The high-resolution global shipping emission inventory by the Shipping Emission Inventory Model (SEIM)

The high-resolution ship emission inventory plays a critical role across multiple disciplines, including atmospheric and marine sciences as well as environmental management. In this study, we present a global high spatiotemporal resolution ship emission inventory developed using the Shipping Emission Inventory Model (SEIMv2.2) at a resolution of 0.1° × 0.1° for the years 2013, 2016–2021. Leveraging 30 billion Automatic Identification System (AIS) signals, SEIMv2.2 integrates real-time vessel positions, speeds, and technical parameters to model ship emissions from the bottom-up for key species, including CO2, NOx, SO2, PM2.5, CO, HC, N2O, CH4, and BC. According to our inventory results, which are freely accessible online (https://zenodo.org/records/11069531), temporally, global ship emissions exhibited minimal daily fluctuations. Spatially, high-resolution datasets revealed varying patterns of ship emission contributions by different vessel types across maritime regions. Research on the high spatiotemporal resolution ship emission inventory model SEIMv2.2 has been accepted by Earth System Science Data (https://essd.copernicus.org/preprints/essd-2024-258/). Using our inventory, it is possible to reveal the variability of ship emissions across different regions and temporal scales. Taking 2020 as an example, we found that overall ship emissions of NOx, CO, HC, CO2, and N2O declined by 7.4%–13.8%, primarily due to the impacts of the COVID-19 pandemic. In the meanwhile, ship emissions of SO2, PM2.5, and BC dropped significantly by 40.9%–81.9% in 2020 compared to 2019, mainly driven by the implementation of low-sulfur fuel regulations. Focusing on the pandemic's influence, temporally, the largest drop in global ship emissions occurred in February 2020, followed by a gradual recovery in September as trade demand rebounded. Spatially, the shock originated in Asia and gradually extended to Europe and North America. Our analysis of the spatiotemporal variability of global ship emissions during the pandemic highlights the resilience of global maritime emissions, evidenced by the relatively small impact of the pandemic and the rapid pace of recovery. This resilience can be attributed partly to robust trade demand, and partly to the connectivity of maritime trade across continents, stemming from the fact that the trade in one region begins to recover, it often stimulates recovery in its trading partners. Research on the spatiotemporal variability of global ship emissions during the pandemic has been published online (https://www.sciencedirect.com/science/article/pii/S0048969724067895). We further examined the characteristics of ship emissions in the Arctic region (defined as the area north of 60°N), which has been attracting research interests in recent years. Between 2016 and 2021, Arctic ship BC emissions increased by 6%, accounting for 1.5% of global ship emissions in 2021. Seasonally, BC emissions from ships during the Arctic summer (July–September) were 1.3 times higher than those in winter (January–March). In terms of vessel type contributions, cargo ships (including general cargo ships, bulk carriers, and container ships) accounted for 44.8% of BC emissions, followed by fishing vessels at 34.8% and oil tankers at 15.0%, in 2021. In the future, distinguishing between ship emissions driven by different transportation demands, such as transit cargo transport and energy transport is crucial for scientifically predicting future Arctic ship emissions and developing effective Arctic ship emission reduction strategies.

High-spatiotemporal-resolution ship emission inventory of China based on AIS data in 2014

An AIS-based high-resolution ship emission inventory and its uncertainty in Pearl River Delta region, China

Study of narrow waterways congestion based on automatic identification system (AIS) data: A case study of Houston Ship Channel

Ship emission variations during the COVID-19 from global and continental perspectives

This study is conducted to develop a comprehensive ship emission inventory in Strait of Malacca and Singapore (SOMAS) based on Automatic Identification System (AIS) data using the bottom-up method. With spatiotemporal analysis on the maritime traffic in SOMAS, it is implied that limited space and dense, complex shipping routes had created hindrances to the local traffic. Gaussian approach Kernel Density Estimation (KDE) is adopted to find the hotspot of the traffic and emission in SOMAS. Among all emitted pollutants estimated, nitrogen oxides (NO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</inf> ) shared the most proportion among pollutants by maritime. With the argumentation of more NO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</inf> emission generated under slow engine speed, the environment in SOMAS had favoured its generation. With over 100,000 ship trajectories compacted within the port waters and turning points between Johor and Singapore strait, it was found that the highest contributor of emissions came from containership as a result of slow steaming. Other cargo ships also contributed substantially on the emissions as a result of long period of manoeuvring or hotelling. The result presented draws the attention about the environmental impact caused by ship emissions.

Contribution of ship emissions to the concentration of PM2.5: A comprehensive study using AIS data and WRF/Chem model in Bohai Rim Region, China

The rising concern over ship emissions has prompted the exploration of Emission Control Areas (ECAs) in various regions. This study provides a comparative analysis of ship emissions in non-ECA areas and offers insights for implementing ECA policies in the Gulf of Thailand. Utilizing Automatic Identification System (AIS) data from January 1 to 31, 2023, this study models ship emissions of nitrogen oxides (NOx), sulfur oxides (SOx), and particulate matter (PM). Spatial analysis reveals critical emission hotspots associated with ship density, port locations, and major shipping lanes, identifying oil tankers as primary emitters. A comparative analysis with existing ECAs demonstrates that implementing an ECA in the Gulf of Thailand could substantially reduce emissions. The findings offer actionable insights for policymakers, including strategies like adopting low-sulfur fuels, optimizing shipping routes, and incentivizing cleaner technologies. Furthermore, this study highlights the importance of addressing economic challenges and ensuring comprehensive data collection to capture seasonal and demand-driven emission variations. Highlights Oil tankers are the largest contributors, accounting for 75 % of total emissions. AIS data reveals emission hotspots in port areas and major shipping lanes in the Gulf of Thailand. Comparing emissions in the Gulf of Thailand with existing ECA areas highlights the potential for significant emission reductions. A bottom-up approach using AIS data provides precise spatial analysis of emissions. Adopting low-sulfur fuels and clean technologies could significantly reduce ship emissions.

Modeling is a promising approach to understand and predict the safety and efficiency of maritime traffic in ports and waterways. Different types of models have been developed over the years. Nevertheless, several important scientific challenges still remain. For instance, few models consider vessel behavior in ports and waterways under the influence of internal factors including vessel type and size, and external factors, such as wind and visibility. More data and research are needed to understand the influence of internal and external factors on vessel behavior including speed, course and path in ports and waterways; more research is also needed to explore human behavior of the bridge team for vessel maneuvering in ports and waterways. To address the needs listed, this thesis focuses on analyzing the influence of wind, visibility, current and vessel encounters on vessel speed, course and path using Automatic Identification System (AIS) data. Based on this analysis a new maritime traffic model has been developed that considers both internal and external factors, and aims to better predict the individual vessel behavior. The model can be used to provide data for the safety and efficiency assessment of vessel traffic in ports and inland waterways. In the last decades, the AIS system, which is an onboard autonomous and continuous broadcast system that transmits vessel data between nearby vessels and shore stations, has been developed. This is used now by almost all vessels. Therefore, AIS data, including vessel speed, course and path, can serve as a valuable data source to investigate vessel behavior. In this thesis, AIS data from a part of the port of Rotterdam is analyzed to investigate influences of different factors, such as vessel size and type, external conditions and vessel encounters, on vessel behavior. Firstly, vessels are distinguished into influenced and unhindered vessels based on certain thresholds that we obtained from the AIS data. The influenced vessel behavior is compared with the behavior of unhindered vessels, which are not influenced by other vessels or strong external influences of wind, visibility and current. The analysis provides evidence showing that the vessel behavior including vessel speed, course and path is influenced by various factors. Ship speed and path is influenced by internal factors (including vessel type, size, waterway geometry and navigation direction) and external factors (including wind, visibility, current, overtaking and head-on encounters), while ship course is only influenced by overtaking and head-on encounters. It can also be concluded that the AIS data is a useful source to get insights into vessel behavior.

Ship emission inventory and its impact on the PM2.5 air pollution in Qingdao Port, North China

This article examines the impact of automatic identification system (AIS) data thinning on ship emissions inventory results. AIS data thinning is theoretically proven to lead to a smaller result for a ship’s air pollutant emissions inventory. The AIS dynamic data of six sampled ships for 1 day and for 1 year were thinned at 1 min, 3 min, 10 min, 30 min, and 1 h time intervals, and then CO2, NOX, CH, PM, SO2, and other air pollutant emissions were estimated both with and without AIS data thinning in the different time intervals. The results show that AIS data thinning affects the air pollutant emissions inventory results of the ships, and the impact is greater as the thinning interval increases. When the thinning interval is less than 10 min, the impact is less than 10%, but the impact increases to about 10–15% at a 30 min interval and about 15–20% at a 60 min interval. The impacts of thinning on the emissions of ships with acutely fluctuating speeds are more significant because the constantly changing speed is the main reason why data thinning affects the ship emissions inventory. Therefore, these data suggest that the AIS data can be thinned at intervals of 5 or 10 min when establishing a coastal or national ship air pollutant emissions inventory, the AIS data should be thinned at intervals of less than 3 min when establishing the air pollutant emissions inventory of inland river ships, and data thinning is not recommended when establishing a port or smaller-scale ship air pollutant emissions inventory.

The maritime Automatic Identification System (AIS) data is obtained from many different terrestrial and satellite sources. AIS data enables safety, security, environmental protection and the economic efficiency of the maritime sector. The quality of AIS receivers is not controlled in the same manner as AIS transmitters. This has led to a situation where AIS data is not as clean as it should/could be. Added to this is the lack of accuracy and standards in entering the voyage data by the mariners such as next port of call into the AIS equipment installed on vessels. By using analytics IMIS Global Limited has been able to process the AIS data stream to eliminate a large portion of the faulty data. This has allowed the resultant AIS data to be used for more accurate detailed analysis such as the long-term vessel track, port arrival events and port departure events. New data that is derived from processing AIS data has enhanced the information available to maritime authorities enabling a significant increase in safety, security, environmental protection and economic growth. The next generation of maritime data communications technology being based AIS. This is known as the VHF Data Exchange System (VDES) and this technology now enables further opportunities. The value from the large volumes of AIS data is extracted by visual, streaming, historical and prescriptive data analytics. The datAcron project is showing the way with regards to the processing and use of AIS and resultant trajectory data.

Improving maritime traffic surveillance in inland waterways using the robust fusion of AIS and visual data

Carbon footprints: Uncovering spatiotemporal dynamics of global container ship emissions during 2015–2021

AIS (Automatic Identification System) data received from moving vessels over an area of interest can be of very much interest for deriving maritime trajectory patterns. In this paper, a novel approach to extract course patterns from AIS data of vessels is presented. From machine learning and natural language processing principles, a topic model might be used for extracting implicit patterns underlying massive and unstructured collection of incoming data. To apply topic model to AIS data, PQk-means vector quantization to convert AIS data record to code documents is introduced. Then, a topic model is applied to extract course patterns from AIS data. In fact, courses, not only encompasses trajectory locations, but also headings and speeds, are recognized by the proposed algorithm. The performance of PQk-means is evaluated using the relative root mean square error and elapsed time. The potential of the approach is illustrated by a series of experimental results derived from practical AIS data set in a region of North West France.