Mn-modified biochars (BCs) were developed by pre-treatment of feedstock (MBCs) or post-modification of biochar (BCM), for simultaneous adsorption and degradation of a model pollutant, cephalexin. The apparent removal rates of cephalexin in the presence of MBCs (2.49 - 6.39 × 10−2 h−1) and BCM (13.3 × 10−3 h−1) were significantly higher than that in the presence of biochar prepared under similar conditions (4.2 × 10−3 h−1). While the •OH generated from the activation of dissolved O2 by the persistent free radicals (PFRs) and phenolic -OH on BC could cause degradation of cephalexin, its removal was drastically enhanced through direct oxidation by the MnOx and related Mn species on Mn-modified BCs. The removal of cephalexin by MBCs decreased as the solution pH was raised from 5.0 to 9.0, which supports the critical role played by Mn3O4 in its oxidation. Removal of cephalexin in the presence of MBCs and Mn3O4 was enhanced with the introduction of Mn(II) ions, suggesting that the Mn3O4 present on MBCs facilitates the re-oxidation of Mn(II) to highly reactive Mn(III). While MnO2 anchored on BCM also enhanced the cephalexin oxidation, the active sites of BC and MnO2 were partially destroyed during post-modification of BC, compromising the redox cycling of Mn(II)/Mn(III) and the generation of •OH. As a result, the performance of BCM in oxidizing cephalexin was inferior to that of MBCs. These findings shed new light on the development of environmentally benign sorbents capable of simultaneously adsorbing and oxidizing organic pollutants.

The ozone-ultraviolet (UV)-chlorine process is a highly effective method of disinfection in water reuse system, but currently still lacks precise quantification and accurate control. It is difficult to determine the dosage of each disinfectant because of the complex interactions that occur between disinfection units and the complicated mathematical calculation required. In this study, we proposed a dosage optimization model for ozone-ultraviolet-chlorine synergistic disinfection process. The model was able to identify the cost-effective doses of the disinfectants under the constraints of microbial inactivation, decolorization, and residual chlorine retention requirements. Specifically, the simulation of microbial inactivation rates during synergistic disinfection process was accomplished through quantification of the synergistic effects between disinfection units and the introduction of enhancement coefficients. In order to solve this optimization model rapidly and automatically, a MATLAB-based software program with graphical user interface was developed. This software consisted of calibration unit, prediction unit, assessment unit, and optimization unit, and was able to simulate synergistic ozone-UV-chlorine process and identify the optimal dose of ozone, UV, and chlorine. Validation experiments revealed good agreements between the experimental data and the results calculated by the developed software. The developed software is believed to help the water reclamation plants improve disinfection efficiency and reduce the operational costs of synergistic disinfection processes.

Biological metabolism drives much of the variation in CO2 in terrestrial ecosystems but does not explain CO2 oversaturation and emission in net autotrophic lakes and reservoirs. The unexplained CO2 could be attributed to the equilibria between CO2 and the carbonate buffering system, which is seldom integrated into CO2 budgets, let alone its interplay with metabolism on CO2 emissions. Here, we perform a process-based mass balance modeling analysis based on an 8-year dataset from two adjacent reservoirs with similar catchment sizes but contrasting trophic states and alkalinity. We find that in addition to the well-acknowledged driver of net metabolic CO2 production, carbonate buffering also determines the total amount and seasonal dynamics of CO2 emissions from the reservoirs. Carbonate buffering can contribute up to nearly 50% of whole-reservoir CO2 emissions, by converting the ionic forms of carbonate to CO2. This results in similar seasonal CO2 emissions from reservoirs with differing trophic state, even in low alkalinity system. We therefore suggest that catchment alkalinity, instead of trophic state, may be more relevant in predicting CO2 emissions from reservoirs. Our model approach highlights the important role of carbonate buffering and metabolism that generate and remove CO2 throughout the reservoirs on a seasonal scale. The inclusion of carbonate buffering could diminish a major uncertainty in the estimation of reservoir CO2 emissions and increase the robustness of aquatic CO2 emission estimates.

To optimize the water quality index (WQI) assessment model, this study upgraded the parameter weight values and aggregation functions. We determined the combined weights based on machine learning and game theory to improve the accuracy of the models, and proposed new aggregation functions to reduce the uncertainty of the model. A new water quality assessment system was established, and took the Chaobai River Basin as a case study. To optimize the weight, two combined weights were established based on game theory. The weight CWAE was combined by the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM). The weight CWAL was combined by AHP and machine learning (LightGBM). CWAL was judged to be an optimal composite weight by comparing the coefficient of variation (CV) values and the Kaiser-Meyer-Olkin (KMO) extracted values. To reduce the uncertainty of the model, we proposed two aggregation functions, the Sinusoidal Weighted Mean (SWM) and the Log-weighted Quadratic Mean (LQM). The three water quality assessment models (WQIS, WQIL and WQIW) were established based on the optimal weights besides. All three models had good reliability. Both WQIS and WQIW models had low eclipsing problems (25.49% and 18.63%). The accuracy of the models was ranked as WQIS > WQIW > WQIL. The uncertainty of WQIs (0.000) in assessing poor water quality was low, and so was WQIW (0.259) in assessing good water quality. Overall, the WQIS model was recommended for assessing poor water quality and the WQIW model was recommended for assessing good water quality. The assessment results of WQIS showed that the Chaobai River Basin was "slightly polluted", and the water quality upstream was better than that downstream. TN was the main pollutant in the basin, and there was slight pollution with CODMn, CODCr, BOD5, etc. There was little metal contamination, only a few months exceeded Class I. The model established in this study can provide a reference for the same type work of water quality assessment. The assessment results can provide a scientific basis for the protection of the regional water environment.

Understanding response of bacterioplankton community responsible for maintaining ecological functions of aquatic ecosystems to environmental disturbance is an important subject. However, it remains largely unclear how bacterioplankton generalists and specialists responding to dredging disturbance. Illumina MiSeq sequencing and statistical analyses were used to evaluate landscape patterns, evolutionary potentials, environmental adaptability, and community assembly processes of generalists and specialists in response to dredging in eutrophic Lake Nanhu. The Proteobacteria and Actinobacteria dominated bacterioplankton communities of generalists and specialists, and abundances of Proteobacteria decreased and Actinobacteria increased after dredging. The generalists displayed higher phylogenetic distance, richness difference, speciation rate, extinction rate, and diversification rate as well as stronger environmental adaptation than that of specialists. In contrast, the specialists rather than generalists showed higher community diversity, taxonomic distance, and species replacement as well as closer phylogenetic clustering. Stochastic processes dominated community assemblies of generalists and specialists, and stochasticity exhibited a larger effect on community assembly of generalists rather than specialists. Our results emphasized that lake dredging could change landscape patterns of bacterioplankton generalists and specialists, whereas the short-term dredging conducted within one year was unable to reverse community difference between generalists and specialists. Our findings extend our understanding of how bacterioplankton generalists and specialists responding to dredging disturbance, and these findings might in turn call on long-term dredging for better ecological restoration of eutrophic lakes.

Bisphenol analogues (BPs) and natural estrogens (NEs) as two important groups of endocrine-disrupting compounds (EDCs) in drinking water treatment plants (DWTPs) have been hardly investigated except bisphenol A (BPA) and three major NEs including estrone (E1), 17β-estradiol (E2) and estriol (E3). In this study, a GC-MS analytical method was firstly established and validated for trace simultaneous determination of ten BPs and twelve NEs in drinking water, which included BPA, bisphenol B (BPB), bisphenol C (BPC), bisphenol E (BPE), bsiphenol F (BPF), bsiphenol P (BPP), bisphenol S (BPS), bisphenol Z (BPZ), bisphenol AF (BPAF), bisphenol AP (BPAP), E1, E2, E3, 17α-estradiol (17α-E2), 2-hydroestrone (2OHE1), 16hydroxyestrone (16α-OHE1), 4-hydroestrone (4OHE1), 2-hydroxyesstradiol (2OHE2), 4-hydroxyestradiol (4OHE2), 17-epiestriol (17epiE3), 16-epiestriol (16epiE3) and 16keto-estraiol (16ketoE2). This investigation showed that eighteen out of twenty-two targeted compounds were detected in drinking source waters of eight DWTPs with concentrations ranging from not detected to 142.8 ng/L. Although the conventional treatment process of DWTP could efficiently remove both BPs and NEs with respective removal efficiencies of 74.1%-90.9% and 74.5%-100%, BPA, BPS, BPE, BPZ, E1, 2OHE1, and 2OHE2 were found in the finished drinking waters. Chlorination could remove part of BPs and NEs, but the efficiency varied greatly with DWTP and the reason was unknown. In the finished drinking waters of eight DWTPs, the highest chemically calculated estrogen equivalence (EEQ) derived from BPs and NEs was up to 6.11 ngE2/L, which was over 22 times that could do harm to zebrafish, indicating a potential risk to human health. Given the fact that many chlorination products of BPs and NEs likely have higher estrogenic activities, the estrogenic effect of BPs and NEs in finished drinking water should be accurately examined urgently with the inclusion of BPs, NEs as well as their main chlorinated by-products. This study shed new light on the occurrence, removal, and potential estrogenic effects of BPs and NEs in DWTPs.

The limited information on microbial interactions and metabolic patterns in denitrification systems, especially those fed with different carbon sources, has hindered the establishment of ecological linkages between microscale connections and macroscopic reactor performance. In this work, denitrification performance, metabolic patterns, and ecological structure were investigated in parallel well-controlled bioreactors with four representative carbon sources, i.e., methanol, glycerol, acetate, and glucose. After long-term acclimation, significant differences were observed among the four bioreactors in terms of denitrification rates, organic utilization, and heterotrophic bacterial yields. Different carbon sources induced the succession of denitrifying microbiota toward different ecological structures and exhibited distinct metabolic patterns. Methanol-fed reactors showed distinctive microbial carbon utilization pathways and a more intricate microbial interaction network, leading to significant variations in organic utilization and metabolite production compared to other carbon sources. Three keystone taxa belonging to the Verrucomicrobiota phylum, SJA-15 order and the Kineosphaera genus appeared as network hubs in the methanol, glycerol, and acetate-fed systems, playing essential roles in their ecological functions. Several highly connected species were also identified within the glucose-fed system. The close relationship between microbial metabolites, ecological structures, and system performances suggests that this complex network relationship may greatly contribute to the efficient operation of bioreactors.

Excessive amounts of fluoride (F−) and nitrate (NO3−) in groundwater pose a significant threat to human health. However, a quantitative approach to assessing the human health risks caused by these harmful substances is lacking. To optimize the probabilistic assessment process for human health risk (HHR), this study introduced kernel density estimation (KDE) into the stochastic simulation of F− and NO3− content in groundwater samples. The potential HHRs caused by F− and NO3− in Songyuan City were summarized by combining the probabilistic and deterministic assessments. This study found that the concentrations of F− and NO3− did not follow common probability density functions (PDFs), but the KDE method passed the Kolmogorov-Smirnov test with the critical value of 0.067 and 0.062, showing high fitting accuracy. Monte Carlo simulation indicated that the probability of NO3− for children and adult exceeding the standard is 21.95% and 15.14% respectively which is comparable with the results of the deterministic assessment, but the probabilistic assessment emphasized lower probability of HHRs in children caused by excess F−(4.14%). Global sensitivity analysis revealed that excessive NO3− in groundwater has the highest sensitivity of the HHR (>0.1), followed by other factors representing water use habits (>0.01). This study presents a refined probabilistic assessment method for HHR and provides a scientific reference for understanding the state of groundwater environments.

Road-deposited sediments (RDS) on urban impervious surfaces are important carriers of heavy metals (HMs), and can contribute to urban runoff pollution. With the outbreak of COVID-19, chlorinated disinfectants (CDs) have been extensively sprayed on these surfaces. This practice may have a superposed or priming effect on HMs contaminants in RDS, yet this remains unknown. This study examined the effects of seven CDs concentration gradients (0, 250, 500, 1000, 2000, 5000, 60,000 mg/L) on the leaching and chemical forms of HMs (Cd, Cr, Ni, Pb, and Zn) in seven particle size fractions (<44, 44–63, 63–105, 105–149, 149–250, 250–450, 450–1000 μm). The results showed that CDs can promote the leaching of HMs in RDS, at the recommended CDs dose (2000 mg/L), except for Pb, the leaching amounts increased by 21.8%-237.2% compared with the untreated RDS. The alteration in the leaching were primarily attributed to the redistribution of chemical forms of HMs in RDS, specifically, the acid-extractable fractions percentage increased by 0.23%-24.39%, and the reducible fractions percentages decreased by 3.21%-38.35%. The lower oxidation–reduction potential (ORP) and alkalinity of CDs as strong oxidants were responsible for the redistribution of forms. The leaching and chemical forms of HMs vary among different particle sizes, but in any case, finer particle sizes (< 105 μm) still dominate their contribution. The current control measure of street sweeping is ineffective in removing these particles. These findings will facilitate the development of strategies for controlling urban diffuse pollution from RDS during the pandemic. Finally, this study suggests potential directions for future research.

Harmful algal blooms negatively impact freshwater, estuarine, and marine systems worldwide, including those used for drinking water, recreation, and aquaculture, through the production of toxic and non-toxic secondary metabolites as well as hypoxic events that occur when algal blooms degrade. Consequently, water resource managers often utilize chemical, bacterial, physical, and/or plant-based treatments to control algal blooms and improve water quality. However, awareness of available treatments may be limited, and there is ambiguity among the effects of algal bloom treatments across studies. Such variation within the literature and lack of knowledge of other tested treatments leave uncertainty for water resource managers when deciding what treatments are best to control algal blooms and improve water quality. Our primary objective was to synthesize data from 39 published and unpublished studies that used one of 28 chemical, bacterial, physical, and/or plant-based treatments in field experiments on various water quality measurements, including phytoplankton pigments and cell density, cyanobacterial toxins (microcystin), and common off-flavors (i.e., taste and odor compounds; geosmin and 2-methylisoborneol). We hypothesized that treatments would improve water quality. Across all studies and treatment types (227 effect sizes), water quality improvements were observed when measured at the time of greatest decline following treatment or at the end of the experiment. However, these findings were primarily mediated by only four chemicals, namely copper sulfate, hydrogen peroxide, peracetic acid, and simazine. None of the bacterial, physical, or plant-based treatments were shown to significantly improve water quality by themselves. Results from this synthesis quantitatively showed that most treatments fail to improve water quality in the field and highlight the need for more research on existing and alternative treatments.

The optimization of membrane bioreactors (MBRs) involves a critical challenge in structural design for mitigation of membrane fouling. To address this issue, a three-dimensional computational fluid dynamics (CFD) model was utilized in this study to simulate the hydrodynamic characteristics of a flat sheet (FS) MBR. The optimization of the membrane module configuration and operating conditions was performed by investigating key parameters that altered the shear stress and liquid velocity. The mixed liquor suspended solids (MLSS) concentration was found to increase the shear stress, leading to a more uniform distribution of shear stress. By optimizing the appropriate bubble diameter to 5 mm, the shear stress on the membrane surface was optimized with relatively uniform distribution. Additionally, extending the side baffle length dramatically improved the uniformity of the shear stress distribution on each membrane. A novel in-situ aeration method was also discovered to promote turbulent kinetic energy by 200 times compared with traditional aeration modes, leading to a more uniform bubble streamline. As a result, the novel in-situ aeration method demonstrated superior membrane antifouling potential in the MBR. This work provides a new approach for the structural design and optimization of MBRs. The innovative combination of the CFD model, optimization techniques, and novel in-situ aeration method has provided a substantial contribution to the advancement of membrane separation technology in wastewater treatment.

Microplastic footprint in urban river networks can be disturbed by multiple urbanization features, and regional river structures are generally overlooked. In this research, we analyzed the distribution of microplastics and potential impact pattern of river structures on it in a typical urban river network in Nanjing, China. Surface waters of the river network were jointly detected by multiple methods, and the Renkonen similarity index was used to study spatial variabilities of microplastics characteristics. Microplastics were ubiquitous and abundant, showing five (>50 μm) and six (20∼50 μm) hotspots, and heterogeneities in the shape and type of microplastics larger than 100 μm were prominent, presumably influenced by river network scale and connectivity. River structure parameters associated with network connectivity were obtained by combining graph theory and an entropy-based set-pair analysis model. Aiming at the action pathway of river structures, by using correlation and partial least squares regression analysis, we found that river node (confluences and sluices) ratio, river frequency, river network density, and water system circularity were significantly positively correlated with microplastic abundance, and confluences with poor connectivity had a greater indirect intervention intensity on the microplastic distribution. The land use characteristics dominated the fitting of microplastic abundance, which was about 1.2 times better than river structures, and the comprehensive land use intensity and river network connectivity were the critical factors, respectively. Potential ecological risks of microplastics were evaluated, resulting in relatively severe levels. This study proposed targeted measures to control urban microplastic pollution by combining the perspective of river network characteristics. To summarize, our exploration of microplastic footprint based on urban river network structures from the perspective of river network connectivity provides new insights into microplastic management.

Antibiotic resistome can be carried by the bioaerosols and propagate from wastewater treatment plants (WWTPs) to the atmosphere, but questions remain regarding their mobility, bacterial hosts, source, and resistome risk. Here, fine particulate matter (PM2.5) was collected within and around a large WWTP and analyzed by the metagenomic assembly and binning. PM2.5 was discovered with increasing enrichment of total antibiotic resistance genes (ARGs), potentially mobile ARGs, and antibiotic-resistant bacteria (ARB) along the WWTP-downwind-upwind gradient. Some ARGs were found to be flanked by certain mobile genetic elements and generally mediated by plasmids in WWTP-PM2.5. Totally, 198 metagenome assembled genomes assigning to seven phyla were identified as the ARB, and a contig-based analysis indicated that 32 pathogens were revealed harboring at least two ARGs. Despite disparate aerosolization potentials of ARGs or ARB at different WWTP units, high resistome risks were found, along with the dominant contribution of wastewater for airborne ARGs (44.79–62.82%) and ARB (35.03–40.10%). Among the detected WWTP matrices, the sludge dewatering room was characterized by the highest resistome risk associated with PM2.5. This study underscores the dispersion of ARGs and ARB from WWTPs to the atmosphere and provides a reference for managing risks of antibiotic resistance.

Increased algal blooms and loss of aquatic vegetation are critical environmental issues associated with shallow lakes worldwide. The increase in organic matter (OM) in both macrophyte-dominated areas (MDAs) and algae-dominated areas (ADAs) has exacerbated these problems. Most OM in water is concentrated as suspended particulate matter (SPM), which eventually migrates to the sediment. However, the detailed origins and fates of OM in water–SPM–sediment systems with coexisting MDAs and ADAs remain unclear. Therefore, in this study, we conducted monthly field investigations in Lake Taihu, focusing on OM-migration patterns in an MDA and an ADA. The C/N mass ratios, δ13C contents, and OM compositions of the water, SPM, and sediment were analyzed. Our findings revealed that autochthonous sources of OM prevailed in water, whereas terrestrial sources prevailed in SPM and sediment. Rapid decomposition processes of microbial- and algae-derived dissolved OM were discovered along the water–SPM–sediment pathways in both areas. A trend towards a shift from macrophytes to algae in the MDA was also discovered. Overall, the entire lake underwent a burial process of OM in both types of areas, with mineralization mostly occurring during the algal-bloom seasons and more strongly in the ADA. Furthermore, we deduced that a decrease in the OM-burial rate, but an increase in the mineralization rate, might occur after a complete shift from a macrophyte- to an algae-dominated status. Such a shift might change the carbon-cycle process in eutrophic shallow lakes and should be given more attention in future research.

Sedimentary denitrification takes place beneath the oxic layer at the sediment-water interface, where nitrate and antibiotics need to diffuse through the overlying water. However, the antibiotics’ effect on sedimentary N removal and associated N2O production has not been adequately investigated under in situ conditions. Here, isotope pairing techniques, including slurry incubations (potential) and intact core incubations (in situ), combined with metagenomic analysis were applied to investigate the impacts of two protein-inhibiting antibiotics (oxytetracycline and thiamphenicol) on sediment nitrogen removal in a subtropical estuary. Slurry incubations showed that the two antibiotics significantly inhibited denitrification (67-98%) and anammox (49-99%), while intact core incubations presented no antibiotic effect at upstream but significant inhibition (23%-52%) at downstream. Meanwhile, N2O yields were stimulated up to 20 folds in slurry incubations yet showing insignificant response in intact cores. Such contrasting results between up- and down-stream and between slurry and intact core incubations strongly indicated that permeability, which determines diffusion of antibiotics to microbes, is the key to exert the effect of antibiotics on in situ sedimentary nitrogen removal processes regardless the existence of antibiotics resistance genes. This diffusive obstruction may mitigate the toxic effect of antibiotics on nitrogen removal related microbes in natural environments.

Reclaimed water reuse is a promising strategy for addressing water scarcity; however, its potential ecological impact remains largely unknown. In particular, the differential effects of reclaimed water on microbial communities in various habitats remain poorly understood. Here, we aimed to elucidate the distinct effects of reclaimed water on bacterioplankton and bacteriobenthos communities in reclaimed water-receiving river networks from multiple perspectives, including community structure, co-occurrence patterns, assembly mechanisms, and nitrogen cycle function. Significant differences in microbial composition were observed between the plankton and benthic habitats, and the average numbers of amplicon sequence variants (ASVs) that originated from the wastewater treatment plants (WWTP) sites were 310.0 and 613.3, respectively, indicating a stronger association between WWTP and benthic habitats. Random forest and network co-occurrence analyses identified the genus Clostridium_sensu_stricto as a biomarker and key module hub. The assembly of bacteriobenthos communities was driven primarily by deterministic processes (58.74% for River-S and 58.94% for WWTP-S), whereas for bacterioplankton communities, this proportion was reduced to 18.02% (River-W) and 19.09% (WWTP-W). The qPCR revealed a large difference in abundance between the N cycling related genes of bacteriobenthos (average 2.47 × 106 copies/ng) and bacterioplankton (average 3.11 × 103 copies/ng) communities, and different interaction patterns with functional genes. Variance partitioning analysis (VPA) indicated that nitrogen was the most important pollutant, affecting the structure and ecological functions of microbial communities. Moreover, pathway analysis suggested that the reuse of reclaimed water may have enhanced the N-cycling functions of microbial communities and the emission of nitrous oxide.

This study describes the improvements of adsorption capacities for raw calcium oxide (CaO) particles subjected to ultrasonication, activation with nitric acid and thermal treatments. The influence of acids and bases on CaO particle surface was assessed with respect to several variables including treatment methods, adsorption contact times, particle size and specific surface area characteristics, concentration and temperature along with various thermodynamic parameters. Structural analyses and physical characteristics of CaO particles were evaluated using FT-IR and SEM methods. SEM micrographs of samples revealed uniform distributions of CaO particles of average diameter 0.5–2.0 µm. The CaO surfaces showed CH3COOH as having the greatest amounts of adsorbate and modeling of the experimental adsorption isotherm data agreed well with the Freundlich adsorption isotherm. Enhancements in adsorption performance of untreated CaO particles were noted with the ultrasonication, activation with HNO3 and thermal treatment processes. The Langmuir-type adsorption demonstrated that single layer adsorption capacities of adsorbate CH3COOH at 25 oC on sonicated CaO (386.6 mg/g), with nitric acid and thermal activation (354.9 and 320.8 mg/g, respectively) were greater than that of the unsonicated CaO (296.3 mg/g) particles. Adsorption spontaneities of the processes were confirmed by the decreases in adsorption free energy values, ΔGads0, changing from −16.1 to −17.1 kJ mol−1 with temperature range 283–338 K.

Water-quality monitoring and management are crucial for ensuring the safety and sustainability of water resources. However, missing data is a frequent problem in water-quality datasets, which can result in biased results in hydrological modeling and data analysis. While classic statistical methods and emerging machine/deep learning methods have been applied for imputing missing values, most existing studies perform well in specific missing scenarios, but not in universal scenarios. Therefore, existing imputation methods often fail to robustly impute missing values across various scenarios. To address the problem, we propose an imputation method that uses a context-aware voting-ensemble model to dynamically select optimal weights to integrate various imputation models across different missingness scenarios. For first identify the attributes of missingness scenarios that influence imputation accuracy. Then after introducing missing values in collected data according to the missingness scenarios, we measure the accuracy of various imputation models across the missingness scenarios. Weights of imputation models are optimized by estimating non-linear functions with regression model that can capture relationships between missingness scenarios and imputation accuracies of models. The final imputed value of the ensemble model for a missing scenario can be determined by multiplying each imputation model's weight by its imputed value, then summing the products. The method inherits the advantages of state-of-art imputation models, including the ability to learn long-term dependencies in time series, as well as the flexibility of using a dynamic weighting strategy to process various missingness scenarios. To validate the superiority of our method, we evaluate on real-world water-quality data from a river in South Korea. The proposed method achieves higher accuracy and lower variation of imputed values than baseline models across various missingness scenarios. Furthermore, we showed the applicability of our method to various hydrological environment by validating our method on industrial water quality dataset. This study highlights the potential value of the ensemble model with dynamic weighting in robust imputation of water-quality data.

Acidic partial nitritation (PN) has emerged to be a promisingly stable process in wastewater treatment, which can simultaneously achieve nitrite accumulation and about half of ammonium reduction. However, directly applying anaerobic ammonium oxidation (anammox) process to treat the acidic PN effluent (pH 4−5) is susceptible to the inhibition of anammox bacteria. Here, this study demonstrated the adaptation of anammox process to acidic pH in a moving bed biofilm reactor (MBBR). By feeding the laboratory-scale MBBR with acidic PN effluent (pH = 4.6 ± 0.2), the pH of an anammox reactor was self-sustained in the range of pH 5 − 6. Yet, a high total nitrogen removal efficiency of over 80% at a practical loading rate of up to 149.7 ± 3.9 mg N/L/d was achieved. Comprehensive microbial assessment, including amplicon sequencing, metagenomics, cryosection-FISH, and qPCR, identified that Candidatus Brocadia, close to known neutrophilic members, was the dominant anammox bacteria. Anammox bacteria were found present in the inner layer of thick biofilms but barely present in the surface layer of thick biofilms and in thin biofilms. Results from batch tests also showed that the activity of anammox biofilms could be maintained when subjected to pH 5 at a nitrite concentration of 10 mg N/L, whereas the activity was completely inhibited after disturbing the biofilm structure. These results collectively indicate that the anammox bacteria enriched in the present acidic MBBR could not be inherently acid-tolerant. Instead, the achieved stable anammox performance under the acidic condition is likely due to biofilm stratification and protection. This result highlights the biofilm configuration as a useful solution to address nitrogen removal from acidic PN effluent, and also suggests that biofilm may play a critical role in protecting anammox bacteria found in many acidic nature environments.

Understanding the dynamics of antibiotic resistance gene (ARG) transfer and dissemination in natural environments remains challenging. Biofilms play a crucial role in bacterial survival and antimicrobial resistance (AMR) dissemination in natural environments, particularly in aquatic systems. This study focused on hospital and urban wastewater (WW) biofilms to investigate the potential for ARG dissemination through mobile genetic elements (MGEs). The analysis included assessing the biofilm extracellular polymeric substances (EPS), microbiota composition as well as metatranscriptomic profiling of the resistome and mobilome. We produced both in vitro and in situ biofilms and performed phenotypic and genomic analyses. In the in vitro setup, untreated urban and hospital WW was used to establish biofilm reactors, with ciprofloxacin added as a selective agent at minimal selective concentration. In the in situ setup, biofilms were developed directly in hospital and urban WW pipes.We first showed that a) the composition of EPS differed depending on the growth environment (in situ and in vitro) and the sampling origin (hospital vs urban WW) and that b) ciprofloxacin impacted the composition of the EPS. The metatranscriptomic approach showed that a) expression of several ARGs and MGEs increased upon adding ciprofloxacin for biofilms from hospital WW only and b) that the abundance and type of plasmids that carried individual or multiple ARGs varied depending on the WW origins of the biofilms. When the same plasmids were present in both, urban and hospital WW biofilms, they carried different ARGs. We showed that hospital and urban wastewaters shaped the structure and active resistome of environmental biofilms, and we confirmed that hospital WW is an important hot spot for the dissemination and selection of antimicrobial resistance. Our study provides a comprehensive assessment of WW biofilms as crucial hotspots for ARG transfer. Hospital WW biofilms exhibited distinct characteristics, including higher eDNA abundance and expression levels of ARGs and MGEs, highlighting their role in antimicrobial resistance dissemination. These findings emphasize the importance of understanding the structural, ecological, functional, and genetic organization of biofilms in anthropized environments and their contribution to antibiotic resistance dynamics.