Hossain, MM, Banerjee, A, Chatterjee, M, Roy, K and Cronin, MT (2024) QSPR and q-RASPR predictions of the adsorption capacity of polyethylene, polypropylene and polystyrene microplastics for various organic pollutants in diverse aqueous environments. Environmental Science: Nano. ISSN 2051-8153

Text QSPR and q-RASPR predictions of the adsorption capacity of polyethylene, polypropylene and polystyrene microplastics.pdf - Accepted Version Restricted to Repository staff only until 27 July 2025. Download (1MB)

Abstract

Microplastics (MPs) and nanoplastics (NPs) have become significant environmental concerns due to their widespread presence in various ecosystems and their potential to act as carriers and accumulators of organic pollutants, including pesticides, pharmaceuticals and industrial chemicals, many of which are known to cause harm to aquatic organisms and bioaccumulate in the food chain. The adsorption of organic pollutants onto microplastics is a complex process influenced by various factors, including the physicochemical properties of both the microplastics and the pollutants. Understanding this process is crucial for assessing the environmental impact of microplastics and developing effective mitigation strategies. In this study, experimental data on the adsorption of organic pollutants onto microplastics in different aqueous environments were used to develop quantitative structure-property relationship (QSPR) and quantitative read-across structure-property relationship (q-RASPR) models establishing quantitative relationships between the chemical structures of the pollutants and their adsorption behavior, providing insights into the key molecular properties that govern the adsorption process. Apart from developing models separately for different categories of microplastics in a particular aqueous environment, global models were also developed combining all the data points. External predictions from q-RASPR models were more accurate than those from QSPR models, and the final global q-RASPR model, with only one descriptor, showed excellent statistical fit. These models will be helpful for planning pollution control measures and the sustainable management of plastic waste and environmental contamination.

Item Type:	Article
Uncontrolled Keywords:	0399 Other Chemical Sciences; 0907 Environmental Engineering; 1002 Environmental Biotechnology
Subjects:	Q Science > QH Natural history > QH301 Biology R Medicine > RM Therapeutics. Pharmacology R Medicine > RS Pharmacy and materia medica
Divisions:	Pharmacy & Biomolecular Sciences
Publisher:	Royal Society of Chemistry (RSC)
SWORD Depositor:	A Symplectic
Date Deposited:	01 Aug 2024 09:11
Last Modified:	14 Aug 2024 09:30
DOI or ID number:	10.1039/d4en00311j
URI:	https://researchonline.ljmu.ac.uk/id/eprint/23841

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