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.

[Characteristics of Cd Fluxe in Topsoil Around Typical Mining Area in Hezhou, Guangxi].

Guangxi is a typical geological high background area in southwest China, where carbonates, black rock series, basic-ultrabasic rock mass, and metal deposits (mineralized bodies) exhibit strong weathering into loam, resulting in higher cadmium (Cd) content in the soil than that in other areas of China. In order to investigate the degree of influence of mining activities on topsoil environmental quality in the area with high geological background, we chose a mining area and control area in Hezhou for this research and systematically carried out a comparative study on Cd transport routes and transport flux density in topsoil. The results showed that the average atmospheric dry and wet deposition flux densities of Cd in the soil of the mining area and control area were 1.87 g·(hm2·a)-1 and 1.52 g·(hm2·a)-1, accounting for 61.5% and 60.3% of the total input flux density, respectively. The flux density of Cd in the soil by fertilization and irrigation was lower. Surface water infiltration was the main avenue of soil Cd output in both the mining area and control area, accounting for 75.4% and 86.6% of the total output flux density, respectively. The harvest output flux density in the mining area was higher than that in the control area, and the Cd content of rice planted in the mining area was higher than the standard, whereas that of maize was safe. On the whole, the net transport flux densities of soil Cd in the mining area and control area were -3.05 g·(hm2·a)-1 and -4.05 g·(hm2·a)-1, both of which showed Cd leaching in the soil. However, the points of high atmospheric deposition flux density and exceeding Cd content in rice were mainly distributed around the mining area, which may have posed a potential threat to the health of local residents. Therefore, it is suggested to control the soil Cd pollution through monitoring and planting structure adjustment.

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.

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.

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

Cadmium (Cd) contamination in soil and crops caused by mining activities has become a prevalent concern in the world. Given that different crops have varying Cd bioaccumulation factors, crops with low Cd bioaccumulation abilities can be selected for the safe usage of Cd -contaminated lands. This study aimed to investigate Cd contamination in soil and crops and the influencing factors of soil Cd activity in a tin mining area (TMA) and control area (CA) and to put forward suggestions for the safe usage of farmlands by developing prediction models of Cd content in different crop grains. We collected 72 and 40 pairs of rice and maize grain samples, respectively, along with their rhizosphere soil samples and 6176 topsoil samples. The results showed that compared with the CA, the Cd pollution was more severe in the cultivated soil and crop grains around TMA. Furthermore, rice has a strong ability to transport Cd from soil to grains, whereas maize has a poor Cd uptake ability. The total organic carbon, CaO, pH, and Mn in soil play key roles in the transfer of Cd from soil to crop grains. Using these parameters and Cd concentration in soil, two sets of accurate Cd prediction models were developed for maize and rice. Based on the Cd concentration in the topsoil and predicted Cd concentration in crop grains, the safe utilization scheme of farmland was proposed. The proportions of priority protection, safe exploitation, planting adjustment, and strict control were 72.59%, 22.77%, 3.16%, and 1.48% in the TMA, respectively. The values reached 80.51% (priority protection), 19.12% (safe exploitation), 0.37% (planting adjustment), and 0% (strict control) in the CA. Thus, given the difference between Cd accumulation in rice and maize, adjustment of planting crops in contaminated farmlands can be applied to maximize the use of farmland resources.