Integrating Bayesian Belief Networks in a toolbox for decision support on plastic clean-up technologies in rivers and estuaries. (1st March 2022)

• Record Type: Journal Article
• Authors: Leone, Giulia; Catarino, Ana I.; Pauwels, Ine; Mani, Thomas; Tishler, Michelle; Egger, Matthias; Forio, Marie Anne Eurie; Goethals, Peter L.M.; Everaert, Gert
• Is Part Of: Environmental pollution. Volume 296(2022)
• Publication Date: 2022-03-01
• Subjects: Bayesian Belief Networks -- Decision support framework -- Ecological modelling -- Plastic waste mitigation technologies -- Unintentional bycatch; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2021.118721
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: Current mitigation strategies to offset marine plastic pollution, a global concern, typically rely on preventing floating debris from reaching coastal ecosystems. Specifically, clean-up technologies are designed to collect plastics by removing debris from the aquatic environment such as rivers and estuaries. However, to date, there is little published data on their potential impact on riverine and estuarine organisms and ecosystems. Multiple parameters might play a role in the chances of biota and organic debris being unintentionally caught within a mechanical clean-up system, but their exact contribution to a potential impact is unknown. Here, we identified four clusters of parameters that can potentially determine the bycatch: (i) the environmental conditions in which the clean-up system is deployed, (ii) the traits of the biota the system interacts with, (iii) the traits of plastic items present in the system, and, (iv) the design and operation of the clean-up mechanism itself. To efficiently quantify and assess the influence of each of the clusters on bycatch, we suggest the use of transparent and objective tools. In particular, we discuss the use of Bayesian Belief Networks (BBNs) as a promising probabilistic modelling method for an evidence-based trade-off between removal efficiency and bycatch. We argue that BBN probabilistic models are a valuable tool to assist stakeholders, prior to the deployment of any clean-up technology, in selecting the best-suited mechanism to collect floating plastic debris while managing potential adverse effects on the ecosystem. Graphical abstract: Image 1 Highlights: Objective assessment of environmental impact of plastic clean-up technologies Plastic clean-up technologies are fundamental to fight plastic pollution Multiple parameters play a role in causing potential bycatch Bayesian Belief Networks as trade-off tools between plastic removal and bycatch BBN as a step towards a decision support framework for plastic clean-up technologies