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.

Endometriosis is a gynaecological disease characterised by the presence of endometriotic tissue outside of the uterus impacting a significant fraction of women of childbearing age. Evidence from epidemiological studies suggests a relationship between risk of endometriosis and exposure to some organochlorine persistent organic pollutants (POPs). However, these chemicals are numerous and occur in complex and highly correlated mixtures, and to date, most studies have not accounted for this simultaneous exposure. Linear and logistic regression models are constrained to adjusting for multiple exposures when variables are highly intercorrelated, resulting in unstable coefficients and arbitrary findings. Advanced machine learning models, of emerging use in epidemiology, today appear as a promising option to address these limitations. In this study, different machine learning techniques were compared on a dataset from a case-control study conducted in France to explore associations between mixtures of POPs and deep endometriosis. The battery of models encompassed regularised logistic regression, artificial neural network, support vector machine, adaptive boosting, and partial least-squares discriminant analysis with some additional sparsity constraints. These techniques were applied to identify the biomarkers of internal exposure in adipose tissue most associated with endometriosis and to compare model classification performance. The five tested models revealed a consistent selection of most associated POPs with deep endometriosis, including octachlorodibenzofuran, cis-heptachlor epoxide, polychlorinated biphenyl 77 or trans-nonachlor, among others. The high classification performance of all five models confirmed that machine learning may be a promising complementary approach in modelling highly correlated exposure biomarkers and their associations with health outcomes. Regularised logistic regression provided a good compromise between the interpretability of traditional statistical approaches and the classification capacity of machine learning approaches. Applying a battery of complementary algorithms may be a strategic approach to decipher complex exposome-health associations when the underlying structure is unknown.

The Sürgü Stream, located in the Euphrates River basin of Turkey, is used for drinking water source, agricultural irrigation and rainbow trout production. Therefore, water quality of the stream is of great importance. In this study, multivariate statistical techniques (MSTs) and water quality index (WQI) were applied to assess water quality of the stream affected by multiple stressors such as untreated domestic sewage, effluents from fish farms, agricultural runoff and streambank erosion. For this, 16 water quality parameters at five sites along the stream were monitored monthly during one year. Most of parameters showed significant spatial variations, indicating the influence of anthropogenic activities. All parameters except TN (total nitrogen) showed significant seasonal differences due to high seasonality in WT (water temperature) and water flow. The spatial variations in the WQI were significant (p < 0.05) and the mean WQI values ranged from 87.6 to 95.3, indicating “good” to “excellent” water quality in the stream. Cluster analysis classified five sites into three groups, that is, clean region, low polluted region and very clean region. Stepwise temporal discriminant analysis (DA) identified that pH, WT, Cl⁻, SO₄²⁻, COD (chemical oxygen demand), TSS (total suspended solids) and Ca²⁺ are the parameters responsible for variations between seasons, and stepwise spatial DA identified that DO (dissolved oxygen), EC (electrical conductivity), NH₄–N, TN (total nitrogen) and TSS are the parameters responsible for variations between the regions. Principal component analysis/factor analysis revealed that the parameters responsible for water quality variations were mainly associated with suspended solids (both natural and anthropogenic), soluble salts (natural) and nutrients and organic matter (anthropogenic).

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.

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.

To evaluate the seasonal distribution of antibiotic resistance genes (ARGs) and explore the reason for their patterns in different seasons and different systems, two wastewater treatment systems were selected and analyzed using high-throughput qPCR. Linear discriminant analysis (LDA) effect size (LEfSe) was used to discover the differential ARGs (biomarkers) and estimate the biomarkers’ effect size. We found that the total absolute abundances of ARGs in inflows and excess sludge samples had no obvious seasonal fluctuations, while those in winter outflow samples decreased in comparison with the inflow samples. Eleven differentially abundant ARGs (biomarker genes, BmGs) (aadA5-02, aac-6-II, cmlA1-01, cmlA1-02, blaOXA10-02, aadA-02, tetX, aadA1, ereA, qacEΔ1-01, and blaTEM) in summer samples and 10 BmGs (tet-32, tetA-02, aacC2, vanC-03, aac-6-I1, tetE, ermB, mefA, tnpA - 07, and sul2) in winter samples were validated. According to 16S rRNA gene sequencing, the relative abundance of bacteria at the phylum level exhibited significant seasonal changes in outflow water (OW), and biomarker bacteria (BmB) were discovered at the family (or genus) level. Synechococcus and vadinCA02 are BmB in summer, and Trichococcus, Lactococcus, Pelosinus, Janthinobacterium, Nitrosomonadaceae and Sterolibacterium are BmB in winter. In addition, BmB have good correlations with BmGs in the same season, which indicates that bacterial community changes drive different distributions of ARGs during seasonal changes and that LEfSe is an acute and effective method for finding significantly different ARGs and bacteria between two or more classes.In conclusion, this study demonstrated the seasonal changes of BmGs and BmB at two wastewater treatment systems.

A long-term study on a benthic community was conducted in two different localities, one in semi-enclosed bay of Jinhae (n = 10, south coast) and the other in open sea area of Samcheok (n = 10, east coast), Korea, respectively. We aimed to identify the spatiotemporal patterns of macrozoobenthos and the environmental variables influencing such patterns in the two contrasting habitats. The macrozoobenthos assemblages on the soft bottom of the subtidal zone were analyzed over the 3 years, encompassing 12 consecutive seasons, in 2013–2016. Among the 22 environmental variables measured, organic matter, dissolved oxygen, mean grain size, and water depth showed clear differences between two study areas. Accordingly, several ecological indices (such as the number of species, abundance, dominant species, and diversity index (H’)) generally reflected site-specific benthic conditions. The macrofaunal community in the Jinhae showed typical seasonal fluctuations, whereas the Samcheok community showed no significant change over time and space. Region- or site-dependent temporal variabilities of macrofaunal assemblages are depicted through cluster analysis (CA), indicating distinct temporal changes in the composition of dominant species. In particular, the abundance of some dominant species noticeably declined in certain seasons when several opportunistic species peaked. Such faunal succession might be explained by significant changes to specific environmental factors, such as bottom dissolved oxygen, grain size, and water depth. Principle component analysis further identified major environmental factors, i.e., sediment properties in Jinhae and water quality parameters in Samcheok community, respectively. In addition, discriminant analysis confirmed the presence of several site-specific parameters for the faunal assemblage groups identified through CA. Finally, indicator value analysis identified species that were representative across stations and regions in accordance with their habitat preference and/or species tolerance. Overall, the two contrasting nearshore habitats showed distinct community differences, in time and space, that were influenced by site-dependent environmental conditions.

Particulate matter (PM), a complex mix of chemical compounds, results to be associated with various health effects. However there is still lack of information on the impact of its different components. PM2.5 and PM1 samples, collected during the different seasons at an urban, rural and remote site, were chemically characterized and the biological effects induced on A549 cells were assessed. A Partial Least Square Discriminant Analysis has been performed to relate PM chemical composition to the toxic effects observed. Results show that PM-induced biological effects changed with the seasons and sites, and such variations may be explained by chemical constituents of PM, derived both from primary and secondary sources. The first-time here reported biological responses induced by PM from a remote site at high altitude were associated with the high concentrations of metals and secondary species typical of the free tropospheric aerosol, influenced by long range transports and aging.

In this study, three classification techniques (self-organizing maps, hierarchical cluster analysis and discriminant analysis) were applied to identify spatial water pollution levels, temporal water quality response delay phenomena (WQRDP), source pollution types (point, urban non-point, or agricultural non-point). Two models (principal components analysis (PCA), and positive matrix factorization (PMF)) were used to do the further quantitative source apportionment studying. The 27 inflow rivers in spatial were divided into three pollution levels (A, high; B, medium; C, low). The primary pollution pattern in spatial Clusters A, B, and C were point, urban non-point and agricultural non-point separately, in consideration of simultaneous land use types. Source apportionment results identified five typical factors in spatial Cluster A and six typical factors in spatial Cluster B and C as responsible for the data structure, explaining 80%–90% of the total variance of the dataset.

Colon microenvironment and microbiota dysbiosis are closely related to various human metabolic diseases. In this study, a total of 72 healthy female mice were exposed to fluoride (F) (0, 25, 50 and 100 mg/L F⁻) in drinking water for 70 days. The effect of F on intestinal barrier and the diversity and composition in colon microbiota have been evaluated. Meanwhile, the relationship among F-induced colon microbiota alterations and antimicrobial peptides (AMPs) expression and short-chain fatty acids (SCFAs) level also been assessed. The results suggested that F decreased the goblet cells number and glycoprotein expression in colon. And further high-throughput 16S rRNA gene sequencing result demonstrated that F exposure induced the diversity and community composition of colonic microbiota significantly changes. Linear Discriminant Analysis Effect Size (LEfSe) analysis identified 11 predominantly characteristic taxa which may be the biomarker in response to F exposure. F-induced intestinal microbiota perturbations lead to the significantly decreased SCFAs levels in colon. Immunofluorescence results showed that F increased the protein expression of interleukin-17A (IL-17A) and IL-22 (P < 0.01) and disturbed the expression of interleukin-17 receptor A (IL-17RA) and IL-22R (P < 0.05 or P < 0.01). In addition, the increased expression of IL-17A and IL-22 cooperatively enhanced the mRNA expression of AMPs which response to F-induced microbiota perturbations. Collectively, destroyed microenvironment and disturbed AMPs are the primary reason of microbiota dysbiosis in colon after F exposure. Colonic homoeostasis imbalance would be helpful for finding the source of F-induced chronic systemic diseases.