Electrical and Electromagnetic Prospecting for the Characterization of Municipal Waste Landfills: A Review

In this chapter, we review the main results of electrical and electromagnetic prospecting applied to the characterization and monitoring of municipal waste landfills in the last decade. Among all the geophysical surveys, these methods are the most used for subsurface investigations of landfills since they provide a cost-effective approach that allows for detailed and non-invasive imaging of the subsurface in terms of the electrical properties, down to depths which generally vary from a few tens of centimeters to several tens of meters. Nevertheless, the indirect geophysical mapping needs the direct even if punctual information from boreholes and wells for an accurate reconstruction of the contaminated zones. Electrical and electromagnetic methods are used for multiple purposes that include mapping landfill boundaries, measuring waste volume and composition, as well as identifying and tracking leachate plumes. In particular, electrical methods are widely used for leachate detection (both inside and outside the landfill) and for the geometrical reconstruction of the landfill using electrical conductivity and chargeability as the main proxies. Low-frequency electromagnetic methods are mostly used for a hydrogeological characterization and extensive screening of the high-conductive areas associated to the leachate accumulation. These methods have lower resolution compared to the electrical techniques but often allow greater depth of investigation. High-frequency electromagnetic surveys are instead mainly focused on the shallow part of the landfill for detection of defects on the covering liner and characterization of the covering layer. We discuss recent results related to the topic providing updated references in relation to the specific applications and emphasizing the importance of site-specific validation through direct information. At last, a special focus is given to novel trends, emerging techniques and data integration by machine learning-based approaches for mapping and monitoring of municipal solid waste landfills.