The gastrointestinal tract and its enteric nervous system are the first routes of food and xenobiotics uptake. Considering the importance of this organ, this study evaluated intestinal biomarkers of Sphoeroides testudineus integrating the data to generate tools for pollution assessment. The fish were collected in three sites of São Paulo Coast and their intestines were analyzed for biochemical, histology, and neuronal density and morphometry biomarkers. To evaluate the differences among the data, a PERMANOVA was applied, followed by a FA/PCA. The PERMANOVA indicated differences (P < 0.001) between the regions (RA, A1, and A2). Four factors were extracted from the FA/PCA (62% cumulative), showing that the animals from A2 presented severe alterations, mainly in intestinal morphometry and neuronal density. A1 alterations refer mainly to the increase of neuronal metabolism. Our results also evidence a gradient of environmental quality related to the protection level (AR > A1 > A2).

Sediment samples from 53 stations of the southwestern Chukchi sea were investigated to the spatial distributions and assess the state of trace metals contamination using ecological indices. The mean concentrations (mg kg⁻¹) in sediments were: Cr (70.5), Ni (41.0), Cu (16.5), Zn (82.7), As (15.90), Cd (0.27), Pb (15.96), Hg (32.0 μg kg⁻¹). The spatial distribution pattern of trace metals was similar with maximum values in the northern of the Chukchi Sea in the outer shelf sediments, while the high values of Cd were noted at stations located in the southern part of the sea where a strong influence of the Pacific waters penetrating through the Bering Strait. The ecological indices indicated no signs of anthropogenic pollution in the study sediments of the Chukchi Sea. Received data are of value for detecting and tracking future chemical changes in the sediments of the Chukchi sea, particularly in light of environmental changes.

In this study, 214 surface sediment samples were collected from the offshore area of the Dongying coast and were analysed for heavy metals; particularly, their concentrations and pollution status were evaluated. The copper (Cu) and chromium (Cr) distributions were similar, their concentrations were the highest in the northeast areas and the Xiaoqing River estuary, where dominated by fine-grained sediments. Higher concentrations of lead (Pb), zinc (Zn), cadmium (Cd), and arsenic (As) were generally found in the offshore area of the study location, and the highest Cd concentration was observed in the Xiaoqing River estuary. The sediments were not polluted by Cu, Pb, Zn, and Cr; they were not polluted or moderately polluted by Cd and As. Results of the principal component analysis indicated that Cu, Pb, Zn, and Cr were derived from natural sources and Cd and As were derived from anthropogenic sources.

The ecotoxicity of nitrofurazone was analyzed based on a community-based approach using periphytic protozoa. Median lethal concentrations (LC₅₀) within an exposure time of 30 min were determined by an acute toxicity test at 0, 1.5, 3, 6 and 12 mg ml⁻¹ nitrofurazone. Toxicity curve tests demonstrated a decreasing trend with increasing exposure time and was well fitted to the toxicity equation LC₅₀ = 32.85e⁻⁰.⁸¹⁴³ᵗ (t = exposure time; R² = 0.91; P < 0.05). Median inhibition concentrations (IC₅₀) for periphytic protozoan growth rates were obtained by chronic tests at 0, 1, 2, 4 and 8 mg ml⁻¹ nitrofurazone within 10 days exposure and were well fitted to the equation r% = 0.3686e⁻⁰.³⁵Cⁿⁱᵗ (Cₙᵢₜ is the concentration of nitrofurazone; R² = 0.92 and P < 0.05). These findings suggest that the LC₅₀ and IC₅₀ values of nitrofurazone can be predicted for any exposure time using periphytic protozoan communities as a bioassay model.

Phytoplankton assemblage and chlorophyll a along the salinity gradient and oxygen zonation were studied in Ulhas Estuary. Oxic condition exist in euhaline region of the estuary that receives oxygenated coastal waters, while, poly-meso-oligohaline region is hypoxic due to the accumulation of voluminous amount of pollutants from industrial effluents, domestic/sewage and agricultural discharges. Additionally, anthropogenically generated nutrients enhanced phytoplankton biomass (chlorophyll a) and their senescence also led to hypoxia. The dominance of diatoms (>70%) was observed in the euhaline-oxic region, while a tremendous decrease in their contribution (18%) was observed in the oligohaline region. Meso to oligohaline region of the estuary is governed by cyanophytes and chlorophytes due to their affinity towards low salinity and high dissolved inorganic nutrients. Carlson's Trophic State Index (TSI) further substantiated the estuary as eutrophic. Additionally, the study could identify the adaptation of cyanophytes and chlorophytes in oxygen deficient water.

Underwater radiated noise from shipping is globally pervasive and can cause deleterious effects on marine life, ranging from behavioural responses to physiological effects. Acoustic modelling makes it possible to map this noise over large areas and long timescales, and to test mitigation scenarios such as ship speed reduction or spatial restrictions. However, such maps must be validated against measurements to ensure confidence in their predictions. This study carried out a multi-site validation of the monthly and annual shipping noise maps for 2019 produced as part of the Joint Monitoring of Ambient Noise in the North Sea (JOMOPANS) programme. Spectral, spatial, and temporal differences between predictions and measurements were analysed, with differences linked to uncertainty in model input data and additional sources of anthropogenic noise in the measurements. Validating shipping noise models in this way ensures they can be applied with confidence in future management decisions to address shipping noise pollution.

Microplastics (MPs) and nanoplastics (NPs) are emerging environmental pollutants, having a major ecotoxicological concern to humans and many other biotas, especially aquatic animals. The physical and chemical compositions of MPs majorly determine their ecotoxicological risks. However, comprehensive knowledge about the exposure routes and toxic effects of MPs/NPs on animals and human health is not fully known. Here this review focuses on the potential exposure routes, human health impacts, and toxicity response of MPs/NPs on human health, through reviewing the literature on studies conducted in different in vitro and in vivo experiments on organisms, human cells, and the human experimental exposure models. The current literature review has highlighted ingestion, inhalation, and dermal contacts as major exposure routes of MPs/NPs. Further, oxidative stress, cytotoxicity, DNA damage, inflammation, immune response, neurotoxicity, metabolic disruption, and ultimately affecting digestive systems, immunology, respiratory systems, reproductive systems, and nervous systems, as serious health consequences.

Surface carbonate chemistry in the Yellow Sea was investigated based on discrete seawater samples collected from 2017 to 2020 at the Socheongcho Ocean Research Station (S-ORS; 37.423°N, 124.738°E). Records of carbon parameters, including seawater CO₂ partial pressure (pCO₂), revealed considerable seasonal variations, with amplitudes comparable to those observed across the western part of the Yellow Sea. The study site acted as a modest sink (−0.13 mol C m⁻² yr⁻¹) for atmospheric CO₂. Biological processes (primary production and respiration) and physical conditions (temperature and degree of stratification) determined seawater pCO₂, which fluctuated on an intraseasonal timescale between oversaturated and undersaturated with respect to atmospheric pCO₂. Variation in pCO₂ was significant in summer, depending on the biological carbon drawdown and tidal mixing-induced upwelling (increased pCO₂ up to ~1000 μatm). The intraseasonal variability in seawater pCO₂ may bias estimated air–sea CO₂ fluxes, if measurements with a coarser (seasonal) time resolution are used.

The method's development to detect oil-spills, and concentration monitoring of marine environments, are essential in emergency response. To develop a classification model, this work was based on the spectral response of surfaces using reflectance data, and machine learning (ML) techniques, with the objective of detecting oil in Landsat imagery. Additionally, different concentration oil data were used to obtain a concentration-estimation model. In the classification, K-Nearest Neighbor (KNN) obtained the best approximations in oil detection using Blue (0.453–0.520 μm), NIR (0.790–0.891 μm), SWIR1 (1.557–1.717 μm), and SWIR2 (1.960–2.162 μm) bands for 2010 spill images. In the concentration model, the mean absolute error (MAE) was 1.41 and 3.34, for training and validation data. When testing the concentration-estimation model in images where oil was detected, the concentration-estimation obtained was between 40 and 60 %. This demonstrates the potential use of ML techniques and spectral response data to detect and estimate the concentration of oil-spills.