Emerging climate-related risks of legionnaires' disease and other waterborne pathogens in the United States.

Scalable and hardness-tolerant H2O2 electrosynthesis in tap-water-based electrolytes enabled by electrode architecture and reactor engineering.

Experimental study on CO2–water alternating huff-n-puff and pressure-driven imbibition water flooding in interbedded shale reservoirs

Extra-heavy oil is an important strategic energy resource, but its ultra-high viscosity severely limits efficient production. Conventional thermal recovery and chemical flooding are often associated with high energy consumption, environmental concerns, and limited reservoir adaptability. In this study, an intelligent responsive magnetic Janus nanocatalyst (IRMJN) was developed and coupled with microwave irradiation to enable low-energy and controllable in situ upgrading and oil mobilization. IRMJN features a spatially separated multifunctional architecture, in which Fe₃O₄ serves as the magnetic core for rapid recovery, MoS₂ nanosheets are selectively anchored on one side as catalytic active sites, and graphene quantum dots enhance microwave absorption to generate a synergistic nanoscale hotspot effect. Long-chain alkyl groups grafted onto the magnetic side further impart interfacial orientation capability. Under optimized conditions, the IRMJN-microwave system reduced the viscosity of extra-heavy oil by more than 95% at a bulk temperature of 100°C, clearly outperforming microwave treatment alone and conventional catalytic systems. Core flooding tests showed an additional oil recovery of more than 18.5% after water flooding. The catalyst also exhibited excellent magnetic recoverability and cycling stability. These results provide a promising strategy for the green and efficient development of extra-heavy oil resources.

Cholera remains endemic in India, with outbreaks frequently intensifying during the monsoon due to flooding and disruption of water and sanitation systems. Recent floods in Delhi have raised concerns regarding changing epidemiology, antimicrobial resistance (AMR) and circulation of virulent Vibrio cholerae lineages. This was a retrospective analysis of V. cholerae isolates obtained from stool samples at a tertiary care hospital in Delhi from May 2018 to December 2024. Isolates were identified by conventional methods and serotyping. Antimicrobial susceptibility testing was conducted using disk diffusion as per Clinical and Laboratory Standards Institute guidelines. Molecular characterization was performed for virulence genes (ctxB, tcpA, rtxA) and ctxB sequencing. Eighty-five V. cholerae isolates were analysed, predominantly affecting children (78.8%), with a post-monsoon peak. All isolates belonged to serogroup O1, serotype Ogawa, El Tor biotype. High resistance was observed to co-trimoxazole (77.6%) and ampicillin (55.3%), with moderate resistance to ciprofloxacin (24.7%); all isolates remained susceptible to azithromycin and ceftazidime. Virulence genes were detected in most sequenced isolates and ctxB sequencing revealed mutations consistent with contemporary epidemic lineages. The study demonstrates persistent circulation of toxigenic V. cholerae O1 Ogawa strains in Delhi, with seasonal clustering following floods and evolving AMR patterns. Continuous surveillance integrating phenotypic and molecular data is essential to guide therapy and strengthen cholera control in flood-prone urban settings.

Carbon dioxide enhanced oil recovery (CO2-EOR) offers dual benefits of increasing hydrocarbon production and sequestering anthropogenic CO2; however, conventional CO2 flooding suffers from unfavorable mobility ratios and premature gas breakthrough, while polymer flooding alone faces challenges in high-salinity and high-temperature reservoirs. This study experimentally and numerically investigates the synergistic impact of combining xanthan gum polymer with CO2 injection to maximize oil recovery. We conducted laboratory coreflood experiments on packed sandstone (porosity 22%, permeability 1000 mD) saturated with 31°API crude oil (6.0 cP at 70 °C), testing polymer concentrations of 1.0, 1.5, 2.0, and 2.5 g/L. We evaluated five flooding scenarios: water flooding (baseline), CO2 flooding alone, polymer flooding alone, polymer (1.5 g/L) followed by CO2, and CO2 followed by polymer (1.5 g/L). We then upscaled experimental findings using CMG-IMEX reservoir simulator to field scale. Water flooding recovered 70.0% of original oil in place (OOIP). CO2 flooding alone increased recovery to 83.3% OOIP. Polymer flooding alone achieved maximum recovery of 81.3% OOIP at optimal concentration (1.5 g/L), while lower (1.0 g/L) and higher (2.5 g/L) concentrations yielded 79% and 73.5% OOIP, respectively. Sequential injection significantly outperformed single methods: polymer (1.5 g/L) followed by CO2 achieved 89.3% OOIP, while CO2 followed by polymer (1.5 g/L) achieved the highest recovery of 94.3% OOIP. An incremental gain of 24.3% over water flooding and 11.0% over CO2 flooding alone. The combined polymer-CO2 flooding system delivers superior oil recovery compared with individual processes. The optimal strategy-CO2 injection followed by 1.5 g/L xanthan gum polymer-maximizes both microscopic displacement efficiency (CO2-driven oil swelling and viscosity reduction) and macroscopic sweep efficiency (polymer-driven mobility control). This hybrid approach represents a technically viable and economically promising EOR strategy for mature sandstone reservoirs while contributing to sustainable carbon management.

Monitoring glacier-fed river width dynamics in High Mountain Asia from Sentinel-2 time series using a deformable UNet and skeleton evolution framework

Abstract The impact of mass‐wasting events on river systems is typically studied in the immediate aftermath and within close proximity to the source area. However, basin‐wide hydro‐geomorphic responses in large river systems, beyond the immediate postdisaster context, remain poorly understood. In this study, we examine the Eastern Himalayan Syntaxis in the Brahmaputra basin, where the Sendongpu glacier valley has undergone rapid erosion due to major mass‐wasting events in 2017, 2018 and 2021. A nearby large‐scale event also occurred in 2000 along the Yigong River. We assess the river's response to these events using satellite‐derived water turbidity indices, water surface elevation, sandbar area and flood extent, at the finest temporal and spatial resolution available through Google Earth Engine's database. Our time series analysis reveals that, following the 2017 event, fine suspended sediment signals can be traced more than 1000 km downstream to the delta, while coarser sediments primarily affect river morphology within approximately 100 km of the mountain front. In this zone, we observed a notable increase in sandbar area and water surface elevation, indicating extensive deposition and channel infilling. Comparison with the 2000 Yigong outburst highlights the differing geomorphic effects of rapid, high‐magnitude events versus slower, more sustained sediment inputs. These findings highlight the need to integrate mass‐wasting‐driven sediment processes into flood risk assessments and hydropower planning in Himalayan river systems, particularly as such landscapes are increasingly subject to both natural and anthropogenic pressures.

Livestock grazing modulated the effects of flooding on ecosystem multifunctionality in the wetland of the lower Tumen River in Northeast China.

Synergistic Effects of 2-Ethoxyethanol and Dissolved CO2 on Interfacial Tension Reduction, Wettability Alteration, and Oil Swelling through Carbonated Water Flooding in Carbonate-Sandstone Reservoirs

A flood is defined as any moderately highwater flow which overtops the artificial or natural banks. It is one of world’s most destructive natural disaster. During extreme rainfall events; flood water flow from all valleys towards the city, causing serious damage to public and private properties. In such situations; we have to identify the areas which with high risk of flooding. This study aims to analyze the peak discharge in Killi river basin based on 2,5, 10- year return periods and used for the identification of flood prone areas and to adopt appropriate plans to control and reduce the effect of floods. For this both hydrologic and hydraulic modelling are conducted using HEC HMS and HEC RAS. Peak flood discharge is estimated using HEC HMS and flow depth, velocity, water surface level is also determined by using HEC RAS. Depth of water, velocity distribution and water surface height obtained after 2D flow simulation are utilized to decide the degree of flooding. RAS-mapper is an effective tool in HEC-RAS, which can be utilized for inundation of research area. For unsteady flow analysis, each time step was done based on inflow hydrograph using RAS mapper tool in HEC-RAS, which gives the spatial distribution of the river flow. The model performance evaluation of HEC HMS was carried out by using co efficient of determination R 2 AND NRMSE. The outcomes from this study can be utilized for disaster management, flood management, early warning system by authorities in addition to infrastructure growth decisions.

ABSTRACT Flooding in urban river catchments is an increasing concern, especially in areas exposed to both riverine and coastal hazards. This study evaluates flood mitigation strategies for the Antiao River Catchment in the Philippines using integrated hydrologic and hydraulic modeling (Hydrologic Engineering Center—Hydrologic Modeling System [HEC‐HMS] and Hydrologic Engineering Center—River Analysis System [HEC‐RAS]). Three scenarios were assessed: (1) a baseline “Do‐Nothing” case, (2) conventional flood protection dikes (FPDs), and (3) a hybrid approach combining FPD with nature‐based solutions (NbSs), including reforestation, mangrove restoration, and constructed wetlands. Model validation showed strong agreement with national flood hazard maps and empirical peak‐flow estimation methods. Results indicate that while FPD reduces flood extent, it can increase localized flood depths and water surface elevations due to flow confinement. The integrated FPD‐NbS approach was associated with lower flood depths, reduced flow velocities, and moderated water surface elevations relative to the FPD‐only scenario, indicating improved hydraulic performance under extreme flood conditions. These findings support the use of combined structural and nature‐based interventions to enhance urban flood resilience in river catchments influenced by both fluvial and coastal processes.

Greater Chennai Corporation (GCC) faces recurring urban flooding, drought cycles, and fragmented water infrastructure across its 426 km² jurisdiction serving over 7 million residents. This paper examines water-sensitive urban planning (WSUP) strategies for stormwater management in high-risk flood zones of GCC. Through primary surveys (n=50 households; n=22 stakeholders), secondary data analysis, literature review, and spatial analysis of 5-year and 10-year flood return period maps, this study identifies key flood vulnerability drivers including unscientific grey drain design, institutional fragmentation between GCC and CMWSSB, wetland loss, and rapid urbanization. Five integrated WSUD proposals are presented: a multi-scalar blue-green infrastructure framework, a citywide sponge park network, a restored tank-canal chain for macro-drainage revival, permeable neighbourhood retrofits, and wetland buffer protection as an ecological conservation corridor. Drawing from global precedents including Singapore's ABC Waters Programme, China's Sponge City strategy, and Lisbon's Drainage Master Plan, the proposals are adapted to Chennai's tropical monsoon context. Findings demonstrate that integrating nature-based solutions with existing grey infrastructure can reduce peak runoff by 30–70%, improve groundwater recharge, and build long-term flood-drought resilience for one of India's most flood-vulnerable megacities.Key Words: Water-Sensitive Urban Design, Stormwater Management, Urban Flooding, Greater Chennai Corporation, Blue-Green Infrastructure, Sponge City, Nature-Based Solutions, Flood Resilience, WSUD, Urban Planning

Direct methanol fuel cells (DMFCs) offer significant advantages including high energy density and rapid refueling, making them promising power sources for portable electronic products. However, their practical application, particularly in passive systems, is hindered by critical mass transport limitations: water flooding in the cathode and CO2 bubble blockage in the anode. Herein, a novel dual-gradient patterned wettability current collector (CC) was designed to alleviate this mass transport impedance. The design uniquely integrates wedge-shaped gradients with surface energy gradients to create a unified, self-driven mechanism for efficient water and CO2 bubble transport at both electrodes. A mathematical model was developed to quantitatively evaluate the effects of the dual-gradient structure. The results confirm that water removal is enhanced when the cathode current collector features a hydrophobic periphery with a dual-gradient patterned wettability interior on the gas-diffusion-layer side and a fully hydrophilic air-side surface, whereas an inverted pattern facilitates anode CO2 removal. Optimal fabrication parameters on 316 L stainless steel were established by investigating laser scanning conditions and low-surface-energy agent concentrations. The experimental results show that the passive DMFCs incorporating the optimized current collectors delivered marked performance improvements. At 1 mol·L-1 methanol, the novel anode and cathode current collectors increased peak power density by 15.6% and 14.5%, respectively. Electrochemical impedance spectroscopy revealed a 31.4% and 31.9% reduction in mass transfer resistance of the cell with novel anode and cathode current collectors, respectively, confirming improved gas-liquid self-driven efficiency. Furthermore, the new cells exhibited substantially enhanced long-term stability over 18 h of continuous discharge, attributed to the robust wettability achieved via laser-silane modification. Overall, these findings suggest that the proposed dual-gradient wettability design is a promising method for improving internal mass transport, potentially supporting the development of more robust passive DMFCs.

Reservoir fracturing combined with thermal stimulation is a highly promising strategy for the development of challenging hydrates. However, the synergistic influence mechanisms of multiple engineering parameters on productivity remain poorly understood. In this study, based on the geological condition of the SH2 site in the Shenhu Area of the South China Sea, a numerical model was built to investigate the development efficiency of challenging hydrates under fracturing and thermal co-stimulation. Using average gas production rates (m3/d) at recovery rates of 0.70 and 0.85 as assessment indicators, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) algorithms were employed to quantitatively measure multivariable importance. The results indicated that enhancing the inter-well interaction through reservoir fracturing can increase development efficiency by 2 to 5 times; however, it is not the case that larger-scale fracturing is always preferable, as it can lead to more severe water flooding. Additionally, data-driven models revealed that fracture length (SHAP values of 15.55 and 9.19) was the primary factor influencing development efficiency, followed by the fracture conductivity (SHAP values of 6.65 and 6.32), whereas injection pressure (SHAP values of 2.90 and 2.17), injection temperature (SHAP values of 2.41 and 2.13), and production pressure (SHAP values of 2.37 and 1.82) had relatively limited influences. Most importantly, the positive interaction effect between fracture length and fracture conductivity cannot be ignored. In our simulation, the recommended fracture length and conductivity were 40 m and 100 D·cm, respectively. These findings provide important insights and guidance for implementing this novel co-stimulation method in challenging hydrates.

ABSTRACT In this study, a multi-source approach incorporating spatio-temporal information and a feature selection strategy is presented for predicting daily and peak flows in the Susquehanna River Basin utilizing multi-source hydrological and hydrogeological data. In the prediction study, two approaches were compared: Approach 1 uses the selected source data directly as model inputs, while Approach 2 incorporates source data after applying time-lagged feature selection. When the time-delayed features were optimized (Approach 2), the prediction accuracy of the LASSO-ANN model was found to be even better (R2 = 0.96, KGE = 0.97 for overall flow; R2 = 0.78, KGE = 0.89 for peak flow). The results obtained show that data sources and the time lags of the selected features are very important in prediction studies, highlighting the significance of combining multivariate data, LASSO-based feature selection and machine learning to improve hydrological forecasts and support flood preparedness and water management under climate uncertainty.

Traditional water resource management relies on statically configured models and sparse in-situ networks, creating critical gaps in situational awareness that lead to operational failures during floods, droughts, and water quality incidents. This review synthesizes advancements in AI-enabled digital twins—constantly updated, stateful digital representations that synchronize with physical water systems through continuous assimilation of multi-source remote sensing data. Unlike conventional modeling workflows, these closed-loop systems integrate heterogeneous observations (optical, Synthetic Aperture Radar (SAR), thermal, microwave, and altimetry) with in-situ Internet of Things (IoT) measurements to maintain real-time alignment with evolving conditions. We propose a unified reference architecture spanning data ingestion, AI-driven downscaling and retrieval, quality-aware multi-modal fusion, state synchronization via data assimilation, probabilistic forecasting using physics-AI hybrids, and decision support with continuous monitoring. The framework explicitly addresses operational constraints, including latency, missing data, and non-stationarity, while prioritizing uncertainty calibration over point accuracy. Our synthesis evaluates design trade-offs across flood response, reservoir operations, drought monitoring, irrigation management, and water quality applications. We conclude by identifying research priorities: standardized state schemas and uncertainty metrics, interoperable application programming interfaces (APIs), robust domain adaptation, and governance frameworks incorporating human-in-the-loop safeguards. This review provides a roadmap for transforming heterogeneous remote sensing streams into reliable, actionable intelligence for real-time water resource management.

By constructing and dynamically simulating a three-dimensional geological model of a hydrocarbon prospect in the North Sea, several development strategies were tested to assess how hydrocarbon fields in sand injectite systems could be optimized. Significant reductions in water production and a moderate increase in oil can be achieved if a well is placed at a shallow depth in a sand-injectite system within an interval where sand injectites are not normally resolved on seismic data, net-to-gross (N/G) is low (typically less than 0.1) and stand-off to the oil–water-contact (OWC) is maximized. The low N/G of such wells challenges the paradigm that a successful development well must have a high proportion of net sand. While the cumulative volume of oil production may be similar to a development well placed closer to an OWC in a seismically mapped drill target, the cumulative volume of water produced may be significantly reduced. In the simulations presented here a broad K v / K h range was used to test the gross vertical connectivity uncertainty within the sand injectite system. A sweet spot exists at a K v / K h of around 0.1 – above that value water breakthrough and cut were accelerated, while below that sand injectite connectivity and water flood were choked back. The gross architecture and flow potential of both seismically mapped and non-seismically resolved sand injectites should be considered. Scenarios may exist where lateral sweep within a seismically mapped sand injectite is enhanced due to reduced vertical connectivity within the non-seismically resolved sand injectites.

ABSTRACT This figure summarizes the study framework, in which meteorological, topographic, land use, soil and station data are used to set up the SWAT model, followed by calibration, validation, and CMIP6 scenario selection to project discharge, environmental flow, soil water content, and evapotranspiration. Climate change has changed global hydro-meteorological responses, especially in the Bangladesh–India portion of the Ganges–Brahmaputra–Meghna (GBM) basin in Asia, which has suffered from excessive precipitation, temperature, floods, etc. Previous research used large-scale models, which are unlikely to improve projections, introduce greater uncertainty, and lack basin meteorological analysis. This study examines how climate change affects the hydrological and meteorological patterns in the Barak River Basin by applying a hydrologic model (Soil & Water Assessment Tool) coupled with bias-corrected and downscaled climate models. It evaluates both moderate and extreme scenarios to quantify changes in discharge, flow return period, seasonal variability, evapotranspiration (ET), and soil water content (SWC). Under an extreme scenario by the end of the century, results indicate an alteration in precipitation patterns, suggesting a potential increase of 31.38% in precipitation. This influences the basin's hydrology, as the annual maximum discharge is anticipated to increase by up to 67%. Furthermore, the return period of extreme flow events and the environmental flow is predicted to reduce by approximately 35 years and 8%, respectively. Moreover, ET and SWC are projected to fluctuate throughout the watershed. These alterations indicate an increased variability, which will affect water availability, flood risks, and agriculture in the region.

The need for urban greening is increasing by times with accelerated rates of global urbanization. Trees and other green infrastructure are the major assets to livable urban areas, providing valuable environmental services to combat challenges like air and water pollution, urban heat and flooding, as well as to improve social cohesion, human health and well-being. Investments in tree planting and arboriculture yield valuable returns ecologically, economically and by human health and well-being, But trees face many challenges in the unnatural and stressful urban environment and in rapidly changing climate.