Backfill mining is a lucrative method for extracting coal buried under buildings, and water bodies, which can substantially increase the resource usage efficiency by mitigating the strata movement and surface subsidence. Its effectiveness depends on the mechanical properties of granular backfill materials. A permeability test was performed on gangue and fly ash samples under different stress levels using an original seepage test system. The variation patterns of the broken rock's internal pressure and permeability were determined. The test results indicate the weakening of the seepage effect on granular materials and a gradual reduction of washed away fly ash. The permeability values fall into the range of 3.2 x 10(-15) similar to 3.2 x 10(-13)m(-2), and non-Darcy factor is between 3.2 x 10(10) and 3.2 x 10(12) m(-1). This phenomenon was more pronounced in samples with smaller particle sizes. As the axial stress increased, the backfill material showed a decline in permeability and an increase in the non-Darcy flow coefficient. As the content of fly ash increased, the mass loss grew sharply, which occurred mainly at the early seepage stage. The results are considered instrumental in the characterization of water and sand inrush.

Liquefaction hazard analysis is crucial in earthquake-prone regions as it magnifies structural damage. In this study, standard penetration test (SPT) and shear wave velocity (Vs) data of Chittagong City have been used to assess the liquefaction resistance of soils using artificial neural network (ANN). For a scenario of 7.5 magnitude (Mw) earthquake in Chittagong City, estimating the liquefaction-resistance involves utilizing peak horizontal ground acceleration (PGA) values of 0.15 and 0.28 g. Then, liquefaction potential index (LPI) is determined to assess the severity of liquefaction. In most boreholes, the LPI values are generally higher, with slightly elevated values in SPT data compared to Vs data. The current study suggests that the Valley Alluvium, Beach and Dune Sand may experience extreme liquefaction with LPI values ranges from 9.55 to 55.03 and 0 to 37.17 for SPT and Vs respectively, under a PGA of 0.15 g. Furthermore, LPI values ranges from 25.55 to 71.45 and 9.55 to 54.39 for SPT and Vs correspondingly. The liquefaction hazard map can be utilized to protect public safety, infrastructure, and to create a more resilient Chittagong City.

Flash floods are often responsible for deaths and damage to infrastructure. The objective of this work is to create a data-driven model to understand how predisposing factors influence the spatial variation of the triggering factor (rainfall intensity) in the case of flash floods in the continental area of Portugal. Flash floods occurrences were extracted from the DISASTER database. We extracted the accumulated precipitation from the Copernicus database by considering two days of duration. The analysed predisposing factors for flooding were extracted considering the whole basin where each occurrence is located. These factors include the basin area, the predominant lithology, drainage density, and the mean or median values of elevation, slope, stream power index (SPI), topographic wetness index (TWI), roughness, and four soil properties. The Random Forest algorithm was used to build the models and obtained mean absolute percentage error (MAPE) around 19%, an acceptable value for the objectives of the work. The median of SPI, mean elevation and the area of the basin are the top three most relevant predisposing factors interpreted by the model for defining the rainfall input for flash flooding in mainland Portugal.

Amidst global scarcity, preventing pipeline failures in water distribution systems is crucial for maintaining a clean supply while conserving water resources. Numerous studies have modelled water pipeline deterioration; however, existing literature does not correctly understand the failure time prediction for individual water pipelines. Existing time-to-failure prediction models rely on available data, failing to provide insight into factors affecting a pipeline's remaining age until a break or leak occurs. The study systematically reviews factors influencing time-to-failure, prioritizes them using a magnitude-based fuzzy analytical hierarchy process, and compares results with expert opinion using an in-person Delphi survey. The final pipe-related prioritized failure factors include pipe geometry, material type, operating pressure, pipe age, failure history, pipeline installation, internal pressure, earth and traffic loads. The prioritized environment-related factors include soil properties, water quality, extreme weather events, temperature, and precipitation. Overall, this prioritization can assist practitioners and researchers in selecting features for time-based deterioration modelling. Effective time-to-failure deterioration modelling of water pipelines can create a more sustainable water infrastructure management protocol, enhancing decision-making for repair and rehabilitation. Such a system can significantly reduce non-revenue water and mitigate the socio-environmental impacts of pipeline ageing and damage.

Moderate-size earthquakes, and the presence of water saturated soil in the near surface can trigger the liquefaction geohazard causing buildings to settle / tilt or collapse, damaging bridges, dams, and roads. A number of paleo-seismic research have focused on the Himalayan area as a potential site for liquefaction. The present study site is in the south of the tectonically active Himalayan foothills and lies in earthquake Seismic Zone III. Therefore, the region can experience earthquakes from nearby regions and can potentially damage civil infrastructures due to liquefaction. The objective of this paper is to determine the susceptibility of alluvial soil deposits to liquefaction for seismic hazard and risk mitigation. Liquefaction geohazard study of alluvial deposits was carried out using shear wave velocity (Vs) profiling. Preliminary assessment of the soil is made by building the average shear wave velocity map up to 30 m depth (Vs30) and by constructing the corrected shear wave velocity (V-s1) maps. It was observed from the Vs30 map that a major portion of the studied area lies in Site Class CD and only a small portion lies in Site Class D. Moreover, it is also noticed from the V(s1 )map that a smaller of the area has V(s1 )lower than the upper limit of V-s1(& lowast; )(215 m/s) below which liquefaction may occur. The region showing lower values of V(s1 )is further examined for liquefaction hazard as per the guidelines given by Andrus et al. (2004). Resistance of the soil to liquefaction, stated as cyclic resistance ratio (CRR), and the magnitude of cyclic loading on the soil induced by the earthquake shaking, stated as cyclic stress ratio (CSR) are computed for the area. Several maps of factor of safety (FS) for different depths are prepared by taking the ratio of CSR and CRR. When FS < 1, the soil is considered prone to liquefaction. Furthermore, susceptibility of soil to liquefaction against different peak horizontal ground surface acceleration (PHGSA) and varying depth of water table is also evaluated in terms of factor of safety. It is observed from this study that for lower levels of PHGSA (up to 0.175 g) the soil can be considered safe. However, the soil becomes more vulnerable to liquefaction when PHGSA is above 0.175 g and with rising water table. The comparison of the factor of safety (FS) obtained using the SPT-N method and the Vs-derived approach shows consistent results, with both methods confirming the absence of liquefaction in the studied soil layers.

Root-knot nematodes (RKN) are globally distributed and highly pathogenic. By determining the threshold at which damage occurs, we can create effective measures to protect plants from nematodes. In our study, we investigated the impact of ten initial population densities (Pi-log series) of M. javanica, i.e., 0, 2.38, 2.68, 2.98, 3.28, 3.58, 3.88, 4.18, 4.48 and 4.78 juveniles (J2) g(-1) soil on tomato cv. S22 plants in pots. The graphical estimation of yield losses caused by RKN was calculated using Seinhorst's yield loss model based on the relationship between the RKN population and damage to tomato plants. The relationship between initial nematode population density (Pi) and plant yield was analyzed using Seinhorst's model, where T is the tolerance limit, m is the minimum yield, and z is a constant describing yield decline. This allowed us to determine the threshold at which nematode infestation significantly reduces tomato growth. Seinhorst's model, y = m + (1-m) 0.95(Pi/T-1) for Pi > T; y = 1 for Pi <= T for RKN, was fitted to the data of shoot length and fresh weight of infected and uninoculated control plants to estimate the damage threshold level. The impact of M. javanica on plant physiological parameters, including chlorophyll content, carotenoid and nitrate reductase activity, root-gall formation, and disease incidence, was also determined in this study. The tolerance limits for relative tomato shoot length and fresh weight were 3.34 J2 of M. javanica g(-1) soil. The minimum relative values (y(m)) for shoot length and fresh weights were 0.39 and 0.42, respectively. We found that the damage threshold level was between 3.28 and 3.58. The root galls index, nematode population and reproduction factors were 3.75, 113 and 29.42, respectively, at an initial population density (Pi) of 3.58 J2 g(-1) soil. The chlorophyll (0.43 mg g(-1)), carotenoids (0.06 mg g(-1)) and nitrate reductase activity (0.21 mu mol min(-1) g(-1)). Our study highlights the importance of the accurate estimation of damage thresholds, which can guide timely and effective nematode management strategies.

As a typical cold region, Northeast China is characterized by its unique climate, hydrological conditions, and land systems, which collectively shape the diversity and complexity of regional ecosystem services (ESs). This review systematically examines research on ESs in Northeast China from 1997 to 2025, with particular emphasis on recent advances in service classification and spatiotemporal patterns, trade-offs and synergies among ESs, the identification of driving mechanisms, regulatory pathways, and policy effectiveness. The findings reveal obvious spatial heterogeneity and distinct stage-wise changing patterns in ESs across the region, with particularly pronounced trade-offs between food production and regulating services. The primary driving factors are concentrated in natural and human activities dimensions, whereas region-specific variables and policy-related drivers remain underexplored. Current research predominantly employs methods such as correlation analysis and geographically weighted regression; however, the capacity to uncover causal mechanisms and nonlinear interactions remains limited. Future research should strengthen the simulation of ecological processes in cold regions, improve the balance between ES supply and demand, improve policy scenario assessments, and develop dynamic feedback mechanisms. Compared with previous studies focusing on single services or regions, this review provides a multidimensional perspective by synthesizing multiple ES categories, integrating spatiotemporal comparative analysis, and incorporating modeling strategies specific to cold-region dynamics. These efforts will help shift ES research beyond static description toward more systematic regulation and management, providing both theoretical support and practical guidance for sustainable development and ecological governance in Northeast China.

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.

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.

From July 26 to July 28, 2024, a rare heavy rainfall associated with Typhoon Gaemi triggered widespread clustered landslides in Zixing City, Hunan Province, China. The severe disaster caused 50 fatalities and 15 missing persons across 26 villages, damaging 11,869 houses and affecting a total of 128,000 individuals. Timely and accurate event analysis is essential for deepening our understanding of landslide clustering mechanisms and guiding future disaster prevention efforts. To achieve this, remote sensing analysis using satellite and unmanned aerial vehicle (UAV) aerial images was conducted to assess the distribution pattern of landslide clusters and explore their relationship with environmental factors. Field investigations were subsequently carried out to identify the failure mechanisms of representative landslides. The results identified three main landslide clustering areas in the eastern mountainous forest region of Zixing City. The landslides are predominantly shallow soil slides, with their distribution closely linked to rainfall thresholds and lithology. The clustering areas typically received cumulative precipitation exceeding 400 mm during the extreme rainfall event. Lithology significantly influences the composition and thickness of slope soils, which in turn controls sliding patterns and affects landslide distribution density and individual landslide size. Granite residual soils contributed to the highest landslide density, with many large individual landslides. Topography and vegetation also play important roles in landslide formation and movement. This study provides preliminary insights into the clustered landslide event, aiding researchers in quickly understanding its key features.