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Approximately 90% of the world trade is done by sea trade. The world maritime trade fleet is also increasing every year. Since ships use fossil fuels, they contribute to global climate change. It is very important for the environment and human health to calculate emissions in the port areas where ships do cargo operations. In this study, emissions analysis of the two important ports of Turkey's is performed with using the bottom-up calculation method. In addition, hotelling periods of the vessels are monitored for both ports for one year period. As a result, total Nitrogen Oxide (NOx), Sulphur Dioxide (SO2), Carbon Dioxide (CO2), Volatile Organic Compounds (VOC), Particulate Matter (PM) and Carbon Monoxide (CO) emissions of İzmir Port are calculated as 900 tons/year, 589 tons/year, 45320.5 tons/year, 49.7 tons/year, 77.7 tons/year and 36.9 tons/year, respectively. Moreover, total NOx, SO2, CO2, VOC, PM and CO emissions of Mersin Port are calculated as 1998 tons/year, 1339 tons/year, 102330 tons/year, 114.5 tons/year, 178.5 tons/year and 82.5 tons/year, respectively. The amount of emissions of Mersin port, which has a higher number of movements, was higher than that of İzmir port.

Based on data from seven different ship types, this paper provides mathematical relationships that allow us to estimate the main and auxiliary engine power of new ships. With these mathematical relationships we can estimate the power of the engine based on the ship’s length (L), gross tonnage (GT) and age. We developed these approaches using simple linear regression, polynomial regression, K-nearest neighbours (KNN) regression and gradient boosting machine (GBM) regression algorithms. The relationships presented here have a practical application: during the pre-parametric design of new ships, our mathematical relationships can be used to estimate the power of the engines so that more environmentally friendly ships may be built. In addition, with the machine learning methodology, the prediction of the main engine (ME) and auxiliary engine (AE) powers used in the numerical calculation of ship-based emissions provides data for researchers working on emission calculations. We conclude that the GBM regression algorithm provides more accurate solutions to estimate the main and auxiliary engine power of a ship than other algorithms used in the study.

Studies on the global climate change and the measures to be taken to alleviate the problem have long emphasized the critical importance of this issue for humanity’s future. The impact of exhaust gas emissions from maritime transport on air quality and global climate change is also examined by scientists. In particular, the restrictions imposed by the law-makers like International Maritime Organization (IMO) and European Union (EU) on maritime shipping to improve air quality requires flag states, ships, ship owners and seamen to reduce ship emissions. In this study, Nitrogen Oxide (NOx), Sulfur Dioxide (SO2), Carbon Dioxide (CO2), Volatile Organic Compounds (VOC), Particulate Matter (PM) and Carbon Monoxide (CO) emissions of ships coming to Turkey’s four important ports (Ambarlı, Izmir, Mersin and Kocaeli ports) are theoretically calculated with two bottom-up methods. Port durations and ship technical data are the real data obtained by following the ships coming to all ports for 1-year period. Based on the emission calculations for all ports, container vessels, which are the most common vessel types, were selected and emission estimation models were obtained for cruising, maneuvering and port operating modes depending on the type and gross tonnage (GRT) of these vessels. The emission estimation models obtained from the study can be used to estimate approximate exhaust gas emissions from container ships with known GRT values and to model ship-based air pollution. When the average error margins are analyzed, it was estimated with 2.03% error in cruising mode, 1.63% in maneuvering mode and 9.44% in port mode for container ships according to the GRT in the ports of Turkey.

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