Biochar has recently been fascinating for research in many environment areas due to its potential applications. In this research, graphene, and nano zero-valent iron (nZVI) were integrated with biochar and used for copper immobilization in the soil. Initially, the biomass feedstock was pyrolyzed under N₂ atmosphere from 150 to 650 °C and immersed in an aqueous solution containing graphene, and then impregnated with nZVI. Laboratory characterization with different instruments (eg. SEM, TEM, XRD, UV–Vis, VSM, and XPS) showed that graphene sheets and reactive nZVI were loaded on the biochar surface during the development process. The 450 °C was considered as optimum pyrolysis temperature based on the effective surface properties of the obtain biochar material. Boehm titration and functional group analysis confirmed the presence of carboxylic groups, phenolic groups in the corn stack biochar supported graphene oxide/nZVI (CTBC-GO/nZVI). Thermogravimetric analysis showed that nZVI incorporation to biochar surface could improve thermal stability as compared to graphene oxide incorporated biochar and pristine biochar. The material was utilized for copper (Cu) immobilization in the soil and a comparative evaluation was established on the basis of efficiency. The soil experiment showed that the CTBC-GO/nZVI has a superior immobilization efficiency of copper than pristine biochar and GO@BC. The available Cu content decreased by > 65% in CTBC-GO/nZVI amended soil after 14 days. Sequential extraction procedure (SEP) results suggested that CTBC-GO/nZVI promoted the conversion of more accessible Cu into the less accessible and bioavailable forms to reduce the toxicity of Cu. Therefore, CTBC-GO/nZVI composite is a promising and effective amendment for immobilizing Cu in contaminated soils and improving soil properties.This work can put forward a strategy to develop magnetic biochar composites and an application towards toxic heavy metals immobilization in soil.

Microbiome community structure is intimately involved in key biological functions in the gastrointestinal (GI) system including nutrient absorption and lipid metabolism. Recent evidence suggests that disruption of the GI microbiome is a contributing factor to metabolic disorders and obesity. Poor diet and chemical exposure have been independently shown to cause disruption of the GI microbiome community structure and function. We hypothesized that the addition a chemical exposure to overfeeding exacerbates adverse effects on the GI microbiome community structure and function. To test this hypothesis, adult zebrafish were fed a normal feeding regime (Control), an overfeeding regime (OF), or an overfeeding regime contaminated with diethylhexyl phthalate (OF + DEHP), a suspected obesogen-inducing chemical. After 60 days, fecal matter was collected for sequencing, identification, and quantification of the GI microbiome using the 16s rRNA hypervariable region. Analysis of beta diversity indicated distinct microbial profiles between treatments with the largest divergence between Control and OF + DEHP groups. Based upon functional predictions, OF + DEHP treatment altered carbohydrate metabolism, while both OF and OF + DEHP affected biosynthesis of fatty acids and lipid metabolism. Co-occurrence network analysis revealed decreases in cluster size and a fracturing of the microbial community network into unconnected components and a loss of keystone species in the OF + DEHP treatment when compared to Control and OF treatments. Data suggest that the addition of DEHP in the diet may exacerbate microbial dysbiosis, a consequence that may explain in part its role as an obesogenic chemical.

Legacy perfluoroalkyl and poly-fluoroalkyl substances (PFASs) are gradually phased out because of their persistence, bioaccumulation, toxicity, long-distance transport and ubiquity in the environment. Alternatively, emerging PFASs are manufactured and released into the environment. It is accepted that PFASs can impact microbiota, although it is still unclear whether emerging PFASs are toxic towards soil microbiota. However, it could be assumed that OBS could impact soil microorganisms because it had similar chemical properties (toxicity and persistence) as legacy PFASs. The present study aimed to explore the influences of an emerging PFAS, namely sodium p-perfluorous nonenoxybenzene sulfonate (OBS), on archaeal, bacterial, and ammonia-oxidizing archaea (AOA) and bacteria (AOB) communities and ammonia oxidation. Grassland soil was amended with OBS at different dosages (0, 1, 10 and 100 mg/kg). After OBS amendment, tolerant microorganisms (e.g., archaea and AOA) were promoted, while susceptive microorganisms (e.g., bacteria and AOB) were inhibited. OBS amendment greatly changed microbial structure. Potential nitrifying activity was inhibited by OBS in a dose-dependent manner during the whole incubation. Furthermore, AOB might play a more important role in ammonia oxidation than AOA. Overall, OBS influenced ammonia oxidation by regulating the activity, abundance and structure of ammonia-oxidizing microorganisms, and could also exert influences on total bacterial and archaeal populations.

Rice is a main source of dietary cadmium (Cd), thus, how to reduce the Cd concentration in brown rice has received extensive attention worldwide. In three acidic paddy soils slightly to moderately contaminated with Cd, a series of field experiments were conducted to evaluate the effects of different proportions of nitrogen-phosphorus-potassium (N-P-K) fertilizer (urea, calcium magnesium phosphate, and potassium carbonate, respectively) alone or coupled with a topdressing of manganese (Mn) fertilizer at the tillering stage on reducing Cd bioavailability in soil and uptake in rice. The rational application of N-P-K fertilizer not only provided the basic nutrients to promote the normal growth of rice but also increased soil pH and thereby reduced the Cd bioavailability in soil. The Mg(NO₃)₂-extracted Cd concentrations in the three soils were reduced by 26.46–56.53%, while TCLP-extracted Cd were reduced by 19.87–45.41%, with little influence on soil cation exchange capacity (CEC) and organic matter (OM). The application of Mn fertilizer at the tillering stage increased Mn and Cd sequestration in the iron plaque. The Mn content in iron plaque increased by 15.71–58.67% and a significant positive correlation between Cd and Mn was observed at the three sites. Collectively, this combined method of fertilization significantly reduced Cd accumulation in rice tissues, the Cd concentrations in roots of treated plants decreased by 11.18–37.78%, whereas the concentrations in straw decreased by 13.16–41.03%. Particularly to brown rice, in which accumulation decreased by 25.19–44.70%, 37.35–47.84%, and 38.00–60.88% in three typical paddy fields, but no significant effect was observed for the Cd translocation factors (TF) among rice tissues. Thus, the basal application of combined urea and alkaline inorganic fertilizers followed by topdressing of Mn fertilizer may be a promising and cost-effective tactics for the remediation of Cd-contaminated paddy soils.

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.