Ecological risks and recent inputs of banned and current-use pesticides in surface water and sediment from Malawi's tobacco-growing region.

Pharmaceutical footprint in the river ecosystem: Suspect screening approach with high resolution mass spectrometry.

Modulating electrode area in microbial fuel cell enhanced floating beds: synergistic effects on bioelectricity generation and perfluorooctanoic acid degradation.

Microplastics (MPs) are persistent pollutants increasingly found in estuarine environments. However, particles smaller than 300 µm are often underestimated because coarse-mesh screens (≥ 300 µm) exhibit physical selectivity, despite evidence that smaller size fractions predominate in the distribution of MPs in the environment. This study aims to evaluate the abundance, size distribution, and morphology of MPs in surface waters from two mangrove channels on the Ajuruteua Peninsula: Furo do Taici (estuarine) and Furo Grande (marine). When considering particles < 300 µm under rigorous contamination control procedures, we evaluated how hydrodynamic conditions and the selectivity of conventional nets influence the detection of MPs in estuarine systems rich in organic matter. Concentrations were higher in Furo do Taici (2.6 items/m 3 ) than in Furo Grande (0.9 items/m 3 ), with a notable predominance of particles < 300 µm, likely retained due to local hydrodynamics and organic matter. Fibers predominated in both locations (~70% at the Furo do Taici and ~80% at the Furo Grande), exceeding 90% in the > 300 µm fraction, while blue and transparent particles were the most frequent in all size classes. These findings emphasize the need to include sub-300 µm particles in monitoring programs, as their exclusion can lead to an underestimation of pollution levels. The study provides valuable baseline data to improve sampling strategies in tropical estuarine systems.

Monitoring surface water in agricultural landscapes is a key requirement for irrigation management, leak detection, and sustainable water use. Although remote sensing literature extensively addresses water detection, most studies focus on large-scale Surface Water Mapping (SWM) in heterogeneous landscapes, where extensive water bodies such as lakes, rivers, or coastal zones occupy a substantial portion of the scene. In contrast, fine-scale water detection in agricultural environments typically involves small, fragmented, and highly imbalanced targets embedded within predominantly vegetated or cultivated areas. As a result, methods and performance metrics developed for large-scale mapping cannot be directly transferred to fine-scale agricultural scenarios without adaptation. This paper analyses 49 peer-reviewed studies published between 2020 and 2025 that address water detection in agricultural and rural contexts using multispectral, thermal, and Synthetic Aperture Radar (SAR) imagery from satellite and Unmanned Aerial Vehicles (UAV) platforms. Rather than providing a purely descriptive review, the work examines how methodological choices — ranging from spectral indices and decision trees to machine learning, deep learning, and foundation models — interact with sensor characteristics, processing levels, and evaluation metrics. The analysis highlights systematic trade-offs among model complexity, data availability, and robustness, identifies recurrent limitations in multiple accuracy metrics in scenarios where land pixels vastly outnumber water pixels, and synthesizes the practical implications of spectral band selection (VNIR, SWIR, TIR) and platform resolution. A central contribution of this review is the demonstration that, in agricultural water detection, preprocessing choices, sensor characteristics, and the use of appropriate evaluation metrics often have a greater influence on reported performance than the complexity of the detection algorithm itself. Based on these findings, the paper offers comparative insights and methodological recommendations to guide the selection and validation of water-detection approaches in agricultural remote sensing applications. • Review (2020–2025) of fine-scale water detection using remote sensing imagery. • Comparative synthesis of spectral, machine and deep learning models under real-world constraints. • F1-Score and IoU outperform Overall Accuracy for robust evaluation under class im- balance. • Analysis of preprocessing, sharpening, augmentation, and multi-sensor fusion impacts. • Identifies gaps in spatial refinement and hybrid segmentation–decision frameworks.

Over the last decades, the use of boron stable isotopes has steadily increased in the Earth Science literature, including hydrological studies. This paper presents “IsoBorDat”, a global open-access database containing boron stable isotope ratios (expressed as permil δ 11 B) measured in various hydrologic reservoirs such as groundwaters, surface waters, and rainwaters. The repository includes B contamination-related samples that have been (i) characterized in their isotopic signature as raw material or (ii) identified as potential sources of pollution in hydrological systems. IsoBorDat provides information on B content and B isotope ratios, water type, analytical methods, sampling time, and location. Data are available in open, structured .ods format, adhering to the FAIR principles. The database is hosted by the National Research Council of Italy (CNR) and is openly accessible at https://isobordat.cnr.it . We also present a quantitative and qualitative explorative approach applied to the statistical distribution of boron concentrations and isotope compositions across the four water types (meteoric, river, lake, and groundwater) and diverse water contamination sources of IsoBorDat. The statistical analysis reveals general distribution patterns and highlights differences among reservoirs, offering insights into geochemical processes influencing boron behaviour. This work supports the integrated use of isotopic data to better understand natural and anthropogenic influences on hydrological systems. IsoBorDat will be integrated within the ISOTOPE VRE, a Virtual Research Environment dedicated to environmental isotopes. • IsoBorDat is a global open-access database of boron concentrations and δ 11 B in waters • The dataset covers meteoric, river, lake, groundwater and contaminated waters • B and δ 11 B distributions show non-linear and multimodal behaviour • IsoBorDat supports isotope-based interpretation of hydrological systems

It is essential to sustain water quality to support environmental sustainability and public health, particularly in high-Andean watersheds, which are sensitive to hydrological variability. The present study is an integrated assessment of surface water quality conducted in the Ilave District of southern Peru, using the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI). Surface water quality was assessed based on the evaluation of physicochemical parameters, microbiological indicators, and concentrations of selected trace metals during contrasting dry and wet seasonal conditions and the application of the components of the CCME-WQI to assess the quality of the overall surface water. Results from this study indicate significant spatial and temporal variability in surface water quality; however, most monitoring locations were classified as having good to excellent water quality. Lower CCME-WQI index values were primarily attributed to localized microbiological contamination and higher concentrations of selected trace metals during low-flow conditions. In general, the CCME-WQI successfully integrated multi-parameter surface water quality data and identified localized degradation processes; therefore, there is a need for targeted monitoring and science-based management of water resources in areas that are subject to increasing anthropogenic pressures in high-Andean environments.

Changes in salinity indicators in surface water during the operation of a pump station in the Turawa Reservoir catchment (southern Poland)

Climatic changes have altered natural organic matter (NOM) characteristics in raw water, intensifying challenges for operation of water treatment systems, particularly the coagulation process. This study explored the measurement of water charge characteristics, in parallel with a range of water quality parameters over an extensive year-long sampling campaign. Results showed variable charge load (15–197 μEq/L) in the raw water throughout the year, attributed to dynamic humic-organic carbon concentrations (1.1–8.3 mg/L) coinciding with increased average rainfall (2.4 mm/day to 6.1 mm/day). Coagulation tests showed a stronger linear relationship between charge load and coagulant demand than for dissolved organic carbon (DOC) concentration (R 2 = 0.80 vs 0.65). This was because the charge characteristics of the water did not change in a consistent way with the NOM concentration, meaning that bulk indicators of organic matter concentration (DOC and UV 254 ) were not always effective for predicting the appropriate coagulant dose. Post-coagulation zeta potential analysis showed minimised DOC residuals at a zeta potential between −10 and 5 mV, with maximum removal achieved when the average zeta potential approached 0 mV. Analysis of the zeta potential distribution provided a new understanding of ideal operating conditions for coagulation. Median zeta potential of −1.69 mV resulted in a condition where at least 68% of all particles were within a range that was effective for charge neutralisation. The study highlights the importance of refining coagulation control methods to address climate-influenced NOM changes, recommending the adoption of UV 254 coupled with zeta potential as feed-forward and feedback systems, respectively, to provide optimal DOC removal by coagulation. • First comprehensive and systematic measurement of charge in surface water • Strong relationship shown between charge load and coagulant demand • Analysis of zeta potential distributions identified new understanding for coagulation control • Surrogates for organic matter can only be used when water quality characteristics are stable • Importance shown of refining coagulation control systems to account for water quality change

Meteoric water δ18O across the Dinarides: Role of topography, air-mass mixing, and precipitation seasonality

• A comprehensive framework integrating hydrological modeling and remotely sensed crop data is proposed for agricultural water management in data-scarce regions. • Improving the performance of WRF-Hydro model in simulation of snow accumulation and melting by optimizing land surface model parameterization. • Improved crop type prediction by assembling multiple machine-learning algorithms using NDVI time-series data. • Machine learning algorithms have been ensembled to predict crop type maps, improve prediction accuracy and estimate irrigation water needs. Water resources are essential for human activities, with no substitutes. Agriculture consumes a significant portion of water to sustain food production. This study proposes a framework for agricultural water management by integrating hydrological modeling and remotely sensed crop type data in a data-scarce region. It focuses on the Amu River Basin (ARB), the largest watershed in northeastern Afghanistan, accounting for 57% of the region’s surface water. The WRF-Hydro stand-alone model was used to simulate daily discharge for three rivers from 2014 to 2019 and was calibrated and validated with the Global Land Data Assimilation System version 2. Statistical indicators were used to assess the model performance. The overall performance results for three rivers show a correlation coefficient (R) of 0.85–0.42, Nash-Sutcliffe Efficiency (NSE) 0.52 to −8.64, Killing-Gupta Efficiency (KGE) 0.74 to −0.56, and coefficient of determination (R 2 ) of 0.73–0.17. Three machine learning (ML) algorithms, namely, random forest, support vector machine, and gradient boosting, were ensembled via a maximum voting classifier to predict crop type maps for the study period. The model was trained using Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data from the Aqua and Terra satellites. A high-resolution 2020 crop type map from United Nation Food and Agriculture Organization (UNFAO) was used for training. The model predicted the five major crop types from 2014 to 2019 across eight elevation zones. Then irrigation water requirements (IWR) were estimated for major crops via the UNFAO Penman‒Monteith method. The estimated IWRs were combined with the spatially and temporally explicit crop maps derived from the multi-model ensemble to quantify irrigation water demand and assess its balance with available water under different management scenarios. This study addresses water management challenges in data-scarce regions, improving the performance of the WRF-Hydro model in snowmelt-influenced watersheds and enhancing ML model accuracy through ensemble techniques. The findings of the current study could provide a deeper understanding of the challenges and possible solutions in arid and semiarid climates in developing countries.

Aquatic ecosystems are increasingly affected by chemical contamination from anthropogenic activities, yet the fate of pesticides and pharmaceutical residues within organisms remains poorly understood. Parasites can also significantly influence pollutant dynamics, through a high capacity for bioaccumulation. In this study, we quantified 60 emerging contaminants, including pesticides, antibiotics, and other pharmaceuticals, in surface waters, fish tissues (gills, liver, muscle), and intestinal acanthocephalan parasites of wild European chubs (Squalius cephalus) across multiple rivers of the Seine river system, France. Forty compounds were detected in water samples, whereas 28, 18, 17, and 8 were detected in parasites, gills, liver, and muscle tissues, respectively. Six compounds, mainly pharmaceuticals and one pesticide (diflufenican), were detected in more than 50 % of samples in at least one biological compartment. Pollutant detection in water was associated with increased occurrence and levels in fish tissue, although tissue levels did not directly correlate with waterborne levels. Gills exhibited the highest pollutant diversity and levels among tissues. Habitat type influenced the number of pollutants detected in water, with higher values in agricultural areas, but did not directly affect tissue concentrations. Acanthocephalans accumulated several pharmaceuticals, especially paracetamol and fluoxetine, at high levels, supporting their bioaccumulation potential. Moreover, parasite biomass influenced host contamination in a tissue-specific manner, decreasing pollutant levels in gills but increasing them in muscle. Fish size positively, and body condition negatively, correlated with pollutant load. These findings highlight the importance of considering both host physiology and parasitism when assessing contaminant fate in freshwater ecosystems.

Experimental and computational studies of transformation of sulfachloropyridazine during water chloramination: Kinetics, mechanism and toxicity.

The use of antipsychotics is constantly increasing worldwide due to their employment in the treatment of bipolar disorder, schizophrenia, autism, depression and more. These compounds are not efficiently removed by the current wastewater treatment technologies and are consequently recognised as factors of environmental concerns. Unravelling their chemical transformations in the water matrix is therefore of high importance. This study investigates the photodegradation of tiapride (TP), an atypical neuroleptic agent, focusing on its reactivity by direct photolysis. The results unveiled a remarkable inhibitory effect of oxygen on the photodegradation, highlighting how variations in dissolved oxygen levels across systems (wastewater treatment plants and surface waters) may shape TP persistence. The involvement of the triplet excited state of the molecule in the process was demonstrated through fluorescence spectroscopy and laser flash photolysis. The structures of the main transformation products under various oxygenation conditions, identified by LC-HRMS with an ESI source, were proposed together with the transformation pathways of the compound.

SWOT performance in monitoring water level of high-mountain lakes on the Tibetan Plateau

Dynamics and drivers of aerobic methane oxidation in water of a shallow eutrophic lake.

Rouen, a densely populated city, is subject to anthropogenic pressure on its aquatic systems, resulting from both diffuse urban and industrial activities. In September 2019, a major fire at a chemical plant released 9500 tons of chemicals, generating a massive smoke plume. To assess its medium- and long-term impact on water bodies, sampling campaigns were carried out at three surface waters and two groundwater sites. Between 2021 and 2023, water and suspended particulate matter (SPM) were collected using passive samplers. Targeted analyses were performed to determine the concentrations and exported fluxes of selected regulated contaminants, including polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and dioxins/furans. In parallel, untargeted analyses were conducted using chromatography coupled with high-resolution mass spectrometry (GC-HRMS, LC-HRMS) and ultra-high-resolution Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS). Chromatogram reprocessing enabled the annotation of dozens of organic compounds, which were compared with an in-house database. The targeted analyses revealed a strong anthropogenic influence on surface and groundwater quality. The surface rivers located near the city center and the plant area were the most impacted, showing relatively elevated specific particulate PAH fluxes and the highest dioxin concentrations in SPM. But no direct causal link could be established between the targeted contaminants and the fire incident. In contrast, untargeted analyses identified non-regulated organic molecules potentially associated with the fire, in both dissolved and particulate phases, suggesting that the accident left a persistent chemical signature in the aquatic environment. SPM collected at sites closest to the plant exhibited the strongest presence of these potential fire markers.

Insights into microbial carbon sequestration mechanisms in the Eastern Arabian Sea using metagenomic analysis.

Multi-matrix assessment of organic UV-filters in a major freshwater recreational ecosystem in Central Europe: Spatio-temporal distribution and environmental risk assessment.

This study presents findings from the opportunistic cruise, which performed microplastic and chemical contaminant sampling in surface waters, sediments, and ice in a sector of the western Arctic and the southwestern part of the Antarctic Peninsula. Microplastics were detected in 100% of the samples. Floating microplastic densities (100-5000μm) reached up to 314,251 items/km2 in Antarctica and 63,593 items/km2 in the Arctic. The smallest particles (100-300μm) dominated in southwestern Antarctic Peninsula (97%) where the fibers (80%) and fragments (19%) were the main components. In the eastern Arctic, the two size classes (100-300μm and 300-1000μm) were more evenly distributed (58% and 40% respectively) and polymer diversity. Sediment microplastic concentrations were higher in the Arctic (up to 470 items/kg) compared to southwestern Antarctic Peninsula (maximum 399 items/kg). OrganoPhosphate Esters and PhthAlate Esters were also measured for the first time in southwestern Antarctic Peninsula seawater (35.18±18.31ng/L and 72.68±39.71ng/L, respectively) and ice (50.44±24.79ng/L and 16.72±11.46ng/L, respectively). This study demonstrates the utility of cruise ship-based sampling for monitoring remote regions and it contributes critical baseline data for global microplastic assessments.