Faculty of Environmental Engineering, Lublin University of Technology, Nadbystrzycka 40B, 20-618 Lublin, Poland.
Faculty of Environmental Engineering, Lublin University of Technology, Nadbystrzycka 40B, 20-618 Lublin, Poland.
J Hazard Mater. 2022 Mar 5;425:127920. doi: 10.1016/j.jhazmat.2021.127920. Epub 2021 Dec 1.
A greenhouse experiment was carried out to evaluate the influence of drill cuttings addition on the accumulation of heavy metals in soil, in plant biomass (Trifolium pretense L.) cultivated on soils with the addition of this type of waste. The transfer and transformation of heavy metals in the soil with drill cuttings- Trifolium pretense L were discussed. Drilling waste in the amount of 2.5%, 5%, 10% and 15% of dry weight were added to acidic soil. The concentrations of heavy metals in the soil and plant materials were determined by an inductively coupled plasma mass spectrometry method. Results indicated that drilling wastes addition had a positive influence on the growth of Trifolium pretense L. However, the concentrations of heavy metals increased in the prepared mixtures along with the dose of drilling wastes. The drilling wastes addition also changed the metal accumulation capacity in plant parts. Nevertheless, the concentrations of heavy metals in soils and above-ground parts of plants did not exceed the permissible values in respective legal standards. The values of the heavy metals bioconcentration coefficient in Trifolium pretense L at the highest dose of drill cuttings were as follows: in the above-ground parts Cd>Cu>Ni>Cr>Pb>Zn, in roots Cd>Ni>Cr>Zn>Pb>Cu. An artificial neural network model was developed in order to predict the concentration of heavy metals in the plants cultivated on the soils polluted with drill cuttings. The input (drill cuttings dose, pH, organic matter content) and the output data (concentration of heavy metals in the shoot cover) were simulated using an artificial neural network program. The results of this study indicate that an artificial neural network trained for experimental measurements can be successfully employed to rapidly predict the heavy metal content in clover. The artificial neural network achieved coefficients of correlation over 90%.
一项温室实验评估了钻屑添加对土壤中重金属积累的影响，该实验在添加这种废物的土壤中种植三叶草（Trifolium pretense L.），以研究重金属在土壤-三叶草系统中的迁移和转化。向酸性土壤中添加 2.5%、5%、10%和 15%干重的钻屑废物。采用电感耦合等离子体质谱法测定土壤和植物材料中的重金属浓度。结果表明，钻屑废物的添加对三叶草的生长有积极影响。然而，随着钻屑废物剂量的增加，制备混合物中的重金属浓度也随之增加。钻屑废物的添加还改变了植物各部分的金属积累能力。然而，土壤和植物地上部分的重金属浓度均未超过各自法定标准规定的允许值。在钻屑添加量最高的情况下，三叶草各部分重金属的生物浓缩系数值如下：地上部分 Cd>Cu>Ni>Cr>Pb>Zn，根 Cd>Ni>Cr>Zn>Pb>Cu。为了预测在受钻屑污染的土壤中种植的植物中重金属的浓度，建立了一个人工神经网络模型。使用人工神经网络程序模拟输入（钻屑剂量、pH 值、有机质含量）和输出数据（地上部分重金属浓度）。研究结果表明，经过实验测量训练的人工神经网络可以成功用于快速预测三叶草中的重金属含量。人工神经网络的相关系数超过 90%。
