Distinct community assembly processes underlie significant spatiotemporal dynamics of abundant and rare bacterioplankton in the Yangtze River

• Season and landform influenced spatiotemporal patterns of abundant and rare taxa. • Different stochastic processes dominated abundant and rare subcommunity assembly. • River flow and suspended solids regulated assembly processes of rare taxa. The rare microbial biosphere provides broad ecological services and resilience to various ecosystems. Nevertheless, the biogeographical patterns and assembly processes of rare bacterioplankton communities in large rivers remain uncertain. In this study, we investigated the biogeography and community assembly processes of abundant and rare bacterioplankton taxa in the Yangtze River (China) covering a distance of 4300 km. The results revealed similar spatiotemporal patterns of abundant taxa (AT) and rare taxa (RT) at both taxonomic and phylogenetic levels, and analysis of similarities revealed that RT was significantly influenced by season and landform than AT. Furthermore, RT correlated with more environmental factors than AT, whereas environmental and spatial factors explained a lower proportion of community shifts in RT than in AT. The steeper distance–decay slopes in AT indicated higher spatial turnover rates of abundant subcommunities than rare subcommunities. The null model revealed that both AT and RT were mainly governed by stochastic processes. However, dispersal limitation primarily governed the AT, whereas the undominated process accounted for a higher fraction of stochastic processes in RT. River flow and suspended solids mediated the balance between the stochastic and deterministic processes in RT. The spatiotemporal dynamics and assembly processes of total taxa were more similar as AT than RT. This study provides new insights into both significant spatiotemporal dynamics and inconsistent assembly processes of AT and RT in large rivers.     

An interval joint-probabilistic programming method for solid waste management: a case study for the city of Tianjin, China

Currently, environmental protection and resources conservation continue to be challenges faced by solid-waste managers in China. These challenges are being further compounded by rapid socioeconomic devel- opment and population growth associated with increased waste generation rates and decreased waste disposal capacities. In response to these challenges, an interval joint-probabilistic mixed-integer programming （IJMP） method is developed for supporting long-term planning of waste management activities in the city of Tianjin, which is one of the largest municipalities in the northern part of China. In the IJMP, joint probabilistic constraints are introduced into an interval-parameter mixed-integer programming framework, such that uncertainties presented in terms of interval values and random variables can be reflected. Moreover, a number of violation levels for the waste-management-capacity constraints are examined, which can facilitate in-depth analyses of tradeoffs among economic objective and system-failure risk. The results indicate that reasonable solutions have been generated. They are valuable for supporting the adjustment of the city＇s existing waste-management practices and the long- term planning of the city＇s waste-management facilities.     

Microbial community and functional genes in the rhizosphere of alfalfa in crude oil-contaminated soil

A rhizobox system constructed with crude oil-contaminated soil was vegetated with alfalfa (Medicago sativa L.) to evaluate the rhizosphere effects on the soil microbial population and functional structure, and to explore the potential mechanisms by which plants enhance the removal of crude oil in soil. During the 80-day experiment, 31.6% of oil was removed from the adjacent rhizosphere (AR); this value was 27% and 53%higher than the percentage of oil removed from the far rhizosphere (FR) and from the non-rhizosphere (NR), respectively. The populations of heterotrophic bacteria and hydrocarbon-degrading bacteria were higher in the AR and FR than in the NR. However, the removal rate of crude oil was positively correlated with the proportion of hydrocarbon-degrading bacteria in the rhizosphere. In total, 796, 731, and 379 functional genes were detected by microarray in the AR, FR, and NR, respectively. Higher proportions of functional genes related to carbon degradation and organic remediation, were found in rhizosphere soil compared with NR soil, suggesting that the rhizosphere selectively increased the abundance of these specific functional genes. The increase in water-holding capacity and decrease in pH as well as salinity of the soil all followed the order of AR>FR>NR. Canonical component analysis showed that salinity was the most important environmental factor influencing the microbial functional structure in the rhizosphere and that salinity was negatively correlated with the abundance of carbon and organic degradation genes.     

Co- or Ni-modified Sn-MnOx low-dimensional multi-oxides for high-efficient NH3-SCR De-NOx: Performance optimization and reaction mechanism

NH₃-SCR performances were explored to the relationship between structure morphology and physio-chemical properties over low-dimensional ternary Mn-based catalysts prepared by one-step synthesis method.Due to its strong oxidation performance,Sn-MnOx was prone to side reactions between NO,NH₃ and O₂,resulting in the generation of more NO₂ and N₂ O,here most of N₂ O was driven from the non-selective oxidation of NH₃,while ...     

Copper fractal growth during recycling from waste printed circuit boards by slurry electrolysis

• Copper fractal growth was observed during WPCBs recycling by slurry electrolysis. • Dendrites fractal growth could be controlled by additive during electrodeposition. • Additive was proved to be an effective way to refine the copper crystal. • These findings contribute to enrich the study of slurry electrolysis. Superfine copper particles could be directly prepared from waste printed circuit boards by slurry electrolysis. Meanwhile, copper fractal growth could be observed. To better understand this phenomenon, the factors that affect copper dendrites in a point-cathode system were discussed in detail. These results showed that the fractal degree of copper dendrites increased as the increase of applied voltage and the decrease of copper sulfate and gelatin concentrations. Sodium lauryl sulfate and hydrochloric acid concentrations could not significantly impact the fractal degree of copper dendrites, while gelatin concentration could. The minimum copper fractal dimension was 1.069 when gelatin and copper sulfate concentration was 120 mg/L and 0.1 mol/L, respectively with an applied voltage of 11 V. Moreover, the results diffusion-limited aggregation model demonstrated that particle translational speed, particle numbers and binding probability significantly affected copper dendrite patterns. The scanning electron microscopy results indicated that the three additives greatly affected the refinement of the copper crystal. These findings contribute to enrich the theoretical study on metals recovery from e-waste by slurry electrolysis.     

Effects of different types of biochar on the properties and reactivity of nano zero-valent iron in soil remediation

• Biochar enhanced the mobility and stability of zero-valent iron nanoparticles. • Particle performance was best when the BC:nZVI mass ratio was 1:1. • Bagasse-BC@nZVI removed 66.8% of BDE209. The addition of nano zero-valent iron (nZVI) is a promising technology for the in situ remediation of soil. Unfortunately, the mobility and, consequently, the reactivity of nZVI particles in contaminated areas decrease due to their rapid aggregation. In this study, we determined how nZVI particles can be stabilized using different types of biochar (BC) as a support (BC@nZVI). In addition, we investigated the transport behavior of the synthesized BC@nZVI particles in a column filled with porous media and their effectiveness in the removal of BDE209 (decabromodiphenyl ether) from soil. The characterization results of N₂ Brunauer–Emmett–Teller (BET) surface area analyses, scanning electron microscopy (SEM), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR) indicated that nZVI was successfully loaded into the BC. The sedimentation test results and the experimental breakthrough curves indicated that all of the BC@nZVI composites manifested better stability and mobility than did the bare-nZVI particles, and the transport capacity of the particles increased with increasing flow velocity and porous medium size. Furthermore, the maximum concentrations of the column effluent for bagasse–BC@nZVI (B–BC@nZVI) were 19%, 37% and 48% higher than those for rice straw–BC@nZVI (R–BC@nZVI), wood chips–BC@nZVI (W–BC@nZVI) and corn stalks–BC@nZVI (C–BC@nZVI), respectively. A similar order was found for the removal and debromination efficiency of decabromodiphenyl ether (BDE209) by the aforementioned particles. Overall, the attachment of nZVI particles to BC significantly increased the reactivity, stability and mobility of B–BC@nZVI yielded, and nZVI the best performance.     

Translocation and biotoxicity of metal (oxide) nanoparticles in the wetland-plant system

• Aquatic plants are more likely to absorb TiO₂ NPs that are beneficial to them. • Ag NPs inhibited the growth of aquatic plants under both 5- and 60-day exposure. • CeO₂ NPs had positive/negative impact on plant in 5/60-day exposure, respectively. • TiO₂ NPs presence could enhance the photosynthesis and increase the plant biomass. • The ENPs changed plant activity, which resulted in changes of wetland performance. Engineered nanoparticles (ENPs) threaten the environment through wastewater discharging. Generally, constructed wetlands (CWs) are efficient methods for ENPs removal. However, the biotoxicity of ENPs on plants in CWs is unclear. Here, we investigated the distribution and bio-impacts of different ENPs (Ag NPs, TiO₂ NPs, and CeO₂ NPs) in plants under 5- and 60-day exposure to 1 and 50 mg/L concentrations. Results showed that ENPs appeared in the vascular bundle and mesophyll cell space, which induced the variation in antioxidase activities (e.g., superoxide dismutase [SOD], peroxidase [POD], and catalase [CAT] activities) as well as overproduction of malondialdehyde (MDA). Additionally, Ag NPs inhibited photosynthesis rate and root activity during two exposure phases. CeO₂ NPs had positive and negative impacts on plants in 5- and 60-day exposure, respectively. Inversely, TiO₂ NPs enhanced photosynthesis and root activity under 60-day exposure. Finally, the contents of the C, N, and P elements in plants fluctuated in response to ENPs stress. All results have a positive correlation with the wetland performance under ENPs exposure except for TiO₂ NPs treatment. Overall, our study systematically reveals aquatic plants' responses to ENPs and provides a reference for building ecological treatment systems to purify wastewater containing ENPs.     

Toxic effect of sodium perfluorononyloxy-benzenesulfonate on Pseudomonas stutzeri in aerobic denitrification,cell structure and gene expression

• OBS inhibited the growth of P. stutzeri and destroyed its structure. • OBS was toxic to the aerobic denitrification process of P. stutzeri. • OBS induced the production of ROS in P. stutzeri. • OBS affected the expression of key genes involved in denitrification and SOD. The toxicities of sodium perfluorononyloxy-benzenesulfonate (OBS) to animals and plants are similar to those of perfluorooctane sulfonate. However, the mechanism of its toxicity to aerobic denitrifying bacteria is still unclear. In the present study, the ecotoxicity of OBS on an aerobic denitrifying strain, Pseudomonas stutzeri, was evaluated. The results showed that a dosage of OBS clearly affected the growth and aerobic denitrification of P. stutzeri. When compared with an unamended control, the degradation efficiency of the total nitrogen decreased by 30.13% during exposure to 200 mg/L of OBS, and the complete degradation time of nitrate-nitrogen was delayed by 4 h. The lactate dehydrogenase and superoxide dismutase produced by the bacteria increased with the concentration of OBS, and reactive oxygen species were also detected by confocal laser scanning microscope imaging. Transmission electron microscope imaging revealed chromosome deformation of the cells and damage to cell content; moreover, outer membrane vesicles were secreted from the bacteria, which was the important detoxification mechanism of P. stutzeri to OBS. Expression of the genes involved in aerobic nitrification and oxidative stress were also changed under OBS stress, which further confirmed the toxicity of OBS to P. stutzeri. This study reveals the environmental exposure risk of OBS from the perspective of microorganisms.     

Activity and characteristics of “Oxygen-enriched” highly reactive absorbent for simultaneous flue gas desulfurization and denitrification

An “Oxygen-enriched” highly reactive absorbent was prepared by mixing fly ash, lime and a small quantity of KMnO₄ for simultaneous desulfurization and denitrification. Removal of SO₂ and NO simultaneously was carried out using this absorbent in a flue gas circulating fluidized bed (CFB). The highest simultaneous removal efficiency, 94.5% of SO₂ and 64.2% of NO, was achieved under the optimal experiment conditions. Scanning Electron Microscope (SEM) and Accessory X-ray Energy Spectrometer (EDX) were used to observe the surface characteristics of fly ash, lime, “Oxygen-enriched” highly reactive absorbent and the spent absorbent. An ion chromatograph (IC) and chemical analysis methods were used to determine the contents of sulfate, sulfite, nitrate and nitrite in the spent absorbents, the results showed that sulfate and nitrite were the main products for desulfurization and denitrification respectively. The mechanism of removing SO₂ and NO simultaneously was proposed based on the analysis results of SEM, EDX, IC and the chemical analysis methods.     

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured Parzen Estimator

● Hybrid deep-learning model is proposed for water quality prediction. ● Tree-structured Parzen Estimator is employed to optimize the neural network. ● Developed model performs well in accuracy and uncertainty. ● Usage of the proposed model can reduce carbon emission and energy consumption. Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants (WWTPs). However, some water quality metrics are not measurable in real time, thus influencing the judgment of the operators and may increase energy consumption and carbon emission. One of the solutions is using a soft-sensor prediction technique. This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit (BiGRU) combined with Gaussian Progress Regression (GPR) optimized by Tree-structured Parzen Estimator (TPE). TPE automatically optimizes the hyperparameters of BiGRU, and BiGRU is trained to obtain the point prediction with GPR for the interval prediction. Then, a case study applying this prediction method for an actual anaerobic process (2500 m³/d) is carried out. Results show that TPE effectively optimizes the hyperparameters of BiGRU. For point prediction of COD_eff and biogas yield, R² values of BiGRU, which are 0.973 and 0.939, respectively, are increased by 1.03%–7.61% and 1.28%–10.33%, compared with those of other models, and the valid prediction interval can be obtained. Besides, the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation. It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.