Search Results (1,004)

Accurate modeling of pollutant transport during storm events is critical for watershed management and pollution mitigation. This study extends Diskin’s Cell Model, originally developed for rainfall–runoff simulations, to incorporate pollutant transport dynamics. By integrating an Instantaneous Unit Hydrograph (IUH), the model transforms pollutant loads into effective mass transport predictions while ensuring mass conservation. The framework accounts for contamination mobilized by rainfall, including agricultural runoff and industrial discharges, and applies convolution-based routing to capture pollutant dispersion. Calibrations using single-cell, two-cell, and fifteen-cell watersheds validate the model’s predictive capability and demonstrate its effectiveness in estimating pollutant accumulation at downstream locations. The results highlight the model’s potential for scalable water quality assessments, stormwater pollution control, and data-driven watershed management strategies. Full article

The post-storm beach recovery process exhibits variability. Understanding its mechanisms is crucial for advancing the study of beach morphodynamics. This study involved a 25-day continuous field observation on Dongdao Beach, Hailing Island, Yangjiang City, Guangdong Province, following the passage of Typhoon Cempaka. The evolution of beach morphology and the spatiotemporal variations in erosion and accretion were analyzed to explore the key influencing factors, response mechanisms, and recovery modes during the short-term recovery process. The post-storm evolution of beach profile structures is predominantly influenced by major geomorphic units such as berms and sandbars, whereas localized responses are characterized by adjustments of fine-scale features like micro-troughs. The width of the supratidal zone and the position of the berm crest continuously fluctuate, while the slope of the intertidal zone increases or decreases as the berm crest migrates landward or seaward. The erosion–accretion process was complex and occurred in distinct stages, with marked spatial heterogeneity. In some areas, the beach experienced multiple short-term cycles of alternating erosion and accretion. Beach slope plays a significant role in short-term recovery. Three types of response relationships between beach unit-width volume and changes in slope were observed, with flatter beaches being more sensitive to changes in unit-width volume. Based on this, four recovery modes in the post-storm short-term recovery process were explored from the perspective of beach slope. This study provides theoretical support for managing beaches after storms and recommends the implementation of zoned and phased management strategies based on different recovery modes to enhance the efficiency and resilience of coastal recovery. Full article

In a 1975 review, red kangaroos in the arid rangelands of Australia were said to be favoured with an anomalous prosperity following the introduction of ruminant livestock. In the western and central locations reviewed, this was not sustained, but in the sheep rangelands of Southern Australia, it is often claimed that such prosperity continues. Here, as elsewhere, the marsupial herbivore guild (kangaroos, wallabies, bettongs and bandicoots) has been simplified by the extinction of the smaller species (the anomaly), while large kangaroos remain abundant. However, the mammalian herbivore guild has gained complexity with not only the introduction of managed ruminant livestock, some of which run wild, but also game like rabbits. We studied the population dynamics, habitat selection and individual mobility of red, western and eastern grey kangaroos, common wallaroos, Merino sheep, feral goats and European rabbits at Fowlers Gap Station in far northwestern New South Wales, Australia. This site is representative of the arid chenopod (Family: Chenopodiaceae) shrublands stocked with sheep, where sheep and red kangaroos dominate the mammalian herbivores by biomass. The study site comprised two contiguous pairs of stocked and unstocked paddocks: a sloping run-off zone and a flat run-on zone, covering a total area of 2158 ha. This three-year study included initial rain-deficient (drought) months followed by more regular rainfall. Red kangaroos showed avoidance of sheep when given the opportunity and heightened mobility in response to localized drought-breaking storms and dispersion of the sheep flock at lambing. Western grey kangaroos were sedentary and did not dissociate from sheep. These effects were demonstrated at the population level and the individual level through radio-tracking a small cohort of females. The other kangaroo species and goats were transient and preferred other habitats. Rabbits were persistent and localized without strong interactions with other species. The results are discussed with a focus on the red kangaroo and some causes for its resilience in the sheep rangelands. Full article

Climate change, driven by natural factors and human activity, produces significant environmental changes worldwide. One consequence is increased rainfall, which leads to intense and increasingly frequent storms, sudden increases in river flows, and increased likelihood of emergencies linked to natural disasters. This framework proposes a model based on the System Dynamics (SD) approach that aims to monitor the increase in flow in rapid-response catchments (RRCs). The model evaluates humanitarian logistics strategies to manage supplies during emergency situations and it is based on dynamic simulation, whose advantages are the analysis of causal relationships between variables and their behavior over time, mathematical support during the creation of the simulation model, and the creation of a graphical interface that allows the user to carry out a visual analysis of the variables involved in the model. The results show, through a case study, the implementation of a containment plan based on early decision-making from rapid-response catchment monitoring to generate humanitarian logistics strategies preventing material and human damage. Therefore, the main contribution of this framework is the creation of a simulation model that involves the synergy between two different systems: the analysis of RRC behavior and the humanitarian logistics plan to establish provision policies (food, water and medicine) based on the number of people at risk. Full article

Background and Objectives: Hemophagocytic lymphohistiocytosis (HLH) is a rare hyperinflammatory condition characterized by excessive activation of cytotoxic lymphocytes and macrophages, resulting in a cytokine storm, multiorgan damage, and high mortality. HLH is classified into primary (genetic) and secondary (acquired) forms, with diagnosis often challenging due to nonspecific symptoms. Macrophage activation syndrome (MAS) refers to the secondary HLH triggered by rheumatic diseases. In this study, we retrospectively analyzed the clinical and laboratory features of patients with secondary HLH to enhance understanding of this life-threatening condition and summarize emerging management strategies. Materials and Methods: This single-center retrospective study analyzed medical records of patients hospitalized with HLH at the University Hospital in Kraków, Poland, from 2013 to 2024, based on HLH-2009 criteria and HScore > 169 points. Diagnostic criteria included clinical, laboratory, and histological findings, e.g., hemophagocytosis in bone marrow, circulating cytopenia, and elevated ferritin levels. Results: A total of 21 patients met the criteria for HLH diagnosis, with a median age of 35 (range: 19–67) years, including 12 women (57.1%). The median HScore among the patients was 244 (range: 208–304) points. Fever was the most common presenting symptom, occurring in all cases. High ferritin, hypertriglyceridemia, and hypofibrinogenemia in peripheral blood were also prevalent. Bone marrow hemophagocytosis was confirmed in 66.7% of cases (n = 12/18 of available data). Regarding immunosuppressive therapy, glucocorticosteroids were the most frequently used (used in all cases). Four (19.0%) patients died during HLH (cases triggered by lymphoma [twice], Epstein–Barr virus infection, unknown reason). Compared to survivors, these patients had lower counts of white blood cells, neutrophils, and lymphocytes at diagnosis (p < 0.05 for all). Conclusions: Secondary HLH is a severe syndrome requiring rapid diagnosis and timely intervention to improve patient outcomes. Lower white blood cell, neutrophil, and lymphocyte counts present worse prognostic factors. Full article

The fast pace of urban development and increasing intensity of precipitation events have made managing urban stormwater an increasingly difficult challenge. Hydrologic models are commonly used to predict flows and assess the performance of stormwater controls, often based on a hypothetical yet standardized design storm. The Storm Water Management Model (SWMM) is widely used for simulating runoff in urban watersheds. However, calibration of SWMM, as with all hydrologic models, is often plagued with issues such as subjectivity, and an abundance of model parameters, leading to delays and inefficiencies in model development and application. Further development of modeling and simulation tools to aid in design is critical in improving the function of stormwater management systems. To address these issues, we developed an integration of PySWMM (a Python wrapper (tool) for SWMM) and Pymoo (a Python package for multi-objective optimization) to automate the SWMM calibration process. The tool was tested using a case study urban watershed in Fredericksburg, VA. This tool can employ either a single-objective or multi-objective approach to calibrate a SWMM model by minimizing the error between prediction and observed values. This tool uses performance metrics including Nash-Sutcliffe Efficiency (NSE), Percent Bias (PBIAS), and Root Mean Square Error (RMSE) Standardized Ratio (RSR) for both single-event and long-term continuous rainfall-runoff processes. During multi-objective optimization calibration, the model achieved NSE, PBIAS, and RSR values of 0.73, 17.1, and 0.52, respectively; while the validation period recorded values of 0.86, 13.1, and 0.37, respectively. Additionally, in the single-objective optimization test case, the model yielded NSE values of 0.68 and 0.73 for the calibration and validation, respectively. The tool also supports parallelized optimization algorithms and utilizes Application Programming Interfaces (APIs) to dynamically update SWMM model parameters, accelerating both model execution and convergence. The tool successfully calibrated the SWMM model, delivering reliable results with suitable computational performance. Full article

Mountainous cities are especially vulnerable to flooding and water quality degradation due to surrounding steep terrain, variable precipitation, and fragile ecosystems. Existing studies often rely on small-scale scenario simulations or computationally intensive optimization algorithms, limiting their practical application. This study proposes a spatial layout strategy for stormwater management tailored to mountainous environments, using the Xining sponge city pilot area as a case study. Based on the “source–sink” theory, flood risk was assessed at the district scale, and the Storm Water Management Model (SWMM) was applied to evaluate four Low-Impact Development (LID) deployment schemes. A novel indicator—the source–sink coupling optimization degree (SSCOD)—was introduced to quantify LID spatial coordination between source and sink zones and identify optimal configuration thresholds. Results show that the four LID allocations significantly reduce runoff and improve water quality compared to the no-LID baseline. Analyses also reveal diminishing returns: optimal LID performance occurs when SSCOD ranges from 0.345 to 0.423, with 24.24–24.41% of LID facilities placed in high-risk zones. Beyond this range, effectiveness plateaus or declines, leading to potential resource waste. The proposed framework provides a technical basis and practical strategy for guiding stormwater infrastructure planning in mountainous cities, balancing effectiveness with resource efficiency. Full article

Zhejiang Province in China is one of the most typhoon-prone regions globally, making typhoon and storm surge hazard analysis critically important for disaster mitigation. This study integrates the Tropical Cyclone Risk Model (TCRM) with a machine learning-based storm surge forecasting model to analyze typhoon hazards and storm surge risks at four representative coastal sites in Zhejiang Province: Haimen, Ruian, Wenzhou, and Zhapu. Firstly, the input database of the TCRM has been updated and subsequently used to generate a 1000-year synthetic typhoon event catalog for the Northwest Pacific region. Secondly, four machine learning models—Long Short-Term Memory (LSTM), Back Propagation (BP), Support Vector Regression (SVR), and Random Forest (RF)—were developed to forecast storm surge component at the four sites, with sensitivity analysis conducted on the input parameters. Among the four models, RF consistently outperformed the others across all four sites. Thirdly, by integrating the storm surge forecasting model with the Yan Meng (YM) typhoon wind field model, extreme wind speed sequences and extreme surge component sequences were derived for the four coastal sites. Finally, four extreme value distribution models—empirical distribution, Weibull, Gumbel, and Generalized Pareto Distribution (GPD)—were applied to fit the extreme wind and surge sequences. Goodness-of-fit tests indicated that the GPD best captured extreme wind speeds at all four sites and extreme surge levels at Haimen, Ruian, and Wenzhou. Using the optimal distributions, return periods (10-, 50-, 100-, and 200-year) for extreme wind speeds and surge components were calculated, providing actionable references for disaster risk management authorities. Full article

Sepsis and cancer, though distinct in their clinical manifestations, share profound pathophysiological overlaps that underscore their interconnectedness in disease progression and outcomes. Here we discuss the intricate biological mechanisms linking these two conditions, focusing on the roles of inflammation, immune dysregulation, and metabolic alterations. In sepsis, an uncontrolled immune response to infection leads to a cytokine storm, tissue damage, and immune paralysis, while cancer exploits chronic inflammation and immunosuppressive pathways to promote tumor growth and metastasis. Both conditions exhibit metabolic reprogramming, such as the Warburg effect in cancer and glycolysis-driven immune cell activation in sepsis, which fuels disease progression and complicates treatment. Sepsis can exacerbate cancer progression by inducing genomic instability, epigenetic modifications, and a pro-tumorigenic microenvironment, while cancer increases susceptibility to sepsis through immunosuppression and treatment-related complications. The shared pathways between sepsis and cancer present unique opportunities for therapeutic intervention, including anti-inflammatory agents, immune checkpoint inhibitors, and metabolic modulators. Anti-inflammatory therapies, such as IL-6 and TNF-α inhibitors, show promise in mitigating inflammation, while immune checkpoint inhibitors like anti-PD-1 and anti-CTLA-4 antibodies are being explored to restore immune function in sepsis and enhance antitumor immunity in cancer. Metabolic modulators, including glycolysis and glutaminolysis inhibitors, target the metabolic reprogramming common to both conditions, though their dual roles in normal and pathological processes necessitate careful consideration. Additionally, antimicrobial peptides (AMPs) represent a versatile therapeutic option with their dual antimicrobial and antitumor properties. In this review, we also highlight the critical need for integrated approaches to understanding and managing the complex interactions between sepsis and cancer. By bridging the gap between sepsis and cancer research, this work aims to inspire interdisciplinary collaboration and advance the development of targeted therapies that address the shared mechanisms driving these devastating diseases. Ultimately, these insights may pave the way for novel diagnostic tools and therapeutic strategies to improve outcomes for patients affected by both conditions. Full article

Ulva intestinalis holds promise for sustainable aquaculture in the Baltic Sea, but success has so far been limited by high environmental variability. This study examines how environmental factors influence sporogenesis, attachment, and growth of U. intestinalis in the low-salinity Baltic Sea. Optimal sporogenesis was observed at nutrient levels of 4–7 g/L, with peak zoospore release at 22–24 °C. Artificial substrates showed limited attachment success, as competing algae like Pylaiella littoralis and Cladophora glomerata often outperformed Ulva. Mesh cage cultivation demonstrated potential, achieving growth rates similar to controlled systems, though storm-induced turbidity lowered growth. These findings highlight the importance of tailored Baltic Sea cultivation strategies, focusing on nutrient, temperature, water stability, and competition management to enhance Ulva production. As the first pilot experiments in the region, they provide essential input for developing informed strategies that support more detailed trials and future scaled-up production. Full article

As the frequency of strong storms and cyclones increases, understanding wind risk in both existing and newly established plantation forests is becoming increasingly important. Recent advances in the quality and availability of remotely sensed data have significantly improved our capability to make large-scale wind risk predictions. This study models the loss of radiata pine (Pinus radiata D.Don) plantations following a severe cyclone within the Gisborne Region of New Zealand through leveraging repeat regional LiDAR acquisitions, optical imagery, and various surfaces describing key climatic, topographic, and storm-specific conditions. A random forest model was trained on 9713 plots classified as windthrow or no-windthrow. Model validation using 50 iterations of 80/20 train/test splits achieved robust accuracy (accuracy = 0.835; F1 score = 0.841; AUC = 0.913). In comparison to most European empirical models (AUC = 0.51–0.90), our framework demonstrated superior discrimination, underscoring its value for regions prone to cyclones. Among the 14 predictor variables, the most influential were mean windspeed during February, the wind exposition index, site drainage, and stand age. Model predictions closely aligned with the estimated 3705 hectares of cyclone-induced forest damage and indicated that 20.9% of unplanted areas in the region would be at risk of windthrow at age 30 if established in radiata pine. The resulting wind risk surface serves as a valuable decision-support tool for forest managers, helping to mitigate wind risk in existing forests and guide adaptive afforestation strategies. Although developed for radiata pine plantations in New Zealand, the approach and findings have broader relevance for forest management in cyclone-prone regions worldwide, particularly where plantation forestry is widely practised. Full article

Extreme precipitation events are one of the common hazards in eastern Texas, generating a large amount of storm water. Water running off urban areas may carry non-point source (NPS) pollution to natural resources such as rivers and lakes. Urbanization exacerbates this issue by increasing impervious surfaces that prevent natural infiltration. This study evaluated the efficacy of rain gardens, a nature-based best management practice (BMP), in mitigating NPS pollution from urban stormwater runoff. Stormwater samples were collected at inflow and outflow points of three rain gardens and analyzed for various water quality parameters, including pH, electrical conductivity, fluoride, chloride, nitrate, nitrite, phosphate, sulfate, salts, carbonates, bicarbonates, sodium, potassium, aluminum, boron, calcium, mercury, arsenic, copper iron lead magnesium, manganese and zinc. Removal efficiencies for nitrate, phosphate, and zinc exceeded 70%, while heavy metals such as lead achieved reductions up to 80%. However, certain parameters, such as calcium, magnesium and conductivity, showed increased outflow concentrations, attributed to substrate leaching. These increases resulted in a higher outflow pH. Overall, the pollutants were removed with an efficiency exceeding 50%. These findings demonstrate that rain gardens are an effective and sustainable solution for managing urban stormwater runoff and mitigating NPS pollution in eastern Texas, particularly in regions vulnerable to extreme precipitation events. Full article

Floods caused by extreme weather events critically impact human and natural systems. Remote sensing can be a very useful tool in mapping these impacts. However, processing and analyzing satellite imagery covering extensive periods is computationally intensive and time-consuming, especially when data from different sensors need to be integrated, hampering its operational use. To address this issue, the present study focuses on mapping flooded areas and analyzing the impacts of the 2023 Storm Daniel flood in the Thessaly region (Greece), utilizing Earth Observation and GIS methods. The study uses multiple Sentinel-1, Sentinel-2, and Landsat 8/9 satellite images based on backscatter histogram statistics thresholding for SAR and Modified Normalized Difference Water Index (MNDWI) for multispectral images to delineate the extent of flooded areas triggered by the 2023 Storm Daniel in Thessaly region (Greece). Cloud computing on the Google Earth Engine (GEE) platform is utilized to process satellite image acquisitions and track floodwater evolution dynamics until the complete drainage of the area, making the process significantly faster. The study examines the usability and transferability of the approach to evaluate flood impact through land cover, linear infrastructure, buildings, and population-related geospatial datasets. The results highlight the vital role of the proposed approach of integrating remote sensing and geospatial analysis for effective emergency response, disaster management, and recovery planning. Full article

With the acceleration of urbanization, the diffusion mechanism of urban non-point source (NPS) pollution caused by extreme rainfall is not clear, which leads to high cost and difficulty in water environment treatment. In view of the shortcomings of dynamic diffusion simulations of mesoscale pollution, this paper proposes a simulation framework based on cellular automata, GIS geographic technology, and a two-dimensional shallow water model. Taking the 500 m × 500 m grid as the unit, we explore the migration laws of nitrogen and phosphorus pollutants and the response relationship between pollutant diffusion and land use under extreme rainfall scenarios. The results show that (i) the pollution risk increases significantly with diffusion, with the maximum pollution load in high-risk areas increasing by 181%, and the diffusion rate is positively correlated with the rate of change in rainfall intensity; (ii) forest land has the highest grid pollution load loss rate, whereas the water grid has the highest accumulation rate; (iii) this method can accurately identify the hot spots of pollution diffusion, providing a basis for the precise control of high-risk areas. This study can support the targeted governance of pollution sources and land planning optimization in urban storm and flood management, and help reduce environmental health risks in extreme climates. Full article

Grass swales have emerged as a cost-effective and sustainable stormwater management solution, addressing the increasing challenges of urbanization, flooding, and water pollution. This study conducted a bibliometric analysis of 224 publications to assess research trends, key contributors, and knowledge gaps in grass swale applications. Findings highlighted the growing emphasis on optimizing hydrological performance, particularly in response to intensifying climate change and urban flood risks. Experimental and simulation-based studies have demonstrated that grass swale efficiency is influenced by multiple design factors, including vegetation type, substrate composition, hydraulic retention time, and slope gradient. Notably, pollutant removal efficiency varies significantly, with total suspended solids (TSS) reduced by 34.09–89.90%, chemical oxygen demand (COD) by 7.75–56.71%, and total nitrogen (TN) by 32.37–56.71%. Additionally, studies utilizing the Storm Water Management Model (SWMM) and TRAVA models have demonstrated that integrating grass swales into urban drainage systems can result in a 17% reduction in total runoff volume and peak flow attenuation. Despite these advancements, key research gaps remain, including cost-effective design strategies, long-term maintenance protocols, and integration with other green infrastructure systems. Future research should focus on developing innovative, low-cost swale designs, refining optimal vegetation selection, and assessing seasonal variations in performance. Addressing these challenges will enhance the scientific foundation for grass swale implementation, ensuring their sustainable integration into climate-resilient urban planning. Full article