Geogenic coal-derived polycyclic aromatic hydrocarbons (PAHs) migration and risks in karst mines remain understudied, especially acid mine drainage (AMD)-facilitated PAH transport via karst dual hydrologic systems and rapid groundwater flow. This study integrates field monitoring, multivariate statistics and laboratory leaching simulations to clarify PAH origins, AMD-controlled release mechanisms and multi-level environmental risks in karst coal basins. Surface water total PAH concentrations vary substantially with seasons and peak in dry periods. Traditional diagnostic ratios poorly distinguish coal‑PAH sources due to intrinsically overlapping fingerprints and weathering modification. Principal component analysis and molecular fingerprinting verify endogenous coal-strata leaching constitutes the primary source rather than exogenous combustion. Divergent aqueous PAH compositions stem from chromatographic fractionation during groundwater flow instead of separate pollution sources. Strong acidity below pH 3.5 breaks coal matrices and liberates low-molecular PAHs, while mild acidic conditions above pH 3.5 restrict PAH migration via adsorption onto secondary minerals in AMD. High rank coal rich in PAHs features weak mobility due to compact matrices and high-molecular PAHs mostly accumulate in the solid phase. Bituminous coal poses the highest health risk, followed by anthracite, lignite and coal gangue, with low-ring PAHs as primary non-carcinogenic risk contributors. Dry seasons witness aggravated aquatic risks, with dermal contact serving as the primary human exposure route. This study elucidates AMD-mediated PAH fractionation and mobilization in karst coal mines, remedies flaws in conventional source apportionment, identifies the neglected coal-strata leaching pathway and supports seasonal pollution control and risk management for analogous karst mining watersheds.

Faced with the increasing presence of recalcitrant emerging contaminants (ECs) in aquatic environments, the development of highly efficient and environmentally friendly treatment technologies has become particularly urgent. Density functional theory (DFT) calculations offer unparalleled value in mechanistic studies by revealing the interaction mechanisms between contaminants and catalytic materials at the electronic structure level. However, the computational intensity of DFT hinders its use for rapid screening of numerous contaminants. To overcome this limitation, we integrated the molecular fingerprint (MF) features of contaminants into the dataset to construct a machine learning (ML) performance prediction model. This enables high-throughput evaluation of the catalytic performance of catalysts while maintaining good predictive accuracy (R2 = 0.81). By introducing SHapley Additive exPlanations (SHAP) and partial dependence plots (PDP), we elucidated the contribution mechanisms of contaminant molecular structural features, catalyst properties, and reaction conditions to performance prediction, identifying the pivotal roles of total pore volume (Vtotal), catalyst concentration (Catalyst-C), and Peroxymonosulfate concentration (PMS-C). Molecular-scale analysis of pollutants revealed that aromatic-related structures enhance reaction activity, while halogen-related substituents inhibit reaction efficiency. Finally, we verified the reliability of the model through experimental validation. This study provides an effective predictive tool and elucidates the structure activity relationship at the atomic scale, providing a theoretical basis and practical guidance for the design of green catalysts and optimization of wastewater treatment processes.

The prevalence of antibiotic-resistant bacteria (ARB) in wastewater is growing, posing significant risks to human health and the environment, and requiring more sophisticated data-driven treatment strategies to achieve effective pathogen inactivation. Traditional treatment procedures are often inadequate for removing biological pollutants, highlighting the need for improved and proactive treatment methods. This study used machine learning (ML) models to predict the efficiency of ARB inactivation in Fenton-based advanced oxidation processes. After screening and comparing ten tree-based ML algorithms, Gradient Boosting (GB) demonstrated superior predictive accuracy and robustness for ARB inactivation across various Fenton processes. The GB model fit the training data well and showed low prediction error, as indicated by a high coefficient of determination (R2 = 0.99273) and the lowest root mean squared error (RMSE = 0.18190). Using this strong predictive performance, the optimal operational conditions were determined for photo-Fenton treatment, achieving ARB log removals greater than 6.30-log in different water matrices. For real wastewater (RWW), the model-predicted optimal conditions (0.1 mM Fe3+, 0.2 mM EDDS, and 0.3 mM H2O2 under light irradiation 130 mW/cm2 for 76 min) yielded a predicted inactivation of 6.37 log, closely matching the experimentally observed value of 6.30-log inactivation (limit of detection). In ultrapure water, complete inactivation was achieved under identical reagent conditions with a shorter treatment time (25 min), while simulated wastewater required higher reagent dosages (0.2 mM Fe3+, 0.4 mM EDDS, and 0.6 mM H2O2) to achieve equivalent performance within 28 min. These findings indicate that the GB-guided framework can adequately capture the influence of water matrix complexity on ARB inactivation, enabling targeted optimization of treatment conditions. The study indicates the potential integration of ML models with Fenton-based AOPs to develop effective strategies for combating antibiotic resistance in wastewater systems.

Aerobic granular sludge (AGS) is limited by slow granulation and structural instability under variable municipal wastewater conditions. Here, an iron-functionalized carbonized melamine sponge (Fe3+@CMS) was developed as a three-dimensional scaffold to accelerate granulation and improve nitrogen removal. Fe3+@CMS reduced the time required for complete granulation by 20 d and became embedded within the granules as a persistent internal skeleton. It promoted extracellular polymeric substance (EPS) production during granule nucleation, whereas mature Fe3+@CMS-supported granules maintained greater structural integrity with lower EPS accumulation, suggesting partial substitution for the native EPS-dependent framework. After transition to real municipal wastewater, the Fe3+@CMS reactor maintained an average soluble COD removal of 92.4% and >97% ammonium removal, and achieved a final total nitrogen removal efficiency of 84.45%, compared with 44.10% in the control. Fe3+@CMS also suppressed excessive Thiothrix proliferation and favored denitrifying and iron-redox-associated taxa. Metagenomic analysis indicated selective redistribution rather than overall enrichment of nitrogen-cycle genes: napA/napB and nirS showed higher relative abundances, while the DNRA/(nirK + nirS) abundance ratio decreased from 0.184 to 0.107. These findings demonstrate that Fe3+@CMS acts as both a persistent structural scaffold and a selective ecological niche, integrating aerobic-granule stabilization with enhanced denitrification under fluctuating wastewater conditions.

Municipal wastewater treatment is transitioning towards energy and resource recovery. However, conventional activated sludge (CAS) and mainstream anaerobic membrane bioreactors (M-AnMBR), which directly treat the bulk flow of low-strength municipal wastewater for organic removal, are constrained by high carbon footprints or energy demands. Sidestream AnMBR (S-AnMBR) treats a small-volume, carbon-rich stream concentrated from the mainstream wastewater, physically decoupling carbon and water flows via chemically enhanced primary treatment (CEPT), offering a promising solution to these bottlenecks. This study developed an evaluation framework integrating techno-economic analysis, life cycle assessment, and data envelopment analysis coupled with Monte Carlo simulations to comprehensively compare CAS, M-AnMBR (including an aeration-optimized scenario), and S-AnMBR, given a range of uncertainties. Results show that S-AnMBR has a high probability of achieving net-negative energy consumption (80.4%), with a net energy consumption of -0.04 kWh/m3 (90% uncertainty interval: -0.11 to 0.03 kWh/m3), while also exhibiting the lowest carbon footprint (0.24 kg CO2e/m3, with a 90% uncertainty interval of 0.22 to 0.34 kg CO2e/m3). S-AnMBR achieves a cost efficiency of 0.85 (out of 1), comparable to the highest score of 0.88 achieved by CAS. Under the evaluated conditions, M-AnMBR remains uncompetitive despite aeration optimization. Sensitivity analysis reveals that the dominant performance bottlenecks shift markedly as the treatment architecture evolves from CAS to mainstream and sidestream AnMBR processes. While CAS is primarily influenced by external factors (e.g., discount rates and grid carbon emission factors), M-AnMBR relies typically on membrane flux and gas sparging. For S-AnMBR, the dominant constraints shift to the cost and performance of nitrogen-removal membranes and the carbon intensity of CEPT coagulants, highlighting membrane cost reduction as a key opportunity for further techno-economic improvement. By integrating front-end carbon capture, S-AnMBR emerges as a highly resilient and sustainable technological paradigm for next-generation wastewater treatment.

Anaerobic digestion (AD) intensification remains a critical priority for sustainable wastewater treatment and resource recovery. While photonic stimulation via direct light or nanomaterial mediation has recently emerged as a novel strategy to enhance methanogenesis, reported performance metrics vary widely, and fundamental mechanistic validation remains insubstantial. Here, we provide a critical assessment of photonic AD by integrating thermodynamic modeling, evidentiary-rigor frameworks, and net-energy balance calculations to evaluate its engineering feasibility in real-world settings. An L1-L4 mechanistic validation hierarchy is proposed to classify the literature and show that evidence for light-driven microbial pathways (e.g., c-di-GMP signaling or direct interspecies electron transfer) is predominantly correlative and fragmented. High light attenuation in high-solids wastewater matrices presents a fundamental physical constraint to uniform photon delivery. Furthermore, a screening estimate incorporating an LED retrofit wall-plug efficiency assumption of 35% (representing a best-case retrofit scenario) reveals that auxiliary lighting energy demands may outweigh methane yield gains, requiring a multi-fold enhancement in light-attributable methane benefit to reach net-energy parity. Preliminary environmental toxicity assessments also reveal process risks: worst-case-scenario photo-corrosion of semiconductor additives (e.g., CdS) may yield multi-fold increase in heavy metal leaching, posing hazards to downstream effluent quality and biosolids land application standards. Hence, current evidence highlights the potential of photonic AD while underscoring the need for further research to establish its scalability for wastewater treatment. Future efforts should focus on advancing from descriptive performance screening toward standardized light-attenuation modeling, robust mechanistic validation, biosolids safety evaluation, and integrated net-energy accounting, paving the way for reliable and sustainable deployment.

Chlorinated benzenes are persistent groundwater contaminants whose anaerobic transformation can depend on organohalide-respiring microorganisms embedded within metabolically interconnected communities. Here, we asked which microorganisms and metabolic functions characterize a Dehalobacter-containing dechlorinating community, and whether bioinformatics and genome-resolved metabolic modeling could identify candidate interactions supporting organohalide respiration. To address these questions, we integrated dechlorination data from related Dehalobacter-containing enrichment lineages with genome-resolved metagenomics and constraint-based modeling. Transformation of 1,2,4-trichlorobenzene (TCB) to dichlorobenzenes and monochlorobenzene (MCB) was observed in one enrichment lineage, whereas MCB transformation to benzene was observed in a related lineage; in the latter, benzene increased from 0 to 62.3μmol bottle-1 as MCB decreased from 88.3 to 22.0μmol bottle-1 over 120 days. These experiments comprised one observation unit per lineage rather than independent biological reactor replicates and therefore do not establish continuous TCB-to-benzene conversion within a single culture. Genome-resolved reconstruction identified two Dehalobacter populations among 12 community members selected for metabolic modeling. Following structural, phenotypic, and thermodynamic quality control, pairwise random flux sampling predicted a metabolically differentiated exchange network: Methanobacterium consumed H2 in all 11 pairwise models (median model-normalized flux, 22.36 mmol gDW-1 h-1), whereas Methanothrix consumed acetate in all 11 (10.16 mmol gDW-1 h-1) and released CH4 (9.51 mmol gDW-1 h-1), distinguishing hydrogenotrophic and acetoclastic methanogenic functions. Model-guided community reduction across 39 sensitivity scenarios produced objective-minimal candidate communities in 34 scenarios, predominantly bicultures with condition-dependent recruitment of a third member. A Dehalobacter member occurred in every candidate community and Methanothrix recurred across all evaluated scenarios, defining a compact model-predicted Dehalobacter-methanogen backbone rather than demonstrating biological indispensability. Together, these results distinguish observed dechlorination from genome-inferred metabolic potential and model-predicted interactions, providing experimentally testable hypotheses for metabolic dependencies that could inform more predictable anaerobic bioremediation strategies.

Chloramine is widely used in drinking water distribution systems (DWDSs), but ammonia recycling can promote nitrification and disinfectant residual loss. As source water ammonia is controlled, permanent conversion to free chlorine may replace repeated temporary chlorine burns, yet its nitrogen control performance and microbiome-resistome consequences remain unresolved. We analyzed early responses of 16 bulk water metagenomes from four sites at chloramine baseline and free chlorine days 1, 5 and 8, with nitrification-related water quality monitored for 8 months. Conversion achieved nitrogen control benefits, with nitrite lower after conversion at multiple time points (p < 0.05), dominant nitrifying genera suppressed to <0.1%, and ammonia oxidation and hydroxylamine oxidation potentials decreasing from 125.84 to 2.77 TPM and from 20.35 to 0.45 TPM. Community restructuring was site-specific (PERMANOVA stage R[2] = 0.297, p = 0.001; site R[2] = 0.311, p = 0.002). Despite improved nitrification control, 21-50% of each site's baseline ARGs remained detectable after conversion. SEM linked chlorine speciation to community shift (β = 0.695) and ARG/16S shift (β = -0.344), while community shift and MGE/16S shift showed positive paths to ARG/16S shift (β = 0.426 and 0.819). The pathways suggest that the shift toward free chlorine was a potential driver of early microbiome-resistome reorganization. These early responses are consistent with succession from chloramine-resistant communities toward free chlorine-resistant, MGE-associated hosts that may facilitate ARG redistribution. Permanent conversion is a viable nitrification control option, but evaluation should include microbiome and resistome monitoring beyond residual and nitrification indicators.

Phenolic compounds are widespread aromatic contaminants in aquatic environments and are of particular concern before disinfection because of their toxicity and potential contribution to disinfection byproduct formation. Although manganese-based oxidation processes are widely applied to remove phenolic contaminants, reliance on parent-compound disappearance alone may overlook toxic transformation products generated during oxidation. Herein, we demonstrated that ligand-stabilized soluble Mn(III), a reactive intermediate in Mn redox cycling, redirects phenolic transformation from apparent removal toward the formation of dissolved oligomeric products with elevated residual risks. Representative phenolic compounds were investigated in ligand-stabilized soluble Mn(III) systems using Mn(III)-pyrophosphate (Mn(III)-PP) as a model complex. Soluble Mn(III) rapidly promoted parent-compound disappearance; however, this apparent removal was accompanied by the formation of dissolved oligomeric products. Particularly in halogenated phenolic systems, these residual oligomeric mixtures exhibited higher toxicity and greater predicted ecological risks than the corresponding parent compounds. Mechanistic analyses reveal that soluble Mn(III) mediates phenolic transformation through hydrogen-bond-assisted outer-sphere pre-association, which facilitates single-electron transfer, phenoxyl radical formation, and subsequent coupling reactions. These findings uncover a previously overlooked pathway in which ligand-stabilized soluble Mn(III) converts phenolic compounds into persistent dissolved oligomers rather than simply detoxifying them. This work highlights important implications for drinking-water protection and Mn-mediated contaminant transformation in natural aquatic environments.

Mature landfill leachate with high NH4[+]-N, low C/N and a need for deep nitrogen removal requires cost-effective solutions. In this study, an algal-bacterial granular sludge (ABGS) system was developed to address the limitations associated with slow-growing anaerobic ammonium-oxidising bacteria (AnAOB) and establish a functionally diverse microbial consortium under low C/N, sulfate-containing conditions. After 150 days of operation with synthetic wastewater simulating mature landfill leachate, stable ABGS was obtained, comprising Chlorella, ammonia-oxidising bacteria (AOB), AnAOB, heterotrophic denitrifying bacteria (HDB), sulfate-reducing bacteria (SRB) and sulfur-oxidising bacteria (SOB). Without mechanical aeration, Chlorella-derived photosynthetic oxygen supported AOB-driven partial nitrification, enabling PN/A-dominated nitrogen removal accompanied by nitrate-to-nitrite recycling and sulfur transformation. The system achieved 98.27% total inorganic nitrogen removal efficiency (TINRE) and a nitrogen removal rate (NRR) of 393.08 mg N/(L·d). AnAOB reached a total abundance of 2.8%, including Candidatus Brocadia, Candidatus Kuenenia and Candidatus Anammoxoglobus, with Nitrosomonas as the principal AOB genus and Thiobacillus as an enriched sulfur-related genus. A subsequent 10-day real-leachate shock test demonstrated short-term resilience, with average NH4[+]-N and TIN removal efficiencies of 92.3% and 88.9%, respectively. This work provides a new approach to anammox-based nitrogen removal and simultaneous organic carbon reduction in mature landfill leachate treatment.

Oxidative stress induced by heavy metals and antibiotics in municipal sludge inhibits the gut development of black soldier fly larvae (BSFL), compromising pollutant-processing capacity of their gut microbiota. Herein, Deinococcus radiodurans was colonized into the BSFL gut and established an antioxidant cascade regulatory system in concert with the gut microbiota, synergistically reducing the enrichment of antibiotic resistance genes (ARGs) in municipal sludge. Results showed that bioaugmentation with Deinococcus radiodurans scavenges reactive oxygen species and upregulates the expression of G-protein and s14e genes in BSFL gut, maintaining normal gut function. On this basis, we propose two potential mechanisms responsible for inhibiting the horizontal transfer of ARGs (HGT). At the molecular level, bioaugmentation reduces environmental co-selection pressure through heavy metal immobilization mediated by extracellular polymeric substances and SlpA protein, as well as antibiotic degradation driven by gut microbiota, reducing the abundances of the class 1 integron integrase gene (intI1), conjugative transfer gene (traG), and ARGs (aphA and tetA) in municipal sludge by more than one order of magnitude. At the ecological level, bioaugmentation reshapes the microbial community structure of sludge, reducing the abundance of Pseudomonadota carrying mobile genetic elements and ARGs by approximately 26%. Moreover, bioaugmentation disrupted the co-occurrence of the mer operon and ARGs (sul1). Conjugative experiments revealed that co-cultivation with Deinococcus radiodurans reduced the conjugative transfer frequency of the ARG-carrying RP4 plasmid by 91%. Therefore, this study elucidates how antioxidant-producing extremophiles can strengthen BSFL system to inhibit HGT and reduce ARG enrichment.

Ion-adsorption rare earth element (REE) mining can create a composite pollution environment characterized by acidification and enrichment of NH4[+], SO4[2][-], and REEs, yet its effects on riverine microbial community assembly and multi-element metabolic potential remain poorly understood. Here, we combined 16S rRNA amplicon sequencing, metagenomic sequencing, and metagenome-assembled genomes (MAGs) to characterize microbial community assembly and functional reorganization across a mining-disturbance gradient. At the community level, bacterial and archaeal richness and diversity declined significantly with increasing pollution, accompanied by clear community differentiation. Compared with bacteria, archaea exhibited greater environmental specificity, with larger contributions of homogeneous selection and dispersal limitation to community assembly. Meanwhile, microbial functional genes formed a modular network linking carbon, nitrogen, sulfur, iron, arsenic, and methane transformation processes, with particularly prominent cross-module associations among carbon-, nitrogen-, and sulfur-related functions. At the genome level, mining-associated MAGs showed multifunctional integration within individual genomes and potential functional division of labor and complementarity among different microbial members. Together, the community- and genome-level results revealed a cross-scale organization of multi-element metabolic potential, extending from functional network connectivity at the community level to functional configuration at the genome level. Such mining-driven reorganization of microbial communities and metabolic functions may further influence greenhouse-gas-related processes and the mobility and transformation of metal(loid)s, providing new insights into microbial ecological processes and potential ecological risks in mining-impacted rivers.

Hydrocyclones have traditionally been used for grit and particle separation, but recent studies have expanded their role toward selective sludge classification and biological process regulation. This review develops a process-oriented framework to integrate these emerging applications. Four major scenarios are examined: grit and particle separation, activated sludge fractionation and settleability enhancement, endogenous carbon release and biological nutrient removal, and sludge disintegration and reduction. As separation targets shift from dense inorganic particles toward increasingly similar-density sludge fractions, hydrocyclones evolve from particle removal toward selective sludge retention, sludge-structure regulation, and biological process enhancement. Reported studies show sludge volume index reductions of 9-66%, improvements in total nitrogen and total phosphorus removal of 6-30%, and reductions in excess sludge production of 30-35%. Beyond individual process improvements, hydrocyclones can provide synergistic benefits through improved sludge management, reduced chemical and energy demand, and in situ treatment-capacity expansion. These benefits depend on selective classification and appropriate integration with the overall treatment process rather than on separation efficiency alone. Future research should therefore move beyond unit-level performance toward integrated process optimization and system-level assessment of the material, energy, operational, and economic consequences of hydrocyclone-based wastewater treatment.

Efficient biological nitrogen removal in biofilm systems is hampered under low-temperature and carbon-limited ("dual-low") conditions, where impaired microbial metabolism curtails denitrification and promotes nitrous oxide (N2O). Quinone moieties of humic substances can shuttle electrons between microorganisms, yet their role in sustaining denitrification under dual-low stress remains unknown. Here we show that supplementing biofilm microcosms with the model humic analogue anthraquinone-2,6-disulfonate (AQDS) raised nitrate (NO3[-]-N) removal from below 60% to 81% at 4-6 °C and C/N = 4, while curbing cumulative N2O emissions by 12.1-26.8% and lowering net global-warming potential. Metatranscriptomics revealed coordinated upregulation of denitrification genes (narG, nirK, norB and nosZ) and extracellular electron-transfer genes (omcB, mtrC and macA), accompanied by 67.3% higher electron-transfer efficiency, elevated ATP and enhanced membrane potentials. FISH and SEM imaging captured AQDS-driven co-aggregates of electroactive microorganisms and denitrifiers bridged by nanowire-like structures, and selective enrichment of Geobacter sulfurreducens and Pseudomonas fluorescens. We identify quinone-mediated electron shuttling and direct interspecies electron transfer as the mechanistic basis of this microbial electroactive syntrophy, offering a low-energy strategy for sustainable nitrogen removal in cold, carbon-limited wastewater treatment.

Heterogeneous catalytic ozonation mitigates aquatic toxicity, yet comprehensive regulation strategies encompassing catalyst properties, reaction conditions, and pollutant molecular descriptors remain largely unexplored. In this study, three feature categories were quantitatively correlated with comprehensive toxicity using a hyperparameter-optimized random forest model. Carbon number and HOMO defined intrinsic pollutant response, whereas pH, reaction time, and catalyst metal identity represented adjustable variables for engineering regulation. Within the represented chemical space, low-carbon/high-HOMO pollutants exhibited lower predicted toxicity in model-identified regions associated with lower-atomic-number metal catalysts, near-neutral pH, and reaction times below 40 min or above 100 min. Together, these molecular, catalyst, and operational features characterize a coupled multivariable response pattern in predicted comprehensive toxicity. Metal identity, statistically represented by atomic number, was associated with distinct toxicity responses. Density functional theory calculations of two selected representative catalysts, Fe2O3 and SnO2, linked their contrasting O3 activation to differences in local electronic structure and interfacial charge transfer, with Fe2O3 showing stronger O3 adsorption and charge transfer. Furthermore, carbon number and highest occupied molecular orbital (HOMO) energy jointly influence electron distribution in pollutants, governing selectivity of bond cleavage. Low-carbon/high-HOMO pollutants localize electrons at electron-rich functional groups, favoring electrophilic [1]O2/[•]OH attack and more directed transformation toward low-toxicity products. Conversely, high-carbon/low-HOMO pollutants contain dispersed electron-rich and electron-deficient moieties, requiring [•]OH/O2[•-]-dominated oxidation to limit pathway branching and drive transformation to lower predicted toxicity. These findings provide a model-guided basis for identifying catalyst-condition combinations associated with lower predicted toxicity within the represented chemical space.