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

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1611
Author(s):  
Han Jiang ◽  
Di Peng ◽  
Yunjing Wang ◽  
Mingliang Fu
Keyword(s):  
Test Data ◽  
Real World ◽  
Emission Inventory ◽  
Calculated Data ◽  
Automatic Identification ◽  
Identification System ◽  
Emission Model ◽  
Emission Rates ◽  
Inland River ◽  
Ship Emission

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.

ESTIMATION OF EXHAUST SHIP EMISSION FROM MARINE TRAFFIC IN THE STRAITS OF SINGAPORE AND BATAM WATERWAYS USING AUTOMATIC IDENTIFICATION SYSTEM (AIS) DATA

2015 ◽  
Vol 77 (23) ◽  
Author(s):  
Hendra Saputra ◽  
Mufti Fathonah Muvariz ◽  
Sapto Wiratno Satoto ◽  
Jaswar Koto
Keyword(s):  
Exhaust Emission ◽  
Automatic Identification ◽  
Identification System ◽  
Exhaust Gas ◽  
Emission Rates ◽  
Gas Emission ◽  
Automatic Identification System ◽  
Marine Traffic ◽  
Exhaust Gas Emission ◽  
Ship Emission

This study focuses on the Strait of Singapore and Batam Waterways area because it is one of the world’s most congested straits used for international shipping. The study aims to estimate exhaust gas emission and the concentration of emission to several areas around the strait. This is accomplished by evaluating the density of shipping lanes in the strait by using the data which obtained by Automatic Identification System (AIS). MEET methodology is used to estimate emissions from ships. There were 1269 total number of ships through the strait on September 27, 2014 at 06.00 am-08.00 am produces total exhaust emission for NOx, CO, CO2, VOC, PM and SOx were about 12595.35 g/second (15.99%), 25725.19 g/second (32.66%), 11832.31 g/second (15.02%), 5973.23 g/second (7.58%), 443.71 g/second, (0.56%), 22185.57 g/second (28.17%), respectively. The ships under the Singapore flag contribute approximately 22.78% of total emissions in the Strait of Singapore and Batam Waterways followed by Panama, Indonesia and Malaysia 14.47%, 3.67%, 1.91%, respectively. Based on the total emission rates hips under Indonesia and Malaysia rank of seventh and eighth respectively.

scholarly journals Spatial Analysis of the Ship Gas Emission Inventory in the Port of Busan Using Bottom-Up Approach Based on AIS Data

2021 ◽  
Vol 9 (12) ◽  
pp. 1457
Author(s):  
Donghan Woo ◽  
Namkyun Im
Keyword(s):  
Spatial Analysis ◽  
Emission Inventory ◽  
Geographical Area ◽  
Automatic Identification ◽  
Identification System ◽  
Gas Emission ◽  
Gas Emissions ◽  
Bottom Up ◽  
Emissions Inventory ◽  
Coastal Residents

Dense hub port-cities have been suffering from ship gas emissions causing atmospheric pollution and a threat to the health of coastal residents. To control ship gas emissions, many regulations have been established internationally. Analyses of ship gas emission inventories are essential to quantify mass and track emission changes over time in a given geographical area. Based on the gas emissions inventory, applicable regulations such as Emission Control Area (ECA) realization and Vessel Speed Reduction (VSR) may be established. The ship gas emission inventory (CO2, CO, NOx, SOx and PM) from the Busan Port (BP), including the North Port (NP) and Gamcheon Dadae-po Port (GDP), which is the biggest port in the Republic of Korea and which is also surrounded by residential, commercial, and industrial areas, were spatially analyzed. To calculate geographical ship gas emissions in real-time, this study introduces a bottom-up methodology using Automatic Identification System (AIS) data. According to the geographical density analysis of the gas emissions inventory, this study highlights that about 35% of the annual ship gas emissions of BP in 2019 were concentrated in the passageway to NP because of high ship speeds when leaving or arriving at the port. To protect the health of coastal residents, ship speed limit regulations along the passageway should be revised based on our spatial analysis results. The spatial analysis of the ship gas emission inventory in BP will be useful basic data for properly evaluating the local gas emission state on newly established or revised environmental regulations for BP.

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

2021 ◽  
Vol 21 (18) ◽  
pp. 13835-13853
Author(s):  
Xiaotong Wang ◽  
Wen Yi ◽  
Zhaofeng Lv ◽  
Fanyuan Deng ◽  
Songxin Zheng ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Emission Reduction ◽  
Emission Inventory ◽  
Emission Control ◽  
Identification System ◽  
In Situ Observation ◽  
Ship Emissions ◽  
Continuous Growth ◽  
Technical Specifications ◽  
Ship Emission

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.

scholarly journals AIS data case Study : identifying AIS coverage gaps on the Ohio River in CY2018

2021 ◽  
Author(s):  
Patricia DiJoseph ◽  
Brian Tetreault ◽  
Marin Kress
Keyword(s):  
River System ◽  
Technical Note ◽  
Coast Guard ◽  
Automatic Identification ◽  
Identification System ◽  
Army Corps ◽  
Ohio River ◽  
Us Army ◽  
Inland River ◽  
The Us

This Coastal and Hydraulics Engineering Technical Note (CHETN) describes a method for evaluating the received coverage from Automatic Identification System (AIS) shore sites and the availability of historic vessel position reports along the Ohio River. The network of AIS shoreside sites installed and operated by the US Army Corps of Engineers (USACE) and the US Coast Guard (USCG) receive information transmitted from vessels; however, reception of these transmissions is generally line-of-sight between the vessel and the AIS site antenna. Reception may also be affected by factors such as the quality of the transceiver installation aboard the vessel as well as the state of the equipment at the receiving site. Understanding how to define and quantify coverage gaps along the inland river system can inform research utilizing AIS data, provide information on the performance of the AIS network, and provide guidance for efforts to address identified coverage gaps

scholarly journals Spatial Analysis of an Emission Inventory from Liquefied Natural Gas Fleet Based on Automatic Identification System Database

2021 ◽  
Vol 13 (3) ◽  
pp. 1250
Author(s):  
Hoegwon Kim ◽  
Daisuke Watanabe ◽  
Shigeki Toriumi ◽  
Enna Hirata
Keyword(s):  
Natural Gas ◽  
Co2 Emissions ◽  
Emission Inventory ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
Wide Range ◽  
Volatile Organic ◽  
Position Data ◽  
Harmful Effects

Many states are actively working toward regulating CO2 emissions from a wide range of industries. However, due to the international characteristic of shipping, the emissions from shipping have not yet been strictly controlled. Using Automatic Identification System (AIS) data acquired through satellites, this study estimates the emission inventory, such as, CO2, CH4, CH4, N2O, NOx, CO and non-methane volatile organic compounds (NMVOCs) around the world and bunker consumption from a liquified natural gas (LNG) fleet under the assumption that a LNG fleet uses LNG as fuel. Using position data calculated from an AIS database, we made comparisons regarding the LNG trade amount and bunker consumption of LNG fleet, as well as the total CO2 inventory and CO2 emissions from LNG fleet in the vicinity of the coasts of relevant countries. The result provides insights into (1) how the emissions and bunker consumption from LNG fleet is distributed, (2) which countries are taking relatively more advantages of LNG trade, and (3) which countries are suffering possible harmful effects.

scholarly journals Evaluation of PNT Error Limits Using Real World Close Encounters from AIS Data

2021 ◽  
Vol 9 (2) ◽  
pp. 149
Author(s):  
Evelin Engler ◽  
Paweł Banyś ◽  
Hans-Georg Engler ◽  
Michael Baldauf ◽  
Frank Sill Torres
Keyword(s):  
Collision Avoidance ◽  
Real World ◽  
Simplified Model ◽  
Automatic Identification ◽  
Identification System ◽  
Traffic Data ◽  
Close Encounters ◽  
Data Provision ◽  
Erroneous Data ◽  
Better Than

Collision avoidance is one of the main tasks on board ships to ensure safety at sea. To comply with this requirement, the direct ship environment, which is often modelled as the ship’s domain, has to be kept free of other vessels and objects. This paper addresses the question to which extent inaccuracies in position (P), navigation (N), and timing (T) data impact the reliability of collision avoidance. Employing a simplified model of the ship domain, the determined error bounds are used to derive requirements for ship-side PNT data provision. For this purpose, vessel traffic data obtained in the western Baltic Sea based on the automatic identification system (AIS) is analysed to extract all close encounters between ships considered as real-world traffic situations with a potential risk of collision. This study assumes that in these situations, erroneous data can lead to an incorrect assessment of the situation with regard to existing collision risks. The size of the error determines whether collisions are detected, spatially incorrectly assigned, or not detected. Therefore, the non-recognition of collision risks ultimately determines the limits of tolerable errors in the PNT data. The results indicate that under certain conditions, the probability of non-recognition of existing collision risks can reach non-negligible values, e.g., more than 1%, even though position accuracies are better than 10 m.

scholarly journals Real-World Marine Radar Datasets for Evaluating Target Tracking Methods

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4641
Author(s):  
Jaya Shradha Fowdur ◽  
Marcus Baum ◽  
Frank Heymann
Keyword(s):  
Target Tracking ◽  
Target Detection ◽  
Situation Awareness ◽  
Real World ◽  
Autonomous Navigation ◽  
Automatic Identification ◽  
Identification System ◽  
Initial Dataset ◽  
Marine Radar ◽  
German Aerospace

As autonomous navigation is being implemented in several areas including the maritime domain, the need for robust tracking is becoming more important for traffic situation awareness, assessment and monitoring. We present an online repository comprising three designated marine radar datasets from real-world measurement campaigns to be employed for target detection and tracking research purposes. The datasets have their respective reference positions on the basis of the Automatic Identification System (AIS). Together with the methods used for target detection and clustering, a novel baseline algorithm for an extended centroid-based multiple target tracking is introduced and explained. We compare the performance of our algorithm to its standard version on the datasets using the AIS references. The results obtained and some initial dataset specific analysis are presented. The datasets, under the German Aerospace Centre (DLR)’s terms and agreements, can be procured from the company website’s URL provided in the article.

scholarly journals Inland port vessel emissions inventory based on Ship Traffic Emission Assessment Model–Automatic Identification System

2017 ◽  
Vol 9 (7) ◽  
pp. 168781401771287 ◽  
Author(s):  
Yan Zhang ◽  
Jian Gu ◽  
Wei Wang ◽  
Yiqiang Peng ◽  
Xiaojing Wu ◽  
...  
Keyword(s):  
Assessment Model ◽  
Local Context ◽  
Automatic Identification ◽  
Identification System ◽  
Ship Emissions ◽  
Traffic Emission ◽  
Automatic Identification System ◽  
Ship Traffic ◽  
Study Results ◽  
Ship Emission

The designation of ship emission control areas in China evidenced increased attention to ship emissions. Ships calling ports along inland waterways are of particular concern as their emissions exacerbate air pollution in nearby cities. Adapting the Ship Traffic Emission Assessment Model to the local context, this study combines data from Automatic Identification System, vessel profile database, and field investigation results to build a “bottom-up” activity-based inventory of ship emissions. The Nanjing Longtan Container Port was taken as a case study. Results show that total ship emissions for PM10, PM2.5, NOx, SOx, CO, HC, and CO2 in 2014 are 3.45, 2.76, 196.00, 2.90, 20.62, 8.13, and 12,554.29 t, respectively. Accordingly, ship emission reduction measures were proposed based on the analysis of emission characteristics. The methods and conclusions of the study provide a scientific basis for the inventory and control of the ship emissions in China.

scholarly journals Architecture for Trajectory-Based Fishing Ship Classification with AIS Data

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3782 ◽  
Author(s):  
David Sánchez Pedroche ◽  
Daniel Amigo ◽  
Jesús García ◽  
José Manuel Molina
Keyword(s):  
Real World ◽  
Binary Classification ◽  
Classification Problem ◽  
Automatic Identification ◽  
Identification System ◽  
Preparation Process ◽  
Data Preparation ◽  
Real World Data ◽  
Fishing Vessels ◽  
Ship Classification

This paper proposes a data preparation process for managing real-world kinematic data and detecting fishing vessels. The solution is a binary classification that classifies ship trajectories into either fishing or non-fishing ships. The data used are characterized by the typical problems found in classic data mining applications using real-world data, such as noise and inconsistencies. The two classes are also clearly unbalanced in the data, a problem which is addressed using algorithms that resample the instances. For classification, a series of features are extracted from spatiotemporal data that represent the trajectories of the ships, available from sequences of Automatic Identification System (AIS) reports. These features are proposed for the modelling of ship behavior but, because they do not contain context-related information, the classification can be applied in other scenarios. Experimentation shows that the proposed data preparation process is useful for the presented classification problem. In addition, positive results are obtained using minimal information.

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