Phthalic acid esters (PAEs), presented in fabrics, surfactants and detergents, were discharged into the ecosystem during textile-dyeing wastewater treatment and might have adverse effects on water ecosystems. In this study, comprehensive investigations of the content and component distributions of 12 PAEs across different units of four textile-dyeing wastewater plants were carried out in Guangdong Province, China. Ecotoxicity assessments were also conducted based on risk quotients (RQs). On average, 93.54% TOC and 80.14% CODCr were removed following treatment at the four plants. The average concentration of Σ12PAEs in effluent was 11.78 μg/L. PAEs with highest concentrations were dimethylphthalate (6.58 μg/L), bis(2-ethylhexyl)phthalate (2.23 μg/L), and dibutylphthalate (1.98 μg/L). The concentrations of the main toxic PAEs were 2.23 μg/L (bis(2-ethylhexyl)phthalate), 0.19 μg/L (diisononylphthalate) and 0.67 μg/L (dinoctylphthalate); corresponding RQs were 1.4, 0.55, and 0.54 for green algae, respectively. The RQs of Σ12PAEs in effluent of the four plants were >0.1, indicating that Σ12PAEs posed medium or higher ecological risk to fish, Daphnia and green algae. Physicochemical-biochemical system was found to be more effective than biochemical-physicochemical system for TOC and CODCr removal, because pre-physicochemical treatment helped to remove macromolecular organic substances, and reduced the competition with other pollutants during biochemical treatment. However, biochemical-physicochemical system was more effective than physicochemical-biochemical system for elimination of PAEs and for detoxification, since the biochemical treatment might produce the toxic PAEs that could helpfully be settled by post-physicochemical treatment. Moreover, ecotoxicity evaluation was recommended for current textile-dyeing wastewater treatment plants.

Metal-salt amended soils (MA, n = 23), and historically-contaminated urban soils from two English cities (Urban, n = 50), were investigated to assess the effects of soil properties and contaminant source on metal lability and solubility. A stable isotope dilution method, with and without a resin purification step, was used to measure the lability of Cd, Cu, Ni, Pb and Zn. For all five metals in MA soils, lability (%E-values) could be reasonably well predicted from soil pH value with a simple logistic equation. However, there was evidence of continuing time-dependent fixation of Cd and Zn in the MA soils, following more than a decade of storage under air-dried conditions, mainly in high pH soils. All five metals in MA soils remained much more labile than in Urban soils, strongly indicating an effect of contaminant source on metal lability in the latter. Metal solubility was predicted for both sets of soil by the geochemical speciation model WHAM-VII, using E-value as an input variable. For soils with low metal solution concentrations, over-estimation of Cd, Ni and Zn solubility was associated with binding to the Fe oxide fraction while accurate prediction of Cu solubility was dependent on humic acid content. Lead solubility was most poorly described, especially in the Urban soils. Generally, slightly poorer estimation of metal solubility was observed in Urban soils, possibly due to a greater incidence of high pH values. The use of isotopically exchangeable metal to predict solubility is appropriate both for historically contaminated soils and where amendment with soluble forms of metal is used, as in toxicological trials. However, the major limitation to predicting solubility may lie with the accuracy of model input variables such as humic acid and Fe oxide contents where there is often a reliance on relatively crude analytical estimations of these variables.Trace metal reactivity in urban soils depends on both soil properties and the original source material; the WHAM geochemical model predicts solubility using isotopically exchangeable metal as an input.

Organic ultraviolet filters used for protection against radiation in personal care products and other materials (e.g. textiles, plastic products) are considered emerging contaminants of aquatic ecosystem. Benzophenone-3 (BP3) and 3-(4-methylbenzylidene)camphor (4-MBC) are the most commonly used organic UV-filters and have been reported in freshwater environments due to contamination through discharges from wastewater treatment plants and swimming pools or by direct contamination from recreational activities. Our aim was to evaluate the ecotoxicological effects of these UV-filters using the freshwater caddisfly Sericostoma vittatum’ biochemical biomarkers and energy processing related endpoints (feeding behaviour, energy reserves and cellular metabolism). In laboratory trials, both compounds induced feeding inhibition of S. vittatum at 3.55 mg/kg BP3 and at concentrations ≥2.57 mg/kg 4-MBC, decreased carbohydrates content at 3.55 and 6.95 mg/kg of BP3 and 4-MBC respectively, and increased total glutathione levels at concentrations ≥1.45 and 1.35 mg/kg of BP3 and 4-MBC respectively. No significant effects were observed on endpoints associated with oxidative stress, antioxidant defences, phase II biotransformation or neurotoxicity after exposure to both UV-filters. Our results show that environmental relevant concentrations of BP3 and 4-MBC, can negatively impact freshwater insects and demonstrate the importance of monitoring the ecological effects of organic UV-filters using non-model invertebrate species.

The blowout of the Deepwater Horizon (DWH) drilling rig in 2010 released an unprecedented amount of oil at depth (1,500 m) into the Gulf of Mexico (GoM). Sedimentary geochemical data from an extensive area (∼194,000 km2) was used to characterize the amount, chemical signature, distribution, and extent of the DWH oil deposited on the seafloor in 2010–2011 from coastal to deep-sea areas in the GoM. The analysis of numerous hydrocarbon compounds (N = 158) and sediment cores (N = 2,613) suggests that, 1.9 ± 0.9 × 104 metric tons of hydrocarbons (>C9 saturated and aromatic fractions) were deposited in 56% of the studied area, containing 21± 10% (up to 47%) of the total amount of oil discharged and not recovered from the DWH spill. Examination of the spatial trends and chemical diagnostic ratios indicate large deposition of weathered DWH oil in coastal and deep-sea areas and negligible deposition on the continental shelf (behaving as a transition zone in the northern GoM). The large-scale analysis of deposited hydrocarbons following the DWH spill helps understanding the possible long-term fate of the released oil in 2010, including sedimentary transformation processes, redistribution of deposited hydrocarbons, and persistence in the environment as recycled petrocarbon.

Although the toxicity of beta-cypermethrin (beta-CYP) to aquatic organisms has become a significant concern in recent years, its enantioselective effects on non-target organisms is poorly understood. To investigate the enantioselective toxicity of beta-CYP on zebrafish, adult zebrafish were exposed to a series of isometric concentrations of four beta-CYP enantiomers and the beta-CYP racemate for 96 h. In addition, the activities of four antioxidant enzymes and the malondialdehyde (MDA) content in zebrafish liver and brain were tested after 15 and 30 days beta-CYP enantiomers and racemate exposure under environmentally relevant dosages (0.01 and 0.1 μg/L). According to the acute toxicity results, the 1R-cis-αS and 1R-trans-αS enantiomers were more lethal than 1S-cis-αR and 1S-trans-αR. At 0.1 μg/L, the 1R-cis-αS and 1R-trans-αS enantiomers, and the beta-CYP racemate could significantly induce a hepatic MDA content at 30 days post exposure (dpe), while only 1R-cis-αS caused brain lipid peroxidation. An apparent regulation of antioxidase levels was observed in zebrafish liver and brain after exposure to the 1R-cis-αS and 1R-trans-αS enantiomers, and the beta-CYP racemate. In contrast, no significant oxidative stress was observed in zebrafish exposed to 1S-cis-αR and 1S-trans-αR enantiomers under the test concentrations. This work demonstrated the occurrence of enantioselectivity in toxicity and oxidative stress of beta-CYP to adult zebrafish, which should be considered in environmental risk assessments.

HIV high risk population may face not only the threat of HIV infection but also a higher chance of exposure to environmental contaminants. However, no previous studies have examined the body burden of environmental pollutants including heavy metals among HIV high risk populations. The aim of this study was to investigate whether adults aged 20–59 years old at high risk of HIV infection have higher blood levels of heavy metals compared to those with low risk of HIV infection in United States.We used the National Health and Nutrition Examination Survey (NHANES) 1999–2010 to compare exposures to heavy metals including cadmium, lead, and total mercury by HIV risk status.The results showed that people at high risk of HIV had higher blood concentrations of all heavy metals compared to their counterparts with lower HIV risks. In multivariate linear regression models, HIV risk status was significantly associated with increased blood cadmium, lead, and total mercury after adjusting for age, sex, race, education, and poverty income ratio.Our study suggests that people at high risk of HIV have significantly higher body burden of heavy metals including cadmium, lead, and mercury compared to those with low risk of HIV. Further longitudinal study collecting more pollutants are warranted to determine the potential health effects of these elevated pollutants on both HIV-infected and HIV high-risk populations.

Volatile organic compounds (VOCs) from two sampling sites (HB and XB) in a power station centralized area, in Shuozhou city, China, were sampled by stainless steel canisters and measured by gas chromatography-mass selective detection/flame ionization detection (GC-MSD/FID) in the spring and autumn of 2014. The concentration of VOCs was higher in the autumn (HB, 96.87 μg/m3; XB, 58.94 μg/m3) than in the spring (HB, 41.49 μg/m3; XB, 43.46 μg/m3), as lower wind speed in the autumn could lead to pollutant accumulation, especially at HB, which is a new urban area surrounded by residential areas and a transportation hub. Alkanes were the dominant group at both HB and XB in both sampling periods, but the contribution of aromatic pollutants at HB in the autumn was much higher than that of the other alkanes (11.16–19.55%). Compared to other cities, BTEX pollution in Shuozhou was among the lowest levels in the world. Because of the high levels of aromatic pollutants, the ozone formation potential increased significantly at HB in the autumn. Using the ratio analyses to identify the age of the air masses and analyze the sources, the results showed that the atmospheric VOCs at XB were strongly influenced by the remote sources of coal combustion, while at HB in the spring and autumn were affected by the remote sources of coal combustion and local sources of vehicle emission, respectively. Source analysis conducted using the Positive Matrix Factorization (PMF) model at Shuozhou showed that coal combustion and vehicle emissions made the two largest contributions (29.98% and 21.25%, respectively) to atmospheric VOCs. With further economic restructuring, the influence of vehicle emissions on the air quality should become more significant, indicating that controlling vehicle emissions is key to reducing the air pollution.

Mercury (Hg) exposure poses a threat to both fish and human health. Sharks are known to bioaccumulate Hg, however, little is known regarding how Hg is distributed between different tissue groups (e.g. muscle regions, organs). Here we evaluated total mercury (THg) concentrations from eight muscle regions, four fins (first dorsal, left and right pectorals, caudal-from both the inner core and trailing margin of each fin), and five internal organs (liver, kidney, spleen, heart, epigonal organ) from two different shark species, bonnethead (Sphyrna tiburo) and silky shark (Carcharhinus falciformis) to determine the relationships of THg concentrations between and within tissue groups. Total Hg concentrations were highest in the eight muscle regions with no significant differences in THg concentrations between the different muscle regions and muscle types (red and white). Results from tissue collected from any muscle region would be representative of all muscle sample locations. Total Hg concentrations were lowest in samples taken from the fin inner core of the first dorsal, pectoral, and caudal (lower lobe) fins. Mercury concentrations for samples taken from the trailing margin of the dorsal, pectoral, and caudal fins (upper and lower lobe) were also not significantly different from each other for both species. Significant relationships were found between THg concentrations in dorsal axial muscle tissue and the fin inner core, liver, kidney, spleen and heart for both species as well as the THg concentrations between the dorsal fin trailing margin and the heart for the silky shark and all other sampled tissue types for the bonnethead shark. Our results suggest that biopsy sampling of dorsal muscle can provide data that can effectively estimate THg concentrations in specific organs without using more invasive, or lethal methods.

In the last few years, ship emissions have attracted growing attention in the scientific community. The main reason is the constant increase of marine emissions over the last twenty years due to the intensification of port traffic. Thus, this study aimed to evaluate ship emissions (PM10, PM2.5, NOx, SO2, CO, CO2, N2O CH4, NMVOC, and HC) through the activity-based methodology in four of the main ports of Portugal (Leixões, Setúbal, Sines and Viana do Castelo) during 2013 and 2014. The analysis was performed according to ship types (bulk carrier, container, general cargo, passenger, Ro-Ro cargo, tanker and others) and operational modes (manoeuvring, hotelling and during cruising). Results indicated that tankers were the largest emitters in two of the four analysed ports. Regarding cruising emissions, container ships were the largest emitters. . CO2, NOx and SO2 estimated emissions represented more than 95% of the cruising and in-port emissions. Results were also compared with the total national emissions reported by the Portuguese Environment Agency, and if the in-port emissions estimated in the present study would have been taken into account to these totals, emissions of NOx and SO2 would increase 15% and 24% in 2013 and 16% and 28% in 2014. Summing up ships seem to be an important source of air pollution, mainly regarding NOx and SO2.

Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13–24 h prediction tasks (MAPE = 31.47%).