Flow Pathways in Fractured Basalt under Challenging Environmental Conditions: A Synergistic Approach of Electrical Resistivity Tomography and Seismic Refraction Imaging

Understanding the site conceptual flow model in fractured-controlled rocks near landfills is crucial for monitoring groundwater pollution and addressing key environmental concerns in diverse locations worldwide. Characterizing fractured basalt with suitable spatial resolution remains an open area of research in such hazardous environments. The objective of this study is to explore the potential of integrating Electrical Resistivity Tomography (ERT) and Seismic Refraction Imaging (SRI) techniques for the construction and improved understanding of a site conceptual flow model in fractured basalt under hazardous conditions. The research was conducted in an area with a history of environmental contamination, where fractured basalt acts as a significant migration pathway for pollutants. SRI identified a shallow water table and revealed highly heterogeneous contacts between the saturated fractured basalt (1200–1850 m/s) and the massive basalts (>?3000 m/s). Additionally, the presence of conductive and interflow zones (5–10 ?.m) at depths of 5 to 12 m indicated potential vertical hydraulic connections with the lower aquifer, allowing local percolation of leachate around the landfills. These findings demonstrate the advantages of the applied approach in delineating prevailing fracture patterns and clarifying pollution scenarios at composite landfills globally. The insights gained from this research have significant implications for improving the understanding of hydrogeological and transport processes in hazardous environments, enabling sustainable management of groundwater resources and effective monitoring of remediation efforts. The site conceptual flow model plays a critical role in achieving these objectives and addressing the challenges associated with groundwater pollution in such complex settings.

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This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement Nº 768737


                   




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