Soil contamination is a widespread problem and action needs to be taken in order to prevent damage to the groundwater and the life around the contaminated sites. In Sweden, it is estimated that more than 80,000 sites are potentially contaminated, and therefore, there is a demand for investigations and further treatment of the soil. In this paper, we present the results from a methodology applied in a site contaminated with chlorinated solvents, for characterization of the contamination in order to plan the remediation and to follow-up the initial step of in-situ remediation in an efficient way. We utilized the results from three different methods; membrane interface probe for direct measurement of the contaminant concentrations; seismic refraction tomography for investigating the depth to the bedrock interface; and direct current resistivity and time-domain induced polarization tomography to acquire a high-resolution imaging of the electrical properties of the subsurface. The results indicate that our methodology is very promising in terms of site characterization, and furthermore, has great potential for real-time geophysical monitoring of contaminated sites in the future.

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SUMMARY We present a solution for long-term direct current resistivity and time-domain induced polarization (DCIP) monitoring, which consists of a monitoring system and the associated software that automates the data collection and processing. This paper describes the acquisition system that is used for remote data collection and then introduces the routines that have been developed for pre-processing of the monitoring data set. The collected data set is pre-processed using digital signal processing algorithms for outlier detection and removal; the resulting data set is then used for the inversion procedure. The suggested processing workflow is tested against a simulated time-lapse experiment and then applied to field data. The results from the simulation show that the suggested approach is very efficient for detecting changes in the subsurface; however, there are some limitations when no a priori information is used. Furthermore, the mean weekly data sets that are generated from the daily collected data can resolve low-frequency changes, making the approach a good option for monitoring experiments where slow changes occur (i.e. leachates in landfills, internal erosion in dams, bioremediation). The workflow is then used to process a large data set containing 20 months of daily monitoring data from a field site where a pilot test of in situ bioremediation is taking place. Based on the time-series analysis of the inverted data sets, we can detect two portions of the ground that show different geophysical properties and that coincide with the locations where the different fluids were injected. The approach that we used in this paper provides consistency in the data processing and has the possibility to be applied to further real-time geophysical monitoring in the future.

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