Overview and Assessment of Ship Emissions Estimation Models

Abstract

This review provides a comprehensive comparative assessment of ship emissions estimation models across the various ship emissions estimation modelling approaches, with a focus on key approaches: top-down, bottom-up, hybrid approaches, and emerging machine learning and artificial intelligence-based techniques as an innovative approach, offering new perspectives and solutions for the maritime industry through its powerful data analysis and pattern recognition capabilities. It delves into important ship emissions estimation models that use advanced data-driven techniques, ranging from simplified to complex, which are continually evolving to improve accuracy by incorporating real-time data, technological advancements, and the impact of environmental policies. Afterwards, this review provides an overall analysis of the ship emissions models, highlighting current research gaps in data availability and quality, challenges in selecting ship emissions estimation methods, and future research topics. Finally, it concludes with future directions in advanced machine learning methods, digital twins, advanced sensor integration, and global monitoring for precise forecasting and regulatory support, all of which are crucial for decarbonization targets.

Keywords

Ship emissions estimation models / Top-down and bottom-up approaches / Hybrid modelling / Machine learning / Data-driven models / Real-time data integration / Maritime Decarbonisation

References

[1] Almklov DV. Trans-arctic re-routing of container vessels using the MariTEAM model: A comparative analysis of short-and long-term climate change impacts, 2022, Trondheim, Norway: Norwegian University of Science and Technology

[2] Bogaerts T, Masegosa AD, Angarita-Zapata JS, Onieva E, Hellinckx P. A graph CNN-LSTM neural network for short and longterm traffic forecasting based on trajectory data. Transportation Research Part C: Emerging Technologies, 2020, 112: 62-77.

[3] Bojić F, Gudelj A, Bošnjak R. Port-related shipping gas emissions—A systematic review of research. Applied Sciences, 2022, 12: 3603.

[4] Bojić F, Gudelj A, Bošnjak R. An analytical model for estimating ship-related emissions in port areas. Journal of Marine Science and Engineering, 2023, 11: 2377.

[5] Bojić F, Gudelj A, Bošnjak R. A comprehensive model for quantifying, predicting, and evaluating ship emissions in port areas using novel metrics and machine learning methods. Journal of Marine Science and Engineering, 2025, 13: 1162.

[6] Bouman EA, Lindstad HE, Stromman AH. Life-cycle approaches for bottom-up assessment of environmental impacts of shipping. SNAME Maritime Convention, 2016, Bellevue, Washington, USA: Society of Naval Architects and Marine Engineers (SNAME)

[7] Breiman L. Random forests. Machine Learning, 2001, 45: 5-32.

[8] Brown IN, Aldridge MF. Power models and average ship parameter effects on marine emissions inventories. Journal of the Air & Waste Management Association, 2019, 69: 752-763.

[9] CARBCalifornia ocean-going vessels emissions inventory, 2021, California, USA: California Air Resources Board (CARB)

[10] Chang YT, Song Y, Roh Y. Assessing greenhouse gas emissions from port vessel operations at the Port of Incheon. Transportation Research Part D: Transport and Environment, 2013, 25: 1-4.

[11] Chen T, Guestrin C. Xgboost: A scalable tree boosting system. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 2016785794.

[12] Chen ZS, Lam JSL, Xiao Z. Prediction of harbour vessel emissions based on machine learning approach. Transportation Research Part D: Transport and Environment, 2024, 131: 104214.

[13] Corbett JJ, Fischbeck PS, Pandis SN. Global nitrogen and sulfur inventories for oceangoing ships. Journal of Geophysical Research: Atmospheres, 1999, 104: 3457-3470.

[14] Dale T. Development of simplified methods for ship powering performance calculations, 2020, Trondheim, Norway: Norwegian University of Science and Technology-NTNU

[15] De Lima Nogueira SC, Och SH, Moura LM, Domingues E, dos Santos Coelho L, Mariani VC. Prediction of the NO_X and CO₂ emissions from an experimental dual fuel engine using optimized random forest combined with feature engineering. Energy, 2023, 280: 128066.

[16] Denier van der Gon H, Gauss M, Granier C. CAMS2_61 – Global and European emission inventories, 2025

[17] Denier van der Gon H, Gauss M, Granier C, Arellano S, Benedictow A, Darras S, Dellaert S, Guevara M, Jalkanen J, Krueger K. Documentation of CAMS emission inventory products, 2023

[18] Duan K, Li Q, Liu S, Liu Y, Wang S, Li S, Wang X, Ma N, Ma Y. AIS-based operational phase identification using Progressive Ablation Feature Selection with machine learning for improving ship emission estimates. Journal of the Air & Waste Management Association, 2024, 74: 100-115.

[19] Durán-Grados V, Mejías J, Musina L, Moreno-Gutiérrez J. The influence of the waterjet propulsion system on the ships’ energy consumption and emissions inventories. Science of The Total Environment, 2018, 631–632: 496-509.

[20] EEAEMEP/EEA air pollutant emission inventory guidebook 2009. Technical guidance to prepare national emission inventories, 2009

[21] EEAEMEP/EEA air pollutant emission inventory guidebook 2023. Technical guidance to prepare national emission inventories, 2023

[22] Elsisi M, Bergies S, Yu JT, Su CL, Lee PJ (2026) A digital twin-enabled IoT architecture for electrified ship emission monitoring using hybrid deep learning and integrated anomaly detection. IEEE Transactions on Transportation Electrification: 1-1. https://doi.org/10.1109/TTE.2026.3694727

[23] ENTECRitchie A, De Jonge E, Hugi C, Cooper D. Service contract on ship emissions: assignment, abatement and market-based instruments. Task 2c–SO₂ Abatement Final report, 2005

[24] ENTECDefra UK ship emissions inventory, 2010

[25] ENTEC UQuantification of emissions from ships associated with ship movements between ports in the European Community, 2002

[26] EPACurrent methodologies in preparing mobile source port-related emission inventories, 2009, Washington, DC, USA: U. S. Environmental Protection Agency

[27] EPABandemehr A, Muehling B, Corbett J, Comer B, Boyle J. US-Mexico cooperation on reducing emissions from ships through a Mexican emission control area: development of the first national Mexican emission inventories for ships using the waterway network ship traffic, energy, and environmental model (STEEM), Office of International and Tribal Affairs. Energy, and Environmental Model, 2015

[28] EPAInventory PE. Methodologies for estimating port-related and goods movement mobile source emissions, 2022, Washington, DC, USA: United States Environmental Protection Agency

[29] Eyring V, Isaksen IS, Berntsen T, Collins WJ, Corbett JJ, Endresen O, Grainger RG, Moldanova J, Schlager H, Stevenson DS. Transport impacts on atmosphere and climate: Shipping. Atmospheric Environment, 2010, 44: 4735-4771.

[30] Fadaie S, Thornley P, Souppez JB (2025) A systematic review of technologies, measures, and CO₂ emission reduction potential for maritime transport decarbonisation. Advances in Applied Energy: 100255. https://doi.org/10.1016/j.adapen.2025.100255

[31] Feng Y, Wang X, Luan J, Wang H, Li H, Li H, Liu Z, Yang Z. A novel method for ship carbon emissions prediction under the influence of emergency events. Transportation Research Part C: Emerging Technologies, 2024, 165: 104749.

[32] Friedman JH. Multivariate adaptive regression splines. The Annals of Statistics, 1991, 19: 1-67

[33] Fu M, Liu H, Jin X, He K. National-to port-level inventories of shipping emissions in China. Environmental Research Letters, 2017, 12: 114024.

[34] Fuentes García G, Sosa Echeverría R, Baldasano Recio JM, Kahl JD, Antonio Durán RE. Review of top-down method to determine atmospheric emissions in port. Case of Study: Port of Veracruz, Mexico. Journal of Marine Science Engineering, 2022, 10: 96.

[35] Goldsworthy L, Goldsworthy B. Modelling of ship engine exhaust emissions in ports and extensive coastal waters based on terrestrial AIS data–An Australian case study. Environmental Modelling & Software, 2015, 63: 45-60.

[36] Huang L, Wen Y, Geng X, Zhou C, Xiao C, Zhang F. Estimation and spatio-temporal analysis of ship exhaust emission in a port area. Ocean Engineering, 2017, 140: 401-411.

[37] IMOSecond IMO GHG study 2009–MEPC59/INF, 200910

[38] IMOThird IMO greenhouse gas study 2014. Executive Summary, 2014, 4 Albert Embankment, London SE1 7SR: International Maritime Organization (IMO)

[39] IMO(MEPC) MEPCIMO strategy on reduction of GHG emissions from ships, 2023, London, UK: International Maritime Organization (IMO)

[40] IPCCEggleston H, Buendia L, Miwa K, Ngara T, Tanabe K. IPCC guidelines for national greenhouse gas Inventories, 2006, Japan: IGES

[41] Jalkanen JP, Brink A, Kalli J, Pettersson H, Kukkonen J, Stipa T. A modelling system for the exhaust emissions of marine traffic and its application in the Baltic Sea area. Atmospheric Chemistry Physics, 2009, 9: 9209-9223.

[42] Jalkanen JP, Johansson L, Kukkonen J. A comprehensive inventory of the ship traffic exhaust emissions in the Baltic Sea from 2006 to 2009. Ambio, 2014, 43: 311-324.

[43] Jalkanen JP, Johansson L, Kukkonen J. A comprehensive inventory of ship traffic exhaust emissions in the European sea areas in 2011. Atmospheric Chemistry Physics, 2016, 16: 71-84.

[44] Jalkanen JP, Johansson L, Kukkonen J, Brink A, Kalli J, Stipa T. Extension of an assessment model of ship traffic exhaust emissions for particulate matter and carbon monoxide. Atmospheric Chemistry Physics, 2012, 12: 2641-2659.

[45] Jalkanen JP, Johansson L, Wilewska-Bien M, Granhag L, Ytreberg E, Eriksson KM, Yngsell D, Hassellöv IM, Magnusson K, Raudsepp U, Maljutenko I, Winnes H, Moldanova J. Modelling of discharges from Baltic Sea shipping. Ocean Science, 2021, 17: 699-728.

[46] Jasper F, Hanayama S, Zhang S, Pereda P, Comer B, Hauerhof E, van der Loeff WS, Smith T, Zhang Y, Kosaka H. Fourth IMO GHG study 2020, 2021, 4 Albert Embankment, London SE1 7SR: International Maritime Organization (IMO)

[47] Johansson L, Jalkanen JP, Kalli J, Kukkonen J. The evolution of shipping emissions and the costs of regulation changes in the northern EU area. Atmospheric Chemistry Physics, 2013, 13: 11375-11389.

[48] Johansson L, Jalkanen JP, Kukkonen J. Global assessment of shipping emissions in 2015 on a high spatial and temporal resolution. Atmospheric Environment, 2017, 167: 403-415.

[49] Joung TH, Kang SG, Lee JK, Ahn J. The IMO initial strategy for reducing Greenhouse Gas (GHG) emissions, and its follow-up actions towards 2050. Journal of International Maritime Safety, Environmental Affairs, Shipping, 2020, 4: 1-7.

[50] Khoshraftar Z, Ghaemi A. Modeling and prediction of CO₂ partial pressure in methanol solution using artificial neural networks. Current Research in Green and Sustainable Chemistry, 2023, 6: 100364.

[51] Kim YR, Steen S, Kramel D, Muri H, Strømman AH. Modelling of ship resistance and power consumption for the global fleet: The MariTEAM model. Ocean Engineering, 2023, 281: 114758.

[52] Knežević V, Radonja R, Dundović Č. Emission inventory of marine traffic for the port of zadar. Pomorstvo, 2018, 32: 239-244.

[53] Kramel D, Muri H, Kim Y, Lonka R, Nielsen JB, Ringvold AL, Bouman EA, Steen S, Strømman AH. Global shipping emissions from a well-to-wake perspective: the MariTEAM model. Environmental Science Technology, 2021, 55: 15040-15050.

[54] Kuhn M, Johnson K. Applied predictive modeling, 2013, New York, NY: Springer

[55] Li H, Jia P, Wang X, Yang Z, Wang J, Kuang H. Ship carbon dioxide emission estimation in coastal domestic emission control areas using high spatial-temporal resolution data: A China case. Ocean & Coastal Management, 2023, 232: 106419.

[56] Li H, Yang Z. Incorporation of AIS data-based machine learning into unsupervised route planning for maritime autonomous surface ships. Transportation Research Part E: Logistics and Transportation Review, 2023, 176: 103171-103171.

[57] Li Y, Jia P, Jiang S, Li H, Kuang H, Hong Y, Wang S, Zhao X, Guan D. The climate impact of high seas shipping. National Science Review, 2023, 10: nwac279.

[58] Li Y, Zhou R, Zhou Y, Chen ZS. Machine learning techniques in maritime environmental sustainability: A comprehensive review of the state of the art. Computers and Electrical Engineering, 2025, 124: 110395.

[59] Lin Y, Wang C. Prediction of ship CO₂ emissions and fuel consumption using voting-BRL model. Sustainability, 2025, 17: 1726.

[60] Liu D, Liu Z, Kang HS, Siow CL, Guedes Soares C. Traffic complexity assessment on the malacca strait with traffic zone matrix based on AIS data. Ocean Engineering, 2024, 314: 119687.

[61] Liu D, Rong H, Guedes Soares C. Shipping route modelling of AIS maritime traffic data at the approach to ports. Ocean Engineering, 2023, 289: 115868.

[62] Liu H, Fu M, Jin X, Shang Y, Shindell D, Faluvegi G, Shindell C, He K. Health and climate impacts of ocean-going vessels in East Asia. Nature Climate Change, 2016, 6: 1037-1041.

[63] Liu H, Jin X, Wu L, Wang X, Fu M, Lv Z, Morawska L, Huang F, He K. The impact of marine shipping and its DECA control on air quality in the Pearl River Delta, China. Science of The Total Environment, 2018, 625: 1476-1485.

[64] Liu H, Yi W, Jalkanen JP, Luo Z, Majamäki E, Matthias V, Moldanová J, Shi Z, He K. Atmospheric impacts and regulation framework of shipping emissions: achievements, challenges and frontiers. Fundamental Research, 2024, 5: 1073-1076.

[65] Marelle L, Thomas JL, Raut JC, Law KS, Jalkanen JP, Johansson L, Roiger A, Schlager H, Kim J, Reiter A. Air quality and radiative impacts of Arctic shipping emissions in the summertime in northern Norway: from the local to the regional scale. Atmospheric Chemistry Physics, 2016, 16: 2359-2379.

[66] Miola A, Ciuffo B. Estimating air emissions from ships: Meta-analysis of modelling approaches and available data sources. Atmospheric Environment, 2011, 45: 2242-2251.

[67] Moreira L, Vettor R, Guedes Soares C. Neural network approach for predicting ship speed and fuel consumption. Journal of Marine Science and Engineering, 2021, 9: 119.

[68] Moreno-Gutiérrez J, Calderay F, Saborido N, Boile M, Valero RR, Durán-Grados V. Methodologies for estimating shipping emissions and energy consumption: A comparative analysis of current methods. Energy, 2015, 86: 603-616.

[69] Moreno-Gutiérrez J, Durán-Grados V. Calculating ships’ real emissions of pollutants and greenhouse gases: Towards zero uncertainties. Science of The Total Environment, 2021, 750: 141471.

[70] Mujal-Colilles A, Guarasa JN, Fonollosa J, Llull T, Castells-Sanabra M. COVID-19 impact on maritime traffic and corresponding pollutant emissions. The case of the Port of Barcelona. Journal of Environmental Management, 2022, 310: 114787.

[71] Nunes R, Alvim-Ferraz M, Martins F, Sousa S. The activity-based methodology to assess ship emissions-A review. Environmental Pollution, 2017, 231: 87-103.

[72] Osei-Bryson KM, Ngwenyama O. Advances in research methods for information systems research: data mining, data envelopment analysis, value focused thinking, 2013, New York, NY: Springer

[73] Park SY, Woo SH, Lim C. Predicting PM10 and PM2.5 concentration in container ports: A deep learning approach. Transportation Research Part D: Transport and Environment, 2023, 115: 103601.

[74] Peng X, Wen Y, Wu L, Xiao C, Zhou C, Han D. A sampling method for calculating regional ship emission inventories. Transportation Research Part D: Transport and Environment, 2020, 89: 102617.

[75] Schrooten L, De Vlieger I, Panis LI, Styns K, Torfs R. Inventory and forecasting of maritime emissions in the Belgian sea territory, an activity-based emission model. Atmospheric Environment, 2008, 42: 667-676.

[76] Schwarzkopf DA, Petrik R, Hahn J, Ntziachristos L, Matthias V, Quante M. Future ship emission scenarios with a focus on ammonia fuel. Atmosphere, 2023, 14: 879.

[77] Schwarzkopf DA, Petrik R, Matthias V, Quante M, Majamäki E, Jalkanen JP. A ship emission modeling system with scenario capabilities. Atmospheric Environment, 2021, 12: 100132.

[78] Schwarzkopf DA, Petrik R, Matthias V, Quante M, Yu G, Zhang Y. Comparison of the impact of ship emissions in northern Europe and eastern China. Atmosphere, 2022, 13: 894.

[79] Shi X, Chen Z, Wang H, Yeung DY, Wong WK, Woo WC. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Proceedings of the 29th International Conference on Neural Information Processing Systems-Volume 1, 2015, Montreal, Canada: MIT Press

[80] Shi Z, Endres S, Rutgersson A, Al-Hajjaji S, Brynolf S, Booge D, Hassellöv IM, Kontovas C, Kumar R, Liu H (2023) Perspectives on shipping emissions and their impacts on the surface ocean and lower atmosphere: An environmental-social-economic dimension. Elementa: Science of the Anthropocene 11. https://doi.org/10.1525/elementa.2023.00052

[81] Silva JNR, Santos TA, Teixeira AP. Methodology for predicting maritime traffic ship emissions using automatic identification system data. Journal of Marine Science Engineering, 2024, 12: 320.

[82] Silveira PAM, Teixeira AP, Guedes Soares C. Use of AIS data to characterise marine traffic patterns and ship collision risk off the coast of portugal. Journal of Navigation, 2013, 66: 879-898.

[83] Sim S, Park JH, Bae H. Deep collaborative learning model for port-air pollutants prediction using automatic identification system. Transportation Research Part D: Transport and Environment, 2022, 111: 103431.

[84] Smith TW, Jalkanen J, Anderson B, Corbett J, Faber J, Hanayama S, O’keeffe E, Parker S, Johansson L, Aldous L. Third IMO greenhouse gas study 2014, 2015, London, UK: International Maritime Organization (IMO)

[85] Sofiev M, Winebrake JJ, Johansson L, Carr EW, Prank M, Soares J, Vira J, Kouznetsov R, Jalkanen JP, Corbett JJ. Cleaner fuels for ships provide public health benefits with climate tradeoffs. Nature Communications, 2018, 9: 406.

[86] Sun R, Abouarghoub W, Demir E, Potter A. A comprehensive analysis of strategies for reducing GHG emissions in maritime ports. Marine Policy, 2025, 171: 106455.

[87] Tadros M, Ventura M, Guedes Soares C. Review of current regulations, available technologies, and future trends in the green shipping industry. Ocean Engineering, 2023, 280: 114670.

[88] Tadros M, Ventura M, Guedes Soares C. Review of the decision support methods used in optimizing ship hulls towards improving energy efficiency. Journal of Marine Science and Engineering, 2023, 11: 835.

[89] Tadros M, Ventura M, Guedes Soares C. Review of the IMO initiatives for ship energy efficiency and their implications. Journal of Marine Science and Application, 2023, 22: 662-680.

[90] Ten KH, Kang HS, Siow CL, Goh PS, Lee KQ, Huspi SH, Guedes Soares C. Automatic identification system in accelerating decarbonization of maritime transportation: The state-of-the-art and opportunities. Ocean Engineering, 2023, 289: 116232.

[91] Tian Y, Ren L, Wang H, Li T, Yuan Y, Zhang Y. Impact of AIS data thinning on ship air pollutant emissions inventories. Atmosphere, 2022, 13: 1135.

[92] Topić T. A ship emission estimation methodology with spatial mapping capability for assessing regulation effectiveness, 2022, Newcastle upon Tyne: Newcastle University

[93] Topić T, Murphy AJ, Pazouki K, Norman R. Assessment of ship emissions in coastal waters using spatial projections of ship tracks, ship voyage and engine specification data. Cleaner Engineering and Technology, 2021, 2: 100089.

[94] Topić T, Murphy AJ, Pazouki K, Norman R. NO_X emissions control area (NECA) scenario for ports in the North Adriatic Sea. Journal of Environmental Management, 2023, 344: 118712.

[95] Topić T, Murphy AJ, Pazouki K, Norman R. Assessment of ship emissions in coastal waters using spatial projections of ship moves (Ship Density) and port congestion data. 2nd Sustainable Development of Energy, Water and Environment Systems, Buenos Aires, Argentina, 2020

[96] Toscano D, Murena F. Atmospheric ship emissions in ports: A review. Correlation with data of ship traffic. Atmospheric Environment, 2019, 4: 100050

[97] Tran NK, Lam JSL, Jia H, Adland R. Emissions from container vessels in the port of Singapore. Maritime Policy & Management, 2022, 49: 306-322.

[98] Trozzi C. Emission estimate methodology for maritime navigation. US EPA 19th International Emissions Inventory Conference, San Antonio, 2010

[99] Trull O, García-Díaz JC, Peiró-Signes A. Multiple seasonal STL decomposition with discrete-interval moving seasonalities. Applied Mathematics and Computation, 2022, 433: 127398.

[100] United Nations Trade, and Development (UNTaD)Review of maritime transport 2024: Navigating maritime chokepoints, 2024.

[101] Wang C, Callahan J, Corbett JJ. Geospatial modeling of ship traffic and air emissions. ESRI International Conference. California, USA, 2007

[102] Wang C, Corbett JJ, Firestone J. Modeling energy use and emissions from North American shipping: application of the ship traffic, energy, and environment model. Environmental Science & Technology, 2007, 41: 3226-3232.

[103] Wang C, Corbett JJ, Firestone J. Improving spatial representation of global ship emissions inventories. Environmental Science & Technology, 2008, 42: 193-199.

[104] Wang K, Wang J, Huang L, Yuan Y, Wu G, Xing H, Wang Z, Wang Z, Jiang X. A comprehensive review on the prediction of ship energy consumption and pollution gas emissions. Ocean Engineering, 2022, 266: 112826.

[105] Wang S, Wang X, Han Y, Wang X, Jiang H, Zhang Z. Ship fuel and carbon emission estimation utilizing artificial neural network and data fusion techniques. Journal of Software Engineering and Applications, 2023, 16: 51-72.

[106] Wang XT, Liu H, Lv ZF, Deng FY, Xu HL, Qi LJ, Shi MS, Zhao JC, Zheng SX, Man HY. Trade-linked shipping CO₂ emissions. Nature Climate Change, 2021, 11: 945-951.

[107] Wang X, Yi W, Lv Z, Deng F, Zheng S, Xu H, Zhao J, Liu H, He K. Ship emissions around China under gradually promoted control policies from 2016 to 2019. Atmospheric Chemistry and Physics, 2021, 21: 13835-13853.

[108] Weiss K, Khoshgoftaar TM, Wang D. A survey of transfer learning. Journal of Big Data, 2016, 3: 9.

[109] Weng J, Shi K, Gan X, Li G, Huang Z. Ship emission estimation with high spatial-temporal resolution in the Yangtze River estuary using AIS data. Journal of Cleaner Production, 2020, 248: 119297.

[110] Xiao G, Wang T, Chen X, Zhou L. Evaluation of ship pollutant emissions in the ports of Los Angeles and Long Beach. Journal of Marine Science and Engineering, 2022, 10: 1206.

[111] Yang X, Tsoulakos N, Xiao Z, Wei X, Fu X, Yan R. Estimation of shipping emissions from maritime big data: A comprehensive review and prospective. Transportation Research Part E: Logistics and Transportation Review, 2025, 202: 104313.

[112] Yang Y, Liu Y, Li G, Zhang Z, Liu Y. Harnessing the power of machine learning for AIS Data-Driven maritime Research: A comprehensive review. Transportation Research Part E: Logistics and Transportation Review, 2024, 183: 103426.

[113] Yi W, Wang X, He T, Liu H, Luo Z, Lv Z, He K. The high-resolution global shipping emission inventory by the Shipping Emission Inventory Model (SEIM). Earth Syst. Sci. Data, 2025, 17: 277-292.

[114] Yoon JH, Kim SW, Eom JO, Oh J, Kim HJ. Coastal air quality assessment through AIS-based vessel emissions: A Daesan Port case study. Journal of Marine Science and Engineering, 2023, 11122291.

[115] Zhang Y, Gu J, Wang W, Peng Y, Wu X, Feng X. Inland port vessel emissions inventory based on ship traffic emission assessment model–automatic identification system. Advances in Mechanical Engineering, 2017, 971687814017712878.

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