Vessel Automatic Identification System Research Articles

Research on risk data analysis of hazardous chemical vessels based on stream computing

Abstract To effectively improve the safety supervision and information management level of vessels carrying hazardous chemicals, the maritime management department is promoting the construction of hazardous chemical vessel monitoring systems based on artificial intelligent technology for hazardous chemical terminals and shipping companies in their jurisdiction. It can identify specific risks and transmit the risk data in real-time to the data center. To analyze data in real-time, a risk data analysis platform for hazardous chemical vessels based on stream computing is designed and implemented. The platform acquires vessel automatic identification system (AIS), risk, basic information, and other data. Based on the stream computing of Flink, it evaluates the risk status of vessels in real time, counts the risk data, and displays the results in the form of charts. The analysis of risk data for hazardous chemical vessels helps maritime management departments to track the real-time navigation status of key vessels, timely identify potential safety hazards, and effectively improve the level and efficiency of safety supervision.

Enhancement of Small Ship Detection Using Polarimetric Combination from Sentinel−1 Imagery

Speckle noise and the spatial resolution of the Sentinel−1 Synthetic Aperture Radar (SAR) image can cause significant difficulties in the detection of small objects, such as small ships. Therefore, in this study, the Polarimetric Combination-based Ship Detection (PCSD) approach is proposed for enhancing small ship detection performance, which combines three different characteristics of polarization: newVH, enhanced VH, and enhanced VV. Employing the Radar Cross Section (RCS) value in three stages, the newVH was utilized to detect Automatic Identification System (AIS) -ships and small ships. In the first step, the adaptive threshold (AT) method was applied to newVH with a high RCS condition (>−10.36 (dB)) for detecting AIS-ships. Secondly, the first small ship target was detected with the maximum suppression of false alarms by using the AT with a middle RCS condition (>−16.98 (dB)). In the third step, a candidate group was identified by applying a condition to the RCS values (>−23.01 (dB)), where both small ships and speckle noise were present simultaneously. Subsequently, the enhanced VH and VV polarizations were employed, and an optimized threshold value was selected for each polarization to detect the second small ship while eliminating noise pixels. Finally, the results were evaluated using the AIS and small fishing vessel tracking system (V-Pass) based on the detected ship positions and ship lengths. The average matching results from 26 scenes in 2022 indicated a matching rate of over 86.67% for AIS-ships. Regarding small ships, the detection performance of PCSD was 42.27%, which was over twice as accurate as the previous Constant False Alarm Rate (CFAR) ship detection model. As a result, PCSD enhanced the detection rate of small ships while maintaining the capacity for detecting AIS-equipped ships.

Understanding vessel noise across a network of marine protected areas

Protected areas are typically managed as a network of sites exposed to varying anthropogenic conditions. Managing these networks benefits from monitoring of conditions across sites to help prioritize coordinated efforts. Monitoring marine vessel activity and related underwater radiated noise impacts across a network of protected areas, like the U.S. National Marine Sanctuary system, helps managers ensure the quality of habitats used by a wide range of marine species. Here, we use underwater acoustic detections of vessels to quantify different characteristics of vessel noise at 25 locations within eight marine sanctuaries including the Hawaiian Archipelago and the U.S. east and west coasts. Vessel noise metrics, including temporal presence and sound levels, were paired with Automatic Identification System (AIS) vessel tracking data to derive a suite of robust vessel noise indicators for use across the network of marine protected areas. Network-wide comparisons revealed a spectrum of vessel noise conditions that closely matched AIS vessel traffic composition. Shifts in vessel noise were correlated with the decrease in vessel activity early in the COVID-19 pandemic, and vessel speed reduction management initiatives. Improving our understanding of vessel noise conditions in these protected areas can help direct opportunities for reducing vessel noise, such as establishing and maintaining noise-free periods, enhancing port efficiency, engaging with regional and international vessel quieting initiatives, and leveraging co-benefits of management actions for reducing ocean noise.

A Method for Clustering and Analyzing Vessel Sailing Routes Efficiently from AIS Data Using Traffic Density Images

A vessel automatic identification system (AIS) provides a large amount of dynamic vessel information over a large coverage area and data volume. The AIS data are a typical type of big geo-data with high dimensionality, large noise, heterogeneous densities, and complex distributions. This poses a challenge for the clustering and analysis of vessel sailing routes. This study proposes an efficient vessel sailing route clustering and analysis method based on AIS data that uses traffic density images to transform the clustering problem of complex AIS trajectories into an image processing problem. First, a traffic density image is constructed based on the statistics of the preprocessed AIS data. Next, the main sea route regions of traffic density images are extracted based on local image features, geometric structures, and spatial features. Finally, the sailing trajectories are clustered using the extracted sailing patterns. Based on actual vessel AIS data, multimethod comparisons and performance analysis experiments are conducted to verify the feasibility and effectiveness of the proposed method. These experimental results reveal that the proposed method displays potential for the clustering task of challenging vessel sailing routes.

Adaptive multi-source data fusion vessel trajectory prediction model for intelligent maritime traffic

Multi-source vessel automatic identification system (AIS) data gathered in the maritime Internet of Things (IoT) system has diverse data characteristics, such as sparse satellite data and terrestrial receiver’s limited coverage. However, it is challenging for the maritime IoT system to provide efficient and accurate intelligent trajectory prediction services while applying multi-source AIS data. Therefore, improving the adaptability and accuracy of vessel trajectory prediction methodologies adapted to varied AIS datasets is essential to enhancing marine traffic safety and transportation efficiency. This study proposes an adaptive data fusion (ADF) model based on multi-source AIS data for predicting vessel trajectories in maritime traffic. The same marine mobile service identifier (MMSI) and timestamp fuse the multi-source AIS data. Deep learning methods are employed to perform feature learning and increase adaptability. This study also utilizes adjacency distance to enhance the correlation between dataset trajectory trajectories. Finally, we validate the benefits of the ADF model using actual terrestrial and satellite AIS data along the west coast of the United States (US). Regarding average RMSE and MAE, the ADF model reduces the prediction error by 46.51% and 52.7% when fusing multi-source historical trajectories compared to current studies. Moreover, multiple ablation experiments have proved our model’s superiority.

Vessel sailing route extraction and analysis from satellite-based AIS data using density clustering and probability algorithms

The vessel Automatic Identification System (AIS) data collected by satellites have the features of large coverage area and large data volume, and they are instantaneous discrete data rather than time-continuous data, so the data has large dispersion with many noise points. This poses a challenge for vessel sailing route extraction. This paper proposes a vessel sailing route extraction method which consists of the fast Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and the Kernel Density Estimation-based Outlier Factor (KDE-based OF) noise reduction algorithm. The method in this paper firstly improves the clustering discrimination method in the DBSCAN algorithm to separate trajectories in different directions. Secondly, this paper extracts a fast clustering algorithm based on the density clustering algorithm to reduce its computing time overhead with satellite big data. Finally, this paper proposes the KDE-based OF processing algorithm, which calculates the outlier probability distribution value of the trajectory points through the algorithm to eliminate the edge trajectory points with low probability distribution. Based on the actual satellite vessel AIS data, this paper conducts multi-method comparisons and performance analysis experiments. Experiments show that the proposed method has the best stability and advancement.

Assessing compression algorithms to improve the efficiency of clustering analysis on AIS vessel trajectories

In the maritime environment, the Automatic Identification System (AIS) is used to monitor vessel activity concerning security and safety ocean-wide. AIS data has been used to detect anomalous behaviors related to suspicious activities and hazardous events. Typically, clustering analysis is used to investigate anomalous events within the AIS data stream. However, the main challenge in this approach is to determine and execute the dissimilarity measure between trajectories since they differ in size and time. In addition, these calculations are computationally expensive and not scalable. To tackle this issue, compression algorithms can be applied to perform clustering analysis since they are typically used to reduce storage and processing time. Therefore, the proposed analysis will assess how compression algorithms affect clustering results with respect to detecting anomalous vessel trajectories. The analysis results show that a suitable compression algorithm can reduce the overall processing time with little impact on the clustering results while supporting the scalability of this type of analysis.

FB-BiGRU: A Deep Learning model for AIS-based vessel trajectory curve fitting and analysis

The marine vessel Automatic Identification System (AIS) broadcast is a system with no-response mechanism, so there are missing data from the received AIS vessel trajectory. Currently, the mainstream vessel trajectory curve fitting method is achieved by means of deep learning algorithm cyclic training, however, the quality of the original data has a large impact on the trajectory fitting results, and the current method does not consider the interference of missing trajectory points on the fitted trajectory curve. Therefore, based on the data features of AIS, this paper proposes a lightweight deep learning method: Forward Backward Bidirectional Gated Recurrent Unit (FB-BiGRU). The method in this paper consists of a forward Gated Recurrent Unit (GRU) network and a backward Bidirectional Gated Recurrent Unit (BiGRU) network, reducing the range of the trajectory to be fitted by scaling in two directions simultaneously, thus gradually realizing the trajectory fitting function. The characteristics of our method are that training the trajectory data by bi-directional scaling can maximize the linear features of the trajectory curves to enhance the accuracy and efficiency of linear regression with no additional computing resources. The trajectory fitting performance of this method is verified on actual Denmark trajectory datasets, we prove that the accuracy of our proposed method in fitting short-term trajectories has increased by 49.16% and 29.89% on average compared with the (Long Short-Term Memory) LSTM and BiGRU. Furthermore, the average fitting accuracy of our method is 96 m, and the minimum fitting error is 64 m.

A Comprehensive Monitoring and Assessment System for Multiple Fisheries Resources in the Northwest Pacific Based on Satellite Remote Sensing Technology

Monitoring the dynamics of ocean-going fishing vessels is essential for fisheries stock assessment and management. In this paper, coupled fishery and remote sensing methods were applied to propose a satellite-based fishery resource monitoring and assessment system (SFRMAS) that supported chub mackerel (Scomber japonicus), Pacific saury (Cololabis saira) and neon flying squid (Ommastrephes bartramii) fishing activities in the northwest Pacific. A multispecies fishing vessel database was generated in the SFRMAS using visible infrared imaging radiometer suite day/night band (VIIRS/DNB) images, automatic identification system (AIS) data and multimarine remote sensing resources. The results showed that the VIIRS/DNB vessel detection (VVD) and AIS vessel detection approaches improved the extraction accuracy of multispecies fishing vessels. A high productivity area with a higher chlorophyll a concentration had the most concentrated distribution of mackerel vessels. The widest longitudinal migrations of the neon flying squid and the latitudinal ranges of Pacific saury were highly related to sea surface temperature (SST), which was well captured by the movement of the fishing vessels. In the SFRMAS, the accuracy of detected multispecies fishing days and fishing catches was mostly between 60 and 90%, which can provide data support for fisheries stock assessment and management.

Risk factors and navigation accidents: A historical analysis comparing accident-free and accident-prone vessels using indicators from AIS data and vessel databases

This paper presents the results of an explorative analysis aiming to identify indicators and factors associated with navigation accidents (groundings and collisions). The analysis compares cargo vessels with at least one registered navigation accident (grounding or collision) within Norwegian waters with those that have none, in the period 2010–2019. The comparison is made using data based on automatic identification system (AIS) satellite data in combination with information from IHS Fairplay, to construct indicators that reflect different characteristics of the vessels. Hallmarks of vessels involved in navigation accidents have been identified using bivariate and multivariate statistical analysis. The multivariate model was a strong predictor of vessels' accident involvement with 44% of the variance explained. Indicators that predicted reported navigation accidents included: (1) vessel type, (2) higher age, (3) smaller size, (4) longer distance sailed, (5) higher average speed, (6) flying Norwegian flag, (7) gray or black Tokyo MoU rating, and 8) not on US Coast Guard target list. The results are discussed relative to their potential causes as well as limits and practical applications. The study shows the promising potential of utilizing AIS data combined with various data sets to obtain knowledge on risk factors and risk indicators.

Using aerial surveys to fill gaps in AIS vessel traffic data to inform threat assessments, vessel management and planning

AIS (Automatic Identification System) is a widely used source of data to spatially and temporally characterize marine vessel traffic and to subsequently estimate the direct and indirect threats maritime activities pose to marine and coastal environments. However, AIS data do not capture all vessel traffic in an area because not all vessels (e.g., recreational vessels) are required to carry AIS transponders. The goal of this study is to quantify the proportion of vessel traffic not transmitting AIS data by employing dedicated aerial surveys that simultaneously capture AIS data and georeferenced imagery of non-AIS vessels on the Canadian Pacific coast. Results show that 70% of vessel traffic in the surveyed area do not transmit AIS. The majority of these non-AIS vessels are recreational, making up 53% of the total vessels sighted. Analysis of historical AIS data in the survey area shows a steady increase in the voluntary uptake of AIS in the last decade. Results of this study can be used to adjust vessel traffic estimates and associated threats that are exclusively based on AIS data. This information can also benefit managers and policy makers that rely on AIS data when developing vessel traffic management measures to mitigate threats and to inform regional marine spatial plans. Place-specific studies to quantify marine vessel traffic missed by AIS are encouraged in coastal areas with high density of small vessel traffic.

Evaluating the Underkeel Clearance of Historic Vessel Transits in the Southwest Pass of the Mississippi River

The Army Corps of Engineers for decades has used two primary metrics to prioritize maintenance activities for coastal navigation projects: cargo tonnage at the project associated port and the controlling depth in the navigation channel relative to the project design depth. These metrics are generated through normal business practices and provide some insight into the relative importance (tonnage) of the port and the condition (controlling depth) of the channel. They have been incorporated into a risk-based framework that drives funds toward locations where project conditions have declined and the consequences of failure are high. With the maturation of a suite of technology programs, the U.S. Army Corps of Engineers (USACE) has an emerging capability to hindcast the underkeel clearance of vessels that transited through maintained waterways at the reach level. This study demonstrates an underkeel clearance-based assessment of shipping through the Southwest Pass of the Mississippi River over the span of three years by combining marine vessel automatic identification system data, tidal predictions, channel bathymetric surveys, and records of vessel sailing draft from entrances and clearances to domestic ports. The Southwest Pass is prone to rapid and widespread sedimentation and has received in excess of $100M annually for maintenance dredging in the last decade. Mobilizing dredges to this area in response to emergent sedimentation scenarios has proved disruptive to other dredging projects on the Gulf and Atlantic coasts, all of which rely on a finite fleet of dredges to maintain our Nation's navigable waterways. Underkeel clearance-based performance is assessed at varying timescales in context of operational parameters such as draft restrictions limiting the size of vessels authorized to transit through the channel. Assessment of the spatial distribution of vessel traffic with respect to channel shoaling is also made.

Assigning a commodity dimension to AIS data: Disaggregated freight flow on an inland waterway network

Inland waterways play a key role within the freight transportation system by connecting productive heartland areas to international gateways, while keeping costs competitive. Quantifying commodity flow is important because it affects cost-based supply chain decision-making. However, data on commodity movements to inform investment and planning decisions is elusive. Publicly available commodity data on U.S. inland waterways is limited in its spatial aggregation to the location of locks, which is insufficient to identify inter-port commodity flows. Automatic Identification System (AIS) data has the potential to disaggregate freight-flows to the port and river segment levels but it does not identify the commodity carried. This paper characterizes and quantifies vessel trips by port of origin-destination, timestamp, commodity carried, and path (mapped to an inland waterway network), allowing for disaggregated commodity flow analysis, previously unavailable in the public domain in the U.S.This is accomplished through a multi-commodity assignment model which conflates AIS vessel movement data with commodity-specific port throughput. A stochastic approach is introduced to handle uncertainty in cargo-to-vessel ratios. Validation using data from the Arkansas River show agreement between model predictions and aggregated commodity volumes with differences lower than 1.82% by commodity and lock. Ubiquitous AIS data permit the transferability of the proposed work.

Big Data Insights into Container Vessel Dwell Times

Analysis of automatic identification system (AIS) vessel call records can greatly improve our understanding of container vessel dwell times when coupled with information on port volumes and vessel schedules. Port productivity discussions often use vessel time in port—referred to as dwell time, turnaround time, or berth time—as a primary metric. This emphasis implies a need to understand the factors that determine dwell time, especially in port comparisons. Previous dwell time analyses have been handicapped by limited data. This analysis differs in that it uses a multiyear, multiport database covering all relevant vessel calls at major continental U.S. container ports (Baltimore, Boston, Charleston, Houston, Jacksonville, Long Beach, Los Angeles, Miami, Mobile, New Orleans, Northwest Seaport Alliance, New York–New Jersey, Oakland, Palm Beach, Philadelphia, Port Everglades, Savannah, Virginia, and Wilmington, NC), and by including vessel schedules and seasonality. The analysis indicated a much stronger association of dwell time with expected cargo volume at each call than with vessel capacity, and expected cargo volumes helped explain port dwell time differences. The analysis also found that vessel schedules may be the primary determinants of dwell time, and that schedule adherence may thus be equally important as dwell time per se. Seasonality also affected container vessel dwell time, but that influence may be complex as both weather conditions and seasonal cargo peaks probably affect the outcomes. Promising avenues for future research lie in merging AIS vessel call records with other data sets that, unfortunately, may not yet exist or be accessible.

Concirrus Quest Marine's Insurance Business Model: The Role of AI and Big Data

The size of the global marine insurance market in 2018 was approximately $30 billion, but low levels of profitability are common due to intense competition and complex risk assessment problems. Concirrus, an Insurance Technology (InsurTech) disruptor, is transforming the assessment of marine risk through the novel use of Artificial Intelligence (AI) and big data analytics, to develop behavioural risk models. This is part of a broader market trend in insurance, in which traditional underwriting is being replaced by new modes of risk analysis, often based on behavioural risk. In the marine insurance market, the vessel Automatic Identification System can be used as an Internet-connected data source, to provide real-time telematic information regarding the activity of insured vessels. For example, this data includes vessel identification codes, positional information, course, speed and heading, target destination, estimated time of arrival, navigation status, and off-ship calculations. Other market information such as weather data, shipping route data, geographic knowledge, vessel records and insurance records can also be incorporated into sophisticated data analytics to model and better understand risk at a highly granular level. This rich digital and data ecosystem makes it possible to dynamically build and test novel, statistical approaches to model risk and develop new analytics that can be used to mitigate risk by pro-actively influencing business customers. Concirrus is an established marine and automotive InsurTech firm that was founded to capitalise on new behavioural risk modelling techniques and apply them in insurance markets. InsurTech companies disrupt markets by implementing new business models that exploit advances in Artificial Intelligence (AI), high-performance digital technology accessed through cloud computing at relatively low cost, and big data analytics capabilities. Concirrus is an example of an Insurance technology leader, who are taking a digital first approach that avoids legacy system issues such as high maintenance costs, lack of integration and siloed business processes. This case study focuses on Concirrus’ business model and describes the operations of its Quest Marine Insurance Product. The Quest insurance product is an insurance risk analytics platform that combines a wealth of data sets, including complex, longitudinal, and behavioural data so that marine insurers can gain a much more nuanced and detailed understanding of a fleet’s risk profile and can offer connected policies that encourage and reward safer behaviour, which results in lower risk of loss and damage to ships, their contents and crew members. A detailed description and analysis of Concirrus’ value creation process is presented, which is based on Abassi’s model of big data value chains. The business model description identifies several areas of information and analytical competitive advantages, which create business benefits for each of the stakeholders involved in marine insurance: fleet operators; insurance brokers; insurers and reinsurers. More generally, the case study is an exemplar of a technology leader that is disrupting a market through the novel application of AI and digital transformation. The implementation challenges and future trajectory of behavioural insurance are outlined.

Resilience assessment tool for port planning.

US ports and container/intermodal terminals are critical links in the marine transportation system. Disruption at a port can have a crippling economic effect in the coastal zone as well as the rest of the nation. Port stakeholders have a vested interest in the long-term function and viability of ports, but no standardized measures for performance or resilience exist for ports. The goal of this research is to demonstrate the utility of a predictive port resilience assessment tool. The developed tool encompasses a microscopic traffic simulation model (VISSIM) based hybrid multimodal analyzes of port operations and provides a quantifiable assessment of resilience. The application of this tool is shown on six ports in the Southeast US. The waterside port simulation models were developed using vessel automatic identification system data and programed within a VISSIM simulation of landside operations. This hybrid modeling approach was used to visualize vessels and allow them to interact in both time and space with each other and landside infrastructure. Local and regional resilience was quantified through the analysis of time-dependent resilience plots and used as a performance measure in this study. The utility of the predictive port resilience assessment was demonstrated in response to Hurricane Matthew (2016). However, the novel procedure described herein can be applied to any port hazard. This research grows the understanding of the regional consequences of hurricane events and enhances the knowledge in the development of a stakeholder-focused tool to assess resilience.

Improving disasters preparedness and response for coastal communities using AIS ship tracking data

Many coastal communities are heavily dependent on maritime transportation for the ingress and egress of people and goods. Any major transportation disruption can have a significant negative impact on the safety, health and wellbeing of affected communities, this is due to the interruption in the availability of food and the supply medicines and fuel. Therefore, preparedness and the forward planning of an effective response are essential for successful emergency and recovery management. Accordingly, in this study, the concept of using AIS (Automatic Identification System) vessel tracking data has been applied for the study of disaster management in coastal communities. The AIS vessel tracking system has been an important development in navigational safety; this is because it continuously transmits important information to all other vessels about a particular vessel (including its position, identity, speed and route). One of the limitations of the AIS tracking system is that AIS data does not indicate commodity specifications: that is, the quantity of essential goods that each vessel is carrying to specified coastal communities. To overcome the limitation of the current AIS tracking system, we use an artificial neural network as an estimation tool. In the current study, AIS data are assessed and analyzed in addition to the augmentation of the capacity information of vessels; thus, the study develops a predictive model so that a relief manager can determine the actual needs of affected residents and thus be able to make responsible relief decisions (e.g., how much relief a disaster-affected community is likely to need). The study makes a unique contribution as its focus seeks to remedy the total lack of research on how to use AIS data in disaster operations.

AIS-Based Multiple Vessel Collision and Grounding Risk Identification based on Adaptive Safety Domain

The continuous growth in maritime traffic and recent developments towards autonomous navigation have directed increasing attention to navigational safety in which new tools are required to identify real-time risk and complex navigation situations. These tools are of paramount importance to avoid potentially disastrous consequences of accidents and promote safe navigation at sea. In this study, an adaptive ship-safety-domain is proposed with spatial risk functions to identify both collision and grounding risk based on motion and maneuverability conditions for all vessels. The algorithm is designed and validated through extensive amounts of Automatic Identification System (AIS) data for decision support over a large area, while the integration of the algorithm with other navigational systems will increase effectiveness and ensure reliability. Since a successful evacuation of a potential vessel-to-vessel collision, or a vessel grounding situation, is highly dependent on the nearby maneuvering limitations and other possible accident situations, multi-vessel collision and grounding risk is considered in this work to identify real-time risk. The presented algorithm utilizes and exploits dynamic AIS information, vessel registry and high-resolution maps and it is robust to inaccuracies of position, course and speed over ground records. The computation-efficient algorithm allows for real-time situation risk identification at a large-scale monitored map up to country level and up to several years of operation with a very high accuracy.

Vessel Operations in the Arctic, 2015–2017

The Arctic is among the most rapidly-changing regions on Earth. Diminishing levels of sea-ice has increased opportunities for maritime activities in historically inaccessible areas such as the Northern Sea Route and Northwest Passage. Degradation of Arctic marine ecosystems may accompany expanding vessel operations through introduced underwater noise, potential for large oil spills, among other things; and may compound stressors already effecting biological populations due to climate change. Assessments are needed to track changes in vessel traffic patterns and associated environmental impacts. We analyzed Arctic-wide vessel Automatic Identification System data 1 January 2015-31 December 2017 to quantify the amount and spatial distribution of vessel operations, assess possible changes in these operations, and establish a baseline for future monitoring. Nearly 400,000 vessel transits were analyzed. Number of trips, hours of operation, and amount of sea surface exposed to vessel traffic were used to compare operations between 14 delineated waterways. Operations were extensive and diverse: an average of 132,828 trips were made annually by over 5,000 different vessels. Transits were made in all areas studied and all months of the year. Maritime activities were intensive in some areas, but ice-limited in others. Amount of sea surface exposed to vessel traffic exceeded 70% in all but three areas. Bulk carriers, cargo ships, passenger/cruise ships, research survey ships, and vessels supporting oil/gas-related activities were represented. However, fishing vessels, primarily in the Barents, Bering, and Norwegian Seas, surpassed operations of all other vessel types and comprised about one-half of all voyages each year. We observed no overt increasing or decreasing trends in vessel traffic volume in our limited study period. Instead, inter-year variation was evident. While the number of unique vessels and transits increased year-to-year, hours of operation declined in the same period. Abundance/distribution of fisheries resources, economic feasibility of Arctic marine travel as weighed against inherent risks, and other factors likely accounted for inter-year variation in regional activity levels. Measures have been established to protect Arctic marine ecosystems but may need strengthening to address potential ecosystem threats from existing and growing commercial and industrial activities in the region.

Utilization of Vessel Automatic Identification System (AIS) to Estimate the Emission of Air Pollutant from Merchant Vessels in the Port of Kaohsiung

ABSTRACT Vessel-source air pollution may have a major impact on human health and the marine environment. In this study, a method based on vessel activity intensity was adopted to estimate the emission of air pollutants generated by the consumption of fuel oils from merchant vessels in the Port of Kaohsiung. The vessel activities were determined by using the vessel automatic identification system (AIS) to track vessel traffic and position and to calculate the corresponding emission of air pollutants. It was found that container ships, bulk carriers, and oil tankers, emitted approximately 90% of the total air pollutants. The emissions of SO2 and NOx accounted for 43% and 23%, respectively, when compared to the land-based source emission from the city of Kaohsiung. Management strategies were explored that market-based tools are recommended for the mitigation of vessel-source air pollution along with mandatory utilization of low sulfur fuels.