Development of an integrated system for the management and remediation of a contaminated site

Groundwater contamination requires integrated approaches combining advanced characterization, digital technologies, and adaptive management. This thesis presents a comprehensive framework validated through three case studies demonstrating scalability across geological settings, contamination typologies, and data conditions. The framework systematically integrates heterogeneous datasets, geological, hydrogeological, geochemical, and land-use, within unified digital dashboard that support both conceptual understanding and operational decision-making. By coupling 3D geological modeling with temporal monitoring, the framework enables dynamic "4D" Conceptual Site Models that evolve with site conditions. This work adopts a geology-driven approach, where geological interpretation serves as the structural framework that guides data integration, process understanding, and remediation design. The first case study demonstrates the operational implementation of SUSI, a web-GIS platform that centralizes ten years of multi-source monitoring data from a chlorinated solvent-contaminated site. The platform transforms static datasets into an interactive decision-support system that embeds temporal dynamics and enables adaptive real-time management. The second case study applies the same integrated framework to a complex urban site contaminated by chlorinated solvents, demonstrating how a unified and operational data structure supports both remediation design and adaptive management. The high-resolution-site-characterization and 3D modeling jointly form the dynamic conceptual site model, that guided the design of a customized remediation system combining coaxial groundwater circulation wells, air sparging, and targeted chemical injections. Continuous integration of monitoring data within the digital framework enabled real-time performance evaluation and identification of contamination-decontamination dynamics. This iterative feedback between data, allowed progressive refinement of the remediation strategy in response to site evolution and 7 hydrogeological heterogeneity. The third case study extends the framework to a regional scale, integrating geological, geochemical, and land-use data through geospatial and multivariate statistics analysis to discriminate natural from anthropogenic contamination and establish natural background levels for arsenic, radon, and fluoride in the Viterbo area (central Italy). The thesis advances data-driven, adaptive management grounded in geology-driven characterization, temporal monitoring, and iterative refinement. The framework proves transferable across spatial scales, geological settings, and objectives, from site investigation to remediation design and regional environmental assessment. This work establishes foundations for integrated, process-based approaches to managing complex subsurface contamination, advancing environmental geosciences toward sustainable decision-making under uncertainty.