Computational models to confront the complex pollution footprint of plastic in the environment

MacLeod, Matthew; Domercq, Prado; Harrison, Sam ORCID: https://orcid.org/0000-0001-8491-4720; Praetorius, Antonia. 2023 Computational models to confront the complex pollution footprint of plastic in the environment. Nature Computational Science, 3. 486-494. https://doi.org/10.1038/s43588-023-00445-y

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Official URL: http://dx.doi.org/10.1038/s43588-023-00445-y

Abstract/Summary

The threat posed by plastic in the environment is poorly characterized due to uncertainties and unknowns about sources, transport, transformation and removal processes, and the properties of the plastic pollution itself. Plastic creates a footprint of particulate pollution with a diversity of composition, size and shape, and a halo of chemicals. In this Perspective, we argue that process-based mass-balance models could provide a platform to synthesize knowledge about plastic pollution as a function of its measurable intrinsic properties.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1038/s43588-023-00445-y
UKCEH and CEH Sections/Science Areas:	Pollution (Science Area 2017-)
ISSN:	2662-8457
Additional Keywords:	computational science, environmental impact, pollution remediation
NORA Subject Terms:	Ecology and Environment Computer Science
Date made live:	09 Nov 2023 08:47 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/535720

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1038/s43588-023-00445-y

UKCEH and CEH Sections/Science Areas:

Pollution (Science Area 2017-)

ISSN:

2662-8457

Additional Keywords:

computational science, environmental impact, pollution remediation

NORA Subject Terms:

Ecology and Environment
Computer Science