Comparative indexation of potentially toxic elements for soil pollution monitoring using ICP-OES and FTIR spectroscopy in Central Morocco

Abstract

The accumulation of potentially toxic elements (PTEs) in agricultural soils poses significant risks to food safety and ecosystem health, necessitating rapid and cost-effective monitoring approaches. While inductively coupled plasma optical emission spectroscopy (ICP-OES) provides accurate PTE quantification, its high cost, time requirements, and chemical reagent necessitate the search for green, fast, robust, and cost-effective alternatives. This study aims to evaluate the suitability of mid-infrared Fourier transform infrared (MIR-FTIR) spectroscopy combined with partial least squares regression (PLSR) as an alternative rapid method for predicting PTE concentrations and calculating pollution indices in semi-arid agricultural soils of central Morocco. A total of 67 surface soil samples (0–20 cm) were collected from three distinct soil types: Lithic Calciustolls (n=23), Typic Haplusterts (n=23), and Typic Calciustolls (n=21). Ten PTEs (As, Ba, Cd, Cu, Mn, Pb, Se, Sr, Ti, and Zn) were measured by ICP-OES and predicted using MIR-FTIR (4000–400 cm −1 ) coupled with PLSR. Mean PTE concentrations varied substantially across soil types, with Cd ranging from 0.95 to 3.91 mg·kg −1 , Sr from 56.25 to 535.14 mg·kg −1 , and Zn from 38.23 to 59.63 mg·kg −1 . PTE Pollution Index (PI) was calculated using both datasets for comparative pollution assessment. Results demonstrated strong to excellent predictive performance (R² = 0.82-0.95) with the highest correlations for Ba, Zn, and Sr. PI calculations showed exceptional concordance between methods (mean PI: 1.54 for both), with all samples classified as low pollution. FTIR spectroscopy maintains the same geochemical relationships as ICP-OES (correlation differences <0.083), confirming method equivalence for soil pollution indexation. This approach offers significant advantages for large-scale monitoring programs while maintaining classification accuracy for environmental risk assessment.