4.7 Article

Marine litter accumulation along the Bulgarian Black Sea coast: Categories and predominance

Journal

WASTE MANAGEMENT
Volume 84, Issue -, Pages 182-193

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.wasman.2018.11.001

Keywords

Marine litter; Monitoring; Reference beaches; Polymer materials; Bulgarian Black Sea coast; Marine Strategy Framework Directive

Funding

  1. Project MARine Litter, Eutrophication and Noise assessment tools [MARLEN - D34-11/31.03.2015]
  2. Black Sea Basin Directorate - Varna, Bulgaria

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Quantitative assessment of marine litter (ML) along the Bulgarian Black Sea coastline was presented. ML surveys were conducted every season in a total of 8 beach monitoring sites during 2015-2016. Eight main categories of material were determined, based on standard OSPAR monitoring protocol. Annual accumulation of 19,805 nos. was recorded. The highest was the quantity of items related to artificial polymer materials category - 16,690 nos. The most dominant in this category were cigarette butts and filters 4496 nos., followed by plastic caps/lids drinks - 1413 nos. and cups and cup lids - 1194 nos. The highest was the abundance of most of the litter during the summer period, related to the increased number of beach visitors, numerous temporarily working fast - food eateries, some big and small restaurants and cafes in close proximity to the beaches, as well as fishing, wild camping, etc. The contribution of ML types to the coastal pollution, presented by number and weight of items was determined. The highest was the contribution (by number) of cigarette butts and filters - 29.7%, followed by plastic caps/lids of beverages - 9.4%; plastic cups and cup lids - 7.9%, etc. According to the weight drink bottles > 5 L exhibited highest percentage - 30.7%, followed by shopping bags incl. pieces - 15.5% and drink bottles <= 0.5 L - 13.2%. The ML accumulation, presented by number and weight gives more detailed information about litter characteristics, the levels of coastal pollution and improves ML management. (C) 2018 Elsevier Ltd. All rights reserved.

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