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.

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.

Air quality management is underpinned by continuous measurements of concentrations of target air pollutants in monitoring stations. Although many approaches for optimizing the number and location of air quality monitoring stations are described in the literature, these are usually focused on dense networks. However, there are small and medium-size urban areas that only require one monitoring station but also suffer from severe air pollution. Given that target pollutants are usually measured at the same sampling points; it is necessary to develop a methodology to determine the optimal location of the single station. In this paper, such a methodology is proposed based on maximizing an objective function, that balances between different pollutants measured in the network. The methodology is applied to a set of data available for the city of Cartagena, in southeast Spain. A sensitivity analysis reveals that 2 small areas of the studied city account for 80% of the optimal potential locations, which makes them ideal candidates for setting up the monitoring station. The methodology is easy to implement, robust and supports the decision-making process regarding the siting of fixed sampling sites.

Ingestion of anthropogenic litter has been well documented in marine vertebrates, but comparatively little is known about marine invertebrates. We report macrolitter ingestion by the sandy anemone Bunodactis reynaudi at Muizenberg beach in False Bay, South Africa. Monthly surveys from May 2015 to August 2019 collected 491 ingested litter items (9.4 ± 14.9 items·month⁻¹, 39.8 ± 71.5 g·month⁻¹), of which >99% were plastic. The number of ingested items was correlated with the abundance of stranded items and ingestion peaked in autumn when seasonal rains washed more litter into the bay. Most ingested litter was clear (39%), white (16%) and black/purple (15%). Comparison with environmental litter showed selection for flexible plastics, particularly bags/packets and food packaging. Experimental feeding trials found that B. reynaudi selected for pieces of HDPE bag suspended in seawater for 2–20 days, suggesting that biofilms enhance the palatability of flexible plastics. Studies are needed to assess the possible impacts of plastic ingestion on B. reynaudi. While only a small proportion of the population currently ingest litter, ingestion might become more common if environmental litter loads increase. This might negatively affect the anemone’s ability to respond to other environmental changes such as increasing levels of heavy metal pollution.

Advanced oxidation processes based on carbon catalysis is a promising strategy possessing great potential for environmental pollution degradation. Herein, nitrogen-doped biochar nanosheets (NCS-x) were synthesized using a nitrogen-rich biomass (Candida utilis) as sole precursor. The involvement of environmental-friendly molten salt (NaCl and KCl) in pyrolysis process not only facilitated the exfoliation of biochar, but also favored the retention of N element in biochar. When applying as catalyst for peroxymonosulfate activation, the as-obtained NCS-6 exhibited outstanding performance in catalytic degradation of bisphenol A (BPA). A 100% removal efficiency was observed in 6 min with fast reaction kinetic (k = 1.36 min⁻¹). Based on quenching test and in-situ electron paramagnetic resonance analysis, both radical pathway and non-radical pathway were suggested to be involved in BPA degradation, while singlet oxygen was identified as the dominant reactive oxygen species. Furthermore, the ecotoxicity evaluation using Chlorella vulgaris as ecological indicator indicated that BPA solution after degradation was less toxic than the original solution. It is expected that this green and facile strategy holds great promise for value-added conversion of nitrogen-rich biomass to highly efficient biochar nanosheets for environment remediation.