Spatial variability of some heavy metals in arid harrats soils: Combining machine learning algorithms and synthetic indexes based-multitemporal Landsat 8/9 to establish background levels

Characterizing the background content and source(s) of heavy metals (HMs) is important for the evaluation of potential pollution in soils. Samples were collected from the topsoil layers (0.3 m) of 19 soil pedons in the harrats arid region in Saudi Arabia, and the concentrations of Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn were analyzed. Stepwise multiple linear regression (SMLR) and random forest (RF) were used to model and digitally map the studied HMs concentrations and establish their background levels using fifty-two environmental covariates (ECOVs) extracted from time series Landsat 8/9 OLI and digital elevation models. The values of the studied HMs ranged from 0.95 to 22.2 mg kg-1 (Co), 0.8 to 58.8 mg kg-1 (Cr), 1.65 to 34 mg kg-1 (Cu), 2,206 to 25,970 mg kg-1 (Fe), 33.5 to 788 mg kg -1 (Mn), 1.8 to 134.7 mg kg -1 (Ni), 2.25 to 12.5 mg kg -1 (Pb), and 2.1 to 55.9 mg kg -1 (Zn) with a coefficient of variation that ranged between 27.3 % (Pb) and 63.7 % (Ni). These are all very low compared to results from anthropogenically affected areas near the border of the harrats region, precisely mining (i.e., Mahad AD'Dahab) and industrial (i.e., Riyadh and Jubail) areas as well as to the common average ranges for these HMs in soil. The model's predictions and available observations were compared with statistics from 5-fold cross -validation with 3 repetitions. Findings revealed that mean R2 varied between 0.38 (Zn) and 0.54 (Cu) with normalized root mean square errors (NRMSEs) between 18.53 % (Zn) and 26.03 % (Cr) for SMLR, which better than RF that yielded mean R2 that ranged from 0.17 (Ni) to 0.40 (Cu) with NRMSEs between 19.15 % (Co) and 27.76 % (Mn). The studied HMs are considered background concentrations in the area, and therefore are important for any future environmental pollution/monitoring studies. Our study demonstrated the capacity of SMLR to use available ECOVs to predict HMs and generate background levels at a large scale in specific locations. Additionally, this study investigated the relationships between the factors affecting HMs accumulation in natural surface soil in arid regions. The methodology proposed was effective for describing HMs spatial variability and can be easily implemented in other ecosystems.