Positive matrix factorization receptor model and dynamics in fingerprinting of potentially toxic metals in coastal ecosystem sediments at a large scale (Persian Gulf, Iran).

Effective pollution control and remediation strategies are the key to providing a major progress in conservation of coastal and marine biodiversity. For the development of such strategies, quantitative assessment of potentially toxic metals (PTMs) and the accurate identification of the pollutant sources are essential. In this study, we seek to find out spatial PTMs distribution in the coastal sediments of the Persian Gulf (Iran), to assess the potential eco-environmental risks and to identify the metal pollution sources. Total and fraction analysis indicated considerable metal (Zn, Cu, Mn, Fe, Al, Hg, Pb, Cd, As, Cr, Co, Ni and V) pollution levels, albeit in most cases PTMs were predominantly associated with the oxidizable and residual fractions. The obtained PTMs concentrations were in the range of 22.8 - 156.3, 16.6 - 161.9; 2.7 - 88; 10.4 - 107.3; 1.1 - 35.8; 0.8 - 27.9; 0.1 - 1.3; 1.1 - 21.3; 0.04 - 1.9 mg.kg for V, Ni, Cu, Zn, Cr, Co, Hg, Pb, and Cd, respectively. The combined PTM-PCA-PMF modeling approach identified four main metal sources (anthropogenic, vehicle-related, agricultural and lithogenic) in the study area. Several recognizable 'hot-spots' with extremely high metal concentrations were observed in the spatial metal pollution patterns. Some of those locations were predominantly affected by the nearby industrial activities, while others have demonstrated contributions from several sources - not only anthropogenic, but also agricultural and vehicle-related. The same spots of elevated pollution were found to demonstrate higher potential eco-environmental risk. Various indexes indicated more or less similar trends: the eco-environmental risk was gradually increasing towards the northwestern part of the study area with several peaks in the central and eastern parts directly affected by the nearby industrial activities.