DENİZ KİRLİLİĞİNE NEDEN OLAN KAZALARIN İLİŞKİ DÜZEYLERİNİN ANALİZİ

Yıl 2025, Cilt: 17 Sayı: 2, 248 - 273, 25.12.2025

Coşkan Sevgili , Burak Kundakçı

https://doi.org/10.18613/deudfd.1731058

Öz

Deniz kazaları, geçmişten bu yana denizcilik sektörünün en önemli olgularından biridir. Bu kazalar can ve mal kaybına yol açmanın yanı sıra çevre üzerinde de olumsuz etkilere sahiptir. Bu araştırma, kirliliğe neden olan deniz kazalarındaki faktörler arasındaki ilişkileri belirlemeyi amaçlamaktadır. Bu kapsamda, IHS Sea-Web veritabanında 1999-2023 yılları arasında meydana gelen ve deniz kirliliği ile sonuçlanan 208 deniz kazası raporu Ki-kare ve Cramer's V yöntemleri ile analiz edilmiştir. Analiz sonuçlarına göre, özellikle gemi bayrağı, gemi tipi ve gros tonaj arasında anlamlı ilişkiler bulunmuştur. Yüksek tonajlı gemiler ile dökme yük, konteyner ve genel kargo gemileri ağırlıklı olarak kolay bayrak taşımaktadır. Kirlilik miktarı ile anlamlı ilişkisi olan tek değişken gemi statüsü olmuştur. Manevra yapan ve seyirde bulunan gemilerin karıştığı kazalarda kirlilik miktarının yüksek olması daha muhtemel iken yakıt ikmali sırasında meydana gelen kazalarda kirlilik miktarı düşüktür. Araştırma bulgularnın kazalardan kaynaklanan kirliliğin önlenmesi için paydaşların önleyici tedbirler almasına ve farkındalığı artırmasına yardımcı olacağı düşünülmektedir.

Anahtar Kelimeler

Deniz kazaları , deniz kirliliği , Cramer’s V , Ki-Kare

ANALYSIS OF ASSOCIATION LEVELS OF ACCIDENTS CAUSING MARINE POLLUTION

Öz

Marine accidents are one of the most important phenomena of the maritime industry since the past. In addition to causing loss of life and property, these accidents also have a negative impact on the environment. This research aims to determine the relationships between the factors in marine accidents causing pollution. In this context, 208 marine accident reports in the IHS Sea-Web database, which occurred between 1999 and 2023 and resulted in marine pollution, were analyzed by Chi-square and Cramer's V methods. According to the results of the analyses, significant relationships were found, especially between vessel flag, vessel type, and gross tonnage. High-tonnage vessels and bulk carriers, containerships and general cargo vessels were mainly flying the flag of convenience. Vessel status was the only variable that had a significant relationship with the pollution quantity. Vessels in maneuvering and on voyage are more likely to have a high amount of pollution, while the amount of pollution in accidents occurring in bunkering is low. The research findings can provide stakeholders take preventive measures and raise awareness to prevent pollution resulting from accidents.

Anahtar Kelimeler

Marine accidents , marine pollution , Cramer’s V , Chi-square

Etik Beyan

This study does not involve human or animal participants and therefore did not require ethical approval.

Destekleyen Kurum

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this research.

Kaynakça

• Anderson, E.E. and Talley, W.K. (1995). The oil spill size of tanker and barge accidents: determinants and policy implications. Land Econ. 71, 216–228.
• Balogun, A. L., Yekeen, S. T., Pradhan, B. and Yusof, K. B. W. (2021). Oil spill trajectory modelling and environmental vulnerability mapping using GNOME model and GIS. Environmental Pollution, 268, 115812.
• Buber, M. and Toz, A.C. (2018). Selection of the best suitable place as a marine pollution response center : suggestions for Iskenderun Bay, Acta Biologica Turcica, 30(1), 27–32.
• Bye, R.J. and Aalberg, A. L. (2018). Maritime navigation accidents and risk indicators: An exploratory statistical analysis using AIS data and accident reports, Reliability Engineering & System Safety, 176(October 2017), 174–186.
• Cakir, E., Sevgili, C. and Fiskin, R. (2021). An analysis of severity of oil spill caused by vessel accidents, Transportation Research Part D: Transport and Environment, 90, 102662.
• Cao, Y., Wang, X., Yang, Z., Wang, J., Wang, H. and Liu, Z. (2023). Research in marine accidents: A bibliometric analysis, systematic review and future directions. Ocean Engineering, 284, 115048.
• Chen, J., Zhang, W., Li, S., Zhang, F., Zhu, Y. and Huang, X. (2018). Identifying critical factors of oil spill in the tanker shipping industry worldwide. Journal of Cleaner Production, 180, 1-10.
• Chen, J., Zhang, W., Wan, Z., Li, S., Huang, T. and Fei, Y. (2019). Oil spills from global tankers: Status review and future governance. Journal of Cleaner Production, 227, 20-32.
• Chen, J., Di, Z., Shi, J., Shu, Y., Wan, Z., Song, L. and Zhang, W. (2020). Marine oil spill pollution causes and governance: A case study of Sanchi tanker collision and explosion. Journal of Cleaner Production, 273, 122978.
• Chen, J., Chen, H., Shi, J., Wang, Y., Li, H., Xiang, Y., Liu, Y. and Chen, H. (2024). Causal diagnostic model and governance strategies to reduce pollution from ship accidents in Chinese waters. Marine Pollution Bulletin, 207, 116817. https://doi.org/10.1016/j.marpolbul.2024.116817
• Chun, J., Oh, J.H. and Kim, C.K. (2020). Oil spill response policies to bridge the perception gap between the government and the public: A social big data analysis. Journal of Marine Science and Engineering, 8(5), 335.
• Ciappa, A.C. (2023). Oil trajectory analysis for oil spill surveillance by SAR in the Mediterranean Sea. Marine Pollution Bulletin, 190(March), 114825. Čović, M., Ricci, S., Jelaska, I. and Stanivuk, T. (2024). Oil spill trajectory modeling and validation: case study on a marine incident at Adriatic Sea. WMU Journal of Maritime Affairs, 23(1), 103-113.
• Davies, A.J. and Hope, M.J. (2015). Bayesian inference-based environmental decision support systems for oil spill response strategy selection. Marine Pollution Bulletin, 96(1–2), 87–102.
• Eliopoulou, E. and Papanikolaou, A. (2007). Casualty analysis of large tankers. Journal of Marine Science and Technology, 12(4), 240-250.
• Eliopoulou, E., Papanikolaou, A., Diamantis, P. and Hamann, R. (2012). Analysis of tanker casualties after the Oil Pollution Act (USA, 1990). Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 226(4), 301-312.
• EMSA (European Maritime Safety Agency). (2024). Annual Overview of Marine Casualties and Incidents 2024. https://www.emsa.europa.eu/publications/reports/item/5352-annualoverview- of-marine-casualties-and-incidents-2024.html, Access Date: 28.04.2025.
• Hu, J., Zhou, W., Zheng, P. and Liu, G. (2024). A Novel Approach for the Analysis of Ship Pollution Accidents Using Knowledge Graph. Sustainability, 16(13), 5296. https://doi.org/10.3390/su16135296
• ITOPF. (2024). Oil Tanker Spill Statistics 2024. https://www.itopf.org/knowledge-resources/data-statistics/oil-tankerspill- statistics-2024/, Access Date: 02.05.2025.
• Knol, M. and Arbo, P. (2014). Oil spill response in the Arctic: Norwegian experiences and future perspectives. Marine Policy, 50(PartA), 171–177.
• Koseoglu, B., Buber, M. and Toz, A.C. (2018). Optimum site selection for oil spill response center in the Marmara Sea using the AHP-TOPSIS method. Archives of Environmental Protection, 44(4), 38–49.
• Lee, M. and Jung, J.Y. (2015). Pollution risk assessment of oil spill accidents in Garorim Bay of Korea. Marine Pollution Bulletin, 100(1), 297–303.
• Nordam, T., Beegle-Krause, C. J., Skancke, J., Nepstad, R. and Reed, M. (2019). Improving oil spill trajectory modelling in the Arctic. Marine Pollution Bulletin, 140, 65-74.
• Pradhan, B., Das, M. and Pradhan, C. (2021). Forecasting oil spill movement through trajectory modeling: a case study from Bay of Bengal, India. Modeling Earth Systems and Environment, 7(2), 1107–1119.
• Qiao, F., Wang, G., Yin, L., Zeng, K., Zhang, Y., Zhang, M., Xiao, B., Jiang, S., Chen, H. and Chen, G. (2019). Modelling oil trajectories and potentially contaminated areas from the Sanchi oil spill. Science of the Total Environment, 685, 856-866.
• Rea, L.M. and Parker, R.A. (2014). Designing and Conducting Survey Research: A Comprehensive Guide. San Francisco: Jossey-Bass (A Wiley Brand).
• Sevgili, C., Fiskin, R. and Cakir, E. (2022). A data-driven Bayesian Network model for oil spill occurrence prediction using tankship accidents. Journal of Cleaner Production, 370, 133478.
• Sevgı̇lı̇ , C. (2024). Data-driven prediction model for pollution prevention deficiencies on ships. Regional Studies in Marine Science, 78, 103790.
• Su, D. T., Tzu, F. M. and Cheng, C. H. (2019). Investigation of oil spills from oil tankers through grey theory: events from 1974 to 2016. Journal of Marine Science and Engineering, 7(10), 373.
• Talley, W. K. and Anderson, E. E. (1996). Determinants of tanker accident oil spill risk. International Journal of Transport Economics, 23(1), 3–16.
• Toz, A.C. and Buber, M. (2018). Performance evaluation of oil spill software systems in early fate and trajectory of oil spill: comparison analysis of OILMAP and PISCES 2 in Mersin bay spill. Environmental Monitoring and Assessment, 190(9), 551.
• Tromiadis, R. and Stanca, C. (2014). Comparative analysis of tanker ships incidents and their environment impacts. Advanced Materials Research, 837, 775-779.
• Uğurlu, Ö., Köse, E., Yıldırım, U. and Yüksekyıldız, E. (2015). Marine accident analysis for collision and grounding in oil tanker using FTA method. Maritime Policy & Management, 42(2), 163-185.
• Wan, S., Yang, X., Chen, X., Qu, Z., An, C., Zhang, B., Lee, K. and Bi, H. (2022). Emerging marine pollution from container ship accidents: Risk characteristics, response strategies, and regulation advancements. Journal of Cleaner Production, 376, 134266.
• Wang, H., Liu, Z., Wang, X., Graham, T. and Wang, J. (2021). An analysis of factors affecting the severity of marine accidents. Reliability Engineering & System Safety, 210, 107513.
• Xiong, S., Long, H., Tang, G., Wan, J. and Li, H. (2015). The management in response to marine oil spill from ships in China: A systematic review. Marine Pollution Bulletin, 96(1-2), 7-17.
• Zhang, Y., Zhai, Y., Chen, J., Xu, Q., Fu, S. and Wang, H. (2022). Factors contributing to fatality and injury outcomes of maritime accidents: a comparative study of two accident-prone areas. Journal of Marine Science and Engineering, 10(12), 1945.
• Zhang, Z., Hu, Q. and Yin, J. (2025). Maritime-Accident-Induced Environmental Pollution and Economic Loss Analysis Using an Interpretable Data-Driven Method. Sustainability, 17(7), 3023.
• Zhen, Z., Li, D., Li, Y., Chen, S. and Bu, S. (2020). Trajectory and weathering of oil spill in Daya bay, the South China sea. Environmental Pollution, 267, 115562.
• Zhu, Y., Ma, W., Feng, H., Liu, G. and Zheng, P. (2022). Effects of preparedness on successful emergency response to ship accident pollution using a Bayesian network. Journal of Marine Science and Engineering, 10(2), 179.