Estimation of Air Pollutant Emissions from Ships and Their Contributions in Korea

Seol, Sunghee; Jung, Sungwoon; Seong, Mi-Ae; Lim, Jae-Hyun; Yeo, Soyoung; Jin, Hyungah

doi:10.5572/kosae.2021.37.2.324

Cited by 3 publications

(1 citation statement)

References 45 publications

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“…China and Hong Kong have also implemented emission control boundaries along their shores [5]. According to the National Institute of Environmental Research [6], the air pollutant emissions amounts in port areas often surpass those on land. For instance, in Busan, South Korea's largest container port city, vessels are responsible for 71% of the sulfur compounds and 39% of the nitrogen compounds emitted.…”

Section: Introduction 1research Backgroundmentioning

confidence: 99%

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

Yoon,

Kim,

Eom

et al. 2023

JMSE

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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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Section: Introduction 1research Backgroundmentioning

confidence: 99%

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

Yoon,

Kim,

Eom

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

Experimental Study on Prediction for Combustion Optimal Control of Oil-Fired Boilers of Ships Using Color Space Image Feature Analysis and Support Vector Machine

Lee,

Jung,

Choi

2023

JMSE

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The International Maritime Organization strives to improve the atmospheric environment in oceans and ports by regulating ship emissions of air pollutants and promoting energy efficiency. This study deals with the prediction of eco-friendly combustion in boilers to reduce air pollution emissions. Accurately measuring air pollutants from ship boilers in real-time is crucial for optimizing boiler combustion. However, using data obtained through an exhaust gas analyzer for real-time control is challenging due to combustion process delays. Therefore, a real-time predictive modeling approach is proposed to enhance the accuracy of prediction models for NOx, SO2, CO2, and O2 by analyzing the color spectrum of flame images in a quasi-instantaneous combustion state. Experimental investigations were carried out on an oil-fired boiler installed on an actual ship, where the air damper was adjusted to create various combustion conditions. This algorithm is a saturation-based feature extraction filter (SEF) through color spectrum analysis using RGB (red, green, and blue) and HSV (hue, saturation, and value). The prediction model applying the proposed method was verified against exhaust gas analyzer data using a new data set, and real-time prediction performance and generalization were confirmed.

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Analysis of Emission Characteristics and Estimation of Air Pollutants Emitted from Small Ship

Kim¹,

Kim²,

Lee³

et al. 2022

KOSAE

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Estimation of Air Pollutant Emissions from Ships and Their Contributions in Korea

Cited by 3 publications

References 45 publications

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

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

Experimental Study on Prediction for Combustion Optimal Control of Oil-Fired Boilers of Ships Using Color Space Image Feature Analysis and Support Vector Machine

Analysis of Emission Characteristics and Estimation of Air Pollutants Emitted from Small Ship

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