Groundwater is a strategic resource for irrigated agriculture in arid regions, where severe water scarcity and high evaporative demand constrain sustainable crop production. This study evaluates the suitability of groundwater in the Béni Ounif syncline, southwestern Algeria, for agricultural irrigation using 46 groundwater samples collected from the shallow and Continental Intercalaire (CI) aquifers. Irrigation quality was assessed through electrical conductivity (EC), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), Kelly’s ratio (KR), permeability index (PI), potential salinity (PS), magnesium adsorption ratio (MAR), and the irrigation water quality index (IWQI). Results indicate low sodium hazard, with all samples classified as excellent according to SAR. IWQI classified all CI samples as good, while shallow-aquifer waters were 63.33% good and 36.67% excellent. However, high-salinity conditions occurred in 62.5% of CI and 26.67% of shallow samples, highlighting the need for drainage, leaching, and salt-tolerant crops for sustainable agricultural water management under prevailing local conditions.
Poor monitoring infrastructure and groundwater pollution areamong other elements that have hindered access to safe drinking water in rural India, which is a perennial public health problem. This paper discusses howdigital monitoring devices such as IoT-based sensors, mobile testing systems, and remote sensing solutions can reinforce the provision of water qualityassurance in rural areas using a secondary research methodology. Basedonthe evidence provided by the existing literature, governmental reports andfield-based studies, the paper assesses the current level of compliance withwater quality criteria, the ef iciency of digital technologies in rural conditions in India, the overall adoption rates at the community level, the level of inequality across the regions, and the common technical issues. The evidenceindicatesthat in major Indian states, it is estimated that around 25%of the rural water sources are above the allowable levels of contamination and digital interventions have shown considerable progress in detection speed andmonitoring frequency were implemented successfully. Nevertheless, theundisrupted challenges such as unstable power supply, lack of networkconnectivity, lack of sensor maintenance, and low digital literacy amongcommunities still limit the general adoption and sustainability. Comparisons with the regions also show the existence of profound governance andinfrastructure imbalances between Indian states in the north, the eastern region, and the southern parts. The study finds that the best approach to realizingthefull potential of digital water monitoring lies in integrated interventions interms of technological innovation, institutional support, communityinvolvement, and equitable infrastructure investment, which provides aplausible way of attaining sustainable rural water safety and equity in publichealth in India.
: Monthly river water quality dashboards often have missing station records, which makes reporting and month-to-month comparison difficult. It is not clear whether simple linear interpolation can fill short gaps without creating too much error under time-respecting tests. A station-by-station workflow was applied to 8 stations on the Mula, Mutha, and Mula-Mutha rivers from January 2017 to December 2023. The method used strict bracketing start and end values, no extrapolation beyond observed data, and pseudo-missing masks in 2022 to 2023. On masked test points, linear interpolation achieved an MAE of 0.142 with 95% CI [0.129, 0.156] and improved pooled coverage from 0.944 to 0.973. Accuracy is limited to the numeric-only subset because token-coded and censored coliform values were excluded from error metrics, and findings are limited to the monitored stations and time window. The study specifies an easy to check and verify imputation and labelling protocol to support routine monthly water quality reporting for municipal monitoring teams and WASH practitioners.
: One of the determinants of safe continuous and contamination-resistant water delivery is stable hydraulic pressure that ensures delivery systems meet the WASH standard. The change in pressure caused by fluctuation in demand, occasional supply and mechanical interruptions can reduce the reliability of the services, accelerate the degradation of the infrastructure, and increase the probability of the intrusion of contaminants. An adaptive mechanical pressure stabilization and real-time sensing feedback system of variation-based stiffness modulation working on a controlled laboratory testbed is experimentally tested in line with the characteristics of decentralized WASH conditions of operation. The control is increasingly being dominated by the hydro-mechanical dynamics, adaptive control formulation, and performance measures; pressure variance, settling time, and percentage overshoot. The results of test show approximately 45-60 percent reduction of the variation in pressure, 40-50 percent improvement of the settling time and nearly fifty percent reduction of the dynamic overshoot, compared with control procedure of constant stiffness. These quantitative advantages demonstrate an increase in disturbance rejection, enhancement of hydraulic stability and compliance with WASH service pressure constraints. Low power and lightweight sensing combined with supervisory control increases mechanical flexibility allowing it to be operated scalable when operating in a low resource environment. Adaptive mechanical regulation is considered by the results as a valid path to resilient, contamination-resistant and publicly-trustworthy water distribution systems, and the future projections have been made as a field scale validation, hybrid predictive control and network level coordination of distributed stabilization units.
Rapid urbanization has increased pressure on freshwater resources resulting in inherent vulnerabilities of traditional approaches to water protection which are deeply disciplinary and cannot meet the multifaceted, dynamic character of contemporary urban water systems. The paper introduces an interdisciplinary engineering model of advancing the protection of urban water that involves the harmonious incorporation of civil, environmental, chemical, and data engineering disciplines through the use of technologies into one technological solution. The offered system architecture is proposed to integrate Internet of Things (IoT)-based real-time sensing networks with artificial intelligence and machine learning (AI/ML)-based predictive models and a digital twin model of the urban water infrastructures, forming a cyber-physical platform that can be used in terms of continuous monitoring, early anomaly detection, and efficient management of the resources. Contamination of urban water sources by industrial effluents, domestic wastewater and stormwater runoffs are solved using mathematically constructed pollutant transport models, sensor data fusion algorithms, multi-objective optimization schemes, and the resilience-based risk assessment schemes. An urban case study simulation that illustrates two different metropolitan conditions shows that the integrated framework can detect pollutants with an accuracy of 96.7 percent and it can cut the average system response time of 420 seconds to 18 seconds and minimise the annual operational costs of the system by 58.8 percent, relative to the traditional monitoring methodologies. Environmental impacts have had a minimum environmental impact of 62.1 reduction in the associated carbon emissions and a minimum of 77.2 decrease in the treated water losses. The findings confirm the soundness, generalizability as well as applicability of the framework in a heterogeneous urban setting, to offer practical engineering principles to researchers, practitioners as well as policymakers who aim to bridge the ever-present gap between water-management research and practical field implementations.
