Sustainable aquaculture production critically depends on continuous monitoring of water quality parameters such as pH, temperature, turbidity, and dissolved oxygen (DO). Conventional manual monitoring techniques are labor-intensive and incapable of providing real-time insights, often leading to delayed intervention and economic losses. This paper presents OxyTrack Pro, a low-cost, AI-enabled Internet of Things (IoT) framework designed for real-time water quality monitoring and intelligent fish suitability prediction. The proposed system integrates multi parameter sensing using an ESP32 microcontroller with cloud-based analytics and a Random Forest regression model for predictive analysis. Sensor data are transmitted via Wi-Fi to a cloud platform and visualized through a mobile dashboard. Experimental evaluation on 1,200 labeled samples demonstrates a prediction accuracy of 95.2%, with low latency (2.6–3.1 s) and reliable alert generation. The proposed framework offers an affordable and scalable solution for smart aquaculture management. Keywords— Artificial Intelligence, Aquaculture Monitoring, ESP32, Fish Prediction, Internet of Things, Water Quality

Water sustainability plays a critical role in achieving the Sustainable Development Goals (SDGs), as it influences human well-being, ecosystem integrity, and long-term development pathways. Over the past three decades, a substantial body of research has emerged on water sustainability; however, there remains a limited synthesis of how sustainability has been assessed, how assessment approaches have evolved, and the extent to which they align with the multidimensional intent of the SDGs. This study addresses the gap by combining a systematic review conducted using the PRISMA framework and bibliometric analysis from 1995 to 2025. The results show a marked acceleration in research output after 2015 following the formal adoption of the SDGs, with concentrations in a small number of countries and research hubs. Water sustainability assessment is mainly shaped by technically oriented indicator-based frameworks that emphasise water availability, water quality, and management performance. While these approaches have enabled comparability and methodological consistency, they often provide a partial representation of sustainability with limited integration of governance processes, social equity, cultural contexts, indigenous knowledge, and ecosystem services. The findings highlight the need for assessment approaches that go beyond technical metrics to more integrative and context-sensitive frameworks that can inform policy, support adaptive decisions, and reflect the interconnected nature of sustainable development.

Sea Snot is phenomenon that occurs in waters characterized by the collection of mucus or sludge on the surface of the sea which is an accumulation of various types of microorganisms such as phytoplankton and bacteria. The first occurrence in Indonesia was reported in Bima Bay on April 25, 2022. This study aims to assess the water quality of Bima Bay using Sea Surface Temperature (SST), Dissolved Oxygen (DO), and Salinity parameters derived from Landsat satellite imagery and validated through field surveys. Based on the results of determining the quality status of Bima Bay waters, shows that there is a change in quality status at each point from 2022 to 2023. From a total of 50 sample points, it shows that there are 44% of points that still have lightly polluted status and from good to lightly polluted status there are 18%. This shows that there is still lightly pollution of 62% in the waters of Bima Bay after one year of the sea snot phenomenon. The condition of Bima Bay's waters does not have the status of being moderately or heavily polluted. These findings highlight the need for increased environmental awareness among local communities to prevent further degradation of Bima Bay’s waters.

The presence of waterborne pathogens in drinking water and physiochemical parameters, such as pH and chloride levels, that are outside of the usual range, are serious issues with water quality that can jeopardize safety. Thus, the purpose of this study was to isolate the pathogens present in commercially available sachet water and to determine the effect of pH and chloride content on the antibiotic resistance pattern of the bacteria recovered from the sachet drinking water. Seven different brands of sachet samples of commercial drinking water were purchased from different retail shops in Ota, Ogun State, Nigeria. Biochemical tests and the analytical profile index (API) method were used to identify the bacteria. In addition, antibiotic susceptibility testing was carried out using the Kirby-Bauer method. The Multiple Antibiotic Resistance (MAR) index was calculated for all the isolates, and Spearman’s rank correlation coefficient was used to assess the association between pH, chloride, and MAR index of the isolates recovered from the sachet water. The Analytical Profile Index revealed seven dominant bacteria in all the samples, namely, Enterobacter aerogenes, Citrobacter freundii, Chryseobacterium meningosepticum, Escherichia fergusonii, Edwardsiella hoshinae, Escherichia coli, and Chromobacterium violaceum. In addition, the Multiple Antibiotic Resistance index of 0.2 was observed in 71.4% of the isolates. The pH values and chloride content recorded were 6.28 to 6.80 and 11.36 to 21.30 mg/L, respectively. A non-significant correlation was observed between pH and MAR index (ρ = 0.49, P-value = .269) and chloride and MAR index (ρ = - 0.67, P-value = .098). Thus, the multidrug resistance characteristics of the isolates recovered in this study are most likely inherent in the source water or a result of human contamination during water processing. Hence, stricter procedures and hygiene standards should be followed when processing drinking water in sachets.

Summary The Tietê River is an anthropogenically disturbed urban water body polluted by a combination of untreated domestic sewage releasing (carbon, nitrogen and phosphorus pollution) and diffuse pollution that cross São Paulo State in Southeast of Brazil. Along its course, it presents contrasting sites showing elevated levels of nutrients and contaminants (eutrophic sites) and oligotrophic environments, in both water and sediments. In this study, we investigated how pollution influences the taxonomic and functional diversity of microbial communities in the Tietê River watershed, with the aim of understanding their role in pollutant transformation during downstream transport. Four sampling sites along a pollution gradient—from São Paulo city to a relatively pristine area near the river mouth—were evaluated. Results indicated that diversity differences were primarily linked to water quality, with higher diversity observed in less contaminated sites. Heterotrophic metabolism was more prominent in polluted regions, whereas photoautotrophic and lithotrophic microorganisms were more abundant in clean areas. Additionally, genes associated with the metabolism of aromatic compounds and virulence factors were more prevalent in environments with higher anthropogenic influence, suggesting a functional shift geared toward environmental adaptation and bioremediation. We propose that, in areas with high organic matter concentrations, microbial communities tend to adopt an r-strategy lifestyle, characterized by rapid growth and reproduction, while in oligotrophic, less polluted sites, more competitive k-strategists predominate. Although the following hypothesis was not extensively studied, the lower abundance of genes involved in secondary metabolic synthesis in eutrophic sites suggests that pollution may reduce the availability of novel species or traits relevant for biotechnological applications. Additionally, community shifts appear to be influenced by "conditionally rare taxa," which temporarily alter their activity and abundance in response to environmental constraints, playing a critical role in water self-purification processes. Overall, these findings offer new insights into the environmental factors driving self-purification in the Tietê River and shed light on the ecological mechanisms underpinning river resilience.

Abstract. The presence of pesticides in surface water poses a significant risk to the quality of drinking water resources. A critical challenge in water quality management involves quantifying the export, degradation, and persistence of pesticides at the catchment scale. Compound-specific isotope analysis (CSIA) may help to evaluate the contribution of pesticide biodegradation in topsoil and water, as it is generally unaffected by non-degradative processes such as dilution, sorption, and volatilisation. In this study, multi-scale monitoring with CSIA was combined with a mass balance approach to determine the source apportionment and degradation contribution to the overall dissipation of S-metolachlor, a widely used herbicide, in the Souffel catchment (115 km2) during a corn and sugar beet growing season. WWTPs contributed to 52 ± 18 % of the input mass based on daily discharges. However, S-metolachlor from non-point and point sources could not be clearly distinguished due to similar stable isotope signatures. The mass balance, including topsoil, river water, sediment, and wastewater treatment plant (WWTP) effluent, showed that 99±5 % (x¯± SD) of S-metolachlor applied during the study period was degraded over the five-month growing season. Most degradation occurred in the topsoil, with only 12.3±3.1 % degraded in the river. CSIA-based estimates of S-metolachlor degradation corroborated the mass balance results, indicating that 95±20 % of S-metolachlor was degraded over the growing season. Our results demonstrate that pesticide CSIA, applied from upstream to downstream, enabled robust estimation of pesticide degradation across an entire catchment. We anticipate that CSIA will enhance surface water management by improving the diagnosis of pesticide off-site transport and degradation. This approach can support the development of efficient regulatory strategies aimed at preserving and restoring aquatic ecosystems.

Decoding water quality from sparse light

Coastal and marine systems are governed by fragile water-quality dynamics, where disturbances can trigger harmful algal blooms with significant ecological and societal consequences. These pressures have intensified interest in forecasting systems that can anticipate bloom development and support environmental management. This study presents a systematic review of simulation-based and predictive environmental modeling approaches used for marine forecasting of water quality and harmful algal bloom phenomena. Following PRISMA guidelines, 11,185 records were identified, 127 articles were screened in full text for eligibility, and 40 peer-reviewed studies published between 2015 and 2025 were included and synthesized using a structured extraction framework capturing modeling paradigms, forecast targets, data inputs, spatial and temporal scope, validation practices, operational context, and reported limitations. The reviewed literature indicates the dominance of predictive and hybrid modeling approaches, with forecasting efforts primarily focused on coastal systems and short-term applications. Harmful algal blooms and chlorophyll-a emerge as dominant forecast targets, commonly supported by satellite observations, in situ measurements, and environmental forcing variables. Despite substantial methodological advances, persistent challenges related to data availability and quality, validation rigor, system integration, and operational deployment remain evident across modeling paradigms. Overall, the findings suggest that while marine forecasting models have become increasingly sophisticated, their translation into reliable and operational systems remains uneven, highlighting the need for closer alignment.

Polycyclic aromatic hydrocarbons (PAHs) are organic compounds composed of fused aromatic rings, formed during the incomplete combustion of organic materials such as coal, oil, gas, wood, and biomass. This paper reviewed the impact of PAHs in the Niger-Delta Region in the Southern part of Nigeria and explored the activities that released them into the environment. The primary sources of PAHs in the Niger Delta are: Oil Extraction and Gas Flaring, Artisanal Crude Oil Refining, Agricultural Practices (Bush Burning and Biomass Burning), Vehicle Emissions and Industrial Activities, Waste Disposal and Landfills. The spatiotemporal distribution of PAHs in the Niger Delta reflects complex interactions between emission sources, seasonal weather patterns, and landscape characteristics, creating dynamic contamination gradients across the region. It is observed that PAHs in Nigeria waters, soil, and air are above the recommended permissible limits by different regulatory agencies in the developed countries such as European Union, United Kingdom, Netherland, Sweden, Canada, China and the World Health Organization (WHO), this call for concern. The PAHs pose enormous challenges in the region especially its negative effects on Agricultural practices and food sustainability as most communities dependent on subsistence agriculture, and lack access to soil and water remediation technologies or alternative livelihood options.

Understanding the relationship between air pollution and farmers' subjective satisfaction in ore-agriculture zone is crucial for rural revitalization and improving farmers' well-being in China. However, existing research lacks multi-perspective and multi-time-frame analyses of this relationship. This study develops a three-dimensional satisfaction framework encompassing life, environmental, and government dimensions and adopts a multi-temporal approach covering short-, medium-, and long-term horizons. Using data from air pollution measurements and questionnaire surveys (n = 600) focusing on farmers in the ore-agriculture zone. An ordinary least squares (OLS) method is applied to assess the effect of the air quality index (AQI) on farmers' subjective satisfaction across different time frames in the region. The results indicate the following: (1) Air pollution significantly reduces farmers' subjective satisfaction, encompassing life satisfaction, atmospheric environmental satisfaction, and government satisfaction. (2) The short-term effects of air pollution on farmers' subjective satisfaction are insignificant or weakly negatively significant, while medium-term and long-term exposure to air pollution results in a significant reduction in subjective satisfaction, with the most pronounced negative impact observed during medium-term exposure. (3) Individual characteristics and socio-economic factors show significant heterogeneous effects on subjective satisfaction. Above results further illustrate the extension of environmental perception theory to rural resource-dependent areas. These findings provide constructing a multi-dimensional and multi-time-frame analytical framework to fill the gap of single-perspective or long-term-focused satisfaction research. The government should regard mid-term air pollution control as a core task of rural revitalization; value farmers' subjective evaluations of governance work; and strengthen rural infrastructure investments including housing conditions, water quality, and transportation accessibility to mitigate the negative impacts of air pollution.

Agricultural landscapes sit at the center of two coupled crises: accelerating degradation of soil functions and persistent impairment of surface and groundwater quality [...]

228Ra/226Ra isotopic ratios in groundwater from selected aquifers in Jordan and relationship with geological formations.

Geospatial analysis of surface water and groundwater quality using GIS in Ishaka subcounty, Bushenyi District, Uganda.

Surface water and groundwater suitability assessment for drinking and irrigation in a coal-mining area of southwestern China: EWQI, IWQI, and sensitivity analysis.

Scale effect and landscape thresholds affecting water quality and dissolved organic matter.

Machine learning-driven water quality index prediction in the Dau Tieng reservoir, southern Vietnam, with interpretability via SHapley additive exPlanations: A vision for water quality management strategies.

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

Impact of coal mining activities on the water quality: A case study of Jharia Coalfield, Dhanbad, India.

Network-scale spatiotemporal dynamics and drivers of nitrogen-cycling genes and their microbial hosts in a multi-tributary mountain river.

Pollution characteristics and probabilistic health risk assessment of potentially toxic elements in surface water from a representative lake in cold-arid regions.