Taking three types of the most frequently detected microplastics (MPs) in the environment, i.e., polyethylene (PE), polyvinyl chloride (PVC), and polystyrene (PS), as the research objects, their ultraviolet (UV) aging and the environmental behavior of aged MPs to diclofenac (DCF) were explored in this study. The results indicate that after UV aging, the surface roughness, specific surface area and average pore size of the three MPs increased, while their contact angle and point of zero charge decreased. Moreover, UV aging caused an increase in the oxygen content of MPs, with a significant increase in the content of oxygen-containing functional groups such as carbonyl and hydroxyl. Adsorption experimental results show that different MPs had slightly different adsorption capacities for DCF, and PS exhibited stronger DCF adsorption ability with the adsorption capacity of 0.5626mg/g. The adsorption capacities of UV-aged PE, PVC and PS for DCF were 0.4884, 0.6077and 0.6382mg/g, respectively, which were stronger than that of the original MPs. The adsorption amount of DCF by MPs before and after aging both decreased with increasing pH or the increase in salinity and humic acid concentration. The adsorption process for DCF by MPs was divided into three stages, i.e., liquid film diffusion, particle internal diffusion, and adsorption equilibrium, and the adsorption mechanism mainly involved physical adsorption dominated by hydrophobic interactions, van der Waals forces, hydrogen bonding and electrostatic interactions.

This study established a static simulation experimental system, using sodium hypochlorite (NaClO) as a representative disinfectant and iron-oxidizing bacteria (IOB) strain LSS-1as a representative corrosive microorganism, to investigate their combined effects on total iron concentration, corrosion rate, electrochemical characteristics during corrosion, and physicochemical properties of corrosion products. The results indicated that the coupling of NaClO and LSS-1 promoted the corrosion of cast iron coupons. Under the condition of LSS-1 with 4.00mg/L NaClO, the corrosion rate after 30 days increased by 0.06mm/a, and the average total iron concentration increased by 5.76mg/L compared to the condition without LSS-1 and NaClO. Under the coupled condition, the self-corrosion potential (E₀) shifted negatively, and the current density (I₀) increased. Furthermore, the main corrosion products generated under the coupled condition included α-FeOOH, β-FeOOH, γ-FeOOH, Fe₂O₃, Fe₃O₄, and CaCO₃.

The c-type cytochrome (c-Cyts) is a key protein in microbial oxidation of Fe(II). However, the reaction process and influencing factors of ligand-bound Fe(II) with c-Cyts under acidic conditions remain unclear. In this study, Fe(II)-EDTA and a model c-type cytochrome (c-Cyts) were used to investigate the mechanism of microbial Fe(II) oxidation under acidic conditions with various environmental factors, including anoxic/oxic conditions, pH, and initial Fe(II)-EDTA concentration. A stopped-flow spectrophotometer was employed to study the reduction kinetics of c-Cyts, while the electrochemical properties of Fe(II) and the distribution of different Fe(II) species were characterized. Under anoxic conditions, the reduction rate of c-Cyts increased with increasing pH and Fe(II)-EDTA concentration. At 20μmol/L Fe(II)-EDTA, the apparent rate constants (k_app) were 0.841 and 6.802s^-1 at pH 3.5 and pH 5.0, respectively. At pH 5.0, the k_app increased to 13.028s^-1 when the initial Fe(II)-EDTA concentration increased to 80μmol/L. The increase in pH and Fe(II)-EDTA concentration enhanced the proportion of FeEDTA^2- and intensified the redox peaks of Fe(II), indicating greater Fe(II) reactivity and thus a faster c-Cyts reduction rate. Compared to anoxic conditions, c-Cyts reduction was inhibited under oxic conditions. This inhibitory effect of oxygen decreased as the pH increased. At pH 3.5 and 4.0, c-Cyts reduction was completely inhibited. At pH 4.5, the k_app decreased from 5.402s^-1 (anoxic) to 1.658s^-1 (oxic); and at pH 5.0, the k_app slightly decreased from 6.802s^-1 (anoxic) to 6.454s^-1 (oxic). This is attributed to the slower reaction between Fe(II)-EDTA and c-Cyts at lower pH, whereas the competing reaction of Fe(II)-EDTA with oxygen is favored, producing reactive oxygen species that further accelerate the reoxidation of reduced c-Cyts. This study elucidates the critical mechanisms of the reaction between Fe(II)-ligand and c-Cyts, providing deeper molecular-level insight into the microbial Fe(II) oxidation process.

In this study, the effect of sodium citrate (CA) combined with potassium permanganate (PM) pretreatment strategy on the acid production efficiency of waste activated sludge (WAS) anaerobic fermentation was investigated, and the promotion mechanism of CA synergistic PM significantly increased the accumulation of short-chain fatty acids (SCFAs). The results showed that, the production of SCFAs after CA and PM pretreatment reached a peak of 15777.54mg COD/L on the 6th day, which was increased by 6.41 times and 3.18 times compared with the control group and the PM group, respectively. At the same time, the proportion of acetic acid in SCFAs was also significantly increased. The reason is that CA and PM pretreatment enhance the structural destruction of extracellular polymer (EPS) in the sludge, release a large amount of soluble organic matter, and provide a richer substrate for microbial metabolism. Microbial community analysis further revealed that pretreatment reduced the overall diversity of microbial communities, significantly enriched functional flora related to hydrolysis and acid production, and inhibited SCFAs-depleting flora.

Primarily based on relevant literature from the Web of Science core database, it analyses the volume, content, and evolving trends of global and national research publications within this field. From an application perspective, solid waste remote sensing research is categorised into three domains: identification of solid waste disposal sites, site selection and suitability assessment, and environmental impact monitoring of disposal sites. Progress and emerging trends within each domain are systematically examined. Analysis reveals that current landfill identification approaches using low-resolution imagery predominantly employ GIS analysis and index construction, while high-resolution imagery applications frequently utilise deep learning techniques (object recognition, semantic segmentation). For site selection and suitability assessment, GIS platforms are commonly employed to conduct multi-criteria decision analysis integrating remote sensing indices (NDVI, NDWI, etc.) and topographical features (roads, water bodies, residential areas, etc.). Environmental monitoring primarily focuses on vegetation stress, surface thermal anomalies, and methane emissions. Current solid waste remote sensing research faces limitations in operationalising identification applications, lacks universally applicable site selection criteria, and has restricted environmental impact monitoring parameters. Building upon this, recommendations for advancing solid waste remote sensing research are proposed, aligned with environmental management requirements.

The source prevention and control technologies for heavy metals in soil at that time were plagued by problems such as insufficient precision and systematicness. This quantitative analysis methods of soil heavy metal sources and sinks at home and abroad were studied in this paper. Their historical evolution, type characteristics, basic principles, advantages and disadvantages were analyzed, and their roles and intrinsic correlations in source identification and flux analysis were revealed. The results show, after decades of exploration and iteration, source apportionment represented by mathematical statistics, receptor mathematical models, and physicochemical characteristic fingerprints had become the foundation for controlling the sources of heavy metals in soil. However, as the concept of soil pollution prevention and control shifted from traditional investigation and remediation to sustainable risk management of sources and sinks, the importance of precise flux calculation for control effectiveness had become increasingly prominent. In view of this, a "source-sink-end" "three-in-one" risk control system was proposed in this paper: based on scientific "source" identification, centered on flux "sink" characterization, and guaranteed by receptor "end" verification, this system was designed to practice the concept of "prevention first, remediation second", and to promote food security and the sustainable utilization of soil resources.

The soil-water partition coefficient (K_d) of per- and polyfluoroalkyl substances (PFAS) was predicted using machine learning (ML) models, and their partitioning behavior in soils was elucidated. A dataset comprising 1,227 samples of 47 PFAS was employed, with 16 PFAS physicochemical properties and soil parameters used as input features. It was demonstrated that the lgK_d values ranged from -1.40 to 3.95. When the number of CF₂ groups exceeded 5and the molecular weight (MW) was greater than 400g/mol, significant linear relationships were observed between lgKd and both the CF₂ number and MW, with R² values of 0.96 and 0.94, respectively. The influence of soil properties on PFAS adsorption was found to be dependent on the type of PFAS: non-zwitterionic PFAS were mainly affected by organic carbon content and silt content, whereas zwitterionic PFAS were primarily influenced by silt and sand content. After parameter optimization, the ML model exhibited good predictive performance, achieving an R² of 0.85, an RMSE of 0.35, and an MAE of 0.26. MW, organic carbon content, water solubility (lgS), and net charge density were identified as the most important features, with contribution rates of 31.6%, 29.8%, 19.7%, and 10.0%, respectively. Three-dimensional interaction analysis indicated that when soils with high organic carbon content, high cation exchange capacity (CEC), and low pH were combined with PFAS of large MW, lgK_d exceeded 0.40, reflecting strong adsorption. Conversely, under conditions where CEC was below 8.00 cmol/kg, organic matter content was less than 1.00%, and MW was below 380g/mol, lgK_d was lower than 0.40, indicating weaker adsorption and higher potential for environmental migration. The findings provide a reliable tool for predicting the adsorption behavior of PFAS.

To investigate the enhanced effect of ferrihydrite-loaded electrodes on the degradation of organic pollutants in petroleum-contaminated soil using a Microbial Electrochemical System (MES), and to elucidate the underlying synergistic mechanisms, a single-chamber soil MES reactor was constructed. Using oil-contaminated soil as the experimental medium, we compared the performance of the original soil, the conventional electrode setup, and the ferrihydrite-loaded electrode setup. The system's effectiveness was evaluated through electrochemical analysis, assessment of soil carbon degradation and transformation, characterization of iron forms, and determination of enzyme activity. Results indicate that loading electrodes with ferrihydrite significantly improved the system's electrochemical performance, with cumulative charge output increasing by 18%. The carbon degradation capacity was also enhanced, with the total carbon degradation rate increasing by up to 11%, effectively promoting the transformation of recalcitrant carbon, such as humus, into more labile forms. Ferrihydrite facilitated the iron cycle, with iron ion content increasing by up to 28%, thereby enhancing the Fe(II)/Fe(III) redox cycling and strengthening the coupling between carbon and nitrogen transformations. Additionally, the bioelectric field accelerated the phase transformation of ferrihydrite into goethite and hematite. The presence of ferrihydrite stimulated microbial secretion of degradative enzymes, significantly increasing the activity of key enzymes involved in pollutant degradation. Mantel test results further reveal that dehydrogenase and polyphenol oxidase served as the primary drivers of carbon mineralization.

Algal blooms are a major ecological and environmental issue in the tributary bays of the Three Gorges Reservoir. To identify the dominant environmental factors driving algal blooms in the Xiangxi Bay, this study utilized high-frequency monitoring data collected from August 9 to August 30, 2024, with chlorophyll-a (Chl.a) concentration as the bloom indicator. Three machine learning models—Random Forest (RF), Support Vector Regression (SVR), and Partial Least Squares Regression (PLS)—were applied to identify key driving factors. The results showed that algal blooms in Xiangxi Bay exhibited pronounced diurnal variation, with nighttime stable stratification and low disturbance favoring bloom formation, while daytime disturbances and high temperatures inhibited algal accumulation. Water velocity (WV) and air temperature (AT) were identified as immediate driving factors, with contribution rates of 27.1% and 18.1%, respectively. specific conductivity (SP) served as an important regulating factor (17.1%), while dissolved oxygen (DO), pH, and water temperature (WT) were secondary feedback factors. Moreover, the impacts of these factors on Algal blooms exhibited significant nonlinearity and lagged response characteristics.

Using high-frequency continuous monitoring data collected at the dam forebay of Huating Lake from April to June 2022, this study quantified chlorophyll-a (Chl.a) dynamics and examined its phase-dependent responses to hydrological and water-quality drivers during phytoplankton growth and decline stages. Principal component analysis, multivariate correlation analysis, and partial least squares structural equation modeling (PLS-SEM) were integrated to disentangle the relative contributions and interaction pathways of these drivers. Chl.a concentrations ranged from 6.54 to 65.90μg/L (mean: (34.62±12.14)μg/L), exhibiting a pronounced unimodal temporal pattern. The dominant controls on Chl.a differed substantially between growth and decline phases. During the growth phase, Chl.a variability was primarily driven by the combined effects of nutrient availability—especially total phosphorus (TP) and permanganate index (COD_Mn)—and hydrological processes, including water-level fluctuations and outflow discharge. In contrast, Chl.a dynamics during the decline phase were mainly regulated by pH and total nitrogen (TN), indicating a shift in controlling mechanisms across phytoplankton life stages. PLS-SEM results showed that water-quality factors exerted significant positive direct effects on Chl.a in both phases (standardized path coefficients: 0.729 in the growth phase and 0.941 in the decline phase). Hydrological factors did not directly affect Chl.a but influenced it indirectly through water-quality pathways, producing a negative indirect effect during the growth phase (-0.780) and a positive indirect effect during the decline phase (0.583). Variable-level analysis identified COD_Mn and TP, together with outflow discharge and water level, as key drivers during the growth phase, whereas pH, TP, TN, COD_Mn, and water level dominated during the decline phase.