Application of cadmium prediction models for rice and maize in the safe utilization of farmland associated with tin mining in Hezhou, Guangxi, China

Characteristics and risk assessment of heavy metals in groundwater at a typical smelter-contaminated site in Southwest China

To explore the characteristics and evaluate the risk of heavy metals in groundwater at a typical smelter-contaminated site, this study focuses on a representative a historical arsenic smelting plant in Southwest China, where the primary historical products were metallic arsenic (∼1000 tons/year) and arsenic trioxide (∼2000 ton/year). The results demonstrated As and Pb as the main pollutants in soil, and As and Cd as main pollutants in groundwater through soil profiling and quarterly groundwater analysis. The maximum As and Pb in the surface soil were 76800 and 2290 mg/kg, respectively, with As vertically infiltrating the deep gravel-sand layer (18-20 m). The groundwater pollution distribution progressively increased along flow direction, influenced by seasonal surface runoff and infiltration fluctuations. The groundwater pollutant concentrations during the dry season notably surpassed those during the wet season, with maximum As and Cd concentrations of 111.64 mg/L and 19.85 μg/L during the dry season, respectively. Furthermore, the analytic hierarchy process (AHP) was applied to evaluate the comprehensive risk of contaminated-site across pollution source load, regional groundwater intrinsic vulnerability, and evaluation of nearby sensitive receptors. The results revealed that the carcinogenic risk of lead in surface soil was moderate to high, while arsenic posed a high carcinogenic risk, contributing to an overall carcinogenic risk proportion of 89.6% in surface soil. Exposure through groundwater intake was identified as the primary pathway, with carcinogenic and noncarcinogenic risks exceeding those through skin contact. The final weights result demonstrated that the principal risk factors are the intrinsic arsenic load and protective target characteristics of regional groundwater at this site. This study provides a reference for comprehensive assessments of similarly contaminated industrial and smelting sites.

Optimizing Soil Sampling for Accurately Prediction of the Potential Remediation-Effective Area in a Contaminated Agricultural Land

To achieve food security in a contaminated agricultural land, the remediation areas usually need more samples to obtain accurate contamination information and implement appropriate measures. In this study, we propose an optimal encryption sampling design to instead of the detailed survey, which is determined by the variation of heavy metals and the technology capability of remediation, to guide soil sampling for accurately remediation in the potential remediation-effective areas (PRA). The coefficient of screening variation threshold (CSVT), considering spatial variation, technology capacity and acceptable error of sampling, together with the spatial cyclic statistics method of neighbourhood analysis, is introduced to identify and delineate the PRA. Both of the hypothetical analysis and application case studies are conducted to illustrate the advantages and disadvantages of the optimization. The results show that, compared with the detailed survey, the optimal design shows a lower overall accuracy due to its sparsely sampling at the clean area, but it exhibits a similar effect of accurately prediction in boundary delineation and further classification in the PRA in both simulation and application studies. This work provides an effective method for subsequent accurate remediation at the investigation stage and valuable insights into application combination of technology capacity and contaminated agricultural land investigation.

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.

Uptake of zinc from the soil to the wheat grain: Nonlinear process prediction based on artificial neural network and geochemical data

Trace elements in plants primarily derive from soils, subsequently influencing human health through the food chain. Therefore, it is essential to understand the relationship of trace elements between plants and soils. Since trace elements from soils absorbed by plants is a nonlinear process, traditional multiple linear regression (MLR) models failed to provide accurate predictions. Zinc (Zn) was chosen as the objective element in this case. Using soil geochemical data, artificial neural networks (ANN) were utilized to develop predictive models that accurately estimated Zn content within wheat grains. A total of 4036 topsoil samples and 73 paired rhizosphere soil-wheat samples were collected for the simulation study. Through Pearson correlation analysis, the total content of elements (TCEs) of Fe, Mn, Zn, and P, as well as the available content of elements (ACEs) of B, Mo, N, and Fe, were significantly correlated with the Zn bioaccumulation factor (BAF). Upon comparison, ANN models outperformed MLR models in terms of prediction accuracy. Notably, the predictive performance using ACEs as input factors was better than that using TCEs. To improve the accuracy, a two-step model was established through multiple testing. Firstly, ACEs in the soil were predicted using TCEs and properties of the rhizosphere soil as input factors. Secondly, the Zn BAF in grains was predicted using ACE as input factors. Consequently, the content of Zn in wheat grains corresponding to 4036 topsoil samples was predicted. Results showed that 85.69 % of the land was suitable for cultivating Zn-rich wheat. This finding offers a more accurate method to predict the uptake of trace elements from soils to grains, which helps to warn about abnormal levels in grains and prevent potential health risks.

Using machine learning to predict selenium and cadmium contents in rice grains from black shale-distributed farmland area

Cadmium (Cd) and selenium (Se) are widely enriched in soil at black shale outcropping areas, with Cd levels exceeding the standard (2.0 mg/kg in 5.5 < pH ≤ 6.5) commonly. The prevention of Cd hazards and the safe development of Se-rich land resources are key issues that need to be urgently addressed. To ensure safe utilization of Se-rich land in the CdSe coexisting areas, 158 rice samples, their corresponding rhizosphere soils, and 8069 topsoil samples were collected and tested in the paddy fields of Ankang City, Shaanxi Province, where black shales are widely exposed. The results showed that 43 % of the topsoil samples were Se-rich soil (Se > 0.4 mg/kg) wherein 79 % and 3 % of Cd concentrations exceeded the screening value and control value, respectively, according to the GB15618-2018 standard. Meanwhile, 63 % of the rice samples were Se rich (Se > 0.04 mg/kg) and the Cd content exceeded the prescribed limit (0.2 mg/kg) in Se-rich rice by 26 %. There was no significant positive correlation between the Se and Cd contents in the rice grains and the Se and Cd contents in the corresponding rhizosphere soil. The factors influencing Se and Cd uptake in rice were SiO2, CaO, P, S, pH, and TFe2O3. Accordingly, an artificial neural network (ANN) and multiple linear regression model (MLR) were used to predict Cd and Se bioaccumulation in rice grains. The stability and accuracy of the ANN model were better than those of the MLR model. Based on survey data and the prediction results of the ANN model, a safe planting zoning of Se-rich rice was proposed, which provided a reference for the scientific planning of land resources.

Prediction of cadmium contents in rice grains from Quaternary sediment-distributed farmland using field investigations and machine learning

The Quaternary sediment-distributed regions of South China are suitable for rice cultivation, which is crucial for ensuring food security. Spatial correlations between soil cadmium (Cd) and rice Cd contents are generally poor, making the evaluation of rice quality and associated health risks challenging. In this study, we developed machine learning algorithms for predicting rice Cd contents using 654 data pairs of soil-rice samples collected in Guangxi province, China. After a comprehensive comparison, our results showed that the random forest (RF) had the better performance than artificial neural network (ANN) based on all the data (RMSEtesting 0.066 vs. 0.099 and R2testing 0.860 vs. 0.688). The feature importance analysis showed that soil CaO, Cd, elevation, and rainfall were the four most important features affecting the rice Cd contents in the study area. Using the established RF-predicated model, the rice Cd contents were predicted at the provincial level with an additional dataset of 1176 paddy soil samples. The prediction result revealed about 23 % of farmland cultivated rice with Cd content over 0.2 mg kg-1 in the study area. Therefore, it is recommended to implement strict measures by local agricultural departments to reduce rice Cd contents and ensure food safety in these areas. Our study provides valuable insights into the prediction of rice Cd contents, thus contributing to ensuring food safety and preventing Cd exposure-associated health risks.

Carbonate rocks as natural buffers: Exploring their environmental impact on heavy metals in sulfide deposits

Carbonate rocks are closely related to the genesis and spatial distribution of polymetallic sulfide deposits. The natural buffering of carbonate rocks can reduce the ecological impact of heavy metals produced by mining and smelting. Ignoring the buffering effect of carbonate rocks on the heavy metals in the mine environment leads to inaccurate ecological risk assessment, wasting land resources and funds. This study investigates Cd, Zn, and Pb distribution and speciation in the water and soil-rice system in the polymetallic sulfide deposit at Daxin, Guangxi. The study aims to reveal the effects of the natural buffering of carbonate rocks on the migration and transformation of heavy metals. The results show that the water Zn and Cd concentrations decreased from 1857.0 to 0.9 mg L-1 to 0.16 and 0.001 mg L-1, respectively, from the mining area to 4 km downstream. The natural buffering of carbonate increases the water pH from 2.80 to 7.64, resulting in a tendency for Cd, Zn, and Pb to separate from the aqueous phase and enrich the sediments. Soil Cd content in the mining area reached 110.0 mg kg-1 (mean value 55.88 mg kg-1), and rice Cd seriously exceeded the maximum limit. However, the weathering of carbonate reduces the migration ability and bioavailability of Cd. Soil Cd is mainly in the Fe-Mn bound and carbonate-bound fractions, resulting in lower Cd content in downstream soils (mean value 2.73 mg kg-1). Soil CaO, tFe2O3, and Mn hindered the uptake of soil Cd by rice rendering a lower exceedance of Cd in downstream rice. Therefore, this study recommends a farmland management plan under the premise of rice Cd content and integrated soil Cd content, which ensures food safety and fully utilizes farmland resources. This result provides a scientific basis for ecological risk assessment, mine environmental protection, and management in the carbonatite sulfide mine environment.

Cadmium accumulation in paddy soils affected by geological weathering and mining: Spatial distribution patterns, bioaccumulation prediction, and safe land usage

The abnormal enrichment of cadmium (Cd) in soil caused by rock weathering and mining activities is an issue in southern China. Although the soil Cd content in these regions is extremely high, the bioavailability of Cd in the soils differs significantly. The carbonate area (CBA) and tin-mining area (TIA) in Hezhou City were investigated to determine the primary features of soil Cd mobility in these regions and improve environmental management. Lateral and vertical spatial distributions revealed different accumulation and migration mechanisms of soil Cd in the CBA and TIA. Further analyses revealed that mining activities and geological weathering resulted in different soil geochemical parameters, thus yielding significantly lower levels of Cd in rice grains in the CBA than in the TIA. The random forest (RF) model predicted the bioaccumulation factor (BAF) (R2 = 0.69) better than the support vector machine (SVM) model (R2 = 0.68). Subsequently, a novel land management scheme was proposed based on soil Cd and the prediction of Cd in rice to optimize the spatial resources of agricultural land and ensure the safety of rice for consumption. This study provides a novel approach for land management in Cd-contaminated areas.

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.

Rapid identification of high and low cadmium (Cd) accumulating rice cultivars using machine learning models with molecular markers and soil Cd levels as input data

Excessive accumulation of cadmium (Cd) in rice grains threatens food safety and human health. Growing low Cd accumulating rice cultivars is an effective approach to produce low-Cd rice. However, field screening of low-Cd rice cultivars is laborious, time-consuming, and subjected to the influence of environment × genotype interactions. In the present study, we investigated whether machine learning-based methods incorporating genotype and soil Cd concentration can identify high and low-Cd accumulating rice cultivars. One hundred and sixty-seven locally adapted high-yielding rice cultivars were grown in three fields with different soil Cd levels and genotyped using four molecular markers related to grain Cd accumulation. We identified sixteen cultivars as stable low-Cd accumulators with grain Cd concentrations below the 0.2 mg kg^-1 food safety limit in all three paddy fields. In addition, we developed eight machine learning-based models to predict low- and high-Cd accumulating rice cultivars with genotypes and soil Cd levels as input data. The optimized model classifies low- or high-Cd cultivars (i.e., the grain Cd concentration below or above 0.2 mg kg^-1) with an overall accuracy of 76%. These results indicate that machine learning-based classification models constructed with molecular markers and soil Cd levels can quickly and accurately identify the high- and low-Cd accumulating rice cultivars.

Prediction of cadmium bioavailability in the rice-soil system on a county scale based on the multi-surface speciation model

Relative to total cadmium (Cd) content, bioavailable Cd in paddy soil is regarded as a more reasonable indicator for the risk of Cd bioaccumulation in rice. However, there is still a lack of approach to accurately predict the content of bioavailable Cd in paddy soil due to its heterogeneity and complexity. Here, multi-surface speciation model (MSM) was employed to predict the bioavailable Cd and Cd immobilization effect. Moreover, a precise remediation strategy was designed based on screening and scenario simulation of the sensitive factors with MSM. The results demonstrated that MSM can well predict Cd bioaccumulation risk in rice. The contribution of pH to Cd bioavailability was quantified under three analysis scenarios, accounting for 87.51% of the total variance of bioavailable Cd. In addition, the pH alert value (6.31 ± 0.52) for Cd risk was acquired for each rice field on a county scale. A precise map for the application amount of lime materials was constructed by taking CaCO₃ (3.38-15.75 t ha^-1) as a recommended economical and green immobilization agent. This study provides a potentially effective approach for risk assessment of Cd contamination in rice and important reference for precise Cd remediation in paddy soil.

Identification of high ecological risk areas with naturally high background value of soil Cd related to carbonate rocks

The characteristics of high concentrations or high activity levels of heavy metals, especially Cd, in soils caused by the pedogenesis of rocks are attracting increased attention. Carbonate rocks and black shales often coexist during geological deposition, but the risk characteristics of heavy metals are different after their weathering into the soil. The purpose of this study was to investigate the element concentrations of a naturally high background value area, to identify patterns of different risk areas, and to make recommendations for the safe usage of farmland. The results showed that, compared with the soil in the carbonate rock area, the soil in the black shale area was more acidified and most of the heavy metal elements were leached. Based on the soil pH value and the heavy metal concentrations, an identification method for land risk areas within naturally high background values was established, and land planning was carried out using this method. The exceeding rates of Cd in rice for the preferential protection area and strict control area were 0.0 and 50.0%, respectively. Therefore, in naturally high background area, the identified lithology can apply to maximize the use of farmland resources. This method provides a basis for preliminary ecological risk screening in naturally high background value areas using the results of the soil survey. A suggestion for the prevention and control of soil pollution in areas with naturally high background values was put forward. In carbonate rock areas, the soil should be closely monitored to prevent soil acidification.

Source-specific risk assessment for cadmium in wheat and maize: Towards an enrichment model for China

Cadmium (Cd) pollution of agricultural soil is of public concern due to its high potential toxicity and mobility. This study aimed to reveal the risk of Cd accumulation in soil and wheat/maize systems, with a specific focus on the source-specific ecological risk, human health risk and Cd enrichment model. For this we investigated more than 6100 paired soil and grain samples with 216 datasets including soil Cd contents, soil pH and grain Cd contents of 85 sites from China. The results showed that mining activities, sewage irrigation, industrial activities and agricultural practices were the critical factors causing Cd accumulation in wheat and maize cultivated sites. Thereinto, mining activities contributed to a higher Cd accumulation risk in the southwest China and Middle Yellow River regions; sewage irrigation influenced the Cd accumulation in the North China Plain. In addition, the investigated sites were classified into different categories by comparing their soil and grain Cd contents with the Chinese soil screening values and food safety values, respectively. Cd enrichment models were developed to predict the Cd levels in wheat and maize grains. The results showed that the models exhibited a good performance for predicting the grain Cd contents among safe and warning sites of wheat (R² = 0.61 and 0.72, respectively); while the well-fitted model for maize was prone to the overestimated sites (R² = 0.77). This study will provide national viewpoints for the risk assessments and prediction of Cd accumulation in soil and wheat/maize systems.

Prediction model for Cd accumulation of rice (Oryza sativa L.) based on extractable Cd in soils and prediction for high Cd-risk regions of southern Zhejiang Province, China

Soil environmental quality in China for agricultural land always considers the effect of total cadmium (Cd) in soil, ignoring the bioavailability of soil Cd. The 139 paired rice (Oryza sativa L.) and soil samples were collected from the Cd-contaminated paddy fields of southern Zhejiang Province, China. The objectives of this study were to establish accurate prediction models for Cd accumulation in brown rice based on bioavailable Cd and physiochemical properties of soils and to evaluate the safety of rice production in Cd-contaminated paddy. The bioavailable Cd in soils was extracted and evaluated by using CaCl₂, HNO₃, diethylenetriamine pentaacetic acid (DTPA), diffusive gradients in thin-films technique (DGT), and sequential extraction method proposed by the European Community Bureau of Reference; 100 pairs of data were used as training sets, and the remaining 39 sets were used as validation sets. Stepwise multiple linear regression analysis and random forest analysis showed that total Cd in soil could roughly indicate the content of Cd in rice, while extractable Cd could better explain the accumulation of Cd in rice grain and DTPA and DGT extractive technologies are the most evaluative. The validation sets also showed that the prediction model has a good fit. Based on the prediction model for Cd in brown rice based on soil pH and DGT extractive Cd, the Monte Carlo simulation showed that 74.32% and 89.35% of the estimated brown rice hazard quotient (HQ) of the daily Cd intake of adults and children in safe utilization paddy sites could exceed the safe level of 1, respectively. Additionally, the threshold values for extractable Cd by DGT or DTPA for rice safe production were 3.4 μg/kg or 0.13 mg/kg when the pH in soils was below 5.5. The results further proved the threshold concentration of extractable Cd for predicting high-risk soils of Cd contamination in brown rice.

Effects of anthropogenic and natural environmental factors on the spatial distribution of trace elements in agricultural soils

The concentrations of trace elements in agricultural soils directly affect the ecological security and quality of agricultural products. A comprehensive study aimed at quantitatively analyze the effects of anthropogenic and natural environmental factors on the spatial distribution of heavy metals (HMs) and selenium (Se) in agricultural soils in a typical grain producing area of China. Factors considered in this study were parent rock, soil physicochemical properties, topography, precipitation, mine activity, and vegetation. Results showed that the median values of Zn, Cd, Cr, and Cu of 111 topsoil samples exceeded the background values of Guangxi province but were lower than the relevant national soil quality standards, and 85% of soil samples were classified as having rich Se levels (0.40 -3.0 mg kg^-1). The potential ecological risk index of soil heavy metals as a whole was low, with Cd in 9% of the samples posing moderate ecological risk. The concentrations of heavy metals and Se were relatively high in soils from shale rock. Soil properties, mainly Fe₂O₃ and Mn played a dominant role on soil HMs and Se concentrations. Based on GeoDetector, we found that the interaction effects of two factors on the spatial differentiation of soil HMs and Se were greater than their sum effect. Among the factors, Mn enhanced the explanatory power of the model the most when interacting with other factors for soil Zn; the greatest interactive effect was between distance from mining area and Mn for Cd (q = 0.70); Fe₂O₃ significantly promoted the spatial differentiation of soil Cr, Cu and Se when interacting with other factors (q > 0.50). These findings contribute to a better understanding of the factors that drive the distribution of HMs and Se in agricultural soils.

Selection of rice and maize varieties with low cadmium accumulation and derivation of soil environmental thresholds in karst

Cadmium (Cd) is considered the primary dietary toxic element. Previous studies have demonstrated significant differences in heavy metal accumulation among crop species. However, this information in karst areas with low heavy metal activity is missing. In this study, the uptake and accumulation characteristics of cadmium in soil-crop samples of group 504 in the core karst region of East Asia were analyzed. Cadmium low-accumulating maize and rice were screened using cluster and Pareto analytic methods. In addition, a new method, the species-sensitive distribution model (SSD), was proposed, which could be used to estimate the environmental threshold for cadmium in regional cropland. The results showed that both maize and rice soils in the research area were contaminated with varying degrees of cadmium. The total concentrations of cadmium ω(T-Cd) in maize and rice fields are 0.18-1.32 and 0.20-4.42 mg kg^-1, respectively. The ω(T-Cd) of heavy metals in maize kernels and rice grains is 0.002-0.429 and 0.003-0.393 mg kg^-1, respectively. The bioaccumulation factor (BCF) of cadmium in maize ranged from 0.0079 to 0.9701, with a coefficient of variation of 1.71; the BCF of cadmium in rice ranged from 0.0074 to 0.1345, with a coefficient of variation of 0.99. According to cluster and Pareto analyses, the maize crop varieties with low cadmium accumulation suitable for local cultivation were screened as JHY809, JDY808, AD778, SN3H and SY13, and the rice varieties were DMY6188, GY725, NY6368, SY451 and DX4103. In addition, the environmental cadmium threshold ranges of 0.30-10.05 mg kg^-1 and 0.89-24.39 mg kg^-1 for maize and rice soils, respectively, were deduced in this study. This threshold will ensure that 5-95% of maize and rice will not be contaminated with cadmium in the soil.

Probability of cultivating Se-rich maize in Se-poor farmland based on intensive field sampling and artificial neural network modelling

Selenium (Se) is a necessary micronutrient for humans, and its supplementation from crop grains is important to address the ubiquitous Se deficiency in people worldwide. Se uptake by crops largely depend on soil bioavailable Se rather than soil total Se content, which provides possibilities to explore the Se-rich crops in Se-poor area. Here, the possibility of cultivating Se-rich maize grains in Se-poor farmland was tested based on intensive field sampling and mathematical modelling. Sampling was conducted at county scale, and a total of 7779 topsoil samples and 109 maize samples with paired rhizosphere soils samples were collected. Results showed that although the soil Se content in the study county from southwestern China was at a low level (0.01-2.75 mg kg^-1), 54.1% of the maize grain samples satisfied the standard for Se-rich products (0.02-0.30 mg kg^-1). Soil organic matter, iron oxide, and phosphorus levels were correlated negatively with Se bioconcentration factor (BCF) of maize grain. Compared with the multivariate linear regression model, the artificial neural network (ANN) model was more accurate and reliable in predicting maize Se BCF. Prediction using the ANN model showed that 22.7% of the county's farmland was suitable for cultivating naturally Se-rich maize, which increased 21.3% growing areas than that from cultivation based on simply soil total Se. This study provided a new methodological framework for natural Se-rich maize production and verified the probability of cultivating naturally Se-rich maize in Se-poor farmland.

Precise and differentiated solutions for safe usage of Cd-polluted paddy fields at regional scale in southern China: Technical methods and field validation

Cadmium (Cd) contamination in rice grains has become a severe issue worldwide. This study aims to explore feasible technologies applicable to different risk lands and develop a practical solution for safe rice production at a regional scale. Despite inconsistent field conditions in the whole region, various foliar fertilizers could effectively decrease grain Cd content by 20.4-41.6 % and were capable of producing safe grains in low/medium-risk areas. At high-risk sites, foliage dressing coupled with alkaline fertilizers significantly reduced Cd accumulation and increased grain compliance rate to 95.0 %. The cost analysis and questionnaire survey showed the above technologies are low-cost, eco-friendly, and highly acceptable in real-world scenarios. The classification results by conditional inference tree (CIT) for CK and FS scenarios indicated grain Cd content is closely related to the interaction effects of soil Cd and pH. On these bases, the whole area was divided spatially into three different risk zones, and each zone matched a feasible method for safe production, subsequently developing a precise and differentiated solution. The estimation results demonstrate it can effectively improve the precision level of safe utilization of regional polluted lands and save more than half of the total cost, providing a new idea for regional Cd-polluted paddy fields management strategies.

Prediction models for monitoring selenium and its associated heavy-metal accumulation in four kinds of agro-foods in seleniferous area

Se-rich agro-foods are effective Se supplements for Se-deficient people, but the associated metals have potential risks to human health. Factors affecting the accumulation of Se and its associated metals in Se-rich agro-foods were obscure, and the prediction models for the accumulation of Se and its associated metals have not been established. In this study, 661 samples of Se-rich rice, garlic, black fungus, and eggs, four typical Se-rich agro-foods in China, and soil, matrix, feed, irrigation, and feeding water were collected and analyzed. The major associated metal for Se-rich rice and garlic was Cd, and that for Se-rich black fungus and egg was Cr. Se and its associated metal contents in Se-rich agro-foods were positively correlated with Se and metal contents in soil, matrix, feed, and matrix organic contents. The Se and Cd contents in Se-rich rice grain and garlic were positively and negatively correlated with soil pH, respectively. Eight models for predicting the content of Se and its main associated metals in Se-rich rice, garlic, black fungus, and eggs were established by multiple linear regression. The accuracy of the constructed models was further validated with blind samples. In summary, this study revealed the main associated metals, factors, and prediction models for Se and metal accumulation in four kinds of Se-rich agro-foods, thus helpful in producing high-quality and healthy Se-rich.

Use of artificial neural network to evaluate cadmium contamination in farmland soils in a karst area with naturally high background values

In recent years, the naturally high background value region of Cd derived from the weathering of carbonate has received wide attention. Due to the significant difference in soil Cd content and bioavailability among different parent materials, the previous land classification scheme based on total soil Cd content as the classification standard, has certain shortcomings. This study aims to explore the factors influencing soil Cd bioavailability in typical karst areas of Guilin and to suggest a scientific and effective farmland use management plan based on the prediction model. A total of 9393 and 8883 topsoil samples were collected from karst and non-karst areas, respectively. Meanwhile, 149 and 145 rice samples were collected together with rhizosphere soil in karst and non-karst areas, respectively. The results showed that the higher CaO level in the karst area was a key factor leading to elevated soil pH value. Although Cd was highly enriched in karst soils, the higher pH value and adsorption of Mn oxidation inhibited Cd mobility in soils. Conversely, the Cd content in non-karst soils was lower, whereas the Cd level in rice grains was higher. To select the optimal prediction model based on the correlation between Cd bioaccumulation factors and geochemical parameters of soil, artificial neural network (ANN) and linear regression prediction models were established in this study. The ANN prediction model was more accurate than the traditional linear regression model according to the evaluation parameters of the test set. Furthermore, a new land classification scheme based on an ANN prediction model and soil Cd concentration is proposed in this study, making full use of the spatial resources of farmland to ensure safe rice consumption.

Zinc concentration prediction in rice grain using back-propagation neural network based on soil properties and safe utilization of paddy soil: A large-scale field study in Guangxi, China

Zn is an essential nutrient for humans, with crucial biological functions. However, Zn concentration in rice grains is generally low. Therefore, a cereal-based diet may lead to Zn deficiency in people, further leading to a series of health problems, such as immune and brain dysfunction. Previous studies seldom focused on the accumulation of Zn in rice grains based on large-scale field research. In the present study, a large-scale field survey of paddy (n = 40,853) and paired soil-rice samples (n = 1332) was conducted in Guangxi, China. Zn concentration in soil and rice grains was determined, and the associations of its spatial distributions with lithology, soil properties, and Mn nodules were investigated. According to the daily rice intake of different age and sex groups and the values of recommended Zn intake and tolerable Zn upper intake level recommended by National Health Commission of China, the Zn threshold value of the rice grain is 15.47-24.49 mg·kg^-1. Moreover, a back-propagation neural network (BPNN) model was used to predict the Zn bioaccumulation factor (BAF) of rice grains with high accuracy. Soil Zn concentration, Mn concentration, pH, and total organic carbon derived from Pearson's correlation analysis were used as input variables in the BPNN model. Compared with the multiple linear regression model, the developed BPNN model using the training (1198 samples) and testing (134 samples) datasets showed better performance in estimating rice Zn BAF, with R² = 0.93, normalized mean error of 0.009, normalized root mean square error of 0.21. When the BPNN model was applied to the 40,853 paddy soil samples, 85.7% of the agriculture lands were within the rice threshold values. These findings further our understanding of the development and utilization of Zn-rich rice and soil.