Estimation and spatio-temporal analysis of ship exhaust emission in a port area
Atmosphere pollution brought by rapid development of shipping industry becomes increasingly serious, and has brought great challenges to maritime supervision and management. With the popularization of AIS (Automatic Identification System) technology, massive navigation logs recording rich ship activ...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Huang, Liang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Schlagwörter: |
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| Umfang: |
11 |
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| Übergeordnetes Werk: |
Enthalten in: Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy - Chang, Guanru ELSEVIER, 2015, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:140 ; year:2017 ; day:1 ; month:08 ; pages:401-411 ; extent:11 |
| Links: |
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| DOI / URN: |
10.1016/j.oceaneng.2017.06.015 |
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ELV030813387 |
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| 245 | 1 | 0 | |a Estimation and spatio-temporal analysis of ship exhaust emission in a port area |
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| 520 | |a Atmosphere pollution brought by rapid development of shipping industry becomes increasingly serious, and has brought great challenges to maritime supervision and management. With the popularization of AIS (Automatic Identification System) technology, massive navigation logs recording rich ship activities can be collected. These logs make it possible to apply a data-driven method to quantify exhaust emissions from ships. This paper is therefore motivated to research methods of approximately estimating ship exhaust emissions and exploring its spatial distribution and variation rules. Firstly, ship static and dynamic information extracted from AIS logs are utilized to distinguish different ship navigational states. A quantitative model is then created to estimate ship emissions from main engine, auxiliary engine and boiler with varied work conditions in different states. Spatio-temporal analysis methods are further applied to comprehensively discovery knowledge hidden in ship emission inventories. Typical analyses from space, time and attributes have uncovered much useful knowledge for ship emission supervision, such as high-polluted area, peak-emission time, least environmentally friendly ship type, etc. Finally, Ningbo-Zhoushan port plays a role as case study in validating the proposed method. The experiment results illustrate this research can be helpful to make efficient emission control measures and improve the capability of environmental management. | ||
| 520 | |a Atmosphere pollution brought by rapid development of shipping industry becomes increasingly serious, and has brought great challenges to maritime supervision and management. With the popularization of AIS (Automatic Identification System) technology, massive navigation logs recording rich ship activities can be collected. These logs make it possible to apply a data-driven method to quantify exhaust emissions from ships. This paper is therefore motivated to research methods of approximately estimating ship exhaust emissions and exploring its spatial distribution and variation rules. Firstly, ship static and dynamic information extracted from AIS logs are utilized to distinguish different ship navigational states. A quantitative model is then created to estimate ship emissions from main engine, auxiliary engine and boiler with varied work conditions in different states. Spatio-temporal analysis methods are further applied to comprehensively discovery knowledge hidden in ship emission inventories. Typical analyses from space, time and attributes have uncovered much useful knowledge for ship emission supervision, such as high-polluted area, peak-emission time, least environmentally friendly ship type, etc. Finally, Ningbo-Zhoushan port plays a role as case study in validating the proposed method. The experiment results illustrate this research can be helpful to make efficient emission control measures and improve the capability of environmental management. | ||
| 650 | 7 | |a Ship exhaust emission |2 Elsevier | |
| 650 | 7 | |a Exhaust emission visualization |2 Elsevier | |
| 650 | 7 | |a Spatio-temporal analysis |2 Elsevier | |
| 650 | 7 | |a AIS |2 Elsevier | |
| 700 | 1 | |a Wen, Yuanqiao |4 oth | |
| 700 | 1 | |a Geng, Xiaoqiao |4 oth | |
| 700 | 1 | |a Zhou, Chunhui |4 oth | |
| 700 | 1 | |a Xiao, Changshi |4 oth | |
| 700 | 1 | |a Zhang, Fan |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Chang, Guanru ELSEVIER |t Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy |d 2015 |g Amsterdam [u.a.] |w (DE-627)ELV01276728X |
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10.1016/j.oceaneng.2017.06.015 doi GBVA2017022000017.pica (DE-627)ELV030813387 (ELSEVIER)S0029-8018(17)30309-8 DE-627 ger DE-627 rakwb eng 690 690 DE-600 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Huang, Liang verfasserin aut Estimation and spatio-temporal analysis of ship exhaust emission in a port area 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Atmosphere pollution brought by rapid development of shipping industry becomes increasingly serious, and has brought great challenges to maritime supervision and management. With the popularization of AIS (Automatic Identification System) technology, massive navigation logs recording rich ship activities can be collected. These logs make it possible to apply a data-driven method to quantify exhaust emissions from ships. This paper is therefore motivated to research methods of approximately estimating ship exhaust emissions and exploring its spatial distribution and variation rules. Firstly, ship static and dynamic information extracted from AIS logs are utilized to distinguish different ship navigational states. A quantitative model is then created to estimate ship emissions from main engine, auxiliary engine and boiler with varied work conditions in different states. Spatio-temporal analysis methods are further applied to comprehensively discovery knowledge hidden in ship emission inventories. Typical analyses from space, time and attributes have uncovered much useful knowledge for ship emission supervision, such as high-polluted area, peak-emission time, least environmentally friendly ship type, etc. Finally, Ningbo-Zhoushan port plays a role as case study in validating the proposed method. The experiment results illustrate this research can be helpful to make efficient emission control measures and improve the capability of environmental management. Atmosphere pollution brought by rapid development of shipping industry becomes increasingly serious, and has brought great challenges to maritime supervision and management. With the popularization of AIS (Automatic Identification System) technology, massive navigation logs recording rich ship activities can be collected. These logs make it possible to apply a data-driven method to quantify exhaust emissions from ships. This paper is therefore motivated to research methods of approximately estimating ship exhaust emissions and exploring its spatial distribution and variation rules. Firstly, ship static and dynamic information extracted from AIS logs are utilized to distinguish different ship navigational states. A quantitative model is then created to estimate ship emissions from main engine, auxiliary engine and boiler with varied work conditions in different states. Spatio-temporal analysis methods are further applied to comprehensively discovery knowledge hidden in ship emission inventories. Typical analyses from space, time and attributes have uncovered much useful knowledge for ship emission supervision, such as high-polluted area, peak-emission time, least environmentally friendly ship type, etc. Finally, Ningbo-Zhoushan port plays a role as case study in validating the proposed method. The experiment results illustrate this research can be helpful to make efficient emission control measures and improve the capability of environmental management. Ship exhaust emission Elsevier Exhaust emission visualization Elsevier Spatio-temporal analysis Elsevier AIS Elsevier Wen, Yuanqiao oth Geng, Xiaoqiao oth Zhou, Chunhui oth Xiao, Changshi oth Zhang, Fan oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:140 year:2017 day:1 month:08 pages:401-411 extent:11 https://doi.org/10.1016/j.oceaneng.2017.06.015 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 140 2017 1 0801 401-411 11 045F 690 |
| spelling |
10.1016/j.oceaneng.2017.06.015 doi GBVA2017022000017.pica (DE-627)ELV030813387 (ELSEVIER)S0029-8018(17)30309-8 DE-627 ger DE-627 rakwb eng 690 690 DE-600 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Huang, Liang verfasserin aut Estimation and spatio-temporal analysis of ship exhaust emission in a port area 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Atmosphere pollution brought by rapid development of shipping industry becomes increasingly serious, and has brought great challenges to maritime supervision and management. With the popularization of AIS (Automatic Identification System) technology, massive navigation logs recording rich ship activities can be collected. These logs make it possible to apply a data-driven method to quantify exhaust emissions from ships. This paper is therefore motivated to research methods of approximately estimating ship exhaust emissions and exploring its spatial distribution and variation rules. Firstly, ship static and dynamic information extracted from AIS logs are utilized to distinguish different ship navigational states. A quantitative model is then created to estimate ship emissions from main engine, auxiliary engine and boiler with varied work conditions in different states. Spatio-temporal analysis methods are further applied to comprehensively discovery knowledge hidden in ship emission inventories. Typical analyses from space, time and attributes have uncovered much useful knowledge for ship emission supervision, such as high-polluted area, peak-emission time, least environmentally friendly ship type, etc. Finally, Ningbo-Zhoushan port plays a role as case study in validating the proposed method. The experiment results illustrate this research can be helpful to make efficient emission control measures and improve the capability of environmental management. Atmosphere pollution brought by rapid development of shipping industry becomes increasingly serious, and has brought great challenges to maritime supervision and management. With the popularization of AIS (Automatic Identification System) technology, massive navigation logs recording rich ship activities can be collected. These logs make it possible to apply a data-driven method to quantify exhaust emissions from ships. This paper is therefore motivated to research methods of approximately estimating ship exhaust emissions and exploring its spatial distribution and variation rules. Firstly, ship static and dynamic information extracted from AIS logs are utilized to distinguish different ship navigational states. A quantitative model is then created to estimate ship emissions from main engine, auxiliary engine and boiler with varied work conditions in different states. Spatio-temporal analysis methods are further applied to comprehensively discovery knowledge hidden in ship emission inventories. Typical analyses from space, time and attributes have uncovered much useful knowledge for ship emission supervision, such as high-polluted area, peak-emission time, least environmentally friendly ship type, etc. Finally, Ningbo-Zhoushan port plays a role as case study in validating the proposed method. The experiment results illustrate this research can be helpful to make efficient emission control measures and improve the capability of environmental management. Ship exhaust emission Elsevier Exhaust emission visualization Elsevier Spatio-temporal analysis Elsevier AIS Elsevier Wen, Yuanqiao oth Geng, Xiaoqiao oth Zhou, Chunhui oth Xiao, Changshi oth Zhang, Fan oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:140 year:2017 day:1 month:08 pages:401-411 extent:11 https://doi.org/10.1016/j.oceaneng.2017.06.015 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 140 2017 1 0801 401-411 11 045F 690 |
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10.1016/j.oceaneng.2017.06.015 doi GBVA2017022000017.pica (DE-627)ELV030813387 (ELSEVIER)S0029-8018(17)30309-8 DE-627 ger DE-627 rakwb eng 690 690 DE-600 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Huang, Liang verfasserin aut Estimation and spatio-temporal analysis of ship exhaust emission in a port area 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Atmosphere pollution brought by rapid development of shipping industry becomes increasingly serious, and has brought great challenges to maritime supervision and management. With the popularization of AIS (Automatic Identification System) technology, massive navigation logs recording rich ship activities can be collected. These logs make it possible to apply a data-driven method to quantify exhaust emissions from ships. This paper is therefore motivated to research methods of approximately estimating ship exhaust emissions and exploring its spatial distribution and variation rules. Firstly, ship static and dynamic information extracted from AIS logs are utilized to distinguish different ship navigational states. A quantitative model is then created to estimate ship emissions from main engine, auxiliary engine and boiler with varied work conditions in different states. Spatio-temporal analysis methods are further applied to comprehensively discovery knowledge hidden in ship emission inventories. Typical analyses from space, time and attributes have uncovered much useful knowledge for ship emission supervision, such as high-polluted area, peak-emission time, least environmentally friendly ship type, etc. Finally, Ningbo-Zhoushan port plays a role as case study in validating the proposed method. The experiment results illustrate this research can be helpful to make efficient emission control measures and improve the capability of environmental management. Atmosphere pollution brought by rapid development of shipping industry becomes increasingly serious, and has brought great challenges to maritime supervision and management. With the popularization of AIS (Automatic Identification System) technology, massive navigation logs recording rich ship activities can be collected. These logs make it possible to apply a data-driven method to quantify exhaust emissions from ships. This paper is therefore motivated to research methods of approximately estimating ship exhaust emissions and exploring its spatial distribution and variation rules. Firstly, ship static and dynamic information extracted from AIS logs are utilized to distinguish different ship navigational states. A quantitative model is then created to estimate ship emissions from main engine, auxiliary engine and boiler with varied work conditions in different states. Spatio-temporal analysis methods are further applied to comprehensively discovery knowledge hidden in ship emission inventories. Typical analyses from space, time and attributes have uncovered much useful knowledge for ship emission supervision, such as high-polluted area, peak-emission time, least environmentally friendly ship type, etc. Finally, Ningbo-Zhoushan port plays a role as case study in validating the proposed method. The experiment results illustrate this research can be helpful to make efficient emission control measures and improve the capability of environmental management. Ship exhaust emission Elsevier Exhaust emission visualization Elsevier Spatio-temporal analysis Elsevier AIS Elsevier Wen, Yuanqiao oth Geng, Xiaoqiao oth Zhou, Chunhui oth Xiao, Changshi oth Zhang, Fan oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:140 year:2017 day:1 month:08 pages:401-411 extent:11 https://doi.org/10.1016/j.oceaneng.2017.06.015 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 140 2017 1 0801 401-411 11 045F 690 |
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10.1016/j.oceaneng.2017.06.015 doi GBVA2017022000017.pica (DE-627)ELV030813387 (ELSEVIER)S0029-8018(17)30309-8 DE-627 ger DE-627 rakwb eng 690 690 DE-600 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Huang, Liang verfasserin aut Estimation and spatio-temporal analysis of ship exhaust emission in a port area 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Atmosphere pollution brought by rapid development of shipping industry becomes increasingly serious, and has brought great challenges to maritime supervision and management. With the popularization of AIS (Automatic Identification System) technology, massive navigation logs recording rich ship activities can be collected. These logs make it possible to apply a data-driven method to quantify exhaust emissions from ships. This paper is therefore motivated to research methods of approximately estimating ship exhaust emissions and exploring its spatial distribution and variation rules. Firstly, ship static and dynamic information extracted from AIS logs are utilized to distinguish different ship navigational states. A quantitative model is then created to estimate ship emissions from main engine, auxiliary engine and boiler with varied work conditions in different states. Spatio-temporal analysis methods are further applied to comprehensively discovery knowledge hidden in ship emission inventories. Typical analyses from space, time and attributes have uncovered much useful knowledge for ship emission supervision, such as high-polluted area, peak-emission time, least environmentally friendly ship type, etc. Finally, Ningbo-Zhoushan port plays a role as case study in validating the proposed method. The experiment results illustrate this research can be helpful to make efficient emission control measures and improve the capability of environmental management. Atmosphere pollution brought by rapid development of shipping industry becomes increasingly serious, and has brought great challenges to maritime supervision and management. With the popularization of AIS (Automatic Identification System) technology, massive navigation logs recording rich ship activities can be collected. These logs make it possible to apply a data-driven method to quantify exhaust emissions from ships. This paper is therefore motivated to research methods of approximately estimating ship exhaust emissions and exploring its spatial distribution and variation rules. Firstly, ship static and dynamic information extracted from AIS logs are utilized to distinguish different ship navigational states. A quantitative model is then created to estimate ship emissions from main engine, auxiliary engine and boiler with varied work conditions in different states. Spatio-temporal analysis methods are further applied to comprehensively discovery knowledge hidden in ship emission inventories. Typical analyses from space, time and attributes have uncovered much useful knowledge for ship emission supervision, such as high-polluted area, peak-emission time, least environmentally friendly ship type, etc. Finally, Ningbo-Zhoushan port plays a role as case study in validating the proposed method. The experiment results illustrate this research can be helpful to make efficient emission control measures and improve the capability of environmental management. Ship exhaust emission Elsevier Exhaust emission visualization Elsevier Spatio-temporal analysis Elsevier AIS Elsevier Wen, Yuanqiao oth Geng, Xiaoqiao oth Zhou, Chunhui oth Xiao, Changshi oth Zhang, Fan oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:140 year:2017 day:1 month:08 pages:401-411 extent:11 https://doi.org/10.1016/j.oceaneng.2017.06.015 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 140 2017 1 0801 401-411 11 045F 690 |
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10.1016/j.oceaneng.2017.06.015 doi GBVA2017022000017.pica (DE-627)ELV030813387 (ELSEVIER)S0029-8018(17)30309-8 DE-627 ger DE-627 rakwb eng 690 690 DE-600 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Huang, Liang verfasserin aut Estimation and spatio-temporal analysis of ship exhaust emission in a port area 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Atmosphere pollution brought by rapid development of shipping industry becomes increasingly serious, and has brought great challenges to maritime supervision and management. With the popularization of AIS (Automatic Identification System) technology, massive navigation logs recording rich ship activities can be collected. These logs make it possible to apply a data-driven method to quantify exhaust emissions from ships. This paper is therefore motivated to research methods of approximately estimating ship exhaust emissions and exploring its spatial distribution and variation rules. Firstly, ship static and dynamic information extracted from AIS logs are utilized to distinguish different ship navigational states. A quantitative model is then created to estimate ship emissions from main engine, auxiliary engine and boiler with varied work conditions in different states. Spatio-temporal analysis methods are further applied to comprehensively discovery knowledge hidden in ship emission inventories. Typical analyses from space, time and attributes have uncovered much useful knowledge for ship emission supervision, such as high-polluted area, peak-emission time, least environmentally friendly ship type, etc. Finally, Ningbo-Zhoushan port plays a role as case study in validating the proposed method. The experiment results illustrate this research can be helpful to make efficient emission control measures and improve the capability of environmental management. Atmosphere pollution brought by rapid development of shipping industry becomes increasingly serious, and has brought great challenges to maritime supervision and management. With the popularization of AIS (Automatic Identification System) technology, massive navigation logs recording rich ship activities can be collected. These logs make it possible to apply a data-driven method to quantify exhaust emissions from ships. This paper is therefore motivated to research methods of approximately estimating ship exhaust emissions and exploring its spatial distribution and variation rules. Firstly, ship static and dynamic information extracted from AIS logs are utilized to distinguish different ship navigational states. A quantitative model is then created to estimate ship emissions from main engine, auxiliary engine and boiler with varied work conditions in different states. Spatio-temporal analysis methods are further applied to comprehensively discovery knowledge hidden in ship emission inventories. Typical analyses from space, time and attributes have uncovered much useful knowledge for ship emission supervision, such as high-polluted area, peak-emission time, least environmentally friendly ship type, etc. Finally, Ningbo-Zhoushan port plays a role as case study in validating the proposed method. The experiment results illustrate this research can be helpful to make efficient emission control measures and improve the capability of environmental management. Ship exhaust emission Elsevier Exhaust emission visualization Elsevier Spatio-temporal analysis Elsevier AIS Elsevier Wen, Yuanqiao oth Geng, Xiaoqiao oth Zhou, Chunhui oth Xiao, Changshi oth Zhang, Fan oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:140 year:2017 day:1 month:08 pages:401-411 extent:11 https://doi.org/10.1016/j.oceaneng.2017.06.015 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 140 2017 1 0801 401-411 11 045F 690 |
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Enthalten in Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy Amsterdam [u.a.] volume:140 year:2017 day:1 month:08 pages:401-411 extent:11 |
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Enthalten in Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy Amsterdam [u.a.] volume:140 year:2017 day:1 month:08 pages:401-411 extent:11 |
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Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy |
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Huang, Liang @@aut@@ Wen, Yuanqiao @@oth@@ Geng, Xiaoqiao @@oth@@ Zhou, Chunhui @@oth@@ Xiao, Changshi @@oth@@ Zhang, Fan @@oth@@ |
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With the popularization of AIS (Automatic Identification System) technology, massive navigation logs recording rich ship activities can be collected. These logs make it possible to apply a data-driven method to quantify exhaust emissions from ships. This paper is therefore motivated to research methods of approximately estimating ship exhaust emissions and exploring its spatial distribution and variation rules. Firstly, ship static and dynamic information extracted from AIS logs are utilized to distinguish different ship navigational states. A quantitative model is then created to estimate ship emissions from main engine, auxiliary engine and boiler with varied work conditions in different states. Spatio-temporal analysis methods are further applied to comprehensively discovery knowledge hidden in ship emission inventories. 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estimation and spatio-temporal analysis of ship exhaust emission in a port area |
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Estimation and spatio-temporal analysis of ship exhaust emission in a port area |
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Atmosphere pollution brought by rapid development of shipping industry becomes increasingly serious, and has brought great challenges to maritime supervision and management. With the popularization of AIS (Automatic Identification System) technology, massive navigation logs recording rich ship activities can be collected. These logs make it possible to apply a data-driven method to quantify exhaust emissions from ships. This paper is therefore motivated to research methods of approximately estimating ship exhaust emissions and exploring its spatial distribution and variation rules. Firstly, ship static and dynamic information extracted from AIS logs are utilized to distinguish different ship navigational states. A quantitative model is then created to estimate ship emissions from main engine, auxiliary engine and boiler with varied work conditions in different states. Spatio-temporal analysis methods are further applied to comprehensively discovery knowledge hidden in ship emission inventories. Typical analyses from space, time and attributes have uncovered much useful knowledge for ship emission supervision, such as high-polluted area, peak-emission time, least environmentally friendly ship type, etc. Finally, Ningbo-Zhoushan port plays a role as case study in validating the proposed method. The experiment results illustrate this research can be helpful to make efficient emission control measures and improve the capability of environmental management. |
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Atmosphere pollution brought by rapid development of shipping industry becomes increasingly serious, and has brought great challenges to maritime supervision and management. With the popularization of AIS (Automatic Identification System) technology, massive navigation logs recording rich ship activities can be collected. These logs make it possible to apply a data-driven method to quantify exhaust emissions from ships. This paper is therefore motivated to research methods of approximately estimating ship exhaust emissions and exploring its spatial distribution and variation rules. Firstly, ship static and dynamic information extracted from AIS logs are utilized to distinguish different ship navigational states. A quantitative model is then created to estimate ship emissions from main engine, auxiliary engine and boiler with varied work conditions in different states. Spatio-temporal analysis methods are further applied to comprehensively discovery knowledge hidden in ship emission inventories. Typical analyses from space, time and attributes have uncovered much useful knowledge for ship emission supervision, such as high-polluted area, peak-emission time, least environmentally friendly ship type, etc. Finally, Ningbo-Zhoushan port plays a role as case study in validating the proposed method. The experiment results illustrate this research can be helpful to make efficient emission control measures and improve the capability of environmental management. |
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Atmosphere pollution brought by rapid development of shipping industry becomes increasingly serious, and has brought great challenges to maritime supervision and management. With the popularization of AIS (Automatic Identification System) technology, massive navigation logs recording rich ship activities can be collected. These logs make it possible to apply a data-driven method to quantify exhaust emissions from ships. This paper is therefore motivated to research methods of approximately estimating ship exhaust emissions and exploring its spatial distribution and variation rules. Firstly, ship static and dynamic information extracted from AIS logs are utilized to distinguish different ship navigational states. A quantitative model is then created to estimate ship emissions from main engine, auxiliary engine and boiler with varied work conditions in different states. Spatio-temporal analysis methods are further applied to comprehensively discovery knowledge hidden in ship emission inventories. Typical analyses from space, time and attributes have uncovered much useful knowledge for ship emission supervision, such as high-polluted area, peak-emission time, least environmentally friendly ship type, etc. Finally, Ningbo-Zhoushan port plays a role as case study in validating the proposed method. The experiment results illustrate this research can be helpful to make efficient emission control measures and improve the capability of environmental management. |
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