Freshwater mussels are one of the most threatened taxonomic groups in the world, and many species are on the brink of local or global extinction. Human activities have altered mussel living conditions in a plethora of ways. One of the most destructive human-induced impacts on running waters is the catastrophic spill of harmful substances, which results in massive die-offs. Even though Finland is regarded as the world’s top country in terms of environmental regulation quality, riverine systems are not safe. In 2014, River Kokemäenjoki in western Finland experienced the worst NiSO4 spill in the country’s history, visibly affecting the mussel community – including protected Unio crassus – along the river. Because freshwater mussel toxicology is grossly understudied (particularly in Europe), any pollution –linked die-offs offer valuable opportunities to study the issue in natural environment. Here, we report the mussel investigations from 2014 and a follow-up study conducted in 2017 in order to assess the variation in species sensitivity on nickel pollution. In total, 104 sites were sampled, and over 20 000 mussels were identified and counted. Our results indicate that the most impacted species (i.e. that which experienced the highest spill-induced mortality) was Anodonta anatina (62%), followed by Unio pictorum (32%), U. crassus (24%) and Unio tumidus (9%). The underlying reason for the sensitivity of A. anatina is not resolved, hence more research is urgently needed. The low mortality among most of the species in 2017 highlights the temporal nature of the pollution impact and the recovery potential of the mussel community. However, the case is more complex with U. crassus population, which may be experiencing delayed impacts of the spill. Because nickel is one of the most commonly produced industrial metals in the world (hence the pollution incident risk is high) and River Kokemäenjoki hosts mussel community typical for European rivers, our results may benefit many researchers and stakeholders dealing with riverine environments.

Pieces of glass as solid wastes were recycled in the synthesis of highly order MCM-41 that decorated by green fabricated Co₃O₄ nanoparticles using the green extract of green tea leaves forming novel green nano-composite. The synthetic Co₃O₄/MCM-41 exhibit high surface area, low bandgap energy (1.63 eV), and typical spherical morphology decorated by Co₃O₄ nanoparticles. The composite was evaluated as green photocatalyst in effective oxidation of methyl parathion pesticide in the presence of a visible light source. The degradation results revealed complete removal of 50 mg/L and 100 mg/L after 60 min and 90 min, respectively using 0.25 of the catalyst at pH 8. The detection of the TOC in the treated methyl parathion solution gives strong indications about the formation of organic intermediate compounds during the oxidation steps. The main detected intermediate compound are C₆H₅OH(NO₂), C₆H₅OH, (CH₃O)₃P(S), C₆H₄(OH)₂, C₆H₃(OH)₃, C₆H₄(NH₂)OP(O)(OCH₃)₂, (CH₃O)₂P(O)OH, (CH₂)₂C(OH)OH(CHO)OC(O), and HO₂C(CH₂)₂C(O)CHO. The detected intermediate compounds converted into SO₄²⁻, PO₄³⁻, NO₃⁻, and CO₂ under the extensive photocatalytic of them over Co₃O₄/MCM-41. The oxidizing species trapping test verified the controlling of the methyl parathion degradation pathway by the hydroxyl radicals. Finally, the composite showed significant reusability properties and applied five times in the oxidation of methyl parathion with considerable degradation percentages.

Hydraulic fracturing of horizontal wells is a cost effective means for extracting oil and gas from low permeability formations. Hydraulic fracturing often produces considerable volumes of flowback and produced water (FPW). FPW associated with hydraulic fracturing has been shown to be a complex, often brackish mixture containing a variety of anthropogenic and geogenic compounds. In the present study, the risk of FPW releases to aquatic systems was studied using the model benthic invertebrate, Lumbriculus variegatus and field-collected FPW from a fractured well in Alberta. Acute, chronic, and pulse toxicity were evaluated to better understand the implications of accidental FPW releases to aquatic environments. Although L.variegatus is thought to have a high tolerance to many stressors, acute toxicity was significant at low concentrations (i.e. high dilutions) of FPW (48 h LC50: 4–5%). Chronic toxicity (28 d)of FPW in this species was even more pronounced with LC50s (survival/reproduction) and EC50s (total mass) at dilutions as low as 0.22% FPW. Investigations evaluating pulse toxicity (6 h and 48 h exposure) showed a significant amount of latent mortality occurring when compared to the acute results. Additionally, causality in acute and chronic bioassays differed as acute toxicity appeared to be primarily driven by salinity, which was not the case for chronic toxicity, as other stressors appear to be important as well. The findings of this study show the importance of evaluating multiple exposure regimes, the complexity of FPW, and also shows the potential aquatic risk posed by FPW releases.

The metalloid arsenic is one of the most conspicuous groundwater contaminants in the Indian subcontinent and its removal from aqueous medium is the main focus of this study. The study aims at functionalising melanin using iron and copper for the efficient removal of arsenic and rendering water fit for consumption. Melanin obtained from the marine bacteria Pseudomonas stutzeri was functionalised by iron impregnation (Fe-melanin) and copper impregnation (Cu-melanin). Morphological studies using FESEM portrayed the impregnated iron and copper granules on the surface of melanin, while XRD analysis confirmed the presence of Fe₂O₃ and CuO on melanin. Adsorption studies on As (V) and As (III) were conducted using Fe-melanin and Cu-melanin for different operating variables like pH, temperature and contact time. More than 99% per cent of As (III) and As (V) from water was removed at a pH range between 4 and 6 within 50 min in the case of Fe-melanin and 80 min for Cu-melanin. Adsorption equilibrium studies showed better fit with Langmuir adsorption isotherm and had good agreement with Redlich-Peterson’s three-parameter model. The maximum adsorption capacities of Fe-melanin and Cu-melanin obtained from Langmuir adsorption model are 50.12 and 20.39 mg/g, respectively, for As (V) and similarly 39.98 and 19.52 mg/g, respectively, for As (III). Arsenic-binding to the functionalised melanin was confirmed using FT-IR and the XPS analysis. Reuse of the adsorbent was effectively done by desorbing the iron and copper together with the bound As (III) and As (V) and further re-impregnation of iron and copper in melanin. Re-functionalised melanin showed 99% adsorption efficiency up to four cycles of adsorption/desorption.

The increasing availability of water quality datasets has led to a greater focus on hydrologic and water quality analysis, thus requiring more efficient and accurate modelling methods. Data mining techniques have been increasingly used for water quality analysis and prediction of the concentration and load of nitrogen pollutants instead of more traditional simulation methods. In this study, we tested the multilayer perceptron (MLP), k-nearest neighbor (k-NN), random forest, and reduced error pruning tree (REPTree) methods, along with the traditional linear regression, to predict nitrate levels based on long-term data from six watersheds with different land-use practices in the midwestern United States. Both the concentration and load results indicated that REPTree had the best performance, with an R² of 0.61–0.85 and a relative absolute error of <75.8%. The different watershed types, however, influenced the performance of the data mining methods, where all four methods showed a higher accuracy for urban dominant watershed and lower accuracy for agricultural and forest watersheds. Out of these four methods, classification tree methods (REPTree and RF) performed better than cluster methods (MLP and k-NN) for agricultural and forested watersheds. Our results indicated that both the data structure based on the dominant land use and type of algorithmic method should be carefully considered for selecting a data mining method to predict nitrate concentration and load for a watershed.

With the increase in population and industrialization, air pollution has become one of the global problems nowadays. Therefore, air pollutant parameters should be measured at regular intervals, and the necessary measures should be taken by evaluating the results of measurements. In order to prevent air pollution, pollutant parameters must be evaluated within the framework of a model. Recently, in order to obtain objective and more sensitive results with regard to air pollution nowadays, studies, which use machine learning algorithms in artificial intelligence technologies, have been carried out. In this study, PM₁₀ concentrations, which are obtained from 7 stations in Ankara province in Turkey, were trained with machine learning algorithms (LASSO, SVR, RF, kNN, xGBoost, ANN). The PM₁₀ concentrations of the years 2009–2017 of 6 stations in Ankara were given as input, and the PM₁₀ concentrations of the seventh station for the year 2018 were predicted. The model development stage was repeated for each station, and the performance and error rates of the algorithms were determined by comparing the results produced by the algorithms with the actual results. The best results were provided with ANN (R² = 0.58, RMSE = 20.8, MAE = 14.4). The spatial distribution of the estimated concentration results was provided through Geographic Information System (GIS), and spatial strategies for improving air pollution over land use were established.

The air quality in classrooms is a critical factor that affects students’ daily exposure. Although air filtration units (AFUs) are often used to reduce exposure to air pollution in China, the effectiveness of installing AFUs in Chinese classrooms have not been quantitatively studied. Hence, we carried out concurrent air quality experiments in two identical classrooms where one room was equipped with commercial AFUs and the other one relied on the natural introduction of outside air. Measurements were taken during regular school days in the winter at a primary school in Hangzhou, China. Three AFU ventilation modes, i.e., fresh air, mixed air and recirculation mode were evaluated, respectively. We found that classrooms equipped with AFU showed significant PM₂.₅ reductions, but this was accompanied by CO₂ build-ups. In classroom with AFUs on internal recirculation, nearly 70% of the PM₂.₅ concentration at the beginning of the class was reduced by the end of a 40-min class. However, the CO₂ concentration was observed to reach levels that were six times greater than that of the ambient due to inadequate air change rate (<1 h⁻¹). To mitigate the issue of excessive in-classroom CO₂ accumulation when using AFUs, filtered outside air must be brought in during their operation. In comparison with the internal recirculation mode, we demonstrated that the fresh air mode that draws the supply air from outdoor can considerably lower CO₂ build-ups in the classroom with a moderate increase in PM₂.₅ concentration of 15 ± 10 μg/m³. The findings from this study helps policy makers especially in developing countries with serious air pollution issues to determine whether or not to install AFU in primary schools and what ventilation mode is effective in reducing negative health effects.

Antidepressants discharged into natural waters are likely to become a new type of endocrine pollutant, which may impact the interspecific relationship in aquatic ecosystem. Induced defense of cladocerans plays an important role in maintaining the balance of interspecific relationships between cladocerans and higher trophic levels. Here we studied the effects of antidepressant sertraline, a selective serotonin reuptake inhibitor, on the induced defensive traits of Ceriodaphnia cornuta in response to invertebrate predator Chaoborus larvae kairomone, including morphological defense and life history traits. We also conducted the predation experiments to check the selection rate of Chaoborus larvae during directly ingesting C. cornuta that were exposed to Chaoborus larvae kairomone at high concentration of sertraline. Results showed sertraline had an interference effect on the induced morphological defense of C. cornuta in response to Chaoborus larvae kairomone, i.e. the high concentration of sertraline (20 and 100 μg L⁻¹) significantly reduced the horns induction. However, the different concentrations of sertraline generally did not affect the life history traits of C. cornuta, regardless of presence or absence of Chaoborus larvae kairomone. The predation experiment demonstrated that the inhibition of sertraline on the induced morphological defense of C. cornuta can promote the feeding selective efficiency of Chaoborus larvae, and thus cause C. cornuta easily to be predated by Chaoborus larvae. Our results suggested that sertraline at the concentrations that are not direct harmful to life history traits of C. cornuta can seriously affect the predator-prey relationship, indicating that effects of pollutants on interspecific relationships should be considered comprehensively to avoid underestimating the potential risk of pollutants to ecosystems.

In vitro bioassays have been used as a bioanalytical means of detecting dioxin-like compounds (DLCs) in environmental matrices and have been suggested as a tool for quantifying DLCs in sediments. The present study evaluated the relationship between bioanalytical results from the micro-7-ethoxyresorufin-O-deethylase (EROD) bioassay and chemical analytical results in 25 sediment samples collected from rivers across Germany. Sediments were collected, polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) and dioxin-like polychlorinated biphenyls (DL-PCBs) were extracted from the sediments, biological toxicity equivalent quotients (BEQs) were determined by micro-EROD assay and toxicity equivalent quotients (TEQs) were calculated from chemical analysis. Correlations between BEQs and TEQs were evaluated, and linear regression modeling was performed, excluding 6 samples as validation data, to derive equations for predicting TEQs from BEQs. Validation data was tested to evaluate predictive capabilities of the models. Correlations were observed between BEQ and TEQ for PCDD/Fs (r=0.987), PCBs (r=0.623), measured sum of PCDD/F and PCBs (r = 0.975) and calculated sum of PCDD/F and PCBs (r = 0.971). The modeling equations provided low variances as evaluated by mean absolute error (MAE) (≤10.3 pg/g) and root mean square error (RMSE) (≤15.8 pg/g) indicating that expected TEQs could be reasonably well calculated from BEQs. Predicted TEQs from validation data fell within the 95% probability intervals of the test data and had low variances (MAE≤6.5 pg/g) and (RMSE≤10.7 pg/g). Our results indicate that the micro-EROD bioassay can be used as a screening tool for DLCs in sediment and has the capability to be used as an alternate method to chemical analysis for quantifying dioxin-like potential of sediments.