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

Justyna Kujawska,Małgorzata Pawłowska

doi:10.1016/j.jhazmat.2021.127920

Justyna Kujawska, Małgorzata Pawłowska

Open Access

https://doi.org/10.1016/j.jhazmat.2021.127920

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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%.

Highlights

Drill cuttings constitute the spoils obtained in the course of drilling, which is extracted onto the surface along with spent drilling fluid
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
Studies have shown that the drilling wastes addition statistically significantly increased the organic matter content in substrates, and the value of this parameter increased along with the amount of drill cuttings in mixtures (Table 3), reaching 5–37% higher values compared to non-modified soil

Summary

Introduction

Drill cuttings constitute the spoils obtained in the course of drilling, which is extracted onto the surface along with spent drilling fluid. Their composition and amount depend on numerous factors, such as: depth and structure of the well, type of drilled rocks, type of employed drill cuttings and performed actions, as well as the properties of formation water (Abbe et al 2009). The content of chemical compounds in these wastes may vary significantly When considering their impact on the natural environment, the main factors taken into account include the content of heavy metals, inorganic salts, oil derivatives, radionuclides, and colloids. Drill cuttings contain variable amounts of certain toxic trace elements (e.g., Cd, Cr, Pb, Ni, B and Mo) and high soluble salt (Jamrozik et al 2015), and this property may affect the the application of drill cuttings (Zvomuya et al 2011)

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Conclusion