An Analysis of Ship-Source Marine Pollution in Nigeria Seaports

Onwuegbuchunam, D. E.; Ebe, T.E; Okoroji, Lazarus I.; Essien, A

doi:10.3390/jmse5030039

Cited by 19 publications

(14 citation statements)

References 6 publications

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“…However, it is true that this proposal, being purely conceptual, lacks practical validation. Also, applied in the case of Nigerian ports, in [14] an integrated model is suggested to reinforce the weak administrative framework of control over marine pollution in port environments. This weakness, as it happens in numerous ports of the world, is the result of the subcontracting of waste management systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Solutions to Marine Pollution in Canary Islands’ ports: Alternatives and Optimization of Energy Management

Uche-Soria

Rodríguez‐Monroy

2019

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The study of waste generated in ports is a vitally important issue to reduce marine pollution and improve port management systems. In this article, the authors study the management of solid waste generated in the ports of the Canary Islands. For this purpose, a waste management model based on the circular economy is developed. With this model, it is possible to reduce pollution in the port areas of the capital's islands, increase the fraction of recycled waste and obtain additional energy for the ports. The interest of this study lies in the singularities of these islands with respect to the geographic limitations that make them weak energy environments. The proposed solution to solve the current problems and achieve a zone of zero waste (throughout the port of Santa Cruz and its industrial estate) has two main phases: the first, in which solid waste is processed and a part is recycled, and the second, which consists of recovering the energy of the converted fuels. The advantages of the application of this model are that CO2 emissions are reduced, energy savings are realized, waste management is improved in these environments (the recycling fraction is increased considerably), and new jobs are created. This model also contributes to the development of the Special Territorial Plan for Waste Management of the Canary Islands, in accordance with the policies of the European Union required for the coming years.

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Section: Literature Reviewmentioning

confidence: 99%

Solutions to Marine Pollution in Canary Islands’ ports: Alternatives and Optimization of Energy Management

Uche-Soria

Rodríguez‐Monroy

2019

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“…Thus, the ballasted vessels entering the Black Sea ports are required to de-ballast the water from the ballast tanks at the time of loading the goods on board. Water from geographic areas other than the Black Sea, if not treated before discharge into the port area, can bring with them pathogenic elements that can create pandemics in the marine ecosystem [27].…”

Section: The Black Sea's Portsmentioning

confidence: 99%

Ballast Water Management in the Black Sea’s Ports

Rață¹,

Gasparotti²,

Rusu³

2018

JMSE

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The objective of this work is to develop a case study in order to improve the ballast water management in the Black Sea's ports. From this perspective, the present paper provides an extensive explanation about the main issues related to the control of marine non-indigenous species introduction through ballast water discharged by ships during their operations in the ports. Thereafter, it quantifies the amount of the ballast water discharged in the major ports of the Black Sea and the amount of the invading species that could reach these ports. Although, globally speaking, the problem of ballast water management is a reality, only three of the six neighboring countries in the Black Sea basin have signed, in 2004, the ratification of the International Convention on Management of Ships' Ballast Water and Sediments. This is also known as the Water Ballast Management Convention, and it provides regulations concerning ballast water management generated by the shipping activities through a common set of rules.

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“…There are many references focusing on the analysis of ship sensing data. For example, Onwuegbuchunam et al [2] conducted research and analysis on the marine pollution of ships in Nigeria, and the integrated model was proposed as an alternative administrative tool for monitoring and controlling pollution in seaports. Iodice et al [3] proposed a numerical approach to assess air pollution by ship engines in maneuver mode and fuel switch conditions, and it was also used to assess the impact of marine engine pollutant emissions on ambient air quality in coastal areas.…”

Section: Introductionmentioning

confidence: 99%

The Temperature Forecast of Ship Propulsion Devices from Sensor Data

Hua

Yin

2019

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The big data from various sensors installed on-board for monitoring the status of ship devices is very critical for improving the efficiency and safety of ship operations and reducing the cost of operation and maintenance. However, how to utilize these data is a key issue. The temperature change of the ship propulsion devices can often reflect whether the devices are faulty or not. Therefore, this paper aims to forecast the temperature of the ship propulsion devices by data-driven methods, where potential faults can be further identified automatically. The proposed forecasting process is composed of preprocessing, feature selection, and prediction, including an autoregressive distributed lag time series model (ARDL), stepwise regression (SR) model, neural network (NN) model, and deep neural network (DNN) model. Finally, the proposed forecasting process is applied on a naval ship, and the results show that the ARDL model has higher accuracy than the three other models.

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An Analysis of Ship-Source Marine Pollution in Nigeria Seaports

Cited by 19 publications

References 6 publications

Solutions to Marine Pollution in Canary Islands’ ports: Alternatives and Optimization of Energy Management

Solutions to Marine Pollution in Canary Islands’ ports: Alternatives and Optimization of Energy Management

Ballast Water Management in the Black Sea’s Ports

The Temperature Forecast of Ship Propulsion Devices from Sensor Data

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