Sergio Bellagamba

This technical note describes a protocol for the safe excavation of asbestos-bearing serpentinite and the management of the associated spoil during tunnel construction. Asbestos fibers occur naturally in serpentinites belonging to greenstone formations and may pose serious health risks if released and inhaled. Detailed, site-specific risk assessment is therefore required for tunnelling and earthworks involving these lithologies. Where excavation through greenstones cannot be avoided, as in the construction of major national and international infrastructure networks, dedicated safety and waste-management procedures are necessary to minimize occupational and environmental risks. The protocol was implemented considering Italy’s extensive experience with tunnel construction sites and integrates operational controls, environmental monitoring, personal protective equipment, and procedures for the characterization and management of rocks potentially containing asbestos. Drawing on practical site experience, it provides a structured framework for risk assessment, worker protection, exposure prevention, and appropriate spoil classification and handling. The proposed approach is also consistent with circular-economy principles, as more detailed material characterization can enable greater recovery of excavated material for reuse. The present technical note may therefore serve as a practical reference for comparable tunnelling projects involving naturally occurring asbestos.

The detection and monitoring of asbestos–cement roofing remain a critical public health and environmental challenge, especially in urban and suburban areas where asbestos-containing materials are still widespread due to their extensive use in the 20th century. Although hyperspectral and high-resolution multispectral remote sensing have proven effective for mapping asbestos–cement roofs, many existing approaches rely on proprietary software, limiting transparency, reproducibility, and large-scale adoption. This study presents a fully reproducible, cost-free Python-based workflow for the detection and temporal monitoring of asbestos–cement roofing using high-resolution multispectral WorldView-3 imagery. The workflow integrates atmospheric correction (using the Py6S radiative transfer model), spatial preprocessing, supervised pixel-based classification, postprocessing, and building-level aggregation within an open framework. A Maximum Likelihood Classifier is applied to VNIR and SWIR data using empirically defined roof typologies to enhance class separability. Pixel-level results are aggregated to the building scale through adaptive thresholding enabling the translation of spectral classifications into meaningful building-level information. Tested over the city of Mantua (Italy), the approach achieved reliable classification performance and enabled multi-temporal comparison to identify changes potentially due to roof remediation. Evaluation metrics (precision, recall, and F1-score) highlight the importance of carefully choosing the building-level threshold. By relying exclusively on open-source tools, the workflow enhances transparency, reproducibility, and scalability for long-term monitoring.

Asbestos is a well-known carcinogenic substance, and it still represents a common problem in many countries. It was widely used during the twentieth century, thanks to its numerous physical and mechanical characteristics. Many governments banned the manufacturing, importation, processing, and distribution of asbestos-containing products and recommended action plans for the mapping and safe removal of such materials. The remote-sensing approach allows to perform analysis on large portions of territories in a relatively short time, providing an innovative tool for managing urban and natural environments. In this chapter, an overview regarding the application of this technology for asbestos cement roofs mapping is presented. Moreover, the different types of sensors, platforms, and data processing architectures are reported and discussed, to give the readers information that we hope will be useful in designing, setting up, and implementing efficient mapping of asbestos cement roofs based on remote sensing strategies.

This study focuses on the analysis of serpentinite rocks to detect asbestos minerals, with a particular emphasis at discriminating chrysotile from other minerals and matrices (antigorite, lizardite) in serpentinites. The primary objective of this exploratory study is to lay the groundwork for the development of an innovative system for real-time identification of asbestos on the surface of materials and rocks. In more detail, the study emphasizes the advantages of utilization of rapid and reliable portable instrumentation, aided by machine learning (ML) techniques. Serpentinite rock debris from tunnel excavation activities (i.e. tunnel spoil) were collected at the Cravasco railway tunnelling site (Genoa - Italy) of the TEN-T Rhine-Alps Core Corridor. On-site analysis was conducted using the ASD FieldSpec 4 Standard-Res spectrophotoradiometer (350-2500 nm). A two-class classifier was developed based on acquired reflectance spectra, to discriminate between the "Presence of asbestiform fibers" and the "Absence of asbestiform fibers". The classification model was calibrated through stereomicroscopic inspection and electron microscopy carried on selected sample fractions.