Hydrogeological suitability of Coahuila, Northeast Mexico: An integrated bibliometric, GIS, and water quality assessment

Environmental aquatic chemistry in the Research Center for Eco-Environmental Sciences: Efforts for cleaner water.

Establishment of an innovative machine learning-driven drinking water quality assessment model with health considerations.

Quality and safety assessment of bottled water in Jordan markets

Making Waves: Advancing risk profiling of emerging water contaminants at low concentrations.

Linking water quality assessment to source apportionment with machine learning-assisted WQI, PMF, and SOM: A case study of the Jinma River basin

Compact fluorescence sensor with silicon photomultiplier and neural network enhancement for real-time total organic carbon monitoring in water.

Groundwater is an important freshwater resource around the world which supports drinking water supply, agriculture, and industry. Many communities, particularly in low- and middle-income countries, rely on groundwater because it is more stable than surface water. This study investigated the elemental composition of groundwater in Badagry Division, Lagos, Nigeria, to assess water quality and potential health risks. Twelve groundwater samples were randomly collected from major settlements, covering both coastal and inland areas. Twenty elemental components were analyzed, including essential minerals and potentially toxic elements, using Atomic Absorption Spectrophotometry (AAS). Findings revealed that groundwater in Badagry Division has varied quantities of elements, some of which exceed safe levels. Out of the 20 elements examined, 65% met World Health Organization (WHO) and Standard Organization of Nigeria (SON) criteria, while 35% exceeded the allowable levels. Lead, arsenic, cadmium, manganese, sodium, and gallium were all discovered above permissible levels, indicating possible health hazards. Calcium, magnesium, and potassium were all within permissible levels. These exceedances raise serious public health concerns, particularly regarding long-term exposure to heavy metals. The findings underscore the urgent need for groundwater treatment, regular monitoring, and awareness campaigns in Badagry Division. The study provides baseline data, which is important for public health and water policy interventions in the region.

ABSTRACT Water pollution threatens river ecosystems and agriculture, as poor-quality irrigation water drives salinity, sodicity, alkalinity, and toxicity. These impacts reduce soil fertility, impair infiltration, and lower agricultural productivity. This study assessed irrigation water quality in the Kuttiyadi River basin, Kerala, India, using an integrated framework combining hydrochemical characterization, fuzzy logic-based multi-criteria decision-making, and machine learning prediction. Seasonal parameters were benchmarked against IS 11624:2019 standards. A Fuzzy Irrigation Water Quality Index (FIWQI) was built using Fuzzy-AHP, while an Extreme Gradient Boosting (XGBoost) regression model was trained on eight indicators to predict irrigation suitability. This combination of fuzzy logic with advanced ML is a significant contribution, enabling both interpretability and high predictive power. Findings showed generally favourable irrigation conditions, though Piper diagrams revealed seasonal hydrochemical shifts. The XGBoost model achieved strong accuracy (R2 = 0.98 for Training and 0.84 for Testing). The hybrid fuzzy-ML approach is scalable, adaptive, and supports real-time irrigation management, offering policymakers a reliable tool for sustainable river basin agriculture.

Rivers are encountering numerous challenges from increasing anthropogenic threats globally. This study aimed to examine how the water quality differs spatio-temporally across the rivers of Birbhum district, West Bengal, Indiaand identify primary contributing factors to the differences. Field surveys were conducted across three seasons in fifteen sites of five major rivers of the district. Several physico-chemical parameters were measured to assess water quality and pollution status by employing the Weighted Arithmetic Water Quality Index (WAWQI) and the Comprehensive Pollution Index (CPI). Both descriptive and inferential statistics were utilised for data interpretation. The linear correlation among fifteen parameters was determined using the Pearson correlation analysis. Further two-way hierarchical cluster analysis (HCA) grouped the sampling sites and parameters based on their similarity. Principal component analysis (PCA) revealed the highest contributing parameters. The MANOVA was conducted to identify the effect of independent variables such as locations and season on the dependent physico-chemical variables. Locations near urban areas and tourist spots exhibited higher WAWQI and CPI values in "very poor" category. Most deteriorated water quality was found in the Ajoy river with the highest WQI [104.36 ± 22.04] and CPI [1.70 ± 0.19]. Furthermore, Inverse Distance Weighting (IDW) method was employed for visualising the spatial and temporal variations of the water quality and pollution level. The findings showed the sites affected by agricultural runoff, sewage disposal, and mining activities have increased solids and nutrient concentration. Overall, these outcomes may provideessential baseline data and recommendations for reducing water pollution and will contribute to several policy making and resource management.

Hydrochemical Characteristics, Water Quality, and Health Risk Assessment of Groundwater in Zhanjiang Area, Guangdong Province, China

An assessment and prediction system for the quality of public water networks was developed, using Timișoara, Romania, as a case study. This was implemented on a Google Firebase cloud storage system and comprised twelve ML algorithms applied to test samples for drinkability and used in predictions of upcoming samples. The system compares 17 water quality parameters to the World Health Organization and public reports of Timișoara drinking water standards for 804 samples. The system provides real-time data storage, drinkability prediction for the reservoir water system, and rule-based critical water recommendations for elementary treatment in samples. The most accurate and best-calibrated against random forest, gradient boosting, and Logistic Regression algorithms was the decision tree algorithm of the ML models. The experimental findings also determine the regions of the worst and best water quality and propose respective treatment. In contrast to previous research and structures, the paper demonstrates an approved stable solution for smart water monitoring, correlating practical deployment with sophisticated data-based conclusions. The results contribute to enhancing public health, enhancing water management measures, and upscaling the system for larger-scale applications.

This study examined the influence of physicochemical parameters on the Water Quality Index percentage (WQI%) of groundwater and selected surface water sources in Rivers State, Nigeria. The research was conducted as a follow-up to earlier water quality assessments carried out in selected cities within the state, where significant variations in WQI% values were observed depending on whether all water quality parameters or only heavy metal parameters were considered. This observation prompted further investigation to better understand the individual and collective contributions of each parameter to the overall WQI outcome. Water samples were collected from groundwater sources and selected rivers across different locations in Rivers State, and laboratory analyses were carried out around March 2022. The parameters analyzed included pH, temperature, electrical conductivity, turbidity, total dissolved solids (TDS), hardness, chloride, nitrate, phosphate, biological oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), and heavy metals such as lead (Pb), mercury (Hg), cadmium (Cd), copper (Cu), chromium (Cr), and manganese (Mn), as well as microbiological indicators including Escherichia coli and total coliforms. The Weighted Arithmetic Water Quality Index (WAWQI) method was used to compute WQI% values under two conditions: first, by considering all analyzed parameters, and second, by excluding parameters with zero or negligible values (≤0.0001). The comparison of results highlights the sensitivity of WQI% to parameter selection and underscores the importance of comprehensive parameter inclusion in water quality assessment.

A deep learning model based on multi-scale self-attention mechanism and 3D EEM fluorescence spectroscopy for water pollution source apportionment: Emphasis on EEM regional feature analysis.

The rapid development of the textile industry has led to the discharge of large amounts of dyes into aquatic environments, such as rivers and lakes. Long-term exposure to these substances causes many adverse health effects. Therefore, compact, highly sensitive sensors that enable rapid, on-site assessment of water quality have received significant attention. High-performance SERS substrates were successfully fabricated by optimizing and controlling the thickness of the shell of core-shell Au@Ag NPs by using both experimental and FDTD calculations. Results on the individual and simultaneous detection of dyes demonstrate the potential of Au@Ag NPs as substrates for environmental applications, where dyes exist in complex forms. Detection limits for the three organic substances, rhodamine B, crystal violet, and methyl orange, were 1.89 × 10-12, 6.57 × 10-13, and 1.08 × 10-9 M, respectively, demonstrating a significant improvement in sensitivity compared to single Au and Ag NPs. At the same time, tests in Han River water also demonstrated the high applicability of the SERS Au@Ag NP substrate in real samples. This is one of the advances in the development of SERS-based sensors for environmental applications. For the first time, the core-shell Au@Ag NPs were evaluated for their capabilities, including simultaneous detection and sensing in real water samples. This is of great significance in the application of this technique beyond the laboratory scale.

Irrigation water quality is a critical factor of sustainable agricultural development in arid and semi-arid regions such as Jordan. This study investigates how irrigation water quality impacts the economics of wheat and tomato farming in the Jordan Valley. The Inverse Distance Weighting (IDW) method was used for mapping physicochemical characteristics of irrigation water. We quantified how spatial variations in the Irrigation Water Quality Index (IWQI) directly influence agricultural performance by integrating crop yield and net profit calculations with IWQI. Correlation analysis and comparative yield–profit assessments were conducted across six major agricultural zones. The findings showed that Al-Kafrain and Sharhabil regions had significantly greater yields and substantially higher net profits particularly for tomatoes compared to the King Talal area. The Irrigation Water Quality Index (IWQI) showed a strong positive correlation with yield and profits with coefficients for all parameters exceeding 0.89. The results showed a significant profitability difference between regions, exceeding 200%, demonstrating that irrigation water quality is a key staple in the northwest part of Jordan Valley’s economic outcomes. It was revealed that improving irrigation water quality and aligning suitable crop choices with Jordanian water conditions are essential for enhancing agricultural profitability in arid and semi-arid environments.

Mukura Forest is a biodiversity hotspot within the Albertine Rift essential for biodiversity conservation, climate regulation, and provision of water resources. Despite its importance, a comprehensive biodiversity and water quality assessment was lacking. This study surveyed plants, birds, mammals, amphibians, reptiles, terrestrial arthropods, butterflies, water macroinvertebrates, diatoms and water quality. Water macroinvertebrates were collected by using the kick-nets and hand searching, while diatoms were sampled by substrate scraping. Further, water physicochemical properties were measured in situ using HQ40d Multimeter. Terrestrial arthropods were sampled through hand collection, sweep nets and pitfall traps, while butterflies were sampled using the sweep-net and baited traps. Birds were recorded via point counts and opportunistic observations; reptiles and amphibians were sampled through active searching and visual encounter surveys; mammals were trapped using Sherman live traps while vegetation was sampled using circular plots for woody species and nested quadrats for herbaceous plants. Collected specimens were identified using identification keys to the family and to species levels where possible. Results have indicated a total of 1 523 individuals of water macroinvertebrates and 25 diatom species. Results have also indicated 4 044 individuals of arthropods including 24 butterfly species. Avian surveys documented 124 species, with 12 Albertine Rift endemics and the critically endangered Hooded Vulture. Amphibians and reptiles yielded 12 and five species, respectively. Identified small mammals included 12 species, with three new records for Mukura. The floristic assessment identified 255 species, with two newly recorded. These findings underscore the significance of Mukura for biodiversity conservation and water quality. There is a need to identify collected specimens to specie level, make continuous study in Mukura and other tropical forests of Rwanda.

Perfluorooctanoic acid (PFOA) is an emerging environmental pollutant due to its high bioaccumulation, toxicity, environmental persistence, and widespread presence. Its detection is crucial for assessing water quality safety. In this study, a molecularly imprinted photoelectrochemical (MIP-PEC) sensor based on a Z-scheme heterojunction of covalent organic framework-confined graphitic carbon nitride quantum dots (CNQDs@CTP) was developed for the highly sensitive and selective detection of PFOA in water. First, the novel CNQDs@CTP composite was prepared via a self-templating method, which featured a unique hollow structure. The hollow structure and Z-scheme heterojunction significantly improved light absorption and carrier separation efficiency, with an electron lifetime reaching 6.72 × 10-2 s and high incident photon-to-electron conversion efficiency (IPCE). Furthermore, when combined with molecularly imprinted technology, PFOA-specific recognition sites were constructed on the electrode surface, yielding an imprint factor of 9.3. Under optimized conditions, the sensors had a wide detection range (1.00 × 10-11 to 5.00 × 10-6 mol·L-1) and a low detection limit (5.50 × 10-12 mol·L-1). The water sample detection results were highly consistent with the LC-MS/MS results. The sensors combined high sensitivity, low cost, and ease of operation, providing an innovative solution for the accurate and sensitive detection of PFOA in water.

ABSTRACT Water quality assessment is vital for the protection of human health and environmental ecosystems. Water quality is dynamic due to environmental changes, necessitating the need for frequent assessments. Mindu Reservoir is the main source of freshwater supply (providing 80% of freshwater demand) in Morogoro urban and peri-urban areas. This study aims to perform spatial–temporal assessment of water quality parameters to aid conservation operations in Mindu Reservoir. The study has used fortnight interval in in-situ data collection from November 2024 to April 2025 for Turbidity, TSS, EC and pH at 10 pre-established monitoring points. Laboratory procedures involved exploration of numerical values of these parameters, considering variations with depth. Results revealed high TSS (13.083–41.667 mg/L) signifying possible soil loss from the catchment, high Turbidity (33.47–79.87 NTU), low salinity (EC 0–0.25 mS/cm) and classifies Mindu Reservoir as alkaline (pH 7.4–9.55). The studied trends have created a baseline aiding current and future monitoring of water quality parameters.

ABSTRACT This study assessed water quality in Costa Rica’s Arenal Tempisque Irrigation District (ATID) from 2017 to 2023, focusing on its agricultural importance and water resource challenges. Analyses of physicochemical parameters and 83 pesticides in dams, waterways, and wells revealed exceedances of national water quality standards for irrigation and consumption, particularly for biochemical oxygen demand, dissolved oxygen, ammonium, nitrate, and boron. Additionally, 22 pesticides, including herbicides (terbutryn, ametryn) and fungicides (carbendazim, tebuconazole), were detected, with some surpassing international guidelines. While the Dutch Index suggested water quality ranged from good to very poor, it was insufficient for a comprehensive evaluation. The lack of event-based sampling limited the assessment of precipitation impacts on pollutant transport. Future studies should incorporate event-driven sampling during and after heavy rains to better understand water quality dynamics, particularly under extreme weather conditions expected to intensify with climate change.