Ship emission estimation with high spatial-temporal resolution in the Yangtze River estuary using AIS data

Weng, Jinxian; Shi, Kun; Gan, Xiafan; Li, Guorong; Huang, Zhi

doi:10.1016/j.jclepro.2019.119297

Cited by 72 publications

(15 citation statements)

References 31 publications

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“…The present study adopts the state-of-the-art “bottom-up” methodology based on the high-resolution AIS data, which has been gradually refined over the years and is now widely applied in the research of ship emission accounting ( Jalkanen et al, 2009 ; Liu et al, 2016 ; Fan et al, 2016 ; Weng et al, 2020 ). The generic equation of the bottom-up method for calculating ship emissions is shown as Eq.…”

Section: Methodsmentioning

confidence: 99%

WITHDRAWN: Assessment of COVID-19 pandemic effects on ship pollutant emissions in major international seaports

Liu

Law

Duru

2021

Environmental Research

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This study aims to investigate the coronavirus disease (COVID-19) pandemic effects and associated restrictive rules on ship activities and pollutant emissions (CO 2 , SO X , NO X , PM, CO, CH 4 ) in four major seaports, namely the Ports of Singapore, Long Beach, Los Angeles, and Hamburg. We used 2019 as the baseline year to show the business-as-usual emission and compared with the estimated quantity during the July 2020–July 2021 pandemic period. We also project future ship emissions from August 2021–August 2022 to illustrate two potential port congestion scenarios due to COVID-19. The results show that the ship emissions in all four ports generally increased by an average of 79% because of the prolonged turnaround time in port. Importantly, majority of ship emissions occurred during the extended hoteling time at berth and anchorage areas as longer operational times were needed due to pandemic-related delays, with increases ranging from 27 to 123% in the total emissions across ports. The most affected shipping segments were the container ships and dry bulk carriers which the total emissions of all pollutants increased by an average of 94–142% compared with 2019. Overall, the results of this study provide a comprehensive review of the ship emission outlook amid the pandemic uncertainty.

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Section: Methodsmentioning

confidence: 99%

WITHDRAWN: Assessment of COVID-19 pandemic effects on ship pollutant emissions in major international seaports

Liu

Law

Duru

2021

Environmental Research

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“…In addition to policy-driven emission changes, different ports showed distinct monthly emission variations that were highly related to their geographical location and ocean resources. For example, ship emissions in the YRD region had a low point in July as their activities were influenced by typhoons, particularly in the YRD (Weng et al, 2020), while ship emissions in Ningbo-Zhoushan Port, Tianjin Port and Shenzhen appeared to be larger in spring and autumn, probably owing to large-scale fishing ship operations (Chen et al, 2016;Yin et al, 2017). In addition, steep short-term increases in SO 2 emissions were observed for the Tianjin, Ningbo Zhoushan and Shenzhen ports in September 2019.…”

Section: Emission Variation In Major Portsmentioning

confidence: 99%

“…With the advent of the big data era, the characterization of ship emissions has evolved from the earlier "top-down" estimation based on global fuel consumption (Corbett et al, 1999;Endresen et al, 2003) to the "bottom-up" model based on big data from a ship's automatic identification system (AIS) (Jalkanen et al, 2009;Winther et al, 2014;Liu et al, 2016;Johansson et al, 2017;Nunes et al, 2017). AIS-based ship emission inventories have great advantages in improving the spatiotemporal resolution for numerical simulations, as well as providing possibilities for near-real-time emission estimations to meet regulatory needs (Miola and Ciuffo, 2011;Nunes et al, 2017;.…”

Section: Introductionmentioning

confidence: 99%

Ship emissions around China under gradually promoted control policies from 2016 to 2019

Wang

Wen

et al. 2021

Atmos. Chem. Phys.

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Abstract. Ship emissions and coastal air pollution around China are expected to be alleviated with the gradual implementation of ship domestic emission control area (DECA) policies. However, a comprehensive post-assessment on the policy's effectiveness is still lacking. This study developed a series of high-spatiotemporal ship emission inventories around China from 2016 to 2019 based on an updated Ship Emission Inventory Model (SEIM v2.0) and analyzed the interannual changes in emissions under the influence of both ship activity increases and gradually promoted policies. In this model, NOx, SO2, PM and HC emissions from ships in China's inland rivers and the 200 Nm (nautical miles) coastal zone were estimated every day with a spatial resolution of 0.05∘×0.05∘ based on a combination of automatic identification system (AIS) data and the Ship Technical Specifications Database (STSD). The route restoration technology and classification of ocean-going vessels (OGVs), coastal vessels (CVs) and river vessels (RVs) has greatly improved our model in the spatial distribution of ship emissions. From 2016 to 2019, SO2 and PM emissions from ships decreased by 29.6 % and 26.4 %, respectively, while ship NOx emissions increased by 13.0 %. Although the DECA 1.0 policy was implemented in 2017, it was not until 2019 when DECA 2.0 came into effect that a significant emission reduction was achieved, e.g., a year-on-year decrease of 33.3 %, regarding SO2. Considering the potential emissions brought by the continuous growth of maritime trade, however, an even larger SO2 emission reduction effect of 39.8 % was achieved in these 4 years compared with the scenario without switching to cleaner fuel. Containers and bulk carriers are still the dominant contributors to ship emissions, and newly built, large ships and ships using clean fuel oil account for an increasingly large proportion of emission structures. A total of 4 years of consecutive daily ship emissions were presented for major ports, which reflects the influence of the step-by-step DECA policy on emissions in a timely manner and may provide useful references for port observation experiments and local policy making. In addition, the spatial distribution shows that a number of ships detoured outside the scope of DECA 2.0 in 2019, perhaps to save costs on more expensive low-sulfur oil, which would increase emissions in farther maritime areas. The multiyear ship emission inventory provides high-quality datasets for air-quality and dispersion modeling, as well as verifications for in situ observation experiments, which may also guide further ship emission control directions in China.

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“…The CO2 emission factor chosen is 683 g/kWh in the assumption that the fuel type used is mediumspeed diesel (IPPC Tier 1). The ship load factors are divided into three during maneuvering, in the port basin, and berthing for each main engine and auxiliary engine (Weng et al, 2020). The load factors percentage for the main engine and auxiliary engine chosen in this research for all situations is listed in Table 1 and based on the reference obtained from an interview with the port's sustainability manager and harbormaster (Styhre, et al, 2017).…”

Section: Ship's Data Analysismentioning

confidence: 99%

Ship Energy Efficiency Management Plan Development Using Machine Learning: Case Study of CO2 Emissions of Ship Activities at Container Port

Dawangi¹,

Budiyanto²

2021

IJTech

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Ship energy management has an effect both on cost efficiency and the environment due to the huge amount of CO2 emission caused by ship activities. Meanwhile, research regarding efforts to save energy consumption in the container terminal area is scarce. This paper aims to estimate CO2 emissions from ship activities in the container port. The influential variable of CO2 emissions is in consideration to Ship Energy Efficiency Management Plan (SEEMP). The estimation of C02 emission starts from the ship activities when the ship approaches the port, which includes ship maneuvering and ship berthing. Ship's energy consumption and CO2 emission were analyzed using random forest regression (RF) at the default setting, and then the effectiveness was verified using k-folds cross-validation. The analysis result showed there are five influential variables to reduce the CO2 emission: (1) main engine power; (2) auxiliary engine power; (3) waiting time in a port basin; (4) maneuvering time; and (5) berthing time. Among those five variables, maneuvering, waiting in a port basin, and berthing have the same position at the top with the same amount of weight importance from the four attribute selection training results. The random forest model training and k-folds cross-validation confirmed that the model has 98.85% of accuracy. Finally, a fuel-efficient operation is discussed, and it can be concluded that by combining several voyage optimizations with a skilled operator and cold ironing when available, it is possible to reduce the CO2 emission by 20%. The findings and proposed plan in this paper can become a reference to develop Ship Energy Efficiency Management Plan.

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Ship emission estimation with high spatial-temporal resolution in the Yangtze River estuary using AIS data

Cited by 72 publications

References 31 publications

WITHDRAWN: Assessment of COVID-19 pandemic effects on ship pollutant emissions in major international seaports

WITHDRAWN: Assessment of COVID-19 pandemic effects on ship pollutant emissions in major international seaports

Ship emissions around China under gradually promoted control policies from 2016 to 2019

Ship Energy Efficiency Management Plan Development Using Machine Learning: Case Study of CO2 Emissions of Ship Activities at Container Port

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