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Historical trends of microplastic pollution in the Seine River (France) from 1960 to 2020 through the study of sedimentary archives
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Researchers reconstructed the spatial and temporal history of microplastic pollution in the Seine River basin from 1960 to 2020 using sedimentary archives, characterizing contamination trends across the watershed to provide a long-term pollution record for a major European river system.
Nowadays, it is widely demonstrated that microplastic (MP) pollution is ubiquitous in the environment. MP have been characterised in many matrices such as water bodies, sediments, soil, air, biota. Sediments are one of the possible matrices in which microplastic pollution can settle for a long period of time. Therefore, sediments can be used to record historical MP contamination levels. This work involves the spatial and temporal reconstruction of MP contamination in the Seine River basin, upstream and downstream of Paris (France), from circa 1960 to 2020. The methodology developped for this work first involves a sedimentological approach, with the identification of suitable sites, followed by the acquisition and characterisation of sediment cores relevant for the recording of MP (dating, granulometry, sediment properties). Then, the MP content was extracted from the matrix and characterised using infrared analysis. This was carried out using a micro-IRTF imager equipped with a Focal Plane Array (FPA), enabling the simultaneous acquisition of several thousand spectra with a spatial resolution set at 20 µm. This technique makes it possible to superimpose the optical image and the chemical image of the samples. Thus, the polymer type of each MP particle present in each sample along with its size and shape can be registered. In this way, it is possible to match the concentrations of each polymer with the age of the sediments. This work shows the evolving trends of key plastic polymers (PE, PP, PS, PET, PVC) over 7 decades in the Seine River basin and discusses the possible factors that may have lead to these temporal trends. Also see: https://micro2024.sciencesconf.org/559372/document
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