Journal Article

Coastal Air Quality Assessment through AIS-Based Vessel Emissions: A Daesan Port Case Study

Yoon, Jeong-Hyun, Kim, Se-Won, Oh, Jaeyong, Kim, Hye-Jin, Eom, Jeong-On

2023

Overview

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.

• Publisher: MDPI AG
• Subject: Accuracy / Air pollution / Air quality / Air quality assessments / Algorithms / automatic identification system / Carbon dioxide / Case studies / Climate change / coastal emissions / Coastal zone / Consumption / Container ships / Correlation coefficient / Correlation coefficients / Emission measurements / Emission standards / Emissions / Energy consumption / Fuel consumption / Fuel oils / Greenhouse gases / Mathematical analysis / Neural networks / Nitrogen dioxide / Oil consumption / Outdoor air quality / Pollutants / Ports / Qualitative analysis / Quality assessment / Quality control / Shipping industry / Sulfur content / Sulfur dioxide / vessel fuel oil consumption