الفهرس 
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Abstract
The issue of soil contamination is one of the most important subjects that interests decision‐makers all over the world. It is also related to soil fertility and food security. The soils adjacent to the drains in Egypt suffer from increasing concentration of heavy metals, which negatively affects soil and crop quality. Precise spatial distribution maps of heavy metals are an essential key to mitigating the negative impacts on the ecosystem. 10 soil profiles were dug and sixty random soil locations adjacent to El Moheet drain were chosen on the west side of the Nile River, El‐Minia governorate, Egypt. Six heavy metals (Cr, Co, Cu, Cd, Pb, and Zn) were selected to generate their spatial pattern maps using ordinary Kriging (OK). Principal component analysis (PCA) and contamination factors (CF) were applied to evaluate soil contamination levels in the study area. The results showed that the Gaussiang model was a high fit for soil pH, and Pb, the Exponential model was fit for EC, Stable model was fit for OC, Co, Cu, and Cd. In addition, the Spherical model was fit for both Cr and Zn. The MSE values were close to zero in all selected metals, while the values of RMSSE were close to one. The study area showed alkaline pH, variation in soil organic carbon and ECe values. The results showed that the soil heavy metal concentrations were grouped into two clusters using PCA. Three contamination degrees were obtained (moderate, considerable, and very high), with about 70.7% of the study area characterized by considerable heavy metals concentration, where the average heavy metals concentration (mg kg-1) in this degree was 91.23 ± 19.5, 29.44 ± 5.2, 53.83 ± 10.2, 1.12 ± 0.3, 36.04 ± 18.0, and 101.29 ± 35 for Cr, Co, Cu, Cd, Pb, and Zn, respectively. The pollution load index (PLI) showed that all the study area is polluted. Soil contamination levels with the selected heavy metals indicated that, the pollution levels according to modified degree of contamination (mCd) fall under the following categories: 9% low and 91% moderate degree of contamination in Cluster 1 and 35% moderate and 65% high degree of contamination in Cluster 2. The current results reflect the mismanagement and use of low‐quality water for irrigation in the study area, which increased the heavy metals concentration in soil surface layers. In the end, the results of spatial distribution maps of pollutants and their degrees could support decision‐makers as a basis for developing appropriate mitigation plans for heavy metals.