Selection of optimal models for predicting growth stress in Artemisia desertorum by comparison of linear regression and multiple neural networks: Take the construction of a green mine in the Bayan Obo mine as an example.

In recent years, more and more countries are focusing on the control of mining sites and the surrounding ecological environment, and the new environmental concept of green mines has been proposed. By investigating the ecological background of a mine site, pollution and ecological imbalances in the mine can be predicted, managed or transformed. This study investigated the effects of rare earth elements on plant growth in the Baotou Bayan Obo Rare Earth Mine and evaluated soil contamination and subsequent remediation through the measured plant height. Using linear regression, BP(Back Propagation) neural networks, GA-BP(Genetic Algorithm- Back Propagation) neural networks, ELM(Extreme Learning Machine) and GA-ELM(Genetic Algorithm- Extreme Learning Machine) model prediction instruments, the different rare earth solution concentrations were set as input values and the heights of Artemisia desertorum, which as the model plant, were set as output values in the prediction. The results showed that the linear regression predicted the standard error of single La(III), Ce(III) solution and compound La(III) + Ce(III) solution for Artemisia desertorum growth stress was on the high side, 7.02%- 8.92%; the efficiency range of each group of models under BP neural network, GA-BP neural network and ELM neural network were 1.15%- 2.53%, 0.85%- 1.28%, 1.76%- 3.53%; while the efficiency range under GA-ELM neural network was 0.59%- 0.68%, with average error values and predicted values close to the true values. Among them, the MAPE of GA-ELM neural network are significantly lower than other models, and the error decreases with increasing concentration of the compound solution. So GA-ELM neural network can be used as an efficient, fast and reasonable optimal model for predicting the growth stress of Artemisia desertorum in Bayan Obo mining area. The experimental results can provide a theoretical basis for assessing the risk of soil rare earth contamination in the area, evaluating the expectation of later remediation, and provide a degree of new ideas for the construction of green mines.

[Effect of Aging on Stabilization of Cd2+ Through Biochar Use in Alkaline Soil of Bayan Obo Mining Area].

Although biochar has been widely used in the remediation of heavy metal pollution in acidic and neutral soils, less attention has been paid to whether biochar will alter its structural properties and the ability to retain heavy metals after different degrees of aging in alkaline soils. In this study, two artificial accelerated aging methods (freeze-thaw cycle and dry-wet cycle) and a short-term natural aging method were used to simulate the aging process of biochar prepared from corn stalk. We investigated the changes in the soil pH and bioavailability, total content, and transformation of Cd2+ before and after aging treatments. Biochar was separated from the soil for characterization to explore the effect of aging on the passivation of Cd2+ by biochar in the alkaline soil of the mining area. The results showed that adding biochar to alkaline soil in the Bayan Obo mining area had no significant liming effect, and pH decreased after the freeze-thaw and dry-wet accelerated aging treatments. Compared with that in the control, the CaCl2-extractable contents of Cd2+ decreased by 19.32%-30.67%, and the total contents of Cd2+ decreased by 5.02%-7.18%. Aging did not significantly change the transformation of Cd2+ but reduced the distribution of acid-soluble and reducible fractions, indicating that biochar could immobilize Cd2+ for a long time after aging, which was related to the increase in oxygen-containing functional groups and the pore structure of biochar. These results are important for evaluating its long-term application prospects in the mining environment.