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Main objective of this study was to describe chemical composition of MP collected in surface waters of Mediterranean Sea. Samples were collected by manta net during Tara Mediterranean expedition (June–Nov.2014). Our results pointed to certain homogeneity at Mediterranean Sea scale. Main polymers collected were polyethylene (PE) (67.3±2.4%), polypropylene (20.8±2.1%), polystyrene (3.0±0.9%). Still, discrepancies, confirmed by literature, were observed at mesoscale level. Thus, in North Tyrrhenian Sea, proportion of PE was significantly lower than average value of Mediterranean Sea (57.9±10.5%).

The Mediterranean Sea is one of the most studied regions in the world in terms of microplastic (MP) contamination. However, only a few studies have analysed the chemical composition of MPs at the Mediterranean Sea surface. In this context, this study aims to describe the chemical composition as a function of particle size, mass and number concentrations of MPs collected in the surface waters of the Mediterranean Sea. The chemical composition showed a certain homogeneity at the Mediterranean Sea scale. The main polymers identified by Fourier Transform Infra-Red (FTIR) spectroscopy were poly(ethylene) (67.3 +/- 2.4%), poly(propylene) (20.8 +/- 2.1%) and poly(styrene) (3.0 +/- 0.9%). Nevertheless, discrepancies, confirmed by the literature, were observed at a mesoscale level. Thus, in the North Tyrrhenian Sea, the proportion of poly(ethylene) was significantly lower than the average value of the Mediterranean Sea (57.9 +/- 10.5%). Anthropic sources, rivers, or polymer ageing are assumed to be responsible for the variations observed.

International audience; The development of methods to automatically determine the chemical nature of microplastics by FTIR-ATR spectra is an important challenge. A machine learning method, named k-nearest neighbors classification, has been applied on spectra of microplastics collected during Tara Expedition in the Mediterranean Sea (2014). To realize these tests, a learning database composed of 969 microplastic spectra has been created. Results show that the machine learning process is very efficient to identify spectra of classical polymers such as poly(ethylene), but also that the learning database must be enhanced with less common microplastic spectra. Finally, this method has been applied on more than 4000 spectra of unidentified microplastics. The verification protocol showed less than 10% difference in the results between the proposed automated method and a human expertise, 75% of which can be very easily corrected. Highlights ► A machine learning algorithm was developed to determine the chemical nature of microplastics. ► This method allows a fast and reliable automated identification even when several thousand of FTIR spectra have to be studied.► This method is the first part of a software dedicated to the study of microplastics: POSEIDON.

© 2019, The Author(s). Environmental managers require a sensitive and reliable means to prove, with the highest level of confidence possible, where non-native fish species exist and where they do not. Therefore, a nested PCR (nPCR) protocol was developed to detect the environmental DNA (eDNA) of a case-study species, topmouth gudgeon Pseudorasbora parva, which was recently the subject of a national eradication campaign in the UK. The nPCR protocol was tested in the laboratory and in the field in a series of coordinated surveys (eDNA and conventional sampling with traps) at a commercial angling venue in southern England where an initial eDNA survey, based on conventional PCR (cPCR), found P. parva to be present in one of the seven ponds. In the laboratory, the nPCR protocol was on average 100× more sensitive than cPCR, providing a 100% detection rate at DNA concentrations of 3 × 10 −8 ng/µL (8 DNA copies per µL). In the field, nPCR and conventional trapping both detected P. parva in only one of the seven angling ponds, the same infested pond as in the previous cPCR-based study. Following eradication work on the infested pond, no eDNA of P. parva was detected using nPCR in either the formerly-infested pond or the adjacent pond, which had been used to quarantine large commercially-valuable fishes. In management applications where the veracity of negative results may be of equal importance as confirmation of positive detections, nPCR protocols provide a useful addition to the analytical toolkit available to inform decision makers responsible for non-native species management.

Since the 1950s plastic production has exponentially increased. Different expeditions have sampled microplastics in all the world’s oceans in order to better know this pollution. However, a large amount of plastic particles is collected during all campaigns, such as during the Tara Mediterranean Sea campaign. And, methods developed to analyse them are time-consuming. So, how to work to chemically characterize samples from large libraries?