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Population expansion within agricultural lands applies pressure on natural resources, particularly water resources, and leads to contamination through different types of pollutants, such as heavy metals, that consequently alter the ecosystem and impact human health. In the present work, several heavy metals in sediment along the Kitchener drain were assessed using different soil quality and health indices; the Kitchener drain is one of the major drains in the Nile Delta. Sediments were collected from six stations along the drain from upstream to downstream. Soil physical and chemical properties were analyzed as well as four metal pollution indices and five ecological risk indices. Additionally, carcinogenic and noncarcinogenic risks for adults and children were evaluated. The data showed that the Kitchener drain is mainly contaminated with Cd, Pb, and Zn, where the concentrations decreased from upstream to downstream. The eco-toxicological indexes showed that Pb, Zn, and Cr were the most hazardous metals along the drain, mainly at upstream stations. The human health risk indices data revealed that the noncarcinogenic risk of the studied metals can be ordered as follows: Co > Cr > Pb > Mn > Ni > Cd > Cu > Zn for adults, while for children it was Cr > Mn > Co > Pb > Ni > Cd > Cu > Zn. The carcinogenic risk data showed that heavy metals ranged from low to medium in all sites, except for Pb and Zn, which have high carcinogenic risks. The present study showed more contamination upstream compared to downstream which can be attributed to urbanization and human activity, as shown from the land use/landcover map. This highlighted that the major drains inside the Nile Delta suffer from different anthropogenic activities that should be taken into consideration by researchers, scientists, and policymakers. Also, the source of heavy metal pollution, particularly upstream, should be controlled or treated before discharge into the drain. On the other side, downstream (toward the Mediterranean Sea), the heavy metals could affect the trophic levels of the marine ecosystem on the Mediterranean Sea which should be taken into consideration. [ABSTRACT FROM AUTHOR]

This work is a novel trial to integrate geostatistics with fuzzy logic under the geographic information system (GIS) environment to model soil pollution. Soil samples from seventy-one soil profiles in the northern Nile Delta, Egypt, and were analyzed for total concentrations of Cd, Co, Cu, Pb, Ni, and Zn. Metal distribution maps were generated using ordinary kriging methods. They were normalized by linear and non-linear fuzzy membership functions (FMFs) and overlain by fuzzy operators (And, OR, Sum, Product, and Gamma). The final maps were validated using the area under the curve (AUC) of the receiver operating characteristic (ROC). The best-fitted semivariogram models were Gaussian for Cd, Pb, and Ni, circular for Co and Zn, and exponential for Cu. The ROC and AUC analysis revealed that the non-linear FMFs were more effective than the linear functions for modeling soil pollution. Overall, the highest AUC value (0.866; very good accuracy) resulted from applying the fuzzy Sum overly to the non-linearly normalized layers, implying the superiority of this model for decision-making in the studied area. Accordingly, 92% of the investigated soils were severely polluted. Our study would increase insight into soil metal pollution on a regional scale, especially in arid regions. [ABSTRACT FROM AUTHOR]

One of the most significant challenges that global decision-makers are concerned about is soil contamination. It is also related to food security and soil fertility. The quality of the soil and crops in Egypt are being severely impacted by the increased heavy metal content of the soils in the middle Nile Delta. In Egypt's middle Nile Delta, fifty random soil samples were chosen. Inverse distance weighting (IDW) was used to create the spatial pattern maps for four heavy metals: Cd, Mn, Pb, and Zn. The soil contamination levels in the research area were assessed using principal component analysis (PCA), contamination factors (CF), the geoaccumulation index (I-Geo), and the improved Nemerow pollution index (In). The findings demonstrated that using PCA, the soil heavy metal concentrations were divided into two clusters. Moreover, the majority of the study region (44.47%) was assessed to be heavily to extremely polluted by heavy metals. In conclusion, integrating the contamination indices CF, I-Geo, and In with the GIS technique and multivariate model, analysis establishes a practical and helpful strategy for assessing the hazard of heavy metal contamination. The findings could serve as a basis for decision-makers to create effective heavy metal mitigation efforts. [ABSTRACT FROM AUTHOR]