Diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy is a well-known technique for thin film characterization. Since all asbestos species exhibit intense adsorptions peaks in the 4000–400 cm⁻¹ region of the infrared spectrum, a quantitative analysis of asbestos in bulk samples by DRIFT is possible. In this work, different quantitative analytical procedures have been used to quantify chrysotile content in bulk materials produced by building requalification: partial least squares (PLS) chemometrics, the Linear Calibration Curve Method (LCM) and the Method of Additions (MoA). Each method has its own pros and cons, but all give affordable results for material characterization: the amount of asbestos (around 10%, weight by weight) can be determined with precision and accuracy (errors less than 0.1).

The PCM (phase contrast microscopy) method for asbestos counting needs special sample treatments, hence it is time consuming and rather expensive. As an alternative, we implemented a deep learning procedure on images directly acquired from the untreated airborne samples using standard Mixed Cellulose Ester (MCE) filters. Several samples with a mix of chrysotile and crocidolite with different concentration loads have been prepared. Using a 20x objective lens coupled with a backlight illumination system a number of 140 images were collected from these samples, which along with additional 13 highly fibre loaded artificial images constituted the database. About 7500 fibres were manually recognised and annotated following the National Institute for Occupational Safety and Health (NIOSH) fibre counting Method 7400 as input for the training and validation of the model. The best trained model provides a total precision of 0.84 with F1-Score of 0.77 at a confidence of 0.64. A further post-detection refinement to ignore detected fibres < 5 µm in length improves the final precision. This method can be considered as a reliable and competent alternative to conventional PCM.

Asbestos is now banned in 52 countries. Although Italy banned asbestos in 1992, up until that date it had been one of the main producer nations of asbestos and asbestos-containing materials, and asbestos-related contamination is still widespread in the country. To reduce asbestos-related health effects, Italy has adopted many laws and regulations regarding exposure thresholds and remediation tools. Even so, there are legislative gaps that are making it difficult to manage related risks especially in the operative phase. The lack of standard procedures at a national level regarding emergency shutdown and remediation as well as reference thresholds for specific risk situations creates difficulties and different actions when dealing with the cleanup of Italian asbestos superfund sites. The authors propose operative guidelines for asbestos remediation at Italian superfund sites. INAIL, the reference national organization for asbestos-related matters, acting as an advisor to a number of state, regional, and local authorities, examined the main asbestos-related risk situations in Italy and proposed the most appropriate actions to take. The detailed analysis of many actual cases of risk, in part through inspections and the management of cleanup actions at asbestos Italian superfund sites, resulted in proposals to modify existing procedures and thresholds, which were subsequently discussed with all national, regional, and local scientific bodies. After more than two years of work and discussion at a national level, INAIL-DIPIA-Asbestos Group drafted new Guidelines for Asbestos Remediation at Italian Superfund sites, and officially submitted them to the Environment Ministry. The Ministry then adopted the document in regard to all asbestos Italian superfunds. This recently released document is also a useful reference for contaminated sites at a regional and local level. The operative Guidelines for Asbestos Remediation at Italian Superfund sites may also be of use at an international level for countries that have already banned asbestos and are engaged in remediation activity and for countries that have not yet banned asbestos but wish to adopt risk prevention measures.

Asbestos has been widely used in many applications for its technical properties (i.e. resistance to abrasion, heat and chemicals). Despite its properties, asbestos is recognized as a hazardous material to human health. In this paper a study, based on multivariate analysis, was carried out to verify the possibilities to utilize the hyperspectral imaging (HSI), working in the short-wave infrared range (SWIR: 1000-2500 nm), to detect the presence of asbestos-containing materials (ACM) in construction and demolition waste (CDW). Multivariate classification methods including classification and regression tree (CART), partial least squares-discriminant analysis (PLS-DA) and correcting output coding with support vector machines (ECOC-SVM), were adopted to perform the recognition/classification of ACM in respect of the other fibrous panels not containing asbestos, in order to verify and compare Efficiency and robustness of the classifiers. The correctness of classification results was confirmed by micro-X-ray fluorescence maps. The results demonstrate as SWIR technology, coupled with multivariate analysis modeling, is a quite promising approach to develop both “off-line” and “on-line” fast reliable and robust quality control strategies, finalized to perform a first evaluation of the presence of ACM.