Transferability of heavy metal(loid)s from karstic soils with high geochemical background to peanut seeds

Precision management of Cd-contaminated paddy fields with high geochemical backgrounds in karst regions: integrating Bayesian decision tree and spatial zoning

Cadmium (Cd) contamination in paddy fields with high geochemical backgrounds in karst regions poses significant challenges. This study aims to explore practical technologies applicable to different risk causal zones throughout karstic Cd-polluted paddy fields, by integrating a Bayesian decision tree-based risk warning model with spatial zoning. In the study area, 92.1 % of soil and 30.1 % of rice samples exceeded Cd limits, yet the synergy between elevated Cd levels in soil and rice was weak. The model achieved an accuracy of 93.4 % in predicting Cd risk in rice and generated 13 risk classification rules for evaluating Cd risk levels in paddy fields. Notably, the identified risk zones were primarily concentrated in areas with relatively low total Cd concentrations in the soil. Regional management framework, including passivation techniques, phytoremediation, agronomic regulation, was proposed according to 7 causal rules across the fields. Each zone was associated with a customized method for rice safe production, subsequently developing a spatially precise, differentiated solution. This study offers new insights into managing Cd-contaminated paddy fields in karst regions.

Safe utilization of cadmium-rich soil for planting lilies and maize using a random forest model based on soil properties

The factors influencing the uptake of soil Cd by crops are complex and closely related to the different crop varieties. Efficient and safe utilization of land resources with high soil Cd levels has become a significant challenge in the scientific community. This study focuses on the anomalously high Cd distribution area in the northern part of Longshan County, Hunan, China. By systematically collecting and testing Cd content in the edible parts of lily and maize, as well as corresponding root soil Cd, pH, and oxides, the study reveals the differences in the bioconcentration factors of Cd (BCF-Cd) for lily and maize and their influencing factors. Using the random forest method and hyperparameter optimization, optimal prediction models for BCF-Cd in lily and maize were established. The results indicate that the BCF-Cd of lily is significantly higher than that of maize. The primary factors influencing BCF-Cd in lily and maize include soil pH, Mn, OM, and ba. Feature importance analysis identifies pH as the most critical factor affecting BCF-Cd in both lily and maize. Based on the prediction results of the random forest model, this study proposes a zoning scheme for the safe utilization of arable land to maximize benefits while ensuring the medicinal safety of lily and the food safety of maize. This provides scientific evidence for ensuring food security and maximizing the productive value of land resources.

Spatial-machine learning framework for rapid identification of soil cadmium risk in high geochemical background areas

Rapid and accurate identification of cadmium (Cd) risk remains challenging in agricultural lands with high geochemical background levels. While predicting soil Cd mobility using the risk assessment code (RAC) is essential for evaluating ecological risks at regional scales, traditional prediction methods struggle to achieve high spatial prediction accuracy because of complex influencing factors and spatial heterogeneity. This study investigated the spatial distribution patterns of soil Cd mobility in karst regions under the influence of anthropogenic activities and natural background conditions. Our analysis revealed that areas of very high risk were predominantly concentrated in black shale formations and mining zones, reflecting the spatial heterogeneity of soil available Cd. Geographically weighted regression analysis demonstrated both negative and positive local correlation coefficients between soil properties and RAC values, suggesting complex spatial interactions. Incorporating these spatial relationships as covariates in the random forest model resulted in an enhanced prediction accuracy (R2 = 0.96) compared to the non-spatial approach (R2 = 0.80). The machine learning model with integrated spatial information developed in this study provides an improved framework for identifying soil Cd risks and understanding Cd geochemical behaviors, supporting the development of targeted pollution prevention and control strategies in areas with high geochemical background.

Soil health assessment of dressing and smelting slag field based on heavy metal pollution-buffer-fertility three aspects

The soil health of heavy metals in dressing and smelting slag field varies soil physicochemical properties. This study proposed a new soil health index based on heavy metal pollution-buffer-fertility for dressing and smelting slag field. Consequently, spatial distribution of soil physicochemical properties and heavy metals were varied, and correlated to each other. Soil buffer function and fertility played a much more important role in soil health in the dressing and smelting slag field located in Gejiu city, which can result in that soil health indexes were higher than those in Huili county, although the soil heavy metal pollution in the former was severer than that in the latter. Maximum values of soil health indexes for dressing and smelting slag field in Gejiu city were 3.84, 0.61, and 1.75 corresponding to additive, multiplicative, and maximum value composite methods, which were higher than those in Huili county with 2.25, 0.61, and 0.17. The former's high value is concentrated in southeastern regions and low value in some western areas, the latter's high value occurred in southeastern districts and low value in northwestern places. So this study unveils a novel perspective on the soil health consequences associated with soil heavy metal pollution-buffer-fertility three aspects.

High geological background concentrations of As and Cd in karstic soils may not contribute to greater risks to human health via rice consumption

High geological background concentrations of toxic metal(loid)s arsenic (As) and cadmium (Cd) from natural enrichment in soils of karst regions have attracted much attention. In this study, paired soil-rice samples were collected from karst and non-karst regions in Guangxi, China to assess the potential risks of metal(loid) transfer from soil to rice grains, and rice grains to humans. Our results indicate that the karstic soils had greater As (25.7 vs. 12.4 mg·kg-1) and Cd (2.12 vs. 1.04 mg·kg-1) contents than those in non-karstic soils. However, metal(loid) transfer from soil to rice grains (ratio of rice grains to soil content) of As and Cd was 40 % and 49 % lower in karst regions, which may relate to their 42 % and 61 % lower HNO3-extractable As and CaCl2-extractable Cd, resulting in similar As/Cd contents in karstic and non-karstic rice grains. In vitro assay using a modified physiologically-based extraction test shows that karstic rice grains had a lower As/Cd bioaccessibility than non-karstic grains, which can be attributed to their ∼50 % greater P content, which negatively correlated with As/Cd bioaccessibility. Additionally, karstic rice grains had 39 % greater phytate and exhibited 45 % and 9.4 % lower As and Cd bioaccessibility in the gastric phase with phytate supplement at 0.6 %. Our work indicates that despite the greater As/Cd contents in karstic soils, the risks of As/Cd transfer from soil to rice grains as well as their exposure risks to humans via rice consumption may not be greater than non-karst regions.

Predicting the risk of arsenic accumulation in soil-rice system in Asian monsoon region

Rice is a staple food for a significant portion of the global population. Arsenic (As) accumulated in rice grains influences rice quality which threatens human health. In this study, we used three machine learning models to predict arsenic accumulation in rice based on over 300 surveys. The prediction results of soil arsenic indicate that high-arsenic soil areas are mainly distributed in South and Southeast Asia such as India, China, and Thailand. In addition, higher bioaccumulation factors (BAF), associated with higher temperature, are predominantly observed in eastern India and southern Myanmar. However, arsenic content in soil is relatively lower in these areas. About 5.5 billion population may be threatened by the consumption of high-arsenic rice. It can be concluded that temperatures may influence the BAF except for soil arsenic, and soil physicochemical properties. Further research on the relationship between climate parameters and BAF should be conducted to address and adapt to future climate change. Additionally, understanding the mechanism of arsenic accumulation under different climatic conditions is crucial for developing agricultural technologies to reduce arsenic accumulation in rice.

Source apportionment of Cd in karst soil based on the delayed geochemical hazard model

Soil Cd contamination has become increasingly prominent in karst regions. Studies have generally elucidated the natural sources of Cd in high-background areas and analyzed their migration and enrichment mechanisms. This study comprehensively analyzed the total content and speciation of Cd in high-background areas using the delayed geochemical hazard (DGH) model to identify the sources of Cd in the region. The results indicated that Cd in the research area followed a pattern of gradual geochemical disasters. In Quaternary soil, brick-red soil, and submergenic paddy soil with hydromorphic characteristics, 32%, 7.69%, and 30% of soil Cd samples exceeded the critical threshold of the releasable total amount, respectively. Based on the DGH model, it was concluded that Cd in this region was mainly influenced by human activities. Field investigations corroborated this conclusion and aligned with the findings. Compared with the traditional source apportionment receptor models (mainly PCA and PMF), the DGH model not only saved considerable time and cost, but also avoided uncertainty associated with the results and complex and varied data processing and computational analysis processes. Moreover, the DGH model was able to identify the factors having the greatest impact on the ecological risk of Cd in the research area, thus facilitating targeted prevention and management planning based on the characteristics or chemical properties of their elements.

Machine learning-based prediction of cadmium pollution in topsoil and identification of critical driving factors in a mining area

Mining activities have resulted in a substantial accumulation of cadmium (Cd) in agricultural soils, particularly in southern China. Long-term Cd exposure can cause plant growth inhibition and various diseases. Rapid identification of the extent of soil Cd pollution and its driving factors are essential for soil management and risk assessment. However, traditional geostatistical methods are difficult to simulate the complex nonlinear relationships between soil Cd and potential features. In this study, sequential extraction and hotspot analyses indicated that Cd accumulation increased significantly near mining sites and exhibited high mobility. The concentration of Cd was estimated using three machine learning models based on 3169 topsoil samples, seven quantitative variables (soil pH, Fe, Ca, Mn, TOC, Al/Si and ba value) and three quantitative variables (soil parent rock, terrain and soil type). The random forest model achieved marginally better performance than the other models, with an R2 of 0.78. Importance analysis revealed that soil pH and Ca and Mn contents were the most significant factors affecting Cd accumulation and migration. Conversely, due to the essence of controlling Cd migration being soil property, soil type, terrain, and soil parent materials had little impact on the spatial distribution of soil Cd under the influence of mining activities. Our results provide a better understanding of the geochemical behavior of soil Cd in mining areas, which could be helpful for environmental management departments in controlling the diffusion of Cd pollution and capturing key targets for soil remediation.

Ecological and health risk assessments of heavy metals and their accumulation in a peanut-soil system

Heavy metals pollution is a notable threat to environment and human health. This study evaluated the potential ecological and health risks of heavy metals (Cu, Cr, Cd, Pb, Zn, Ni, and As) and their accumulation in a peanut-soil system based on 34 soil and peanut kernel paired samples across China. Soil As and Cd posed the greatest pollution risk with 47.1% and 17.6% of soil samples exceeding the risk screen levels, respectively, with 26.5% and 20.6% of the soil sites at relatively strong potential ecological risk level, respectively, and with the geo-accumulation levels at several soil sites in the uncontaminated to moderately contaminated categories. About 35.29% and 2.94% of soil sites were moderately and severely polluted based on Nemerow comprehensive pollution index, respectively, and a total of 32.4% of samples were at moderate ecological hazard level based on comprehensive potential ecological risk index values. The Cd, Cr, Ni, and Cu contents exceeded the standard in 11.76, 8.82, 11.76 and 5.88% of the peanut kernel samples, respectively. Soil metals posed more health risks to children than adults in the order As > Ni > Cr > Cu > Pb > Zn > Cd for non-carcinogenic health risks and Ni > Cr ≫ Cd > As > Pb for carcinogenic health risks. The soil As non-cancer risk index for children was greater than the permitted limits at 14 sites, and soil Ni and Cr posed the greatest carcinogenic risk to adults and children at many soil sites. The metals in peanut did not pose a non-carcinogenic risk according to standard. Peanut kernels had strong enrichment ability for Cd with an average bio-concentration factor (BCF) of 1.62. Soil metals contents and significant soil properties accounted for 35-74% of the variation in the BCF values of metals based on empirical prediction models.

Identification of soil parent materials in naturally high background areas based on machine learning

Recently, farmlands with high geological background of Cd derived from carbonate rock (CA) and black shale areas (BA) have received wide attention. However, although both CA and BA belong to high geological background areas, the mobility of soil Cd differs significantly between them. In addition to the difficulty in reaching the parent material in deep soil, it is challenging to perform land use planning in high geological background areas. This study attempts to determine the key soil geochemical parameters related to the spatial patterns of lithology and the main factors influencing the geochemical behavior of soil Cd, and ultimately uses them and machine-learning methods to identify CA and BA. In total, 10,814 and 4323 surface soil samples were collected from CA and BA, respectively. Hot spot analysis revealed that soil properties and soil Cd were significantly correlated with the underlying bedrock, except for TOC and S. Further research confirmed that the concentration and mobility of Cd in high geological background areas were mainly affected by pH and Mn. The soil parent materials were then predicted using artificial neural network (ANN), random forest (RF) and support vector machine (SVM) models. The ANN and RF models showed higher Kappa coefficients and overall accuracies than those of the SVM model, suggesting that ANNs and RF have the potential to predict soil parent materials from soil data, which might help in ensuring safe land use and coordinating activities in high geological background areas.

Spatial prediction and influencing factors identification of potential toxic element contamination in soil of different karst landform regions using integration model

The prediction of contamination distribution of potentially toxic elements (PTEs) in soils of Guangxi province, China and the identification of their controlling factors pose great challenges due to diverse bedrock types, intense leaching and weathering, and discontinuous terrain distributions. Herein, we integrated the random forest (RF) and empirical Bayesian kriging (EBK) to interpret and predict complex PTEs contamination distribution from three different karst landform regions (fenglin, fengcong, isolated peak plain) in Guangxi province. The modeling results are compared with the commonly used ordinary kriging and regression-kriging. In this study, our developed RF-EBK model combines the advantages of the RF and EBK model to promote the prediction accurately and efficiently. In this study, it was shown that the integration RF-EBK model exhibited desirable for Cd and As concentrations, with R² of 0.89 and 0.83, respectively. The average RMSE and MAE of integration RF-EBK model decreased by 39% and 44%, respectively, relative to the regression-kriging with the second highest accuracy. Furthermore, the modeling results showed that approximately 41.96% and 18.96% of total area was classified as Cd and As polluted and above regions (I_geo >0) in Guangxi province, respectively. Higher Cd concentration was observed in the soil of fenglin and fengcong regions than that in isolated peak plain region due to the secondary enrichment and parent rock inheritance, while the As concentration exhibited no significant difference among the three regions. The modeling results indicated that the elevated Cd concentration might be associated with soil CaO concentration and alkaline soil environment, whereas As concentration tended to be increased with the elevating Fe₂O₃ concentrations in weakly acidic soil environment. This result confirmed the applicability and effectiveness of integration model in predicting complex spatial patterns of soil PTEs and identifying their controlling factors.

Remediation effect and mechanism of low-As-accumulating maize and peanut intercropping for safe-utilization of As-contaminated soil

Phytoremediation by intercropping is a potential method to realize both production and remediation. Maize and peanut are the main crops planted in arsenic(As) contaminated areas in south China and vulnerable to As pollution. Experiments were conducted on arsenic-polluted soil with low As-accumulating maize monoculture (M), peanut monoculture (P), and intercropping with different distances between the maize and peanut (0.2 m, 0.35 m, and 0.5 m, recorded as MP0.2, MP0.35, and MP0.5, respectively). The results indicated that the As content in the maize grains and peanut lipids in the intercropping system decreased significantly, meeting the food safety standard of China (GB 2762-2017). Moreover, the land equivalent ratio (LER) and heavy metal removal equivalence ratio (MRER) of all intercropping treatments were greater than 1, indicating that this intercropping agrosystem has the advantage of production and arsenic removal, among which the yield and LER of MP0.35 treatment were the highest. Additionally, the bioconcentration factors (BCF) and translocation factor (TF) of MP0.2 increased by 117.95% and 16.89%, respectively, indicating that the root interaction affected the absorption of As in soil by crops. This study preliminarily demonstrated the feasibility of this intercropping system to safely use and remedy arsenic-contaminated farmland during production.

Estimation of heavy metal soil contamination distribution, hazard probability, and population at risk by machine learning prediction modeling in Guangxi, China

Due to superposition of diverse pollution sources, soil heavy metal concentrations have been detected to exceed the recommended maximum permissible levels in many areas of Guangxi province, China. However, the heavy metal contamination distribution, hazard probability, and population at risk of heavy metals in the entire Guangxi province remain largely unclear. In this study, machine learning prediction models with different standard risk values determined according to land use types were used to identify high-risk areas and estimate populations at risk of Cr and Ni based on 658 topsoil samples from Guangxi province, China. Our results showed that soil Cr and Ni contamination derived from carbonate rocks was relatively serious in Guangxi province, and that their co-enrichment during soil formation was associated with Fe and Mn oxides and alkaline soil environment. Our established model exhibited excellent performance in predicting contamination distribution (R² > 0.85) and hazard probability (AUC>0.85). Pollution of Cr and Ni exhibited a pattern of decreasing gradually from the central-west areas to the surrounding areas with the polluted area (I_geo>0) of Cr and Ni accounting for approximately 24.46% and 29.24% of total area in Guangxi province, respectively, but only 10.4% and 8.51% of total area was classified as Cr and Ni high-risk regions. We estimated approximately 1.44 and 1.47 million people were potentially exposed to the risk of Cr and Ni contamination, which were mainly concentrated in the Nanning, Laibin, and Guigang. These regions are main heavily-populated agricultural regions in Guangxi, and thus heavy metal contamination localization and risk control in these regions are urgent and essential from the perspective of food safety.

Nickel bioaccessibility in soils with high geochemical background and anthropogenic contamination

Abnormally high concentrations of metals including nickel (Ni) in soils result from high geochemical background (HB) or anthropogenic contamination (AC). Metal bioaccessibility in AC-soils has been extensively explored, but studies in HB-soils are limited. This study examined the Ni bioaccessibility in basalt and black shale derived HB-soils, with AC-soils and soils without contamination (CT) being used for comparison. Although HB- and AC-soils had similar Ni levels (123 ± 43.0 vs 155 ± 84.7 mg kg^-1), their Ni bioaccessibility based on the gastric phase of the Solubility Bioaccessibility Research Consortium (SBRC) in vitro assay was different. Nickel bioaccessibility in HB-soils was 6.42 ± 3.78%, 2-times lower than the CT-soils (12.0 ± 9.71%) and 6-times lower than that in AC-soils (42.6 ± 16.3%). Based on the sequential extraction, a much higher residual Ni fractionation in HB-soils than that in CT- and AC-soils was observed (81.9 ± 9.52% vs 68.6 ± 9.46% and 38.7 ± 16.0%). Further, correlation analysis indicate that the available Ni (exchangeable + carbonate-bound + Fe/Mn hydroxide-bound) was highly correlated with Ni bioaccessibility, which was also related to the organic carbon content in soils. The difference in co-localization between Ni and other elements (Fe, Mn and Ca) from high-resolution NanoSIMS analysis provided additional explanation for Ni bioaccessibility. In short, based on the large difference in Ni bioaccessibility in geochemical background and anthropogenic contaminated soils, it is important to base contamination sources for proper risk assessment of Ni-contaminated soils.

Natural Factors on Heterogenetic Accumulations of PTEs in Sloping Farmland in a Typical Small Mountainous Watershed in Southwest China

High potential toxic element (PTE) concentrations in soils that exceed local regulatory threshold values have been reported in non-polluted mountainous areas worldwide. However, there have been few studies that have comprehensively investigated the contribution of natural factors including the parental material, pedogenesis processes and physiochemical properties of soils on the distribution of PTEs in these soils. Therefore, in this study, we studied the distribution of 13 PTEs in sloping farmland soils collected from a mountainous watershed in Guizhou Province, Southwest China. The contributions of natural influencing factors were analyzed using a geostatistical analysis and a geographic detector method. All of the PTEs were unevenly distributed, especially Sb, and the average contents of V, Cr, Co, Ni, Cu, Zn, As, Mo, Cd, Sb, Tl, Pb and Hg were 57.15, 36.20, 4.61, 12.61, 13.36, 63.50, 11.94, 0.78, 0.37, 6.44, 0.48, 27.42 and 0.36mg/kg, respectively. The proportion of samples with Cd, Hg and As exceeding the screening value of the soil pollution risk of agricultural land in China was 46.7%, 5.9% and 4.4%, respectively. Except for Cd and Pb, the q values of the PTEs calculated from the geographical detector were above 0.05, indicating that altitude changes, which affect the pedogenesis process, have a great impact on the spatial distribution. Stratigraphic factors contributed greatly to the distribution of Co, Ni and Cu, which indicates their similarity in parental material. The combined effect of clay content, topographic factors and agricultural land types had the strongest explanatory power for V, Cr, Mo and Pb. The distributions of As, Sb, Tl and Hg are strongly associated with a potential source of mercury ore, and their accumulation is also enhanced by the adsorption on soil clay. Agricultural As also contributes to its distribution.