A chemometrics-based approach for the chemical prediction of lead (Pb) levels in surface soil, Dammam, Saudi Arabia

Abstract

AbstractHigh levels of trace metals in top soil may impose serious health problems to humans and the environment. Thus, there is a need to assess the geochemical conditions of surface soils where various human activities are intensified. This study aims to evaluate contaminations associated with trace metals within Dammam, Saudi Arabia. The study also aims to hyphenate the results into a chemometrics-based approach for predicting the concentration of lead (Pb) due to its toxicity. No previous work was found on Pb prediction in top soils using chemometrics-based approach. Surface soil samples were collected from the different zones and analyzed in the laboratory for their trace metals’ constituents. According to the study’s findings, all trace metals were below international allowable metals with the highest mean concentration of Ba (309.7 mg/kg), Zn (7.9 mg/kg), and Cr (10.2 mg/kg) in industrial, agriculture and residential areas, respectively. The prediction steps involve the application of two AI-based techniques; Gaussian process regression (GPR) and Least-square (L-Boost), as well as a linear Step-Wise-Linear Regression (SWLR). The modelling was featured in two scenarios, M1 and M2, based on the input–output relationship designated according to the correlational feature extraction approach. The performance results of the models indicate that the second scenario (M2) showed higher performance skills than the first scenario (M1) in all three approaches. Overall, the performance accuracy of the models showed that the non-linear GPR-M2 showed higher performance accuracy than all the model combinations applied in the current work with 99% accuracy and an MSE of 0.012.

Keywords

• trace metals
• lead (Pb)
• Saudi Arabia
• surface soil
• chemometrics