An AIS-Based Study to Estimate Ship Exhaust Emissions Using Spatio-Temporal Approach
The global shipping industry facilitates the movement of approximately 80% of goods across the world but accounts for nearly 3% of total greenhouse gas (GHG) emissions every year, and other pollutants. One challenge in reducing shipping emissions is understanding and quantifying emission characteris...
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MDPI AG
2025-05-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/5/922 |
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| author | Akhahenda Whitney Khayenzeli Woo-Ju Son Dong-June Jo Ik-Soon Cho |
| author_facet | Akhahenda Whitney Khayenzeli Woo-Ju Son Dong-June Jo Ik-Soon Cho |
| author_sort | Akhahenda Whitney Khayenzeli |
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| description | The global shipping industry facilitates the movement of approximately 80% of goods across the world but accounts for nearly 3% of total greenhouse gas (GHG) emissions every year, and other pollutants. One challenge in reducing shipping emissions is understanding and quantifying emission characteristics. A detailed method for calculating shipping emissions should be applied when preparing exhaust gas inventory. This research focused on quantifying CO<sub>2</sub>, NO<sub>x,</sub> and SO<sub>x</sub> emissions from tankers, containers, bulk carriers, and general cargo in the Republic of Korea using spatio-temporal analysis and maritime big data. Using the bottom-up approach, this study calculates vessel emissions from the ship engines while considering the fuel type and operation mode. It leveraged the Geographic Information System (GIS) to generate spatial distribution maps of vessel exhausts. The research revealed variability in emissions according to ship types, sizes, and operational modes. CO<sub>2</sub> emissions were dominant, totaling 10.5 million tons, NO<sub>x</sub> 179,355.2 tons, and SO<sub>x</sub> 32,505.1 tons. Tankers accounted for about 43.3%, containers 33.1%, bulk carriers 17.3%, and general cargo 6.3%. Further, emissions in hoteling and cruising were more significant than during maneuvering and reduced speed zones (RSZs). This study contributes to emission databases, providing a basis for the establishment of targeted emission control policies. |
| format | Article |
| id | doaj-art-85c5cc374f514a2ca4ddcb9ae092aeb9 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-85c5cc374f514a2ca4ddcb9ae092aeb92025-08-20T03:48:01ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-05-0113592210.3390/jmse13050922An AIS-Based Study to Estimate Ship Exhaust Emissions Using Spatio-Temporal ApproachAkhahenda Whitney Khayenzeli0Woo-Ju Son1Dong-June Jo2Ik-Soon Cho3Department of Maritime AI & Cyber Security, Graduate School of National Korea Maritime & Ocean University, Busan 49112, Republic of KoreaResearch Institute of Medium & Small Shipbuilding, Changwon 51965, Republic of KoreaDepartment of Maritime AI & Cyber Security, Graduate School of National Korea Maritime & Ocean University, Busan 49112, Republic of KoreaDivision of Maritime AI & Cyber Security, National Korea Maritime & Ocean University, Busan 49112, Republic of KoreaThe global shipping industry facilitates the movement of approximately 80% of goods across the world but accounts for nearly 3% of total greenhouse gas (GHG) emissions every year, and other pollutants. One challenge in reducing shipping emissions is understanding and quantifying emission characteristics. A detailed method for calculating shipping emissions should be applied when preparing exhaust gas inventory. This research focused on quantifying CO<sub>2</sub>, NO<sub>x,</sub> and SO<sub>x</sub> emissions from tankers, containers, bulk carriers, and general cargo in the Republic of Korea using spatio-temporal analysis and maritime big data. Using the bottom-up approach, this study calculates vessel emissions from the ship engines while considering the fuel type and operation mode. It leveraged the Geographic Information System (GIS) to generate spatial distribution maps of vessel exhausts. The research revealed variability in emissions according to ship types, sizes, and operational modes. CO<sub>2</sub> emissions were dominant, totaling 10.5 million tons, NO<sub>x</sub> 179,355.2 tons, and SO<sub>x</sub> 32,505.1 tons. Tankers accounted for about 43.3%, containers 33.1%, bulk carriers 17.3%, and general cargo 6.3%. Further, emissions in hoteling and cruising were more significant than during maneuvering and reduced speed zones (RSZs). This study contributes to emission databases, providing a basis for the establishment of targeted emission control policies.https://www.mdpi.com/2077-1312/13/5/922exhaust gasspatio-temporalbig dataGISemission databaseinternational maritime organization (IMO) |
| spellingShingle | Akhahenda Whitney Khayenzeli Woo-Ju Son Dong-June Jo Ik-Soon Cho An AIS-Based Study to Estimate Ship Exhaust Emissions Using Spatio-Temporal Approach Journal of Marine Science and Engineering exhaust gas spatio-temporal big data GIS emission database international maritime organization (IMO) |
| title | An AIS-Based Study to Estimate Ship Exhaust Emissions Using Spatio-Temporal Approach |
| title_full | An AIS-Based Study to Estimate Ship Exhaust Emissions Using Spatio-Temporal Approach |
| title_fullStr | An AIS-Based Study to Estimate Ship Exhaust Emissions Using Spatio-Temporal Approach |
| title_full_unstemmed | An AIS-Based Study to Estimate Ship Exhaust Emissions Using Spatio-Temporal Approach |
| title_short | An AIS-Based Study to Estimate Ship Exhaust Emissions Using Spatio-Temporal Approach |
| title_sort | ais based study to estimate ship exhaust emissions using spatio temporal approach |
| topic | exhaust gas spatio-temporal big data GIS emission database international maritime organization (IMO) |
| url | https://www.mdpi.com/2077-1312/13/5/922 |
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