The European Interreg Italy–France 2014–2020 Maritime Project SPlasH! (Stop to Plastics in H₂O!) focused on the study of microplastics (MPs) in the marine port environment to evaluate their presence, abundance, and mechanisms of diffusion to the open sea. In the framework of this project, a worldwide review of 74 studies was carried out, providing an overview of MP investigation techniques, focusing on sampling strategies, laboratory methodologies, and identification of MPs collected in seawater, and specifically evaluating their applicability to the marine port environment. Nets were the most commonly used device for MP surface sampling, but their use can be difficult in narrow spaces within the port basins, and they must be coupled to discrete sampling devices to cover all port basins. In the laboratory, density separation (NaCl, ZnCl₂, NaI, sodium lauryl sulfate (SLS)), filtration (polycarbonate, polyamide, glass, cellulose, ANOPORE inorganic membrane filters), sieving, visual sorting, and digestion methods (acidic, enzymatic, alkaline, oxidative) were used to separate MPs from seawater. Digestion becomes essential with water samples with great inorganic and organic loads as deriving from a port. Although many studies are based only on visual MP identification under a microscope, analytical identification techniques unequivocally determine the particle nature and the identity of the plastic polymers and are necessary to validate the visual sorting of MPs. Fourier-transform infrared spectroscopy (FTIR) is the most used analytical identification technique.

Statistical approaches to study extreme events require, by definition, long time series of data. In many scientific disciplines, these series are often subject to variations at different temporal scales that affect the frequency and intensity of their extremes. Therefore, the assumption of stationarity is violated and alternative methods to conventional stationary extreme value analysis (EVA) must be adopted. Using the example of environmental variables subject to climate change, in this study we introduce the transformed-stationary (TS) methodology for non-stationary EVA. This approach consists of (i) transforming a non-stationary time series into a stationary one, to which the stationary EVA theory can be applied, and (ii) reverse transforming the result into a non-stationary extreme value distribution. As a transformation, we propose and discuss a simple time-varying normalization of the signal and show that it enables a comprehensive formulation of non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with a constant shape parameter. A validation of the methodology is carried out on time series of significant wave height, residual water level, and river discharge, which show varying degrees of long-term and seasonal variability. The results from the proposed approach are comparable with the results from (a) a stationary EVA on quasi-stationary slices of non-stationary series and (b) the established method for non-stationary EVA. However, the proposed technique comes with advantages in both cases. For example, in contrast to (a), the proposed technique uses the whole time horizon of the series for the estimation of the extremes, allowing for a more accurate estimation of large return levels. Furthermore, with respect to (b), it decouples the detection of non-stationary patterns from the fitting of the extreme value distribution. As a result, the steps of the analysis are simplified and intermediate diagnostics are possible. In particular, the transformation can be carried out by means of simple statistical techniques such as low-pass filters based on the running mean and the standard deviation, and the fitting procedure is a stationary one with a few degrees of freedom and is easy to implement and control. An open-source MATLAB toolbox has been developed to cover this methodology, which is available at https://github.com/menta78/tsEva/ (Mentaschi et al., 2016).