Search Results (632)

With the continued advancement of industrialization and urbanization in northwestern China, heavy metal contamination of loess sites has become an increasingly serious environmental issue. Heavy metals such as copper (Cu) and lead (Pb) are toxic, persistent, and readily retained by the mineral-pore framework of loess, which makes remediation difficult and threatens regional ecological security and human health. This study investigated electrokinetic (EK) remediation of artificially prepared loess co-contaminated with Cu and Pb using tartaric acid (TA), citric acid (CA), and disodium ethylenediaminetetraacetate (EDTA) as catholytes. Cu was generally removed more effectively than Pb because Pb was less adsorbed and immobilized more strongly. All three chelators enhanced metal desorption and migration through complexation, particularly in the cathode-side section. EDTA produced the greatest enhancement because it formed stable, negatively charged complexes with Cu and Pb over a broad pH range. Relative to the deionized-water control, EDTA increased the overall removal efficiencies of Cu and Pb to 55.4% and 27.2%, respectively, and promoted their transfer from the soil to the anolyte. These findings demonstrate that chelator-assisted EK treatment, particularly with EDTA, can effectively improve Cu and Pb removal from loess, while field application requires control of energy use, residual chelator, and post-treatment metal mobility. Full article

Coastal wetlands are important sinks for heavy metals, yet the linkage between vertical redistribution, quantitative source contributions, and ecological–health risk drivers remain insufficiently understood. This study collected the stratified sediment samples from three depth intervals (0–20, 20–40, and 40–60 cm) from 28 representative sites in the coastal wetlands of western Bohai Bay, and evaluated the spatial distribution, vertical variation, pollution status, potential sources, and ecological–human health risks of eight heavy metals (Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb). The mean concentrations were below the Class I limits of the Marine Sediment Quality Standard. Most metals were close to or slightly below regional background values, whereas Cu and As showed mild enrichment and Cr was higher than values reported for Bohai Bay and Hangzhou Bay. Vertical profiles were generally homogeneous, with weak enrichment of specific metals, probably due to sediment resuspension, tidal disturbance, and bioturbation. Pollution assessment based on the geoaccumulation index (Igeo), Nemerow pollution index (P_N), and pollution load index (PLI) consistently indicated low contamination levels, with Hg and Pb as the main contributors to the regional pollution load. Source apportionment indicated that heavy metals were jointly influenced by lithogenic background (38.9%), atmospheric deposition (31.1%), and local anthropogenic activities (30.0%). Ecological risk assessment showed that the integrated risk index (RI) remained within the low-risk category, although Hg consistently fell within the moderate-risk range and Cd approached the moderate-risk threshold. Health risk assessment showed that both non-carcinogenic and carcinogenic risks were within acceptable limits for all receptor groups. Children had higher risks in core residential areas, whereas adults showed higher risks in non-core industrial–agricultural zones because of increased exposure frequency. The source–risk analysis indicates that non-carcinogenic risk is mainly associated with background-derived Cr, whereas carcinogenic risk is primarily linked to anthropogenic As inputs. These findings indicate that source contributions and risk contributions are not necessarily consistent, highlighting the need for source-oriented risk management in industrialized coastal wetlands. Full article

Pesticide and veterinary drug residues, heavy metals and other hazardous contaminants in agricultural products and food systems pose severe threats to food safety and agro-ecological security. Conventional detection techniques are plagued by complicated operations, long testing cycles and insufficient sensitivity, which fail to meet the practical requirements for rapid, accurate on-site detection and in situ remediation. This paper systematically introduces the fundamental physicochemical properties of typical carbon-based nanomaterials, including graphene, carbon nanotubes, carbon quantum dots and biomass-derived carbon. It comprehensively reviews the latest research advances of these materials in the detection of heavy metal ions, pesticide residues, mycotoxins and illegal additives, as well as in the non-destructive monitoring of food quality. Meanwhile, relevant applications of carbon-based nanomaterials in the adsorption, enrichment and catalytic remediation of heavy metals and organic pollutants in farmland soil and water environments are summarized. The intrinsic mechanisms underlying their performance in high-precision detection and environmental remediation are elaborated from the perspectives of optical sensing response and adsorption–separation effects. Furthermore, the current technical limitations and bottlenecks restricting the practical application of carbon-based nanomaterials are discussed. Combined with the industrial demands for rapid screening of agro-food safety risks and in situ treatment of farmland environments, the future development prospects of carbon-based nanomaterials in agriculture and food safety fields are outlined. This work aims to provide theoretical references for the development and industrialization of high-performance carbon-based sensing and remediation materials, and to facilitate the risk prevention and control of agro-food safety as well as the green and sustainable development of agricultural ecosystems. Full article

Understanding Cd and Pb in vitro bioaccessibility (IVBA) is important for evaluating human health risks in mine-impacted soils. In this field study, we assessed Cd and Pb bioaccessibility in a heavy metal contaminated mine impacted soil that received Low and High Compost applications (180 and 360 Mg ha⁻¹, respectively), and Native Prairie soils within close proximity to the impacted soil, using three in vitro methods (Unites States Environmental Protection Agency (US EPA) pH 1.5, US EPA pH 2.5, and Ohio State University pH 1.8). Total Cd concentrations under High Compost (10.3 mg kg⁻¹) exceeded the US EPA regional screening level (RSL)–residential soil Cd concentration for ingestion non-cancer risk in children (7.8 mg kg⁻¹), while Low Compost and Native Prairie soils were below the RSL. Total Pb (<75 mg kg⁻¹) in all sites was below the US EPA RSL for Pb for non-cancer risk in children (200 mg kg⁻¹). Within each extraction method, Cd IVBA remained consistently high (>70% of total) across all three sites for at least US EPA pH 1.5 and OSU pH 1.8. In contrast, Pb IVBA varied across methods, with the pH 2.5 extraction consistently yielding lower Pb IVBA as compared to the other IVBA methods. These findings suggest that Cd poses a challenge for risk mitigation at this site, while Pb shows more promising stabilization outcomes. Findings highlight the importance of tailoring amendment strategies and selecting appropriate in vitro assays when assessing remediation effectiveness and risk within multi-metal contaminated mine-impacted sites, while emphasizing the need for long-term field validation of metal stability under real-world conditions. Full article

Heavy metals are persistent environmental contaminants that accumulate in soils and vegetation, posing significant risks to ecological systems and human health. Pinus nigra needles are widely recognized as effective biomonitors for reflecting spatial and temporal variations in atmospheric heavy metal deposition. However, the complex, nonlinear interactions among multiple pollutants, environmental factors, and site-specific conditions limit the effectiveness of conventional statistical approaches in accurately modeling and predicting contamination patterns. This study investigated the spatial and seasonal distribution of heavy metals in soils and Pinus nigra needles across different environmental settings in Adıyaman, Türkiye, including urban traffic zones, an organized industrial area, a cement factory vicinity, and a clean reference site. Metal concentrations were determined using inductively coupled plasma mass spectrometry (ICP–MS) following standardized acid digestion procedures. To address the limitations of traditional methods and capture complex nonlinear relationships, advanced machine learning (ML) algorithms—multilayer perceptron, Random Forest, XGBoost, LightGBM, CatBoost, and Gradient Boosting—were employed to model elevation based on heavy metal concentrations. The dataset was divided into training (80%) and testing (20%) subsets, and model performance was evaluated using R², RMSE, MAE, MAPE, and EVS. Among the models, XGBoost exhibited superior predictive performance. Excluding Cd, Cr, and Cu, it achieved R² = 0.9996 (RMSE = 0.068) in training and R² = 0.9526 (RMSE = 17.77) in testing. Including these metals further improved performance to R² = 0.9999 (RMSE = 0.054) for training and R² = 0.9890 (RMSE = 5.55) for testing. The results confirm that Pinus nigra needles are reliable bioindicators of heavy metal accumulation. More importantly, the integration of biomonitoring data with ML techniques provides a powerful framework for capturing complex environmental interactions and improving predictive accuracy, thereby supporting more effective environmental monitoring, risk assessment, and sustainable management strategies. Full article

Cadmium is a common contaminant of both saline and freshwater environments. It has no biological function but readily penetrates tissues, causing significant oxidative stress and consequent damage throughout the organism. In this work, we examined the effects of this metal on the nauplii of Artemia salina, a model for testing toxicity in larval zooplanktonic species, and to evaluate the impact of contaminants on natural populations. An environmentally realistic concentration (15 µg/L) and two higher concentrations (150 and 1500 µg/L) were used. The presence of metals in culture media and nauplii was verified by atomic absorption spectroscopy, while oxidative stress levels were assessed by measuring hydroperoxides, carbonyl groups, ROS, and total antioxidant capacity. Mortality and growth rates were the conventional toxicity endpoints analysed. In parallel, morphological alterations were assessed in toto and in section. Attention was directed to the gut, the primary site of metal uptake and reabsorption, and to effects on protein patterns. The results demonstrated significant dose-independent Cd uptake by the nauplii and dose-dependent oxidative stress. Consequent alterations in brush border organisation and yolk and lipid resorption were assessed. Modest teratogenic effects were detected in naupliar eye pigmentation and paired-eye organisation. Oxidative stress and the marked changes in protein pattern justify the alterations observed, while the upregulation of the hsp26 and hsp60 genes indicates naupliar attempts to mitigate damage. In conclusion, this study confirms the effects of Cd toxicity on the development of Artemia salina nauplii and demonstrates interference with reserve resorption. This evidence indicates that this heavy metal can have a profound impact on zooplanktonic communities, which are essential to the stability of the entire aquatic food web. Full article

Heavy metal contamination in urban street dust is often highly heterogeneous, limiting the effectiveness of conventional geostatistical mapping approaches. In Mexico City, previous studies have reported very low spatial autocorrelation for key elements, making interpolation-based methods unsuitable for representing contamination patterns. This study proposes a multiscale cartographic framework to analyze and visualize heavy metal contamination in street dust from 482 sampling sites using the contamination factor (CF) and pollution load index (PLI) at three levels of spatial analysis: (i) city-scale patterns identified through hierarchical clustering of districts based on median CF values, (ii) district-scale variability assessed through statistical comparisons of PLI distributions, and (iii) site-scale identification of contamination hotspots using observed PLI values. Results revealed five contamination clusters and significant differences in pollution load among districts (Kruskal–Wallis, p < 0.05); PLI values in Xochimilco and Tláhuac are significantly lower than in Cuauhtémoc, Gustavo A. Madero, and Magdalena Contreras. Higher contamination levels were concentrated in northern and central districts, whereas lower levels predominated in the south. Site-scale analysis identified localized hotspots associated with transportation infrastructure, industrial areas, and commercial corridors, reflecting the influence of local emission sources. The results demonstrate that contamination patterns operate simultaneously at city, district, and site scales and cannot be adequately represented through interpolation alone. The proposed framework provides a practical approach for visualizing heterogeneous contamination datasets, supporting environmental decision-making, and may apply to other metropolitan regions characterized by weak spatial autocorrelation and localized pollution processes. Full article

Soil pollution constitutes a critical global environmental challenge driven by industrialization, intensive agriculture, urban expansion, mining, and the application of synthetic agrochemicals. This article presents seven annotated Python-based Machine Learning (ML) workflows for soil pollution assessment, structured around five contaminant groups: heavy metals, pesticides, microplastics, per- and polyfluoroalkyl substances (PFAS), and excess macronutrients. The contribution has three distinct components. First, a literature synthesis drawing on more than 100 peer-reviewed studies contextualizes each contaminant group within current spectroscopic, geochemical, and ML-based detection frameworks. Second, a conceptual six-step workflow links field sampling, ML-based analysis, and scenario-based risk modelling to soil ecosystem service (SES) assessment. Third, seven executable Python scripts—implementing Random Forest regression, XGBoost with SHAP explainability, 1-D Convolutional Neural Networks, LSTM time-series forecasting, PCA-based dimensionality reduction, Monte Carlo uncertainty propagation, and GeoPandas geospatial mapping—serve as illustrative demonstrations using a benchmark dataset. All reported performance metrics are derived from synthetic data and represent workflow demonstrations, not validated field results. Radionuclides are acknowledged as an important contaminant class but fall outside the defined scope of this study. The scripts are provided as reproducible templates for adaptation to real contaminated-site datasets. Full article

Conceptual Site Models (CSMs) are essential tools for characterizing contaminated sites, integrating hydrobiogeochemical information to support remediation planning. Historical datasets are often underutilized, while additional investigations can be costly, limiting our understanding of contaminant dynamics. This study aims to develop a sustainable and cost-effective framework for constructing an enhanced CSM of the Mantua Lakes through the integration of historical (2008) and recent (2024–2025) sediment and water quality datasets, resulting in more than 2000 data points. Objectives included the reconstruction of a 3D geological model (55 boreholes), the estimation of contaminant masses in sediments, and the evaluation of temporal trends in contaminant distribution and natural attenuation. Sediment cores (collected at 25 cm intervals) and surface water samples were analyzed for arsenic, cadmium, chromium, mercury, and heavy petroleum hydrocarbons. A harmonized set of 39 georeferenced points enabled a multi-temporal comparison. Voronoi polygons and volumetric calculations were used to estimate contaminant mass within sediment layers. Bathymetric and stratigraphic data, consisting of 458 depth points and 64 isobaths, were integrated into a 3D geodatabase and extended into a 4D framework to capture temporal evolution. Sediments exhibited overall reductions in contaminants, particularly cadmium and hydrocarbons, while arsenic and chromium showed localized variations. Water column concentrations mirrored sediment trends, indicating significant bioattenuation. Integrating historical and recent data strengthens CSMs, provides quantitative mass estimates, and offers a comprehensive framework for understanding contaminant dynamics, natural attenuation processes, and sustainable site management. Full article

This review covers literature published up to June 2026. Detecting various water pollutants quickly and reliably remains a challenge. Boron-doped diamond (BDD) electrodes, particularly when fabricated as nanostructured thin films such as nanocones or nanowalls, offer a wide electrochemical window, low background current, and excellent chemical stability, making them promising tools for electrochemical sensing. However, unmodified BDD electrodes face an inherent trade-off among conductivity, active site density, and interfacial stability, a phenomenon termed herein the “sensitivity-selectivity-stability triangle bottleneck”, which severely limits practical performance. In this review, we demonstrate how multi-scale structural regulation can circumvent this bottleneck. Specifically, a triple strategy comprising boron doping, surface modification, and post-annealing treatment is proposed and evaluated. First, the effect of boron doping level on conductivity and active site density is discussed. Second, two common surface modification approaches are examined: carbon nanomaterials (which increase surface area and form conductive networks) and metal nanoparticles (which enhance catalytic activity and interfacial charge transfer). Third, post-annealing is highlighted as a key synergistic step that locks the modified layer and stabilizes the interface. Together, these three components form an integrated framework. To provide concrete guidance, the performance of each strategy is compared for representative water pollutants, including heavy metal ions, phenolic compounds, and emerging contaminants such as antibiotics and pesticides, with emphasis on sensitivity, selectivity, and stability. Representative detection limits achieved include 0.01 μg/L for Pb²⁺, 5 nM for acetaminophen, and 0.32 fM for PCB-77, demonstrating the effectiveness of the triple structural regulation strategy. Finally, in line with the theme of this Nanomaterials Special Issue on nanostructured thin films, current challenges in structural regulation are summarized, and future directions, including multi-parameter optimization, AI-assisted high-throughput screening, and real-world testing, are outlined. The goal is to offer practical structure-performance guidelines for designing BDD-based electrochemical sensors that are both high-performing and durable. Full article

Groundwater is vital to subsurface ecosystems and to maintaining water supplies for human and environmental needs. Heavy metal pollution of water bodies poses a significant threat to environmental health and human well-being. In this paper, a detailed spatiotemporal analysis of heavy metal pollution at a network of 35 sampling sites is presented for the summer, autumn, and winter seasons. The water samples were analyzed based on the concentration (μg/L) of eight priority metals, such as Lead (Pb), Mercury (Hg), Cadmium (Cd), Arsenic (As), Chromium VI (Cr-VI), Copper (Cu), Zinc (Zn), as well as Iron (Fe). Measuring contamination levels, identifying space hotspots, and explaining seasonal variations were the key tasks. The results show that Fe, Zn, and Cu concentrations are consistently high across seasons, with mean values ranging from 234.3 to 253.2 µg/L (Fe), 163.2 to 175.6 µg/L (Zn), and 107.0 to 109.2 µg/L (Cu), indicating a widespread geogenic or diffuse source. The most significant seasonal deviation was observed in the fall when there were unusually high levels of Cd and Hg, with mean concentrations reaching 0.476 µg/L and 0.312 µg/L, respectively, suggesting a strong seasonal contamination event or mobilization process. The spatial analysis showed that the locations (e.g., L6, L8, L10, L28, L29) exhibited common hotspots for different metals, with maximum concentrations reaching up to 793.3 µg/L (Fe), 602.3 µg/L (Zn), and 508 µg/L (Cu). Principal Component Analysis (PCA) effectively separated seasonal trends and classified metals into anthropogenic (Pb, Cd, Hg, Cr (VI)) and geogenic/diffuse (Fe, Zn, Cu) groups. The health risk assessment indicated no significant non-carcinogenic risk, although children are more vulnerable, while arsenic levels in winter approached the upper acceptable carcinogenic limit (up to 1.1 × 10⁻⁴). Overall, the study highlights the importance of multi-seasonal monitoring by capturing temporally abrupt contamination events and provides a novel integrated framework that combines seasonal analysis, spatial hotspot identification, and multivariate techniques, distinguishing it from conventional single-season or non-integrated studies. Full article

Heavy metal (HM) contamination near mining operations in Kazakhstan poses a serious threat to the environment. However, data on the state of chernozem soils in this region is limited. This study assessed the bioaccumulation of HMs and translocation within the soil–sunflower (Helianthus annuus L.) system in a southern Calcic Chernozem in the Kostanay region (Northern Kazakhstan), which is located 50 km from the nearest mining facility. The content of seven HMs (Cd, Co, Cr, Cu, Ni, Pb, and Zn) and arsenic (As), as well as five macroelements (K, Ca, S, Mg, and P), was determined in 18 soil samples from the complete soil pedon (0–150 cm) and in eight anatomical parts of six sunflower plants at physiological maturity. Most metals exhibited a deficiency relative to upper continental crustal Clarke values (Clarke of Concentration (CC) < 1 for Cr, Cu, Ni, Pb, and Zn), with a moderate lithogenic anomaly for Cd (CC = 1.65–3.57) and a localized Co anomaly in the Bk horizon (56.26 mg kg⁻¹), indicating no pronounced HM contamination at the investigated agricultural site. Metal distribution exhibited strong organ specificity in sunflower plants. Cd, Cu, and Zn accumulated preferentially in the leaves, whereas Ni and Co were more concentrated in the seeds and stems, respectively. Only cadmium exceeded the threshold values for both BCF > 1 (1.01) and TF > 1 (1.47), confirming the status of sunflower as a cadmium accumulator. These results provide a preliminary reference dataset of the organ-specific distribution of heavy metals in H. annuus L. plants, which can serve as a local baseline for sunflower growth in uncontaminated southern Chernozems. This information can contribute to future environmental monitoring purposes in the region, acting as an exploratory benchmark. Full article

Landfill leachate represents a long-term source of contamination that may significantly affect groundwater and receiving water bodies through the migration of organic, inorganic, and toxic pollutants. This study evaluated the long-term migration of landfill leachate and its potential environmental impacts using the LandSim Release 2 probabilistic software model applied to two municipal waste landfills in the Republic of Serbia: the regional sanitary landfill “Gigoš” in Jagodina and the sanitary landfill “Meteris” in Vranje. The modelling framework integrated laboratory leachate analyses, hydrogeological conditions, engineered barrier system characteristics, and receptor-oriented contaminant transport assessment. Model validation was performed through comparison of simulated and laboratory-measured concentrations. Two scenarios were analyzed for each site: an engineered sanitary landfill scenario with a functional containment system and a conservative barrier-failure scenario representing complete loss of engineered barrier functionality. Ten representative leachate parameters were included, covering nitrogen compounds, inorganic ions, toxic substances, and heavy metals/metalloids. The results showed that engineered protection systems significantly delay contaminant migration and reduce receptor concentrations, while barrier-failure conditions lead to earlier pollutant breakthrough and higher environmental risk. The simulations demonstrated that under the engineered sanitary landfill scenario, receptor concentrations of all analyzed contaminants remained below the corresponding maximum allowable concentrations, with contaminant migration occurring only after several centuries. In contrast, the conservative barrier-failure scenario resulted in substantially earlier contaminant breakthrough, with nitrogen compounds and phenols representing the greatest environmental concern due to their rapid migration and exceedance of regulatory thresholds, while the “Meteris” landfill generally exhibited higher receptor concentrations than the “Gigoš” landfill. These findings highlight the importance of predictive modelling and long-term monitoring for sustainable landfill management and groundwater protection. Full article

Reprocessing historical lead–zinc (Pb–Zn) slag offers a circular-economy pathway for secondary metal recovery, yet it can remobilize legacy contaminants where containment is inadequate, transferring risk to the surrounding land. Sustainable management of such sites requires frameworks that link contamination assessment to actionable remediation. We integrated ICP-OES geochemistry, native-plant biomonitoring, and US EPA RAGS-based risk modeling at an active Pb–Zn slag reprocessing site in Shymkent, Southern Kazakhstan. Twenty-four soil samples along four cardinal transects, two reference samples, and four composite plant samples (Centaurea pseudosquarrosa + Plantago lanceolata) were analyzed for ten metals by ICP-OES. UCC-referenced indices classified six metals as geoaccumulation Class 6 at most points (enrichment factors up to 90,871, confirming an exclusively anthropogenic origin). Peak concentrations reached 9350 mg·kg⁻¹ Pb, 290 mg·kg⁻¹ Cd, and 10,900 mg·kg⁻¹ As—exceeding Kazakhstan MPC by 72×, 290×, and 5450×. Worst-case carcinogenic risk reached 4.3 × 10⁻³ (43× above the US EPA threshold), driven almost entirely by arsenic (93%); ecosystem risk (RCR_total = 223) was dominated by cadmium (43%), arsenic (27%), and mercury (16%)—a disconnect between mass-based and toxicity-based prioritization. On this basis we propose a three-tier remediation framework (engineered containment, phytostabilization, monitored attenuation) that couples resource recovery with contamination control, is transferable to analogous Pb–Zn legacy sites, and supports sustainable land use, urban resilience, and responsible secondary-resource use. Full article

The contamination of aquatic environments by cadmium and other toxic heavy metals represents a major environmental concern requiring efficient and operationally sustainable remediation strategies. In this work, iron oxide materials were synthesized through a microwave-assisted hydrothermal method and evaluated for Cd(II) removal from aqueous systems. Different precursor compositions and organic additives were initially screened in order to identify the most suitable adsorbent formulation. The selected Fe-Tart material was characterized by FTIR, SEM-EDS, and XRD analyses, revealing hydroxylated and poorly crystalline iron oxide structures with heterogeneous surface organization. Batch adsorption experiments were performed under controlled conditions to investigate the influence of pH and equilibrium adsorption behavior, while adsorption data were analyzed using Langmuir and Freundlich isotherm models. Cd(II) uptake showed strong pH dependence, with adsorption progressively increasing from acidic to near-neutral conditions and reaching approximately 80% removal at pH 7–8. The Langmuir model provided the best fitting results (R² = 0.988), suggesting preferential occupation of energetically comparable surface sites with a maximum adsorption capacity of 6.51 mg g⁻¹. The adsorption behavior was interpreted within a pH-dependent surface complexation framework involving hydroxylated iron oxide surfaces. Although the adsorption capacity remained lower than that reported for some highly engineered adsorbents, the results indicate that microwave-assisted synthesis may provide a relatively simple and rapid route for preparing iron oxide-based materials potentially applicable to water remediation systems. Full article