Frontiers of Environmental Science & Engineering最新文献

Abstract

Electrochemical disinfection (ECD) is a promising disinfection technique for wastewater reclamation; however, the impacts of ECD on the microbiome in secondary effluent wastewater remain unknown. In this study, Propidium monoazide-qPCR (PMA-qPCR) and the plate count method were used to evaluate the inactivation performance, and the PMA-16S rRNA gene sequences of living cells were targeted to study the microbiome. A discrepancy was found between PMA-qPCR and the plate count method in the evaluation of cell count, with increases of 1.5 to 2.2 orders of magnitude in the disinfection rate after 150 s of disinfection. However, the cell count recovered and occasionally exceeded original levels within 3 d after disinfection. Biodiversity was suppressed after ECD, but the microbiome after 150 s disinfection retained a higher level of evenness and stability in the community with a median Shannon index (> 3.7). Pathogenic bacteria remained high in relative abundance even after 150 s of 25 V disinfection, but the biofilm-forming population was effectively suppressed by ECD. The co-occurrence network revealed a centralized and fragile network as disinfection persisted, demonstrating the destabilizing effects of ECD on the microbiome. Functional pathways for cell membrane synthesis and organic compound degradation were enriched after ECD. The reaction of the microbiome after ECD was similar to other disinfection techniques in terms of community structure.

The pollution of antibiotics in aquatic environments has received extensive attention. Yet, research on antibiotic contamination in river-lake systems, a significant form of modern aquatic environments, still needs to be explored. This study focuses on the Chaohu Basin (China) investigating the occurrence characteristics, influencing factors, and risk assessments of antibiotics in the river-lake system. The total antibiotic concentrations in the water phase and sediment phase were 3.14–1887.49 ng/L and 0.92–1553.75 ng/g, respectively. Clindamycin was the predominant antibiotic in the water phase, whereas tetracycline prevailed in the sediment phase. Notable differences in concentration and structural composition of antibiotics between the tributaries (river system) and Chaohu Lake were observed, indicating the involvement of various geochemical processes in the attenuation of antibiotics during transport to the receiving lake. Spatial analysis suggested that the western river is the primary source of antibiotics in Chaohu Lake. Controlling nutrient influx in heavily polluted areas is crucial to addressing the escalating issue of antibiotic pollution in the river-lake system. The widespread occurrence of clindamycin in the waters is likely due to wastewater treatment plant discharges, and high-intensity human activities continue to exacerbate antibiotic contamination. Risk assessment indicated that sulfamethoxazole, tetracycline, lincomycin, and clindamycin ranked in the top four with the highest risks to the most sensitive aquatic organisms. Nonetheless, the antibiotics presented no risk to consumer health. This study provides valuable insights for controlling antibiotic pollution in riverlake systems.

Heavy metals (HMs) represent pervasive and highly toxic environmental pollutants, known for their long latency periods and high toxicity levels, which pose significant challenges for their removal and degradation. Therefore, the removal of heavy metals from the environment is crucial to ensure the water safety. Biochar materials, known for their intricate pore structures and abundant oxygen-containing functional groups, are frequently harnessed for their effectiveness in mitigating heavy metal contamination. However, conventional tests for optimizing biochar synthesis and assessing their heavy metal adsorption capabilities can be both costly and tedious. To address this challenge, this paper proposes a data-driven machine learning (ML) approach to identify the optimal biochar preparation and adsorption reaction conditions, with the ultimate goal of maximizing their adsorption capacity. By utilizing a data set comprising 476 instances of heavy metal absorption by biochar, seven classical integrated models and one stacking model were trained to rapidly predict the efficiency of heavy metal adsorption by biochar. These predictions were based on diverse physicochemical properties of biochar and the specific adsorption reaction conditions. The results demonstrate that the stacking model, which integrates multiple algorithms, allows for training with fewer samples to achieve higher prediction accuracy and improved generalization ability.

Neural networks (NNs) have been used extensively in surface water prediction tasks due to computing algorithm improvements and data accumulation. An essential step in developing an NN is the hyperparameter selection. In practice, it is common to manually determine hyperparameters in the studies of NNs in water resources tasks. This may result in considerable randomness and require significant computation time; therefore, hyperparameter optimization (HPO) is essential. This study adopted five representatives of the HPO techniques in the surface water quality prediction tasks, including the grid sampling (GS), random search (RS), genetic algorithm (GA), Bayesian optimization (BO) based on the Gaussian process (GP), and the tree Parzen estimator (TPE). For the evaluation of these techniques, this study proposed a method: first, the optimal hyperparameter value sets achieved by GS were regarded as the benchmark; then, the other HPO techniques were evaluated and compared with the benchmark in convergence, optimization orientation, and consistency of the optimized values. The results indicated that the TPE-based BO algorithm was recommended because it yielded stable convergence, reasonable optimization orientation, and the highest consistency rates with the benchmark values. The optimization consistency rates via TPE for the hyperparameters hidden layers, hidden dimension, learning rate, and batch size were 86.7%, 73.3%, 73.3%, and 80.0%, respectively. Unlike the evaluation of HPO techniques directly based on the prediction performance of the optimized NN in a single HPO test, the proposed benchmark-based HPO evaluation approach is feasible and robust.

It has been confirmed that microplastics (MPs) are present in the environment. This study simulated secondary PE-MPs via aging and mechanical processes to evaluate their effects on Pak choi (Brassica rapa L.) over 21 d. Two common pollutants, dichlorodiphenyltrichloroethane (DDT) and naphthalene, were used in the combined toxicity tests. The results indicated that the growth of Pak choi was significantly inhibited after exposure to secondary PE-MPs, and the combined effects were antagonistic, owing to the adsorption capacity of secondary PE-MPs to DDT and naphthalene. Oxidative stress in Pak choi can be markedly affected, leading to oxidative damage to plant cells. The moisture content, soil bulk density, soil density, cation exchange capacity (CEC), and FDA hydrolase in the planted soils increased in the treated groups, and the TOC content changed significantly. We also found that the microbial composition of the soil in the DDT and naphthalene groups showed more significant alterations than that in the other groups. Alpha diversity analysis showed that species diversity increased in the combined groups but indicated a clear downward trend in the single MPs groups. This study suggests that secondary PE-MPs harm the growth of Pak choi and can change soil properties, revealing the harm to the ecosystem of MPs in the soil.

Tin mine tailings (TMT) and fuming slag (FS) contain many heavy metals (As, Cr, Cu, Zn and Mn) that cause severe pollution to the environment. Herein, geopolymers were prepared using TMT, FS and flue gas desulfurization gypsum (FGDG) to immobilize heavy metals, and their compressive strength and heavy metal leaching toxicity were investigated. It was first determined that T4F5 (TMT:FS = 4:5) sample exhibited the highest compressive strength (7.83 MPa). T4F5 achieved 95% immobilization efficiency for As and Cr, and nearly 100% for Cu, Zn and Mn, showing good immobilization performance. A series of characterization analyses showed that heavy metal cations can balance the charge in the geopolymer and replace Al in the geopolymer structure to form covalent bonds. In addition, about 2%–20% of heavy metal Fe was immobilized in hydration products, heavy metal hydroxides and non-bridging Si–O and Al–O coordination with silica-aluminate matrices. AsO₃³⁻ was oxidized into AsO₄³⁻, which may form Ca–As or Fe–As precipitates. Cr₂O₇²⁻ was converted to CrO₄²⁻ under alkaline environment and then combined with OH⁻ to form Cr(OH)₃ precipitates. Mn²⁺ may react directly with dissolved silicate to form Mn₂SiO₄ and also form Mn(OH)₂ precipitates. The unstable Mn(OH)₂ can be further oxidized to MnO₂. The heavy metal cations were immobilized in the silicoaluminate lattice, while the anions tended to form insoluble precipitates. These results may benefit the industry and government for better handling of TMT, FS and solid wastes containing the abovementioned five heavy metals.

Recently, research on hydrogel materials with a porous structure and superior water absorption capabilities significantly grown. However, the hydrogel under gravity-driven separation conditions often exhibit an unstable pore structure, poor mechanical properties, and limited functionality. To this end, this work presents a novel approach that combines a macro-micro double bionic strategy with a triple crosslinking method to develop a multifunctional alginate composite hydrogel filter (2%-SA-κ-CG-PVA-Ca²⁺, 2%-SKP-Ca²⁺ for short) with a stable pore structure and superior mechanical properties, which possessed an umbrella-shaped structure resembling that of jellyfish. The 2%-SKP-Ca²⁺ filter was synthesized using polyvinyl alcohol (PVA) as a stable structure-directing agent, and sodium alginate (SA) and κ-carrageenan (κ-CG) as polymer hydrogels. The distinctive umbrella-shaped hydrogel of 2%-SKP-Ca²⁺ filter, formed through the triple crosslinking method, overcomes the limitations of unstable pore structure and poor durability seen in hydrogels prepared by traditional crosslinking methods. Furthermore, the utilization of the 2%-SKP-Ca²⁺ filter in water treatment demonstrates its good selective permeability, excellent resistance to fouling, and extended longevity, which enables it to simultaneously achieve the multifunctional water purification and the coating of multi-substrate anti-fouling coatings. Therefore, not only does this research provide an efficient, multifunctional, highly pollution-resistant preparation method for designing a new filter, but it also confirms the application prospect of the macro-micro dual bionic strategy developed in this study in complex water treatment.

Nanoplasctics (NPs), which are very small in particle size, exert toxic effect to organisms. Additionally, compared to original NPs, photodegraded NPs would pose higher toxicity. This is because their relatively higher specific surface areas and the presence of additives which can more easily leach. How original NPs and aged NPs affect plant growth has not been widely investigated. This work chose polyvinyl chloride NPs (PVC-NPs) that were subjected to up to 1000 h UV light radiation to explore the impact of PVC-NPs on the growth of pea seedlings (Pisum Sativum L.). The results indicated the existence of PVC-NPs with longer UV light radiation time and higher concentrations had more negative influences on pea seedlings’ growth such as germination rate (decreased by 10.6%–22.5%), stem length (decreased by 2.8%–8.1%), dry weight (decreased by 6.3%–7.1%) and fresh weight (decreased by 6.7%–14.8%). It was also noted that photodegraded PVC-NPs resulted in damage to leaf stomata and roots, hindering photosynthesis and absorption of nutrients and hence the decrease in chlorophyll and soluble sugar contents. According to transcriptomic investigation results, the presence of aged PVC-NPs primarily influenced protein processing in endoplasmic reticulum (upregulated metabolic pathway) and phenylpropanoid biosynthesis (downregulated metabolic pathway) of pea seedlings. These results provide an in-depth understanding of how NPs influence the growth of plants.

Mathematical modeling of anaerobic digestion is a powerful tool to predict gas yields and optimize the process. The Anaerobic Digestion Model No. 1 (ADM1) is a widely implemented model for this purpose. However, modeling full-scale biogas plants is challenging due to the extensive substrate and parameter characterization required. This study describes the modification of the ADM1 through a simplification of individual process phases, characteristic components and required parameters. Consequently, the ability of the simplified model to simulate the co-digestion of grass silage and cattle slurry was evaluated using data from a full-scale biogas plant. The impacts of substrate composition (crude carbohydrate, protein and lipid concentration) and variability of carbohydrate degradability on simulation results were assessed to identify the most influential parameters. Results indicated that the simplified version was able to depict biogas and biomethane production with average model efficiencies, according to the Nash-Sutcliffe efficiency (NSE) coefficient, of 0.70 and 0.67, respectively, and was comparable to the original ADM1 (average model efficiencies of 0.71 and 0.63, respectively). The variability of crude carbohydrate, protein and lipid concentration did not significantly impact biogas and biomethane output for the data sets explored. In contrast, carbohydrate degradability seemed to explain much more of the variability in the biogas and methane production. Thus, the application of simplified models provides a reliable basis for the process simulation and optimization of full-scale agricultural biogas plants.

Under the pressure of global droughts and water shortage, it is essential to evolve toward a sustainable and robust water system. One possible avenue is the maximum reuse of treated wastewater, but the quality of which determines its reuse. Therefore, inorganic (Cd, Pb, Cr, Ni, Cu, and As) and organic (xenoestrogens and polycyclic aromatic contaminants, PACs) contaminants were monthly monitored in an effluent of the wastewater treatment plant (WWTP), the surrounding surface waters and the local groundwater in Belgium. Dissolved and particulate concentrations of inorganic contaminants in these water bodies were analyzed. In addition, Diffusive Gradients in Thin-films (DGT) was used in situ to obtain bioavailable metal fractions. In the WWTP effluent and surface waters, only Ni exceeds the Annual Average-Environmental Quality Standard (AA-EQS), while in the groundwater, dissolved As was the predominant element. Moreover, in the surface and effluent waters the highest lability degrees were observed for Cd and Ni. The concentrations of these metal species in the effluent water were lower than in the other water bodies. Micro-organic pollutants, xenoestrogens and PACs were analyzed by dual Estrogen and Aryl hydrocarbon Receptor - Chemical Activated LUciferase gene eXpression (ER & AhR-CALUX) assays. Since the annual averaged (AA) bioequivalent concentration of E2 (0.18 ng/L) is below the AA-EQS standard (0.4 ng/L), and the bioequivalent concentration of benzo[a]pyrene never exceeded the maximum admissible concentration (MAC), the reclamation and reuse of treated wastewater for groundwater replenishment and agricultural irrigation should pose no environmental problems, at least in a short-term.