Environmental Geochemistry and Health最新文献

Former mining activities in the Bétaré-Oya region of eastern Cameroon have generated long-lasting environmental impacts due to the accumulation of unrehabilitated mine residues. This study provides an integrated assessment of the mineralogical, geochemical, spectroscopic, and microtextural characteristics of mine tailings, contaminated soils, and downstream sediments to evaluate their contamination potential. X-ray Diffraction (XRD) analyses show that tailings are dominated by quartz (up to 55%), kaolinite, muscovite, and metallic sulfides including chalcopyrite, arsenopyrite, and galena, while soils and sediments exhibit more heterogeneous silicate-clay assemblages. Fourier-Transform Infrared Spectroscopy (FTIR) identifies strong absorption bands associated with carbonates (1430-875 cm^-1), sulfates (1120-980 cm^-1), and clay-related hydroxyl groups. Geochemically, total carbon (C) ranges from 1.0 to 6.9% in tailings and 1.2-6.4% in soils, whereas sulfur (S) reaches up to 6.5% in some tailings and sediments. Calcium carbonate (CaCO₃) is highly variable, with maximum values of 16% in tailings, reflecting processing residues. Major oxides indicate strong iron enrichment in tailings (Fe₂O₃ up to 13.4 wt%), coupled with elevated Al₂O₃ (up to 35.2 wt%) and SiO₂ variability (25-60 wt%). Silver (Ag) displays anomalous enrichment, reaching up to 8 g/t in tailings, 5 g/t in soils, and 11 g/t in sediments, exceeding typical natural background levels (< 0.1 g/t). Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDX) reveals porous and fractured microtextures, dissolution fronts, and micron-scale hotspots of Pb, Zn, Ag, and As within altered sulfides and secondary Fe-oxides. Collectively, these mineralogical and geochemical signatures indicate a high potential for contaminant release and downstream transfer, particularly during intense tropical weathering and seasonal flooding. The findings underscore the urgent need for site rehabilitation, improved tailings management, and sustained environmental monitoring to mitigate long-term risks to local ecosystems and agricultural zones.

The occurrence characteristics of heavy metals (HMs) in aquatic environments have garnered widespread attention. Industrial wastewater, agricultural runoff, and domestic sewage discharged along the Yellow River in Henan Province pose potential threats to both the river ecosystem and human health. Consequently, the present study elucidated HM sources in the Henan section of the Yellow River via field investigations and model validation. The total concentrations of seven heavy metals (Cu, Pb, Zn, Cr, Ni, Cd, As) in riverbank soils of the studied area ranged from 0.28 (Cd)-798.55 (Mn) mg/kg.. HM levels were generally higher in tributaries than in the main stream, with significant inter-tributary variation (P < 0.05). The spatial distribution of potential ecological risk showed a pyramidal structure, dominated by medium-risk zones (61.50%), followed by high-risk and low-risk areas. Health risk assessment indicated elevated risks for children compared to adults, with oral ingestion identified as the primary exposure pathway (> 90%). Correlation analysis revealed Zn was significantly correlated with other HMs (P < 0.05). Source apportionment via Principal Component Analysis (PCA) combined with the Positive Matrix Factorization (PMF) model quantified contributions as: industrial emissions (28.50%) > agricultural non-point sources (21.70%) > geological background (15.30%) > industrial wastewater (12.80%) > transportation emissions (10.50%) > mining activities (7.00%). Industrial sources were identified as the primary contributor to both ecological and health risks. This study provides a reference and a basis for formulating effective measures to prevent and control HMs enrichment in agricultural soils.

Heavy metal contamination in aquatic environments poses a severe threat to ecosystems and human health due to the non-biodegradability, bioaccumulation, and toxicity of metals such as lead, cadmium, mercury, chromium, and arsenic. Conventional treatment methods often suffer from limitations, including high operational costs, incomplete removal, and secondary pollution. In this context, biocatalytic and enzymatic systems have emerged as promising green alternatives for heavy metal remediation. This review comprehensively examines the current state of enzymatic and biocatalytic approaches for removing heavy metals from water systems. Specific focus is placed on naturally occurring and genetically engineered enzymes, including metallothioneins, phytochelatins, oxidoreductases, and peroxidases, as well as microbial biocatalysts and enzyme-immobilized composites. The underlying mechanisms, such as enzymatic reduction, chelation, biosorption, and bioaccumulation, are discussed in detail. Key factors affecting efficiency, including pH, temperature, enzyme stability, and metal ion speciation, are critically analyzed. Additionally, recent advancements in nano-biocatalysts and immobilized enzyme systems are highlighted for their potential in enhancing selectivity and recyclability. This review not only elucidates the strengths and limitations of biocatalytic systems but also outlines the future directions toward scalable, cost-effective, and sustainable water treatment technologies.

Pharmaceutical contamination of aquatic systems poses an increasing environmental concern due to the persistence, bioactivity, and incomplete removal of these compounds by conventional wastewater treatment processes. This study investigates the adsorptive removal of three structurally distinct pharmaceuticals: ketotifen fumarate (KF), doxycycline hyclate (DXC), and nystatin (Nyst), using raw bentonite (RB). By combining batch experiments with an interpretable machine learning (ML) framework, adsorption kinetics, equilibrium, and thermodynamics were evaluated. Additionally, four Ant Lion Optimizer (ALO)-optimized models: Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), were employed to predict adsorption capacity under diverse conditions. RB exhibited high adsorption capacities: 178.86 ± 1.26 mg/g for KF, 222.91 ± 2.02 mg/g for DXC, and 190.25 ± 2.86 mg/g for Nyst. Adsorption equilibrium was best described by the Freundlich isotherm, indicating multilayer adsorption on a heterogeneous surface. Thermodynamic and spectroscopic analyses revealed a dual mechanism involving electrostatic attraction, hydrogen bonding, cation exchange, and van der Waals interactions, with DXC and Nyst adsorption being endothermic and KF adsorption exothermic. SHAP (SHapley Additive exPlanations) analysis identified adsorbent dosage, initial concentration, and pH as dominant operational factors, while the molecular descriptor nC (number of carbon atoms) emerged as key to differentiating pharmaceuticals, linking larger molecular size to stronger adsorption. The XGBoost model achieved the highest accuracy (R² = 0.972, RMSE = 0.1225), demonstrating robust generalizability. These findings highlight RB as a low-cost, scalable adsorbent and establish an interpretable ML approach capable of linking molecular structure to adsorption behavior.

This work represents the first integrated assessment in the Litani River Basin, Lebanon's longest river, which flows into the Mediterranean Sea, combining multi-class organic micropollutant occurrence, specifically pharmaceuticals and aromatic hydrocarbons, in both surface water and sediment with a quantitative environmental risk assessment. Surface water and sediment samples were collected across consecutive dry (summer) and wet (winter) seasons from nine sites along the Upper Litani River Basin and analyzed using advanced analytical methods. Among the 27 tested pharmaceuticals, ibuprofen (4.7 µg/L), caffeine (23.6 µg/L), telmisartan (833.5 ng/L), carbamazepine (658.5 ng/L), gemfibrozil (71.0 ng/L), mefenamic acid (64.7 ng/L), and diphenhydramine (65.9 ng/L) were the most frequently detected, with some reaching notably high concentrations in the µg/L range in surface water. Sediments revealed ubiquitous contamination by ibuprofen, telmisartan, climbazole, diphenhydramine, and azithromycin. Spatial profiling identified pollution hotspots closely linked to the discharge of untreated municipal sewage, and hospital and industrial effluents. Environmental risk assessment results highlighted substantial ecological risks posed by telmisartan, ibuprofen, diclofenac, climbazole, and caffeine. Among the aromatic hydrocarbons, xylene, ethylbenzene, and benzene were frequently detected in the sediments at ecologically hazardous concentrations, while naphthalene and benzo[a]pyrene exhibited seasonal and spatial variability in occurrence and risk potential. The findings from this study contribute to the state of knowledge of pollution levels of emerging contaminants in a major Mediterranean river and permit to inform and guide future mitigation and management strategies. The environmental risk assessment applied in this study enabled the translation of measured concentrations into ecologically meaningful indicators allowing the identification of priority contaminants and high-risk locations.

With rapid urbanization and industrialization, pollution of rare earth elements (REEs) in air, soil, and water is increasing in urban areas. These critical high-tech elements are becoming more abundant in urban dust and other environmental settings. It is difficult to differentiate and quantify the sources of REE pollution, as natural and anthropogenic sources overlap. This work couples isotopic tracing techniques (Pb, Sr, and Nd isotopes) with receptor models (positive matrix factorization (PMF) and absolute principal component scores/multiple linear regression (APCS/MLR)) to obtain robust source apportionment of REEs in urban environments. Isotopic fingerprints, including Gd anomalies, have been shown to serve as powerful tracers for distinguishing vehicular emissions, industrial discharges, and soil erosion. Non-exhaust vehicular emissions, especially those from brake and tire wear, have been identified as the main sources of REE release into the environment. The combination of isotopic methods and receptor models enhances the accuracy of source apportionment and contributions, facilitating better environmental management. The review highlights the need for standardized isotope libraries and sophisticated modelling tools to characterize sources, thereby improving source apportionment and informing sustainable control strategies for urban pollution. Focusing on controlling industrial and vehicle emissions can be an effective strategy to reduce REE contamination levels and human exposure.

Urban uncontrolled landfills in Kinshasa generate metal-rich leachates that contaminate surrounding freshwater streams, sediments, and biota. To assess the extent of this contamination, sediment from six freshwater streams in Kinshasa (N'djili, Limete, Lemba, Selembao, Mont-Ngafula, Makala) were analyzed to assess contamination by heavy metals-including Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Mo, Cd, Sn, Sb, Pb and Hg-and bioaccumulation in terrestrial (Lumbricus terrestris) and aquatic (Tubifex tubifex, Nais elinguis, Enchytraeus albidus) oligochaetes. Sediments were digested following the Swiss Federal Soil Ordinance (OSol 814.12) and analyzed using ICP-MS for metals and a Direct Mercury Analyzer (CV-AAS) for total Hg. Oligochaete tissues were freeze-dried, acid-digested (HNO₃-HClO₄), and analyzed using the same instrumentation. Sediment pollution levels were assessed using the Geoaccumulation Index (Igeo), Enrichment Factor (EF), contamination factor (CF), and the overall Ecological Risk Index (RI), while ecological thresholds were compared to the Canadian Sediment Quality Guidelines. Bioaccumulation factors (BAF) were calculated to quantify metal transfer from sediments to organisms. Sediments showed strong contamination at landfill-impacted sites, with Hg reaching 3.8 mg·kg⁻¹ dry weight-far exceeding Canadian SQGs (TEL: 0.17 mg·kg⁻¹) and PELs (0.486 mg·kg⁻¹). Cu and Zn were also highly elevated (up to 687.9 and 995.3 mg·kg⁻¹, respectively). RI values were highest at Limete (1014-3552.7), indicating very high ecological risk. Aquatic oligochaetes exhibited greater bioaccumulation than terrestrial species, with Hg up to 0.876 mg·kg⁻¹ and Cu up to 93.1 mg·kg⁻¹. High BAFs were observed, particularly for Cd (118.2 at Mont-Ngafula) and Sn (263.1 at Makala), confirming strong sediment-to-organism transfer. Fine, organic-rich sediments and proximity to landfill leachates were positively correlated with metal contamination and bioavailability.

This study investigated the hydrogeochemical characteristics of surface waters in the Congonhas Mineral District (CMD), located in the southern portion of Quadrilátero Ferrífero, Brazil. A total of 38 sites were monitored between 2021 and 2024 to understand seasonal and spatial variability across distinct lithologies and land uses. Hydrogeochemical patterns revealed dominant mixed bicarbonate facies associated with metavolcano-sedimentary terrains, while domains of granitoids exhibited alkali enrichment. Waters under the influence of larger Urban settlements were enriched in Na, Cl, sulfate, and nutrients. By integrating geospatial classification, seasonal sampling, and robust statistical techniques, we investigated the behavior of Fe and Mn, key elements influenced by both natural geological sources and mining activities. Reference values for geochemical background and baseline thresholds, based on samples from preserved and mixed land use areas, respectively, were estimated using three distinct statistical approaches. Among these, the upper tolerance limit (UTL) method was considered the most consistent and suitable. Spatial and seasonal patterns revealed elevated Fe and Mn levels during the rainy season, particularly in areas influenced by mining and urbanization. The proposed reference values provide a realistic basis for identifying contamination, and can give support for more realistic regulatory frameworks, and definition of strategies for water quality management. The obtained results highlight the relevance of tailored guidelines in mining contexts, where reference values adopted by regulatory agencies may not reflect local geochemical conditions.

Cadmium (Cd) pollution poses a serious threat to aquatic environmental safety and sustainable agricultural development. Biogenic iron (Fe)-manganese (Mn) oxides (BFMO), mediated by Mn-oxidizing bacteria, are promising natural adsorbents for Cd removal. Although iron-manganese oxides have been widely studied for application in wastewater treatment, BFMO synthesized via a fully biologically driven process using novel strains still face limitations in terms of material structure and the availability of active sites. In this study, BFMO was synthesized using a newly isolated strain of Stenotrophomonas sp. Z-MRQA-3, and its mineralogical properties, Cd(II) immobilization performance, and underlying mechanisms were systematically investigated. The results demonstrated that BFMO possesses a high specific surface area (244.52 m²/g), a hierarchical porous structure, and abundant surface functional groups, which collectively contribute to its superior adsorption capacity. Under conditions of adsorbent dosage of 0.5 g/L, initial Cd(II) concentration of 50 mg/L, and pH 7.2, the removal efficiency of Cd(II) reached 96.52%, with an adsorption capacity of 80.83 mg/g. The adsorption process followed the pseudo-second-order kinetic model and the Langmuir isotherm model, with a theoretical maximum adsorption capacity of 89.29 mg/g. Mechanistic studies indicated that Cd(II) immobilization occurs mainly through surface complexation, ion exchange, and co-precipitation, facilitated by the redox-active multivalent Mn and oxygen-containing functional groups. This study aims to investigate the unique advantages of in situ synthesizing BFMO using specific bacterial strains. This work offers fundamental insights and practical prospects for developing green, efficient, and sustainable technologies for remediating Cd-contaminated water.