Precise precipitation data are essential for the effective execution of water resource management and risk reduction initiatives, particularly during periods of extreme precipitation. This study introduces a new precipitation product (NPP) that integrates in situ and ERA5 estimations to enhance the identification and quantification of extreme precipitation events. We evaluated the newly generated dataset using a diverse array of statistical indices, in conjunction with two satellite precipitation products (IMERG and PERSIANN-DIR). We also implemented principal component analysis (PCA) to evaluate the efficacy of precipitation products. The PCA algorithm facilitates the interpretation of the primary factors that influence severe precipitation by reducing the number of dimensions in the dataset. We also evaluated the efficacy of newly produced products and SPPs using 11 extreme indices. We found that the NPP is the only newly integrated precipitation product that can track the heaviest precipitation at the highest elevations (> 3000 m), which is similar to how it leads on most extreme indices for tracking extreme events. The NPP monitors the maximum number of days for R50, while others suggest a significant level of underestimation. The PERSIANN PDIR's performance is unsatisfactory, and the IMERG is only marginally capable of monitoring the new dataset's performance. The results suggest that PCA pattern 4 significantly influences the efficacy of all datasets due to displaying circular wind patterns, which can produce localized precipitation zones and potentially complicate accurate capture by satellite products. This research provides a valuable resource for water resource managers and meteorologists to improve flood predictions and risk reduction strategies. A novel precipitation product, in conjunction with PCA insights, establishes a robust framework for improving the mitigation of severe weather impacts in vulnerable regions.

The integral process of ecotechnology postulates that it is possible to change from a globalized context, which consists of mass productions as well as a high consumption of resources, to a context that is concerned with the resolution of local issues, also seeking to eradicate the relations of domination and strong inequality gaps. Although ecotechnology makes use of ancestral techniques and practices, nevertheless, it is not seen as an outdated, obsolete alternative or as a marginal technology; on the contrary, it is of great significance due to its process of knowledge exchange, which is based on scientific inputs for its development and construction. This highlights the need to generate a social model that contains democratic, fair and plural knowledge, which would allow the reduction of inequality gaps and marginalization. Thus, within this framework, the social adoption of ecotechnology allows us to legitimize original and cultural knowledge in a forceful way, as well as to seek the necessary change of paradigm and generate a process of implementation in an integral way, leading to the confrontation of problems through strategic technological answers with a high social impact.

In the Sokoto–Rima catchment, over 70% of the population depends on groundwater for subsistence farming. The use of the conventional techniques in the assessment of water quality is expensive because it requires several parameters, so developing an accurate and reliable model is essential in the management of water resources for effective agricultural practices. This study applied multivariate statistics, geospatial analyses and machine learning (ML) models to groundwater chemistry and irrigation indices to determine the classification and the spatial–temporal distribution. MS Excel was to used calculate the irrigation suitability parameters, such as SAR, KR, ESP, Na%, PI and ESP, and then PAST4.0 statistical software and machine learning algorithms such as multiple linear regression (MLR), a Decision Tree (DT), Random Forest (RF), a Support Vector Model (SVM) and K-NN Neigbors (K-NN) were used for the model predictions, based on a composite index that combined all the indices (SAR, KR, EST, Na, MAR, PI) into a single score representing the overall irrigation water quality and then SAR (and subsequently other indices like KR, EST, Na, MAR, PI, etc.). The IDW interpolation technique was used to generate the spatial distribution maps for each parameter of the groundwater dataset for this study. The results from the fifty-element (50) water chemistry dataset obtained from the archive of the Federal Ministry of Water Resources predicted four (4) clusters from the hierarchical cluster analysis, with two (2) principal components, PC1 and PC2, representing the major geochemical processes controlling the groundwater quality. A very strong correlation association was observed between EC-Ca (0.85), KR-SAR (0.95) and Na%-ESP (0.85). The machine learning models indicated for the composite index showed a low MSE of 0.00 and a high R of 1.00 for multiple linear regression and R values of 0.6 and 0.63 and MSE values of 68.5 and 67.86, respectively, for the DT and RF models. Predicting PI as the target variable with KR, SAR, MAR, Na% and ESP demonstrated a notable predictive capability, with a low RMSE of 13.3 and a high R of 0.9836, with RF. While KNN showcases a robust performance for Na% as the target variable, as did the DT and RF for ESP, MLR showed a strong predictive performance for SAR.

Ageing phenomena are inevitable in urban water distribution systems (WDSs). One of the most popular techniques to reduce the consequences of water losses caused by ageing is the management of hydraulic parameters such as pressure reduction in the water mains. In this study, aiming to investigate the effect of pressure reduction on leakage, EPANET 2.2 software is used to simulate an urban water distribution network. The application of Machine Learning (ML) models such as ANFIS (Adaptive Neuro-Fuzzy Inference System)-Genetic Algorithm (GA), ANFIS-Particle Swarm Algorithm (PSO), and Extreme Learning Machine (ELM) is evaluated to reduce damage due to high operating pressure in a WDS while considering the measured values of head loss and velocity data through hydraulic simulation caused by diurnal demand patterns. In order to investigate the difference between the historical and estimated values, the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Akaike Information Criterion (AIC), and R are used. A real-world case study is selected to apply the proposed models. After the application of Machine Learning, the obtained results indicate that the ELM technique provides an appropriate tool for predicting pressure in the WDS with minimum error and high desired accuracy. This means that the implementation of the results of the proposed ML model in a real urban WDS is feasible and plays a key role in reducing water losses.

This study focuses on designing and evaluating a household-level rainwater harvesting system (RWHS) aimed at enhancing groundwater conservation, reducing costs, and mitigating urban flooding. Utilizing WaterGEMS for hydraulic analysis, the research highlights the effectiveness of an RWHS in advancing sustainable urban water management. Initially, this study involved designing the water supply system without a rainwater harvesting component. Subsequently, the rainwater potential and storage tank volumes were computed for 3, 5, and 10-marla houses using four methods, with the SamSam model being identified as the optimal approach. Further, the calculated storage volume was divided into the rain barrel volume and the underground tank volume. Three scenarios were developed based on different percentages of water demand being met through harvested rainwater, with case I (covering gardening, house cleaning, and laundry, meeting 18% of the demand) deemed the most viable. Simulations using the Storm Water Management Model (SWMM) revealed an average of 8.6% reduction in water demand, a 17.38% decrease in electricity consumption, and significant reductions in peak urban flooding for return periods of 2, 5, and 10 years, accompanied by 20-24% less flooding at the society level and 17% energy savings. The implementation costs for RWHS were determined as PKR 550,253 (USD 1974) for 3-marla, PKR 670,890 (USD 2407) for 5-marla, and PKR 1,112,283 (USD 3992) for 10-marla houses, underscoring the substantial potential of RWHS in bolstering urban water supply sustainability through efficient rainwater management and utilization.

Floods are among the most devastating natural disasters worldwide, inflicting severe damage on human life, infrastructure, and socioeconomics. Long-term flood forecasting is crucial for sustainable flood risk management, necessitating the development of accurate and efficient prediction models. This study aims to demonstrate the application of machine learning models in long-term flood risk prediction on the downstream watershed of the Ottawa River in Ontario, Canada. Specifically, it investigates the use of Support Vector Machine (SVM), Artificial Neural Network (ANN), and Extreme Learning Machine (ELM) models for long-term flood forecasting. An additional objective is to assess the impact of various variables on flood risk assessment, utilizing the Pearson method to compare the correlation of inputs like precipitation, rainfall, snow, temperature, wind speed, and humidity with the output water level index. The performance of the three applied models for flood risk forecasting was evaluated. Results indicate that the ELM model outperforms the others, achieving a higher accuracy in both training and testing phases. The ELM model yielded very high correlation coefficients (R) of 0.860 and 0.901; low Root Mean Square Error (RMSE) values of 0.417 and 0.374; and Mean Absolute Error (MAE) values of 0.523 and 0.463 for the training and testing phases, respectively. In addition, a sensitivity analysis of the best-calibrated ELM model revealed a significant dependency on the humidity parameter. The findings underscore the potential of machine learning models, particularly the ELM, in enhancing long-term flood forecasting accuracy. This research contributes to the growing body of knowledge on machine learning applications in natural disaster risk assessment and offers valuable insights for effective flood risk management.

Water scarcity is a global issue, and with the world population projected to reach 9-10 billion by the year 2050 and changing climate, it will become more serious. The food supply would also be adversely affected by the shortage of water. Ensuring food security for both present and future generations depends on sustainable water management and advancing agricultural productivity through technologies that optimize water use amidst climate change. Therefore, there is an urgent need to improve crop production and water use efficiency, especially in arid and semi-arid regions. In Pakistan's Sindh province, water scarcity presents a significant challenge to agriculture, emphasizing the need for the sustainable management of water resources. The drip irrigation system can save approximately 70-80% of irrigation water compared to traditional methods; however, its acceptance is low in the province despite government interventions to address water scarcity issues and promote sustainable food production. This study explores farmers' perceptions and the factors influencing the non-adoption of drip irrigation. Understanding these factors will guide strategies to enhance the acceptance of drip irrigation, thereby improving agricultural production and water conservation. The adoption of drip irrigation by farmers was influenced by factors such as education level, technical knowledge, labor demands, access to extension services, and availability of irrigation water sources. It was observed that establishing technical backups, raising farmers' awareness of water's value, and shifting their preferences from short-term to long-term gains are essential for efficient and sustainable use of available water resources. The findings of this study will significantly contribute to the capacity building of farmers' organizations and extension services, promoting farmer-to-farmer learning and enhancing sustainable agricultural practices.

Abstract:

Fly ash, a coal combustion by-product, can be converted into zeolites with enhanced adsorption capacity for removing toxic pollutants. This study modified fly ash-derived zeolites with amino propyl imidazole, creating imidazolium-based zeolites for loading Fe₃O₄ NPs. The resulting magnetic film (Fe₃O₄ NPs@zeolite) showed an excellent adsorption of heavy metals and dyes, making it effective for detoxifying industrial wastewater.

Introduction:

Chromium and selenium contamination from industrial and geological sources causes severe health and ecological problems. Chromium, found in wastewater, and selenium, emitted from geothermal and volcanic activities, pose risks such as hair loss and kidney damage. Synthetic zeolites, modified with imidazolium-based ionic liquids and magnetic nanoparticles, offer a promising solution for removing these contaminants from water, providing effective and sustainable water treatment options.

Methods:

NaX-UP zeolites were synthesized from fly ash by mixing with NaOH and heating, followed by sodium aluminate addition and stirring. The product was washed, dried, and modified with 1-(3-aminopropyl) imidazole to form IL-NaX-UP zeolites. Fe₃O₄ NPs were then synthesized on these zeolites, resulting in a magnetic thin film.

Results:

The as-fabricated magnetic films exhibited excellent functionality and durability in the adsorption of Cr(VI), Se(IV), Congo Red, and RhB [1]. The magnetic films are very suitable for practical applications as they can achieve maximum removal performance over a wide pH range. According to the pseudo-second-order kinetic model, the magnetic films have fast adsorption kinetics compared to earlier evidence from the literature.

Conclusion:

The resulting magnetic films, incorporating PSS, PVA, and chitosan, demonstrated an excellent adsorption of Cr(VI), Se(IV), Congo Red, and RhB, and were effective in treating water, creating drinkable water by meeting WHO and EPA standards.

PFASs, often referred to as "forever" chemicals, are widespread in various environments, including soils and aquatic systems, due to their extensive use. Surface waters in several European countries, particularly in marinas and ports with heavy boat traffic, need further investigation as potential sources of contamination. Reliable methods for the extraction and quantification of these emerging compounds are crucial. This study aimed to enhance an existing solid-phase extraction method for analyzing surface water from marinas and ports with varying salinities (2, 9, and 17 PSU).

The objectives were to 1) optimise the solid-phase extraction method, considering matrix salinity effects and cross-contamination; 2) validate the extraction and quantification method for 18 EPA 537.1 PFASs in estuarine surface waters, using Ultra-High-Performance Liquid Chromatography–Quadrupole Time–Of–Flight Tandem Mass Spectrometry, and 3) apply the optimised method to PFAS quantification in three Portuguese marinas.

All the ICH criteria were successfully validated at 9 PSU. The limits of quantification ranged from 117.80 ng/L to 385 ng/L, with the exception of that for PFHpA (645.85 ng/L). The PFAS levels (PFOA, HFPO-DA, PFBS, PFHxS, and PFOS) were relatively low, with a maximum of <0.32 ng/L observed only for PFOA. In Freixo Marina, the total average concentrations were slightly higher (∑PFAS= 1.02 ng/L) compared to those found in Cais da Ribeira Port (∑PFAS= 0.94 ng/L) and Afurada Marina (∑PFAS= 0.81 ng/L). The PFOS concentrations were below the limit values set by the Environmental Quality Standards (36000 ng/L of PFOS for inland surface water), similar to findings in other Portuguese river studies. This study facilitated the development of a precise and reliable method for the extraction and quantification of PFASs in estuarine surface waters, particularly from marinas. This method can be readily applied to analyzing PFASs in other estuarine samples.

There is a potential significance for improving the monitoring and management of irrigation water needs in semi-arid areas in Mediterranean countries like Greece. This study deals with the estimation of actual evapotranspiration (ET_a) above selected cotton fields located in the semi-arid region of Thessaly, Greece, for the growing season of 2022. The described methodology estimates daily crop evapotranspiration values using a combination of Sentinel-2 and Sentinel-3 satellite images and meteorological data derived from the Weather Research and Forecast (WRF) model. The methodology consists of seventeen separate steps for the estimation of energy parameters and the final estimation of actual daily evapotranspiration values at a 20 × 20 m spatial resolution. The basic idea of the process is based on the Two-Source Energy Balance (TSEB) methodology. The Sen-ET SNAP graphical user interface developed by the European Space Agency is used to estimate ET_a, with the use of Sentinel 2 and Sentinel 3 satellite data. One of the innovations of the study is the use of the WRF model instead of the initially proposed ERA-5 for the retrieval of the relevant meteorological data. Another innovation is the computation of the regional adjustment factor, which leads to realistic values for daily ET_a. The results are very promising; however, this study still needs validation through in situ experiments.