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.

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.

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.

Ground subsidence is a common urban geological hazard in several regions worldwide. The settlement of loess fill foundations exhibits more complex subsidence issues under the coupled effects of geomechanical and seepage-driven processes. This study selected 21 ascending Sentinel-1 A radar images from April 2023 to October 2024 to monitor the loess fill foundation in Shaanxi, China. To minimize errors caused by the orbital phase and residual flat-earth phase, this research combined PS-InSAR technology with the three-threshold method to improve the SBAS-InSAR processing workflow, thereby exploring time-series deformation of the loess fill foundation. Compared with conventional SBAS-InSAR technology, the improved SBAS-InSAR technique provided more consistent deformation time-series results with leveling data, effectively capturing the deformation characteristics of the fill foundation. Additionally, geographic information system (GIS) spatial analysis techniques and statistical methods were employed to analyze the overall characteristics and spatiotemporal evolution of the ground surface deformation in the study area. On the other hand, the major drivers of the subsidence in the study area were also discussed based on indoor experiments and engineering geological data. The results showed gradual and temporal shifts of the subsidence center toward areas with the maximum fill depths. In addition, two directions of uneven subsidence were observed within the fill foundation study area. The differences in the fill depth and soil properties caused by the building foundation construction were the main factors contributing to the uneven settlement of the foundations. Foundation deformation was also positively and negatively affected by surface water infiltration. This study integrates remote sensing and engineering geological data to provide a scientific basis for accurately monitoring and predicting loess fill foundation settlement. It also offers practical guidance for regional infrastructure development and geological hazard prevention.

Background: With growing concern during the COVID-19 pandemic, indoor environmental quality has received significant attention. Radon, a radioactive gas produced from the decay of radium found in soil, rocks, and building materials, can accumulate indoors, posing serious health risks such as lung cancer. University environments, where occupants spend significant time indoors, are particularly susceptible to prolonged radon exposure. Method: This study focused on the estimation of indoor radon concentrations from multiple university buildings in Shanghai. A field investigation was conducted between June 2020 and August 2022. Continuous radon measurements were conducted in the dormitories and classroom buildings. Environmental factors include indoor air temperature and relative humidity. Results: Radon concentrations were influenced by season, floor level, and measurement period, with the highest concentrations recorded during summer and on lower floors due to reduced ventilation. The mean radon concentration in dormitories was 14.8 +/- 9.2 Bq/m3, and in classrooms 12.6 +/- 6.7 Bq/m3, both below national safety limits and lower than those in the pre-pandemic era. Seasonal effect, floor level, and time of measurement were the significant factors for indoor radon concentrations. Conclusion: This study has identified the main factors that affect indoor radon concentration in university campus. The radon concentrations at the lower floor levels remain the highest in the building. The results provide evidence for conducting refined radon monitoring and risk assessment in campus environment, especially during the summer.

Moisture accumulation within road pavements, particularly in unbound granular materials with or without thin sprayed seals, presents significant challenges in high-rainfall regions such as Queensland. This infiltration often leads to various forms of pavement distress, eventually causing irreversible damage to the pavement structure. The moisture content within pavements exhibits considerable dynamism and directly influenced by environmental factors such as precipitation, air temperature, and relative humidity. This variability underscores the importance of monitoring moisture changes using real-time climatic data to assess pavement conditions for operational management or incorporating these effects during pavement design based on historical climate data. Consequently, there is an increasing demand for advanced, technology-driven methodologies to predict moisture variations based on climatic inputs. Addressing this gap, the present study employs five traditional machine learning (ML) algorithms, K-nearest neighbors (KNN), regression trees, random forest, support vector machines (SVMs), and gaussian process regression (GPR), to forecast moisture levels within pavement layers over time, with varying algorithm complexities. Using data collected from an instrumented road in Brisbane, Australia, which includes pavement moisture and climatic factors, the study develops predictive models to forecast moisture content at future time steps. The approach incorporates current moisture content, rather than averaged values, along with seasonality (both daily and annual), and key climatic factors to predict next step moisture. Model performance is evaluated using R2, MSE, RMSE, and MAPE metrics. Results show that ML algorithms can reliably predict long-term moisture variations in pavements, provided optimal hyperparameters are selected for each algorithm. The best-performing algorithms include KNN (the number of neighbours equals to 15), medium regression tree, medium random forest, coarse SVM, and simple GPR, with medium random forest outperforming the others. The study also identifies the optimal hyperparameter combinations for each algorithm, offering significant advancements in moisture prediction tools for pavement technology.

In this essay, by summarizing the research progress and achievements of various scholars at home and abroad in recent years on the material properties and corrosion resistance of magnesium phosphate cement (MPC), we review the factors influencing on the properties of MPC, and analyze the effects of raw materials, retarders, and admixtures on the properties of MPC. Two different hydration mechanisms of MPC are discussed, and finally the research progress of MPC in the field of anti-corrosion coatings for steel and ordinary concrete (OPC) is highlighted, and suggestions and prospects are given.

Soil salinity is a severe abiotic stress that damages plant growth and development. As an antioxidant and free radical scavenger, melatonin is well known for helping plants survive abiotic conditions, including salinity stress. Here, we report that the salt-related gene MsSNAT1, encoding a rate-limiting melatonin biosynthesis enzyme, is located in the chloroplast and contributes to salinity stress tolerance in alfalfa. We found that the MsSNAT1 overexpressing alfalfa lines exhibited higher endogenous melatonin levels and increased tolerance to salt stress by promoting antioxidant systems and improving ion homeostasis. Furthermore, through a combination of transcriptome sequencing, dual-luciferase assays and transgenic analysis, we identified that the basic leucine zipper (bZIP) transcription factor, MsbZIP55, is associated with salt response and MsSNAT1 expression. EMSA analysis and ChIP-qPCR uncovered that MsbZIP55 can recognize and directly bind to the MsSNAT1 promoter in vitro and in vivo. MsbZIP55 acts as a negative regulator of MsSNAT1 expression, thereby reducing melatonin biosynthesis. Morphological analysis revealed that overexpressing MsbZIP55 conferred salt sensitivity to transgenic alfalfa through a higher Na+/K+ ratio and lower antioxidant activities, which could be alleviated by applying exogenous melatonin. Silencing of MsbZIP55 by RNA interference in alfalfa resulted in higher expression of MsSNAT1 and promoted salt tolerance by enhancing the antioxidant system enzyme activities and ion homeostasis. Our findings indicate that the MsbZIP55-MsSNAT1 module plays a crucial role in regulating melatonin biosynthesis in alfalfa while facilitating protection against salinity stress. These results shed light on the regulatory mechanism of melatonin biosynthesis related to the salinity stress response in alfalfa.

Soil organic carbon (SOC) in the active layer (0-2 m) of the Tibetan Plateau (TP) permafrost region is sensitive to climate change, with significant implications for the global carbon cycle. Environmental factors-including parent material, climate, vegetation, topography, soil, and human activities-inevitably drive SOC variations. However, vegetation and climate are likely the two most influential factors impacting SOC variations. To test this hypothesis, we conducted experiments using 31 environmental variables combined with the recursive feature elimination (RFE) algorithm. These experiments showed that RFE retained all vegetation variables [Land cover types (LCT), normalized difference vegetation index (NDVI), leaf area index (LAI), and gross primary productivity (GPP)] as well as two climate variables [Moisture index (MI) and drought index (DI)], supporting our hypothesis. We then analyzed the relationship between SOC and the retained vegetation and climate variables using random forest (RF), Shapley additive explanations (SHAP), and GeoDetector models to quantify the independent and interactive drivers of SOC distribution and to identify the optimal conditions for SOC accumulation. The RF model explained 68% and 42% of the spatial variability in SOC at depths of 0-1 m and 1-2 m, respectively, with SOC stocks higher in the southeast and lower in the northwest. Additionally, SOC stock at 0-1 m was significantly higher (p 0.05). Spearman correlation coefficients results indicated that NDVI, LAI, GPP, and MI had highly significant positive correlations with SOC (p < 0.01), whereas DI had a highly significant negative correlation with SOC (p < 0.01). SHAP analysis revealed environmental thresholds for SOC variations, with notable shifts at NDVI (0.40), LAI (7), GPP (250 g C m(-)(2) year(-)(1)), MI (0.40), and DI (0.50). The spatial distribution of these thresholds aligns with the 400 mm equivalent precipitation line. Additionally, GeoDetector results emphasized that interactions between climate and vegetation factors enhance the explanatory power of individual variables on SOC variations. The swamp meadow type, with an NDVI range of 0.73-0.84, LAI range of 11.06-15.94, and MI range of 0.46-0.56, was identified as the most favorable environment for SOC accumulation. These findings are essential for balancing vegetation and climate conditions to sustain SOC levels and mitigate climate change-driven carbon release.