Deciphering spatial patterns of potential toxic elements using GIS and multivariate statistics for sustainable...

The current research aims to map the spatial distribution of potentially toxic elements (PTEs) (As, Co, Cr, Cu, Fe, Mn, Ni, V, and Zn) in Qena southern of Egypt, identify the sources of PTEs using geostatistical analysis (principal component analysis, cluster analysis, and correlation matrix(, and determine the level of contamination in the study area to assess soil contamination with selected potentially toxic elements. To accomplish this, thirty soil samples were gathered and examined for the PTEs. The variability of the soil chemical content was estimated using multivariate geostatistical analysis. All PTEs, with the exception of As, show a strong positive association with one another in the correlation matrix. Two components illustrated through the principal component analysis (PCA). The primary source of the first component, which accounts for the majority of the variance, is a combination of industrial activity and agricultural activities. It is rich in Co, Cr, Cu, Fe, Mn, Ni, V, and Zn. As is abundant in the second component, and lithology may be the source. According to the Pollution Load Index (PLI), 61% of the study area (west and south of the study area) was contaminated, and 31% of the study area (east of the study area) was unpolluted. The Degree of Contamination (DC) index was used to classify the research region as low, moderate, and considerably contaminated, with percentages of 21, 51, and 28% of the study area, respectively. The Nemerow Pollution Index (NPI) classifies the majority of samples (63.33%) as slightly to moderately polluted. The study's conclusions showed that to evaluate the potential risk to the environment, PTEs concentrations in the study area need to be monitored. This study aligns with key UN Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health) and SDG 15 (Life on Land), by to safeguarding both human well-being and terrestrial ecosystem health. Moreover, the study provides a scientific basis for sustainable environmental management, supporting the transition towards safer and more resilient communities.