Quantifying ship-borne emissions in Istanbul Strait with bottom-up and machine-learning approaches

Quantifying the shipping emissions through the development of emission inventories provides important data on the current state of a region. We aimed to generate an emission inventory between 2010 and 2020, with bottom-up-based Entec and EPA methodologies for Istanbul Strait, and we used machine learning-based regression analysis to overcome the lack of data and to predict the future with data from previous years. Most of the emissions were Carbon Dioxide (CO₂) with a rate of 93.9%. Following the CO₂, Nitrogen Oxide (NO_X) with 2.5%, Sulfur Dioxide (SO₂) with 1.6%, Particulate Matter (PM) with 0.2%, and Hydrocarbons (HC) with 0.1%, respectively. Emissions from ships passing from South to North (S–N) were on average 2.89% higher each year due to the Strait's surface current. The results indicated that although the number of ships decreased over the years, the emissions did not decrease since the total gross tonnage of the passing ships increased.