A structure–function based approach to floc hierarchy and evidence for the non-fractal nature of natural sediment flocs

Spencer, Kate, Wheatland, Jonathan, Bushby, Andy, Carr, Simon ORCID logo ORCID: https://orcid.org/0000-0003-4487-3551 , Droppo, Ian and Manning, Andrew (2021) A structure–function based approach to floc hierarchy and evidence for the non-fractal nature of natural sediment flocs. Scientific Reports, 11 (1). p. 14012.

[thumbnail of s41598-021-93302-9.pdf]
Preview
PDF - Published Version
Available under License CC BY

Download (1MB) | Preview
Official URL: https://doi.org/10.1038/s41598-021-93302-9

Abstract

Natural sediment flocs are fragile, highly irregular, loosely bound aggregates comprising minerogenic and organic material. They contribute a major component of suspended sediment load and are critical for the fate and flux of sediment, carbon and pollutants in aquatic environments. Understanding their behaviour is essential to the sustainable management of waterways, fisheries and marine industries. For several decades, modelling approaches have utilised fractal mathematics and observations of two dimensional (2D) floc size distributions to infer levels of aggregation and predict their behaviour. Whilst this is a computationally simple solution, it is highly unlikely to reflect the complexity of natural sediment flocs and current models predicting fine sediment hydrodynamics are not efficient. Here, we show how new observations of fragile floc structures in three dimensions (3D) demonstrate unequivocally that natural flocs are non-fractal. We propose that floc hierarchy is based on observations of 3D structure and function rather than 2D size distribution. In contrast to fractal theory, our data indicate that flocs possess characteristics of emergent systems including non-linearity and scale-dependent feedbacks. These concepts and new data to quantify floc structures offer the opportunity to explore new emergence-based floc frameworks which better represent natural floc behaviour and could advance our predictive capacity.

Item Type: Article
Journal / Publication Title: Scientific Reports
Publisher: Nature Research
ISSN: 2045-2322
Departments: Institute of Science and Environment > STEM
Additional Information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Depositing User: Insight Administrator
SWORD Depositor: Insight Administrator
Date Deposited: 03 Sep 2021 11:31
Last Modified: 13 Jan 2024 12:30
URI: https://insight.cumbria.ac.uk/id/eprint/6163

Downloads

Downloads per month over past year



Downloads each year

Edit Item