Temporal and spatial evolution of different heavy metal fractions and correlation with environmental factors after prolonged acid mine drainage irrigation: A column experiment.

Acid mine drainage (AMD) has global significance due to its low pH and elevated heavy metal content, which have received widespread attention. After AMD irrigation in mining areas, heavy metals are distributed among soil layers, but the influencing factors and mechanisms remain unclear. AMD contamination of surrounding soil is primarily attributed to surface runoff and irrigation and causes significant environmental degradation. A laboratory soil column experiment was conducted to investigate the temporal and spatial distribution of the heavy metals Cd and Cu, as well as the impact of key environmental factors on the migration and transformation of these heavy metals following long-term soil pollution by AMD. After AMD addition, the soil exhibited a significant increase in acidity, accompanied by notable alterations in various environmental parameters, including soil pH, Eh, Fe(II) content, and iron oxide content. Over time, Cd and Cu in the soil mainly existed in the exchangeable and carbonate-bound fractions. In spatial terms, exchangeable Cu increased with increasing depth. Pearson correlation analysis indicated significant negative correlations between pH and Cu, Cd, and Eh in pore water, as well as negative correlations between pH and the exchangeable fraction of Cd (F1), carbonate-bound fraction of Cd (F2), and exchangeable fraction of Cu (F1) in the solid phase. Additionally, a positive correlation was observed between pH and the residual fraction of Cu (F5). Furthermore, the soil total Cd content exhibited a positive correlation with pyrophosphate-Fe (Fep) and dithionite-Fe (Fed), while CdF1, CdF2, total Cu, and CuF1 displayed positive correlations with Fep. Our findings indicate that the presence of AMD in soil leads to alterations in the chemical fractions of Cd and Cu, resulting in enhanced bioavailability. These results offer valuable insights for developing effective remediation strategies for soils near mining sites.

Heteroaggregation kinetics of nanoplastics and soot nanoparticles in aquatic environments.

Heteroaggregation between polystyrene nanoplastics (PSNPs) and soot nanoparticles (STNPs) in aquatic environments may affect their fate and transport. This study investigated the effects of particle concentration ratio, electrolytes, pH, and humic acid on their heteroaggregation kinetics. The critical coagulation concentration (CCC) ranked CCCPSNPs > CCCPSNPs-STNPs > CCCSTNPs, indicating that heteroaggregation rates fell between homoaggregation rates. In NaCl solution, as the PSNPs/STNPs ratio decreased from 9/1 to 3/7, heteroaggregation rate decreased and CCCPSNPs-STNPs increased from 200 to 220 mM due to enhanced electrostatic repulsion. Outlier was observed at PSNPs/STNPs= 1/9, where CCCPSNPs-STNPs= 170 mM and homoaggregation of STNPs dominated. However, in CaCl2 solution where calcium bridged with STNPs, heteroaggregation rate increased and CCCPSNPs-STNPs decreased from 26 to 5 mM as the PSNPs/STNPs ratio decreasing from 9/1 to 1/9. In composite water samples, heteroaggregation occurred only at estuarine and marine salinities. Acidic condition promoted heteroaggregation via charge screening. Humic acid retarded or promoted heteroaggregation in NaCl or CaCl2 solutions by steric hindrance or calcium bridging, respectively. Other than van der Waals attraction and electrostatic repulsion, heteroaggregation was affected by steric hindrance, hydrophobic interactions, π - π interactions, and calcium bridging. The results highlight the role of black carbon on colloidal stability of PSNPs in aquatic environments.

Effects of Fe(II) bio-oxidation rate and alkali control on schwertmannite microstructure and adsorption of oxyanions: Characteristics, performance and mechanism.

Schwertmannite has attracted increasing interest for its excellent sorption of oxyanions such as AsO43-, CrO42-, and Sb(OH)6-. Controlling biomineralization by adjusting the Fe(II) oxidation rate and implementing alkali control can enhance the yield and adsorption performance of schwertmannite. However, the adsorption improvement mechanism is still unclear. The morphology, crystallinity, specific surface area (SSA) and oxyanion adsorption of schwertmannite synthesized with alkali control of solution pH and different Fe(II) oxidation rates were analyzed in this study. The differences in the adsorption mechanisms of As(V), Cr(VI) and Sb(V) on schwertmannite obtained under different synthesis conditions were also studied. Reducing the Fe(II) oxidation rate or maintaining the solution pH through alkali control significantly increased the SSA of schwertmannite and the proportion of outer-sphere sulfate. Alkali-controlled schwertmannite (Sch-C) exhibited superior As(V) and Sb(V) adsorption performance and slightly greater Cr(VI) adsorption than non-alkali-controlled schwertmannite. The As(V) and Sb(V) adsorption capacities of Sch-C greatly improved because the ultra-high SSA increased the surface hydroxyl content and reduced the passivation effect of amorphous precipitates on the mineral surface, allowing continuous sulfate exchange at inner mineral sites. An increased surface hydroxyl content had little effect on Cr(VI) adsorption, but an increased proportion of outer-sphere sulfate caused a slight increase in Cr(VI) adsorption. Sb(V) has a stronger hydroxyl exchange ability than As(V), but due to its octahedral structure, it exchanges only with outer-sphere sulfate on schwertmannite and hardly exchanges with inner-sphere sulfate.

Divergent redistribution behavior of divalent metal cations associated with Fe(II)-mediated jarosite phase transformation.

The Fe(II)/Fe(III) cycle is an important driving force for dissolution and transformation of jarosite. Divalent heavy metals usually coexist with jarosite; however, their effects on Fe(II)-induced jarosite transformation and different repartitioning behavior during mineral dissolution-recrystallization are still unclear. Here, we investigated Fe(II)-induced (1 mM Fe(II)) jarosite conversion in the presence of Cd(II), Mn(II), Co(II), Ni(II) and Pb(II) (denoted as Me(II), 1 mM), respectively, under anaerobic condition at neutral pH. The results showed that all co-existing Me(II) retarded Fe(II)-induced jarosite dissolution. In the Fe(II)-only system, jarosite first rapidly transformed to lepidocrocite (an intermediate product) and then slowly to goethite; lepidocrocite was the main product. In Fe(II)-Cd(II), -Mn(II), and -Pb(II) systems, coexisting Cd(II), Mn(II) and Pb(II) retarded the above process and lepidocrocite was still the dominant conversion product. In Fe(II)-Co(II) system, coexisting Co(II) promoted lepidocrocite transformation into goethite. In Fe(II)-Ni(II) system, jarosite appeared to be directly converted into goethite, although small amounts of lepidocrocite were detected in the final product. In all treatments, the appearance or accumulation of lepidocrocite may be also related to the re-adsorption of released sulfate. By the end of reaction, 6.0 %, 4.0 %, 76.0 % 11.3 % and 19.2 % of total Cd(II), Mn(II), Pb(II) Co(II) and Ni(II) were adsorbed on the surface of solid products. Up to 49.6 %, 44.3 %, and 21.6 % of Co(II), Ni(II), and Pb(II) incorporated into solid product, with the reaction indicating that the dynamic process of Fe(II) interaction with goethite may promote the continuous incorporation of Co(II), Ni(II), and Pb(II).

Prediction of Cr(VI) and As(V) adsorption on goethite using hybrid surface complexation-machine learning model.

This study aimed to develop surface complexation modeling-machine learning (SCM-ML) hybrid model for chromate and arsenate adsorption on goethite. The feasibility of two SCM-ML hybrid modeling approaches was investigated. Firstly, we attempted to utilize ML algorithms and establish the parameter model, to link factors influencing the adsorption amount of oxyanions with optimized surface complexation constants. However, the results revealed the optimized chromate or arsenate surface complexation constants might fall into local extrema, making it unable to establish a reasonable mapping relationship between adsorption conditions and surface complexation constants by ML algorithms. In contrast, species-informed models were successfully obtained, by incorporating the surface species information calculated from the unoptimized SCM with the adsorption condition as input features. Compared with the optimized SCM, the species-informed model could make more accurate predictions on pH edges, isotherms, and kinetic data for various input conditions (for chromate: root mean square error (RMSE) on test set = 5.90 %; for arsenate: RMSE on test set = 4.84 %). Furthermore, the utilization of the interpretable formula based on Local Interpretable Model-Agnostic Explanations (LIME) enabled the species-informed model to provide surface species information like SCM. The species-informed SCM-ML hybrid modeling method proposed in this study has great practicality and application potential, and is expected to become a new paradigm in surface adsorption model.

Combined collection systems of sewage and rainfall runoff seriously affect the spatial distributions of natural estrogens and their conjugates in river water: Insights from the Pearl River of China.

So far, little has been known about how the combined collection systems of sewage and rainfall runoff (CCSs) affect emerging contaminants in river water. To fill up the knowledge gap, this study was conducted to investigate the spatial distributions of three natural estrogens (NEs, i.e., estrone (E1), 17ß-estradiol (E2) and estriol (E3)) and their conjugates (C-NEs) in the Pearl River in the wet and dry seasons. Results showed that the respective average concentrations of NEs and C-NEs at different locations alongside the Pearl River in the wet season were 7.3 and 1.8 times those in the dry season. Based on estrogen equivalence (EEQ), the average estimated EEQ level in the Pearl River waters in the wet season was nearly 10 times that in the dry season. These seemed to imply that the CCSs in the wet season not only cause untreated sewage into the receiving water body, but greatly decrease the removal efficiency of NEs and C-NEs in wastewater treatment plant. Furthermore, the estimated annual loads of E1, E2, and E3 to the Pearl River in the wet season accounted for about 88.6 %, 100 %, and 99.3 % of the total annual loads. Consequently, this work for the first time demonstrated that the CCSs in cities with high precipitation are unfavorable for controlling of emerging contaminants.

Influence of dissolved organic matter with different molecular weight from chicken manure on ferrihydrite adsorption and re-release of antimony(V).

Applying organic fertilizer is the main way to enhance soil fertility through the interfacial reaction between mineral and dissolved organic matter (DOM). However, the interfacial reaction between minerals and DOM may influence antimony(V) (Sb(V)) mobility in agricultural soils around antimony mines. In our study the ferrihydrite (Fh) was chosen as a representative mineral, to reveal the effect of its interaction with chicken manure organic fertilizer (CM-DOM) with Fh on Sb(V) migration. In this study, we investigated different organic matter molecular weights and C/Fe molar ratios. Our findings indicated that the addition of CM-DOM decreased the adsorption of Sb(V) by Fh and promoted the re-release of Sb(V) adsorbed on Fh. This effect was enhanced by increasing the C/Fe molar ratio. Fh mainly affects its interaction with Sb(V) through electrostatic gravitational interaction and ligand exchange, but the presence of CM-DOM weakens the electrostatic interaction between Fh and Sb(V) as well as competes with Sb(V) for the hydroxyl reactive site on Fh surface. In addition, the smaller molecular weight fraction (<10 kDa) of CM-DOM has higher aromaticity and hydrophobicity, which potentially leads to more intense competition with Sb(V) for the reaction sites on Fh. Therefore, the application of organic fertilizer may promote Sb(V) migration, posing significant risks to soil ecosystems and human health, which should be a concern in field soil cultivation.

Speciation and Possible Origins of Organosulfur Compounds in Rice Paddy Soils Affected by Acid Mine Drainage.

Although sulfur cycling in acid mine drainage (AMD)-contaminated rice paddy soils is critical to understanding and mitigating the environmental consequences of AMD, potential sources and transformations of organosulfur compounds in such soils are poorly understood. We used sulfur K-edge X-ray absorption near edge structure (XANES) spectroscopy to quantify organosulfur compounds in paddy soils from five AMD-contaminated sites and one AMD-uncontaminated reference site near the Dabaoshan sulfide mining area in South China. We also determined the sulfur stable isotope compositions of water-soluble sulfate (δ34SWS), adsorbed sulfate (δ34SAS), fulvic acid sulfur (δ34SFAS), and humic acid sulfur (δ34SHAS) in these samples. Organosulfate was the dominant functional group in humic acid sulfur (HAS) in both AMD-contaminated (46%) and AMD-uncontaminated paddy soils (42%). Thiol/organic monosulfide contributed a significantly lower proportion of HAS in AMD-contaminated paddy soils (8%) compared to that in AMD-uncontaminated paddy soils (21%). Within contaminated soils, the concentration of thiol/organic monosulfide was positively correlated with cation exchange capacity (CEC), moisture content (MC), and total Fe (TFe). δ34SFAS ranged from -6.3 to 2.7‰, similar to δ34SWS (-6.9 to 8.9‰), indicating that fulvic acid sulfur (FAS) was mainly derived from biogenic S-bearing organic compounds produced by assimilatory sulfate reduction. δ34SHAS (-11.0 to -1.6‰) were more negative compared to δ34SWS, indicating that dissimilatory sulfate reduction and abiotic sulfurization of organic matter were the main processes in the formation of HAS.

Integrated transcriptomics and metabolomics analyses reveal the aerobic biodegradation and molecular mechanisms of 2,3',4,4',5-pentachlorodiphenyl (PCB 118) in Methylorubrum sp. ZY-1.

2,3',4,4',5-pentachlorodiphenyl (PCB 118), a highly representative PCB congener, has been frequently detected in various environments, garnering much attention across the scientific community. The degradation of highly chlorinated PCBs by aerobic microorganisms is challenging due to their hydrophobicity and persistence. Herein, the biodegradation and adaptation mechanisms of Methylorubrum sp. ZY-1 to PCB 118 were comprehensively investigated using an integrative approach that combined degradation performance, product identification, metabolomic and transcriptomic analyses. The results indicated that the highest degradation efficiency of 0.5 mg L-1 PCB 118 reached 75.66% after seven days of inoculation when the bacteria dosage was 1.0 g L-1 at pH 7.0. A total of eleven products were identified during the degradation process, including low chlorinated PCBs, hydroxylated PCBs, and ring-opening products, suggesting that strain ZY-1 degraded PCB 118 through dechlorination, hydroxylation, and ring-opening pathways. Metabolomic analysis demonstrated that the energy supply and redox metabolism of strain ZY-1 was disturbed with exposure to PCB 118. To counteract this environmental stress, strain ZY-1 adjusted both the fatty acid synthesis and purine metabolism. The analysis of transcriptomics disclosed that multiple intracellular and extracellular oxidoreductases (e.g., monooxygenase, alpha/beta hydrolase and cytochrome P450) participated in the degradation of PCB 118. Besides, active efflux of PCB 118 and its degradation intermediates mediated by multiple transporters (e.g., MFS transporter and ABC transporter ATP-binding protein) might enhance bacterial resistance against these substances. These discoveries provided the inaugural insights into the biotransformation of strain ZY-1 to PCB 118 stress, illustrating its potential in the remediation of contaminated environments.

Atomic-Level Matching Metal-Ion Organic Hybrid Interface to Enhance Energy Storage of Polymer-Based Composite Dielectrics.

In this work, a distinctive "metal-ion organic hybrid interface" (MOHI) between polyimide (PI) and calcium niobate (CNO) nanosheets is designed. The metal ions in the MOHI can achieve atomic-level matching not only with the inorganic CNO, but also with the PI chains, forming uniform and strong chemical bonds. These results are demonstrated by experiment and theory calculations. Significantly, the MOHI reduces the free volume and introduces deep traps across the filler-matrix interfacial area, thus suppressing the electric field distortion in PI-based composite dielectrics. Consequently, PI-based dielectric containing the MOHI exhibits excellent energy storage performance. The energy storage densities (Ue) of the composite dielectric reach 9.42 J cm-3 and 4.75 J cm-3 with energy storage efficiency (η) of 90% at 25 °C and 150 °C respectively, which are 2.6 and 11.6 times higher than those of pure PI. This study provides new ideas for polymer-based composite dielectrics in high energy storage.

Inhibition photooxidation of pyrite under illumination via altering photogenerated carrier migration pathways: Role of DTC-TETA surface passivation.

The oxidation of pyrite is the main cause of acidic mine drainage (AMD), which is a very serious environmental problem in numerous mining areas around the world. Previous studies have shown that passivation agents create a hydrophobic film on the surface of pyrite, effectively isolating oxygen and water. However, the presence of abundant sulfide minerals in tailings ponds may exacerbate AMD when exposed to solar radiation, due to the semiconductor properties of pyrite. It remains uncertain whether the current surface passivation coating can effectively prevent the oxidation of pyrite under light conditions. This paper is the first to investigate the passivation effect as well as the mechanism of surface passivation coating on pyrite under illumination from the perspective of materials science. The results demonstrated that the triethylenetetramine-bisdithiocarbamate (DTC-TETA) passivation coating on pyrite almost completely suppressed the photooxidation of pyrite under illumination by changing the migration path of photogenerated charge carriers. The formation of NC(S)2-Fe chelating groups provides atomic-level interface channels for DTC-TETA to transfer electrons to pyrite and creates a favorable reduction environment for pyrite. Besides, DTC-TETA coating greatly improves the electron-hole pairs recombination efficiency of pyrite, which significantly inhibits the photogenerated electron reduction of oxygen to generate reactive oxygen species (ROS). Moreover, DTC-TETA coating captures the photogenerated holes, avoiding direct oxidation of pyrite by holes. Density functional theory (DFT) calculations revealed that the DTC-TETA coating increases the adsorption energy barrier for oxygen and water. The results extend the existing knowledge on passivation mechanisms on pyrite and hold significant implications for the future screening, evaluation, and practical application of surface passivating agents.

Deconjugation potentials of natural estrogen conjugates in sewage and wastewater treatment plant: New insights from model prediction and on-site investigations.

Natural estrogen conjugates play important roles in municipal wastewater treatment plant (WWTP), but their deconjugation potentials are poorly understood. This work is the first to investigate the relationships between the enzyme activities of arylsulfatase/ß-glucuronidase and deconjugation potentials of natural estrogen conjugates. This work led to three important findings. First, the enzyme activity of ß-glucuronidase in sewage is far higher than that of arylsulfatase, while their corresponding activities in activated sludge were similar. Second, a model based on ß-glucuronidase could successfully predict the deconjugation potentials of natural estrogen glucuronide conjugates in sewage. Third, the enzyme activity of arylsulfatase in sewage was too low to lead to evident deconjugation of sulfate conjugates, which means that the deconjugation rate of estrogen sulfates can be regarded as zero. By comparing their theoretical removal based on enzyme activity and on-site investigation, it is reasonable to conclude that reverse deconjugation of estrogen conjugates (i.e., conjugation of natural estrogens to form conjugated estrogens) likely exist in WWTP, which explains well why natural estrogen conjugates cannot be effectively removed in WWTP. Meanwhile, this work provides new insights how to improve the removal performance of WWTP on natural estrogen conjugates. SYNOPSIS: This work is the first to show how arylsulfatase/ß-glucuronidase could affect deconjugation of natural estrogen conjugates and possible way to enhance their removal in wastewater treatment plant.

Non-targeted screening and photolysis transformation of tire-related compounds in roadway runoff.

Roadway runoff serves as a crucial pathway for transporting contaminants of emerging concern (CECs) from urban environments to receiving water bodies. Tire-related compounds originating from tire wear particles (TWPs) have been frequently detected, posing a potential ecological threat. Yet, the photolysis of tire-related compounds within roadway runoff remains inadequately acknowledged. Addressing this deficit, our study utilized high-resolution mass spectrometry (HRMS) to characterize the chemical profile of roadway runoff across eight strategically selected sites in Guangzhou, China. 219 chemicals were identified or detected within different confidence levels. Among them, 29 tire-related contaminants were validated with reference standards, including hexa(methoxymethyl)melamine (HMMM), 1,3-diphenylguanidine (DPG), dicyclohexylurea (DCU), and N-cyclohexyl-2-benzothiazol-amine (DCMA). HMMM exhibited with the abundance ranging from 2.30 × 104-3.10 × 106, followed by DPG, 1.69 × 104-8.34 × 106. Runoff sample were exposed to irradiation of 500 W mercury lamp for photodegradation experiment. Photolysis results indicated that tire-related compounds with a low photolysis rate, notably DCU, DCMA, and DPG, are more likely to persist within the runoff. The photolytic rates were significantly correlated with the spatial distribution patterns of these contaminants. Our findings underscore TWPs as a significant source of pollution in water bodies, emphasizing the need for enhanced environmental monitoring and assessment strategies.

Prediction of adsorption of metal cations by clay minerals using machine learning.

Adsorption of heavy metals by clay minerals occurs widely at the solid-liquid interface in natural environments, and in this paper, the phenomenon of adsorption of Cd2+, Cu2+, Pb2+, Zn2+, Ni2+ and Co2+ by montmorillonite, kaolinite and illite was simulated using machine learning. We firstly used six machine learning models including Random Forest(R), Extremely Forest(E), Gradient Boosting Decision Tree(G), Extreme Gradient Boosting(X), Light Gradient Boosting(LGB) and Category Boosting(CAT) to feature engineer the metal cations and the parameters of the minerals, and based on the feature engineering results, we determined the first order hydrolysis constant(log K), solubility product constant(SPC), and higher hydrolysis constant (HHC) as the descriptors of the metal cations, and site density(SD) and cation exchange capacity(CEC) as the descriptors of the clay minerals. After comparing the predictive effects of different data cleaning methods (pH50 method, Box method and pH50-Box method) and six model combinations, it was finally concluded that the best simulation results could be achieved by using the pH 50-Box method for data cleaning and Extreme Gradient Boosting for modelling (RMSE = 4.158 %, R2 = 0.977). Finally, model interpretation was carried out using Shapley explanation plot (SHAP) and partial dependence plot(PDP) to analyse the potential connection between each input variable and the output results. This study combines machine learning with geochemical analysis of the mechanism of heavy metal adsorption by clay minerals, which provides a different research perspective from the traditional surface complexation model.

Turning harmful Mn2+ to treasure: In-situ formed ε-MnO2 for removing heavy metals from acid mine drainage.

Acid mine drainage (AMD) contains high concentrations of heavy metals, causing serious environmental pollution. Current neutralization techniques fail to recover and utilize valuable heavy metals, and generate large quantities of hazardous sludge. Manganese (Mn) is generally present at high levels in AMD. Therefore, this paper proposed a technology to recover Mn from AMD, by adding KMnO4 to converting Mn into ε-MnO2. Ultra-Violet C (UVC) was used to photolyze the residual KMnO4. The study then evaluated the processes and mechanisms involved in the technology. The photolysis of KMnO4 in strong acidic conditions was determined, and new mechanisms were proposed. MnO2 produced by the photolysis process was formed through the reaction between Mn(III) and KMnO4. In the absence of KMnO4, Mn(III) underwent further photolysis and was reduced to Mn2+. The maximum adsorption capacities of in-situ formed ε-MnO2 for Pb2+, Cd2+, and Fe3+ were 449.80, 122.05, and 779.88 mg/g, respectively. Higher Mn-OH levels and MnO2 regeneration were crucial in improving adsorption performance. Proton exchange and inner-circle complexation were the main pathways for Pb2+ and Cd2+ adsorption by in-situ formed ε-MnO2. A phase transformation occurred when a substantial amount of Fe3+ was adsorbed, leading to the gradual transformation to MnFe binary oxides. When applying in-situ formed ε-MnO2 technology for actual AMD treatment, 98.62 % of Mn in AMD was recovered within 24 h in the presence of ε-MnO2 for possible further reuse in industries, with a final recovery of 0.76 kg/m3. Further, this technique removed other heavy metals and reduced the sludge volume by 20.99 % when used as a pre-treatment step for neutralization. These results demonstrated the broad potential of this treatment technology.

Enhancing compound confidence in suspect and non-target screening through machine learning-based retention time prediction.

The retention time (RT) of contaminants of emerging concern (CECs) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is crucial for database matching in non-targeted screening (NTS) analysis. In this study, we developed a machine learning (ML) model to predict RTs of CECs in NTS analysis. Using 1051 CEC standards, we evaluated Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Artificial Neural Network (ANN) with molecular fingerprints and chemical descriptors to establish an optimal model. The SVR model utilizing chemical descriptors resulted in good predictive capacity with R2ext = 0.850 and r2 = 0.925. The model was further validated through laboratory NTS compound characterization. When applied to examine CEC occurrence in a large wastewater treatment plant, we identified 40 level S1 CECs (confirmed structure by reference standard) and 234 level S2 compounds (probable structure by library spectrum match). The model predicted RTs for level S2 compounds, leading to the classification of 153 level S2 compounds with high confidence (ΔRT <2 min). The model served as a robust filtering mechanism within the analytical framework. This study emphasizes the importance of predicted RTs in NTS analysis and highlights the potential of prediction models. Our research introduces a workflow that enhances NTS analysis by utilizing RT prediction models to determine compound confidence levels.

Efficient removal of heavy metals in water utilizing facile cross-link conjugated linoleic acid micelles.

Micellar-enhanced ultrafiltration (MEUF) technology is an effective method to treat low-concentration heavy metal wastewater. However, the leakage of surfactants in the ultrafiltration (UF) process will inevitably cause secondary pollution. In this study, a biosurfactant of conjugated linoleic acid (CLA) with conjugated double bonds was selected to bind its micelles by simple thermal crosslinking to obtain morphologically stable stearic acid (SA) nanoparticles. The pure SA nanoparticles were obtained by repeated dialysis. The stability of the SA nanoparticles was verified by comparing the particle size distribution and solubility of the materials before and after crosslinking at different pH levels. The effectiveness of SA nanoparticle-enhanced UF in removing heavy metals was verified by exploring the adsorption performance of SA nanoparticles. The dialysis device was used to simplify the UF device, wherein SA nanoparticles were assessed as adsorbents for the elimination of Cu2+, Pb2+, and Cd2+ ions from aqueous solutions under diverse process parameters, including pH, contact time, metal ion concentration, and coexisting ions. The findings indicate that the SA nanoparticles have no evidence of secondary contamination in UF and exhibit compatibility with a broad pH range and coexisting ions. The maximum adsorption capacities for Cu2+, Pb2+, and Cd2+ were determined to be 152.77, 403.56, and 271.46 mg/g, respectively.

Effects of Microplastics on Endophytes in Different Niches of Chinese Flowering Cabbage (Brassica campestris).

Microplastics (MPs) are present in soil as emerging contaminants and pose a threat to soil as well as plants. Here, the effects of MPs on Chinese flowering cabbage from a microbiology perspective were explored. MP size and concentration significantly affected endophytic communities of plant root and petiole (p < 0.05). Under MP treatments, the root, petiole, and leaf exhibited a substantial abundance of pathogenic biomarkers, such as Pseudomonas, Burkholderia, Ralstonia, and Escherichia, resulting in the slow growth and morbidity of the plant. Difference analysis of metabolic pathways revealed that MPs significantly upregulated the pathogenic metabolic pathways (p < 0.05), and the presence of Vibrio infectious and pathogenic metabolic pathways was detected in all three niches of the plant. Moreover, MPs significantly inhibited the contents of carotenoids, iron, vitamin C, and calcium in edible niches of the plant (p < 0.05), and most of the high-abundant biomarkers were negatively correlated with their nutritional qualities.

Prediction of heavy metal removal performance of sulfate-reducing bacteria using machine learning.

A robust modeling approach for predicting heavy metal removal by sulfate-reducing bacteria (SRB) is currently missing. In this study, four machine learning models were constructed and compared to predict the removal of Cd, Cu, Pb, and Zn as individual ions by SRB. The CatBoost model exhibited the best predictive performance across the four subsets, achieving R2 values of 0.83, 0.91, 0.92, and 0.83 for the Cd, Cu, Pb, and Zn models, respectively. Feature analysis revealed that temperature, pH, sulfate concentration, and C/S (the mass ratio of chemical oxygen demand to sulfate) had significant impacts on the outcomes. These features exhibited the most effective metal removal at 35 °C and sulfate concentrations of 1000-1200 mg/L, with variations observed in pH and C/S ratios. This study introduced a new modeling approach for predicting the treatment of metal-containing wastewater by SRB, offering guidance for optimizing operational parameters in the biological sulfidogenic process.

Pyrrhotite-dependent microbial reduction and magnetic separation for efficient vanadium detoxification and recovery in contaminated aquifer.

Microbial reduction under anaerobic condition is a promising method for remediating vanadate [V(V)] contamination in aquifers, while V(V) may be re-generated with redox fluctuations. The inability to remove vanadium after remediation has become a key issue limiting bioremediation. In this study, we proposed the use of pyrrhotite, a natural mineral with magnetic properties, to immobilize V(V) to insoluble V(IV) under microbial action and remove vanadium from the aquifer using a magnetic field, which could avoid the problem of V(V) recontamination under redox fluctuating conditions. Up to 49.0 ± 4.7 % of vanadium could be removed from the aquifer by the applied magnetic field, and the vanadium in the aquifer after the reaction was mainly in the acid-extractable and reducible states. pH had a strong effect on the magnetic recovery of V(V), while the influence of initial V(V) concentration was weak. Microbial community structure analysis showed that Thiobacillus, Proteiniphilum, Fermentimonas, and Desulfurivibrio played key roles for V(V) reduction and pyrrhotite oxidation. Structural equation model indicated the positive correlation between these genera with the magnetic recovery of vanadium. Real time-qPCR confirmed the roles of functional genes of V(V) reduction (napA and nirK) and SO42- reduction (dsrA) in such biological processes. This study provides a novel route to sustainable V(V) remediation in aquifers, with synchronous recovery of vanadium resources without rebound.