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In-Stream Marine Litter Collection Device Location Determination Using Bayesian Network

Author

Listed:
  • Abdullah Battawi

    (Industrial and System Engineering Department, Mississippi State University, Starkville, MS 39762, USA)

  • Ellie Mallon

    (Osprey Initiative, LLC, Mobile, AL 36606, USA
    Coastal Research and Extension Center, Mississippi State University, Biloxi, MS 39532, USA)

  • Anthony Vedral

    (Coastal Research and Extension Center, Mississippi State University, Biloxi, MS 39532, USA)

  • Eric Sparks

    (Coastal Research and Extension Center, Mississippi State University, Biloxi, MS 39532, USA
    Mississippi-Alabama Sea Grant Consortium, Ocean Springs, MS 39564, USA)

  • Junfeng Ma

    (Industrial and System Engineering Department, Mississippi State University, Starkville, MS 39762, USA)

  • Mohammad Marufuzzaman

    (Industrial and System Engineering Department, Mississippi State University, Starkville, MS 39762, USA)

Abstract

Increased generation of waste, production of plastics, and poor environmental stewardship has led to an increase in floating litter. Significant efforts have been dedicated to mitigating this globally relevant issue. Depending on the location of floating litter, removal methods would vary, but usually include manual cleanups by volunteers or workers, use of heavy machinery to rake or sweep litter off beaches or roads, or passive litter collection traps. In the open ocean or streams, a common passive technique is to use booms and a collection receptacle to trap floating litter. These passive traps are usually installed to intercept floating litter; however, identifying the appropriate locations for installing these collection devices is still not fully investigated. We utilized four common criteria and fifteen sub-criteria to determine the most appropriate setup location for an in-stream collection device (Litter Gitter—Osprey Initiative, LLC, Mobile, AL, USA). Bayesian Network technology was applied to analyze these criteria comprehensively. A case study composed of multiple sites across the U.S. Gulf of Mexico Coast was used to validate the proposed approach, and propagation and sensitivity analyses were used to evaluate performance. The results show that the fifteen summarized criteria combined with the Bayesian Network approach could aid location selection and have practical potential for in-stream litter collection devices in coastal areas.

Suggested Citation

  • Abdullah Battawi & Ellie Mallon & Anthony Vedral & Eric Sparks & Junfeng Ma & Mohammad Marufuzzaman, 2022. "In-Stream Marine Litter Collection Device Location Determination Using Bayesian Network," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6147-:d:818580
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    References listed on IDEAS

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    1. Melanie Bergmann & Mine B. Tekman & Lars Gutow, 2017. "Sea change for plastic pollution," Nature, Nature, vol. 544(7650), pages 297-297, April.
    2. A. Awasthi & H. Omrani, 2009. "A hybrid approach based on AHP and belief theory for evaluating sustainable transportation solutions," International Journal of Global Environmental Issues, Inderscience Enterprises Ltd, vol. 9(3), pages 212-226.
    3. Honghao Zhang & Yong Peng & Guangdong Tian & Danqi Wang & Pengpeng Xie, 2017. "Green material selection for sustainability: A hybrid MCDM approach," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-26, May.
    4. Hosseini, Seyedmohsen & Barker, Kash, 2016. "A Bayesian network model for resilience-based supplier selection," International Journal of Production Economics, Elsevier, vol. 180(C), pages 68-87.
    5. Daniel R. Petrolia & Jerrod Penn & Ruth Quainoo & Rex H. Caffey & J. Matthew Fannin, 2019. "Know Thy Beach: Values of Beach Condition Information," Marine Resource Economics, University of Chicago Press, vol. 34(4), pages 331-359.
    6. Kannan Govindan & Devika Kannan & Madan Shankar, 2015. "Evaluation of green manufacturing practices using a hybrid MCDM model combining DANP with PROMETHEE," International Journal of Production Research, Taylor & Francis Journals, vol. 53(21), pages 6344-6371, November.
    7. Duan, Gang & Aghalari, Amin & Chen, Li & Marufuzzaman, Mohammad & Ma, Junfeng, 2021. "Vessel routing optimization for floating macro-marine debris collection in the ocean considering dynamic velocity and direction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
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