Organic micropollutants in rivers are emitted via diffuse and point sources like from agricultural practice or wastewater treatment plants (WWTP). Extensive laboratory and field experiments have been conducted to understand emissions and fate of these pollutants in freshwaters. Nevertheless, data is often difficult to compare since common protocols for appropriate approaches are largely missing. Thus, interpretation of the observed changes in substance concentrations and of the underlying fate of these compounds downstream of the chemical input into the river is still challenging. To narrow this research gap, (1) process understanding and (2) measurement approaches for field-based investigations are critically reviewed in this article. The review includes, on the one hand, processes that change the volume of the water (hydrological processes) and, on the other hand, processes that affect the substance mass within the water (distribution and transformation). Environmental boundary conditions for the purpose of better comparability of different attenuation studies, as well as promising state-of-the-art measurement approaches from different disciplines, are presented. This overview helps to develop a tailored procedure to assess turnover mechanisms of organic micropollutants under field conditions. In this respect, further research needs to standardize interdisciplinary approaches to increase the informative value of collected data.

This study investigated the protective effects of melatonin (MLT), a potent antioxidant, in male Wistar rats exposed to environmentally relevant doses of bisphenol A (BPA) in utero. Pregnant Wistar rats were randomly assigned into five groups. Group 1 (control) received 0.2 mL 1% dimethyl sulfoxide (DMSO)/99% canola oil as vehicle; group 2 received BPA at 25 μg/kg/day; group 3 received BPA at 250 μg/kg/day; group 4 received BPA at 25 μg/kg/day with concurrent MLT 1 mg/kg/day while group 5 received BPA at 250 μg/kg/day with concurrent MLT 1 mg/kg/day. Treatments were by gavage from gestational day (GD) 10–21. The BPA-treated rats showed dose-dependent significant reduction in body weight, gonosomatic index, sperm motility, livability and count. Also, BPA caused significant reduction in the levels of serum testosterone and luteinizing hormone while it caused significant increases in the levels of follicle stimulating hormone as well as estradiol. Furthermore, BPA induced testicular oxidative stress including significant decreases in the activities of testicular SOD, GSH and GPx as well as GST, increasing the levels of testicular MDA and H₂O₂. It further induced interstitial necrosis and germinal cell degeneration in the testis with a subsequent diminution of the tubular and luminal diameter. However, co-treatment with MLT offered protection against testicular damage induced by BPA. Melatonin is likely to protect against alterations of the male reproductive system caused by BPA through a direct action on the mechanism of anti-oxidants as well as through the inhibition of necrosis.

In situ passivation of heavy metals by biochar mainly focuses on the effect of biochar’s pH, surface oxygen-containing functional groups (OCFGs), and ash content. In this paper, starting with the measurement of biochar’s electrical properties under different pyrolysis atmospheres and temperatures, the changes in the zeta potential of biochar and the consequent effects on cadmium immobilization in soil are studied. The results show that the zeta potential of biochar from the pyrolysis of high temperature (800 °C) is higher than that of biochar at low temperatures, so its electronegativity is weaker than that of biochar at low temperatures, but the protective effect on wheat is stronger than that of biochar obtained at low temperatures. The zeta potential of biochar obtained under a CO₂ atmosphere was higher than that of biochar prepared under a N₂ atmosphere, so its protective effect on wheat was stronger than that of biochar under N₂. The reason is that biochar particles with a high zeta potential and weak electronegativity have higher cohesion and are better at in situ passivation of Cd in soils. Namely, biochar obtained at high pyrolysis temperatures (800 °C) and prepared under a CO₂ atmosphere has better effect on Cd immobilization.

Water quality prediction is an effective method for managing and protecting water resources by providing an early warning against water quality deterioration. In general, the existing water quality prediction methods are based on a single shallow model which fails to capture the long-term dependence in historical time series and is more likely to cause a high rate of false alarms and false negatives in practical water monitoring application. To resolve these problems, a new model combining recurrent neural network (RNN) with improved Dempster/Shafer (D-S) evidence theory (RNNs-DS) is proposed in this paper. Among them, the RNNs which can handle the long-term dependence in historical time series effectively are used to realize the preliminary prediction of water quality. And the improved D-S evidence theory is used to synthesize the prediction results of RNNs. In addition, an improved strategy based on correlation analysis method is presented for evidence theory to obtain the number of evidence, which reduces uncertainty in evidence selection effectively. Besides, a new basic probability assignment function which based on modified softmax function is proposed. The new function can effectively solve the problems of weight allocation failure in the traditional function. Then, data about permanganate index, pH, total phosphorus, and dissolved oxygen from Jiuxishuichang monitoring station near Qiantang River, Zhejiang Province, China is used to verify the proposed model. Compared with support vector regression (SVR) and backpropagation neural network (BPNN) and three RNN models, the new model shows higher accuracy and better stability as indicated by four indices. Finally, the engineering application of the RNNs-DS algorithm has been realized on the self-developed water environmental monitoring and forecasting system, which can provide effective support for early risk assessment and prevention in water environment.

The aim of this study was to determine the bioavailability of metals in field soils contaminated with chromated copper arsenate (CCA) mixtures. The uptake and elimination kinetics of chromium, copper, and arsenic were assessed in the earthworm Eisenia andrei exposed to soils from a gradient of CCA wood preservative contamination near Hartola, Finland. In soils contaminated with 1480–1590 mg Cr/kg dry soil, 642–791 mg Cu/kg dry soil, and 850–2810 mg Ag/kg dry soil, uptake and elimination kinetics patterns were similar for Cr and Cu. Both metals were rapidly taken up and rapidly excreted by Eisenia andrei with equilibrium reached within 1 day. The metalloid As, however, showed very slow uptake and elimination in the earthworms and body concentrations did not reach equilibrium within 21 days. Bioaccumulation factors (BAF) were low for Cu and Cr (< 0.1), but high for As at 0.54–1.8. The potential risk of CCA exposure for the terrestrial ecosystem therefore is mainly due to As.

The endogenous release of nutrients from sediments contributes to the eutrophication of landscape water to a certain degree, which depends on the characteristics of sediments. The study explored the characteristics of nitrogen (N) and phosphorus (P) released from two different sediments, which were deposited from reclaimed water (SRW) or surface water (SSW) respectively in Xi’an moat. This paper aimed to compare the effects of nutrient release from SRW and SSW on the water quality. Results showed that the maximum increase rates reached 1.21 mg TN/(L·day) and 0.11 mg TP/(L·day), respectively, in the overlying water of SRW, which were 1.6 and 2.8 times those of SSW. The released amounts of SRW were 0.192 mg TN/g and 0.038 mg TP/g, which were 4.1 and 12.7 times those of SSW. Meanwhile, the densities of benthic algae in SRW and SSW were 5.605 × 10⁹ and 2.846 × 10⁸ cells/L, respectively. Moreover, the species number and individual sizes of benthic algae in SRW were also larger than those in SSW, which played an important role in the nitrogen circulation. Unexpectedly, oxidation reduction potential (ORP) level of SRW was lower than that of SSW, although SRW has a higher dissolved oxygen level. Therefore, the N and P concentrations in the overlying water of SRW were considerably higher than those of SSW, which was mainly attributed to the higher nutrient contents and lower ORP in SRW.

The vine mealybug (VMB), Planococcus ficus (Hemiptera: Pseudococcidae), is a key insect pest of vineyards. While pheromone-based mating disruption (MD) has been successfully tested against a wide range of insect pests, knowledge about its efficacy against key mealybug species, such as P. ficus, is scarce. In this study, a novel MD product, Isonet® PF, was evaluated by testing 300, 400, and 500 dispensers/ha at four study sites located in Northern (Veneto) and Southern (Sicily) Italy. Experiments were carried out over 2 years by monitoring the mealybug populations in wine grape and table grape vineyards managed with and without the application of MD. Pheromone dispensers were periodically collected during the grapevine-growing season, extracted, and analyzed by GC-MS, to determine their pheromone content and the release in mg/ha/day. The results showed that use of the MD dispenser Isonet® PF reduced the percentage of VMB-infested bunches and the number of VMB specimens per bunch compared with the untreated controls. This was recorded over 2 years at all experimental sites. Differences in the incidence of infested bunches among the three tested rates of Isonet® PF were not detected. Overall, the results presented here contribute to optimizing the sex pheromone dosage used in MD control programs against VMB allowing a reduction of broad-spectrum insecticides currently employed to manage this important pest.

An experimental study was carried out using in pilot-scale constructed wetland systems, operated in parallel to treat raw sewage. Each system consisted of a vertical flow (VF) unit that was filled with biochar as the main media, followed by a horizontal flow (HF) unit filled with crushed cement mortar. Hydraulic loading (HL) ranged 340–680 mm/day was applied on the VF wetland units, where high total nitrogen (TN) mass removal rate (20–23 g N/m² d) was obtained, demonstrating that biochar media had a beneficial effect on the degradation of nitrogenous pollutants. Total phosphorus (TP) removal percentage (concentration based) was ≥ 86% in HF wetlands packed with mortar materials. In one system, the flow direction of the sewage was directed by the deployment of downflow pipes and vertical baffles, aiming to facilitate the formation of aerobic and anaerobic zones in the wetland matrices. The effects of such arrangement were analyzed by comparing pollutant removal efficiencies in the two systems. On average, 99, 96, 93, and 86 percentage removals were obtained for ammonia (NH₄-N), TN, biochemical oxygen demand (BOD), and TP, respectively, during the experiments. Biochar and crushed mortar proved to be a highly effective combination as media in subsurface flow constructed wetlands for wastewater treatment.

The consumption of psychoactive pharmaceuticals has increased worldwide, and wastewater treatment plants are not able to eliminate them from the effluent. An extensive review was carried out to assess the environmental risk (ERA model) based on secondary data about potential impacts on non-target organisms of seven psychoactive drugs consumed worldwide (alprazolam, bromazepam, citalopram, clonazepam, diazepam, lorazepam, and oxazepam). Risk quotients (RQs) were calculated according to the European Medicines Agency (EMA) on ERA of Medicinal Products For Human Use based on (i) the predicted and measured environmental concentrations (PEC and MEC, respectively) of the psychoactive drug in surface water, groundwater, and wastewater effluent and (ii) the predicted no-effect concentration (PNEC) derived from ecotoxicological assays or ECOSAR software. Furthermore, this study reviews and discusses non-standardized ecotoxicity assays, such as sublethal and behavioral effects on different organisms. In total, 903 MEC entries of psychoactive drugs and 162 data on ecotoxicological assays were gathered from the literature survey addressing behavioral effects (115), acute/chronic effects (35), and sublethal effects (12). Citalopram and diazepam were the only substances that are likely to pose an environmental risk (RQ > 1) to surface waters. Even though there is considerable amount of data on behavioral effects of psychoactive drugs to aquatic species, results are currently not integrated into the EMA risk assessment framework. The large amount of data on psychoactive drug concentrations and effects on non-target organisms collected, interpreted, and discussed in the present study should be used as a baseline for future improvement of ERA strategies.

River water temperature is a key control of many physical and bio-chemical processes in river systems, which theoretically depends on multiple factors. Here, four different machine learning models, including multilayer perceptron neural network models (MLPNN), adaptive neuro-fuzzy inference systems (ANFIS) with fuzzy c-mean clustering algorithm (ANFIS_FC), ANFIS with grid partition method (ANFIS_GP), and ANFIS with subtractive clustering method (ANFIS_SC), were implemented to simulate daily river water temperature, using air temperature (Tₐ), river flow discharge (Q), and the components of the Gregorian calendar (CGC) as predictors. The proposed models were tested in various river systems characterized by different hydrological conditions. Results showed that including the three inputs as predictors (Tₐ, Q, and the CGC) yielded the best accuracy among all the developed models. In particular, model performance improved considerably compared to the case where only Tₐ is used as predictor, which is the typical approach of most of previous machine learning applications. Additionally, it was found that Q played a relevant role mainly in snow-fed and regulated rivers with higher-altitude hydropower reservoirs, while it improved to a lower extent model performance in lowland rivers. In the validation phase, the MLPNN model was generally the one providing the highest performances, although in some river stations ANFIS_FC and ANFIS_GP were slightly more accurate. Overall, the results indicated that the machine learning models developed in this study can be effectively used for river water temperature simulation.