Estimating Carbon Dioxide and Particulate Matter Emissions from Ships using Automatic Identification System Data

Tracking and measuring national carbon footprints is key to achieving the ambitious goals set by the Paris Agreement on carbon emissions. According to statistics, more than 10% of global transportation carbon emissions result from shipping. However, accurate tracking of the emissions of the small boat segment is not well established. Past research looked into the role played by small boat fleets in terms of greenhouse gases, but this has relied either on high-level technological and operational assumptions or the installation of global navigation satellite system sensors to understand how this vessel class behaves. This research is undertaken mainly in relation to fishing and recreational boats. With the advent of open-access satellite imagery and its ever-increasing resolution, it can support innovative methodologies that could eventually lead to the quantification of greenhouse gas emissions. Our work used deep learning algorithms to detect small boats in three cities in the Gulf of California in Mexico. The work produced a methodology named BoatNet that can detect, measure and classify small boats with leisure boats and fishing boats even under low-resolution and blurry satellite images, achieving an accuracy of 93.9% with a precision of 74.0%. Future work should focus on attributing a boat activity to fuel consumption and operational profile to estimate small boat greenhouse gas emissions in any given region.

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“…Several authors have proposed using AIS to monitor the carbon emissions of the fleet [66][67][68][69][70].…”

Section: Related Work Small Boat Fleet and Carbon Emissionsmentioning

confidence: 99%

BoatNet: automated small boat composition detection using deep learning on satellite imagery

2023

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“…Smith et al [9] implemented a full-scale ship emission inventory analysis using AIS data. Sérgiomabunda et al [10] estimated a ship emission inventory near the strait of Gibraltar. Coello et al [11] estimated an emission inventory for the UK fishing fleet.…”

Section: Research Review and Objectivementioning

confidence: 99%

“…Two major methods with which to derive ship emissions inventories are top-down (fuel-based) and bottom-up (activity-based) [9]. Smith et al [9] and the IMO MEPC [3] adopted both methodologies, while Jalkanen and Kukkonen [8]; Sérgiomabunda et al [10]; Coello et al [11]; Winther et al [12]; and Yao et al [13] adopted the bottom-up methodology. In this study, we adopted the bottom-up methodology to derive fuel consumption.…”

Section: Research Review and Objectivementioning

confidence: 99%

“…Power prediction for the ship is one of the most important factors in deriving bunker consumption. Smith et al [9] and the IMO MEPC [3] used the IHS database; Coello et al [11] used the statistical fuel consumption; Jalkanen and Kukkonen [8] used the STEAM 3 model; and Winther et al [12] and Yao et al [13] adopted the methodology of Kristensen and Lützen [14], which uses the International Towing Tank Conference (ITTC) performance prediction method to obtain the resistance coefficient; and Sérgiomabunda et al [10] used the ITTC performance prediction method. We adopted the ITTC recommended procedures and guidelines.…”

Section: Research Review and Objectivementioning

confidence: 99%

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

et al. 2021

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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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“…This paper proposes a system, operating in real time, able to estimate the amount of pollutant gases emitted by ships crossing the Strait of Gibraltar (the west of the Strait of Gibraltar and part of the northern Moroccan Atlantic coast). The system is based on a method for estimating carbon dioxide (CO 2 ) and particulate matter (PM) emissions using AIS data [6]. CO 2 and PM are known respectively to be harmful to the environment (greenhouse gas) and human health (carcinogenic gas) [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Ship Exhaust Real-Time Monitoring System based on Automatic Identification System

Mabunda¹,

Astito²,

Hamdoune³

2014

IJCA

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Maritime traffic is a major source of pollutant gases and we believe that it is important to quantify these gases in order to assess their impact on the environment. This paper proposes a maritime monitoring system able to estimate in real time the emissions of carbon dioxide (CO 2 ) and particulate matter (PM) from ships crossing the Strait of Gibraltar area. The system works based on the Automatic Identification System (AIS) : it consists of an AIS receiver connected to a computer in which was installed an application able to determine, store and display emissions from ships. The computer application was performed according to a method for estimating CO 2 and PM emissions from AIS data. The system was installed near the Strait of Gibraltar (exactly in Tangier -Morocco) and tests were carried out for about a month (between March 26 and April 23, 2014). The system recorded emissions from 900 to 4000 tons of CO 2 and 2 to 9 tons of PM each day.

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Estimating Carbon Dioxide and Particulate Matter Emissions from Ships using Automatic Identification System Data

Cited by 5 publications

References 7 publications

BoatNet: automated small boat composition detection using deep learning on satellite imagery

BoatNet: automated small boat composition detection using deep learning on satellite imagery

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

Ship Exhaust Real-Time Monitoring System based on Automatic Identification System

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