Liquid Water Content Research Articles

Frozen and unfrozen moisture content estimation in coral calcareous sand during artificial freezing

In tropical areas where coral calcareous sands are prevalent, artificial freezing techniques are frequently employed during construction. However, the fundamental thermodynamic behaviors and moisture dynamics of calcareous sands under freezing conditions are poorly understood. Therefore, we conducted laboratory tests and developed a numerical model to capture the total moisture, liquid water, and ice contents of calcareous sand during artificial freezing. The thermal fiber Bragg grating (T − FBG) and frequency − domain reflectometry methods were used in the study. Freezing characteristic curves were quantitatively analyzed with taking into account the initial moisture content and ambient temperature. The results indicate that T − FBG effectively estimates the total moisture content in unfrozen and frozen calcareous sand, as well as ice content in frozen soil, with less than 0.029 m3/m3 error. Ice melting induced by T − FBG heating is affected by the initial moisture content, heating duration, power, and ambient temperature. However, the maximum change is below 0.008 m3/m3, which is negligible. The van Genuchten model accurately describes the liquid moisture–temperature relationship of unsaturated calcareous sand, with an R2 exceeding 0.98. The residual–initial moisture content relationship follows a quadratic function. During freezing, the temperature reduction aligns with the Kozlowski model, and the liquid moisture–temperature relationship follows a cubic polynomial function.

MVD and LWC prediction in icing environment based on genetic algorithm optimized neural network

Mean volumetric diameter (MVD) and liquid water content (LWC) are two important meteorological parameters that affect aircraft icing, but they are difficult to measure accurately in practice. If these two parameters can be obtained in real time and accurately, it can provide some guidance for ice accumulation prediction and the establishment of aircraft airworthiness certification standards. This paper proposes a prediction model for icing meteorological parameters based on genetic algorithm optimized neural network. With ice thickness and icing rate of different measurement point combinations, ambient temperature, flight speed and wing angle of attack as input parameters, and icing meteorological parameters MVD and LWC as output parameters, the genetic algorithm optimized icing meteorological parameter prediction model is constructed, and the prediction model is used to predict the icing meteorological parameters of the numerical calculation test group data and the icing wind tunnel experimental data. The results show that the prediction model based on genetic algorithm optimized Elman neural network predicts the icing meteorological parameters of the test group within the relative error of the test group 10%, and the relative error of the experimental data is within the relative error of the experimental data 20%. This method has certain feasibility.

Cloud characteristics and their role in the 2024 United Arab Emirates extreme rainfall events

ABSTRACT Intensified by climate change, extreme rainfall events are frequently increasing and have become severe particularly in tropical and subtropical regions. On 16 April 2024, the United Arab Emirates (UAE) experienced a record-breaking flood event, characterized by ~ 250 mm of rain within a 24-hour period and surpassed the previous record of 1949. Later, on 2 May 2024, another rainfall event occurred but with low rainfall (~20 mm in 12 hours). These consecutive incidents raised a need to understand convective mechanism, so present study focus to analyse it using various cloud properties over Dubai. Our findings inferred that the April event was mostly driven by extensive cloud such as cumulonimbus which stretched up to ~ 16 km, with prominent features of high optical thickness (~150), small-sized cloud droplets (~23.89 µm), and considerable liquid water content (~2185 g/m−2). Further, we found high (~400–1800 J kg−1) and comparatively low (~0–700 J kg−1) potential energies, which trigerred convection on April 16 and May 02 respectively. Thus our study gained insights related to occurrence of convective activities at regional level using atmospheric and cloud parameters. These outcomes can be used as an input in climate models to improve weather patterns even in arid environments like Dubai, where cloud seeding is a regular practice.

Combining observations and simulations to investigate the small-scale variability of surface solar irradiance under continental cumulus clouds

Abstract. The amount of solar radiation reaching the Earth surface (surface solar irradiance, SSI) is critical for a variety of applications, ranging from surface–atmosphere interactions to solar energy. SSI is characterized by a large spatiotemporal variability, in particular in the presence of cumulus clouds. This results in complex spatial patterns of shadows and sunlight directly related to clouds' geometry and physical properties. Although key in many respects, the instantaneous spatial distribution of SSI remains largely unexplored. Here, we use unique observations from a dense network of pyranometers deployed during the HOPE field campaign to investigate the SSI spatial distribution. For cumulus scenes, bimodal distributions are found, with one mode corresponding to cloud shadows and the other to sunlit areas with enhanced SSI exceeding clear-sky values. Combining large-eddy simulations of cumulus clouds with Monte Carlo ray tracing, we demonstrate the capability of advanced numerical tools to reproduce the observed distributions and quantify the impact of cloud geometrical and physical properties on both modes. In particular, cloud cover strongly modulates their amplitudes, in addition to their position and width, which are also sensitive to cloud height, geometrical depth, and liquid water content. Combining observations and simulations, we also explore sampling strategies to estimate the SSI spatial distribution with a limited number of sensors, suggesting that 10 pyranometers integrated over 10 min can capture most details of the full distribution. Such a strategy could be used for future campaigns to further investigate SSI distributions and their impact on land–atmosphere exchanges or photovoltaic farm management.

Effects of sand grain roughness height on the performance of wind turbine blade section under extreme weather conditions

Wind turbine blades are prone to icing and their performance is affected significantly by the roughness caused by icing and erosion. Numerical models are constructed to simulate the effects of sand grain roughness on the mass of accreted ice and aerodynamic performance. The sand grain roughness height is considered by applying the NASA and Shin et al. models, and the cloud characteristics studied are the liquid water content (LWC), median volume diameter (MVD) and air temperature covering freezing drizzle and in-cloud icing conditions resulting in glaze ice and rime ice, respectively. The numerical model applied the multi-shot approach, and the effects of the number of shots on the ice accretion and aerodynamic performance was examined to determine the optimum number of shots which can be used in the simulation in order to minimize the computational time without affecting the accuracy. The relationship between the sand grain roughness height and aerodynamic performance is studied, revealing that the shallowest roughness heights cause less performance degradation. The mass of ice increases with increasing LWC from 0.05g/m3 to 0.3g/m3 and MVD from 20 µm to 100 µm, which results in a reduction in the lift-to-drag ratio (CL/CD).

The Characteristics of the Chemical Composition of PM2.5 during a Severe Haze Episode in Suzhou, China

During the past decade, the air quality has been greatly improved in China since the implementation of the “Clean Air Act”. However, haze events are still being reported in some regions of China, and the pollution mechanism remains unclear. In this study, we investigate the chemical characteristics of the pollution mechanism of the PM2.5 composition in Suzhou from October 18 to December 15, 2020. A notable declining trend in temperature was observed from 18 to 27 November, which indicates the seasonal transition from fall to the winter season. Four representative periods were identified based on meteorological parameters and the PM2.5 mass concentrations. The heavy pollution period had the typical characteristics of a relatively low temperature, a high relative humidity, and mass loadings of atmospheric pollutants; nitrate was the dominant contributor to the haze pollution during this period. The nitrate formation mechanism was driven by the planetary boundary layer dynamics. The potential source contribution function model (PSCF) showed that the major PM2.5 composition originated from the northwest direction of the sampling site. The aerosol liquid water content presented increasing trends with an increasing relative humidity. The pH was the highest during the heavy pollution period, which was influenced by the aerosol liquid water content and the mass loadings of NO3−, SO42−, NH4+, and Cl−. The comprehensive analysis in this paper could improve our understanding of the nitrate pollution mechanism and environmental effects in this region.

Performance of the multi-U-style structure based flow field for polymer electrolyte membrane fuel cell

The design of the reactant gas flow field structure in bipolar plates significantly influences the performance of proton exchange membrane fuel cells (PEMFCs). In this study, we introduced four innovative U-shaped flow field designs, namely: In-Out Multi-U, Out-In Multi-U, Distro In-Out Multi-U, and Distro Out-In Multi-U. To investigate the impact of these various flow fields on PEMFC performance, we conducted computational fluid dynamics (CFD) numerical simulations, validated through model experiments. Our results indicate that the Distro Out-In Multi-U flow field offers notable advantages compared to the conventional parallel flow field (CPFF) and conventional serpentine flow field (CSFF). These benefits include reduced inlet and outlet pressures, lower liquid water content, more uniform liquid water distribution, and a more even current density distribution. Furthermore, the Distro Out-In Multi-U design demonstrates improved efficiency, consuming less H2 (91.9%) than the CSFF while achieving a higher net power density output (10.1%). As a result, for the same power output, the Distro Out-In Multi-U utilizes only 83.5% of the H2 consumed by the CSFF. In summary, the U-shaped structured flow field exhibits superior output performance, enhanced energy efficiency, and improved resistance to flooding. These findings suggest that the U-shaped flow field design holds significant potential as a reactive flow field for PEMFCs.

Experimental Study on Water and Salt Migration and the Aggregate Insulating Effect in Coarse-Grained Saline Soil Subgrade under Freeze–Thaw Cycles

Understanding multiphase transformations and the migration of heat, water, vapor, and salt in coarse-grained saline soil under groundwater recharge and environmental freeze—thaw cycles is crucial for ensuring the stability of highway infrastructures. To clarify the water, heat, vapor, and salt migration patterns in coarse-grained saline soil, as well as the salt-insulating effect of the aggregate insulating layer, an experimental study was conducted in a soil column model under pressureless water replenishment with fluorescein-labeled liquid water under freeze—thaw cycles. The results showed that the temperature in the saline soil columns periodically changed and that hysteresis effects occurred during temperature transfer. External water replenishment and the content of liquid water inside the soil exhibited nonlinear changes with environmental temperatures. After multiple freeze—thaw cycles, two water and salt accumulation zones formed within the coarse-grained saline soil subgrade. The migration of liquid water resulted in a water and salt accumulation zone in the nonfrozen zone, whereas the migration of water vapor yielded a water and salt accumulation zone in the frozen zone. To prevent water and salt migration, a 20 cm thick gravel insulating layer could be laid at a distance of 10 cm from the bottom of the roadbed, which could provide a satisfactory salt-insulating effect. The research results provide a theoretical basis and guidance for regulating the stability of subgrades in saline soil areas.

Glide-snow avalanches: a mechanical, threshold-based release area model

Abstract. Glide-snow avalanches release at the ground–snow interface due to a loss in basal friction. They pose a threat to infrastructure because of the combination of unreliable mitigation measures, limited forecasting capabilities, and a lack of understanding of the release process. The aim of this study was to investigate the influence of spatial variability in basal friction and snowpack properties on the avalanche release area distribution and the release location. We developed a pseudo-3D, mechanical, threshold-based model that consists of many interacting snow columns on a uniform slope. Parameterizations in the model are based on our current understanding of glide-snow avalanche release. The model can reproduce the power law glide-snow avalanche release area distribution as observed on Dorfberg (Davos, Switzerland). A sensitivity analysis of the input parameters showed that the avalanche release area distribution was mostly influenced by the homogeneity (correlation length and variance) of the basal friction and whether the basal friction was reduced suddenly or in small increments. Larger release areas were modeled for a sudden decrease and a more homogeneous basal friction. The spatial variability of the snowpack parameters had little influence on the release area distribution. Extending the model to a real-world slope showed that the modeled location of avalanche releases qualitatively matched the observed locations. The model can help narrow down the length scales and timescales for field investigations. Simultaneously, it can grow in complexity with our increasing understanding of glide-snow avalanche release processes. Input parameters such as the basal friction or snowpack parameters could potentially all be connected to the liquid water content. This would allow for the use of meteorological measurements to drive the model. The model has the potential to help identify potentially dangerous conditions for large or numerous avalanches which would help improve glide-snow avalanche forecasting.

Molecular interaction between ammonium sulfate and secondary organic aerosol from styrene

Secondary organic aerosol (SOA) and inorganic aerosol (SIA) are two major constituents of PM2.5, which are well-studied separately. However, the molecular-level investigation of mechanical interactions between them is still limited, which will affect the understanding of toxicity and optical properties of PM2.5. Styrene contains a benzene ring and a highly reactive double bond, mainly contributing to urban SOA formation. Here, chamber experiments were conducted to explore the effects of the (NH4)2SO4 seeds, as an example of SIA, on styrene SOA formation under varying relative humidity (RH) conditions. The particle-phase products were determined based on a high-resolution orbitrap mass spectrometer. The results showed that in the styrene-H2O2-hv systems, (NH4)2SO4 seeds facilitated the gas-particle partitioning of SOA precursors, leading to slightly higher SOA yields than the unseeded systems at 8.5 % RH. While RH exceeded 44.3 %, the presence of (NH4)2SO4 seeds increased the particle liquid water content, thus enhancing the partitioning of hydrophilic H2O2 to particle phase and the further oxidation of SOA, finally decreasing SOA yields. In the styrene-NOx-hv systems, NH4+ and SO42− were involved in the particle-phase reactions, producing prominent nitrogen- and sulfur-containing compounds of C8H7O2N and C8H9O6NS. In the styrene-O3 dark reaction systems, the existence of (NH4)2SO4 seeds significantly changed the oligomer components under humid conditions. The particle-phase formation pathways of oligomers affected by (NH4)2SO4 seeds were also illustrated based on tandem mass spectra data. This study emphasizes the importance of inorganic seed effects on particle-phase reactions and improves our understanding of the SOA formation pathways in the ambient atmosphere.

Relationship between vertical variation of cloud microphysical properties and thickness of the entrainment interfacial layer in Physics of Stratocumulus Top stratocumulus clouds

AbstractThis study examines the vertical variations of cloud microphysics and their correlation with the thickness of the entrainment interfacial layer (EIL) in stratocumulus clouds, observed in the Physics of Stratocumulus Top (POST) aircraft measurement campaign. From the mixing fraction analysis, we identified EIL between the free atmosphere and cloud top for all 15 POST flights, and found that EIL thickness significantly influenced the vertical variation of cloud microphysics and thermodynamics. In several flights, a trend toward stronger homogeneous mixing traits with increasing depth from the cloud top was found, indicative of the vertical movement of mixed (i.e., entrainment‐affected and diluted) parcels. However, in one flight, this trend was limited to the middle part of the cloud only, with the correlation between virtual potential temperature and liquid water content being strongly negative near the cloud top, suggesting limited downward movement of mixed parcels. Another important finding is that there was a robust negative correlation between long‐wave cooling rate near the cloud top and EIL thickness, highlighting differences in radiative cooling rates between mixed and unmixed parcels due to differences in liquid water content between them. These insightful findings will be crucial for enhancing our understanding of the role of EIL in modulating entrainment and the vertical movement of mixed parcels in stratocumulus clouds.

Novel method for inversion of microphysical properties of clouds using Raman lidar data

Aerosol–cloud–precipitation interactions are important in the balance of Earth’s radiation budget. To further explore the relationship between clouds and precipitation, and to improve operational weather modification, it is necessary to study the microphysical parameters of liquid water clouds. Here, an inversion method that uses a back propagation (BP) neural network based on a genetic algorithm (GA), namely a GABP, is proposed to invert cloud microphysical parameters using ground-based dual-field-of-view (FOV) Raman lidar data. To verify the feasibility of the method, long-term continuous observations were conducted in the Liupan Mountains (China). Results revealed that the proposed inversion method using the GABP is feasible for retrieving the liquid water content (LWC) and the cloud droplet effective radius after training a large number of data measured simultaneously by the Raman lidar and a microwave radiometer. When inverting LWC, the root mean square error (RMSE) of the GABP algorithm was found in the range 0–0.005, whereas the RMSE of the BP algorithm fluctuated in the range 0–0.01. It was evident that the GABP algorithm yields better inversion results and finer detail. When maintaining other variables and comparing the inversion results of signals in the inner and outer FOVs, the RMSE of the inner FOV signal was within 0.005 at near-ground heights (i.e., <2 km), whereas the outer FOV signal exceeded 0.005 at certain heights. This study developed a feasible solution for detecting characteristic cloud microphysical parameters using a Raman lidar, which could be used to study aerosol–cloud–precipitation interactions, and thereby have considerable practical importance for improving artificial rainfall operations.

Insights Into the Influence of Anthropogenic Emissions on the Formation of Secondary Organic Aerosols Based on Online Measurements

AbstractTo investigate the combined impacts of anthropogenic and biogenic emissions on the formation of secondary organic aerosols (SOA), SOA molecular tracers, their corresponding volatile organic compound precursors, and other air pollutants were measured online during the winter and summer seasons of 2022 in an industrial city, Zibo, China. The results indicate that the average concentrations of SOA tracers were 16.1 ± 9.8 ng m−3 in winter and 99.4 ± 57.2 ng m−3 in summer. During winter, anthropogenic SOA (ASOA, the sum of SOA derived from naphthalene and mono‐aromatic volatile organic compounds) dominated, whereas isoprene SOA (SOAI) prevailed in summer. Correlation analysis between SO42− and both SOAI and high‐order monoterpene SOA tracers (SOAM‐H) (R = 0.46–0.72, p &lt; 0.001) revealed that higher aerosol acidity facilitated the formation of SOAI and SOAM‐H, with SO2 emissions playing a significant role in leading to higher acidity. Most biogenic SOA (BSOA) tracers exhibited a significant positive correlation with NO3−, particularly in winter, implying the remarkable influence of NOx emissions on BSOA formation. The levels of BSOA tracers increased with NH3, indicating that NH3 can enhance the formation of BSOA. In summer, SOA formation correlated with Ox (Ox = O3 + NO2), indicating the substantial impact of atmospheric oxidizing capacity on SOA formation. During winter, aerosol liquid water content (ALWC) correlated well with SOAI tracers (i.e., 3‐hydroxyglutaric acid (3‐HGA) and 3‐hydroxy‐4,4‐dimethylglutaric acid (3‐HDMGA)), and 2,3‐dihydroxy‐4‐oxopentanoic acid (DHOPA) (R &gt; 0.5, p &lt; 0.001), indicating the important contribution of aqueous‐phase formation of SOA. These findings underscore the significant role of anthropogenic pollutant emissions in the formation of ASOA and BSOA in urban environments.

Spatio‐temporal wet snow dynamics from model simulations and remote sensing: A case study from the Rofental, Austria

AbstractThe formation and concentration of liquid water (LW) in the snowpack constitute key processes linking snow and runoff. Hence, the LW content of the snowpack represents a crucial target variable to investigate for snowmelt‐induced runoff predictions. In this study, we capture the wet snow dynamics at higher than hectometre resolution in the alpine headwater catchment Rofental, Tyrol, Austria (98.1 km2) by means of distributed model simulations and remote sensing data for the 5 year period 10/2017–09/2022. The model simulations are conducted using the intermediate complexity open‐source snow‐hydrological model openAMUNDSEN. Simulation results are compared to wet snow maps (WSM) derived from Sentinel‐1 data. Our investigations indicate that distributed snow models of intermediate complexity, such as openAMUNDSEN and satellite‐based wet snow data are well capable of capturing the wet snow dynamics in high spatial and temporal resolutions. The areal extents of wet snow as well as the upward movement of the wet snow line to higher elevation with progressing snowmelt are captured well by both approaches. In order to evaluate the snow simulations, we use fractional snow cover (FSC) data based on Sentinel‐2, which proved to provide valuable small‐scale snow and snow redistribution patterns in alpine catchments. The comparison of model simulations with FSC maps with more than 50% of the non‐glaciated area being cloud‐free (i.e. 364 images) results in an accuracy of 0.91. This study represents a further step towards a serviceable operational snow‐hydrological monitoring and modelling framework for mountain regions including wet snow dynamics in high spatial and temporal resolutions.

Climate‐Driven Projections of Future Global Wetlands Extent

AbstractWetlands are crucial components of the Earth's system, interacting with various processes such as the hydrological cycle, energy exchanges with the atmosphere, and global nitrogen and carbon cycles. The future trajectory of wetlands is anticipated to be influenced not only by direct human activities, but also by climate change. Here we present our assessment of climate‐driven global changes in wetlands extent, focusing on the main wetland complexes. We used an approach based on the Topographic Hydrological model (TOPMODEL) and soil liquid water content projections from 14 models of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Our analysis reveals a consistent decrease in wetlands extent in the Mediterranean, Central America, and northern South America, with a substantial loss of 28% in the western Amazon Basin for the end of the 21st century (2081–2100) under the SSP370 scenario. Conversely, Central Africa exhibits an increase in wetlands extent, except in the Congo Basin. Nevertheless, most of the areas studied (80%) present uncertain results, due to conflicting projections of changes between the models. Notably, we show that there is significant uncertainty among CMIP6 models regarding liquid soil water content in high latitudes. By narrowing our focus to 10 models, which seem to better represent the thawing of permafrost, we obtain a better inter‐model agreement. We then find a modest declines in the overall global area (&lt;5%), but an average loss of 13% beyond 50°N. Specific areas like the Hudson Bay Lowlands experiencing a 21% decrease and the Western Siberian Lowlands a 15% decrease.

Detection and Retrieval of Supercooled Water in Stratocumulus Clouds over Northeastern China Using Millimeter-Wave Radar and Microwave Radiometer

Supercooled water in mixed-phase clouds plays a significant role in precipitation formation, atmospheric radiation, weather modification, and aircraft flight safety. Identifying supercooled water in mixed-phase clouds is a crucial-frontier scientific issue in atmospheric detection research. In this study, we propose a new algorithm for identifying supercooled water based on the multi-spectral peak characteristics in cloud radar power spectra, combined with radar reflectivity factor and mean Doppler velocity. Using microwave radiometer data, we conducted retrieval analyses on two stratocumulus cases in the spring over the northeastern Daxing’anling region, China. The retrieval results show that the supercooled water in the spring stratocumulus clouds over the region is widespread, with liquid water content (LWC) ranging around 0.1 ± 0.05 g/m3, and particle sizes not exceeding 10 μm. The influence of updrafts on supercooled water is evident, with both showing good consistency in spatiotemporal variation trends. Comparing the liquid water path (LWP) variations retrieved from cloud radar and microwave radiometer, both showed good consistency in variation trends and high LWC areas, indicating the reliability of the identification algorithm developed in this study.

17O NMR spectroscopy protocol for the determination of water content in liquids produced by pyrolysis and hydrothermal liquefaction

BackgroundThere is an urgent need to replace fossil-based fuels and chemicals with bio-based, renewable alternatives. Water content is a critical parameter in these liquid products since water affects their quality and properties. However, currently existing methods for bio-oil water content determination have limitations and thus, there is a need to find methods that are versatile, work for a wide water content and sample consistency range repeatably and reliably and are safe for the user and the environment. ResultsIn this research, a17O NMR spectroscopy protocol for water content determination of pyrolysis and hydrothermal liquefaction (HTL) liquids was developed and compared with the standard method Karl Fischer (KF) titration. The approach showed linearity over a wide concentration range, and the changes to the measurement parameters caused only minor effects to the results (≤0.8 percentage points) indicating robustness. The method is also accurate since the absolute differences between experimental and theoretical water contents varied from 0.08 % to 2.09 %. Additionally, the precision of the method, based on the relative standard deviations (RSD) of the three replicate measurements of pyrolysis and HTL samples, was good (RSD <1.82 %). The method was applied to samples containing 1–98 wt% water. Overall, the 17O NMR spectroscopy and KF titration results were well aligned with each other suggesting that the 17O NMR spectroscopy is a potential alternative for the conventional KF titration. SignificanceThis is the first study on the use of 17O NMR spectroscopy protocol for water content quantification. The results indicate that the protocol is an accurate, linear, and precise technique for water content determination of a wide range of samples. Furthermore, the method does not require hazardous chemicals or calibration standards, and the sample preparation is straightforward. The non-destructiveness of the method also enables further studies on the sample, e.g. by 1H NMR spectroscopy.

Prediction model for icing growth characteristics of high-speed railway contact lines

The sliding electrical contact is the only means by which high-speed trains obtain energy. When icing occurs on the contact lines, the impact vibrations of the pantograph-catenary system are further exacerbated, electrical arcing becomes more frequent, and abnormal wear is caused, seriously threatening the safety of the energy supply for high-speed railways. To address the unclear mechanisms, unpredictable patterns, and challenging characterization of contact lines icing, this paper proposes a dynamic simulation method for the first time. Furthermore, a surrogate model for predicting contact line icing is developed using deep learning algorithms. First, based on grid updating, flow field analysis, and icing calculations, key icing parameters are obtained to establish a numerical model of contact lines icing under time-varying meteorological parameters. Then, the effects of factors such as wind speed, temperature, and liquid water content on the dynamic evolution characteristics of contact line icing are analyzed. Finally, using the CNN-GRU algorithm, a prediction model for contact line icing is constructed to predict the icing mass and contours. This research clarifies the evolution patterns of contact lines icing, addresses challenges in monitoring and predicting icing states, and lays a theoretical foundation for high-speed railways' safe and stable operation under icing conditions.

Depth-specific distribution of bacterial MAGs in permafrost active layer in Ny Ålesund, Svalbard (79°N)

Arctic soil microbial communities may shift with increasing temperatures and water availability from climate change. We examined temperature and volumetric liquid water content (VWC) in the upper 80 cm of permafrost-affected soil over 2 years (2018–2019) at the Bayelva monitoring station, Ny Ålesund, Svalbard. We show VWC increases with depth, whereas in situ temperature is more stable vertically, ranging from −5°C to 5 °C seasonally. Prokaryotic metagenome-assembled genomes (MAGs) were obtained at 2–4 cm vertical resolution collected while frozen in April 2018 and at 10 cm vertical resolution collected while thawed in September 2019. The most abundant MAGs were Acidobacteriota, Actinomycetota, and Chloroflexota. Actinomycetota and Chloroflexota increase with depth, while Acidobacteriota classes Thermoanaerobaculia Gp7-AA8, Blastocatellia UBA7656, and Vicinamibacteria Vicinamibacterales are found above 6 cm, below 6 cm, and below 20 cm, respectively. All MAGs have diverse carbon-degrading genes, and Actinomycetota and Chloroflexota have autotrophic genes. Genes encoding β -glucosidase, N-acetyl-β-D-glucosaminidase, and xylosidase increase with depth, indicating a greater potential for organic matter degradation with higher VWC. Acidobacteriota dominate the top 6 cm with their classes segregating by depth, whereas Actinomycetota and Chloroflexota dominate below ∼6 cm. This suggests that Acidobacteriota classes adapt to lower VWC at the surface, while Actinomycetota and Chloroflexota persist below 6 cm with higher VWC. This indicates that VWC may be as important as temperature in microbial climate change responses in Arctic mineral soils. Here we describe MAG-based Seqcode type species in the Acidobacteriota, Onstottus arcticum, Onstottus frigus, and Gilichinskyi gelida and in the Actinobacteriota, Mayfieldus profundus.

Numerical investigations on electrolytic performance of proton exchange membrane electrolysis cell with parallel flow field

In this study, a three-dimensional simulation model of PEMEC with parallel flow field was established, the heat and mass transfer, electrochemical reactions were described to investigate the electrolytic performance. The distribution characteristics of temperature, liquid water and gas products in the electrolysis cell were obtained. The results show that during the operation of PEMEC system, the liquid water content decreases sequentially along the flow channel direction, while there is a significant temperature gradient in the flow direction of the electrolysis cell. The increase of the initial temperature can increase the current density of the electrolytic cell when the temperature increases from 60 ℃ to 80 ℃, the current density can be increases by 7.86 %, but it can also lead to the dehydration of PEMEC, resulting in thermal degradation of PEMEC, and further the reactivity and proton transfer performance were decreased. It is also found that the increase of liquid water inlet flow not only increased the reactant concentration, accelerated the chemical reaction rate, but also took away more heat generated by ohmic polarization as a coolant, which was conducive to the improvement of electrolytic performance. As the inlet flow rate increased from 1 mL/s to 3 mL/s, the current density at 2.4 V and 1.4 V can be increased by 3.66 % and 1.77 %, respectively, and the maximum temperature can be reduced by 7.4 K.