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Data

What are the most common items of waste found in rivers and oceans?

About this data

Source
Morales-Caselles et al. (2021) – processed by Our World in Data
Last updated
September 1, 2022
Date range
2021–2021

Sources and processing

This data is based on the following sources

Morales-Caselles et al. – An inshore-offshore sorting system revealed from global classification of ocean litter

This study included samples more than 12 million litter items retrieved from 7 major river and ocean environments globally. To build such a large inventories of macro-litter items, the authors compiled a total of 36 datasets providing counts of litter by item typology in river waters and riverbed, shoreline, nearshore waters (<100 km from shoreline) and nearshore seafloor (<100 m depth, <100 km from shoreline), open waters (>100 km from shoreline) and deep seafloor (>100 m depth, >100 km from shoreline).

These litter items were classified according to their material composition, type of product and probable origin.

On average, 80% of the items were made of plastic, followed by metal (7% ± 7%), glass (5% ± 6%) and fabric (3% ± 3%).

Retrieved on
September 1, 2022
Retrieved from
https://www.nature.com/articles/s41893-021-00720-8
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Morales-Caselles, C., Viejo, J., Martí, E., González-Fernández, D., Pragnell-Raasch, H., González-Gordillo, J. I., ... & Cózar, A. (2021). An inshore-offshore sorting system revealed from global classification of ocean litter. Nature Sustainability, 4(6), 484-493.

This study included samples more than 12 million litter items retrieved from 7 major river and ocean environments globally. To build such a large inventories of macro-litter items, the authors compiled a total of 36 datasets providing counts of litter by item typology in river waters and riverbed, shoreline, nearshore waters (<100 km from shoreline) and nearshore seafloor (<100 m depth, <100 km from shoreline), open waters (>100 km from shoreline) and deep seafloor (>100 m depth, >100 km from shoreline).

These litter items were classified according to their material composition, type of product and probable origin.

On average, 80% of the items were made of plastic, followed by metal (7% ± 7%), glass (5% ± 6%) and fabric (3% ± 3%).

Retrieved on
September 1, 2022
Retrieved from
https://www.nature.com/articles/s41893-021-00720-8
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Morales-Caselles, C., Viejo, J., Martí, E., González-Fernández, D., Pragnell-Raasch, H., González-Gordillo, J. I., ... & Cózar, A. (2021). An inshore-offshore sorting system revealed from global classification of ocean litter. Nature Sustainability, 4(6), 484-493.

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Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: What are the most common items of waste found in rivers and oceans?”. Our World in Data (2026). Data adapted from Morales-Caselles et al.. Retrieved from https://archive.ourworldindata.org/20260511-092124/grapher/most-common-waste-rivers-oceans.html [online resource] (archived on May 11, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Morales-Caselles et al. (2021) – processed by Our World in Data

Full citation

Morales-Caselles et al. (2021) – processed by Our World in Data. “What are the most common items of waste found in rivers and oceans?” [dataset]. Morales-Caselles et al., “An inshore-offshore sorting system revealed from global classification of ocean litter” [original data]. Retrieved May 15, 2026 from https://archive.ourworldindata.org/20260511-092124/grapher/most-common-waste-rivers-oceans.html (archived on May 11, 2026).

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Data URL (CSV format)
https://ourworldindata.org/grapher/most-common-waste-rivers-oceans.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/most-common-waste-rivers-oceans.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/most-common-waste-rivers-oceans.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/most-common-waste-rivers-oceans.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/most-common-waste-rivers-oceans.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/most-common-waste-rivers-oceans.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/most-common-waste-rivers-oceans.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/most-common-waste-rivers-oceans.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear