Radon (222Rn) in groundwater represents a natural radiological hazard and a potential tracer of subsurface processes. This study investigates the spatial variability and controlling mechanisms of radon in 66 domestic groundwater samples collected from Bien Hoa City, Vietnam, a rapidly urbanizing tropical region. Measured radon concentrations ranged from 0.18 ± 0.12 to 1.36 ± 0.45 Bq/L, with a mean value of 0.44 ± 0.27 Bq/L, well below the guideline limits recommended by the USEPA (11.1 Bq/L), UNSCEAR (40 Bq/L), and WHO (100 Bq/L), significantly below international guideline limits, indicating negligible radiological risk. Multivariate statistical analysis reveals a weak but statistically significant negative correlation between radon concentration and pH (r = - 0.36, p < 0.01), while no meaningful relationships are observed with total dissolved solids (TDS) or electrical conductivity (EC). These results suggest that the measured hydrochemical parameters alone are insufficient to fully explain the observed spatial variability of radon; instead, the observed radon distribution is more consistent with the influence of geological heterogeneity within the study area. Importantly, spatial analysis suggests a transition in the factors controlling radon variability: in near-river environments, radon variability is likely influenced primarily by hydrodynamic processes and salinity-induced degassing, whereas in inland areas, geochemical conditions, particularly pH, appear to play a more important role. This spatial shift suggests the coupled influence of physical and geochemical processes on radon behavior in tropical aquifers. The findings provide baseline data on groundwater radon distribution in Bien Hoa City, support the use of radon as a hydrogeological indicator, and contribute to groundwater quality assessment in rapidly developing regions.

The Ganges River, vital for drinking water, agriculture, fisheries, and socio-religious practices, is increasingly threatened by contaminants, compromising its ecological and biological integrity. Present study was undertaken to identify contaminants in the river water using chemical profiling integrated with bacterial and plant bioassays, complemented by in silico analysis. GC-MS analysis of hexane and chloroform extracts revealed the presence of pharmaceuticals, phenolic substances, siloxanes, hormone-disrupting agents, and antibiotics, including spironolactone, nalmefene, desmethylverapamil, bisphenol B, aminopterin, and minocycline. Solvent extracts of the Ganges water demonstrated concentration-dependent mutagenicity in Ames Salmonella reversion assay, exceeding more than two-fold spontaneous reversion frequency. Chloroform extracts exhibited significant mutagenic potential with strain TA98 compared to TA100, suggesting prevalence of frameshift mutagens in the test samples. DNA repair-defective Escherichia coli K-12 mutants revealed decline in survival after 6 h of exposure; recA and lexA mutants displayed higher sensitivity compared to wild-type counterparts, confirming DNA-damaging potential of the samples. Allium cepa assay demonstrated suppression of mitotic index from 51.63% to 20.62%, along with increased chromosomal abnormalities from 2.33% to 26.17%. In Vigna radiata seeds, germination and seedling growth were reduced, followed by root tip damage and reduced root cell viability as detected by confocal microscopy. Computational docking analysis and 100-ns molecular dynamics simulations revealed stable binding of selected contaminants to molecular targets involved in nucleotide biosynthesis, DNA replication, topological regulation, and cell division. These findings suggested that the bioactive compounds may induce genetic and cellular damage, contributing to the biological effects associated with contaminant mixtures in river water.

In this study, a simple colorimetric method was developed using cupric acetate monohydrate to detect and differentiate arsenic species in groundwater in the Dongargarh-Kotri region, Chhattisgarh, Central India. The method produced precipitate that was distinctly green for As(III) and blue for As(V). The method achieved a detection limit of 5.5 μ g L - 1 in collected groundwater samples and required only one reagent, without any pre- or post-treatment steps. This method also offers a semi-quantitative estimation of the relative proportions of As(III) and As(V). The successful application of this method to contaminated groundwater samples demonstrates its potential as a rapid, low-cost, and field-deployable screening tool for arsenic speciation and monitoring in resource-limited regions.

Information on the spatial distribution of soil organic carbon (SOC) is essential for environmental monitoring and assessment. However, limitations of commonly used environmental covariates and satellite multispectral data constrain the accurate prediction of SOC at large spatial scales. In this study, 647 topsoil (0-20 cm) samples and the corresponding Vis-NIR hyperspectral data were collected from croplands in Northeast China. Machine learning (ML) algorithms, including random forest (RF), extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), were combined with environmental covariates, satellite multispectral covariates, and hyperspectral-informed intermediate variables derived by spatializing the principal components of laboratory-based hyperspectral measurements to predict SOC across croplands in Northeast China. The results showed similar spatial patterns of SOC across the ML models, with high SOC contents in the north and northeast and low contents in the west and southwest. Incorporating multisource satellite data, spectral indices, and moving-window averages of satellite multispectral data improved the prediction performance of SOC, with the R[2] of RF model (R[2] = 0.55) increased by 11%, in comparison with those using satellite multispectral data alone (R[2] = 0.44). While combining environmental covariates and the developed hyperspectral-informed intermediate variables in the RF model yielded the highest R[2] of 0.61. Although environmental covariates were the most important predictors of SOC (e.g., mean annual temperature accounts for 32% of the total variable importance), the developed hyperspectral-informed intermediate variable PC2 accounted for the additional 16% importance of the SOC prediction. These results highlight the importance of incorporating laboratory-based soil hyperspectral information into spatial covariates to improve large-scale SOC mapping.

This study provides a summary of ambient concentrations of 30 commonly analysed perfluoroalkyl substances (PFAS) at 54 sites adjacent to various land use types across the east coast of Queensland, Australia. Sites were sampled for surface water every 2 months for a year (2019-2020). The results showed higher concentrations of PFAS in surface waters adjacent to major city areas (e.g. urban, industrial, residential land use types), and the lowest in surface waters adjacent to remote and regional areas (e.g. national parks, agriculture land use types). Perfluorooctanesulfonic acid (PFOS) was the most reported compound (in 53% of samples), ranging from < 0.1 to 0.4 ng L[-1] in remote areas, < 0.1 to 0.6 ng L[-1] in outer regional areas, < 0.1 to 4.7 ng L[-1] in inner regional areas, and < 0.1 to 37.0 ng L[-1] in major cities. No PFAS were reported at nine of the 54 sites during the six monitoring rounds, and 16 out of 30 PFAS were not reported at any of the sites sampled over the year. Although PFAS (such as PFOS) are described as ubiquitous, the results from this ambient monitoring program indicate that this assumption does not necessarily apply to all ambient surface waters in Queensland.

This study evaluated total mercury (THg) distribution and accumulation in soil, litter, and surface-active soil invertebrates at the Águas Emendadas Ecological Station (ESECAE), a savanna reserve in central Brazil. We assessed whether THg differed between soil and litter; whether soil pH and organic matter influenced soil THg, the degree of THg accumulation in invertebrates relative to soil and litter; whether feeding habits structured epigeic soil fauna according to THg concentrations; and whether biomagnification could be inferred within the local trophic network. THg concentrations were significantly higher in litter (51.7 ± 5.6 ng g[-][1]) than in soil (34.9 ± 13.5 ng g[-][1]; t = 4.402, df = 7, p = 0.0032). Soil organic matter was positively associated with soil THg (R[2] = 0.455, p = 0.039), whereas pH showed no significant relationship. Invertebrate THg concentrations ranged from 0.095 to 970.70 ng g[-][1], reaching values approximately 15 times higher than those in soil or litter. Overall, 83.75% of samples showed bioaccumulation potential, and macroaccumulation predominated relative to both soil (80.38%) and litter (68.53%). Grouping by feeding habits revealed consistent accumulation patterns, indicating trophic-level biomagnification. Predator frequency increased from 9.1% in the lowest THg group to 45.5% in the highest, while phytophagous organisms decreased from 26.1% to 2.3%. Our results underscore the complexity of mercury cycling in terrestrial food webs and highlight the role of ecological traits and environmental parameters in modulating mercury bioavailability and transfer.

Riverine hypoxia, oxygen depletion in flowing waters, poses ecological risk, yet its spatial and temporal dynamics remain poorly characterized. This study presents a novel risk-based spatiotemporal framework for assessing riverine hypoxia across Indian river systems, utilizing 18,976 site-year summary data points (annual minimum, mean, maximum) across 87 sub-basins over 17 years. We conceptualized 'contextual hypoxic risk' as the probability of summary statistics falling below a critical threshold (4.0 mg L[-][1]), distinguishing emerging, intermittent, and perennial hypoxic conditions. Hypoxic risks were estimated under an empirical Bayes (Poisson-Gamma hierarchical framework) to address overdispersion and stabilize standard error. Sub-basin-level aggregation mitigated site selection bias, yielding hydrologically meaningful and analytical units that facilitates policy decision. Of observations, 47% (8914) were emerging hypoxic events, 7% (1328) intermittent, and 1% (190) perennial. At least one severe hypoxic event (DO < 2.0 mg L[-][1]) occurred at approximately 25% of sites-significantly exceeding the global average (12.6%). Spatial analysis revealed pronounced heterogeneity, with hypoxic risk ranging from 0 to 50%, and northwestern and Gangetic sub-basins exhibiting high hypoxic risks and faster intensification. While hypoxic occurrences revealed upward national trend, hypoxic risk showed transient patterns. Space-time interaction analysis indicated statistically significant accelerating risk trajectories-particularly for perennial and intermittent types-across six key sub-basins. By coupling probabilistic risk modelling with spatial smoothing and trajectory steepness diagnostics, this framework quantifies both spatial variability and temporal intensification of riverine hypoxia. The scalable, reproducible framework is applicable to other developing regions. Findings underscore urgent need for adaptive, sub-basin-scale management to prevent progressive establishment of chronic hypoxic regimes in India's river systems.

The European Union Water Framework Directive (WFD) requires member states to assess the ecological status of surface waters using biological quality elements (BQEs). Although the WFD includes intercalibration procedures to harmonise class boundaries, differences between national assessment systems may affect the comparability of ecological status classifications, particularly in transboundary rivers. This study evaluated the consistency of Portuguese and Spanish ecological status assessments in 32 water bodies across Iberian transboundary rivers in the Minho, Lima, Douro, Tagus and Guadiana basins, using data collected between 2019 and 2021. Comparisons included fish, macrophytes, benthic macroinvertebrates and diatoms. Biological assemblages, ecological quality ratios (EQRs) and ecological quality classifications were analysed. Results differed markedly among BQEs. Fish showed no significant differences in assemblage composition or EQR values between countries and a moderate positive correlation in ecological quality classifications. Macroinvertebrates displayed weak compositional separation, no differences in EQR values and a weak, nonsignificant positive correlation between countries. Macrophytes showed significant compositional differences despite similar EQR distributions and a weak, nonsignificant positive correlation in ecological quality classifications. Diatoms showed the strongest divergence, with clear assemblage separation, higher EQR values in Spain and a very weak, nonsignificant negative correlation between countries. Final ecological classifications also showed a weak, nonsignificant positive correlation between countries. These findings show that formal intercalibration alone does not guarantee operational comparability in shared river systems. Cross-border assessment consistency depends strongly on the BQEs considered and on methodological differences between national monitoring systems, highlighting the need for improved harmonisation of monitoring procedures and taxonomic practices.

To investigate the spatiotemporal variability of lead (Pb) in mining environments, 100 topsoil samples were collected from different locations across a semi-arid mining district in western Iran over a 4-month monitoring period, with sampling conducted at different sites in each month. Key explanatory variables, namely, pH, electrical conductivity (EC), soil organic carbon (SOC), zirconium (Zr), and elevation and slope as topographic attributes alongside normalized difference vegetation index (NDVI), were integrated into the Geographic and Temporal Weighted Regression (GTWR) framework. Pb was selected because of its strong association with mining activities, high environmental persistence, and ecological significance. The effectiveness of the GTWR model was evaluated and compared with Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR) and Temporally Weighted Regression (TWR) using multiple statistical evaluation metrics and model-fitting criteria. In addition, the ecological implications of Pb contamination were assessed using the risk quotient (RQ) index. The evaluation metrics demonstrated the superiority of GTWR (R[2] = 0.79) over the other models. The results indicate that Pb distribution was influenced by geological, geochemical, and biological factors, with Zr showing a predominantly spatially positive association, EC exhibiting spatially variable effects, and NDVI and pH showing relatively stable temporal associations with Pb. Furthermore, RQ values exceeded the ecological threshold throughout the study area, indicating widespread potential ecological risk. Integrating GTWR with ecological risk assessment provides a useful framework for characterizing heterogeneous Pb patterns and supporting the identification of priority areas for environmental monitoring and risk management in mining-affected landscapes.

Ground-level ozone (O3) has become a major challenge for air quality improvement in China, but its short-term variability is controlled by complex interactions among pollutant persistence, precursor conditions, and meteorological factors. In this study, hourly air quality observations and ERA5 reanalysis meteorological data for an urban monitoring site in Wuhan during 2019-2025 were integrated to evaluate O3 temporal variability and interpretable machine learning prediction. Three models, namely, deep neural network (DNN), random forest (RF), and extreme gradient boosting (XGBoost), were compared using baseline and lag-enhanced feature sets, and SHapley Additive exPlanations (SHAP) were used to interpret predictive contributions. O3 remained at relatively high levels during 2019-2025, with exceedances of the national grade II standard in 2019, 2023, and 2024, while most primary pollutants generally declined. O3 exhibited a unimodal diurnal cycle with afternoon peaks at 14:00-16:00 and a seasonal pattern of summer > spring > autumn > winter. The enhanced XGBoost model achieved the best short-term prediction performance, with R[2] = 0.9756 and RMSE = 6.35 μg m[-3] on the test set. SHAP analysis showed that the 1-h lagged O3 concentration was the dominant predictor, indicating strong short-term persistence rather than independent chemical forcing. NO2 and lagged NO2 showed negative predictive contributions, consistent with the expected role of NO titration under high-NOₓ conditions, whereas temperature and radiation-related variables showed positive contributions. These results suggest that interpretable machine learning can help identify the statistical conditions associated with O3 variability, but the absence of direct VOC observations means that O3 sensitivity and VOC-related control implications should be further verified using VOC measurements or chemical transport/observation-based modeling.

Plastic pollution raised great environmental concerns due to its persistence and widespread presence. To model ecological impacts, such as ingestion by wildlife, information on the abundance and dimensions of plastic particles is fundamental. Our study examined the relationships between size dimensions (length, area, circumference) of individual particles and the length of particles, their concentration, and cumulative surface area. Plastic particles were collected with trawl nets from upstream and downstream sites in two Dutch rivers in 2023 and 2024. Particle dimensions, including length (defined as the longest axis), surface area, circumference, and the number of particles, were measured. Geometric means of particle concentrations and cumulative surface area concentrations were calculated for equal-sized bins based on length and plotted against particle length on a log-log scale. Reduced major axis and ordinary linear least squares regressions determined the power relationships between size dimensions of individual particles. For length-based bins, we evaluated how concentrations and total surface area co-varied with length classes. Size dimensions were strongly and positively related, indicating differences in shape across size classes. Contrary to previous research, the length of plastic particles was not strongly correlated to their concentration in the Meuse River and varied between sampling locations. The cumulative surface area concentration increased significantly with length. The findings of the study should be viewed with caution, as they were based on two Dutch rivers. Even greater caution is warranted for the relationships between particle length and concentration, and between particle length and cumulative surface area, as they were based solely on measurements in the Meuse.

Contamination by microplastics is an emerging environmental problem that is widely distributed across continental aquatic environments and may cause ecological impacts. This study evaluated the occurrence, concentration, and chemical and morphological characteristics of microplastics in surface waters and sediments from Lake Canaçari, comparing two areas with different land-use patterns within a floodplain system in the Central Amazon. Microplastics were detected in all sampled areas. In surface water, concentrations ranged from 0 (no microplastic particles detected in one sample) to 1,000 microplastics·m[-][3] (405 ± 298 microplastics·m[-][3]), while in sediments, they ranged from 7 to 307 microplastics·kg[-][1] (77 ± 104 microplastics·kg[-][1]). No significant differences in microplastic concentrations were observed between fishing and human-settlement areas. The particles were predominantly blue fibers (< 1,000 μm). Among the spectroscopically analyzed particles, polyethylene was the most frequently identified polymer, while copper phthalocyanine pigments, including Pigment Blue 15 and Pigment Green 7, were also detected by FTIR and/or Raman spectroscopy. Overall, the results suggest that Lake Canaçari may act as a transport pathway, redistribution zone, and retention compartment for microplastics; however, seasonal sampling is needed to confirm how these processes vary throughout the hydrological cycle. These results reinforce the role of Amazonian floodplain systems in the regional dynamics of microplastics and highlight the need for monitoring and management strategies that incorporate seasonal hydrological connectivity at the basin scale.

Doochi, a traditional Korean dish made from boiled shark skin and intestine, may be a source of mercury exposure. In 2013, total mercury was measured in untreated fragments of boiled shark skin from 32 purchased servings of Doochi and of 32 raw skins sold as its ingredient. In 2022, another serving was separated into the skin and intestine, each fully homogenized and analyzed in triplicate at an accredited laboratory. The geometric mean was 0.349 µg/g in the boiled skin fragments and 0.122 and 0.137 µg/g in the outer and inner layers of the raw skin. After homogenization, the boiled skin contained 2.667-2.882 µg/g and the intestine 0.295-0.333 µg/g. The homogenized boiled skin exceeded the European total mercury limit of 1.0 µg/g for shark, and its estimated methylmercury concentration exceeded the Korean limit of 1.0 µg/g for deep-sea fish. Fragment concentrations varied 167-fold and were more dispersed than in raw skin (Levene's test, p < 0.001). Hazard quotients against the methylmercury reference dose were 0.15 to 0.57 under traditional ceremonial consumption, and exceeded 1 at 10 to 36 servings per year depending on the assumptions; the corresponding intakes were 7 to 25% of the provisional tolerable weekly intake under that pattern and reached it at 22 to 81 servings per year. Under the assumptions of this analysis, exposure at the traditional consumption frequency was below the reference dose, but frequency warrants attention as the dish becomes more widely available, particularly for individuals of lower body weight.

Tarballs, the weathered residues of petroleum hydrocarbons, are a recurring feature along the west coast of India, yet their role as carriers of toxic and carcinogenic compounds remains insufficiently quantified. This study presents a comprehensive assessment of the distribution, composition, abundance and carcinogenic potential of polycyclic aromatic hydrocarbons (PAHs) in pelagic and beached tarballs collected along the Eastern Arabian Sea and the adjacent coastal regions (2018-2024). Results indicate that both pelagic and beached tarballs are dominated by low and medium-molecular-weight(LMW) PAHs, with phenanthrene consistently identified as the most abundant compound. Total PAH concentrations were markedly higher in pelagic tarballs than in beached tarballs. Diagnostic PAH ratios and principal component analysis suggest that the observed differences in PAH composition between pelagic and beached tarballs are consistent with the effects of environmental weathering. Assessment based on benzo[a]pyrene toxic equivalents (BaP-TEQ) and carcinogenic PAH fraction (cPAH%) indicates higher carcinogenic potential in pelagic tarballs, whereas beached tarballs exhibited greater spatial variability in PAH composition. Despite evidence of weathering, carcinogenic PAHs persisted in both tarball types, with some locations exhibiting notably elevated contributions. Overall, the study demonstrates that pelagic tarballs function as mobile vectors of carcinogenic hydrocarbons in the marine environment, while beached tarballs act as persistent secondary sources of contamination.. These findings highlight the potential environmental concern associated with tarball-derived PAHs and underscore the need for sustained monitoring and management of petroleum residues along coastal ecosystems.

Increasing metal contamination in aquatic ecosystems threatens environmental sustainability, aquatic organisms, and public health by promoting the accumulation of toxic elements in fish tissues. This study evaluated the bioaccumulation of toxic metals and essential elements in the liver, gills, and muscles of wild and farm-reared Cirrhinus mrigala collected from the River Jhelum and local fish farms, respectively. Fish health was assessed through hematological, biochemical, and histopathological analyses. Results showed higher metal concentrations in wild fish compared with farm-reared specimens. The liver exhibited highest accumulation, followed by the gills and muscles. Among essential elements, calcium (Ca) and magnesium (Mg) were the most abundant, whereas nickel (Ni), chromium (Cr), and arsenic (As) were the predominant toxic metals. Wild fish exhibited marked hematological and biochemical alterations, while histopathological analyses revealed severe tissue damage in the gills, liver, and muscles. Human health risk assessment indicated minimal risk from farm-reared fish consumption, whereas wild fish from the River Jhelum may pose significant health concerns. Overall, the study highlights the ecological deterioration of the investigated river system and emphasizes the urgent need for effective mitigation strategies, including improved waste management practices, continuous environmental monitoring, and regular limnological assessments to safeguard aquatic ecosystems and public health.