Detecting LNAPL migration in real time using electrical resistivity and statistical models

TL;DR

This study shows how electrical resistivity can detect diesel spills in soil in real time, with results showing strong correlations and potential for environmental monitoring.

Contribution

The study introduces a refined electrical resistivity method and validates its effectiveness for real-time LNAPL spill detection using statistical models.

Findings

Electrical resistivity significantly decreases with increasing LNAPL concentration in loam and sandy loam soils.

The power regression model best fits the ER-LNAPL relationship with an R² up to 0.942 and p < 0.001.

Soil properties strongly influence LNAPL migration dynamics and ER measurement outcomes.

Abstract

To address the issue of detecting immediate Light Non-Aqueous Phase Liquid (LNAPL) spillage in subsurface soils, a refined method of Electrical Resistivity (ER) measurement was developed and evaluated in this study. A controlled experiment was conducted using a custom-designed nine-sector soil box to simulate stratified soil conditions. Diesel, an LNAPL surrogate, was injected at varying flow rates (5 ml/min, 25 ml/min, and 50 ml/min) to replicate immediate spillage scenarios. Electrical resistivity measurements were taken over 24 h using a multifunction installation tester, soil samples were analyzed to determine LNAPL concentration. The experimental results showed a significant decrease in ER values with increasing LNAPL concentration, particularly in loam and sandy loam soils, which exhibited higher permeability and facilitated faster LNAPL migration. Non-linear regression models—logarithmic, quadratic, and power—were applied to analyze the relationship between ER and LNAPL concentration. The power model demonstrated the best fit, showing a strong negative correlation (R² up to 0.942) and high statistical significance (p < 0.001). This study highlights the efficacy of ER as a real-time monitoring tool for detecting immediate LNAPL spillage and provides valuable insights into the influence of soil properties on LNAPL migration dynamics. These findings contribute to advancing geotechnical engineering practices and offer a foundation for developing rapid response strategies for environmental monitoring and remediation. Future research should expand on these findings by incorporating larger datasets and diverse soil conditions to further validate the observed relationships.

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