Search Results (43)

The study of the impact of anthropogenic and natural pollution on living organisms has become a major social issue. In this context, the objective of this work is to assess the use of the polychaete annelid Hediste diversicolor as a bioindicator organism for the quality of the marine environment. The concentration of four heavy metals (lead, copper, zinc, and cadmium) was determined in natural populations of H. diversicolor captured from four locations along the Tunisian coast using atomic absorption spectroscopy. Concentration ranges (µg/g dry weight) across all sites were as follows: Cd (0.12–0.43), Cu (3.80–6.45), Zn (18.35–42.78), and Pb (22.64–63.91). Statistical analysis confirmed significant spatial variation (Pb: F = 12.15, p < 0.001; Zn: F = 3.32, p = 0.04; Cd: F = 48.66, p < 0.001; Cu: F = 9.08, p < 0.001), with peak Pb in Bizerte and Cu in Sfax. These results highlight the influence of local environmental factors, such as industrial and urban pollution on metal accumulation in Hediste diversicolor. In this study, the accumulation of the analyzed elements in the tissues of H. diversicolor follows an increasing order as follows: Cd < Cu < Zn < Pb. Additionally, lead metal concentrations were higher than those of cadmium, zinc, and copper for all four studied locations. To our knowledge, this is the first study in Tunisia to assess heavy metal accumulation in H. diversicolor. The recorded levels were similar to, or lower than, those reported in other studies worldwide. These findings underscore the potential of H. diversicolor as a sensitive and effective bioindicator for monitoring coastal contamination and guiding environmental management strategies in Tunisia. Full article

Cadmium (Cd) pollution poses a severe threat to rice safety and human health, while traditional linear models exhibit significant limitations in predicting rice Cd accumulation due to environmental complexities. This study systematically evaluated the predictive performance of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Residual Neural Networks (ResNet), using a multi-source soil–rice dataset comprising 57,200 samples from Hunan Province. The results showed that the RF model performed best on the test set (R² = 0.62), with the dominant features being soil’s available Cd (contributing 9.74%) and precipitation during the rice-filling stage (joint contribution of 15.96%). However, the model’s predictive performance experienced a sharp decline on the independent 2023 validation set comprising 393 samples from Yizhang County and Lengshuitan District, with R² values ranging from −0.12 to −0.31. This highlighted the fundamental limitations of static data-driven paradigms. Agronomic management measures, simplified by heterogeneous data and binary encoding, failed to effectively represent the actual intervention intensity. The study demonstrated that while machine learning models captured nonlinear relationships in laboratory environments, they struggled to adapt to the dynamic interactions and spatiotemporal heterogeneity of farmland systems. Future efforts should focus on developing hybrid models guided by mechanistic insights, integrating dynamic environmental processes and real-time data, and promoting localized “one model per region” strategies to enhance predictive robustness. This study provides methodological insights for the technological transformation of agricultural artificial intelligence, emphasizing that the deep integration of data-driven approaches and mechanistic understanding is crucial for overcoming the “last mile” challenge. Full article

Cadmium (Cd) is a highly hazardous heavy metal that has an extensive impact throughout the world. 2,4-Epibrassinolide (BR) is an endogenous hormone that can enhance plant tolerance to various abiotic stresses. Herein, Vigna angularis cultivar “Zhen Zhuhong” was grown hydroponically and treated with 0, 1, and 2 mg·L⁻¹ cadmium chloride (CdCl₂) at the V1 stage, and foliar sprayed with or without 1 μM BR solution to analyze the effects of BR treatment on the physiology of Vigna angularis seedling leaves under Cd stress. BR treatment significantly alleviated the growth inhibition induced by Cd stress, which was associated with an increase in the plant height (11.15–17.83%), leaf area (35.59–56.72%), leaf dry weight (45.57–50.65%), and above-ground dry weight (50.86–55.17%). In addition, BR treatment induced significant reductions in Cd accumulation across different tissues of V. angularis, with decreases of 20.38–35.93% in leaves, 21.24–32.74% in stems, and 15.38–16.00% in petioles. Compared with the Cd treatment, BR treatment significantly enhanced the activities of peroxidase (5.02–13.22%), ascorbate peroxidase (27.13–70.28%), catalase (20.46–32.30%), and superoxide dismutase (16.54–21.81%), and increased the ascorbic acid content (27.55–45.52%), which contributed to a reduction in the accumulation of reactive oxygen species, cellular membrane damage, and cytoplasmic exosmosis. RNA-seq and real-time quantitative reverse transcription PCR analyses revealed that the BR treatment under Cd stress significantly upregulated the expression of genes involved in chlorophyll biosynthesis, transformation, and degradation, thereby enhancing the chlorophyll cycle. Furthermore, the BR treatment significantly increased the number of grana lamellae in the mesophyll cells, which enhanced the biosynthesis of chloroplasts. The increase in the chlorophyll content improved the capture of light energy, electron transport in photosynthesis, and the biosynthesis and metabolism of carbohydrates in the leaves of V. angularis under Cd stress. Full article

This study investigates the relationships between farmers’ perceptions, their agriculture practices, and objective soil health indicators in a viticultural subzone of the Madrid region, aligning with the EU’s Farm to Fork Strategy. A dual-methodology approach was employed, combining detailed soil chemical and physical analyses with a structured survey of thirty-four local farmers. Soil samples were analyzed for pH, nutrient concentrations (nitrogen, phosphorus, and potassium), and heavy metals (nickel, lead, and cadmium), while the survey captured farmers’ perceptions regarding soil contamination and sustainable practices. Results showed significantly higher levels of nitrogen (0.09% vs. 0.04%), phosphorus (125 vs. 65 mg/kg), and potassium (3100 vs. 1550 mg/kg) in fertilized plots (p < 0.05), while heavy metals remained within safe limits, compared to those not using fertilizers, as confirmed by Mann–Whitney U tests (p < 0.05). However, the impact on heavy metal accumulation was minimal, with only a slight decrease in nickel levels in fertilized plots. Additionally, the survey revealed low adoption rates of conservation agriculture techniques and limited training on sustainable practices, despite strong environmental commitment among farmers. These findings underscore the need for regular soil testing, targeted educational initiatives, and the increased promotion of conservation practices to better align subjective assessments with scientific evidence, ultimately enhancing both productivity and ecological resilience in sustainable viticulture. Full article

Human biomonitoring (HBM) is a critical scientific tool for assessing human exposure by quantifying chemicals and their metabolites in biological specimens such as blood and urine. This approach provides a comprehensive and accurate evaluation of internal exposures from diverse sources and exposure routes. In Ireland, establishing a national HBM programme requires a systematic chemical prioritisation process that aligns global frameworks with local public perceptions. This study integrates insights from international initiatives such as the European Joint Programme Human Biomonitoring for Europe (HBM4EU) and the Partnership for the Assessment of Risks from Chemicals (PARC)—along with HBM programmes from EU countries (Germany, France, Belgium, Norway, Slovenia, Czech Republic, and Sweden) and non-EU countries (US, Canada, South Korea, China, and New Zealand). In addition, a national survey was conducted to capture the perceptions of people in Ireland regarding chemicals of concern to develop a comprehensive priority list of chemicals and biomarkers. The broader chemical groups identified include heavy metals (lead, cadmium, mercury, arsenic, and chromium VI), plasticisers (phthalates), bisphenols, pesticides, flame retardants, PFASs (per- and polyfluoroalkyl substances), PAHs (polycyclic aromatic hydrocarbons), POPs (persistent organic compounds), VOCs (volatile organic compounds), and UV (ultraviolet) filters. This integrated, participatory approach provides a roadmap for a robust, adaptable chemical list that supports evidence-based policy decisions in HBM in Ireland and enhances public health outcomes. Full article

Heavy metal contamination of soil and water is a growing environmental concern, especially mercury, lead, and cadmium. Therefore, fast and reliable methodologies to assess contamination in the field are in demand. However, many methodologies require laborious, expensive, and cumbersome equipment that is not convenient for rapid field analysis. Mobile phone technology coupled with bioluminescent assays provides accessible hands-on alternatives that has already been shown to be feasible. Previously, we demonstrated that firefly luciferases can be harnessed as luminescence color-tuning sensors for toxic metals. An assay based on such a principle was already successfully applied for teaching biochemistry laboratory lessons, which demonstrates the effect of cadmium on enzyme function based on bioluminescence color change. For analytical detection of cadmium in water, here, we developed a novel bioluminescence assay using the cadmium-sensitive Amydetes vivianii firefly luciferase coupled with a cell phone provided with a program to quantify cadmium concentration based on luminescence color discrimination. The application has proven to be efficient with high precision between 0.10 and 2 mM of cadmium, being appliable to diluted water samples (0.1–2 µM) upon concentration and relying on reference cadmium standards values. The light emitted by the reference standards and samples in a dark box is captured by the smartphone’s camera, which, using computer vision, automatically quantifies cadmium according to the RGB color. CadmiLume is a simple and easy luminescent enzymatic biosensor for cadmium contamination in water samples, which instantaneously can provide results with the convenience of a smartphone in the palm of one’s hands. Full article

Alum mining leads to significant heavy metal and acid pollution within soils. Phytoremediation is a common strategy used to treat alum mine soils, but its efficiency is frequently compromised by the alum-mining-induced impairment of plant growth. To improve the strength of plants against mine pollution, this study constructed the artificial yeast strain ScHB (heavy metal-binding protein-containing Saccharomyces cerevisiae) expressing the de novo designed protein HBGFP (heavy metal-binding green fluorescence protein) and investigated its effect on the phytoremediation of alum mine soils with soil physiochemical assays and heavy metal quantification. This protein was composed of an N-terminal signal peptide, an HB (heavy metal-binding) domain, and a GFP (green fluorescence protein) domain, as well as a C-terminal glycolphosphatidylinositol-anchoring fragment. The exposure of the HBGFP on the ScHB surface increased the growth rate of the yeast cells and enhanced cadmium capture from the cadmium-containing medium. After culturing Medicago sativa in the alum mine soils for 30 days, ScHB remarkably increased the plants’ average height from 17.5 cm to 27.9 cm and their biomass from 3.03 g/plant to 4.35 g/plant, as well as increasing the accumulation of antioxidant agents in the plants. Moreover, the ScHB cells strongly improved the soil quality, with an increase in the soil pH values from 5.47 to 6.21 to 6.9, and increased the levels of soil organic matter, total nitrogen, available phosphorus, and living bacteria. Furthermore, ScHB efficiently improved the plants’ abilities to remove soil heavy metals, decreasing the levels of cadmium, lead, chromium, and copper by 90%, 86%, 97%, and 88%, respectively. This study developed a genetic engineering method to improve the efficiency of phytoremediation against pollution from alum mining. Full article

Background: Environmental exposures to heavy metals/metalloids such as arsenic, cadmium, and mercury have been implicated in adverse cardiovascular health outcomes. Using data from the All of Us research program, we investigated the associations between these metals/metalloids and six cardiovascular-related biomarkers: systolic blood pressure (SBP), HDL cholesterol, LDL cholesterol, C-reactive protein (CRP), total cholesterol, and triglycerides. Methods: This study explored the relationship between outcome cardiovascular variables (SBP, CRP, LDL, HDL, triglycerides, and total cholesterol) and predictor metal/metalloid variables (cadmium, mercury, and arsenic) among 136 participants (53.4 percent women). We initially conducted linear regression to determine the association between variables of interest. Bayesian Kernel Machine Regression (BKMR) analysis was subsequently performed to capture potential non-linear relationships, as well as interactions among metal/metalloid exposures. In the BKMR analysis, posterior inclusion probabilities (PIPs) quantified the contribution of each metal/metalloid to the outcomes, with higher PIP values indicating a greater likelihood of a specific exposure being a key predictor for a given cardiovascular biomarker. Within the BKMR framework, univariate, bivariate, and overall exposure–response analyses provided insights into the individual and combined effects of metal/metalloid exposures. These analyses identified the factors with the strongest associations and highlighted interactions between exposures. Results: In this study, the average age of male participants was 58.2 years, while female participants had an average age of 55.6 years. The study population included 104 individuals identifying as White (mean age: 57.5 years), 10 as Black or African American (mean age: 63.2 years), 7 as Hispanic (mean age: 48.2), 3 as Asian (mean age: 49.7 years), and 12 as Other race (mean age: 48.8 years). In our study, men exhibited higher levels of SBP, triglycerides, mercury, and arsenic, while women had higher levels of CRP, LDL cholesterol, HDL cholesterol, total cholesterol, and cadmium. Black people exhibited higher levels and greater variability in markers of cardiovascular risk and inflammation (e.g., blood pressure and CRP), Asians consistently showed the lowest levels across most biomarkers, while White people, Hispanics, and the “Other” group demonstrated moderate levels with some variability. In linear regression, we identified significant positive associations between mercury and HDL cholesterol, arsenic and triglycerides, and arsenic and total cholesterol. In BKMR analysis, PIP results revealed that mercury had the highest predictive contribution for SBP, HDL cholesterol, and triglycerides; cadmium for CRP; and arsenic for LDL and total cholesterol. Univariate and bivariate exposure–response analyses in BKMR demonstrated non-linear exposure–response patterns, including U-shaped and inverted U-shaped patterns for cadmium, particularly CRP and total cholesterol. Traditional linear regression techniques would have missed these patterns. Conclusion: Our study results highlight the influence of environmental metal/metalloid exposures on cardiovascular biomarkers, providing evidence of non-linear and interactive effects that warrant further investigation to understand their role in cardiovascular disease risk better. Full article

This study investigates the geological composition and material distribution within the Lavrion repository located in Greece through an aerial magnetometry survey using a novel aerial drone, CERBERUS, coupled with advanced data processing techniques. The deployment of drone-based magnetometry provided a high-resolution, non-invasive approach to capturing magnetic field data over complex and potentially hazardous terrain (soils highly contaminated), facilitating the rapid and precise mapping of the study area. As a final result, a 3D magnetic susceptibility model was developed, representing a detailed view of the magnetic susceptibility variations within the repository. This model enabled the comprehensive visualization of high-susceptibility zones associated with ferromagnetic materials and low-susceptibility zones correlating with diamagnetic materials like lead, arsenic, cadmium, and zinc. The combined methodologies underscore the effectiveness of drone-based aerial magnetometry in geophysical studies, highlighting its potential for mining exploration and waste management. This study demonstrates that the integration of drone technology with magnetic data processing offers a powerful tool for analysing subsurface structures in a safe, efficient, and non-invasive manner. Full article

This study investigated the elemental composition of Boerhavia elegans, addressing the gap in comprehensive trace element profiling of this medicinal plant. The research aimed to determine the distribution of macronutrients, micronutrients, and beneficial and potentially toxic elements across different plant parts (seeds, leaves, stems, and roots). Using ICP-OES analysis, two digestion methods were employed to capture both complex and labile elements. The study revealed distinct elemental distribution patterns, with iron and nickel concentrating in stems, manganese and zinc in leaves, and copper in roots. Magnesium emerged as the most abundant macronutrient, particularly in leaves. Importantly, all detected toxic elements (arsenic, chromium, lead, and cadmium) were below WHO safety limits. These findings provide crucial insights into the nutritional and safety profile of B. elegans, potentially informing its use in traditional medicine and highlighting its potential as a source of essential elements. Full article

Heavy metal pollution has become increasingly severe, with distinctive physiological characteristics of rice leading to significant accumulation of arsenic (As) and cadmium (Cd) in grains, posing serious health risks. Biological soil crusts (BSC) are common in paddy soils and exhibit a strong capacity to bind trace heavy metals. This study investigated the effects of exogenous iron (Fe)/manganese (Mn) and urea on the effectiveness of BSC (20 mg L⁻¹) in removing As (2 mg L⁻¹) and Cd (100 μg L⁻¹) and analyzed the heavy metal distribution. Fe/Mn addition increased As adsorption by BSC from 51.2% to 83.0% but reduced Cd adsorption from 73.2% to 50.3%, whereas urea inhibited As uptake but enhanced Cd capture. Under co-contamination, the As removal ability of the BSC remained unchanged, but Cd removal improved. As was primarily present in the non-EDTA exchangeable fraction (79.0%), which increased to 96.4% and 85.8% in the presence of Fe/Mn, and urea, respectively. Cd was mainly in the sorbed fraction (51.6%), which increased to 61.0% with urea. These results confirm that BSC exhibits a strong ability to adsorb As and Cd under irrigated water with combined As and Cd contamination, iron/manganese and urea can also enhance this ability. The application of exogenous Fe/Mn providing the raw material for the mineralization process and the presence of urea enhancing the biological activity of the colonies. This study provides an eco-friendly strategy for remediating As and Cd in paddy fields. Full article

Accurate soil total phosphorus (TP) prediction is essential to support sustainable agricultural practices and formulate ecological conservation protection policies, particularly in complex karst landscapes with high spatial variability and high phosphorus and cadmium content and interactions, complicating nutrient management. This study uses GIS and geostatistical methods to analyze the spatial distribution, influencing factors, and predictive modeling of soil TP in the karst region of northern Mashan County, Guangxi, China. Using 427 surface soil samples, we developed five predictive models: ordinary kriging (OK), regression kriging (RK) and geographically weighted regression kriging (GWRK) combined with environmental variables such as land uses, soil types, and topographic factors; residual mean-centered kriging (MM_OK), and residual median-centered kriging (MC_OK). Our results indicate that higher TP levels were observed in agricultural lands (paddy fields and dry land, at 766 and 913 mg·kg⁻¹, respectively) may due to fertilization, while forests and shrublands showed lower TP levels (383 and 686 mg·kg⁻¹, respectively), reflecting natural phosphorus cycling. The high-value areas of soil TP concentration are in the karst areas in the west and east of the study area, and the low-value area is in the Hongshui River valley in the north of Mashan. The spatial distribution of soil TP is affected by land use, soil type, and topography. The GWRK model exhibited superior accuracy (80.6%), with predicted concentration of TP closely aligning with observed TP values, effectively capturing fine spatial variations, and showing the lowest mean standardized error, average standard error, and mean absolute error. GWRK also achieved the highest R² (0.67), demonstrating robust predictive capability. MM_OK and MC_OK models performed well and showed smoother spatial transitions, while the OK model displayed the lowest predictive accuracy (62%). By utilizing spatially adaptive weighting, GWRK and its residual-centered kriging method improve soil TP’s prediction accuracy and smoothness in karst areas, providing a reference for targeted soil conservation and sustainable agricultural practices in spatially complex karst environments. Full article

Background/Objective: Environmental exposures, such as heavy metals, can significantly affect physical activity, an important determinant of health. This study explores the effect of physical activity on combined exposure to cadmium, lead, and mercury (metals), using data from the 2013–2014 National Health and Nutrition Examination Survey (NHANES). Methods: Physical activity was measured with ActiGraph GT3X+ devices worn continuously for 7 days, while blood samples were analyzed for metal content using inductively coupled plasma mass spectrometry. Descriptive statistics and multivariable linear regression were used to assess the impact of multi-metal exposure on physical activity. Additionally, Bayesian Kernel Machine Regression (BKMR) was applied to explore nonlinear and interactive effects of metal exposures on physical activity. Using a Gaussian process with a radial basis function kernel, BKMR estimates posterior distributions via Markov Chain Monte Carlo (MCMC) sampling, allowing for robust evaluation of individual and combined exposure-response relationships. Posterior Inclusion Probabilities (PIPs) were calculated to quantify the relative importance of each metal. Results: The linear regression analysis revealed positive associations between cadmium and lead exposure and physical activity. BKMR analysis, particularly the PIP, identified lead as the most influential metal in predicting physical activity, followed by cadmium and mercury. These PIP values provide a probabilistic measure of each metal’s importance, offering deeper insights into their relative contributions to the overall exposure effect. The study also uncovered complex relationships between metal exposures and physical activity. In univariate BKMR exposure-response analysis, lead and cadmium generally showed positive associations with physical activity, while mercury exhibited a slightly negative relationship. Bivariate exposure-response analysis further illustrated how the impact of one metal could be influenced by the presence and levels of another, confirming the trends observed in univariate analyses while also demonstrating the complexity varying doses of two metals can have on either increased or decreased physical activity. Additionally, the overall exposure effect analysis across different quantiles revealed that higher levels of combined metal exposures were associated with increased physical activity, though there was greater uncertainty at higher exposure levels as the 95% credible intervals were wider. Conclusions: Overall, this study fills a critical gap by investigating the interactive and combined effects of multiple metals on physical activity. The findings underscore the necessity of using advanced methods such as BKMR to capture the complex dynamics of environmental exposures and their impact on human behavior and health outcomes. Full article

Hydrogen sulfide is present in active or extinct volcanic areas (mofettas). The habitable premises in these areas are affected by the presence of hydrogen sulfide, which, even in low concentrations, gives off a bad to unbearable smell. If the living spaces considered are closed enclosures, then a system can be designed to reduce the concentration of hydrogen sulfide. This paper presents a membrane-based way to reduce the hydrogen sulfide concentration to acceptable limits using a cellulosic derivative–propylene hollow fiber-based composite membrane module. The cellulosic derivatives considered were: carboxymethyl–cellulose (NaCMC), P1; cellulose acetate (CA), P2; methyl 2–hydroxyethyl–cellulose (MHEC), P3; and hydroxyethyl–cellulose (HEC), P4. In the permeation module, hydrogen sulfide is captured with a solution of cadmium that forms cadmium sulfide, usable as a luminescent substance. The composite membranes were characterized by SEM, EDAX, FTIR, FTIR 2D maps, thermal analysis (TG and DSC), and from the perspective of hydrogen sulfide air removal performance. To determine the process performances, the variables were as follows: the nature of the cellulosic derivative–polypropylene hollow fiber composite membrane, the concentration of hydrogen sulfide in the polluted air, the flow rate of polluted air, and the pH of the cadmium nitrate solution. The pertraction efficiency was highest for the sodium carboxymethyl–cellulose (NaCMC)–polypropylene hollow fiber membrane, with a hydrogen sulfide concentration in the polluted air of 20 ppm, a polluted air flow rate (Q_H2S) of 50 L/min, and a pH of 2 and 4. The hydrogen sulfide flux rates, for membrane P1, fall between 0.25 × 10⁻⁷ mol·m²·s⁻¹ for the values of Q_H2S = 150 L/min, C_H2S = 20 ppm, and pH = 2 and 0.67 × 10⁻⁷ mol·m⁻²·s⁻¹ for the values of Q_H2S = 50 L/min, C_H2S = 60 ppm, and pH = 2. The paper proposes a simple air purification system containing hydrogen sulfide, using a module with composite cellulosic derivative–polypropylene hollow fiber membranes. Full article

The combination of ion exchange membranes with carbon quantum dots (CQDs) is a promising field that could lead to significant advances in water treatment. Composite membranes formed by sulfonated poly(ether ether ketone) (SPEEK) with embedded CQDs were used for the detection and removal of heavy metal ions, such as lead and cadmium, from water. SPEEK is responsible for the capture of heavy metals based on the cation exchange mechanism, while CQDs detect their contamination by exhibiting changes in fluorescence. Water-insoluble “red” carbon quantum dots (rCQDs) were synthesized from p-phenylenediamine so that their photoluminescence was shifted from that of the polymer matrix. CQDs and the composites were characterized by several techniques: FTIR, Raman, UV/VIS, photoluminescence, XPS spectroscopies, and AFM microscopy. The heavy metal ion concentration was analyzed by inductively coupled plasma–optical emission spectroscopy (ICP-OES). The concentration ranges were 10.8–0.1 mM for Pb²⁺ and 10.0–0.27 mM for Cd²⁺. SPEEK/rCQDs showed a more pronounced turn-off effect for lead. The composite achieved 100% removal efficiency for lead and cadmium when the concentration was below a half of the ion exchange capacity of SPEEK. The regeneration of membranes in 1 M NaCl was also studied. A second order law was effective to describe the kinetics of the process. Full article