Abstract
Coastal regions worldwide face increasing air pollution due to maritime activities. This technical note focuses on assessing the air pollution in the Daesan port area, Republic of Korea, using hourly emission measurements. Leveraging Automatic Identification System (AIS) data, we estimate vessel-induced air pollutant emissions and correlate them with real-time measurements. Vessel navigational statuses are categorized from the AIS data, enabling an estimation of fuel oil consumption. Random Forest models predict specific fuel oil consumption and maximum continuous ratings for vessels with unknown engine details. Using emission factors, we calculate the emissions (CO2, NO2, SO2, PM-10, and PM-2.5) from vessels visiting the port. These estimates are compared with actual air pollutant concentrations, revealing a qualitative relationship with an average correlation coefficient of approximately 0.33.
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