This study evaluated the microbiological quality of raw cow milk from commercial dairy farms in western Turkey over a 3-year period (2023-2025) as part of an environmental monitoring framework. A total of 1800 samples were collected from mechanized farms in the major milk-producing districts of İzmir and analyzed for total colony count (TCC), somatic cell count (SCC), coliforms, Escherichia coli, Staphylococcus aureus, and Salmonella spp. A considerable proportion of samples exceeded regulatory limits, with 64% (1152/1800) and 22% (396/1800) non-compliance for TCC and SCC, respectively. Coliforms and E. coli were detected in 69% (1242/1800) and 52% (936/1800) of samples, while S. aureus was identified in 15%; Salmonella sp. was not detected. Seasonal differences were evaluated using the Kruskal-Wallis test (p < 0.05). Significant seasonal variation was observed for most parameters: SCC peaked in summer, coliforms and E. coli in spring, and S. aureus in winter, whereas TCC showed no significant seasonal trend. The observed microbial patterns indicate contamination pathways influenced by environmental conditions and farm-level hygiene practices. These findings suggest that microbiological indicators in raw milk may provide useful information on hygiene conditions and environmental influences within dairy production systems. Overall, the results highlight persistent hygiene challenges and emphasize the importance of continuous monitoring for environmental risk assessment and improved farm management. This study provides a large-scale 3-year evaluation of microbiological quality trends in raw milk and contributes valuable evidence for environmental monitoring and dairy hygiene management.

Predicting fecal indicator bacteria (FIB) to protect public health at Chicago beaches remains a significant challenge due to complex, site-specific environmental dynamics. This study presents a machine learning pipeline utilizing site-specific random forest regressor models to predict log-transformed enterococci concentrations. The models integrate meteorological, hydrodynamic, and temporal-lag features. A hybrid evaluation framework is implemented; models are trained for regression but evaluated on their ability to classify bacterial exceedances at a 320 CCE/100 mL threshold, chosen as a conservative value given data sparsity at the Chicago Park District's Beach Action Value of 1000 CCE/100 mL, which is used to issue swim advisories at these recreational beaches. This is achieved by dynamically identifying an optimal F1 score threshold for each model's raw count predictions, linking regression performance to classification utility. Model interpretability was a primary focus, employing SHAP (SHapley Additive exPlanations) for each beach and in aggregate. This analysis provided critical insights into the distinct drivers at each location. For the Chicago beaches, the models identified hydrodynamic factors, specifically maximum wave height and maximum dominant wave period, as the most significant hydrometeorological predictors of enterococci concentrations, highlighting the importance of nearshore physical processes in this region. The resulting pipeline provides an interpretable, validated, and site-specific forecasting system capable of identifying high-risk contamination events with moderate accuracy but struggling with sensitivity.

Fish occupy a central position in aquatic ecosystems and serve as important bioindicators for environmental monitoring, as well as powerful translational models for understanding toxic mechanisms conserved across higher vertebrates. In recent years, omics techniques have proven to be powerful tools to address complex environmental questions that conventional toxicology methods cannot answer. Despite this potential, a critical translational gap remains between molecular findings and their use in ecological risk assessment frameworks. This review critically synthesizes advances across omics techniques including epigenomics, transcriptomics, metabolomics and proteomics and their integration. Special emphasis is placed on methodological considerations and practical aspects of these techniques in fish environmental toxicology and environmental monitoring. Evidence from single-omics studies suggests conserved biomarker signatures across species while characterizing complex phenomena like non-monotonic dose-response relationships, mixture toxicity and transgenerational and stereoselective effects with implications for population level monitoring. Multi-omics studies, especially those involving triple omics, further enhance mechanistic resolution by reconstructing adverse outcome pathways. We further evaluate using case studies when additional molecular layers provide critical insight and when they offer limited advantage, a strategic distinction with direct implications in environmental monitoring programmes. Finally, current limitations and future directions that will ultimately bridge the translational gap and hold promise for advancing mechanistic ecotoxicology and predictive environmental monitoring are discussed.

Mountain headwater streams are highly sensitive freshwater ecosystems where hydromorphological gradients strongly influence habitat structure and biological communities, highlighting the need for integrated ecological assessment. This study uses Israfil Creek (Denizli, Türkiye) as a model snowmelt-fed mountain headwater to investigate how hydromorphological gradients and metric sensitivity structure ecological patterns. A total of 55 macroinvertebrate taxa were identified, with Chironomidae as the most diverse group. Community composition showed a clear longitudinal gradient, with cold-adapted taxa (e.g., Diamesa spp.) dominating upstream and more tolerant taxa (e.g., Chironomus riparius and Gyraulus albus) increasing downstream. Substrate composition shifted from lithal dominance in headwaters to finer sediments and particulate organic matter downstream. Saprobic indices indicated predominantly xenosaprobic-oligosaprobic conditions upstream, with increasing mesosaprobic influence downstream. Although Biological Monitoring Working Party (BMWP) scores consistently indicated high ecological quality, Ephemeroptera, Plecoptera, and Trichoptera (EPT) percentages declined longitudinally, revealing differences in metric sensitivity. Hydroclimatic analysis highlighted strong elevational control on snow persistence and pronounced precipitation seasonality. Overall, ecological patterns were governed primarily by hydromorphological gradients rather than conventional water quality signals, and reliance on single metrics may obscure subtle ecological transitions. These findings emphasize the importance of integrated, multimetric approaches for assessing climatically sensitive mountain stream ecosystems.

Assessment of environmental pollution in the Arctic is becoming increasingly important in the context of active industrial development and global climate change. In this study, soils and bottom sediments from the northern regions of Western and Central Siberia were analyzed for persistent organic pollutants including polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs). Total contaminant concentrations were quantified and spatial trends in the relative distribution of individual PAH and PCB groups were identified across Arctic subregions defined by the CAFF working group. Potential pollution sources were evaluated using non-metric multidimensional scaling (nMDS) and ANOSIM. Furthermore, the specific characteristics of lake bottom sediment pollution under the influence of active oil and gas production were examined. Total PAH concentrations in background soils and sediments ranged from 0.3 to 112.6 µg/kg dry weight, exhibiting a significant northward increasing gradient, heavily dominated by low-molecular-weight (2-3 ring) compounds. In contrast, total PCB concentrations remained uniformly low, ranging from 0.2 to 9.0 µg/kg dry weight regardless of the subregion. The nMDS ordination demonstrated a pronounced decrease in the diversity of PAH compositions with increasing latitude and proximity to nearest industrial center, reflecting the combined effects of temperature-dependent atmospheric fractionation and regional emissions. Compositional fingerprinting confirmed that the background PCB pollution was predominantly driven by the historical regional footprint of Soviet "Sovol" technical mixtures. Compared to pristine background baselines, lake sediments within active oil and gas fields exhibited an increase in both the total content and the relative abundance of heavy PAHs. In contrast, local hydrocarbon extraction activities did not influence either the total loads or the homologous profiles of PCBs, leaving the stabilized regional historical background unaltered.

Oceanic islands are strongly affected by marine debris transported across long distances, highlighting the transboundary nature of plastic pollution and the challenges it poses for marine governance. While identifying the sources of most marine debris remains difficult, fisheries-related items can act as traceable indicators of origin and responsibility. This study presents a detailed inventory of plastic debris from lobster fisheries in eastern North America recovered in the Azores archipelago (NE Atlantic). A total of 1577 items were recorded, mainly lobster escape vents and trap tags. Analysis showed that most trap tags originated from the USA (63%), mainly from the state of Maine (76%), whereas the remaining tags were from Canada. Tags dated from 1983 to 2023, indicating that some debris had drifted in the North Atlantic for over three decades. The average time between tag registration and collection was 12 ± 7 years for US tags. These findings demonstrate how remote islands are affected by transboundary plastics and underscore the challenges faced by management authorities of island nations. The results support the need for coordinated international policies, improved gear marking and reporting systems, and preventive measures targeting lobster trap fisheries to reduce long-term plastic leakage into the marine environment.

Escalating environmental pollution, particularly from persistent organic pollutants, dyes, pharmaceuticals, and other recalcitrant contaminants in wastewater and air, demands innovative, efficient, and sustainable remediation technologies. Nanomaterials-based photocatalysis is a solar-driven advanced oxidation process that generates reactive electron-hole pairs that initiate redox reactions for pollutant degradation. However, this conventional method relies heavily on resource-intensive, time-consuming trial-and-error experimentation. This approach struggles with the complex, non-linear interplay among nanomaterial properties, operational parameters, and environmental conditions. Machine learning (ML) and artificial intelligence (AI) are emerging as transformative tools to overcome these barriers. By leveraging large experimental and computational datasets, ML models enable accurate prediction of photocatalytic degradation efficiency, identification of key performance-influencing factors via feature importance analysis, and rational design of optimized photocatalysts. This review synthesizes recent advances (primarily from 2020 to 2026) in integrating ML/AI with photocatalytic nanomaterials for various environmental pollutant degradation. The review found that boosting-based ensemble algorithms and hybrid machine learning models possess the highest predictive performance in water and air pollutant degradations. They also predict degradation rate constants and concentrations. This is due to their generalization capability for the non-linear complex photocatalytic process. However, the scarcity and inconsistency of datasets, as well as the difficulty in identifying the input variables that may result in black-box problems, remain major challenges.

A molecularly imprinted polymer thin film (MIP) on a polyethylene terephthalate (PET) substrate for the selective extraction and solid-state fluorescence detection of dibenzothiophene (DBT) as a marker of oil spills in marine environments was developed. The MIP thin films were produced via UV-initiated polymerization of a polymeric mixture, giving a mechanically robust thin film without the necessity to modify the substrate surface. This solid-state fluorometric technique eliminated the need for extraction by providing a direct measurement of fluorescence from the solid phase, reducing sample pre-treatment requirements. Linear responses were observed over a concentration range of 5.0 to 80.0 µg L⁻1 (R2 = 0.9906). Furthermore, a detection limit of 1.74 µg L⁻1 (n = 15) was established. Acceptable selectivity towards DBT was shown by the MIP thin films in comparison to structurally similar aromatic compounds such as naphthalene and indole, which resulted in minimal interference with the signal. The reliability of the thin-film was further proven when the film was applied to untreated seawater samples, resulting in negligible matrix effects and quantitative results. In addition, the flexibility of the PET substrate will allow an easy adaptation to portable sensing units. This new MIP thin-film with rapid fluorescent detection offers a highly sensitive, selective and user-friendly tool for the early detection and assessment of oil spills in marine environments.

Freshwater ecosystems are increasingly threatened by persistent organic pollutants, particularly organochlorine pesticides (OCPs), due to their persistence, bioaccumulation potential, and associated ecological and human health risks. This study investigated the occurrence, distribution, and human health risks of OCPs in water and sediments from Apa Dam Lake (Türkiye). Water and sediment samples were collected from three shoreline stations in April, July, and October 2024 and analyzed for 28 target OCPs using GC-ECD. Human health risks were evaluated through oral ingestion and dermal exposure pathways. Physicochemical parameters indicated good water quality during the study period. OCP concentrations in water were low and remained below regulatory limits, whereas sediments showed higher concentrations and detection frequencies, particularly for HCH isomers and DDT derivatives. Although average sediment concentrations remained generally low, HCH isomers and Σheptachlor epoxide exceeded sediment guideline values. Isodrin exhibited the highest concentrations among the detected compounds and was the major contributor to sediment contamination. Health risk assessment revealed negligible non-carcinogenic risks, with hazard quotient (HQ) and hazard index (HI) values below 1. Oral carcinogenic risks remained within acceptable limits (ILCR: 10[-7]-10[-6]), whereas dermal exposure produced higher carcinogenic risk estimates (up to 10[-][3]), mainly associated with DDT-related compounds. Overall, OCP contamination in Apa Dam Lake was low compared with many aquatic ecosystems. However, the occurrence of OCP residues in sediments, exceedance of sediment guideline values for some OCPs, and potential dermal carcinogenic risks highlight the need for continuous monitoring to support effective OCP management in freshwater ecosystems.

Microplastics (MPs) are persistent and ubiquitous contaminants in aquatic ecosystems, yet their interactions with submerged aquatic plants remain poorly understood. While MP-induced phytotoxicity has been extensively investigated in terrestrial plants, quantitative evidence for MP uptake and internal accumulation in submerged species is still limited. In this study, we investigated the phytotoxicity and accumulation patterns of fluorescent microplastics (FMPs) in two submerged aquatic plants, Bacopa lanigera and Rotala indica, using fluorescence spectroscopy. Plants were exposed to FMPs of two particle sizes (50 nm and 1 µm) across three exposure concentrations (0.001%, 0.01%, and 0.05%). Plant growth, chlorophyll content, fluorescence emission, and FMP accumulation were systematically evaluated. Our results demonstrated clear size- and concentration-dependent responses. Smaller particles (50 nm) showed significantly higher uptake and induced stronger phytotoxic effects than 1 µm particles, with pronounced growth inhibition and chlorophyll reduction observed at the highest concentration (0.05%). Fluorescence-based analysis enabled quantitative estimation of both surface-associated and internalized FMPs within plant tissues. Maximum surface accumulation reached 207 ppm, while internal (cross-sectional) accumulation reached up to 75 ppm, regardless of plant species. Under the respective experimental conditions, B. lanigera exhibited higher estimated FMP accumulation, whereas R. indica showed greater growth inhibition. These findings provide quantitative evidence of microplastic uptake and internal accumulation in submerged aquatic plants and highlight particle size as a critical determinant of phytotoxicity. Moreover, this study establishes a fluorescence-based methodological framework for estimating microplastic concentrations in aquatic plant tissues, contributing to improved ecological risk assessment of microplastics in freshwater ecosystems.

In urban regions of Europe, road traffic noise ranks as the second most significant environmental stressor, following air pollution. This study measures urban road traffic noise using a noise map that is validated by in situ noise measurements, and it predicts a future scenario for increased traffic in 2030 by altering the traffic composition and overall vehicle count whilst evaluating the noise exposure at building façade points. The investigation used the noise mapping software IMMI and employed the CNOSSOS-EU method to assess road traffic noise. There are strong connections between the actual noise measurements and the model results, with an average difference of 1.09 dB(A) on the roadside, whilst on the receptor side it was 0.81 dB(A). Urban transport development provides the basic data to predict the noise levels affecting the designated area. Based on the 35% increase expected in average day and night traffic flow in 2030, the results indicate an additional 307.14% of people will be exposed to noise levels between 55 and 60 dB(A) throughout the day (Lday), and an additional 450.00% of people will be exposed to noise levels between 50 and 55 dB(A) at night (Lnight). By 2030, the continual increase in urban traffic, driven by population growth, will result in significantly heightened noise levels in densely populated areas. Furthermore, average day and night noise levels in high-traffic zones are anticipated to exceed 55 dB(A) without strategic urban planning and stringent enforcement of noise laws, which would exceed WHO health safety standards.

Accurate and efficient monitoring of suspended sediment concentration (SSC) is essential for the sustainable management of fluvial environments, reservoir operations, and sediment regulation systems. Conventional measurement techniques, such as manual sampling, are often constrained by high operational costs, extended processing times, and limited applicability to real-time monitoring. In response to these limitations, vision-based approaches have been proposed as viable alternatives; however, their performance is frequently compromised by suboptimal feature extraction and high sensitivity to background interference. This study introduces a novel and lightweight classification framework, termed Mobile Vision Transformer Convolutional Block Attention Module (MobileViT-CBAM), which combines the computational efficiency of the MobileViT architecture with the enhanced feature refinement capabilities of the Convolutional Block Attention Module (CBAM). The integration is designed to improve feature representation and mitigate background noise, thereby enhancing classification robustness. The dataset employed for model development was constructed using high-resolution imagery acquired from a mobile sediment-water circulation system and was subsequently partitioned into training (49%), validation (21%), and testing (30%) subsets. Experimental results indicate that the proposed model achieves superior classification accuracy (98.28%) compared to conventional architectures such as ResNet-34 (97.93%) and GoogleNet (97.20%). Furthermore, it demonstrates strong performance across multiple evaluation metrics, including average precision (95.81%), recall (95.52%), specificity (99.72%), and F1 score (97.26%). With a compact model size of only 19.2 MB, MobileViT-CBAM is well-suited for efficient SSC monitoring and shows promise for further validation in hydrological and environmental management contexts.

River floodplains are dynamic ecosystems that trap and store pollutants transported through riverine processes, atmospheric deposition, and adjacent land use. Their role as sediment and nutrient retention zones also promotes the accumulation of persistent organic pollutants and (potentially) toxic elements. Amphibians, whose eggs and larvae develop directly in aquatic environments, are highly sensitive to such pollution and are therefore widely used as bioindicators of freshwater ecosystem health. This study provides a preliminary assessment of 16 priority polycyclic aromatic hydrocarbons (PAHs) and toxic and potentially toxic elements (arsenic [As], selenium [Se], cadmium [Cd], lead [Pb], mercury [Hg], and chromium [Cr]) in embryos of the European green frogs (Pelophylax esculentus complex) collected from the UNESCO-protected floodplain area of Drava River in continental Croatia. Several PAHs were detected in all embryo samples, including naphthalene, acenaphthene, acenaphthylene, fluorene, phenanthrene, fluoranthene, and pyrene, with concentrations ranging 0.012-23.22 µg/kg. When grouped by molecular weight, low-molecular-weight PAHs dominated the overall pollution profile, whereas high-molecular-weight compounds were detected sporadically. Diagnostic PAH ratios suggested mixed pollution sources, with patterns consistent with both petrogenic and pyrogenic inputs. In contrast, most analysed elements (As, Cd, Pb, and Hg) remained below limit of detection (LOD) in all samples. Only Se and Cr were quantified, with concentrations ranging 12.52-17.41 µg/kg and 10.35-12.00 µg/kg, respectively. Overall, the results indicate that amphibian embryos in the floodplain area of the Drava River are exposed to detectable levels of PAHs, while element concentrations appear low. These findings provide initial insight into pollution exposure in early amphibian life stages and highlight the need for further monitoring and expanded ecological risk assessments in floodplain ecosystems.

Microplastic pollution in agricultural soils poses growing environmental risks, yet the retention behavior of different polymer types across contrasting soil textures remains poorly understood. This study compared the retention of polypropylene (PP) and polyvinyl chloride (PVC) microplastics (< 50 μm) in sand, clay, and silt loam soils using a single initial microplastic concentration under a standardized 72 h batch contact condition. Retention was quantified by solution depletion, in which the microplastic mass removed from the aqueous phase was normalized to oven-dry soil mass to obtain the retained amount at 72 h (qₑ). A soil-free control confirmed acceptable procedural recovery (~ 95%). The retained amount at 72 h differed significantly among the six microplastic-soil combinations (one-way ANOVA, p = 0.0001), ranging from 157.14 to 585.71 mg/kg. PVC in silt loam showed the highest retention (585.71 mg/kg), whereas PP in silt loam showed the lowest (157.14 mg/kg), indicating that retention was governed by the combination of polymer type and soil texture rather than by any single soil property. Pearson's correlation analysis did not identify a statistically significant association between retention and organic matter, cation exchange capacity, or clay content (all p > 0.5, n = 6). Scanning electron microscopy provided visual evidence of microplastic attachment to soil surfaces. These findings provide a comparative basis for microplastic retention behavior in agricultural soils and highlight the need for future multi-concentration adsorption isotherm studies to establish mechanistic property-retention relationships.

The herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) is widely used in agricultural systems, raising concerns about its potential genotoxic effects on non-target organisms. This cross-sectional observational field study evaluated nuclear abnormalities in exfoliated buccal mucosa cells from 36 grazing cattle maintained on a farm where 2,4-D was routinely used in Goiás, Brazil, and investigated their relationship with physiological variables, including rectal temperature, body weight, and body condition score (BCS). A total of 1,000 cells per animal (36,000 cells) were analyzed to quantify micronuclei and other nuclear abnormalities. Statistical analyses included the Shapiro-Wilk test, Mann-Whitney test, rank-biserial correlation, Spearman correlation, Benjamini-Hochberg correction for multiple comparisons, and multivariable Poisson regression with robust standard errors. Overall, low frequencies of micronuclei and other nuclear abnormalities were observed. Although nominal associations were identified in the unadjusted analyses, none remained statistically significant after Benjamini-Hochberg correction for multiple comparisons. In the multivariable Poisson regression model, body condition score remained inversely associated with micronucleus frequency (incidence rate ratio = 0.274; 95% confidence interval: 0.128-0.587; p = < 0.001). These findings suggest that physiological status may contribute to variation in cytological biomarkers under field conditions. Because individual exposure to 2,4-D was not directly measured and no untreated control group was included, the findings should be interpreted as exploratory. This study contributes to the use of cattle as sentinel organisms in environmental biomonitoring and reinforces the need for future studies incorporating direct exposure assessment and complementary biomarkers within the One Health framework.