Environmental Geochemistry and Health最新文献

Phenolic resin waste (PRW) is a carbon-rich industrial byproduct, and its improper disposal leads to environmental pollution and resource loss. In this study, a porous carbon material (PRWPC) with a well-developed porous structure and a large specific surface area (1760.6107 m² g^-1) was prepared from PRW via microwave-assisted alkaline activation and applied for methylene blue (MeB) removal from aqueous solution. Under the optimized conditions with an initial MeB concentration of 100 mg L^-1, an adsorbent dosage of 10 mg, a contact time of 40 min, a temperature of 328 K, and pH = 11, PRWPC exhibits high adsorption performance, achieving a maximum adsorption capacity of 1482.35 mg g^-1 with a removal efficiency of 98.8%. Kinetic analysis indicates that the adsorption process follows a pseudo-second-order model, while equilibrium data are well described by the Langmuir isotherm, suggesting monolayer adsorption dominated by micropore filling. Thermodynamic analysis reveals that the adsorption process is spontaneous and endothermic. Overall, this study demonstrates that microwave-assisted conversion of phenolic resin waste provides a feasible, low-cost, and sustainable strategy for the efficient removal of cationic dyes from wastewater.

Multiple permeable reactive barriers (multi-PRBs) are an effective in-situ technology for acid mine drainage (AMD) treatment. However, their practical implementation is hindered by unclear mechanisms and a lack of decision models. In this study, a coupled processes numerical model was developed to simulate the synergistic removal of TFe and SO₄2⁻ through multi-PRBs with the optimized sequence of limestone, followed by biochar and then D201 resin. Machine learning integrated with the Non-dominated Sorting Genetic Algorithm (ML-NSGAII) was proposed for optimization, in which a Backpropagation Neural Network (BPNN) served as a highly accurate surrogate model (R² > 0.99) to predict system performance, reducing the computational load by 99.7% compared to conventional methods. Spearman correlation analysis and SHAP model interpretation revealed hydraulic load and filler size as the most influential parameters. Application of the TOPSIS-entropy weight method to the Pareto-optimal solution set yielded a final design that significantly enhanced system service life and treatment capacity while reducing costs. This research provides a practical and computationally efficient strategy for designing multi-PRBs for AMD treatment.

The amount of potassium (K⁺) that plants can use might not be fully shown by exchangeable potassium numbers, since this method doesn't take into account the effect of non-exchangeable potassium (NEK). K⁺ availability and release in soils can be assessed through quantity-intensity (Q/I) relationships. A study was conducted in the calcareous regions of Muzaffarpur district, Bihar, focusing on the potassium concentrations in rice roots, shoots, and grains, as well as various soil characteristics. 92 samples were analyzed to determine the different K⁺ forms present in the soil as well as assessed NEK reserves and Q/I isotherms. The potential buffering capacity of Zone 1 (24.87 cmol kg^-1 (mol L^-1)^-1/2) was higher than Zone 2 (21.67 cmol kg^-1 (mol L^-1)^-1/2). Zone 1 exhibits an elevated equilibrium activity ratio (AR_e^0K) than Zone 2. The free energy values suggest that soil from both zones has moderate to significant K⁺ deficiencies. A positive correlation was observed between the exchangeable and NEK forms of K⁺ and Step-K and CR-K. AR_e^K exhibited a positive correlation with K⁺ saturation, K₀, -ΔG, K_L, K_V, and K_KDO. The potassium concentration in rice is greatest in the grains, followed by the shoots, and least in the roots. Zone 1 soil exhibited the highest availability of potassium. Random Forest models accurately predict potassium availability and uptake, thereby enhancing soil fertility and precision agriculture, which in turn leads to improved crop yields and soil health. Consequently, comprehending the dynamics of potassium release and availability in calcareous soils, is essential for effective fertilizer management.

River water quality in monsoon-driven subtropical basins exhibits strong seasonal variability driven by hydroclimatic forcing and increasing anthropogenic pressure, posing challenges for reliable assessment and management. Despite advances in water quality modeling, most Water Quality Index (WQI) prediction frameworks require extensive sampling and lack interpretability, limiting rapid baseline assessment during critical periods. This study develops the first integrated Explainable Artificial Intelligence (XAI) framework combining Machine Learning (ML), Deep Learning (DL), and Physics-Informed Neural Networks (PINNs) to predict, interpret, and spatially characterize seasonal water quality dynamics in the Padma River Basin, Bangladesh. Forty-four surface water samples collected during winter and monsoon seasons were evaluated using WQI assessment, explainable modeling, probabilistic uncertainty analysis, and spatial regionalization. Results show that seasonal variability dominates over spatial variability (p < 0.0001), with winter low-flow conditions promoting solute concentration and localized degradation, while monsoon discharge drives basin-wide dilution and recovery. Model performance is strongly region-dependent: Deep Neural Networks achieve the highest accuracy in winter (R² = 0.98; RMSE = 1.16), whereas Ridge Regression and Voting Ensemble models perform more robustly during the monsoon (R² ≈ 0.97; RMSE ≈ 1.01). Explainable AI analysis identifies NO₃^- emerged as the dominant contaminant (24.0 ± 36.3 mg/L winter, 47.5 ± 68.7 mg/L monsoon, with isolated samples exceeding WHO limits), whereas pH and DO exhibit dual seasonal influences. PINN-based data augmentation improves model generalization under limited sampling while preserving hydrochemical consistency. Monte Carlo simulations quantify prediction uncertainty and reveal seasonal shifts in WQI probability distributions, while spatial autocorrelation analysis identifies localized winter degradation hotspots and widespread monsoon improvement. The proposed physics-informed and explainable AI framework enhances predictive reliability, interpretability, and decision relevance, offering a transferable approach for uncertainty-aware water quality assessment and adaptive management in monsoon-affected, data-limited river basins.

The Vietnamese Mekong Delta (VMD), a cornerstone of national food security, is increasingly affected by salinity intrusion arising from the combined influences of upstream hydropower development, climate change, and sea-level rise. Despite growing attention to this issue, the long-term hydrological mechanisms shaping these changes remain insufficiently understood. This study examines freshwater-salinity dynamics along the Co Chien River over the period 2000-2024, applying nonparametric Mann-Kendall (MK) tests and Sen's slope estimators to identify spatio-temporal trends, alongside a comparative assessment of hydrological variability between coastal and inland zones. Spearman correlation analysis is used to distinguish the relative contributions of climatic variability and upstream hydrological regulation. The findings indicate a pronounced landward shift of the salinity boundary, with inland monitoring stations exhibiting relative increases in minimum salinity (Smin) exceeding 3% per year. Of particular significance is the role of declining dry-season upstream discharge, which emerges as the principal driver of salinity intrusion, exerting a stronger influence than ENSO-related climatic variability. A notable spatial paradox is identified: while coastal areas experience consistently high yet comparatively stable salinity conditions, inland transition zones are characterised by pronounced hydrological instability. These patterns point to the limitations of predominantly localised engineering responses and underline the need for more anticipatory, inter-regional approaches to water governance. Integrating upstream discharge thresholds into early-warning systems offers a pathway towards enhancing the resilience of livelihoods in the delta's most vulnerable transitional landscapes.

Rice husk, an agricultural waste, was employed as a raw material to extract cellulose using an optimized chemical and physical treatment approach, offering a sustainable solution for value-added applications. The extraction process included pre-treatment to eliminate waxy impurities, oxidative treatment with 4% H₂O₂, hydrolysis using 70% HNO₃, and ultrasonic treatment to isolate nanoscale fibrils. Characterization of the extracted cellulose was conducted using Fourier Transform Infrared (FT-IR) spectroscopy, Scanning Electron Microscopy (SEM), particle size analysis, Gel Permeation Chromatography (GPC), X-ray Diffraction (XRD), and Thermogravimetric Analysis (TGA). FT-IR analysis confirmed the removal of non-cellulosic components and the presence of β-(1 → 4)-glycosidic linkages, while SEM revealed fibrils with reduced diameters ranging from 800 to 900 nm. Particle size analysis indicated a mono-dispersed nanoscale distribution. XRD analysis demonstrated crystalline cellulose-I, with the crystallinity index calculated at 70 ± 3%, attributed to the effective elimination of lignin and hemicellulose. TGA showed a decomposition temperature of 333 °C with minimal residue, confirming the high thermal stability and purity of the product. GPC analysis indicated a high molecular weight and narrow polydispersity index, further verifying the superior quality of the extracted cellulose. Batch adsorption experiments further demonstrated the effectiveness of rice husk-derived nanocellulose in immobilizing Pb²⁺ ions, highlighting its potential for mitigating anthropogenic metal contamination in environmental systems. The combination of high crystallinity, thermal stability, and nanoscale morphology makes the extracted cellulose highly suitable for advanced applications, such as biocomposite nanofibers in packaging. This study underscores the potential of converting agricultural waste into high-value materials, aligning with sustainable development goals and promoting eco-friendly industrial applications.

The main objective of this study is to evaluate the nitrate contamination in the groundwater of the Suruliyar River basin using modern technologies such as machine learning (ML) and Monte Carlo Simulation (MCS) for a cost-effective and time-efficient assessment of areas at risk. In the basin, nearly 152 samples collected in both seasons (pre-monsoon (PRM), (76) and post-monsoon (POM), (76) seasons) with an area extent of 1332 km² and 1486 km² of land area show nitrate level exceeding 45 ppm during the PRM and POM seasons. The source for this contamination is the unregulated use of fertilizers, with minor contributions from sewage and livestock waste. The Nitrate Pollution Index (NPI) indicates that, 898 km² and 1326 km² sampled areas fall under significant to very significant pollution categories during the PRM and POM seasons. Machine learning predictions of nitrate level were most accurately predicted using the Support Vector Machine (SVM) model, which achieved accuracies of 87.50% and 81.25% in the PRM and POM seasons. Traditional health risk assessment reveals that 83% and 89% of samples pose risk to children, by Hazard Quotient (HQ) > 1 during the PRM and POM seasons. The MCS results further support this finding, showing maximum 95th percentile HQ values of 5.5510 and 7.4938 for children in the respective seasons, confirming their higher vulnerability to nitrate contamination compared to other age groups. This research provides critical insights that can support policymakers and authorities in implementing measures to reduce nitrate pollution and its health complications, to guarantee the Sustainable Development Goal (SDG) 3 and 6 for the sustainable development.

Exposure to heavy metals is a global health concern, especially for children under the age of five. In South Africa, industrial and mining activities have contributed to environmental accumulation of metals. Early learning programmes (ELPs) or preschools are primary spaces for learning and play, making them critical for mitigating early life metal exposure. This study examined arsenic (As), cadmium (Cd) and lead (Pb) levels in soil and dust from selected ELPs in two metropolitan municipalities in Gauteng Province, South Africa, to assess potential exposure risks for children under five. As part of a nationally represented survey of early childhood outcomes, 70 ELPs were stratified into three fee bands (low, medium, high). Soil and dust samples were collected from outdoor play areas and indoor classrooms and analysed using inductively coupled plasma-optical emission spectroscopy (ICP-OES) and inductively coupled plasma-mass spectrometry (ICP-MS). Dust results were semi-quantitative (no surface area measured) and not used in risk calculations. Potential Pb exposure in children was evaluated using the US EPA Integrated Exposure Uptake Biokinetic (IEUBK) model to predict blood Pb levels. Geographic Information System (GIS) mapping identified spatial patterns and hotspots of metal concentrations relative to potential pollution sources. Soil Pb levels were below South African reference values; however, 9.0% of samples exceeded Canadian guidelines, while dust Pb was detected in all samples. Soil As was detected in 95% of samples, with higher concentrations in low-fee schools (p = 0.002); 10.7% exceeded Canadian guidelines. Cd concentrations were low across all sites. Estimated As exposures suggested minimal non-carcinogenic health risk to children through soil ingestion, while IEUBK modeling predicted a geometric mean blood Pb levels of 1.72 µg/dL (95% CI 0.69-4.31), with 6.6% exceeding the CDC blood lead reference value. Children exposed to As at school for approximately 2 years had a combined lifetime cancer risk of ~ 2.2 × 10⁻⁴, exceeding the USEPA's acceptable threshold. Hotspot and kernel density estimation analysis identified localised elevated soil As and Pb levels near areas of concentrated mining activity, indicating legacy industrial sources as likely contributors. Localised hotspots of Pb and As highlight the need for continued environmental monitoring and targeted interventions to ensure safe learning environments for young children, particularly given the carcinogenic risk associated with As exposure.

The multifaceted utility of biochar in environmental applications stems from its porous structure, ample surface area, and rich oxygen-containing functional groups. However, interactions between biochar and its surroundings can lead to the release of potentially harmful components, necessitating a comprehensive understanding of environmental impacts. This review categorizes adverse biochar effects on their detrimental components, surface attributes, structure, and size, delving on water, soil, plants, animals and atmospheric ecosystems. It also presents different methodologies for detecting environmental risks associated with biochar application, offering guidance for future toxicity assessment and avoidance strategies. Biochar created via high-temperature pyrolysis under limited oxygen can harbor various known contaminants and emerging threats (persistent free radicals and metal cyanides), posing risks like phytotoxicity, cytotoxicity and neurotoxicity. The ecotoxic potential of biochar concerning specific contaminants, comprehensive strategies to mitigate this entire spectrum of contaminants within biochar are lacking. This review comprehensively explores the formation mechanisms of these contaminants and their potential risks to ecosystems and underscores the need for effective contamination control strategies during biochar production. It emphasizes the significance of designing pyrolysis units that ensure separation of pyrolysis liquids from solids, minimizing organic contaminant condensation onto biochar. Reducing total levels of PTE holds promise through strategies such as co-pyrolysis of biomass containing both metal-rich and metal-free components, complemented by the inherent decrease in PTE levels with higher pyrolysis temperatures. With these recommended strategies, there is potential to produce biochar posing minimal environmental risks, empowering sustainable applications in diverse environmental contexts.

In this study, the spatiotemporal distributions, influencing factors, sources, and ecological risks of atmospheric microplastic deposition in a valley city from 2019 to 2023 were investigated. On average, dry deposition accounted for 75.90% of the microplastic deposition. The deposition fluxes exhibited significant spatiotemporal differences. The deposition fluxes in summer and winter were the highest (814.36 p m^-2 d^-1, on average) and lowest (178.65 p m^-2 d^-1, on average), respectively. The average annual and seasonal deposition fluxes were strongly influenced by the precipitation intensity and frequency, the frequency of daily average wind speeds ≥ 2 m s^-1, the boundary layer height, the air temperature and the ultraviolet radiation dose. In addition, the average annual deposition fluxes were strongly influenced by the inner city travel intensity and number of tourists, and the average seasonal deposition fluxes were strongly influenced by the seasonal precipitation amount. The spatial distributions of deposition fluxes were influenced by population density. Approximately 42.11% of the microplastic deposition originated from local sources, and the nonlocal sources were mainly from the northwestern region of the study area. The pollution level, hazard level and ecological risk of microplastic deposition during the pandemic period were lower than those during the non-pandemic period. Our results suggested that atmospheric microplastic deposition was influenced by both natural and anthropogenic factors.