New York Bay is one of the most important transition regions of ships trading to east America. The region plays an important role in the commerce of the New York metropolitan area. The area is surrounded with the coasts that have various levels of environmental sensitivity. The area accommodates high diversity of native ecosystems and species that are rather vulnerable in case of oil spill. Thus getting well informed about the likelihood, or fate, of oil spills around this region is of great importance so that proactive measures can be taken. The purpose of this study is to investigate the oil spill and predict the future accidents likely to be encountered around the Bay of New York. Two trajectory models have been conducted for the study. ADIOS (Automated Data Inquiry for Oil Spills), has been conducted for natural degradation calculations, and, GNOME (General NOAA Operational Modeling Environment), has been conducted for surface spread simulation. The results gained through these efforts are hoped to be useful for many organizations dealing with oil spill response operations and contribute to an effective and efficient coordination among the relevant institutions.

In this paper, a single-stage, horizontal type centrifugal pump, which can be used in a chemical tanker’s cargo operations, was modelled with MATLAB/Simulink software. The modelled pump was run with seven different fluids handled in chemical tankers which are ethyl alcohol, N-Propyl alcohol, phenol, chloroform, castor oil, 55% nitric acid and water. Therefore, the pump’s performance curves and data sets were obtained for each situation. After these, a neural network was created with MATLAB/ Neural Network Fitting Tool application. Inputs of the network were volumetric flow, head, shaft power, torque, and net positive suction head. The output was the pump efficiency and it is estimated for each fluid from the numeric data. Mean squared error was very close to zero (1.1817e-6) and R² provided a prediction accuracy of 99.996%. According to these results, artificial neural network (ANN) had a satisfactory performance to predict the efficiency of a chemical tanker’s centrifugal cargo pump.

This study aimed to evaluate the research output of oil spill response and citations from 2000 to 2022 through a bibliometric analysis. The primary findings were as follows: there has been a noticeable increase in the number of publications. The most active journal was Marine Pollution Bulletin. Environmental sciences ranked as the most popular subject area. The United States of America (USA) published the greatest number of single-authored, internationally collaborative, first-authored papers. Oil spill(s) was the most widely queried research term, ranking first in the article title, abstract, and author keyword analysis, respectively. The Deep-Water Horizon disaster, the largest marine oil spill in 2010, was the most frequently analyzed oil spill accident in the research as a sample. This study makes a significant contribution to the field of oil spill response science by being one of the few that applies network visualization and mapping technique. Further research is recommended in light of longer-term data and the diverse Web of Science (WoS) categories found in oil spill science, which may be visualized using a variety of bibliometric visualization applications.