Soil chemical washing has the disadvantages of long reaction time, slow reaction rate and unstable effect. Thus, there is an urgent need to find a cost-effective and widely applicable alternative power to facilitate the migration of washing solutions in the soil, so as to achieve efficient removal of heavy metals, reduce the risk of soil compaction, and mitigate the damage of soil structure. Therefore, the study used a combination of freeze-thaw cycle (FTC) and chemical washing to obtain three-dimensional images of soil pore structure using micro-X-ray microtomography, and applied image analysis techniques to study the effects of freeze-thaw washing on the characteristics of different pore structures of the soil, and then revealed the effects of pore structure on the removal of heavy metals. The results showed that the soil pore structure of the freeze-thaw washing treatment (FT) became more porous and complex, which increased the soil imaged porosity (TIP), pore number (TNP), porosity of macropores and irregular pores, permeability, and heavy metal removal rate. Macroporosity, fractal dimension, and TNP were the main factors contributing to the increase in TIP between treatments. The porous structure resulted in larger effective pore diameters, which contain a greater number of branching pathways and pore networks, allowing the chemical washing solutions to fully contact the soil, increasing the roughness of the soil particle surface, mitigating the risk of soil compaction, and decreasing the contamination of heavy metals. The results of this study contribute to provide new insights into the management of heavy metal pollution in agricultural soils.

The vadose zone acts as a natural buffer that prevents contaminants such as arsenic (As) from contaminating groundwater resources. Despite its capability to retain As, our previous studies revealed that a substantial amount of As could be remobilized from soil under repeated wet-dry conditions. Overlooking this might underestimate the potential risk of groundwater contamination. This study quantified the remobilization of As in the vadose zone and developed a prediction model based on soil properties. 22 unsaturated soil columns were used to simulate vadose zones with varying soil properties. Repeated wet-dry cycles were conducted upon the As-retaining soil columns. Consequently, 13.9-150.6 mg/kg of As was remobilized from the columns, which corresponds to 37.0-74.6 % of initially retained As. From the experimental results, a machine learning model using a random forest algorithm was established to predict the potential for As remobilization based on readily accessible soil properties, including organic matter (OM) content, iron (Fe) content, uniformity coefficient, D30, and bulk density. Shapley additive explanation analyses revealed the interrelated effects of multiple soil prop-erties. D30, which is inter-related with Fe content, exhibited the highest contribution to As remobilization, fol-lowed by OM content, which was partially mediated by bulk density.

Nitrogen is an essential element for life but its excessive release into the environment in the form of reactive nitrogen causes severe damage, including acidification and eutrophication. One of the main sources of nitrogen pollution is the use of fertilizers in agricultural soils. Feammox is a recently described pathway that couples ammonium (NH4+) oxidation with iron (Fe) reduction. In this study, the enrichment and bioaugmentation of anaerobic sludge under conditions that promote Feammox activity were investigated. The first enrichment stage (E1) achieved 28% of ammonium removal after 28 days of incubation, with a production of 30 mg/L of Fe2+. E1 was then used as inoculum for two enrichments at 35 degrees C with different carbon sources: sodium acetate (E2) and sodium bicarbonate (E3). Neither E2 nor E3 showed significant NH4+ removal, but E2 was highly effective in iron reduction, reaching Fe2+ concentrations of 110 mg/L. Additionally, an increase in nitrate (NO3-) concentration was observed, which may indicate the occurrence of this pathway in the Feammox process. The Monod kinetic model, analyzed using AQUASIM software, showed a good fit to the experimental data for NH4+, NO3-, and Fe2+. Sequencing analysis revealed the presence of phyla associated with Feammox activity. Although there was only a slight difference in NH4+ removal between the bioaugmented and non-augmented control sludge, the bioaugmented sludge was statistically superior in nitrate production and iron reduction. This study provides valuable insights into the enrichment and bioaugmentation of the Feammox process potential large-scale wastewater treatment applications.

Forest growth in tropical regions is regulated in part by climatic factors, such as precipitation and temperature, and by soil factors, such as nutrient availability and water storage capacity. We examined a decade of growth data from Eucalyptus clonal plantations from over 113,000 forest inventory plots across a 10 million-ha portion of Mato Grosso do Sul in southwestern Brazil. From this full dataset, three subsets were screened: 71,000 plots to characterize growth and yield across water table depth classes, 17,000 plots to build generalized models, and 50,000 plots for clone-based analyses. Average precipitation varied little across the region (1150 to 1270 mm yr(-1)), but water table depth ranged from less than 10 m to over 100 m. Where the water table was within 10 m of the surface, about 20 % of the total water used by trees came from this saturated zone. Water tables deeper than 50 m contributed very little to tree water use. Sites with a water table within 10 m averaged 47 m(3) ha(-1) yr(-1) in stem growth (mean annual increment, MAI) across a full rotation, compared to less than 37 m(3) ha(-1) yr(-1) for sites with water tables deeper than 50 m. Drought-induced canopy damage rose from 7 % to 30 % along the water tables depth gradient, while tree mortality rose nearly fourfold. The optimal stocking level was about 1360 trees ha(-1) where water tables were accessible, declining to 1080 trees ha(-1) where they were not. Among the 15 most planted Eucalyptus clones, increases in MAI from the lowest to highest water table depths ranged from + 4.8 to + 16.8 m(3) ha(-1) yr(-1) , reflecting significant genotype-environment interactions. On average, MAI decreased by 0.8 m(3) ha(-1) yr(-1) (ranging from 0.4 to 1.4) for every 10 m increase in water table depth. Similarly, the Site Index at base age 7 years declined from 31 m to 27 m, with an average reduction of 0.25 m per 10 m increase in water table depth. Physiographic modeling of water table depths offers useful information for forest management practices like forest inventory and planning, clonal allocation, optimized planting densities, fertilization strategies, coppice techniques, and other landscape-specific strategies like tree breeding zones.

The environmental threat, pollution and damage posed by heavy metals to air, water, and soil emphasize the critical need for effective remediation strategies. This review mainly focuses on microbial electrochemical technologies (MET) for treating heavy metal pollutants, specifically includes Chromium (Cr), Copper (Cu), Zinc (Zn), Cadmium (Cd), Lead (Pb), Nickel (Ni), and Cobalt (Co). First, it explores the mechanisms and current applications of MET in heavy metal treatments in detail. Second, it systematically summarizes the key microbial communities involved, analyzing their extracellular electron transfer (EET) processes and summarizing strategies to enhance the EET efficiencies. Next, the review also highlights the synergistic microbial interactions in bioelectrochemical systems (BES) during the recovery and removal (remediation) processes of heavy metals, underscoring the crucial role of microorganisms in the transfer of the electrons. Then, this paper discussed how factors including pH values, applied voltages, types and concentrations of electron donors, electrode materials, biofilm thickness and other factors affect the treatment efficiencies of some specific metals in BES. BES has shown its great superiority in treating heavy metals. For example, for the treatments of Cr6+, under low pH conditions, the recovery and removal rate of Cr-6(+) by double chambers microbial fuel cell (DCMFC) can generally reach 98-99%, with some cases even achieving 100% (Gangadharan & Nambi, 2015). For the treatments of heavy metal ions such as Cu2+, Zn2+ and Cd2+, BES can also achieve the rates of treatments of more than 90% under the corresponding conditions of appropriate pH values and applied voltages(Choi, Hu, & Lim, 2014; W. Teng, G. Liu, H. Luo, R. Zhang, & Y. Xiang, 2016; Y. N. Wu et al., 2019; Y. N. Wu et al., 2018). After that, the review outlines the future challenges and the research opportunities for understanding the mechanisms of BES and microbial EET in heavy metal treatments. Finally, the prospect of future BES researches are pointed out, including the combinations with existing wastewater treatment systems, the integrations with the wind energy and the solar energy, and the application of machine learning (ML) in future BES. This article has certain significance and value for readers to better understand the working principles of BES and better operate and control BES to deal with heavy metal pollutants.

Early water stress detection is important for water use yield and sustainability. Traditional methods using the Internet of Things (IoT), such as soil moisture sensors, usually do not provide timely alerts, causing inefficient water use and, in some cases, crop damage. This research presents an innovative early water stress detection method in lettuce plants using Thermal Infrared (TIR) and RGB images in a controlled lab setting. The proposed method integrates advanced image processing techniques, including background elimination via Hue-Saturation- Value (HSV) thresholds, wavelet denoising for thermal image enhancement, RGB-TIR fusion using Principal Component Analysis (PCA), and Gaussian Mixture Model (GMM) clustering to segment stress regions. The leaves stressed areas annotated in the RGB image through yellow pseudo-coloring. This approach is predicated on the fact that when stomata close, transpiration decreases, which causes an increase in the temperature of the affected area. Experimental results reveal that this new approach can detect water stress up to 84 h earlier than conventional soil humidity sensors. Also, a comparative analysis was conducted where key components of the proposed hybrid framework were omitted. The results show inconsistent and inaccurate stress detection when excluding wavelet denoising and PCA fusion. A comparative analysis of image processing performed on a single- board computer (SBC) and through cloud computing over 5 G showed that SBC was 8.27% faster than cloud computing over a 5 G connection. The proposed method offers a more timely and accurate identification of water stress and promises significant benefits in improving crop yield and reducing water usage in indoor farming.

Soil moisture is a key parameter in the exchange of energy and water between the land surface and the atmosphere. This parameter plays an important role in the dynamics of permafrost on the Qinghai-Xizang Plateau, China, as well as in the related ecological and hydrological processes. However, the region's complex terrain and extreme climatic conditions result in low-accuracy soil moisture estimations using traditional remote sensing techniques. Thus, this study considered parameters of the backscatter coefficient of Sentinel-1A ground range detected (GRD) data, the polarization decomposition parameters of Sentinel-1A single-look complex (SLC) data, the normalized difference vegetation index (NDVI) based on Sentinel-2B data, and the topographic factors based on digital elevation model (DEM) data. By combining these parameters with a machine learning model, we established a feature selection rule. A cumulative importance threshold was derived for feature variables, and those variables that failed to meet the threshold were eliminated based on variations in the coefficient of determination (R2) and the unbiased root mean square error (ubRMSE). The eight most influential variables were selected and combined with the CatBoost model for soil moisture inversion, and the SHapley Additive exPlanations (SHAP) method was used to analyze the importance of these variables. The results demonstrated that the optimized model significantly improved the accuracy of soil moisture inversion. Compared to the unfiltered model, the optimal feature combination led to a 0.09 increase in R2 and a 0.7% reduction in ubRMSE. Ultimately, the optimized model achieved a R2 of 0.87 and an ubRMSE of 5.6%. Analysis revealed that soil particle size had significant impact on soil water retention capacity. The impact of vegetation on the estimated soil moisture on the Qinghai-Xizang Plateau was considerable, demonstrating a significant positive correlation. Moreover, the microtopographical features of hummocks interfered with soil moisture estimation, indicating that such terrain effects warrant increased attention in future studies within the permafrost regions. The developed method not only enhances the accuracy of soil moisture retrieval in the complex terrain of the Qinghai-Xizang Plateau, but also exhibits high computational efficiency (with a relative time reduction of 18.5%), striking an excellent balance between accuracy and efficiency. This approach provides a robust framework for efficient soil moisture monitoring in remote areas with limited ground data, offering critical insights for ecological conservation, water resource management, and climate change adaptation on the Qinghai-Xizang Plateau.

Background and aimsAlpine swamp meadows play a vital role in water conservation and maintaining ecological balance. However, the response mechanisms of its area and hydrological functions under global climate change remain unclear, particularly the impact of permafrost degradation on water storage capacity, which urgently requires quantification.MethodsWe integrated multi-temporal Landsat data (2000-2023) and phenological features to construct a classification framework for alpine swamp meadows. A multi-source remote sensing-based water balance assessment method was developed. Random forest importance evaluation and piecewiseSEM were employed to quantify the impacts and pathways of multidimensional driving factors on changes in alpine swamp meadow area and water storage.ResultsThe phenology-based classification method effectively extracted alpine swamp meadows with a mean producer's accuracy of 92.84%, user's accuracy of 92.14%, and a Kappa coefficient of 0.95. The study found that the spatial expansion of alpine swamp meadows in the watershed showed an initial decrease followed by an increase trend, while the water storage capacity continued to decline, indicating a significant decoupling between the two.ConclusionUnder climate change, increased precipitation and reduced snow cover albedo have led to the expansion of alpine swamp meadows, while enhanced evapotranspiration and the degradation of permafrost aquicludes have caused a systematic decline in their water storage capacity. These findings provide a scientific basis for assessing the health of alpine ecosystems and managing water resources under climate change.

Soil freeze-thaw state influences multiple terrestrial ecosystem processes, such as soil hydrology and carbon cycling. However, knowledge of historical long-term changes in the timing, duration, and temperature of freeze-thaw processes remains insufficient, and studies exploring the combined or individual contributions of climatic factors-such as air temperature, precipitation, snow depth, and wind speed-are rare, particularly in current thermokarst landscapes induced by abrupt permafrost thawing. Based on ERA5-Land reanalysis, MODIS observations, and integrated thermokarst landform maps, we found that: 1) Hourly soil temperature from the reanalysis effectively captured the temporal variations of in-situ observations, with Pearson' r of 0.66-0.91. 2) Despite an insignificant decrease in daily freeze-thaw cycles in 1981-2022, other indicators in the Qinghai-Tibet Plateau (QTP) changed significantly, including delayed freezing onset (0.113 d yr- 1), advanced thawing onset (-0.22 d yr- 1), reduced frozen days (-0.365 d yr- 1), increased frozen temperature (0.014 degrees C yr- 1), and decreased daily freeze-thaw temperature range (-0.015 degrees C yr- 1). 3) Total contributions indicated air temperature was the dominant climatic driver of these changes, while indicators characterizing daily freeze-thaw cycles were influenced mainly by the combined effects of increased precipitation and air temperature, with remarkable spatial heterogeneity. 4) When regionally averaged, completely thawed days increased faster in the thermokarstaffected areas than in their primarily distributed grasslands-alpine steppe (47.69%) and alpine meadow (22.64%)-likely because of their stronger warming effect of precipitation. Locally, paired comparison within 3 x 3 pixel windows from MODIS data revealed consistent results, which were pronounced when the thermokarst-affected area exceeded about 38% per 1 km2. Conclusively, the warming and wetting climate has significantly altered soil freeze-thaw processes on the QTP, with the frozen soil environment in thermokarstaffected areas, dominated by thermokarst lakes, undergoing more rapid degradation. These insights are crucial for predicting freeze-thaw dynamics and assessing their ecological impacts on alpine grasslands.

Natural marine clays exhibit distinct dynamic behavior compared to remolded counterparts due to their inherent structural properties. Dynamic and static triaxial tests were conducted on both marine clay types to evaluate stress-strain behavior, double amplitude strains, pore water pressure, and dynamic elastic modulus, as well as post-cyclic strength attenuation. The results indicate that due to the structural properties, the effective stress path of undisturbed samples is more ductile than that of remolded samples. Also, there is a clear inflection point in the strain development curve of undisturbed samples. The structure exerts a certain degree of restraint on the strain development of the undisturbed samples, and has a distinct impact on the variation of pore water pressure at varying dynamic stress levels. Both marine clay types exhibited gradual reductions in dynamic elastic modulus and marked undrained strength attenuation. Critically, the attenuation of dynamic elastic modulus in undisturbed samples aligned with post-cyclic strength loss, while remolded samples exhibited greater dynamic elastic modulus loss relative to strength degradation. These findings clarify the role of soil structure in cyclic response and strength degradation, offering insights for the long-term stability assessment of structures and disaster mitigation in marine clay engineering.