Geochemistry and spatial distribution of selected heavy metals in surface soil of Sohag, Egypt: a multivariate statistical and GIS approach

• Published in: Environmental earth sciences 2016-09, Vol.75 (18), p.1, Article 1257
• Main Authors: Ali, Mohamed H., Mustafa, Abdel-Rahman A., El-Sheikh, Ahmed A.
• Format: Article
• Language: English
• Subjects: Agrochemicals; Anthropogenic factors; Biogeosciences; Cadmium; Chromium; Earth and Environmental Science; Earth Sciences; Environmental Science and Engineering; Geochemistry; Geographic information systems; Geology; Heavy metals; Hydrology/Water Resources; Lead; Multivariate analysis; Original Article; Principal components analysis; Soil surfaces; Soils; Spatial distribution; Statistical analysis; Surface chemistry; Terrestrial Pollution; Urbanization; Wastewater irrigation
• ISSN: 1866-6280; 1866-6299
• DOI: 10.1007/s12665-016-6047-x

In this study, selected heavy metals were estimated in surface soil samples of Sohag, Egypt, with the aim to characterize their accumulation patterns. Median concentrations of Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn were 0.89, 39, 162, 74, 72,000, 1434, 72, 25 and 164 µg g −1 , respectively. The higher average concentrations of these HMs compared with corresponding concentrations in average shale suggesting their anthropogenic inputs. Application of principal component analysis and cluster analysis successfully grouped the investigated HMs according to their anthropogenic or geogenic origin. The uncontrolled excessive application of agrochemical products, wastewater irrigation, industrial and urbanization emissions are significant contributors for elevated concentrations of Cd, Cu, Zn, Cd and Pb in the agricultural surface soil, whereas Ni, Mn, Co and Fe amounts are controlled by parent material influences. Chromium was found to exhibit a mixed source in the study cultivated system. Geographical information system (GIS) techniques were used to illustrate the spatial distribution trends of the investigated elements. Integration of GIS mapping and multivariate statistical analysis identified locations affected by the major polluting sources responsible for HMs concentrations in the study area (i.e., hotspot areas). In addition, this study provides a reliable approach for HMs source apportionment in the area using APCS–MLR receptor model. The present work is important to document the status of HM pollution and offer baseline data for the future research in the study region.