Cadmium in topsoils of the European Union - An analysis based on LUCAS topsoil database

Investigation of spatially varying relationships between cadmium accumulation and potential controlling factors in the topsoil of island of Ireland based on spatial machine learning approaches

BACKGROUND

Cadmium (Cd) contamination in soils is a pressing environmental issue due to its toxicity and persistence. Given the diverse geological formations and intensive agricultural activities in Ireland, understanding the distribution and sources of soil Cd is particularly important.

METHODS

This study used multiple GIS-based and spatial machine learning (SML) techniques to investigate the spatial distribution and controlling factors of Cd in 16,783 topsoil samples across the island of Ireland. Three analytical methods were applied: hot spot analysis to detect clusters of high and low Cd concentrations, Geographically Weighted Pearson Correlation Coefficients (GWPCC) to explore how Cd relationships with other soil properties vary across space, and Random Forest (RF) to rank the contributing factors in Cd accumulation.

RESULTS

Hot spot analysis revealed strong spatial overlap between Cd concentrations and key geochemical variables including CIA, Fe, P, pH, SOC, and Zn. GWPCC further highlighted their spatially varying relationships, with significantly strong positive correlations between Cd and pH, Zn, and P in the central midlands. The local correlation coefficients obtained from the GWPCC ranged from negative to the highest values of 0.80, 0.92 and 0.86, respectively, which were significantly higher than the results of traditional Pearson correlation coefficients. These patterns were associated with impure limestones, Zn mineralization, and phosphate fertilizer inputs. Furthermore, the RF model ranked Zn (39.4 %) and P (17.6 %) as the most influential factors, with their importance increasing in limestone-dominated areas (50.9 % and 27.4 %), which emphasized the external contributions from local Zn mineralization and phosphate fertilizers in addition to natural accumulation.

CONCLUSION

This study demonstrated the effectiveness of integrating SML techniques with geochemical analysis for identifying Cd sources in the topsoil of Ireland, highlighting the roles of lithology and agricultural activities in Cd accumulation. The results provided valuable insights for contamination management and environmental policy development in Ireland and elsewhere.

Prediction of zinc, cadmium, and arsenic in european soils using multi-end machine learning models

Heavy metal contamination in soil is a major environmental and public health concern, especially in regions with substantial industrial and agricultural activities. Conventional predictive models often focus on single contaminants, limiting their utility for comprehensive environmental monitoring. This study addressed these limitations by developing an advanced multi-end ensemble convolutional neural network model capable of simultaneously predicting the concentrations of cadmium, arsenic, and zinc in European soils. A comprehensive dataset with 18 diverse factors was prepared, including soil properties, climatic factors, and anthropogenic activities. Moreover, the model compared four ensemble learning techniques in contamination prediction, including simple averaging, snapshot ensembles, integrated stacking, and separate stacking. Among these, the separate stacking model with random forest regressor meta-model achieved the highest accuracy, with a mean spared error of 0.0378, a mean absolute error of 0.0785, and a coefficient of determination of 0.79 in the testing phases. Sensitivity analysis highlighted farming area, road length, nitrogen content, and mean annual temperature as key factors influencing metal concentrations. To enhance accessibility, a GUI-based web application was developed, allowing users to enter relevant factors and receive real-time predictions of contamination levels. This application empowers stakeholders, such as environmental regulators and policymakers, to make informed, data-driven decisions for targeted remediation. These findings underscore the critical role of integrated machine learning approaches in environmental science, offering a powerful tool for identifying contamination hotspots, supporting soil health management, and promoting sustainable land use.

Global Potential Risk of Thallium in Topsoil: A Cropland-Focused Quantification Framework

Thallium (Tl) pollution from natural and anthropogenic sources is increasingly recognized for its environmental and health risks, with localized threats in polluted areas despite low global background levels. Utilizing over 20,000 topsoil Tl measurements with 21 related environmental variables, a CatBoost classification model (AUC = 0.89, recall = 0.80, balanced accuracy = 0.84) was applied to predict whether global topsoil Tl concentrations exceeding 1 mg/kg, identifying both known and unreported hotspots. A CatBoost regression model (R2 = 0.62) further predicted Tl concentration distributions, highlighting regional variations. This study reveals that high-risk areas are highly overlapped with anthropogenic factors (mining activities and land cover) and geological conditions (mineralized zones, lithology, and geological structures), collectively influencing 14.81% of the model outputs. By integrating cropland cover maps with our predictions, we found that approximately 9.9% of the world's cropland has a greater than 47% probability of Tl concentrations exceeding 1 mg/kg, particularly in South America (34.7%), Asia (12.3%), and Africa (10.8%). These findings underscore the need for heightened attention to soil Tl testing in high-risk croplands to ensure agricultural safety.

Regulation strategies of microplastics with different particle sizes on cadmium migration processes and toxicity in soil-pakchoi system

It is still unclear whether there are differences in the effects of microplastics with different particle sizes on the environmental behavior of cadmium (Cd) in the soil-crop system. By introducing the polystyrene microplastics (PS-MPs) with 0.2, 2, and 20 μm, this study explored the regulation strategies of different-sized MPs on the migration and toxicity of Cd in the soil-pakchoi system. Compared to the Cd treatment, the mobility factor of Cd in the soil decreased by 12.97 %, 34.73 %, and 40.12 % with increasing particle sizes of PS-MPs, while the relative binding intensity increased significantly. The 0.2 μm PS-MPs had no effect on Cd content in pakchoi, however, 2 and 20 μm PS-MPs significantly reduced the Cd content in shoots and roots of pakchoi by 47.40 %-29.67 % and 44.56 %-20.92 %, respectively. Additionally, 20 μm PS-MPs reduced the enrichment of the heavy Cd isotope in pakchoi. The results of principal component analysis (PCA) and structural equation modeling (SEM) indicated that 2 and 20 μm PS-MPs may promote the growth of pakchoi by regulating soil properties, nutrient uptake, Cd accumulation, and antioxidant system activity. This study provides evidence for the importance of particle size of MPs in regulating the environmental behavior of heavy metals.

Review: Strategies for limiting dietary cadmium in cereals

Cadmium (Cd) is a toxic metal, which in some production areas reaches levels above allowed limits in cereals. Thus, reducing its concentration in cereals is crucial for mitigating health risks and complying with food safety regulations. This review evaluates strategies to reduce Cd accumulation in cereal grains by mitigating soil Cd contamination and its bioavailability to plants. It covers methods for Cd estimation in soil and explores biological, chemical, and genetic approaches to limit Cd uptake by crops. The effectiveness of these strategies depends on genetic factors, soil properties, and crop type. Key approaches include traditional breeding, genome editing, digital and predictive soil mapping, and silicon (Si) and selenium (Se) supplementation. Traditional breeding, enhanced by modern genetic tools, enables the development of high-yielding, low-Cd cultivars but is time-consuming. Genome editing, particularly CRISPR-Cas9, offers precise gene modifications to reduce Cd uptake but faces regulatory constraints. Digital and predictive soil mapping provide high-resolution maps for targeted interventions but require extensive calibration. Silicon supplementation is a promising approach, as it competes with Cd for uptake sites, and limits Cd translocation to edible plant parts. Additionally, Si enhances plant tolerance to abiotic stresses, making it a multifunctional solution. Selenium supplementation can also reduce Cd accumulation while offering health benefits. However, the effectiveness of both Si and Se vary with dosage and crop type. An integrated approach combining these strategies is essential for effective Cd reduction in cereals. Continued research, technological advancements, and supportive policies are crucial for ensuring safe and sustainable cereal production.

Engineering of nicotianamine synthesis enhances cadmium mobility in plants and results in higher seed cadmium concentrations

Efficient biofortification, i.e., the enrichment of edible plant organs with micronutrients available for human consumption, is pursued through breeding and genetic engineering approaches. Enriching for iron (Fe) and zinc (Zn), two of the most critical trace elements, in cereal grains can be achieved by boosting the synthesis of nicotianamine (NA), a key metal chelator in plants. However, metal transport and distribution pathways are not entirely specific and may lead to the adventitious accumulation of potentially highly toxic non-essential metals such as cadmium (Cd). We found evidence for the formation of intracellular Cd-NA complexes driving Cd uptake and accumulation in two different yeast species and therefore studied Arabidopsis thaliana mutants as well as NA synthase overexpression lines in wild-type and mutant backgrounds that showed varying degrees of NA deficiency or overproduction relative to controls. NA synthesis was enhanced by metal excess and conferred Cd and Zn tolerance. Importantly, when cultivated on soil containing environmentally relevant Cd levels, NA-overproducing lines accumulated not only more Fe and Zn in their seeds but also more Cd. Thus, the engineering of NA synthesis can result in an unintended food safety risk that should be mitigated by carefully monitoring Cd phytoavailability in soils and, ideally, the use of low Cd germplasm for the engineering of biofortified crops.

Machine learning for predictive mapping of exceedance probabilities for potentially toxic elements in Czech farmland

For efficient decision-making and optimal land management trajectories, information on soil properties in relation to safety guidelines should be processed from point inventories to surface predictive maps. For large-scale predictive mapping, very few practical implementations have attempted to clarify how well indicator models can be built from large covariate sets combined with spatial proxies. This paper summarizes the performance of the weighted indicator-based random forest model which was used to predict exceedance probabilities for several potentially toxic elements (PTEs) in Czech farmland. The method was implemented for data mining in the Czech high-density monitoring data which had to be firstly regressed to achieve analytical harmony, and the reliability of the regression-based harmonisation was used as the input weights for the final model. The indicator-based models were trained for each PTE (As, Be, Cd, Co, Cr, Cu, Hg, Ni, Pb, V, and Zn) with two different sets of indicators, reflecting the two-tier nature of the Czech safety guidelines, which differentiate between soil textures of topsoil. The two separate predictive outputs are combined into a single probability map using a pragmatic meta-model of linear weights derived from a soil texture map generated by a compositional spatial model. Through validation with data splitting, the accuracy of the models showed relatively high predictive power for the probability distributions, but with pronounced differences between PTEs as the root mean square error in terms of exceedance probabilities ranged from 11 % (V) to 32 % (Cd and Cr) for independent validation. In addition, models based on high-resolution auxiliary variables allowed a meaningful and quantitative identification of the most important natural and anthropogenic drivers for areas with an increased rate of non-compliance with the protection thresholds for cultivated soils. Variable importance calculations showed the dominant influence of spatially explicit covariates (represented by geographical distances to quantile-based groups of points), but still significant contributions from other predictors. Among the natural factors, lithological information came to the fore, mainly due to continuous response variables such as mineral exploration density or geophysical ancillary variables (from remotely sensed gravimetry and radiometry). Among anthropogenic factors, particulate matter in the atmosphere was identified as the most important human-related pressure, followed by several land-use effects.

A Soil Monitoring Law for Europe

Over 60% of European soils are unhealthy according to the Soil Mission board estimates and the indicators presented in the European Union (EU) Soil degradation dashboard. The situation may worsen if no policy interventions are taken. The unsustainable use of natural resources, in particular the degradation of soils, precipitates biodiversity loss, exacerbated by the climate crisis. In particular, in the EU alone, soil degradation costs over €50 billion per year due to the loss of essential services they provide and to the impact on human health. Here a more precise estimation of the soil degradation cost related to a set of soil degradation processes, ranging between €40.9 and 72.7 billion per year is presented. This newly updated estimate compared to the Impact assessment of the Soil Monitoring Law takes into account the costs of soil erosion, contamination, phosphorus losses, soil carbon losses, nitrogen losses, soil compaction, and soil sealing. However, this estimation might double if it is added to the costs of soil biodiversity loss, floods, droughts, off-site effects of soil erosion, and health consequences of soil contamination. Therefore, further research is needed to address this knowledge gap and estimate the missing costs. Soil degradation is a critical issue with transboundary implications that requires urgent attention and action at the EU level. The costs of soil degradation are substantial, both in terms of environmental impacts and economic consequences, highlighting the importance of investing in sustainable soil management practices and a harmonized EU soil monitoring system. By addressing soil degradation through the proposed Soil Monitoring Law, investing significant amounts for research and innovation in the Soil Mission, and promoting international cooperation, the EU can take solid steps toward protecting its soil resources and achieving a sustainable future for all.

Selective binding and fluorescence sensing of Zn(II)/Cd(II) using macrocyclic tetra-amines with different fluorophores: insights into the design of selective chemosensors for transition metals

Selective binding and optical sensing of Zn(II) and Cd(II) by L1, HL2, L3, H2L4 and H2L5 receptors were analysed in aqueous solutions by coupling potentiometric, UV-vis absorption and fluorescence emission measurements, with the aim to determine the effect of complex stability on selective signalling of metals with similar electronic configurations. All receptors share the same cyclic tetra-amine binding unit attached to a single quinoline (Q) or 8-hydroxyquinoline (8-OHQ) unit (L1 and HL2, respectively), two Q or 8-OHQ moieties (L3 and H2L4, respectively), and, finally, two Q and two acetate groups (H2L5). The crystal structures of the Cd(II) and Zn(II) complexes show that L3 and H2L4 feature a cavity in which the larger Cd(II) complex is better fitted than the Zn(II) complex, leading to the formation of more stable Cd(II) complexes. In turn, Zn(II) forms more stable complexes with L1 and HL2, owing to its high tendency to give 5-coordinated complexes. Considering optical selectivity, Zn(II) gives the most emissive complex with L3, while the corresponding Cd(II) complex is basically quenched. The gathered structure of the Zn(II) complex, in which the two Q units are associated with one another-a structural motif not observed in the [CdL3]2+ complex-leads to poor solvation of the Q units, favouring complex emission. Among 8-OHQ-containing receptors, the most emissive complex is formed by Cd(II) with HL2, containing a single 8-OHQ moiety. H2L4 forms non-emissive complexes: the presence of two coordinating 8-OHQ moieties weakens metal interactions with the tetra-amine unit, favouring PET to the excited fluorophore that quench the emission.

Modification of peroxidase activity and proteome in maize exposed to cadmium in the presence of galactoglucomannan oligosaccharides

We tested the effects of galactoglucomannan oligosaccharides (GGMOs) and/or cadmium (Cd) on peroxidase activity and the proteome in maize (Zea mays L.) roots and leaves. Our previous work confirmed that GGMOs ameliorate the symptoms of Cd stress in seedlings. Here, the plants were hydroponically cultivated for 7 days, and the protein content and peroxidase activity were estimated in intracellular, neutral cell wall, and acidic cell wall protein fractions. The peroxidase activity varied between the plant organs as well as among the fractions and treatments. The GGMOs in the presence of Cd did not significantly influence content of peroxidases but modulated their activity, which implies posttranslational regulation. The changes in the content of various proteins (e.g., related to the defence reactions, cell wall structure/metabolism, and activation of plant hormones) caused by GGMOs and Cd indicate possible protective mechanisms that improve the vitality of maize seedlings exposed to metal stress. GGMOs partially reverted Cd-induced protein disbalance, which was a reoccurring phenomenon of mitigation in leaves.

Exploring environmental risk in soils: Leveraging open data for non-sampling assessment?

Soil contamination by heavy metals (HM) is a critical area of research. Traditional methods involving sample collection and lab analysis are effective but costly and time-consuming. This study explores whether geostatistical analysis with GIS and open data can provide a faster, more precise, and cost-effective alternative for HM contamination assessment without extensive sampling. Concentrations of nine HMs (Cu, Pb, Ni, Co, Mn, As, Cd, Sb, Cr) were analysed from 498 soil samples collected in two mining areas in Portugal: the Panasqueira and Aljustrel mines. Corresponding data were extracted from the Lucas TOPSOIL 1 km raster maps. Several contamination indices, Contamination Factor (Cf), Modified Contamination Degree (mCd), Geoaccumulation Index (Igeo), Nemerow Pollution Index (Pn), Potential Ecological Risk Index (PERI), and Pollution Load Index (PLI) were calculated for both datasets. A confusion matrix was used to evaluate the percentage of correct classifications, while a concordance analysis assessed the alignment of accurately classified points between the two data sources. In the soil samples, very high contamination levels for As were observed in 42% of the samples, according to the Cf, with high levels for Sb found in approximately 30% of the samples. The mCd revealed that approximately 11% of soil samples exhibited very high levels of contamination, while the Pn indicated that 78.9% of the soil samples fell within the seriously polluted domain. Similar contamination trends were observed for the other indices. In contrast, the results for the LUCAS points showed significant discrepancies. No high contamination levels were found for any metal. The misclassification rates for mCd, Pn, PERI, and PLI were 84.25%, 97.55%, 95%, and 82%, respectively, when compared to the field data. This study concludes that while open data raster maps offer rapid overviews, they fall short of providing the detailed precision required for reliable contamination assessments. The significant misclassification rates observed highlight the limitations of relying solely on these tools for critical environmental decisions. Consequently, traditional sampling and laboratory analysis remain indispensable for accurate risk assessments of HM contamination, ensuring a more reliable foundation for decision-making and environmental management.

Classification of soil contamination by heavy metals (Cr, Ni, Pb, Zn) in wildfire-affected areas using laser-induced breakdown spectroscopy and machine learning

The assessment of soil contamination by heavy metals is of high importance due to its impact on the environment and human health. Standard high-sensitivity spectroscopic techniques for this task such as atomic absorption spectrometry (AAS) and inductively coupled plasma spectrometry (ICP-OES and ICP-MS) are effective but time-consuming and costly, mainly due to sample preparation and lab consumables, respectively. In the present study, a laser-based spectroscopic approach is proposed, laser-induced breakdown spectroscopy (LIBS), which, combined with machine learning (ML), can provide a tool for rapid assessment of soil contamination by heavy metals. A dataset comprising 523 soil samples, from the areas of Mati, Kineta, Varympompi, and Evia (Greece) after the wildfires of 2018 and 2021, was employed to train and validate various ML models. The analysis focused on Cr, Ni, Zn, and Pb concentrations, utilizing environmental and human health screening values for soil classification. Two classification schemes were employed: the first identified samples "outside the danger zone" of contamination, while the second focused on samples "inside the safe zone". The models achieved over 93% performance for Cr, Ni, and Zn in the first scheme and 97% for Pb in the second. These findings demonstrate that LIBS, coupled with ML, can provide a reliable and efficient solution for preliminary assessment of soil contamination, particularly suited for large-scale operations of environmental monitoring and remediation efforts.

The Mechanisms of Cadmium Toxicity in Living Organisms

Cadmium (Cd) is a toxic metal primarily found as a by-product of zinc production. Cd was a proven carcinogen, and exposure to this metal has been linked to various adverse health effects, which were first reported in the mid-19th century and thoroughly investigated by the 20th century. The toxicokinetics and dynamics of Cd reveal its propensity for long biological retention and predominant storage in soft tissues. Until the 1950s, Cd pollution was caused by industrial activities, whereas nowadays, the main source is phosphate fertilizers, which strongly contaminate soil and water and affect human health and ecosystems. Cd enters the human body mainly through ingestion and inhalation, with food and tobacco smoke being the primary sources. It accumulates in various organs, particularly the kidney and liver, and is known to cause severe health problems, including renal dysfunction, bone diseases, cardiovascular problems, and many others. On a cellular level, Cd disrupts numerous biological processes, inducing oxidative stress generation and DNA damage. This comprehensive review explores Cd pollution, accumulation, distribution, and biological impacts on bacteria, fungi, edible mushrooms, plants, animals, and humans on a molecular level. Molecular aspects of carcinogenesis, apoptosis, autophagy, specific gene expression, stress protein synthesis, and ROS formation caused by Cd were discussed as well. This paper also summarizes how Cd is removed from contaminated environments and the human body.

Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest

The world's largest mangrove forest (Sundarbans) is facing an imminent threat from heavy metal pollution, posing grave ecological and human health risks. Developing an accurate predictive model for heavy metal content in this area has been challenging. In this study, we used machine learning techniques to model sediment pollution by heavy metals in this vital ecosystem. We collected 199 standardized sediment samples to predict the accumulation of eleven heavy metals using ten different machine learning algorithms. Among them, the extremely randomized tree model exhibited the best performance in predicting Fe (0.87), Cr (0.89), Zn (0.85), Ni (0.83), Cu (0.87), Co (0.62), As (0.68), and V (0.90), achieving notable R2 values. On the other hand, the random forest outperformed for predicting Cd (0.72) and Mn (0.91), whereas the decision tree model showed the best performance for Pb (0.73). The feature attribute analysis identified FeV, CrV, CuZn, CoMn, PbCd, and AsCd relationships resembled with correlation coefficients among them. Based on the established models, the prediction of the contamination factor of metals in sediments showed very high Cd contamination (CF ≥ 6). The Moran's I index for Cd, Cr, Pb, and As were 0.71, 0.81, 0.71, and 0.67, respectively, indicating strong positive spatial autocorrelation and suggesting clustering of similar contamination levels. Conclusively, this research provides a comprehensive framework for predicting heavy metal sediment pollution in the Sundarbans, identifying key areas needing urgent conservation. Our findings support the adoption of integrated management strategies and targeted remedial actions to mitigate the harmful effects of heavy metal contamination in this vital ecosystem.

Phytomanagement of a metal-contaminated agricultural soil with Sorghum bicolor, humic / fulvic acids and arbuscular mycorrhizal fungi near the former Pb/Zn metaleurop Nord smelter

As many contaminated agricultural soils can no longer be used for food crops, lignocellulosic energy crops matter due to their ability to grow on such soils and to produce biomass for biosourced materials and biofuels, thereby reducing the pressure on the limited arable lands. Sorghum bicolor (L.) Moench, can potentially produce a high biomass suitable for producing bioethanol, renewable gasoline, diesel, and sustainable aircraft fuel, despite adverse environmental conditions (e.g. drought, contaminated soils). A 2-year field trial was carried out for the first time in the northern France for assessing sorghum growth on a Cd, Pb and Zn-contaminated agricultural soil amended with humic/fulvic acid, alone and paired with arbuscular mycorrhizal fungi. Sorghum produced on average (in t DW ha-1): 12.4 in year 1 despite experiencing a severe drought season and 15.3 in year 2. Humic/fulvic acids (Lonite 80SP®) and arbuscular mycorrhizal fungi did not significantly act as biostimulants regarding the shoot DW yield and metal uptake of sorghum. The annual shoot Cd, Pb and Zn removals averaged 0.14, 0.20 and 1.97 kg ha-1, respectively. Sorghum cultivation and its metal uptake induced a significant decrease in 0.01 M Ca(NO3)2-extractable soil Cd, Pb and Zn concentrations by 95%, 73% and 95%, respectively, in year 2. Soluble and exchangeable soil Cd, Pb and Zn would be progressively depleted in subsequent crops, which should result in lower pollutant linkages and enhanced ecosystem services. This evidenced sorghum as a relevant plant species for phytomanaging the large area (750 ha) with metal-contaminated soil near the former Pb/Zn Metaleurop Nord smelter, amidst ongoing climate change. The potential bioethanol yield of the harvested sorghum biomass was 5589 L ha-1. Thus sorghum would be a promising candidate for bioethanol production, even in this northern French region.

Accumulation and Release of Cadmium Ions in the Lichen Evernia prunastri (L.) Ach. and Wood-Derived Biochar: Implication for the Use of Biochar for Environmental Biomonitoring

Biochar (BC) boasts diverse environmental applications. However, its potential for environmental biomonitoring has, surprisingly, remained largely unexplored. This study presents a preliminary analysis of BC's potential as a biomonitor for the environmental availability of ionic Cd, utilizing the lichen Evernia prunastri (L.) Ach. as a reference organism. For this purpose, the lichen E. prunastri and two types of wood-derived biochar, biochar 1 (BC1) and biochar 2 (BC2), obtained from two anonymous producers, were investigated for their ability to accumulate, or sequester and subsequently release, Cd when exposed to Cd-depleted conditions. Samples of lichen and biochar (fractions between 2 and 4 mm) were soaked for 1 h in a solution containing deionized water (control), 10 µM, and 100 µM Cd2+ (accumulation phase). Then, 50% of the treated samples were soaked for 24 h in deionized water (depuration phase). The lichen showed a very good ability to adsorb ionic Cd, higher than the two biochar samples (more than 46.5%), and a weak ability to release the metal (ca. 6%). As compared to the lichen, BC2 showed a lower capacity for Cd accumulation (-48%) and release (ca. 3%). BC1, on the other hand, showed a slightly higher Cd accumulation capacity than BC2 (+3.6%), but a release capacity similar to that of the lichen (ca. 5%). The surface area and the cation exchange capacity of the organism and the tested materials seem to play a key role in their ability to accumulate and sequester Cd, respectively. This study suggests the potential use of BC as a (bio)monitor for the presence of PTEs in atmospheric depositions and, perhaps, water bodies.