Abstract Water quality monitoring plays a critical role in safeguarding human health and environmental sustainability. However, existing machine learning models such as KNN, SVM, and CNN struggle with imbalanced and small-sample datasets, reducing their effectiveness for real-time water quality assessment. To overcome these limitations, this study introduces an innovative hybrid African Vulture Optimization Algorithm–Recurrent Neural Network (AVOA-RNN) framework. The novelty of the approach lies in three aspects: (i) the integration of AVOA with RNN to automatically tune hyper-parameters and select discriminative features, (ii) the incorporation of Synthetic Minority Oversampling Technique (SMOTE) to mitigate class imbalance and enhance minority-class recognition, and (iii) the evaluation on a newly collected Cauvery River water-quality dataset. Experimental results demonstrate that AVOA-RNN achieves 97% classification accuracy, outperforming CNN, LSTM, GA-RNN, and PSO-RNN baselines by 6–15%. These findings highlight the robustness, adaptability, and superior predictive power of the proposed framework for imbalanced water quality datasets.

Assessment of the ecological-health risk and heavy metal pollution in drinking water was carried out in the Enyigba-Ameka Pb–Zn mining region in order to establish present level of heavy metal enrichments. Thirty-five water samples were collected from various source of drinking water in two seasons (dry and wet) and analyzed according to the American Public Health Association (APHA) standard. Data was interpreted using nine indexical models. Water quality index (WQI) values obtained showed that 9%, 20%, and 71% of the samples indicated “moderately polluted water, polluted water, and excessively polluted water” respectively during dry season and 14%, 37%, and 49%, representing “moderately polluted water, polluted water, and excessively polluted water” respectively during wet season. Synthetic pollution index (SPI) values for dry season were 9%, 17%, 48%, and 26% indicating slightly polluted water, moderately polluted water, highly polluted water, and unfit respectively for drinking purposes while for wet season, 6%, 28%, 43%, and 23% representing slightly polluted water, moderately polluted water, highly polluted water, and unfit respectively for drinking purposes. Hazard quotient (HQ) revealed that cadmium (Cd) was the major heavy metal influencing undesirable health conditions in both the adult and children populations in both seasons. Contamination index (CI) classified 35 water samples as low contamination in dry season and 28 in wet season while heavy metal evaluation index (HEI) classified 35 samples as low contamination in both seasons. The study’s findings provide insights into contamination by heavy metals for the protection of human health and water supply in the Enyigba-Ameka Pb–Zn mining region.

The Philippines is recognized as a major contributor to global marine plastic pollution; however, information on microplastic contamination within protected coastal ecosystems remains limited. This study assessed the occurrence of microplastics in surface waters and in two rabbitfish species, Siganus fuscescens and Siganus guttatus, from selected coastal sites within the Siargao Island Protected Landscape and Seascape. Surface water samples (20 L per station) and fish specimens (S. fuscescens, n = 80; S. guttatus, n = 22) were collected and examined. Microplastics in both water samples and fish gastrointestinal tracts were extracted using alkaline digestion followed by density separation. Water samples collected from six stations revealed an average concentration of 1000 particles/m3. Most were fibers (95%) and black in color (32%), ranging from 0.01 to 0.10 mm size (26%). The Kruskal–Wallis’s test indicated significant difference in microplastic abundance across sampling stations (p = 0.01185). Additionally, 80 (78.43%) out of 102 examined fish samples contained microplastics. S. fuscescens contained an average of 3.32 particles individual⁻1, while S. guttatus contained 2.76 particles individual⁻1, with a higher frequency of occurrence observed in S. guttatus (95.45%). Microplastics found on the studied fish species were mostly fibers (98.4%), black in color (38%), and in the 0.11–0.20 mm size range (34.65%). It was also noted that with increasing microplastic size, the numbers found in the gastrointestinal tract of the examined fish species decreases significantly. While there was no significant difference in microplastic abundance between species (p = 0.48585), notable differences were observed across sampling sites for S. fuscescens (p = 0.02848). These findings highlight the need for expanded microplastic research in other areas of Siargao, particularly in assessing the potential environmental and health impacts. There is also a need to conduct several measures such as promoting proper waste disposal, reduce plastic consumption, increase public awareness, and encourage innovative solutions to mitigate plastic contamination on the island.

Water quality in dam reservoirs is a growing global concern due to increasing climate pressures and human impacts. In Morocco’s semiarid regions, dams are essential for water supply but are rarely studied in terms of seasonal water quality dynamics. Few studies have addressed how climatic and geochemical interactions drive seasonal water quality variation in Moroccan reservoirs; this study fills that gap by providing a detailed assessment of Tamalout Dam. Monthly water samples were collected from six stations between January and December 2023. Descriptive statistics, Spearman’s correlation, and principal component analysis (PCA) revealed two dominant processes: geochemical mineralization driven by evaporation and lithology and biological degradation linked to organic matter inputs. Despite seasonal fluctuations, overall water quality remained acceptable with a low risk of eutrophication. However, oxygen depletion in deeper layers during warm periods indicates a potential hypoxia risk. These findings provide a scientific basis for practical water quality monitoring, sustainable reservoir management, and climate-adaptive strategies in semiarid regions.

Natural clinoptilolite zeolite was investigated for potential use as an adsorbent for the removal of Al, Fe and Zn from natural acid mine drainage (AMD). The AMD (pH of 3.01) was polluted water obtained from an abandoned coal mine in South Africa. A batch reactor experiment was set up for the treatment conducted at the mixing ratios of 67:1, 20:1, 14:1, 6.7:1 and 5:1 by weight of AMD to zeolite. The reactors were agitated for 15, 30, 60, 120, 180 and 300 min, then the AMD was analysed. Kinetics of metals removal and the adsorbent’s efficacy, were measured. Absorption ability was simulated using the Langmuir and Freundlich adsorption isotherms. The optimal ratios were 6.7:1 and 5:1 by weight of AMD to zeolite, which increased the pH levels of AMD to the ideal circumneutral values of 6–8 within 15 min of the treatment. Results showed that the natural zeolite effectively removed 82–99% of Al, Fe and Zn from AMD. Both isotherms showed a realistic depiction of the zeolite’s rate and capacity for adsorption of the contaminant metals; however, the Langmuir equation fitting was more suitable than that of the Freundlich isotherm.