Microplastic Contamination in Snow from Western Italian Alps.

Recent studies have documented the presence of microplastics (MPs) in remote areas, including soils or sediments collected in mountain and glacier environments, but information on their presence in snow is scant. The present study aimed at exploring the presence of MPs in residual snow collected in four locations of the Aosta Valley (Western Italian Alps), with different accessibility and human presence. Overall, the µ-FTIR analyses confirmed the presence of 18 MPs in snow, 7 (39%) items were fibres, while 11 (61%) were fragments. Polyethylene (PE; 7 MPs) was the main polymer, followed by polyethylene terephthalate (PET; 3 MPs), high density PE (HDPE; 3 MPs), polyester (2 MPs), while only 1 MP made by low density PE, polypropylene and polyurethane were found. The mean (± SE) concentration of MPs in snow ranged between 0.39 ± 0.39 MPs/L and 4.91 ± 2.48 MPs/L, with a mean of 2.32 ± 0.96 MPs/L for the sampling locations. The concentration of MPs did not statistically differ among locations. Our results suggest that MPs presence in high-mountain ecosystems might depend on deposition through atmospheric precipitations or local sources due to human activities. For these reasons, policies aiming at reducing plastic use and dispersal in mountain areas may be effective in preventing local MP contamination.

Innovative unattended SEM-EDS analysis for asbestos fiber quantification.

Scanning electron microscopy with energy dispersive spectrometry (SEM-EDS) is the only affordable analytical technique that can discriminate both morphology and elemental composition of inorganic fibers. SEM-EDS is indeed required to quantify asbestos in confounding natural matrixes (e.g. ophiolites), but is also time-consuming, operator dependent, and strongly relies on the stochastic distribution of the fibers on the filter surface. The balance between analytical time/cost and the method sensibility allows only about 0.5% of the filter to be analyzed, strongly affecting the statistical significance of results. To improve sensitivity and precision and enhance productivity, an unattended quantitative measurement of the asbestos fibers by SEM-EDS is proposed. The method identifies the particle shape first and determines their chemical composition later, saving EDS analytical time. Our approach was tested on four asbestos standards and the relative error on replicated measurements was < 10%. The proposed unattended method quantifies asbestos in natural confounding matrix, also with a very low asbestos content.

Mapping asbestos-cement roofing with hyperspectral remote sensing over a large mountain region of the Italian Western Alps.

The World Health Organization estimates that 100 thousand people in the world die every year from asbestos-related cancers and more than 300 thousand European citizens are expected to die from asbestos-related mesothelioma by 2030. Both the European and the Italian legislations have banned the manufacture, importation, processing and distribution in commerce of asbestos-containing products and have recommended action plans for the safe removal of asbestos from public and private buildings. This paper describes the quantitative mapping of asbestos-cement covers over a large mountainous region of Italian Western Alps using the Multispectral Infrared and Visible Imaging Spectrometer sensor. A very large data set made up of 61 airborne transect strips covering 3263 km2 were processed to support the identification of buildings with asbestos-cement roofing, promoted by the Valle d'Aosta Autonomous Region with the support of the Regional Environmental Protection Agency. Results showed an overall mapping accuracy of 80%, in terms of asbestos-cement surface detected. The influence of topography on the classification's accuracy suggested that even in high relief landscapes, the spatial resolution of data is the major source of errors and the smaller asbestos-cement covers were not detected or misclassified.