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Oil Spill Environmental Risk Assessment and Mapping in Coastal China Using Automatic Identification System (AIS) Data

Author

Listed:
  • Gaoru Zhu

    (Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport of the People’s Republic of China, Beijing 100028, China)

  • Zhenglei Xie

    (College of Marine Science & Engineering, Nanjing Normal University, Nanjing 210023, China
    Key Laboratory of Coastal Salt Marsh Ecosystems and Resources, Ministry of Natural Resources, Nanjing 210007, China)

  • Honglei Xu

    (Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport of the People’s Republic of China, Beijing 100028, China)

  • Nan Wang

    (College of Marine Science & Engineering, Nanjing Normal University, Nanjing 210023, China)

  • Liguo Zhang

    (Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport of the People’s Republic of China, Beijing 100028, China)

  • Ning Mao

    (Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport of the People’s Republic of China, Beijing 100028, China)

  • Jinxiang Cheng

    (Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport of the People’s Republic of China, Beijing 100028, China)

Abstract

The rapid expansion in shipping traffic, oil tankers, and oil field exploration in coastal and marine areas has, inevitably, resulted in the occurrence of many oil spill accidents. Oil spill accidents, which cause serious socio-economic, health, and environmental risks in coastal and marine areas, are a global concern. An oil spill pollution risk distribution map, combining multiple spill sources, is an effective tool by which to identify high-risk areas, which may help decision-makers in adopting contingency response and integrated coastal management. However, the assessment of oil spill distribution and risk assessment has been restricted, due to their heavy dependence on laboratory experiments and model simulations lacking reliable shipping data, which often derive inaccurate mapping results. This study combines the automatic identification system (AIS) and other data to precisely quantify the spatial extent of accident risk in coastal China. Based on oil quantity, oil spill rate, and accident probability, the ship, oil storage tank, submarine pipeline, and oil platform accidents spill risk index is analyzed. Next, combined with the sensitive degree of a coastal area, considering environmental and social issues, the oil spill environmental risk index is calculated. The oil spill pollution risk level is classified into five categories based on the oil spill pollution risk index, namely the low-risk zone, relatively low-risk zone, moderate-risk zone, relatively high-risk zone, and high-risk zone. The relatively high oil spill environmental risk concentration zone is located in the Bohai Sea, inter-border area between the Yellow Sea and Bohai Sea, the Yangtze River estuary, south of the Taiwan Strait, and the Pearl River estuary. The high-risk zone in the Bohai Sea is 36,018 km 2 in area, with an average risk value of 32.23, whereas the high-risk area in the Pearl River estuary is only 14,007 km 2 . The high-risk area proportions in Tianjin are 23.5%, while those in Fujian, Hainan, Jiangsu, and Guangxi are very low. The low-risk area proportion in Hainan Province is 62%, while the value in Tianjin is only 2.9%. This study will be helpful in assisting decision-makers in mapping the influence area of oil spills and adopting the important strategies and effective management and conservation countermeasures for ship accidents in the coastal areas of China.

Suggested Citation

  • Gaoru Zhu & Zhenglei Xie & Honglei Xu & Nan Wang & Liguo Zhang & Ning Mao & Jinxiang Cheng, 2022. "Oil Spill Environmental Risk Assessment and Mapping in Coastal China Using Automatic Identification System (AIS) Data," Sustainability, MDPI, vol. 14(10), pages 1-16, May.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:5837-:d:813391
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