Environmental Pollution Indices and Multivariate Modeling Approaches for Assessing the Potentially Harmful Elements in Bottom Sediments of Qaroun Lake, Egypt

This research intends to offer a scientific foundation for environmental monitoring and early warning which will aid in the protection management of Qaroun Lake. Lake is increasingly influenced by untreated wastewater discharge from many anthropogenic activities, making it vulnerable pollution. For that, six pollution indices, namely contamination factor (Cf), enrichment (EF), geo-accumulation index (Igeo), degree (Dc), load (PLI), potential ecological risk (RI), were utilized assess bottom sediment determine different geo-environmental variables affecting lake system. Cluster analysis (CA), principal component (PCA) used explore sources heavy metal. Moreover, efficiency partial least-square regression (PLSR) multiple linear (MLR) tested Dc, PLI, RI depending on selected elements. The samples carefully collected 16 locations two investigated years 2018 2019. Total concentrations Al, As, Ba, Cd, Co, Cr, Cu, Fe, Ga, Hf, Li, Mg, Mn, Mo, Ni, P, Pb, Sb, Se, Zn, Zr quantified using inductively coupled plasma mass spectra (ICP-MS). According Cf, EF, Igeo results, demonstrated significant derived sources. Dc all categorized under very high contamination. Further, results showed that at risk. Meanwhile, PLI data indicated 59% was polluted 41% had < 1. PLSR MLR models based studied elements presented highest as alternative approaches sediments. examples, validation (Val.) best performance these with R2val = 0.948–0.989 model accuracy ACCv 0.984–0.999 PLSR, 0.760–0.979 0.867–0.984 MLR. Both there no clear overfitting or underfitting between measuring, calibrating, validating datasets. Finally, combinations Igeo, RI, CA, PCA, represent valuable applicable methods assessing potentially harmful elemental