Marine Biodiversity and Ecosystem Functioning
EU Network of Excellence

 
Main Menu

· Home
· Contacts
· Data Systems
· Documents
· FAQ
· Links
· MarBEF Open Archive
· Network Description
· Outreach
· Photo Gallery
· Quality Assurance
· Register of Resources
· Research Projects
· Rules and Guidelines
· Training
· Wiki
· Worldconference

 

Register of Resources (RoR)

 People  |  Datasets  |  Literature  |  Institutes  |  Projects 

[ report an error in this record ]basket (1): add | show Print this page

one publication added to basket [348489]
Integrating Bayesian Belief Networks in a toolbox for decision support on plastic clean-up technologies in rivers and estuaries
Leone, G.; Catarino, A.I.; Pauwels, I.; Mani, T.; Tishler, M.; Egger, M.; Forio, M.A.E.; Goethals, P.L.M.; Everaert, G. (2022). Integrating Bayesian Belief Networks in a toolbox for decision support on plastic clean-up technologies in rivers and estuaries. Environ. Pollut. 296: 118721. https://dx.doi.org/10.1016/j.envpol.2021.118721
In: Environmental Pollution. Elsevier: Barking. ISSN 0269-7491; e-ISSN 1873-6424
Peer reviewed article  

Available in  Authors 

Author keywords
    Bayesian Belief Networks; Decision support framework; Ecological modelling; Plastic waste mitigation technologies; Unintentional bycatch

Project Top | Authors 
  • PhD Decision support framework for plastic clean-up technologies in rivers and estuaries: minimizing unintentional bycatch while maintaining efficient plastic removal under realistic environmental conditions

Authors  Top 
  • Leone, G.
  • Catarino, A.I.
  • Pauwels, I.
  • Mani, T.
  • Tishler, M.
  • Egger, M.
  • Forio, M.A.E.
  • Goethals, P.L.M.
  • Everaert, G.

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.

All data in the Integrated Marine Information System (IMIS) is subject to the VLIZ privacy policy Top | Authors 


If any information here appears to be incorrect, please contact us
Back to Register of Resources
 
Quick links

MarBEF WIKI

Erasmus Mundus Master of Science in Marine Biodiversity and Conservation (EMBC)
Outreach

Science
Responsive Mode Programme (RMP) - Marie Nordstrom, copyright Aspden Rebecca

WoRMS
part of WoRMS logo

ERMS 2.0
Epinephelus marginatus Picture: JG Harmelin

EurOBIS

Geographic System

Datasets

 


Web site hosted and maintained by Flanders Marine Institute (VLIZ) - Contact data-at-marbef.org