Spatial and temporal allocation of ship exhaust emissions in Australian coastal waters using AIS data: Analysis and treatment of data gaps

Goldsworthy, Brett

doi:10.1016/j.atmosenv.2017.05.028

Cited by 22 publications

(7 citation statements)

References 26 publications

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“…In addition, it was used to evaluate the influence of policies in the emission control region on air quality in European countries. Goldsworthy [24] used path regeneration to retrieve AIS trajectories of ships missing in coastal transit, and efficiently approximated ship emissions in locations without AIS data. Zeng [25] utilized the emission factor technique based on shipping activity to compute the ship emission inventory of entering and exiting Xiamen Port in 2018 and evaluated the ecological efficiency of the port based on environmental and social indicators and external cost evaluation tools.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Inland Ship Emission Results from a Real-World Test and an AIS-Based Model

et al. 2021

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Inland shipping is pivotal to the comprehensive transport system of China. However, ship emission has become a major air polluter in inland river regions, and relevant emission inventories are urgently needed. Currently, the Automatic identification System based (AIS-based)emission model is widely used in calculating the ocean-going ship emission inventory. However, due to the lack of AIS data in the river area, the inland ship emission inventory mainly uses the fuel consumption method. With the continuous improvement of AIS data quality in the river area, the AIS-based emission model can be adopted in the development of inland ship emission inventory. However, there are few studies on the evaluation of the accuracy of the inland ship emissions using the AIS-based emission model. This study makes a comparison between test data and model-calculated data to evaluate the accuracy of the AIS-based emission models. Inland ship activities are divided into being at berth, maneuvering (port departure and port arrival), and on cruise modes in an AIS-based emission model. The model-calculated CO2, HC, and NOx emission rates can cover those onboard emission test data, but the values from the model are much lower. The total average ratios of test data to model-calculated data for CO2, CO, HC, and NOx are 2.66, 19.12, 2.46, and 3.16 when engine loads are below 60%. In upstream cruise mode, average emission rates of CO2, CO, HC, and NOx from the real-world test are 1.91–6.48, 8.78–27.83, 3.05–8.96, and 4.06–5.96 times higher than those from the AIS-based model, respectively. However, those are only 1.08–1.51, 6.74–9.67, 2.03–3.75, and 1.65–2.75 times higher than those from the AIS-based model in downstream cruise mode.

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Section: Introductionmentioning

confidence: 99%

Comparison of Inland Ship Emission Results from a Real-World Test and an AIS-Based Model

et al. 2021

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“…Detailed tracking of vessels around coastal areas necessitates an adequate density of ground stations. For remote areas beyond these ground stations, satellite AIS collection is pivotal but can be compromised by signal collisions (Goldsworthy, 2017).…”

Section: Data Cleaningmentioning

confidence: 99%

“…Given the high volume of vessels in port waters, Tianjin Port's 2018 AIS data accumulation is substantial, making direct usage for subsequent analyses resource-intensive. Moreover, the AIS data transmission intervals are significantly more frequent than the time frames required for ship emission calculations (Goldsworthy, 2017). Therefore, it is imperative to integrate this vast expanse of AIS data, pinpointing key nodes that best describe the original trajectory from densely sampled points.…”

Section: Ship Trajectory Aggregationmentioning

confidence: 99%

Maritime greenhouse gas emission estimation and forecasting through AIS data analytics: a case study of Tianjin port in the context of sustainable development

Xie,

Li,

Yang

et al. 2023

Front. Mar. Sci.

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The escalating greenhouse gas (GHG) emissions from maritime trade present a serious environmental and biological threat. With increasing emission reduction initiatives, such as the European Union’s incorporation of the maritime sector into the emissions trading system, both challenges and opportunities emerge for maritime transport and associated industries. To address these concerns, this study presents a model specifically designed for estimating and projecting the spatiotemporal GHG emission inventory of ships, particularly when dealing with incomplete automatic identification system datasets. In the computational aspect of the model, various data processing techniques are employed to rectify inaccuracies arising from incomplete or erroneous AIS data, including big data cleaning, ship trajectory aggregation, multi-source spatiotemporal data fusion and missing data complementation. Utilizing a bottom-up ship dynamic approach, the model generates a high-resolution GHG emission inventory. This inventory contains key attributes such as the types of ships emitting GHGs, the locations of these emissions, the time periods during which emissions occur, and emissions. For predictive analytics, the model utilizes temporal fusion transformers equipped with the attention mechanism to accurately forecast the critical emission parameters, including emission locations, time frames, and quantities. Focusing on the sea area around Tianjin port—a region characterized by high shipping activity—this study achieves fine-grained emission source tracking via detailed emission inventory calculations. Moreover, the prediction model achieves a promising loss function of approximately 0.15 under the optimal parameter configuration, obtaining a better result than recurrent neural network (RNN) and long short-term memory network (LSTM) in the comparative experiments. The proposed method allows for a comprehensive understanding of emission patterns across diverse vessel types under various operational conditions. Coupled with the prediction results, the study offers valuable theoretical and data-driven support for formulating emission reduction strategies and optimizing resource allocation, thereby contributing to sustainable maritime transformation.

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“…The quality of the information provided by the studies has significantly contributed to the quantity and quality of the information in the sector of account information and energy consumption with CO 2 emissions. Many researchers aimed to provide significant results for the ERP implementation system of management accounting, especially after the systems spreading in wider areas and spreading in the large scale of companies (Goldsworthy, 2017). They showed that there is an absence of tending to the issue of ERP in bookkeeping writing.…”

Section: Literature Reviewmentioning

confidence: 99%

The Influence of Accounting Information System and Energy Consumption on Carbon Emission in the Textile Industry of Indonesia: Mediating Role of the Supply Chain Process

Dalle¹,

Hayat²,

Karim³

et al. 2020

IJEEP

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The purpose related to the current article is to investigate the impact of accounting information system (AIS) and structure of energy consumption (SEC) on the carbon emission (CE) of the textile industry in Indonesia. The aim also consist upon the investigation of mediating impact of supply chain process (SCP) on the links of AIS and CO 2 emission, SEC and CO 2 emission. The employees related to the AIS and environment sustainability are the respondent who provide the data through questionnaires that were evaluated by the help of PLS-SEM. The results show that AIS and SEC have positive nexus with the CE of the textile industry in Indonesia. The results are show that SCP has positive mediation among the links of AIS and CO 2 emission, SEC and CO 2 emission. The regulation developing authorities and new researchers are the users of this article who gel help while preparing the policies and also in the exploration of this area in the future.

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Spatial and temporal allocation of ship exhaust emissions in Australian coastal waters using AIS data: Analysis and treatment of data gaps

Cited by 22 publications

References 26 publications

Comparison of Inland Ship Emission Results from a Real-World Test and an AIS-Based Model

Comparison of Inland Ship Emission Results from a Real-World Test and an AIS-Based Model

Maritime greenhouse gas emission estimation and forecasting through AIS data analytics: a case study of Tianjin port in the context of sustainable development

The Influence of Accounting Information System and Energy Consumption on Carbon Emission in the Textile Industry of Indonesia: Mediating Role of the Supply Chain Process

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