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.

Iron pipes connected by bell-spigot joints are utilized in buried pipeline systems for urban water and gas supply networks. The joints are the weak points of buried iron pipelines, which are particularly vulnerable to damage from excessive axial opening during seismic motion. The axial joint opening, resulting from the relative soil displacement surrounding the pipeline, is an important indicator for the seismic response of buried iron pipelines. The spatial variability of soil properties has a significant influence on the seismic response of the site soil, which subsequently affects the seismic response of the buried iron pipeline. In this study, two-dimensional finite element models of a generic site with explicit consideration of random soil properties and random mechanical properties of pipeline joints were established to investigate the seismic response of the site soil and the buried pipeline, respectively. The numerical results show that with consideration of the spatial variability of soil properties, the maximum axial opening of pipeline joints increases by at least 61.7 %, compared to the deterministic case. Moreover, in the case considering the variability of pipeline-soil interactions and joint resistance, the spatial variability of soil properties remains the dominant factor influencing the seismic response of buried iron pipelines.

Malan loess is widely distributed on the Chinese Loess Plateau and poses great challenges to geotechnical, ecological, and agricultural practices due to its unique structure and collapsibility. It is essential to understand the evolution of these properties with depth to assess soil stability and reduce engineering risks in the area. This study investigates the mechanical properties and microstructural evolution of Malan loess with depth and employs multivariate statistical methods to explore their complex interrelationships. Oedometer-collapse tests reveal a 94.2 % reduction in collapsibility coefficient (delta s) from 0.0722 at 1 m to 0.0042 at 9 m, indicating a significant reduction in collapsibility with increasing depth. According to the results of the direct shear test, it showed that the shear strength initially decreases and then increases due to the combined effect of the water content and dry density. Scanning electron microscopy (SEM) images reveal the densification of the loess structure, with changes in particle contact from point to face contact and the evolution from macropores to mesopores and small pores as depth increases. Quantitative analysis by Avzio showed a decrease of 61.5 % in macropores area and an increase of 62.5 % in small pores area. The results obtained by Pearson's correlation analysis and random forest model showed that among these microstructural characteristics, the total pore area (%IncMSE = 22.77 %) is the most important factor influencing the collapsibility properties of loess and water content (%IncMSE = 17.72 %) acts a key role in controlling shear strength. Additionally, compared to traditional methods, the random forest model offers a more insightful understanding of nonlinear relationships and multifactorial coupling effects. These findings provide scientific guidance for geotechnical engineering in loess regions, aiding in risk mitigation and promoting sustainable construction.

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.

Cherry blossom crown gall has caused serious damage to plant growth, and is highly contagious and extremely difficult to control. The antagonism of pathogens by rhizosphere bacteria has attracted widespread attention. However, there is still limited research on the cherry blossom crown gall. In this study, we explored the control effect of rhizosphere bacteria Pseudomonas aurantiaca ST-TJ4 on cherry blossom crown gall. We also investigated the long-term survival status of ST-TJ4 in the cherry blossom roots and the induction of plant defense resistance. The results showed that ST-TJ4 had obvious inhibition effect on the population of Agrobacterium tumefaciens, which could reduce the number of A. tumefaciens by 70% to 90%, and its population kept the advantage in the rhizosphere soil and cherry blossom roots. The incidence of crown gall in the therapy group and the prevention group was reduced by 37.5% and 50%, respectively, and the disease index was reduced from 80 to 20 and 10, respectively. At the 150th day, ST-TJ4 could still be isolated from the rhizosphere soil and root surface, indicating that ST-TJ4 could survive in soil for a long time and had long-term performance. Compared with the control group, the therapy group and prevention group could reduce the levels of H2O2, malondialdehyde (MDA) and the oxidative damage, and up-regulated the expression of active oxygen-related genes DHAR1, SOD1, GR1 and CAT to activate defense response. On the other hand, it could up-regulate the expression of SA1, SA2 and JA1 genes related to the induction of salicylic acid (SA) and jasmonic acid (JA), and lead to the increase of SA hormone level. Collectively, P. aurantiaca ST-TJ4 had the potential to be applied for biocontrol of cherry blossom crown gall by reducing root pathogen colonization and inducing plant resistance.

Landslides, which are a type of process-based geological hazard, exhibit stagewise characteristics that serve as important guidance for the prevention and mitigation of slope engineering disasters. The cross-correlation and randomness of soil parameters can influence the evolution of landslide characteristics. This paper, based on the spatial variability of slope soil parameters, combines copula theory and the material point method (MPM) to establish a Monte Carlo-random material point method considering the cross-correlation of soil parameters. This resulting method is called copula-RMPM. It investigates the probability distributions of slope instability and landslide large deformation characteristics, such as sliding distance, landslide thickness, collapse range, and volume of sliding mass. The results indicated that in the study of soil parameter characteristics, failure probability increases with increased correlation coefficient. Also, failure probability showed a positive correlation with the variability coefficient of cohesion and internal friction angle, with failure probability being more sensitive to the variability coefficient of the internal friction angle. The landslide large deformation characteristics generally follow the normal distribution; they exhibit significant fluctuations in sliding distance and sliding mass area despite the relatively small variability coefficient. Compared with the results of random field simulation of soil parameters, the probability of landslide large deformation characteristics obtained by deterministic soil parameters is often lower. Therefore, the probability distribution of landslide large deformation characteristics obtained by the Monte Carlo-random material point method considering the cross-correlation of soil parameters is more meaningful for engineering guidance.

This paper proposes a frequency wavenumber-finite element hybrid method with kinetic source model for dynamic analysis of pile founded nuclear island from fault to structure. This method benefits from the effective synthesis of broadband ground motions by the fault source model, the realism of frequency wavenumber for earthquake simulation from fault to the site and the mesh refinement capabilities of the finite element in modeling the nuclear structure and the near soil. This method achieves the expression of source rupture, wave propagation, site response, soil-structure interaction, soil nonlinearity and structure response accurately, which solves the multi-scale problem from crustal layer to nuclear structure. Under finite-fault excitation, the correctness of the proposed method is validated by comparing with the frequency wavenumber method. Then, a full process seismic simulation of a pile founded nuclear island built on a non-rock site is conducted. The influence of source parameter and soil-structure interaction is studied. Results indicate that the change of source parameter can lead to difference nuclear island failure direction. With the increase of dip angle, the appearance of maximum stress is in advance. The soil nonlinearity could greatly amplify the soil-structure interaction effect and the loads on piles. The connection between the containment vessel and the raft is vulnerable and the piles on the edge of the raft is prone to damage. This hybrid method could accomplish an appropriate seismic evaluation of the nuclear structures and the conclusions may provide reference for seismic design of nuclear structure.

In the dynamic response analysis of slopes, the displacement of sliding surfaces is an important indicator for assessing stability. However, due to the uniform dynamic parameters of the Newmark slide block method, it is difficult to accurately analyze the displacements of large-scale slopes. To address this issue, the spatial distribution characteristics of dynamic parameters need to be considered to accurately analyze the stability of slopes. Under the combined action of rainfall and reservoir water level change, the Shiliushubao old landslide in the Three Gorges Reservoir area remains stable. To investigate the seismic stability of slopes, simulated seismic waves were employed. Firstly, the dynamic triaxial test, designed with cyclic loading, was employed to investigate the variation rules of the dynamic parameters of slope soil, and to establish a functional relationship. Then, the stochastic seismic motion model was used to generate artificially seismic waves in the Three Gorges Reservoir Area. Finally, to assess the stability of the old landslide, finite element software, GeoStudio 2018 was used to obtain the spatial distribution characteristics of the dynamic parameters and to calculate the permanent displacements of the reservoir bank slope by inputting random ground motion loads and dynamic characteristic functions. It is demonstrated that under the most unfavorable working conditions of heavy rainfall and the earthquake in the specific region, the permanent displacement of the Shiliushubao old landslide will be less than the critical permanent displacement, the old landslide is not to experience instability again.

Gas station sites pose potential risks of soil and groundwater contamination, which not only threatens public health and property but may also damage the assets and reputation of businesses and government entities. Given the complex nature of soil and groundwater contamination at gas station sites, this study utilizes field data from basic and environmental information, maintenance information for tank and pipeline monitoring, and environmental monitoring to develop machine learning models for predicting potential contamination risks and evaluating high-impact risk factors. The research employs three machine learning models: XGBoost, LightGBM, and Random Forest (RF). To compare the performance of these models in predicting soil and groundwater contamination, multiple performance metrics were utilized, including Receiver Operating Characteristic (ROC) curves, Precision-Recall graphs, and Confusion Matrix (CM). The Confusion Matrix analysis revealed the following results: accuracy of 85.1-87.4 %, precision of 86.6-88.3 %, recall of 83.0-87.2 %, and F1 score of 84.8-87.8 %. Performance ranking across all metrics consistently showed: XGBoost > LightGBM > RF. The area under the ROC curve and precision-recall curve for the three models were 0.95 (XGBoost), 0.94 (LightGBM), and 0.93 (RF), respectively. While all three machine learning approaches demonstrated satisfactory predictive capabilities, the XGBoost model exhibited optimal performance across all evaluation metrics. This research demonstrates that properly trained machine learning models can serve as effective tools for environmental risk assessment and management. These findings have significant implications for decision-makers in environmental protection, enabling more accurate prediction and control of contamination risks, thereby enhancing the preservation of ecological systems, public health, and property security.

In this study, the physiological response of potted apple trees to combined drought and heat stress was evaluated. After establishing different levels of soil water availability, the trees were exposed to a five-day simulated heatwave with daily maximum temperatures of 40 degrees C. Stem water potential, leaf gas exchange, chlorophyll fluorescence, and tree transpiration were monitored before, during and after the combined application of heat and water stress, therefore providing insights into the extent and rapidity of the recovery. Drought caused stomatal closure that limited net photosynthesis and transpiration both at leaf and at tree level, leading to structural damage through leaf loss. On drought-stressed plants, chlorophyll fluorescence was significantly reduced by heat stress, suggesting additional leaf damage although net photosynthesis was not lower than under drought stress alone. On the other hand, well-watered trees showed low midday stem water potentials and high transpiration rates during the heatwave, while net photosynthesis was not affected. Water use efficiency of well-watered trees at 33 degrees C was reduced to 60 % of that at 23 degrees C. After the heatwave, transpiration rate in well-watered trees immediately declined to pre-stress levels, underscoring the strong atmospheric control on transpiration in apple trees. In drought-stressed trees, predawn stem water potential reached pre-stress values already on the first day of recovery. Stomatal conductance, net photosynthesis, and chlorophyll fluorescence, however, required a longer period to recover, indicating that drought stress induced transient hydraulic limitations. Nevertheless, all parameters fully recovered within five days after the end of the heatwave, showing that apple trees can withstand periods of combined heat and drought stress. The key role of water in modulating the response to heat stress highlights the need for improved irrigation management in apple orchards under climate change.