Spatial-multivariate modelling of seagrass ecological quality index (SEQI) in Central to Eastern Indonesia.

Improved water quality assessment and prediction for small watersheds in human settlements of the Chengdu plain, southwestern China.

Coupling stable isotopes and hydrochemical parameters to elucidate the linkages between hydrological connectivity and water quality in an urban river-lake system.

Bio-optical water quality trends and back-reef benthic community structure in southwestern Puerto Rico.

Hydro-environmental dynamics of Kaptai Lake using satellite derived biophysical metrics and an ensemble Machine Learning Framework.

Modelling nutrient loads in data-scarce large catchments using spatially regularized ensemble calibration.

Macrobrachium nipponense is favored by Chinese farmers due to its advantages such as stronger adaptability, fewer diseases and investments, better economic benefits and is eco-friendliness. However, investigations regarding M. nipponense culture remain limited to laboratory aquarium tanks. There are no established guidelines on optimal stocking density for M. nipponense related to commercial scale farming practices in earthen ponds. In this study, the effects of stocking density on growth performance, water quality, immune indices and the economic benefits of M. nipponense reared in earthen ponds were evaluated. Juvenile prawns (0.22 ± 0.03 g) were stocked at four densities: D90: 90 individuals (inds)/m², D135: 135 inds/m², D180: 180 inds/m², and D225: 225 inds/m². The results demonstrate that growth performance of M. nipponense in low densities was higher than that in high densities in the early stages, while in the later growth stages, no significant differences were found across all groups. Neither the aquatic environment nor prawn antioxidant capacity was significantly impacted across all groups, as they underwent significant changes along with changing of the seasons. Taking into account the above results and the total yield, the yield and proportion of large-sized prawns, net profit and return on investment, the final recommended stocking density is 1.8 million inds/ha (D180 density). The results of this study will provide guidance for the commercial farming of M. nipponense and give valuable insights for promoting sustainable development. • Low density promotes early growth of Macrobrachium nipponense but final weight converges across densities. • Stocking density did not significantly affect water quality or prawn antioxidant capacity, though they varied seasonally. • High-density ponds show lower microbial diversity yet harbor more nitrogen-cycling bacteria. • Optimal stocking density is 1.8 million individuals/ha, based on growth, yield, and economics.

Mining operations with local geogenic processes and other anthropogenic activities significantly alter quality of natural water resources systems. In complex geological settings these interactions influence contaminant transport behavior leading to shifts in hydrogeochemical signatures of (sub)-surface waters. Hence, a methodological framework is crucial to coherently link these geogenic and anthropogenic signatures with contamination sources and evaluate associated health risks in mining regions. The hydrogeochemical processes were initially studied using Gibbs and Piper plots, along with mineral saturation indices to establish baseline conditions in limestone mining environments. Thereafter, Entropy Water Quality Index (EWQI) was applied to delineate contamination hotspots. Non-carcinogenic risks from potentially toxic elements (PTEs) were evaluated to validate effects of contaminated water exposure. Finally, Principal Component Analysis (PCA) was employed to distinguish natural geochemical signatures from anthropogenic inputs. The results showed that (sub)-surface water quality was controlled by geogenic processes, particularly carbonate and silicate weathering, with additional inputs from anthropogenic activities. The piper plot classified most samples into CaMgHCO3 facies (53%) reflecting dissolution of limestone and dolomite minerals. The mineral saturation indices highlighted thermodynamically favorable conditions for carbonate dissolution. EWQI values exceeding 150 indicated contamination hotspots characterized by extremely poor water quality. The health risk assessment validated these findings, with Hazard Index (HI) indicating significant non-carcinogenic risks, particularly for children. HI values exceeded the threshold of 1 in both groundwater (1.70-5.66) and surface water (1.60-17.65) samples across seasons. PCA further differentiated geogenic controls from anthropogenic sources such as mining and agriculture. This study establishes a holistic hydrogeochemical framework for quantifying both geogenic and anthropogenic processes and associated health and environmental risks in mining regions globally.

Distribution patterns and ecological networks of pathogenic microorganisms in a tropical urban river: insights from the Mirongo River, Tanzania.

Exploring seasonal coastal water quality parameter interactions through the self-organizing map neural network in eastern Mediterranean: Lebanon as a case study.

China's Environmental Disaster Reduction Satellite 2 (HJ-2) recently launched charge-coupled device (CCD) sensors, designed to monitor environmental and ecological changes. This study marks the first application of HJ-2A/B CCD imagery for quantifying chlorophyll-a (Chl-a) in lakes with diverse optical properties and trophic statuses, highlighting its potential for comprehensive water quality monitoring. Chl-a, a key indicator of algae biomass and nitrogen levels, was evaluated using empirical algorithms (EMs), semi-analytical algorithms (e.g. quasi-analytical algorithms (QAAs) and data-driven machine learning (ML) algorithms. Results showed that EMs struggled with lake-specific characteristics, while QAA demonstrated reliability (R² > 0.75, RPD > 2). ML algorithms, leveraging their data-driven adaptability, outperformed both the EM and the QAA, with CatBoost (CB) achieving the highest accuracy (R² = 0.97, RMSE = 6.69 μg/L, MAE = 4.78 μg/L, RPD = 5.16). CB-generated spatial Chl-a distribution maps highlighted HJ-2A/B CCD's significant potential for practical water quality monitoring across lakes with varying optical and trophic conditions. This study not only validates HJ-2A/B CCD's utility in Chl-a quantification but also underscores the superiority of ML approaches in handling complex, data-driven challenges. The findings provide a robust foundation for future large-scale and long-term applications of HJ-2A/B CCD imagery, offering valuable insights for environmental managers and researchers.

A baseline assessment of Enterococcus in low-contamination surface waters and their antibiotic resistance.

Hierarchical excitation-emission matrix (EEM) decomposition: Relaxed implementation of Kasha's rule to enhance resolution.

Access to safe drinking water remains a pressing global challenge, with waterborne diseases causing over 505,000 deaths annually. Total Coliform (TC) and Fecal Coliform (FC) bacteria are key indicators of water quality and safety, but traditional testing methods are time-consuming, expensive, and require specialized laboratory equipment and expertise. Recently, machine learning approaches have emerged as promising solutions for faster and more cost-effective bacterial assessments. Existing studies have addressed various aspects of microbiological water quality monitoring, including bacterial detection and concentration estimation, often focusing on specific indicators, water body types, or geographic contexts. This study proposes a novel data-driven modeling approach to enable the quantification of FC and TC bacteria across diverse types of water bodies from other easily measurable parameters. Over eight years, more than 5000 water samples were collected from 139 different locations in Andhra Pradesh, India. From the collected data, bacterial quantification models were trained. Random Forest proved to be the best-performing algorithm, with R-squared values of 0.81 and 0.74 for FCs and TCs, respectively. Furthermore, a complete digital twin system is proposed to streamline the application of the models. Thus, this study complements existing research by advancing data-driven coliform assessment and contributes to the United Nations Sustainable Development Goal 6: Clean Water and Sanitation. • A data-driven approach to the quantification of fecal and total coliform bacteria. • A comparative evaluation of six machine learning algorithms for bacterial estimation. • A SHapley Additive exPlanations (SHAP)-based feature importance analysis. • A digital twin system to streamline the application of the learning models. • Contributions to the UN Sustainable Development Goal 6: Clean Water and Sanitation.

Restoration of the shallow lake Kralingse Plas, the Netherlands, with emphasis on lanthanum-modified bentonite.

Super-intensive aquaculture offers a viable strategy to maximize shrimp production while minimizing environmental impact. This study developed and evaluated a membrane-integrated recirculating aquaculture system (M-RAS) for the cultivation of whiteleg shrimp (Litopenaeus vannamei). The system incorporated ultrafiltration (UF) membranes to pretreat seawater and remove suspended solids and pathogens prior to pond entry. Four cultivation ponds (20 m2 each) were stocked at a density of 400 shrimp/m2 and operated over a 100-day cycle. Nutrient residues from uneaten feed and shrimp waste were treated using a biofiltration unit comprising solid removal, denitrification, nitrification, and sludge separation. Water quality was maintained through routine siphoning, partial water exchange, periodic harvesting, and addition of Ca(OH)2, sugar, and nitrifying bacteria. Key water quality parameters, including DO, salinity, turbidity, pH, and nitrogen compounds, remained within acceptable ranges, although temperature (25.7–28.4 °C) was slightly below optimal. Cultivation performance metrics were favorable, with a survival rate of 86.85% ± 2.10%, daily weight gain of 0.25 ± 0.03 g/day, final average body weight of 25.09 ± 0.71 g, productivity of 4.71 ± 0.31 kg/m2, and a feed conversion ratio of 1.57 ± 0.07. These results demonstrate the potential of M-RAS as an effective and sustainable system for intensive shrimp farming.

Cyanobacteria, among the most ancient and versatile microorganisms on Earth, thrive across different aquatic ecosystems, ranging from freshwater to marine environments. In addition to being important producers of structurally diverse secondary metabolites, these photosynthetic prokaryotes are crucial to the global cycling of carbon and nitrogen. With their wide range of biological activities, such as antibacterial, antiviral, anticancer, and anti-inflammatory properties, cyanobacterial metabolites are attractive options for use in biotechnology and medicine. In addition, some cyanometabolites have toxic properties that affect aquatic ecosystems and present problems for human health and water quality. This dual significance for ecology and biomedicine emphasizes how crucial it is to do systematic research on cyanobacterial metabolites. The chemical diversity, biological roles, and biosynthetic origins of cyanobacterial secondary metabolites are summarized in this review, with a focus on their translational potential and ecological responsibilities. Innovative approaches for accelerating metabolite discovery and characterization are also covered, including recent developments in metabolite profiling, genome mining, and artificial intelligence (AI)-assisted biosynthetic gene cluster prediction. The present review integrate ecological, biochemical, and computational perspectives emphasizes the cyanobacteria is ongoing significance as sources of bioactive chemicals for environmental sustainability, pharmaceutical research, and biotechnology.

Integrating non-thermal plasma technology into aquaculture and fisheries: A review of its potential for enhancing fish health, water quality, and post-harvest practices

ClO₂-induced VBNC state in Enterococcus faecalis poses a health risk in drinking water systems.

Macroinvertebrate-based assessments in Brazilian freshwaters: A systematic review