The objectives of this paper will be to discuss the issues related to the determination of the limits of detection (LOD) in laser-induced breakdown spectroscopy (LIBS) analytical applications. The derivation of the commonly used ‘3-sigma over slope’ rule and its evolution towards the new official definition recently adopted by the International Union of Pure and Applied Chemistry (IUPAC) will be illustrated. Methods for extending the calculation of the LOD to LIBS multivariate analysis will also be discussed, using as an example the detection of Cu traces in cast iron samples by LIBS.

The rapid identification of beached marine micro-plastics is essential for the determination of the source of pollution and for planning the most effective strategies for remediation. In this paper, we present the results obtained by applying the laser-induced breakdown spectroscopy (LIBS) technique on a large sample of different kinds of plastics that can be found in a marine environment. The use of chemometric analytical tools allowed a rapid classification of the pellets with an accuracy greater than 80%. The LIBS spectrum and statistical tests proved their worth to quickly identify polymers, and in particular, to distinguish C-O from C-C backbone pellets, and PE from PP ones. In addition, the PCA analysis revealed a correlation between appearance (surface pellets roughness) and color (yellowing), as reported by other recent studies. The preliminary results on the analysis of metals accumulated on the surface of the pellets are also reported. The implication of these results is discussed in view of the possibility of frequent monitoring of the marine plastic pollution on the seacoast.

The detection and classification of microplastics in the marine environment is a complex task that implies the use of delicate and expensive instrumentation. In this paper, we present a preliminary feasibility study for the development of a low-cost, compact microplastics sensor that could be mounted, in principle, on a float of drifters, for the monitoring of large marine surfaces. The preliminary results of the study indicate that a simple sensor equipped with three infrared-sensitive photodiodes can reach classification accuracies around 90% for the most-diffused floating microplastics in the marine environment (polyethylene and polypropylene).

Resin pellets, which are highly prone to environmental dispersion, can absorb pollutants such as polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) once they enter marine environments. Principal aim of the study was to statistically confirm the correlation between pollutant content and the yellowing parameter - indicative of chemical aging of pellets - behind the use of pellets as a cost-effective alternative for chemical pollution assessment in the marine environment. In parallel, we first tested the use of the LIBS (Laser-Induced Breakdown Spectroscopy technique to detect the presence of metals on the surface of pellets. We conducted Italy's first national survey of resin pellets through a citizen science initiative. A total of 2270 pellets were collected from 73 beaches across 13 Italian regions and analyzed based on morphological characteristics, including discolouration, fouling presence, and porosity. From selected subsamples we extracted four PAH and six PCB congeners and examined the surface heavy metal content using LIBS technique. Pollutant concentration values were found to be correlated with the discoloration parameter of yellowing, further validating the use of resin pellets as an effective proxy for environmental pollution in place of traditional methods like mussels or others environmental matrix, as first proposed in the International Pellets Watch (IPW) framework.

Several samples coming from the recently discovered (February 2019) Late Bronze Age/Early Iron Age Chovdar necropolis in Azerbaijan were analysed using the X-ray fluorescence (XRF) technique. The analysis allowed a preliminary classification of the samples in eight groups based on their composition, obtained from the XRF spectra using the fundamental parameter method. A more detailed classification was then obtained using the graph clustering method.