The present study is the first comprehensive monitoring of 13 selected endocrine disrupting compounds (EDCs) in untreated urban and industrial wastewater in Serbia to assess their impact on the Danube River basin and associated freshwaters used as sources for drinking water production in the area. Results showed that natural and synthetic estrogens were present in surface and wastewater at concentrations ranging from 0.1 to 64.8 ng L⁻¹. Nevertheless, they were not detected in drinking water. For alkylphenols concentrations ranged from 1.1 to 78.3 ng L⁻¹ in wastewater and from 0.1 to 37.2 ng L⁻¹ in surface water, while in drinking water concentrations varied from 0.4 to 7.9 ng L⁻¹. Bisphenol A (BPA) was the most abundant compound in all water types, with frequencies of detection ranging from 57% in drinking water, to 70% in surface and 84% in wastewater. Potential environmental risks were characterized by calculating the risk quotients (RQs) and the estrogenic activity of EDCs in waste, surface and drinking water samples, as an indicator of their potential detrimental effects. RQ values of estrone (E1) and estradiol (E2) were the highest, exceeding the threshold value of 1 in 60% of wastewater samples, while in surface water E1 displayed potential risks in only two samples. Total estrogenic activity (EEQₜ) surpassed the threshold of 1 ng E2 L⁻¹ in about 67% of wastewater samples, and in 3 surface water samples. In drinking water, EEQₜ was below 1 ng L⁻¹ in all samples.

Antibiotic resistance surveillance data is lacking in many parts of the world, limiting effective therapy and management of resistance development. Analysis of urban wastewater, which contains bacteria from thousands of individuals, opens up possibilities to generate informative surveillance data in a standardized and resource-efficient way. Here, we evaluate the relationship between antibiotic resistance prevalence in E. coli from wastewater and clinical samples by studying countries with different resistance situations as assessed by traditional clinical surveillance. Composite, influent wastewater samples were collected over 24 h from treatment plants serving major cities in ten European countries. Using a broth screening method, resistance to six antibiotic classes was analyzed for 2507 E. coli isolates (n = 247–252 per country). Resistance prevalence in wastewater E. coli was compared to that in clinical E. coli reported by the European Antibiotic Resistance Surveillance Network. Resistance prevalence was lower in wastewater than clinical E. coli but followed similar geographic trends. Significant relationships were found for resistance to aminopenicillins (R² = 0.72, p = 0.0019) and fluoroquinolones (R² = 0.62, p = 0.0072), but not for aminoglycosides (R² = 0.13, p = 0.31) and third-generation cephalosporins (R² = 0.00, p = 0.99) where regression analyses were based on considerably fewer resistant isolates. When all four antibiotic classes were taken into account, the relationship was strong (R² = 0.85, p < 0.0001). Carbapenem resistance was rare in both wastewater and clinical isolates. Wastewater monitoring shows promise as method for generating surveillance data reflecting the clinical prevalence of antibiotic resistant bacteria. Such data may become especially valuable in regions where clinical surveillance is currently limited.

Recently polymer inclusion membranes (PIMs) have been proposed as materials for passive sampling, nonetheless a theoretical base to describe the mass transfer process through those materials, under such conditions of monitoring, has not been elucidated. Under the assumption that: (i) the transport of the metal ion occurs at steady state conditions, (ii) the concentration gradients are linear, and (iii) the kinetics of the chemical reactions in the extraction process on the membrane are elemental; an equation for the passive sampling of copper (II) using a PIM system containing Kelex-100 as carrier is derived. The prediction capacity of this sampler under different conditions of temperature, metal concentration, flow velocity, ionic strength and pH is analyzed as well. Among the dependencies of the PIM on the physicochemical conditions, effects of concentration, temperature and flow velocity tend to increment copper (II) flux across the membrane, being the parameter temperature the one with the most pronounced effect at T ≥ 30 °C. Ionic strength had no great effect on passive sampler response, however the sampler is dependent on the acidity of the medium. The comparable metal ion concentrations estimated from the PIM sampler to those obtained by direct measurements of the sampling medium suggest that PIMs can be robust materials when used as passive sampler devices.

Supported carbon quantum dots (CQDs), used as fluorescent sensors for the detection of metal ions, have rarely been used to remove heavy metals from water. Nitrogen-doped CQDs immobilized in hydrophilic silica hydrogels exhibited a more superior sensitivity and selectivity for the detection of Re(VII) and Cr(VI) than other metal ions, including Fe(III), Fe(II), Zn(II), Cu(II) and Mn(II). For the first time, low limits of detection (LOD) of 2.3 μM for Re(VII) detection and 65 nM for Cr(VI) detection were reported by a facile method. Based on the high selectivity of fluorescent silica hydrogels for Re(VII) and Cr(VI) detection, the removal of Re(VII) and Cr(VI) by graphene oxide (GO) in water was monitored with the hydrogels used as a turn-off fluorescent sensing platform. The consistent results of the sorption isotherms of each metal on GO, which were obtained from the fluorescence spectra and by UV absorption, further verified the possibility of monitoring metal removal by fluorescence detection. Remarkably, GO removed 1186 mg/g of Re(VII) but only 178 mg/g of Cr(VI). The density functional theory (DFT) calculations indicated that both Re(VII) and Cr(VI) formed stable bonds with silica hydrogels, confirming that the interactions between the metal ions and the substrate would promote the fluorescence quenching of the supported CQDs. On the other hand, Re(VII) interacted more strongly with the carboxyl groups of GO than Cr(VI). In addition, a real-time detection system was designed to alarm the service life of a GO filter used for Re(VII) removal.

The 2011 spill at platforms B and C of the Penglai 19-3 oil field in the Bohai Sea has been the worst oil spill accident in China. To assess long-term effects, a comprehensive monitoring program of chemical and biological variables (within a 2.2 km radius of the spill site) was conducted five years after the spill. Comparison of nutrient, Chl-a and oil concentrations in seawater, TOC, PAHs, heavy metals concentrations within the sediments, and the abundance and biomass of macrobenthic organisms to values obtained before and after the oil spill in previous studies indicate habitat recovery has occurred within the Bohai Sea following the episodic oil release. Observed elevated oil concentration in the water column and higher concentrations of two heavy metals, five PAHs, TOC, TOC/TN and lower values of δ¹³C, together with a reduction in macrobenthic biomass in near-field samples, suggest the influence of contaminants from chronic releases of oil and operational waste discharges within the vicinity of the oil platforms.

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.

Current studies indicate that long-term exposure to ambient fine particulate matter (PM₂.₅) is related with global mortality, yet no studies have explored relationships of PM₂.₅ and its species with DNAm PhenoAge acceleration (DNAmPhenoAccel), a new epigenetic biomarker of phenotypic age. We identified which PM₂.₅ species had association with DNAmPhenoAccel in a one-year exposure window in a longitudinal cohort. We collected whole blood samples from 683 elderly men in the Normative Aging Study between 1999 and 2013 (n = 1254 visits). DNAm PhenoAge was calculated using 513 CpGs retrieved from the Illumina Infinium HumanMethylation450 BeadChip. Daily concentrations of PM₂.₅ species were measured at a fixed air-quality monitoring site and one-year moving averages were computed. Linear mixed-effect (LME) regression and Bayesian kernel machine (BKM) regression were used to estimate the associations. The covariates included chronological age, body mass index (BMI), cigarette pack years, smoking status, estimated cell types, batch effects etc. Benjamini-Hochberg false discovery rate at a 5% false positive threshold was used to adjust for multiple comparison. During the study period, the mean DNAm PhenoAge and chronological age in our subjects were 68 and 73 years old, respectively. Using LME model, only lead and calcium were significantly associated with DNAmPhenoAccel. For example, an interquartile range (IQR, 0.0011 μg/m³) increase in lead was associated with a 1.29-year [95% confidence interval (CI): 0.47, 2.11] increase in DNAmPhenoAccel. Using BKM model, we selected PM₂.₅, lead, and silicon to be predictors for DNAmPhenoAccel. A subsequent LME model showed that only lead had significant effect on DNAmPhenoAccel: 1.45-year (95% CI: 0.46, 2.46) increase in DNAmPhenoAccel following an IQR increase in one-year lead. This is the first study that investigates long-term effects of PM₂.₅ components on DNAmPhenoAccel. The results demonstrate that lead and calcium contained in PM₂.₅ was robustly associated with DNAmPhenoAccel.

On-road remote sensing technology measures the concentration ratios of pollutants over CO₂ in the exhaust plume in half a second when a vehicle passes by a measurement site, providing a rapid, non-intrusive and economic tool for vehicle emissions monitoring and control. A key assumption in such measurement is that the emission ratios are constant for a given plume. However, there is a lack of study on this assumption, whose validity could be affected by a number of factors, especially the engine operating conditions and turbulence. To guide the development of the next-generation remote sensing system, this study is conducted to investigate the effects of various factors on the emissions dispersion process in the vehicle near-wake region and their effects on remote sensing measurement. The emissions dispersion process is modelled using Large Eddy Simulation (LES). The studied factors include the height of the remote sensing beam, vehicle speed, acceleration and side wind. The results show that the measurable CO₂ and NO exhaust plumes are relatively short at 30 km/h cruising speed, indicating that a large percentage of remote sensing readings within the measurement duration (0.5 s) are below the sensor detection limit which would distort the derived emission ratio. In addition, the valid measurement region of NO/CO₂ emission ratio is even shorter than the measurable plume and is at the tailpipe height. The effect of vehicle speed (30–90 km/h) on the measurable plume length is insignificant. Under deceleration condition, the length of the valid NO/CO₂ measurement region is shorter than under cruising and acceleration conditions. Side winds from the far-tailpipe direction have a significant effect on remote sensing measurements. The implications of these findings are discussed and possible solutions to improve the accuracy of remote sensing measurement are proposed.

An accurate estimation of population exposure to particulate matter with an aerodynamic diameter <2.5 μm (PM₂.₅) is crucial to hazard assessment and epidemiology. This study integrated annual data from 1146 in-home air monitors, air quality monitoring network, public applications, and traffic smart cards to determine the pattern of PM₂.₅ concentrations and activities in different microenvironments (including outdoors, indoors, subways, buses, and cars). By combining massive amounts of signaling data from cell phones, this study applied a spatio-temporally weighted model to improve the estimation of PM₂.₅ exposure. Using Shanghai as a case study, the annual average indoor PM₂.₅ concentration was estimated to be 29.3 ± 27.1 μg/m³ (n = 365), with an average infiltration factor of 0.63. The spatio-temporally weighted PM₂.₅ exposure was estimated to be 32.1 ± 13.9 μg/m³ (n = 365), with indoor PM₂.₅ contributing the most (85.1%), followed by outdoor (7.6%), bus (3.7%), subway (3.1%), and car (0.5%). However, considering that outdoor PM₂.₅ makes a significant contribution to indoor PM₂.₅, outdoor PM₂.₅ was responsible for most of the exposure in Shanghai. A heatmap of PM₂.₅ exposure indicated that the inner-city exposure index was significantly higher than that of the outskirts city, which demonstrated that the importance of spatial differences in population exposure estimation.

β-Blockers (BB) are one of the most commonly prescribed pharmaceuticals used for treating cardiovascular and acute anxiety-related disorders. This class of drugs inhibit β-adrenoceptor signalling and given their growing, widespread use, BB are routinely detected in surface waters at nM concentrations. This is concerning as trace levels of BB impart developmental and reproductive dysfunction in non-target aquatic organisms, with potential for ecological risks. To date, environmental pharmaceutical risks to non-target animals are not part of the monitoring framework due to the lack of bioassays for assessing their biological effects. Behavioral endpoints have the advantage of a systems-level integration of multiple sensory signals and motor responses for toxicity screening; however, they are not currently used for risk assessment of environmental contaminants. The zebrafish (Danio rerio) embryo photomotor response (zfPMR) has been used in high-throughput behavioral screenings for neuroactive drug effects at high, therapeutic concentrations. Our objective here was to examine if we could utilize the zfPMR for screening environmental levels of BB. Embryos were placed into 96-well plates, exposed to chemicals and/or municipal wastewater effluent (MWWE), and their zfPMRs were measured with video-analysis. To specifically target BB, embryos were co-treated with isoproterenol, a β-adrenergic agonist that stimulates the zfPMR, and the inhibition of isoproterenol-induced response was used as a biomarker of BB exposure. Our results reveal that the inhibition of isoproterenol-stimulated zfPMRs can be used as a biosensor capable of detecting BB in the parts-per-billion to parts-per-trillion in water samples, including diluted MWWE. The method developed detects BB in spite of the presence of other neuroactive compounds in water samples. This systems level approach of rapid screening for BB effects provides the most promising evidence to date that behavioral neuromodulation can be potentially applied for environmental effects monitoring of pharmaceuticals.