Deep learning for asbestos counting.

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.

Asbestos in soil and water: A review of analytical techniques and methods.

In this review the main standard and novel analytical techniques and methods for sampling, sample preparation, detection and quantification of asbestos in soil and water are described, compared and discussed in terms of advantages and limitations. An overview of common analytical methods applied for identification and quantification of airborne asbestos is preliminary provided, as they have been widely studied, due to the well-known human pathologies related to fibers inhalation. Despite the presence of asbestos in soil and water may also constitute a health risk, it has been less investigated and regulated.

Classification and management of asbestos-containing waste: European legislation and the Italian experience.

Objective: Production of a new classification of Asbestos Containing Products (ACPs), materials (ACM) and Asbestos Containing Waste (ACW), in addition to a correct identification of landfills where ACW should be disposed of in Europe.

Methods: Analysis of the European and Italian legislation, study of waste classification and management in the main European countries, data analysis of mapping of Italian landfills and quantification of ACW disposed there.

Findings: Classification according to unique criteria (physical state, substances with which asbestos minerals have been blended, function of the asbestos, etc.