμCs to detect less than 1% of hydrogen in nitrogen, which makes them suitable for the targeted application.

Stress-induced changes in hydrothermal gas discharges along active faults near Mt. Etna volcano (Sicily, Italy)

Near-continuous monitoring both of gas emissions (CO2, CH4 and H2S) and of water temperature at Santa Venera al Pozzo thermal springs (SE foot of Mt. Etna volcano, Sicily, Italy) was conducted from December 2017 to April 2019, using a novel and cheaper Chromatography Monitoring System (CMS) coupled with a water temperature sensor. The results showed methane as predominant gas and temporal changes in gas concentrations that were in part due to daily fluctuations, which caused small amplitude variations, and in part due to non-environmental causes. These latter were correlated with the occurrence of strong earthquakes and slow tectonic events related to magmatic intrusions, but not with input of magmatic gases into the thermal aquifer, given the non-magmatic origin of all monitored gases. Methane spikes were observed during many volcano-tectonic events and call for a deep source of this gas. H2S was detected only during the strongest local tectonic events, including a Mw 4.9 earthquake, suggesting that this gas has a common origin as CH4 (i.e., mixing between microbial and thermogenic gas), but it is released only when tectonic stress is applied for sufficiently long periods as to cause H2S oversaturation in the hydrothermal aquifer. Water temperature decreases were also observed immediately after the two strongest earthquakes in the area, which helped us produce a comprehensive model to explain the observed geochemical variations. Our approach allowed revealing the great sensitivity of gases such as CH4 and especially H2S to tectonic stress, thus making them valuable indicators of impending strong tectonic or volcano-tectonic events.

Apohemoglobin‐haptoglobin (Apohb‐Hp) Complex as a Vasculo‐protective Agent in Hypoxia Induced Vaso‐occlusion in Sickle Cell Disease

Sickle Cell Disease is associated with episodes of vaso‐occlusive crises (VOCs) caused by hemoglobin S (HbS) polymerization and resultant microvascular endothelial dysfunction due to hemolysis, inflammation, defects in nitric oxide metabolism and oxidative stress. In the acute setting, care for VOC involves supportive care including hydration and aggressive pain management. There are no specific therapies known to alter the course of the disease during an acute VOC. Apohemoglobin‐haptoglobin (Apohb‐Hp) complex is a two‐protein formulation that scavenges free hemoglobin (Hb) as well as free heme and has been shown to have a role in alleviating vasoconstriction and hypertension induced by cell free Hb1. The objective of this study was to evaluate microcirculatory effects of Apohb‐Hp during hypoxia induced vaso occlusion in sickle cell mice.We used intravital microscopy on the dorsal skin fold to observe the microcirculatory effects of Apo Hb‐Hp, haptoglobin (Hp), vehicle control in HbSS‐Townes mice (6‐8 week and ~25 g) and Hb AA mice. Each group consisted of 11 mice, they were initially kept in normal conditions (21% O2) for an hour followed by a hypoxic state (7% O2) for another hour and finally brought back to normal conditions (21% O2) for an hour before being sacrificed. In some cases, some mice were followed for 72 hours before being sacrificed. ApoHb‐Hp and Hp were injected intravenously at a concentration of 59 mg/mL and 86 mg/mL respectively at a single dosing quantity of 150 mg/kg. The Hb binding capacity of Apohb‐Hp was 15 mg/mL and the heme binding capacity was 0.5 mg/mL, the Hb binding capacity of Hp was 30 mg/mL with no heme binding capacity. During baseline, hypoxia and normoxia systemic parameters (mean arteriole pressure and heart rate), blood gases (pO2,A, pO2,v, sO2,A­, sO2,v), hematological properties (total Hb concentration, hematocrit, and concentration of lactate) and microcirculatory hemodynamics (velocity, diameter, flow and functional capillary density) were measured. After the mice were sacrificed a count of present macrophages, neutrophiles and lymphocytes were quantified in the plasmaThere was no notable changes in blood gas concentrations with any of the treatments. Apohb‐hp treatment treated animals showed an improvement in microcirculatory dynamics. Apohb‐Hp treated animals showed a statically higher diameter in arterioles (&lt;40 um and &gt;40 um) as well as venules with a diameter between 20‐60 um when compared to controls and hp treated mice, suggesting that treatment prevented vasoconstriction in arterioles. Apohb‐hp treatment also improved flow rates in arterioles and venules when compared to the control and hp treated mice. Furthermore, the functional capillary density of the mice followed for 72 hours after hypoxia showed that the mice treated with apohb‐hp had a greater number of functional capillaries compared to the control and hp treated group. Lastly, the amount macrophages, neutrophiles and lymphocytes decreased when treated with apohb‐hp illustrating another promising result as VOCs are generally associated with an active immune response.These results suggest a role for ApoHb‐Hp complex in alleviating microvascular changes occurring during hypoxia mediated acute vaso‐occlusion in SCD and merits further study for development as a potential therapeutic.

Air Quality during the COVID–19 Lockdown and Unlock Periods in India Analyzed Using Satellite and Ground-based Measurements

A nationwide lockdown was imposed in India from 24 March 2020 to 31 May 2020 to contain the spread of COVID-19. The lockdown has changed the atmospheric pollution across the continents. Here, we analyze the changes in two most important air quality related trace gases, nitrogen dioxide (NO2) and tropospheric ozone (O3) from satellite and surface observations, during the lockdown (April–May 2020) and unlock periods (June–September 2020) in India, to examine the baseline emissions when anthropogenic sources were significantly reduced. We use the Bayesian statistics to find the changes in these trace gas concentrations in different time periods. There is a strong reduction in NO2 during the lockdown as public transport and industries were shut during that period. The largest changes are found in IGP (Indo-Gangetic Plain), and industrial and mining areas in Eastern India. The changes are small in the hilly regions, where the concentrations of these trace gases are also very small (0–1 × 1015 molec./cm2). In addition, a corresponding increase in the concentrations of tropospheric O3 is observed during the period. The analyses over cities show that there is a large decrease in NO2 in Delhi (36%), Bangalore (21%) and Ahmedabad (21%). As the lockdown restrictions were eased during the unlock period, the concentrations of NO2 gradually increased and ozone deceased in most regions. Therefore, this study suggests that pollution control measures should be prioritized, ensuring strict regulations to control the source of anthropogenic pollutants, particularly from the transport and industrial sectors.Highlights• Most cities show a reduction up to 15% of NO2 during the lockdown• The unlock periods show again an increase of about 40–50% in NO2• An increase in tropospheric O3 is observed together with the decrease in NO2Supplementary InformationThe online version contains supplementary material available at 10.1007/s40710-022-00585-9.

Prediction of gas concentration evolution with evolutionary attention-based temporal graph convolutional network

Accurate prediction of gas concentration is of great importance in many safe-based systems and applications. However, prediction accuracy of gas concentration is limited by not only the temporal evolution of gas concentration but also the spatial characteristics of gas dispersion. To capture the spatial and temporal dependences simultaneously, an evolutionary attention-based temporal graph convolutional network (EAT-GCN) is proposed, which has three outstanding features: (1) graph convolutional network (GCN) is used to capture spatial dependence by learning topological structures of a gas sensor network; (2) gated recurrent unit (GRU) is adopted to retain temporal dependence by learning dynamic changes of gas concentration, and (3) evolutionary attention is introduced to improve the ability of GRU to pay different degrees of attention to the sub-window features within multiple time steps. Finally, kernel extrapolation distribution mapping algorithm is employed to visualize the predicted results of gas concentration and update the gas distribution map. Compared with CNN, GCN, GRU, T-GCN, A3T-GCN and EA-GRU models, the proposed EAT-GCN model improves the prediction accuracy by 13.46%, 124.21%, 33.92%, 23.39%, 46.63%, and 23.97%, respectively. Experiments demonstrate that the designed model captures spatiotemporal correlation from gas concentration data and achieves better prediction accuracy than state-of-the-art baseline methods.