Vapor Pressure Research Articles

Estimating and Modeling Pinus contorta Transpiration in a Montane Meadow Using Sap-Flow Measurements

This study quantifies the transpiration of encroached lodgepole pine (Pinus contorta var. murryana (Grev. & Balf.) Engelm.) in a montane meadow using pre-restoration sap-flow measurements. Lodgepole pine transpiration and its response to environmental variables were examined in Rock Creek Meadow (RCM), Southern Cascade Range, CA, USA. Sap-flow data from lodgepole pines were scaled to the meadow using tree survey data and then validated with MODIS evapotranspiration estimates for the 2019 and 2020 growing seasons. A modified Jarvis–Stewart model calibrated to 2020 sap-flow data analyzed lodgepole pine transpiration’s correlation with solar radiation, air temperature, vapor pressure deficit, and soil volumetric water content. Model validation utilized 2021 growing season sap-flow data. Calibration and validation employed a Markov Chain Monte Carlo (MCMC) approach through the DREAM(ZS) algorithm with a generalized likelihood (GL) function, enabling parameter and total uncertainty assessment. The model’s scaling was compared with simple scaling estimates. Average lodgepole pine transpiration at RCM ranged between 220.6 ± 25.3 and 393.4 ± 45.7 mm for the campaign (mid-July 2019 to mid-August 2020) and 100.2 ± 11.5 to 178.8 ± 20.7 mm for the 2020 partial growing season (April to mid-August), akin to MODIS ET. The model aligned well with observed normalized sap-velocity during the 2020 growing season (RMSE = 0.087). However, sap-velocity, on average, was underpredicted by the model (PBIAS = −6.579%). Model validation mirrored calibration in performance metrics (RMSE = 0.1233; PBIAS = −2.873%). The 95% total predictive uncertainty confidence intervals generated by GL-DREAM(ZS) enveloped close to the theoretically expected 95% of total observations for the calibration (94.5%) and validation (81.8%) periods. The performance of the GL-DREAM(ZS) approach and uncertainty assessment in this study shows promise for future MJS model applications, and the model-derived 2020 transpiration estimates highlight the MJS model utility for scaling sap-flow measurements from individual trees to stands of trees.

Tricolored Bat (Perimyotis subflavus) microsite use throughout hibernation

Abstract White-nose syndrome (WNS) has caused dramatic population declines in several bat species, including the Tricolored Bat (Perimyotis subflavus). Several studies have documented bats using colder roosting temperatures after infection; however, this strategy may have costs such as increased freezing risks or greater predation risks and it is unknown when during hibernation bats begin to utilize these colder temperatures. Our aim was to examine Tricolored Bat roost locations in a WNS-positive site in relation to roost microclimate and other environmental conditions throughout the hibernation season. We conducted monthly censuses of tricolored bats across 2 hibernation seasons (November to March 2020 to 2021 and October to March 2021 to 2022) in a WNS-positive hibernaculum in northwestern South Carolina and recorded skin and adjacent wall temperature, tunnel section, and distance from the entrance for each bat species. We continuously measured hibernacula temperature and relative humidity during both hibernation seasons. Most bats roosted in the back part of the tunnel where temperatures were warmer and more stable, and vapor pressure deficit (VPD) and human disturbance were low. However, >20% of bats roosted in the front, where roost temperatures were significantly colder and VPD was higher but more variable; human disturbance was also higher in this section. The proportion of bats in each tunnel section did not vary among months and we did not find evidence of significant movement to the front section of the tunnel as hibernation progressed based on marked bats; however, bats in the front section roosted higher on the wall suggesting that they may be avoiding human disturbance or predators. Our results support the notion that no optimum hibernation temperature exists for tricolored bats and that high VPD and disturbance are likely important factors driving microsite use. Protection of Tricolored Bat hibernacula that offer a range of microclimates or a network of sites in close proximity that offer different microclimates may be helpful for recovery of this species.

Spatial–Temporal Variations in Water Use Efficiency and Its Influencing Factors in the Li River Basin, China

As a vital indicator for measuring the coupled carbon–water cycle of an ecosystem, water use efficiency (WUE) can also reflect the adaptive capacity of plants in different ecosystems. Located in Southwest China, the Li River Basin has a representative karst landform, and the uneven rainfall in the region leads to severe water shortage. In this study, we analyzed the spatial–temporal transformation characteristics of the WUE of the basin and its relationship with different influencing factors from 2001 to 2020 based on a correlation analysis and trend analysis. The main conclusions are as follows: (1) The average value of WUE in the Li River Basin was 1.8251 gC· mm−1·m−2, and it kept decreasing at a rate of 0.0072 gC· mm−1·m−2·a−1 in the past 20 years. With respect to the spatial distribution of the multi-year average of WUE, it exhibits a gradual increasing trend from west to east. (2) Between gross primary productivity (GPP) and evapotranspiration (ET), it was found that ET was the primary influencing factor of WUE. Precipitation was positively correlated with WUE in the Li River Basin, accounting for 67.22% of the total area of the basin. The air temperature was negatively correlated with WUE, and the area was negatively correlated with WUE, accounting for 92.67% of the basin area. (3) The normalized difference vegetation index (NDVI) and leaf area index (LAI) were negatively correlated with WUE, and the proportions of negatively correlated areas to the total area of the basin were similar; both were between 60 and 70%. The growth of vegetation inhibited the increase in WUE in the basin to a certain extent. Regarding Vapor Pressure Deficit (VPD), the proportions of positive and negative correlation areas with WUE were similar, accounting for 49.58% and 50.42%, respectively. (4) The occurrence of drought events and the enhancement in its degree led to a continuous increase in WUE in the basin; for different land cover types, the correlation of the standardized precipitation evapotranspiration index (SPEI) was in the following order from strongest to weakest: grassland > cropland > forest > shrubland.

A revision of the Ni-Zn phase diagram based on vapor-solid diffusion experiments

The technique of vapor-solid diffusion couples was systematically studied for its potential in phase diagram investigation using the well-established Ni-Zn system which is highly suitable for this purpose due to the high vapor pressure difference of the two constituents. More than 50 samples synthesized by direct vapor-solid reaction, varying the parameters temperature, time and overall system composition, were investigated by SEM/EDX focusing on diffusion profile line-scans. Supporting powder-XRD and DTA experiments were performed as well. Based on these experimental results, significant changes to the currently accepted Ni-Zn phase diagram, specifically concerning the β, β1, and γ phase fields, are proposed. Furthermore, a relatively simple experimental procedure to prepare vapor-solid diffusion experiments is introduced and its application in phase diagram investigation is discussed in detail.

Released control of vapor pressure deficit on rainfed rice evapotranspiration responses to extreme droughts in the subtropical zone

Released control of vapor pressure deficit on rainfed rice evapotranspiration responses to extreme droughts in the subtropical zone

Spatiotemporal patterns and drivers of extreme fire severity in Spain for the period 1985–2018

Extreme fire severity patterns driven by climate change have deep consequences on vegetation, subsequent fire regimes and have consequences on the effectiveness of extinction. In this work, we aimed to determine if extreme fire severity defined as the 90th percentile of RdNBR has changed in Spain in the last 3 decades, if extreme fire severity patterns have differed among the different vegetation types and what drivers have influenced extreme fire severity in both summer and non-summer fires. We have used perimeters of large wildfires (≥100 ha, n = 1719) from 1985 to 2018 in four pyroregions and the main dominant vegetation types. We have analyzed time series for each pyroregion and vegetation type to determine if there were significant trends and breakpoints in extreme fire severity. We have also analyzed the effects of drought, weather, topography and vegetation on extreme fire severity in seasonal wildfires.Extreme fire severity increased in summer fires from 1985 to 2018, especially in the East and North pyroregions, while the Southwest pyroregion reached highest values in summer. There has been an increase of severity in shrubland, mosaic vegetation and conifer forests in summer. Broadleaved forests had the lowest values of fire severity and did not show any trend in the period considered. The most important drivers of extreme fire severity are, in order of importance, pyroregion, elevation, slope, vapor pressure deficit (VPD) and vegetation type for summer fires, and VPD, vegetation types, pyroregion, windspeed and drought for non-summer fires. Global change is increasing climate-related events. Our results indicate that extreme fire severity in summer has been on the rise in recent decades in Spain, suggesting that, in a climate warming context, without strategic management of these increasingly flammable landscapes, further increases may be likely.

The microclimatic effects of the native shrub Ephedra californica (Mormon tea) in California drylands.

The impacts of climate change can be profound in many ecosystems worldwide, including drylands such as arid and semi-arid scrublands and grasslands. Foundation plants such as shrubs can provide microclimatic refuges for a variety of taxa. These shrubs can directly influence micro6 environmental measures, and indirectly increase the local environmental heterogeneity as a result. We examined the hypothesis that, in comparison to an open gap, foundation shrubs improve the microclimate beneath their canopy and that microclimate is in turn a significant predictor of annual vegetation. The following predictions were made: 1) mean air temperature (NSAT), ground temperature (SGT), and vapour pressure deficit (VPD) will be significantly lower under the shrubs than in the open microsites; 2) shrub canopy size predicts microclimate; 3) site-level aridity estimates and percent shrub cover influence annual plant abundance and richness; and 4) the site13 level mean of NSAT and VPD predict annual plant abundance and richness. Our study took place in Southwestern California, U.S.A. We used a handheld device with a probe to measure microclimatic variables such as near-surface air temperature (NSAT), near-surface relative humidity (NSRH), and surface ground temperature (SGT) at the shrub species Ephedra californica and in the open gap, across six sites in California, United States. Air temperature and RH were then used to calculate VPD. The mean number of vascular plant species across each site was also recorded. Only SGT was significantly reduced under shrub canopies. Canopy volume was not a significant predictor of all three microclimatic variables, demonstrating that even small, low-stature shrubs can have facilitative effects. Furthermore, total shrub cover and aridity at sites significantly predicted mean plant richness and abundance. There were significantly more plants associated with shrubs and there were significantly more species associated with the open. Mean air temperature and VPD at the site-level significantly predicted vegetation abundance and richness, though microsite-level differences were only significant for richness. Foundation shrubs are a focal point of resiliency in dryland ecosystems. Understanding their impact on microclimate can inform us of better management, conservation, and restoration frameworks.

Estimating transpiration globally by integrating the Priestley-Taylor model with neural networks

Abstract Transpiration (T), the component of evapotranspiration (ET) controlled by the vegetation, dominates terrestrial ET in many ecosystems; however, estimating it accurately, especially at the global scale, remains a considerable challenge. Existing approaches mostly rely on the relationship between T and photosynthesis, but untangling this relationship is difficult and leads to diverging T estimates. Limited in-situ measurements and the inability to directly measure transpiration from space further complicate the reliable assessment of this crucial process in the terrestrial water cycle. Here, we developed a new hybrid Priestley-Taylor (PT) model combined with an Artificial Neural Network (ANN) using globally available remote sensing and reanalysis data of soil moisture, vapor pressure deficit and windspeed. We also take advantage of the newly released global sap flow measurement network SAPFLUXNET. In the proposed approach, we avoid the parameterization of stomatal conductance by training the ANN on the PT-Coefficient α, obtained by inverting the PT equation. The results showed that our model framework can estimate T in different forest ecosystems based on few predictors. By utilizing forcings from independent datasets, we eliminate the reliance on in-situ measurements for predicting T. Through upscaling actual observations to a larger scale, this model framework helps alleviate the scarcity of T products. Intercomparison of T with ET partitioning methods based on eddy covariance data, shows high performances (KGE of 0.69 in Europe and 0.60 in North America), slightly improving estimates compared to other models. Analysis of contribution of T to ET across 100 FLUXNET sites result in a global mean of 55.2%. We believe that modelling T independent from the carbon cycle can support our understanding of land-atmosphere feedbacks and climate extremes in future research.

Diurnal Vegetation Moisture Cycle in the Amazon and Response to Water Stress

AbstractWater stress in the Amazon is exacerbated by rising temperatures and reduced moisture levels. However, understanding forest responses to increased aridity is hindered by limited in situ water potential observations in the Amazon. Remote sensing of water content has emerged as a promising metric. Vegetation Water Content (VWC) diurnal dynamics is hypothesized to reflect water stress responses. Conventional sensors' low sampling rates impede capturing and studying sub‐daily VWC dynamics. Leveraging Global Navigation Satellite System Reflectometry (GNSS‐R) with unprecedented sampling rates, this study reveals significant disparities in morning and evening VWCs in the Amazon, for example, by 1.1 and 1.0 kg/ during the wet and dry seasons of 2019. A strong correlation between VWC (the difference between evening and morning VWCs) and vapor pressure deficit is observed in Amazonian peatland. This highlights the potential of VWC from innovative remote sensing techniques in elucidating water stress dynamics in critical ecosystems.

Characterizing nonlinear, nonstationary, and heterogeneous hydrologic behavior using ensemble rainfall–runoff analysis (ERRA): proof of concept

Abstract. A classical approach to understanding hydrological behavior is the unit hydrograph and its many variants, but these often assume linearity (runoff response is proportional to effective precipitation), stationarity (runoff response to a given unit of rainfall is identical, regardless of when it falls), and spatial homogeneity (runoff response depends only on spatially averaged precipitation). In the real world, by contrast, runoff response is typically nonlinear, nonstationary, and spatially heterogeneous. Quantifying this nonlinearity, nonstationarity, and spatial heterogeneity is essential to unraveling the mechanisms and subsurface properties controlling hydrological behavior. Here, I present proof-of-concept demonstrations illustrating how nonlinear, nonstationary, and spatially heterogeneous rainfall–runoff behavior can be quantified, directly from data, using ensemble rainfall–runoff analysis (ERRA), a data-driven, model-independent method for quantifying rainfall–runoff relationships across a spectrum of time lags. I show how ERRA uses nonlinear deconvolution to quantify how catchments' runoff responses vary with precipitation intensity and to estimate their precipitation-weighted runoff response distributions. I further illustrate how ERRA combines nonlinear deconvolution with de-mixing techniques to reveal how runoff response depends jointly on precipitation intensity and nonstationary ambient conditions, including antecedent wetness and vapor pressure deficit. I demonstrate how ERRA's de-mixing techniques can be used to quantify spatially heterogeneous runoff responses in different parts of a catchment, even if those subcatchments are not separately gauged. I also illustrate how ERRA's broken-stick deconvolution capabilities can be used to quantify multiscale runoff responses that combine hydrograph peaks lasting for hours and recessions lasting for weeks, well beyond the average spacing between storms. ERRA can unscramble these multiple effects on runoff response even if they are overprinted on each other through time and even if they are corrupted by autoregressive moving average (ARMA) noise. Results from this approach may be informative for catchment characterization, process understanding, and model–data comparisons; they may also lead to a better understanding of storage dynamics and landscape-scale connectivity. An R script is provided to perform the necessary calculations, including uncertainty analysis.

Economic Effects Assessment of Forest City Construction: Empirical Evidence from the County-Level Areas in China

Forests are both an irreplaceable natural resource and a vital economic asset for all humankind. Based on the data of counties in mainland China from 2007 to 2020, the article explores the direct impact and spatial spillover effects of the policy implementation on the economic growth of counties with the help of the forest city pilot policy and the policy evaluation model. The results reveal that policy implementation can have a positive economic growth effect on the pilot counties, which, in turn, can significantly increase the size of the county′s GDP, the level of GDP per capita, and the total amount of nighttime lighting brightness. The implementation of forest city construction can bring about 2.74% of total GDP size, about 2.63% of per capita GDP development level, and about 7.25% of nighttime light brightness to the county on average. Cost–benefit analysis also indicates that forest city construction can bring about a comprehensive economic benefit of approximately CNY 686.453 million (approximately USD 96.82 million) to the counties. The rapid improvement in labor productivity, significant influx of high-end factors, and continuous expansion of market potential are important mechanisms through which policy implementation promotes economic growth in pilot counties. While promoting economic growth in the pilot counties, forest city construction can also have positive spatial spillover effects on neighboring areas in the pilot counties. Furthermore, when the deficits in atmospheric vapor pressure and annual evapotranspiration are used as instrumental variables for forest city construction, the empirical estimates are not significantly altered. In the process of building forest cities, county governments should be wary of issues such as the high cost of forest maintenance. This study provides a Chinese model and policy reference for other countries and regions in the world to deal with the relationship between forest city construction and county economic growth.

Self-diffusion of liquid deuterium hydride and liquid tritium.

We present a quasi-elastic neutron scattering study of liquid deuterium hydride carried out using the Disk Chopper Spectrometer at the National Institute of Standards and Technology. Under saturated vapor pressure, the self-diffusion constant of deuterium hydride obeys an Arrhenius law D=D0exp-EA/kBT, where the prefactor D0 is given by D0=9.5±1.2Å2/ps and the activation energy is given by EA = 58 ± 2K. We apply the quantum law of corresponding states to the known diffusion constants of the hydrogen isotopologues. From this application, we estimate that D0≈9.1Å2/ps and EA ≈ 75K in liquid tritium. Young's theory of quantum-mechanical effects in van der Waals fluids [Young, Phys. Rev. A 23, 1498 (1981)] is shown to apply to the diffusion constants of the liquid hydrogens. Our results underscore the importance of nuclear quantum effects in shaping the properties and behavior of the hydrogen isotopologues.

Microclimate monitoring in commercial tomato (Solanum Lycopersicum L.) greenhouse production and its effect on plant growth, yield and fruit quality

IntroductionHigh annual tomato yields are achieved using high-tech greenhouse production systems. Large greenhouses typically rely only on one central weather station per compartment to steer their internal climate, ignoring possible microclimate conditions within the greenhouse itself.MethodsIn this study, we analysed spatial variation in temperature and vapour pressure deficit in a commercial tomato greenhouse setting for three consecutive years. Multiple sensors were placed within the crop canopy, which revealed microclimate gradients.Results and discussionDifferent microclimates were present throughout the year, with seasonal (spring – summer – autumn) and diurnal (day – night) variations in temperature (up to 3 °C, daily average) and vapour pressure deficit (up to 0.6 kPa, daily average). The microclimate effects influenced in part the variation in plant and fruit growth rate and fruit yield – maximum recorded difference between two locations with different microclimates was 0.4 cm d-1 for stem growth rate, 0.6 g d-1 for fruit growth rate, 80 g for truss mass at harvest. The local microclimate effect on plant growth was always larger than the bulk climate variation measured by a central sensor, as commonly done in commercial greenhouses. Quality attributes of harvested tomato fruit did not show a significant difference between different microclimate conditions. In conclusion, we showed that even small, naturally occurring, differences in local environment conditions within a greenhouse may influence the rate of plant and fruit growth. These findings could encourage the sector to deploy larger sensor networks for optimal greenhouse climate control. A sensor grid covering the whole area of the greenhouse is a necessity for climate control strategies to mitigate suboptimal conditions.

Characteristics and Driving Factors of Energy Balance over Different Underlying Surfaces in the Qinghai Plateau

The study of the surface energy balance characteristics of different ecosystems in the Qinghai Plateau is of great significance for a deeper understanding of land surface processes, the water cycle, and global climate change. This study aims to compare the seasonal variations in energy balance and partitioning of four typical ecosystems on the Qinghai Plateau—swamp meadows, subalpine mountain meadows, alpine shrublands, and alpine deserts. Mantel analysis and path analysis were used to explore the regulatory mechanisms of meteorological elements on energy fluxes and the Bowen ratio (β). The results showed the following: (1) Net radiation (Rn), sensible heat flux (H), and latent heat flux (LE) all exhibited a single-peak pattern of change, and the energy partitioning was closely related to the hydrothermal conditions. Swamp meadows and subalpine mountain meadows were dominated by LE throughout the year and the growing season, while H dominated in the non-growing season. Meanwhile, alpine shrublands and alpine deserts were dominated by H throughout the year. (2) β reflected the characteristics of turbulent fluxes variations and the moisture level of the underlying surface. Swamp meadows and subalpine mountain meadows were relatively moist, with the value of β all being less than 1. Alpine shrublands and deserts were comparatively arid, with the values of β all exceeding 1. The energy closure rate ranged from 48% to 90%, with better energy closure conditions observed during the growing season compared to the non-growing season. (3) Meteorological factors collectively regulated the variations in energy fluxes and its partitioning, with H and LE being primarily influenced by Rn, relative humidity (RH), and soil moisture (Ms). β was significantly affected by RH, Ms, and the saturated vapor pressure deficit (VPD). The sensitivity of the ecosystems to changes in fluxes increased with decreasing moisture, especially in alpine deserts, with Ms, VPD and RH being the most affected. Swamp meadows were significantly associated with air temperature (Ta), soil temperature (Ts), and wind speed; subalpine mountain meadows with Ta and Ts; and alpine shrublands with Ta. These results provided a basis for further analyses of the energy balance characteristics and partitioning differences of different ecosystems on the Qinghai Plateau.

Thermodynamic modelling of systems involved in natural gas dehydration with triethylene glycol using a group contribution association model

Natural gas (NG) dehydration through absorption into Triethylene Glycol (TEG) is one of the most important applications in the NG industry. The optimal design of the TEG dehydration process requires a deep understanding of the thermodynamic behavior of mixtures containing TEG, water, hydrocarbons, and other compounds present in natural gas. In this work, the recently developed Universal Mixing Rule – Cubic Plus Association (UMR-CPA) group contribution equation of state (EoS) is extended to these systems. UMR-CPA combines the PR-CPA EoS with the UNIFAC group contribution activity coefficient model through the Universal Mixing Rules. Parameters for pure water, TEG and NG components were determined by accurately fitting vapor pressure, density and heat capacity data. For non-associating compounds, the model leads to overall deviations of 1.2 % in vapor pressures and 6.1 % in isobaric heat capacities. Water properties are also quite accurately described, with overall deviations of approximately 0.4 %, 1.2 % and 5.7 % in vapor pressures, liquid densities and isobaric heat capacities, respectively. The model was then applied to mixtures of water and TEG with gases and hydrocarbons by correlating the proper group interaction parameters. Very satisfactory results were obtained for both vapor-liquid and liquid-liquid phase equilibria in these systems, where also an adequate reproduction of the minimum of hydrocarbon solubility in water was noted. Finally, the UMR-CPA EoS was further validated through the prediction of the phase behavior of ternary systems including TEG and/or water and NG compounds. Very good predictions were achieved for the low TEG and water content in the vapor phase of the TEG-H2O-CH4 ternary system, with absolute deviations of around 0.05 and 23.26 ppm, respectively. Overall, the model yields accurate predictions, suggesting its suitability for designing the TEG dehydration process.

A generalizable and interpretable model for early warning of pest-induced crop diseases using environmental data

Pest infestations and resulting crop diseases threaten global food security. Traditional pest and disease monitoring methods are time-consuming and prone to delays, thus necessitating the development of effective prediction strategies that facilitate early and timely detection. In response to this challenge, this research proposes a generalizable and interpretable machine learning model to predict two major rice pests—Green Leafhopper and Yellow Stem Borer—and a No Pest class using environmental data collected from various regions in India. The dataset, comprising factors such as temperature, humidity, and rainfall, underwent rigorous preprocessing. Key features like temperature difference, humidity difference, and vapor pressure deficit were engineered to enhance the model’s performance. Twelve baseline models were trained and their performance was evaluated using F1 scores and AUC values due to the imbalanced nature of the dataset. Through statistical analysis of baseline models, Random Forest, Balanced Random Forest, XGBoost, and CatBoost models, were selected for hyperparameter tuning via Optuna. The tuned CatBoost model demonstrated superior performance, achieving AUC values of 0.99 for Green Leafhoppers, 0.98 for Yellow Stem Borers, 1.00 for No Pest class and an overall F1 score of 0.9414 with a mean AUC value of 0.9912 across all classes. Additionally, Explainable Artificial Intelligence techniques, particularly SHAP, were employed to interpret the model’s predictions, identifying the relative importance of environmental factors in pest occurrence. This interpretability aligns the model’s predictions with established agronomic knowledge, enhancing its practical utility for early pest and disease detection. The generalizability of the proposed model suggests it can be adapted to other crops and regions, offering a valuable tool for early warning systems in agriculture, promoting sustainable practices, and reducing crop losses due to pests and diseases.

Eutectogels: The Multifaceted Soft Ionic Materials of Tomorrow.

Eutectogels, a rising category of soft materials, have recently garnered significant attention owing to their remarkable potential in various domains. This innovative class of materials consists of a eutectic solvent immobilized in a three-dimensional network structure. The use of eco-friendly and cost-effective eutectic solvents further emphasizes the appeal of these materials in multiple applications. Eutectogels exhibit key characteristics of most eutectic solvents, including environmental friendliness, facile preparation, low vapor pressure, and good ionic conductivity. Moreover, they can be tailored to display functionalities such as self-healing capability, adhesiveness, and antibacterial properties, which are facilitated by incorporating specific combinations of the eutectic mixture constituents. This perspective article delves into the current landscape and challenges associated with eutectogels, particularly focusing on their potential applications in CO2 separation, drug delivery systems, battery technologies, biocatalysis, and food packaging. By exploring these diverse realms, we aim to shed light on the transformative capabilities of eutectogels and the opportunities they present to address pressing industrial, academic, and environmental challenges.

The Usability of Metallurgical Production Waste as a Siliceous Component in Autoclaved Aerated Concrete Technology

The reconstruction of buildings is a complex process that often requires the consideration of the construction load when selecting correct building materials. Autoclaved aerated concrete (AAC)—which has a lower bulk density (compared to traditional masonry materials)—is very beneficial in such applications. A current trend in AAC development is the utilization of secondary raw materials in high-performance AAC, characterized by higher bulk density and compressive strength than regular AAC. The increase in bulk density is achieved by increasing the content of quartz sand in the mixing water. In this study, part of the siliceous component was replaced by ladle slag, foundry sand, furnace lining, and chamotte block powder. These materials are generated as by-products in metallurgy. The substitution rates were 10% and 30%. The samples were autoclaved in a laboratory autoclave for 8 h of isothermal duration at 190 °C with a saturated water vapor pressure of 1.4 MPa. The physical–mechanical parameters were determined, and the microstructure was described by XRD and SEM analyses. The results were compared with traditional AAC, with silica sand being used as the siliceous component. The measurement results show that sand substitution by the secondary raw material is possible, and it does not have a significant impact on the properties of AAC, and in a proper dosage, it can be beneficial for AAC production.

Spatial and Temporal Patterns of Vegetation Water Use Efficiency and Its Response to Climatic and Environmental Factors in Gansu Province

Vegetation water use efficiency (WUE) is a crucial indicator for elucidating the interconnections between the carbon and water cycles of ecosystems and for discerning the response of vegetation ecosystems to climate change. Gansu Province in northwestern China is facing significant ecological water-related challenges. However, the response of vegetation WUE to climate environmental factors in this region remains unclear. In this study, the MODIS vegetation gross primary productivity (GPP) and evapotranspiration (ET) datasets were used to calculate the vegetation WUE in Gansu Province and, combined with meteorological data, Theil–Sen median trend analysis and partial correlation analysis were used to determine the spatial and temporal characteristics of vegetation WUE in this region and its response to climate environmental factors. Finally, the random forest model was used to rank the importance of climate environmental factors. The results indicate the following: (1) The average values of vegetation WUE, GPP, and ET in Gansu Province from 2000 to 2020 were 1.46 gC·mm−1·m−2, 510.22 gC·m−2, and 343.68 mm, respectively, and their spatial distribution was high in the southeast and low in the northwest, which was closely related to the distribution of vegetation in the region. (2) Over the past 20 years, the vegetation WUE in this region showed a slowly decreasing trend in general, with a decrease rate of 16.57%. There were significant differences in the WUE of different vegetation types, among which forest WUE was the highest and grassland WUE the lowest. (3) The trend prediction of WUE in Gansu Province was performed by using the rescaled extreme difference method, and the Hurst index was 0.45, which means that the vegetation WUE in this region is expected to increase in the future. (4) In general, precipitation was the main factor influencing the change in vegetation WUE in Gansu Province, followed by vapor pressure deficit (VPD), temperature, and soil moisture. This study provides strategy support for the coupling process of vegetation ecosystems and the sustainable development of agriculture and animal husbandry in Gansu Province and has scientific reference value for promoting and planning the sustainable development of vegetation in arid and semi-arid areas.

The relationship of δD and δ18O in soil water and its implications for soil evaporation across distinct rainfall years in winter wheat field in the North China Plain

Soil evaporation plays a key role in regulating local climate and water loss. Stable isotope ratios of water (²H/¹H and ¹⁸O/¹⁶O) are effective tracers for studying water flux. This study examines three isotope-based indicators deuterium excess (d-excess), the slope of the soil water evaporation line (SEL), and line-conditioned excess (lc-excess) across three wheat growing seasons: wet, ordinary, and dry years. The influencing factors of d-excess, SEL, and lc-excess, respectively, soil, vegetation, and meteorology, were analyzed using various methods. Wheat yields varied significantly, reaching 6.69 t ha⁻¹ in wet years, 8.66 t ha⁻¹ in dry years, and 9.28 t ha⁻¹ in ordinary years. The lc-excess was highest in ordinary years, and d-excess peaked during dry years. A negative correlation between d-excess and SEL slope, and between lc-excess and SEL slope, was observed in dry and ordinary years (P<0.05), but not in wet years (P>0.05). Multivariate regression showed that net radiation (Rn) was the primary factor influencing SEL, contributing 54.19 %, 11.58 %, and 29.27 % in wet, dry, and ordinary years, respectively. Leaf area index (LAI) was the most significant factor affecting lc-excess (37.91 % in wet years, 32.22 % in dry, and 30.92 % in ordinary years). Vapor pressure deficit (VPD) affected d-excess in wet and ordinary years, while air (Ta) and soil temperature (Ts) were key in dry years. Variation partitioning revealed meteorological factors primarily influenced SEL, lc-excess, and d-excess in wet years, while soil, vegetation, and climate interactions had greater effects in dry and ordinary years. The lc-excess, integrating multiple factors, is a better indicator of soil evaporation than SEL.