Dr Claus Svendsen
UK Centre for Ecology & Hydrology
Maclean Building, Benson Lane, Crowmarsh Gifford, Wallingford
OX10 8BB, Oxfordshire
Tel : +1491 692676
Dr Wendel Wohlleben, BASF SE, Germany, firstname.lastname@example.org
Dr Antonia Praetorius, University of Amsterdam (UvA), The Netherlands, email@example.com
Dr Prof Mark R Wiesner, Duke University, North Carolina, USA firstname.lastname@example.org
The overarching objective of this project is to develop and deliver a pragmatic open-source mechanistic model of how environmental factors affect plastic fragmentation and degradation rates. The model will be applicable across a range of conditions in multiple environmental compartments. Our approach builds on an understanding of how fragmentation rates depend on energy dissipation rates experienced by plastics and links to intrinsic chemical and physical properties of polymers. It recognises that these properties change over time through aging/degradation (e.g. additive loss, cross linking, crack formation) induced by extrinsic environmental factors (UV, mechanical, chemical and biological). The resulting model will readily interface with other LRI models addressing size dependent aging (additive loss), fate and transport processes (residence time in each environmental compartments), affecting polymer degradation and fragmentation rates and resulting size distributions over time (mutual feedback needed).
To accomplish this, we bring together elements of fundamental physical chemistry, fluid mechanics, material science and data science and extend our experience with existing fragmentation theory, to yield a unified view of how key environmental factors affect degradation and fragmentation rates for polymer particles. This includes establishing how such rates depend on the “intensity” of each factor within their natural ranges. By joining this with understanding of how the conditions within any one compartment can effectively be described as a combination (or sequence) of “intensities” and “available reaction times” for each factor, we aim to rationalise the otherwise infinite number of combinations of “input ranges” for environmental factors. We will ensure that we can maximise the overall operational domain while keeping the scalable model element parsimoniously driven by only key parameters needed around each environmental factor, to represent the factor combinations active within different environmental compartments and their matrices (Fig 1). This approach also enables other objectives, namely to deliver a model requiring only input parameters that are either known or can be experimentally derived for a broad variety of polymer types and sizes, and validating them in environmentally realistic conditions, covering condition ranges relevant for waters, air, soils and sediments.
In doing so, we will advance beyond state-of-the-art in our understanding and ability to predict microplastic fragmentation in the environment, and deliver a model usable by a variety of end users and interoperable with other model systems, to help guide exposure and risk assessment. Importantly for this our model will predict to what extent nanoplastics are generated, and if they are, how long they persist. Accounting for the rates by which each fragmentation process may lead to complete terminal breakdown of the nanoplastics (likely aggregation dependent) into non-particle entities (i.e. dissolved chemical compounds) will enable prediction of “exiting mass” from the particle based elements of fate and exposure modelling, delivering more realistic hazard and risk assessments.
This model and its scientific impact will be delivered through four main objectives:
- Build an inter-consortia “LRI Cluster”, encompassing LRI projects ECO56-59, to ensure interoperability, dissemination and impact of concepts, models and data.
- Conceptualise and develop an open-source mechanistic model of Micro and NanoPlastic FRAGMentation in the EnvironmeNT (FRAGMENT-MNP), ensuring interoperability with other models through the LRI Cluster.
- Provide an experimental database of key parameters to FRAGMENT-MNP, leveraging in-house databases, existing literature and targeted gap-filling experiments (including parameters shared with or needed across LRI ECO56-59 Cluster).
Validate the predictions of the model using targeted simulation studies of combined factors, up to realistic environmental conditions (sharing materials within LRI Cluster).