GNGTS 2024 - Atti del 42° Convegno Nazionale

Session 3.2 GNGTS 2024 Quanttatve integraton of geoelectrical data for mapping of leachate plumes: applicaton to a MSW landfll in Central Italy D. Melegari 1 , G. De Donno 1 , E. Piegari 2 1 DICEA (Sapienza - University of Rome, Rome, Italy) 2 DISTAR (Federico II - University of Naples, Naples, Italy) Introducton Waste management is one of the main challenges for contemporary society, as highlighted by the United Natons SDGs (Target 12.4). Specifcally, the uncontrolled accumulaton of leachate in municipal solid waste (MSW) landflls represents a potental risk for the environment, since leachate is a liquid with a high pollutant content that can contaminate aquifers (Assamoi B., Lawryshyn Y.; 2012) and afect the stability of landflls. Therefore, mapping and monitoring the waste mass down to signifcant depths is required for an appropriate management of the landfll sites. Among the geophysical techniques, electrical resistvity tomography (ERT) and induced polarizaton (IP) methods are perfectly suited for this purpose given the electrical propertes of leachate (highly conductve and chargeable) compared to the unsaturated waste mass (Soupios P., Ntarlagiannis D.; 2017). In the present study we present an applicaton of a machine learning-based approach for a quanttatve integraton of ERT and IP data for imaging of leachate levels in a MSW landfll located in Central Italy. The quanttatve interpretaton of resistvity, chargeability and normalized chargeability data is provided by using the Fuzzy C-means clustering algorithm which allows for the identfcaton of the leachate accumulaton zones and for assessing the reliability of the reconstructon by means of the membership value. The results of the cluster analysis are validated by the computaton of the Silhoute coefcient and supported by well data. Study area, data acquisiton and processing The landfll is located in Central Italy on a steep slope, in which the leachate accumulaton can trigger instability phenomena (Fig. 1a). We focused our investgaton on four selected terraced steps with four electrical lines (L1-L4) 300 to 500 meters long (Fig. 1b), using a mult-parameter reconstructon through electrical resistvity tomography (ERT) and tme-domain induced polarizaton (TDIP) methods. Experimental datasets were acquired through the Syscal Pro resistvity-meter (IRIS Instruments) with stainless steel electrodes spaced 5 m apart, using the dipole-dipole (DD) array and a roll-along confguraton. For IP acquisiton, we used a current