Environmental Risk Assessment and Resource Recovery Potential of Asphaltite Ash in Şırnak (Türkiye): A Geochemical and Machine Learning Approach

Abstract

In this study, asphaltite samples obtained from five different sites (Harbul, Milli, Avgamasya, Seridahlı and Karatepe) in Şırnak province were ashes and analyzed by ICP-OES method after microwave-assisted acid solubilization. According to the results of the analysis, the abundance order of the major elements was determined as Fe (52.400-61.200 mg/kg) > Zn (10.860-39.805 mg/kg) > Ni (2.532-4.755 mg/kg) > Mo (2.246-3.186 mg/kg), and it was confirmed that the relative standard deviation (RSD) values in repeated measurements were below 5%. Analyses revealed that especially Cd, Mo, Se and Zn elements were enriched at high levels in all samples. Assessments using the Metal Pollution Index (MPI), Geoaccumulation Index (Igeo) and Potential Ecological Risk Index (RI) showed that all sites were in the very high ecological risk class (RI>600). It has been determined that the Cd-induced risk is dominant, especially in the Harbul area. In contrast, the high concentrations determined in terms of Mo and Zn at the Seridahlı site indicate the economic recovery potential of this ash; according to calculations, the metal recovery value of Seridahlı ash is approximately 323 USD/ton. Geochemical network analysis revealed strong genetic relationships between Mo-Zn-Cu-Se elements, while regression-based machine learning models confirmed that Cd acts independently of other elements and its critical role in terms of environmental risk. The findings show that Şırnak asphaltite ashes pose a serious environmental risk on the one hand, and on the other hand have a significant secondary raw material potential that can be evaluated with appropriate management processes.

Keywords

• Heavy metals
• Machine learning
• Geochemical network analysis
• Spectroscopy

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