Composition and abundance of benthic marine litter in the fishing grounds of Iskenderun Bay, northeastern Levantine coast of Turkey

2021 ◽  
Vol 172 ◽  
pp. 112840
Author(s):  
Ferhat Büyükdeveci ◽  
Sedat Gündoğdu
Keyword(s):  
Marine Litter ◽  
Iskenderun Bay

scholarly journals Investigation of benthic marine litter in the Yumurtalık Fishing Port

2021 ◽  
Vol 9 (2) ◽  
pp. 272-276
Author(s):  
Özgür Yılmaz ◽  
Celal Erbaş ◽  
Mahmut Ali Gökçe
Keyword(s):  
Marine Litter ◽  
Iskenderun Bay ◽  
Almost All

Marine litter, which can be seen in almost all seas in recent years, is a serious problem for the environment. Similarly, the same problem also exists in Turkey's seas. This study was carried out in 4 different areas, each of which is 100m², in the Yumurtalık Fishing Port in İskenderun Bay. Marine litter was removed from the seabed by diving in these selected areas in April 2016 and June 2017. In the study, a total of 157.2 kg of rubber (49.5%), 118.9 kg of glass (37.4%), 21.61 kg of plastic (6.8%) and 19.94 kg of metal (6.3%) marine litter were detected and removed. It was determined that the marine litter extracted in 2017 was less by weight compared to 2016 (p

Marine Litter in the Russian Gulf of Finland and South-East Baltic: Application of Different Methods of Beach Sand Sampling

2021 ◽  
Author(s):  
Alexandra A. Ershova ◽  
Tatjana R. Eremina ◽  
Irina P. Chubarenko ◽  
Elena E. Esiukova
Keyword(s):  
Gulf Of Finland ◽  
Beach Sand ◽  
Marine Litter

Daily accumulation rates of marine litter on the shores of Rapa Nui (Easter Island) in the South Pacific Ocean

2021 ◽  
Vol 169 ◽  
pp. 112535
Author(s):  
Martin Thiel ◽  
Bárbara Barrera Lorca ◽  
Luis Bravo ◽  
Iván A. Hinojosa ◽  
Hugo Zeballos Meneses
Keyword(s):  
Pacific Ocean ◽  
South Pacific ◽  
Easter Island ◽  
Accumulation Rates ◽  
The South ◽  
Rapa Nui ◽  
Marine Litter ◽  
South Pacific Ocean

A preliminary investigation of marine litter pollution along Mandvi beach, Kachchh, Gujarat

2021 ◽  
Vol 165 ◽  
pp. 112100
Author(s):  
Durga Prasad Behera ◽  
Prabhu Kolandhasamy ◽  
Sivaraj Sigamani ◽  
Lakshmi Prabha Devi ◽  
Yusof Shuaib Ibrahim
Keyword(s):  
Preliminary Investigation ◽  
Marine Litter

scholarly journals A Survey of Marine Coastal Litters around Zhoushan Island, China and Their Impacts

2021 ◽  
Vol 9 (2) ◽  
pp. 183
Author(s):  
Xuehua Ma ◽  
Yi Zhou ◽  
Luyi Yang ◽  
Jianfeng Tong
Keyword(s):  
Rapid Development ◽  
Univariate Analysis ◽  
Local Environment ◽  
Marine Litter ◽  
Social Ecological ◽  
The Social ◽  
Significant Difference ◽  
Valuable Method ◽  
Beach Litter ◽  
The Impact

Rapid development of the economy increased marine litter around Zhoushan Island. Social-ecological scenario studies can help to develop strategies to adapt to such change. To investigate the present situation of marine litter pollution, a stratified random sampling (StRS) method was applied to survey the distribution of marine coastal litters around Zhoushan Island. A univariate analysis of variance was conducted to access the amount of litter in different landforms that include mudflats, artificial and rocky beaches. In addition, two questionnaires were designed for local fishermen and tourists to provide social scenarios. The results showed that the distribution of litter in different landforms was significantly different, while the distribution of litter in different sampling points had no significant difference. The StRS survey showed to be a valuable method for giving a relative overview of beach litter around Zhoushan Island with less effort in a future survey. The questionnaire feedbacks helped to understand the source of marine litter and showed the impact on the local environment and economy. Based on the social-ecological scenarios, governance recommendations were provided in this paper.

Floating marine litter and their risks to cetaceans off Portugal

2021 ◽  
Vol 170 ◽  
pp. 112603
Author(s):  
Sara Sá ◽  
Jorge Bastos-Santos ◽  
Hélder Araújo ◽  
Andreia Torres Pereira ◽  
Marisa Ferreira ◽  
...  
Keyword(s):  
Marine Litter

scholarly journals Adoption and diffusion of marine litter clean-up technologies across European seas: Legal, institutional and financial drivers and barriers

2021 ◽  
Vol 170 ◽  
pp. 112611
Author(s):  
Sofia Frantzi ◽  
Roy Brouwer ◽  
Emma Watkins ◽  
Pieter van Beukering ◽  
Maria Conceição Cunha ◽  
...  
Keyword(s):  
Marine Litter ◽  
Adoption And Diffusion ◽  
And Diffusion ◽  
European Seas

Evaluation of antibacterial, teratogenicity and antibiofilm effect of sulfated chitosans extracted from marine waste against microorganism

2021 ◽  
pp. 088391152110142
Author(s):  
Velu Gomathy ◽  
Venkatesan Manigandan ◽  
Narasimman Vignesh ◽  
Aavula Thabitha ◽  
Ramachandran Saravanan
Keyword(s):  
Biofilm Formation ◽  
Pathogenic Bacteria ◽  
Antimicrobial Agents ◽  
Inhibitory Effect ◽  
Diffusion Method ◽  
Marine Litter ◽  
Ionization Time ◽  
Gram Positive Bacteria ◽  
Mineral Components ◽  
Ft Ir

Biofilms play a key role in infectious diseases, as they may form on the surface and persist after treatment with various antimicrobial agents. The Staphylococcus aureus, Klebsiella pneumoniae, S. typhimurium, P. aeruginosa, and Escherichia coli most frequently associated with medical devices. Chitosan sulphate from marine litter (SCH-MW) was extracted and the mineral components were determined using atomic absorption spectroscopy (AAS). The degree of deacetylation (DA) of SCH was predicted 50% and 33.3% in crab and shrimp waste respectively. The elucidation of the structure of the SCH-MW was portrayed using FT-IR and 1H-NMR spectroscopy. The molecular mass of SCH-MW was determined with Matrix-Assisted Laser Desorption/Ionization-Time of Flight (MALDI-TOF). The teratogenicity of SCH-MW was characterized by the zebrafish embryo (ZFE) model. Antimicrobial activity of SCH-MW was tested with the agar well diffusion method; the inhibitory effect of SCH-MW on biofilm formation was assessed in 96 flat well polystyrene plates. The result revealed that a low concentration of crab-sulfated chitosan inhibited bacterial growth and significantly reduced the anti-biofilm activity of gram-negative and gram-positive bacteria relatively to shrimp. It is potentially against the biofilm formation of pathogenic bacteria.

scholarly journals A Citizen Science Unmanned Aerial System Data Acquisition Protocol and Deep Learning Techniques for the Automatic Detection and Mapping of Marine Litter Concentrations in the Coastal Zone

Drones ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Apostolos Papakonstantinou ◽  
Marios Batsaris ◽  
Spyros Spondylidis ◽  
Konstantinos Topouzelis
Keyword(s):  
Deep Learning ◽  
Data Acquisition ◽  
Citizen Science ◽  
Coastal Zone ◽  
Automatic Detection ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Marine Litter ◽  
Density Maps ◽  
Learning Techniques

Marine litter (ML) accumulation in the coastal zone has been recognized as a major problem in our time, as it can dramatically affect the environment, marine ecosystems, and coastal communities. Existing monitoring methods fail to respond to the spatiotemporal changes and dynamics of ML concentrations. Recent works showed that unmanned aerial systems (UAS), along with computer vision methods, provide a feasible alternative for ML monitoring. In this context, we proposed a citizen science UAS data acquisition and annotation protocol combined with deep learning techniques for the automatic detection and mapping of ML concentrations in the coastal zone. Five convolutional neural networks (CNNs) were trained to classify UAS image tiles into two classes: (a) litter and (b) no litter. Testing the CCNs’ generalization ability to an unseen dataset, we found that the VVG19 CNN returned an overall accuracy of 77.6% and an f-score of 77.42%. ML density maps were created using the automated classification results. They were compared with those produced by a manual screening classification proving our approach’s geographical transferability to new and unknown beaches. Although ML recognition is still a challenging task, this study provides evidence about the feasibility of using a citizen science UAS-based monitoring method in combination with deep learning techniques for the quantification of the ML load in the coastal zone using density maps.

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