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

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.

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Analysis of Tugboat Activities using AIS Data for the Tianjin Port

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120916734 ◽

2020 ◽

Vol 2674 (5) ◽

pp. 498-509

Author(s):

Shukai Chen ◽

Feng Wang ◽

Xiaoyang Wei ◽

Zhijia Tan ◽

Hua Wang

Keyword(s):

Spatial Distribution ◽

Spatial Analysis ◽

Service Time ◽

Automatic Identification ◽

Identification System ◽

Crucial Importance ◽

Two Stage ◽

Temporal And Spatial ◽

Shed Light

The tugboat is the vessel that helps to maneuver large ships for berthing and un-berthing operations. To achieve efficient tugboat operations, investigating the features of tugboat activities is of crucial importance. This study aims to use automatic identification system (AIS) data to identify the maneuver services and analyze the characteristics of tugboat activities. A two-stage algorithm is developed to extract the time, locations, and involved tugboats for berthing and un-berthing operations from AIS data. The AIS data from Tianjin port, China, are used in the case study to demonstrate the effectiveness of the proposed method and analyze the pattern of tugboat activities. First, some important features of tugboat jobs are presented, such as the daily number of jobs and the spatial distribution of jobs. Then, a temporal and spatial analysis is conducted to investigate tugboat assignment, service time, tugboat utilization, and locations of berthing and un-berthing operations. The obtained results and implications could shed light on the deployment of tugboat berths, tugboat scheduling, and evaluation of tugboat fleet operation.

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Fuel Oil Consumption Monitoring and Predicting Gas Emission Based on Ship Performance using Automatic Identification System (AISITS) Data

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/557/1/012017 ◽

2020 ◽

Vol 557 ◽

pp. 012017

Author(s):

A T A Wijaya ◽

I M Ariana ◽

D W Handani ◽

H N Abdillah

Keyword(s):

Fuel Oil ◽

Automatic Identification ◽

Identification System ◽

Oil Consumption ◽

Gas Emission ◽

Automatic Identification System ◽

Ship Performance

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

Jurnal Teknologi ◽

10.11113/jt.v77.6687 ◽

2015 ◽

Vol 77 (23) ◽

Cited By ~ 1

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.

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Supplementary material to "Nepal Emission Inventory (NEEMI): a high resolution technology-based bottom-up emissions inventory for Nepal 2001–2016"

10.5194/acp-2019-113-supplement ◽

2019 ◽

Author(s):

Pankaj Sadavarte ◽

Maheswar Rupakheti ◽

Prakash V. Bhave ◽

Kiran Shakya ◽

Mark G. Lawrence

Keyword(s):

High Resolution ◽

Emission Inventory ◽

Bottom Up ◽

Emissions Inventory ◽

Supplementary Material

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

Sustainability ◽

10.3390/su13031250 ◽

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.

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Development of the CREATE Inventory in Support of Integrated Climate and Air Quality Modeling for Asia

Sustainability ◽

10.3390/su12197930 ◽

2020 ◽

Vol 12 (19) ◽

pp. 7930 ◽

Cited By ~ 1

Author(s):

Jung-Hun Woo ◽

Younha Kim ◽

Hyeon-Kook Kim ◽

Ki-Chul Choi ◽

Jeong-Hee Eum ◽

...

Keyword(s):

Climate Change ◽

Air Quality ◽

Biomass Burning ◽

Environmental Protection Agency ◽

Emission Inventory ◽

Emission Inventories ◽

Bottom Up ◽

Emissions Inventory ◽

Transport Experiment ◽

Natural Emissions

A bottom-up emissions inventory is one of the most important data sets needed to understand air quality (AQ) and climate change (CC). Several emission inventories have been developed for Asia, including Transport and Chemical Evolution over the Pacific (TRACE-P), Regional Emission Inventory in Asia (REAS), and Inter-Continental Chemical Transport Experiment (INTEX) and, while these have been used successfully for many international studies, they have limitations including restricted amounts of information on pollutant types and low levels of transparency with respect to the polluting sectors or fuel types involved. To address these shortcomings, we developed: (1) a base-year, bottom-up anthropogenic emissions inventory for Asia, using the most current parameters and international frameworks (i.e., the Greenhouse gas—Air pollution INteractions and Synergies (GAINS) model); and (2) a base-year, natural emissions inventory for biogenic and biomass burning. For (1), we focused mainly on China, South Korea, and Japan; however, we also covered emission inventories for other regions in Asia using data covering recent energy/industry statistics, emission factors, and control technology penetration. The emissions inventory (Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment (CREATE)) covers 54 fuel classes, 201 subsectors, and 13 pollutants, namely SO2, NOx, CO, non-methane volatile organic compounds (NMVOC), NH3, OC, BC, PM10, PM2.5, CO2, CH4, N2O, and Hg. For the base-year natural emissions inventory, the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and BlueSky-Asia frameworks were used to estimate biogenic and biomass burning emissions, respectively. Since the CREATE emission inventory was designed/developed using international climate change/air quality (CC/AQ) assessment frameworks, such as GAINS, and has been fully connected with the most comprehensive emissions modeling systems—such as the US Environmental Protection Agency (EPA) Chemical Manufacturing Area Source (CMAS) system—it can be used to support various climate and AQ integrated modeling studies, both now and in the future.

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Extracting Global Shipping Networks from Massive Historical Automatic Identification System Sensor Data: A Bottom-Up Approach

Sensors ◽

10.3390/s19153363 ◽

2019 ◽

Vol 19 (15) ◽

pp. 3363 ◽

Cited By ~ 4

Author(s):

Zhihuan Wang ◽

Christophe Claramunt ◽

Yinhai Wang

Keyword(s):

Multiple Scales ◽

Spatial Clustering ◽

Sensor Data ◽

Automatic Identification ◽

Identification System ◽

Bottom Up ◽

Ship Traffic ◽

Terminal Level ◽

Shipping Networks

The increasing availability of big Automatic Identification Systems (AIS) sensor data offers great opportunities to track ship activities and mine spatial-temporal patterns of ship traffic worldwide. This research proposes a data integration approach to construct Global Shipping Networks (GSN) from massive historical ship AIS trajectories in a completely bottom-up way. First, a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is applied to temporally identify relevant stop locations, such as marine terminals and their associated events. Second, the semantic meanings of these locations are obtained by mapping them to real ports as identified by the World Port Index (WPI). Stop events are leveraged to develop travel sequences of any ship between stop locations at multiple scales. Last, a GSN is constructed by considering stop locations as nodes and journeys between nodes as links. This approach generates different levels of shipping networks from the terminal, port, and country levels. It is illustrated by a case study that extracts country, port, and terminal level Global Container Shipping Networks (GCSN) from AIS trajectories of more than 4000 container ships in 2015. The main features of these GCSNs and the limitations of this work are finally discussed.

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Evaluation of Air Pollutant Emission Inventories in East Asia

10.5194/egusphere-egu2020-18338 ◽

2020 ◽

Author(s):

Younha Kim ◽

Jung-hun Woo ◽

Youjung Jang ◽

Minwoo Park ◽

Bomi Kim ◽

...

Keyword(s):

Spatial Distribution ◽

Air Quality ◽

East Asia ◽

Emission Inventory ◽

Air Pollutant ◽

Emission Inventories ◽

Top Down ◽

Bottom Up ◽

Emissions Inventory ◽

Quality Study

Concentration of air pollutants such as tropospheric ozone and aerosols are mainly affected by meteorological variables and emissions. East Asia has large amount of anthropogenic and natural air pollutant emissions and has been putting lots of efforts to improve air quality. In order to seek effective ways to mitigate future air pollution, it is essential to understand the current emissions and their impacts on air quality. Emission inventory is one of the key datasets required to understand air quality and find ways to improve it. Amounts and spatial-temporal distributions of emissions are, however, not easy to estimate due to their complicate nature, therefore introduce significant uncertainties.In this study, we had developed an updated version of our Asian emissions inventory, named NIER/KU-CREATE (Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment) in support of climate-air quality study. We first inter-compare multiple bottom-up inventories to understand discrepancies among the dataset(sectoral, spatial). We then inter-compare those bottom-up emissions to the satellite-based top-down emission estimates to understand uncertainties of the databases. The bottom-up emission inventories used for this study are: CREATE, MEIC(Multiresolution Emission Inventory for China), REAS (Regional Emission inventory in ASia), and ECLIPSE(Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants). The satellite-derived top-down emission inventory had been acquired from the DECSO (Daily Emission derived Constrained by Satellite Observations) algorithm data from the GlobEmissions website.The analysis showed that some discrepancies, in terms of emission amounts, sectoral shares and spatial distribution patterns, exist among the datasets. We analyzed further to find out which parameters could affect more on those discrepancies. Co-analysis of top-down and bottom-up emissions inventory help us to evaluate emissions amount and spatial distribution. These analysis are helpful for the development of more consistent and reliable inventories with the aim of reducing the uncertainties in air quality study. More results of evaluation of emissions will be presented on site.&#160; &#160;&#160;&#160;Acknowledgements : This work was supported by National Institute of Environment Research (NIER-2019-03-02-005), Korea Environment Industry & Technology Institute(KEITI) through Public Technology Program based on Environmental Policy Program, funded by Korea Ministry of Environment(MOE)(2019000160007). This research was supported by the National Strategic Project-Fine particle of the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT(MSIT), the Ministry of Environment(ME), and the Ministry of Health and Welfare(MOHW) (NRF-2017M3D8A1092022).

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

Atmosphere ◽

10.3390/atmos12121611 ◽

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.

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