The effect of amendment addition drill cuttings on heavy metals accumulation in soils and plants: Experimental study and artificial network simulation

Abstract

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%.

Keywords: Artificial neural network; Drill cuttings; Heavy metals; Trace pollutant.