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Photocatalytic membranes were fabricated via vacuum assisted layer-by-layer (LBL) self-assembly of 0D-2D Bi 2 MoO 6 -g-C 3 N 4 on a PDA coated thin-film composite PVDF substrate by Schiff base reaction. The composite membrane possesss excellent photocatalytic activity under visible light. [Display omitted] A Self-cleaning surface can efficaciously solve the problem of irreversible contamination buildup on filtration membranes. Photocatalytic membranes were fabricated via vacuum assisted layer-by-layer (LBL) self-assembly of 0D-2D Bi 2 MoO 6 -g-C 3 N 4 on a PDA coated thin-film composite PVDF substrate by Schiff base reaction. The rejection rate of the simulated polysaccharide was more than 90%, and that of the simulated protein was more than 80%. The combination of the membrane and the photocatalyst promoted the degradation of tetracycline hydrochloride by the composite membrane to 67.85% when original membranes had minor effect. Under visible light, reversible radiation pollutants (R r) gradually replaced irreversible pollutants (R ir) as the main pollutants. The flux recovery ratio (FRR) of 0D-2D Bi 2 MoO 6 -g-C 3 N 4 /PVDF membrane was 85% after being irradiated with visible light for 30 min. The flux recovery rate of contaminated photocatalytic membrane remained 75%, and the rejection was maintained in a stable range after four cycles of the cleaning operation under visible light. The results indicated that the excellent photocatalytic performance of 0D-2D Bi 2 MoO 6 -g-C 3 N 4 photocatalysis material and the increase of multi-dimensional functional layer morphology on pollutant contact area improved the mechanical stability, interception performance and self-cleaning performance of the composite membrane. This work not only builds a new type of composite coating membranes, but also help us to further understand the relationship between the dimensions of photocatalytic materials and the improvement of photocatalytic membrane performance. [ABSTRACT FROM AUTHOR]

Acute lung injury (ALI) is a serious illness that develops suddenly, progresses rapidly, has a poor treatment response and a high mortality rate. Studies have found that circular RNAs (circRNA) play a critical role in several diseases, but their role in ALI remains unclear. The aim of this study was to identify circRNAs that are associated with ALI and investigate their potential molecular mechanisms. A comparison of lung circRNA and microRNA expression profiles in mice with ALI and controls was performed by RNA-sequencing. A bioinformatic analysis was conducted to identify differentially expressed (DE) RNAs, to construct competitive endogenous RNA (ceRNA) networks, and to analyze their function and pathways. Then, a protein-protein interaction (PPI) network was generated by the Search Tool for the Retrieval of Interacting Genes database, and hub genes were identified using Cytoscape. Furthermore, a key ceRNA subnetwork was constructed based on these hub genes. Overall, we found 239 DE circRNAs and 42 DE microRNAs in ALI mice compared to controls. Additionally, the molecular mechanism of ALI was further understood by building ceRNA networks based on these DE genes. ALI-induced circRNAs are mostly function in the inflammatory response and metabolic processes. Moreover, DE circRNAs are primarily involved in the nuclear factor (NF)-kappa B and PI3K-Akt signaling pathways. Seven hub genes were derived from the PPI network of 191 genes, followed by the construction of circRNA-miRNA-hub gene subnetworks. In this study, circRNA profiles are remarkably changed in mice with LPS-triggered ALI, and their potential contribution to the disease is revealed. • Differential expression of circRNAs involved in ALI and their interactions with miRNAs and mRNAs. • Bioinformatics pipeline to study circRNA-miRNA-mRNA regulation network. • 7 hub genes (Ptpn6, Itga7, Bcl2l1, Tlr4, Vwf, Nfkb2, and Kdr). • 24 circRNA-6 miRNA- 7 hub genes interaction. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • MCU-C 3 N 4 was fabricated by supramolecular strategy. • MCU-C 3 N 4 /PVDF membranes were assembled by vacuum filtration with crosslinkers. • MCU-C 3 N 4 /PVDF membranes had excellent photocatalytic degradation of RhB and TC. • MCU-C 3 N 4 /PVDF membranes exhibited well self-cleaning properties. Abstract Recently, the application of membranes faces a big challenge due to membrane fouling, to alleviate this situation, the hybridization of photocatalysis and membrane filtration has aroused significant attention. In this study, we firstly introduced melamine, cyanuric acid and urea in dimethyl sulfoxide (DMSO) as precursors to fabricated the MCU(DMSO)-C 3 N 4 material with excellent photocatalytic performance, and immobilized it on PVDF membranes by vacuum filtration, subsequently adding polyethylene glycol and glutaraldehyde as crosslinkers from MCU-C 3 N 4 /PVDF membrane. The results demonstrate that with the MCU-C 3 N 4 ratio increasing, the membrane flux was gradually decreased. Besides, the photocatalytic efficiencies of MCU-C 3 N 4 /PVDF for rhodamine B (RhB) and tetracycline hydrochloride (TC) degradation are 84.24% and 71.26% respectively, which are about 8 times higher than that of the original membrane. To evaluate antifouling performance of photocatalytic membranes, we conducted a four-stage filtration system, and the flux recovery ratio (FRR) of MCU-C 3 N 4 /PVDF membranes reached over 80% (optimum 91%) under visible light irradiating (λ > 420 nm) for 30 min. Meanwhile, under visible light irradiation reversible fouling (Rr) gradually became the dominant fouling factor instead of the irreversible fouling (Rir), indicating the excellent antifouling performance of MCU-C 3 N 4 /PVDF membranes. This novel method to modify membranes with MCU-C 3 N 4 gives insight to photocatalytic and self-cleaning properties of photocatalytic composite membranes, providing theoretical basis for their broad application. [ABSTRACT FROM AUTHOR]

A low-cost and metal-free semiconductor-based photocatalyst driven by visible light has attracted great interest for water remediation. A relatively larger-surface area carbon nitride consisting of thin nanosheets is synthesized, firstly adopting melamine (M), cyanuric acid (C), urea (U) and dimethyl sulfoxide (DMSO) as the starting materials to form the supramolecular carbon nitride MCU(DMSO)-C 3 N 4 . Physical, chemical, and optical properties of the resulting samples were characterized. The photocatalytic performance of the MCU(DMSO)-C 3 N 4 was evaluated under visible light irradiation using rhodamine B (RhB), tetracycline hydrochloride (TC), and ciprofloxacin (CIP) as target pollutants. Results showed that MCU-C 3 N 4 exhibited much higher photocatalytic activity than pure g-C 3 N 4 , and when M, C, and U in DMSO with certain molar ratio (1:1:1) exhibited the highest activity. The larger surface area with more active reaction sites, increased bandgap with stronger REDOX ability, and higher separation efficiency of photogenerated electron-hole pairs contributed to higher photocatalytic activity. The photo-degradation is dominant by the active species (h + and O 2 − ) and converted product ( OH). This exploration of MCU(DMSO)-C 3 N 4 may open a window to the design of low-cost and highly efficient photocatalysis degradation systems for various wastewater treatments. [ABSTRACT FROM AUTHOR]

Due to the mass production and daily use of plastic products, the potential toxicity of microplastics to the water environment has attracted worldwide attention. In this work, the effect of typical microplastics (PET) on the performance of activated sludge from membrane bioreactors (MBR) was evaluated. The impacts on biological removal efficiency were unconspicuous with continuous dosing of 60 particles/L. However, further investigations revealed that PET particle accumulation caused adverse impacts on settleability and dewaterability. The SVI value increased from 53.3 ml/g MLSS to 69.9 ml/g MLSS and the CST in the PET reactor increased by 22%. Nevertheless, hydrophobicity was reduced by 49.2%. Mechanism studies exposed that the PET microplastics accumulation improved extracellular polymeric substances (EPS) from 116.96 mg/L to 138.70 mg/L and caused cell membrane damage. The abundance and diversity of microbial community reduced in activated sludge in PET reactor compared with control reactor. These phenomena revealed a possible hypothesis that the microplastic particles increased EPS and cytotoxicity of activated sludge. However, the rate of transmembrane pressure (TMP) build-up was significantly mitigated in PET-MBR compared to that in a control-MBR (1.27 folds), which attributes that physical scour of particles may still alleviate membrane contamination in MBR. • Membrane biofouling was alleviated due to physical scouring of PET particles. • The physicochemical properties of sludge were changed after long-term PET exposure. • Accumulation of PET particles caused EPS secretion and cell membrane damage. • Long-term exposure to PET changed the microbial community of MBR system. [ABSTRACT FROM AUTHOR]

The bacteria (including pathogenic bacteria) attached to road deposited sediments (RDS) may interrelate with the microbe in the atmosphere, soil and water through resuspension and wash-off, and is of great significance to human and ecological health. However, the characteristics of bacterial communities with different time scale on RDS were unknown to dates. Climate change prolonged the dry days between rain events in many areas, making the varied trend of bacterial communities might be more significant in short term. This study revealed the characteristics of bacterial communities on RDS in urban and suburban areas through seasonal and daily scale. The correlations between other factors (land use, particle size, and chemical components) and the bacterial communities were also analyzed. It was found that the season showed a higher association with the bacterial community diversity than land use and particle size in urban areas. The bacterial community diversity increased substantially throughout the short-term study period (41 days) and the variation of dominant bacteria could be fitted by quadratic function in suburbs. In addition, urbanization notably increased the bacterial community diversity, while the potential pathogenic bacteria were more abundant in the suburban areas, coarse RDS (>75 μm), and in spring. The chemical components on RDS showed special correlations with the relative abundance of dominant bacteria. The research findings would fill the knowledge gap on RDS bacterial communities and be helpful for the future research on the assembly process of bacterial communities. [Display omitted] • Season had a higher association with bacterial community diversity. • Bacterial diversity increased and was fitted by quadratic function at daily scale. • Urbanization notably increased the bacterial diversity on RDS. • Pathogenic bacteria were abundant in suburbs, coarse particles and spring. • Chemical components showed special correlations with dominant bacteria. [ABSTRACT FROM AUTHOR]

Microplastics (MPs) have recently attracted much attention due to their widespread distribution in the aquatic environment. Microplastics can act as a vector of heavy metals in the aquatic environment, causing a potential threat to aquatic organisms and human health. This review mainly summarized the occurrence of microplastics in the aquatic environment and their interaction with heavy metals. Then, we considered the adsorption mechanisms of MPs and heavy metals, and further critically discussed the effects of microplastics properties and environmental factors (e.g., pH, DOM, and salinity) on the adsorption of heavy metals. Finally, the potential risks of combined exposure of MPs and heavy metals to aquatic biota were briefly evaluated. This work aims to provide a theoretical summary of the interaction between MPs and heavy metals, and is expected to serve as a reference for the accurate assessment of their potential risks in future studies. • Microplastics serve as vectors of heavy metals in the aquatic environment. • The possible mechanisms of heavy metal adsorption by microplastics are complex. • Microplastics properties and environmental factors affect heavy metal adsorption. • Co-exposure of microplastics and heavy metals may have different effects on aquatic biota. [ABSTRACT FROM AUTHOR]

The accurate estimation of coastal water quality parameters (WQPs) is crucial for decision-makers to manage water resources. Although various machine learning (ML) models have been developed for coastal water quality estimation using remote sensing data, the performance of these models has significant uncertainties when applied to regional scales. To address this issue, an ensemble ML-based model was developed in this study. The ensemble ML model was applied to estimate chlorophyll-a (Chla), turbidity, and dissolved oxygen (DO) based on Sentinel-2 satellite images in Shenzhen Bay, China. The optimal input features for each WQP were selected from eight spectral bands and seven spectral indices. A local explanation strategy termed Shapley Additive Explanations (SHAP) was employed to quantify contributions of each feature to model outputs. In addition, the impacts of three climate factors on the variation of each WQP were analyzed. The results suggested that the ensemble ML models have satisfied performance for Chla (errors = 1.7%), turbidity (errors = 1.5%) and DO estimation (errors = 0.02%). Band 3 (B3) has the highest positive contribution to Chla estimation, while Band Ration Index2 (BR2) has the highest negative contribution to turbidity estimation, and Band 7 (B7) has the highest positive contribution to DO estimation. The spatial patterns of the three WQPs revealed that the water quality deterioration in Shenzhen Bay was mainly influenced by input of terrestrial pollutants from the estuary. Correlation analysis demonstrated that air temperature (Temp) and average air pressure (AAP) exhibited the closest relationship with Chla. DO showed the strongest negative correlation with Temp, while turbidity was not sensitive to Temp, average wind speed (AWS), and AAP. Overall, the ensemble ML model proposed in this study provides an accurate and practical method for long-term Chla, turbidity, and DO estimation in coastal waters. • A machine learning (ML) based ensemble model was proposed for the estimation of water quality parameters. • The feature selection was conducted for the ML-based ensemble model. • The ML-based ensemble model improved the estimation accuracy of non-optical active constituent. • SHAP method was employed to quantify the contribution of the input features. • Climate factors driving mechanism was considered on interannunal dynamics of water quality. [ABSTRACT FROM AUTHOR]

To evaluate the long-term climate change impacts on groundwater fluctuations of the Ardabil plain, Iran, a groundwater level (GWL) modeling was proposed in this study. Accordingly, the outputs of Global Climate Models (GCMs) under the sixth report of Coupled Model Intercomparison Project (CMIP6) and future scenario of the Shared Socioeconomic Pathway 5-8.5 (SSP5-8.5), were used as climate change forcing to the Machine learning (ML) models. The GCM data were first downscaled and projected for the future via Artificial Neural Networks (ANNs). Based on the results, compared to 2014 (the last year of the base period), the mean annual temperature may increase by 0.8 °C per decade until 2100. On the other hand, the mean precipitation may decrease by about 8% compared to the base period. Then, the centroid wells of clusters were modeled by Feedforward Neural Network (FFNN), examining different input combination sets to simulate both autoregressive and non-autoregressive models. Since each of the ML models can extract different kinds of information from a dataset, after finding the dominant input set via FFNN, GWL time series were modeled via various ML methods. The modeling results indicated that the ensemble of shallow ML models could lead to a 6% more accurate outcome than the individual shallow ML models, and 4% than the deep learning models. Also, the simulation results for future GWLs illustrated that temperature can impact groundwater oscillations directly, whereas precipitation may not have uniform impacts on the GWLs. The uncertainty evolving in the modeling process was quantified and observed to be in acceptable range. Modeling results showed that the main reason for the declining GWL in the Ardabil plain could be primarily linked to the excessive exploitation of the water table, while climate change impact could be also notable. • The average temperature will increase at different rates in different seasons. • The average precipitation will change variously in different months. • Ensemble model is more accurate than other ML models. • Temperature has a direct impact on groundwater fluctuations. • Precipitation impacts the groundwater in complex ways. [ABSTRACT FROM AUTHOR]