Marine coastal contamination caused by human activity is a major issue worldwide. The implementation of effective pollution monitoring programs, especially in coastal areas, is important and urgent. The use of biological, physiological, or biochemical measurements to monitor the impacts of pollution has garnered increasing interest, particularly for the development of new non-invasive tools to assess water pollution. Fish skin mucus is in direct contact with the marine environment, making it a favourable microenvironment for the formation of biofilm bacterial communities. In this study, we developed a non-invasive technique, sampling fish skin mucus to determine and analyse bacterial community composition using next-generation sequencing. We hypothesised that bacterial communities associated with the skin mucus of a common harbour benthic blennioid triplefin fish, Forsterygion capito, would reflect conditions of different marine environments. We detected clear differences in bacterial community alpha-diversity between contaminated and reference sites. Beta-diversity analysis also revealed differences in the bacterial community structure of the skin mucus of fish inhabiting different geographical areas. The relative abundance of different bacterial orders varied among sites, as determined by linear discriminant analysis (LDA) and effect size (LEfSe) analyses. The observed variation in bacterial community compositions correlated more strongly with variation in hydrocarbons than to various metal concentrations. Using advanced DNA sequencing technologies, we have developed a novel non-invasive, low-cost and effective tool to monitor the impacts of pollution through analysis of the bacterial communities associated with fish skin mucus.

The Keban Dam Reservoir, located on the Euphrates River, is the second largest reservoir of Turkey. Water quality of this reservoir is of great importance because it is widely used for recreation, aquaculture production, fishing, and irrigation. In this study, discriminant analysis, principal component analysis (PCA), factor analysis (FA) and cluster analysis (CA) were conducted to evaluate the seasonal and spatial variations in surface water quality of the reservoir. Also, total phosphorus (TP) content in sediments, water type and trophic status of the reservoir were determined. For this, 19 water quality variables and TP in sediments were monitored seasonally at 11 sampling stations on the reservoir during one year. Hierarchical CA classified 11 stations into three groups, i.e., upstream (moderate polluted), midstream (low polluted) and downstream (clean) regions. PCA/FA allowed to group the variables responsible for variations in water quality, which are mainly related to mineral dissolution (natural), organic matter and nutrients (anthropogenic), and physical parameters (natural). Discriminant analysis (DA) gave better results for both data reduction and spatio-temporal analysis. Stepwise temporal DA identified eight variables: water temperature (WT), chemical oxygen demand (COD), nitrate nitrogen (NO₃–N), soluble reactive phosphorus (SRP), chlorophyll-a (Chl-a), potassium (K⁺), magnesium (Mg²⁺), and calcium (Ca²⁺), which are the most significant variables responsible for temporal variations in water quality of the reservoir, while stepwise spatial DA identified three variables: K⁺, chloride (Cl⁻), and sulphate (SO₄⁻²), which are the most significant variables responsible for spatial variations. According to Ontario sediment-quality guidelines, sediments of the reservoir can be considered as unpolluted in terms of mean TP content. The water type of the reservoir was calcium-bicarbonate. According to trophic state index values based on TP and Chl-a, upstream region (moderate polluted) of the reservoir was in the eutrophic status, whereas other regions were in the mesotrophic status.

Humans are exposed to various anthropogenic chemicals in daily life, including endocrine-disrupting chemicals (EDCs). However, there are limited data on chronic, low-level exposure to such contaminants among the general population. Here hair analysis was used to investigate the occurrence of four polychlorinated biphenyls (PCBs), seven polybrominated diphenyl ethers (PBDEs) and two bisphenols (BPs) in 204 Chinese women living in the urban areas of Baoding and Dalian and 311 pregnant French women. All the PCBs and PBDEs tested here were more frequently detected in the hair samples of the French women than in those of the Chinese women. In both cohorts, PCB 180 and BDE 47 were the dominant PCB and PBDE congener, respectively. PCB 180 was found in 82% of the French women and 44% of the Chinese women, while the corresponding values of BDE 47 were 54% and 11%, respectively. A discriminant analysis further demonstrated the difference in PCBs and PBDEs exposure profile between the two cohorts. These results demonstrate that hair analysis is sufficiently sensitive to detect exposure to these pollutants and highlight differences in exposure between populations even at environmental levels. Although BPA and BPS were found in 100% of the hair samples in both cohorts, the French women had significantly higher levels of BPA and BPS than the Chinese women. The median concentrations of BPA were one order of magnitude higher than BPS in both the Chinese (34.9 versus 2.84 pg/mg) and the French women (118 versus 8.01 pg/mg) respectively. Our results suggest that both French and Chinese populations were extensively exposed to BPA and BPS.

Chronic ambient fine particulate matter (PM₂.₅) exposure correlates with various adverse health outcomes. Its impact on the circulating metabolome−a comprehensive functional readout of the interaction between an organism's genome and environment−has not however been fully understood. This study thus performed metabolomics analyses using a chronic PM₂.₅ exposure mouse model. C57Bl/6J mice (female) were subjected to inhalational concentrated ambient PM₂.₅ (CAP) or filtered air (FA) exposure for 10 months. Their sera were then analyzed by liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). These analyses identified 2570 metabolites in total, and 148 of them were significantly different between FA- and CAP-exposed mice. The orthogonal partial least-squares discriminant analysis (OPLS-DA) and heatmap analyses displayed evident clustering of FA- and CAP-exposed samples. Pathway analyses identified 6 perturbed metabolic pathways related to amino acid metabolism. In contrast, biological characterization revealed that 71 differential metabolites were related to lipid metabolism. Furthermore, our results showed that CAP exposure increased stress hormone metabolites, 18-oxocortisol and 5a-tetrahydrocortisol, and altered the levels of circadian rhythm biomarkers including melatonin, retinal and 5-methoxytryptophol.

Antibiotics resistance genes (ARGs) are considered as an emerging pollutant among various environments. As a sink of ARGs, a comprehensive study on the spatial and temporal distribution of ARGs in the estuarine sediments is needed. In the present study, six ARGs were determined in sediments taken along the Yangtze Estuary temporally and spatially. The sulfonamides, tetracyclines and fluoroquinolones resistance genes including sul1, sul2, tetA, tetW, aac(6’)-Ib, and qnrS, were ubiquitous, and the average abundances of most ARGs showed significant seasonal differences, with relative low abundances in winter and high abundances in summer. Moreover, the relative high abundances of ARGs were found at Shidongkou (SDK) and Wusongkou (WSK), which indicated that the effluents from the wastewater treatment plant upstream and inland river discharge could influence the abundance of ARGs in sediments. The positive correlation between intI1 and sul1 implied intI1 may be related to the occurrence and propagation of sulfonamides resistance genes. Correlation analysis and redundancy discriminant analysis showed that antibiotic concentrations had no significant correlation to their corresponding ARGs, while the total extractable metal, especially the bioavailable metals, as well as other environmental factors including temperature, clay, total organic carbon and total nitrogen, could regulate the occurrence and distribution of ARGs temporally and spatially. Our findings suggested the comprehensive effects of multiple pressures on the distribution of ARGs in the sediments, providing new insight into the distribution and dissemination of ARGs in estuarine sediments, spatially and temporally.

In China, the cadmium (Cd) levels in paddy fields have increased, which has led to the excessive uptake of Cd into rice grains. In this study, we determined the physicochemical properties of soil samples, including the pH, soil organic matter (SOM) content, cation exchange capacity (CEC), and total Cd content (Cdsoil) in order to establish a quadratic discriminant analysis (QDA) model for assessing the risk of Cd in rice and to calculate its prior probability. Decision tree and logistic regression models were also established for comparison. The results showed that the accuracy rate was 74% with QDA, which was significantly higher than that obtained using the decision tree (67%) and logistic regression (68%) models. The correlation coefficients between the soil pH and the other three factors (CEC, SOM, and Cdsoil) were higher in the inaccurate set than the accurate set, whereas the correlation coefficients were smaller in the inaccurate set than the accurate set.

Foliage dust contains heavy metal that may have harmful effects on human health. The elemental contents of tree leaves and foliage dust are especially useful to assess air environmental pollution. We studied the elemental concentrations in foliage dust and leaves of Acer pseudoplatanus along an urbanization gradient in Vienna, Austria. Samples were collected from urban, suburban and rural areas. We analysed 19 elements in both kind of samples: aluminium, barium, calcium, copper, iron, potassium, magnesium, sodium, phosphor, sulphur, strontium and zinc. We found that the elemental concentrations of foliage dust were significantly higher in the urban area than in the rural area for aluminium, barium, iron, lead, phosphor and selenium. Elemental concentrations of leaves were significantly higher in urban than in rural area for manganese and strontium. Urbanization changed significantly the elemental concentrations of foliage dust and leaves and the applied method can be useful for monitoring the environmental load.

To establish the quality of waters it is necessary to identify both point and non-point pollution sources. In this work, we propose the combination of clean analytical methodologies and chemometric tools to study discrete and diffuse pollution caused in a river by tributaries and precipitations, respectively. During a two-year period, water samples were taken in the Guadalquivir river (selected as a case study) and its main tributaries before and after precipitations. Samples were characterized by analysing nutrients, pH, dissolved oxygen, total and volatile suspended solids, carbon species, and heavy metals. Results were used to estimate fluvial and atmospheric inputs and as tracers for anthropic activities. Multivariate analysis was used to estimate the background pollution, and to identify pollution inputs. Principal Component Analysis and Cluster Analysis were used as data exploratory tools, while box-whiskers plots and Linear Discriminant Analysis were used to analyse and distinguish the different types of water samples.