Application of exploratory and Spatial Data Analysis (SDA), singularity matrix analysis, and fractal models to delineate background of potentially toxic elements: A case study of Ahvaz, SW Iran.

It is essential to study spatial distribution of Potentially Toxic Elements (PTEs) in surface soil, and separate PTEs geochemical background from their human sources, and also determine their correlation with other environmental factors in order to assess their impacts on humans, provide realistic environmental geochemical maps, and carry out soil management. For this purpose, this study was designed to investigate the surface soil in Ahvaz, Southwest of Iran. The applied methods were exploratory data analysis (EDA), including boxplot, Q-Q plot, probability plot (PP), concentration-area (C-A), number-size (N-S) fractal model, and singularity index (SI) model. The obtained results revealed that the fractal models led to a more realistic distinction among the geochemical population compared to the EDA methods such as probability plot. Furthermore, the C-A model was found to be more effective on the separation of subpopulation compared to the N-S and PP models. The studied elements exhibited a similar pattern implying that pollution is a function of geochemical dispersion regarding the surface soil in Ahvaz (Zn â‰« Pb > Cu > As). The studied metals-major elements plot also indicated that there was no meaningful relationship between Pb, Zn, Cu, and major elements in the study area. Plots of association of Pb, Zn, Cu, and As distinctly showed two general geogenic and anthropogenic populations. Moreover, the results of SI revealed that the highly contaminated area was consistent with the main defined hotspots and anthropogenic sources of elements as well as places affected by the contaminated area that have not been reported in previous studies. Furthermore, a combination of geochemical and geographical model comprising different statistical models was developed to more effectively separate geogenic from anthropogenic sources. Also, the geochemical background for the studied elements (Pb 180 mg/kg; Cu 200 mg/kg; Zn 90 mg/kg; As 65 mg/kg) was shown to be higher than the Iranian soil quality guideline with Pb, Zn, Cu, and As of 100, 80, 200, and 18 mg/kg, respectively.

Multifractal approach for delineating uranium anomalies related to phosphatic deposits in Area-3, Northern Palmyrides, Syria.

Multifractal Concentration-Number (C-N) modeling approach has been developed and applied to Airborne Gamma Spectrometry (AGS) data related to Area-3, Northern Palmyrides, Syria. The application of the multifractal approach basically aimed at separating uranium anomalies from background. The AGS technique has been applied for uranium exploration in Syria, where four radioactive parameters were recorded, T.C, eU, eTh, and K%. Log-log plots practiced on those radioactive variables indicate the presence of different uranium anomaly ranges. Those radioactive ranges have been verified and controlled by both geology field and surface spectrometric gamma sample rocks analysis. The area range of 5.37-13.20 eU includes uranium concentration more than 120 ppm, The area range of 2.95-5.37 eU includes uranium concentration of 50 ppm, and the area range of 1.40-2.95 eU includes uranium concentration of 7 ppm. Positive correlation has been consequently found between radioactive anomalous eU ranges and uranium concentrations. Such correlation indicates the importance of multi fractal approach to be extensively used as a fractal analysis-smart sampling tool in phosphate and uranium prospecting programs, where a positive correlation between phosphate content, radioactivity and uranium concentration exist.

Fractal theory modeling for interpreting nuclear and electrical well logging data and establishing lithological cross section in basaltic environment (case study from Southern Syria).

Fractal theory modeling technique is newly proposed in this research for interpreting the combination of nuclear well logging, including natural gamma ray, density and neutron-porosity, and the electrical well logging of long and short normal, for establishing the lithological cross section in basaltic environments. The logging data of Kodana well, localized in Southern Syria are used for testing and applying the proposed technique. The established cross section clearly shows the distribution and the identification of four kinds of basalt which are hard massive basalt, hard basalt, pyroclastic basalt and the alteration basalt products, clay. The concentration- Number (C-N) fractal modeling technique is successfully applied on the Kodana well logging data in southern Syria, and can be used efficiently when several wells with much well logging data with a high number of variables are required to be interpreted.