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Microplastic contamination in the highly polluted Tietê River (São Paulo, Brazil): an unsustainable human-nature relationship
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Researchers analyzed microplastic contamination in the Tiete River downstream of Sao Paulo, one of the most polluted rivers in Brazil. They found microplastics in both water and sediment samples across wet and dry seasons, with fibers and fragments being the dominant types. The study confirms that heavily urbanized areas are major sources of microplastic pollution that contaminates downstream river ecosystems.
Most domestic and industrial waste, along with plastic litter from São Paulo city, flows into the Tietê River. Microplastics (MPs) from water column and sediment samples of the Tietê River, a marginal lagoon, and Peixe River (tributary) were analyzed to verify microplastic contamination downstream the São Paulo metropolitan region. Water samples were collected after the rainy season (April 2021) and during the dry season (August 2021), with a plankton net. Sediment samples were collected only in April 2021, with a dredge. Samples were submitted to wet peroxidation (HO + Fe (II) at 70 °C) and then passed through metal sieves (minimum 0.053 mm, maximum 4 mm). Microscopic counting characterized microplastic size, form, and color. µFTIR analysis was performed to verify particle chemical composition. For water, there was an increasing gradient of MPs: Peixe River < Tietê lagoon < Tietê River. For sediments, contamination in the main river and the marginal lagoon is of the same magnitude (10 MPs/kg). Size, form, and color proportions were similar when comparing the water and sediment. Small-sized transparent and blue fibers predominated. The main types of polymers were PET (23.5%), followed by HDPE, polyester, and polyethylene (14.7% each). The lentic condition of the lagoon did not increase particles in the sediment when compared to the main river. Contamination in the Tietê River water column was higher after the rains in São Paulo even 300 km downstream. Integrated water-sediment analyses were important to understand distinct processes on both spatial and temporal scales.
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