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Macrolitter dataset from a highly frequented roadway in Nantes, France

Land-based sources of riverine macrolitter are now recognized as a major concern, but few field data on litter amount, composition and sources are available. This is especially the case for macrolitter hotspots like high frequented roadways that could generate large amount of macrolitter potentially...

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Autores principales: Tramoy, Romain, Ledieu, Lauriane, Ricordel, Sophie, Astrié, Daniel, Tassin, Bruno, Gasperi, Johnny
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9118109/
https://www.ncbi.nlm.nih.gov/pubmed/35599826
http://dx.doi.org/10.1016/j.dib.2022.108237
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author Tramoy, Romain
Ledieu, Lauriane
Ricordel, Sophie
Astrié, Daniel
Tassin, Bruno
Gasperi, Johnny
author_facet Tramoy, Romain
Ledieu, Lauriane
Ricordel, Sophie
Astrié, Daniel
Tassin, Bruno
Gasperi, Johnny
author_sort Tramoy, Romain
collection PubMed
description Land-based sources of riverine macrolitter are now recognized as a major concern, but few field data on litter amount, composition and sources are available. This is especially the case for macrolitter hotspots like high frequented roadways that could generate large amount of macrolitter potentially reaching rivers. This dataset provides macrolitter amount and composition over one year from a retention pond collecting stormwater and carried macrolitter from a 800 m portion of a highly frequented roadway (around 90,000 vehicles per day). The typology of macrolitter was defined using the TSG-ML/OSPAR classifications. A total of 36,439 items in which 84% of plastics were individually counted, classified and weighted by category for a total mass of 88.5 kg (60% of plastics). Raw data are available in Mendeley Data (DOI:10.17632/t6ryv6crjd.4). Top 10 items represent 92% by count of the total with plastic fragments (31%), cigarette butts (18%), EPS fragments (17%) or foam packaging (11%) as most common items. Top 10 items represent 72% by mass of the total with plastic fragments (24%) and Cardboard (13%) as most common items, followed by foam packaging (6%), wood fragments (6%), industrial plastic sheets (5%), rubber fragments (4%) and EPS fragments (4%). More than 94% of plastic items are below 1.9 g/item. This dataset is related to the research paper Amount, composition and sources of macrolitter from a highly frequented roadway.
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spelling pubmed-91181092022-05-20 Macrolitter dataset from a highly frequented roadway in Nantes, France Tramoy, Romain Ledieu, Lauriane Ricordel, Sophie Astrié, Daniel Tassin, Bruno Gasperi, Johnny Data Brief Data Article Land-based sources of riverine macrolitter are now recognized as a major concern, but few field data on litter amount, composition and sources are available. This is especially the case for macrolitter hotspots like high frequented roadways that could generate large amount of macrolitter potentially reaching rivers. This dataset provides macrolitter amount and composition over one year from a retention pond collecting stormwater and carried macrolitter from a 800 m portion of a highly frequented roadway (around 90,000 vehicles per day). The typology of macrolitter was defined using the TSG-ML/OSPAR classifications. A total of 36,439 items in which 84% of plastics were individually counted, classified and weighted by category for a total mass of 88.5 kg (60% of plastics). Raw data are available in Mendeley Data (DOI:10.17632/t6ryv6crjd.4). Top 10 items represent 92% by count of the total with plastic fragments (31%), cigarette butts (18%), EPS fragments (17%) or foam packaging (11%) as most common items. Top 10 items represent 72% by mass of the total with plastic fragments (24%) and Cardboard (13%) as most common items, followed by foam packaging (6%), wood fragments (6%), industrial plastic sheets (5%), rubber fragments (4%) and EPS fragments (4%). More than 94% of plastic items are below 1.9 g/item. This dataset is related to the research paper Amount, composition and sources of macrolitter from a highly frequented roadway. Elsevier 2022-05-04 /pmc/articles/PMC9118109/ /pubmed/35599826 http://dx.doi.org/10.1016/j.dib.2022.108237 Text en © 2022 The Author(s). Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Tramoy, Romain
Ledieu, Lauriane
Ricordel, Sophie
Astrié, Daniel
Tassin, Bruno
Gasperi, Johnny
Macrolitter dataset from a highly frequented roadway in Nantes, France
title Macrolitter dataset from a highly frequented roadway in Nantes, France
title_full Macrolitter dataset from a highly frequented roadway in Nantes, France
title_fullStr Macrolitter dataset from a highly frequented roadway in Nantes, France
title_full_unstemmed Macrolitter dataset from a highly frequented roadway in Nantes, France
title_short Macrolitter dataset from a highly frequented roadway in Nantes, France
title_sort macrolitter dataset from a highly frequented roadway in nantes, france
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9118109/
https://www.ncbi.nlm.nih.gov/pubmed/35599826
http://dx.doi.org/10.1016/j.dib.2022.108237
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