Based on the acidic and phosphorus-rich properties of phosphogypsum (PG), it was dope-modified (PO-PG) to cement (PO) and used for lead removal from acid mine wastewater. The results showed that PO-PG removed more than 99% of lead at different concentrations (15~100mg/L) with suitable dosage; under the reaction conditions of initial pH=3, PO-PG dosage of 0.2g/L, and 25℃, the effect of PO-PG on the removal of lead from 30mg/L Pb²⁺ was completely removed in 20min instead of 120min for PO; meanwhile, PO-PG had a high affinity for lead, and the removal effect of lead remained stable in the simulated wastewater with complex composition. Mechanistic analysis showed that isomorphous replacement and surface heterogeneous chemical precipitation were the main mechanisms for lead removal by PO-PG, Ca(OH)₂, CaCO₃ and Ca₅(PO₃)₃ OH were the main lead removal factors in PO-PG, and Pb²⁺ is removed by lattice displacement and other chemical reactions with Ca²⁺ to form a water-insoluble precipitate, and the products of the lead removal mainly included PbCO₃, Pb₃(CO₃)₂(OH)₂, Pb(OH)₂ and Pb₅(PO₄)₃ OH precipitates. In summary, the doping of acidic PG can accelerate the hydration rate of alkaline PO, thus improving the reaction efficiency and alleviating the problem of high pH in PO-treated effluent, in addition to the presence of phosphates in PG can be an effective species for lead removal. This study provides a cost-effective and efficient new method for the removal of lead from acid mine wastewater and can realize the resource utilization of phosphogypsum.

The molecular compositions of organic components in winter PM_2.5 samples from a typical urban area of Chongqing were analyzed by electrospray ionization coupled with ion mobility spectrometry-time of flight mass spectrometry (ESI-IMS-TOF-MS). Sulfur-containing organics (CHOS+CHNOS) were important components in organic aerosol, and their relative abundance accounted for more than 70% on average. IMS-derived collision cross section, collision-induced dissociation, and Kendrick mass defect analyses verified the presence of organosulfates (OSs). Molecular characterization results indicated that sulfur-containing organics were dominated by carbohydrate and lignin species and had higher oxidation degree in Chongqing urban area compared with other cities. Biogenic and anthropogenic precursors were important sources of sulfur-containing organics. The relationships between aerosol liquid water content, acidity and inorganic sulfate with sulfur-containing organics suggested that aqueous-phase chemistry and acid-catalyzed chemistry play important roles in the formation of sulfur-containing organics.

Oxidative potential (OP) is a crucial indicator for evaluating the capacity of PM_2.5 to trigger oxidative stress. Therefore, this study employed dithiothreitol (DTT) method to measure the OP of PM_2.5. During the observation period, the results indicated that the daily average concentration of atmospheric PM_2.5 in Taiyuan severely exceeded the standard, with a maximum concentration reaching 150.91µg/m³, signifying severe air pollution. The daily average values for volume-normalized (DTT_v) and mass-normalized (DTT_m) DTT activity were (2.90±1.07)nmol/(min·m³) and (38.34±18.91)pmol/(min·µg), respectively. Meanwhile, a significant positive correlation was observed between PM_2.5 mass concentration and DTT_v (r=0.916, P<0.01), while a negative correlation was found with DTT_m. Furthermore, DTT_v exhibited significant correlations (P<0.05) with organic carbon (OC), elemental carbon (EC), metallic elements (Fe, Mn, Zn, Pb), and ionic components (K⁺, Cl^-, etc.) within PM_2.5. These phenomena suggested that DTT activity primarily depends on specific components of PM_2.5. The study further integrated the positive matrix factorization (PMF) model with the multiple linear regression algorithm. Quantitative analysis revealed that solid fuel combustion sources, such as coal combustion, were the most important sources of OP in Taiyuan, contributing 54.7%, followed by motor vehicle sources (23.3%) and dust sources (22.0%).

This review summarizes the synthesis and regulation mechanisms of quorum sensing (QS) systems in Gram-negative and Gram-positive bacteria, focusing on key signalling molecules including acyl-homoserine lactones (AHLs), autoinducing peptides (AIPs), and autoinducer-2 (AI-2), as well as their applications in environmental remediation. The results demonstrate that QS-mediated regulation of bacterial collective behaviors primarily involves two critical processes: synthesis/release of signalling molecules and subsequent recognition-triggered behavioral responses. Notably, S-adenosylmethionine (SAM) serves as a common substrate for multiple QS signal biosynthesis pathways, potentially reflecting co-evolutionary adaptation between QS systems and bacterial coordination. Certain signalling molecules exhibit cross-kingdom functionality, not only adjusting conspecific bacterial behaviors but also mediating communication between phylogenetically unrelated bacteria and plants. Microbial communities leverage QS to synchronize population-level activities, optimize community architecture, and modulate synthesis of key degradation enzymes, thereby enhancing biofilm-mediated remediation efficiency. Finally, the development directions of QS-based microbial remediation technologies are discussed. The research results provide theoretical foundations and practical insights for studies on microbial remediation technologies.

This study analysed the spatiotemporal characteristics of dissolved oxygen (DO) concentrations upstream and downstream of sluices during dry and wet years, using data from three automated water quality monitoring stations and field measurements along the Huangjiang River in Guangdong Province. Multiple statistical methods were employed to identify the relative contributions of key influencing factors to DO variability across years under different precipitation conditions. Upstream DO concentrations were generally higher in dry years ((8.02±0.10) mg/L) than in wet years ((7.26±0.08) mg/L). In contrast, DO levels in the downstream tidal section increased from (4.45±0.10) mg/L (dry year) to (7.33±0.09) mg/L (wet year), primarily due to improved water quality. Periodic fluctuations were observed in both years, with higher DO levels during the flood season and lower levels during the non-flood season throughout the river channel. Influenced by the gate control and different external inputs, DO fluctuations upstream and downstream were driven by different factors. Rainfall and water temperature explained 44% to 87% of the DO variability upstream. While ammonia nitrogen, and COD_Mn were the most influential factors downstream, accounting for 53% to 75% of the variability. Furthermore, the “lacustrine” upstream section was especially sensitive to climate variations. In this area, the loss of phytoplankton biomass caused by stormwater runoff was a major factor contributing to DO difference during dry and wet years. While DO downstream is more easily influenced by water pollutants, especially the significant decrease in oxygen-demanding substances.

Based on the InVEST model, Hierarchy of Needs Theory, and ecosystem service supply-demand analysis, we utilized Linkage Mapper and other tools to extract ecological sources, corridors, and pinch points in the Sichuan Basin from 2005 to 2020, following the least-cost path theory. The ecosystem service supply-demand relationship was analyzed to construct the ecological security pattern of the basin. Spatially, ecosystem service supply and demand in the Sichuan Basin exhibited a negative correlation. Despite an overall ecological surplus (supply exceeding demand), spatial mismatches between supply and demand intensified regional contradictions. From 2005 to 2020, the area of ecological sources in the basin was 68700km², 63900km², 63100km², and 64800km², respectively, presenting a “dense periphery-sparse center” ring-shaped distribution. The total length of ecological corridors across four periods increased continuously (6693.38km, 8342.29km, 8594.62km, and 14130.94km), forming a “periphery-connected, sparse-center, dense-east” network structure. Ecological pinch points were clustered at source junctions, while obstacle points were concentrated near fragmented source patches, particularly in the eastern parallel ridge-valley region. Integrating ecosystem service supply-demand dynamics and existing ecological security patterns, we proposed an optimized protection framework termed “Three Belts, Four Zones, and Five Cores”. Three Belts: East-west axial belts in the basin’s southern and northern regions, and a north-south axial belt in the eastern basin. Four Zones: Ecological security protection zone (eastern Sichuan), ecological restoration zones (western and northern Sichuan), and an ecological fragility recovery zone (central Sichuan). Five Cores: Key connectivity nodes in the southern basin and the eastern parallel ridge-valley area.

This review systematically explores the application of machine learning technology in the field of microplastics, covering classification and identification, quantitative analysis, and prediction of adsorption properties. By combing through recent literature, it has been found that technologies such as convolutional neural networks (CNN) and support vector machines (SVM) are of great significance for improving the accuracy and efficiency of microplastic detection. In classification and identification, CNN models can accurately distinguish the types and shapes of microplastics; during quantitative analysis, machine learning can quickly determine the concentration of microplastics with the help of image and spectral data. In terms of predicting adsorption properties, models based on quantitative structure-property relationships (QSPR) have shown higher accuracy and robustness than traditional models. However, there are currently challenges such as poor data quality, difficulties in collection and annotation, and a lack of model interpretability. Future research should focus on diversifying datasets and enhancing model interpretability to promote the further application of machine learning technology in microplastic research.

To explore the influence of trace elements and change in environmental conditions at low temperature on the nitrification performance of biofilm reactor, a simulated wastewater containing NH₄⁺-N was treated. The effects of trace elements, low temperature, aeration rate and flow rate on nitrification performance of the biofilm reactor were studied. The microbial community structure was analyzed by 16S rRNA high-throughput sequencing technique. The results showed that trace elements significantly affected the nitrification performance (P<0.0001). After adding trace elements to the influent, the removal load of NH₄⁺-N increased from 0.93kg/(m³·d) to 1.63kg/(m³·d) and the generation load of NO₃^--N increased from 0.23kg/(m³·d) to 1.21kg/(m³·d). Low temperature can affect nitrifying bacteria. Nitrite oxidizing bacteria (NOB) are sensitive to low temperature shock, while ammonia oxidizing bacteria (AOB) are resistant to it. The decrease in aeration rate led to a lack of dissolved oxygen (DO) in the reactor, which further affected the nitrification performance. The change in flow rate had no significant effect on the nitrification performance. Analysis of the microbial community structure at low temperatures showed that the nitrobacteria of Nitrosomonas and Nitrospira were enriched during operation, which ensured that the reactor can still have stable nitrification performance. The research provides experimental evidence and theoretical guidance for improving the nitrification performance and enhancing the low-temperature resistance of biofilm reactor in wastewater nitrification treatment practice.

To elucidate the interaction mechanisms between iron-manganese minerals and antibiotics/antibiotic resistance genes (ARGs), enhance the understanding of their environmental degradation behaviors, and advance remediation technologies, this study systematically investigates the multifaceted degradation mechanisms of antibiotics by iron-manganese minerals. The mechanisms are explored through the following pathways: synergistic catalysis through surface Brønsted acid sites, Lewis acid sites, and hydroxyl groups promoting antibiotic hydrolysis; semiconductor-mediated photocatalytic degradation via electron-hole pair generation; direct oxidation by redox-active components such as Fe(III)/Mn(IV) coupled with activation of persulfate/hydrogen peroxide to yield reactive species for complete mineralization; concomitant radical-induced damage to ARGs through phosphodiester bond cleavage and base pair destruction, effectively inhibiting their horizontal transfer and evolution. The practical efficacy of iron-manganese minerals has been demonstrated in diverse environmental matrices including soils, aquatic systems, sludge, and livestock manure, with degradation efficiency dynamically regulated by pH, organic matter content, co-existing ions, and moisture conditions. Future research should prioritize establishing integrated databases mapping antibiotic-ARG co-degradation pathways and toxicity profiles, developing in situ dynamic characterization techniques for mineral interface reactions, engineering environment-adaptive mineral- based composite materials.

Cooperative strategies that mitigate competition among dominant functional microorganisms are crucial for efficient nitrogen and phosphorus removal in wastewater treatment. This study investigated a novel single-stage sequencing batch reactor operating under an anaerobic/anaerobic/oxic/anaerobic (A/A₁/O/A₂) mode for 100days to regulate the dynamic balance of phosphorus-accumulating organisms (PAOs), denitrifying PAOs (DPAOs), and denitrifying glycogen-accumulating organisms (DGAOs). The optimized system, featuring a recycle loop and reduced aerobic phase duration, achieved nitrogen and phosphorus removal efficiencies of (95.13%±0.35%) and (94.70%±0.96%), respectively. Mechanistic analysis suggested that the A₁ phase created an anoxic environment conducive to DPAO-mediated denitrifying phosphorus removal, while the A₂ phase supported DGAO-driven denitrifying nitrogen removal using polyhydroxyalkanoates (PHAs) and glycogen (Gly). Extracellular polymeric substance (EPS) analysis revealed increases of 35.38mg/gVSS in protein (PN) and 12.39mg/gVSS in polysaccharide (PS) content, enhancing sludge aggregation. Microbial community analysis demonstrated significant enrichment of Dechloromonas and Ca. Competibacter, with their abundances increasing from 2.24% and 1.53% in R1to 7.61% and 7.94% in R2, respectively. The A/A₁/O/A₂ mode effectively created a synergistic environment for key DPAOs and DGAOs, achieving superior nitrogen and phosphorus removal performance compared to conventional modes.