Tiered prediction models for port vessel emissions inventories

Cammin, Philip; Yu, Jingjing; Voß, Stefan

doi:10.1007/s10696-022-09468-5

Cited by 6 publications

(3 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method is based on historical data and the technological level and cannot fully consider possible future technological advances and improvements, and the effects of emission reduction measures cannot be monitored and predicted. This approach should be combined with SE prediction modeling to assess the ship pollutant discharge inventory [47].…”

Section: Discussionmentioning

confidence: 99%

Spatial-Temporal Ship Pollution Distribution Exploitation and Harbor Environmental Impact Analysis via Large-Scale AIS Data

Chen,

Dou,

Song

et al. 2024

JMSE

View full text Add to dashboard Cite

Ship pollution emissions have attracted increasing attention in the maritime field due to the massive growth of maritime traffic activities. It is important to identify the ship emissions (SEs) magnitude and corresponding spatial and temporal distributions for the purposes of developing appropriate strategies to mitigate environment pollution. The aim of this study was to estimate ship pollution emissions with various typical merchant ship types under different sailing conditions. We estimated the emission variation with a ship traffic emission assessment model (STEAM2), and then the ship pollution emission distribution was further visualized using ArcGIS. We collected data from the automatic identification system (AIS) for ships in New York Harbor and further analyzed the spatiotemporal distribution of pollutant emissions from ships. The experimental results demonstrate that the ship pollutant emission volume in the New York Harbor area in 2022 was 3340 t, while the pollution in terms of CO, SO2, CXHX, PM10, NOX, and PM2.5 was 136, 1421, 66, 185, 1384, and 148 t, respectively. The overall SEs from container ships, passenger ships, and tankers account for a large amount of pollution discharge. The pollutant emissions of container ships are significantly greater than that of their counterparts. Moreover, the spatiotemporal distributions of ship pollutant discharge can vary significantly among different ship types and sailing conditions.

show abstract

Section: Discussionmentioning

confidence: 99%

Spatial-Temporal Ship Pollution Distribution Exploitation and Harbor Environmental Impact Analysis via Large-Scale AIS Data

Chen,

Dou,

Song

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

“…Also, there are other hybrid methodologies that consist of combining the different approaches of the aforementioned models [16]. More general settings of emission inventories are described in [25,33,34].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Further efforts should be made to obtain more accurate shipping emission estimations or inventories, so it is desirable to obtain more accurate input data (technical information on vessels, engines, load, and emission factors) to arrive at a global and universally accepted methodology [16,25,33,34]. Methodological approaches to learning inventory policies using machine learning could also be seen in [35] for a different application setting.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Towards Cleaner Ports: Predictive Modeling of Sulfur Dioxide Shipping Emissions in Maritime Facilities Using Machine Learning

et al. 2023

Self Cite

View full text Add to dashboard Cite

Maritime ports play a pivotal role in fostering the growth of domestic and international trade and economies. As ports continue to expand in size and capacity, the impact of their operations on air quality and climate change becomes increasingly significant. While nearby regions may experience economic benefits, there are significant concerns regarding the emission of atmospheric pollutants, which have adverse effects on both human health and climate change. Predictive modeling of port emissions can serve as a valuable tool in identifying areas of concern, evaluating the effectiveness of emission reduction strategies, and promoting sustainable development within ports. The primary objective of this research is to utilize machine learning frameworks to estimate the emissions of SO2 from ships during various port activities, including hoteling, maneuvering, and cruising. By employing these models, we aim to gain insights into the emission patterns and explore strategies to mitigate their impact. Through our analysis, we have identified the most effective models for estimating SO2 emissions. The AutoML TPOT framework emerges as the top-performing model, followed by Non-Linear Regression with interaction effects. On the other hand, Linear Regression exhibited the lowest performance among the models evaluated. By employing these advanced machine learning techniques, we aim to contribute to the body of knowledge surrounding port emissions and foster sustainable practices within the maritime industry.

show abstract

Cybersecurity Considerations for the Design of an AI-Driven Distributed Optimization of Container Carbon Emissions Reduction for Freight Operations

Paternina-Arboleda,

Nestler,

Kascak

et al. 2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Tiered prediction models for port vessel emissions inventories

Cited by 6 publications

References 55 publications

Spatial-Temporal Ship Pollution Distribution Exploitation and Harbor Environmental Impact Analysis via Large-Scale AIS Data

Spatial-Temporal Ship Pollution Distribution Exploitation and Harbor Environmental Impact Analysis via Large-Scale AIS Data

Towards Cleaner Ports: Predictive Modeling of Sulfur Dioxide Shipping Emissions in Maritime Facilities Using Machine Learning

Cybersecurity Considerations for the Design of an AI-Driven Distributed Optimization of Container Carbon Emissions Reduction for Freight Operations

Contact Info

Product

Resources

About