Particulate Matter (PM1, 2.5, 10) Concentration Prediction in Ship Exhaust Gas Plume through an Artificial Neural Network

Abstract

In the last decade the reduction of carbon dioxide emissions in the transport sector, including the marine sector, has become the direction of its strategic development. Increased air pollution in the air is one of the main reasons for premature deaths around the globe. It was determined that while many methods provide adequate information about pollution levels, improvements could be made to avoid major errors. The traditional methods are either expensive or require a lot of data and human resources to correctly evaluate those data arrays. To avoid these problems, artificial neural networks (ANN) and other machine learning methods are widely used nowadays. Many ANN models for ship pollution evaluation in ports either included the whole port area or went even further and included cities near port areas. These studies show that ANNs can be effectively used to evaluate air pollution in a wide area. However, there is a lack of research on ANN usage for individual ship pollution or ship plume evaluation. This study attempts to fill this gap by developing an ANN model to evaluate an individual ship’s plumes by combining several data sources such as AIS data, meteorological data, and measured the ship’s plume pollutants concentration. Results show good correlation; however, additional limitations have to be overcome regarding data filtering and the overall accuracy of the model.

Routing a Fleet of Drones from a Base Station for Emission Detection of Moving Ships by Genetic Algorithm

“…Ship emission detection technologies play a critical role in reducing the impact of ship emissions on the environment and public health [3]. Ship emission detection technologies are still developing, and future research should focus on improving the technology's accuracy, efficiency, and cost-effectiveness [18]. Drones equipped with sniffing technology have been tested and applied to detect atmospheric pollution on ships.…”

“…The research problem includes ship emission detection methods, detection accuracy optimization, and detection effectiveness evaluation. Research methods related to ship detection issues include neural networks [18], simulation analysis [19], machine learning [20], optimization [21], and utilizing data such as Automatic Identification System (AIS) data [22], satellite images, and videos. Detection methods involve remote sensing, onboard measurement [23], and imaging pattern recognition [24].…”

“…Research Problem Detection Solution [18] Individual ship pollution or ship plume evaluation Onboard NN [20] A big data analytics framework for ship performance monitoring Onboard ML [25] Apply an energy consumption approach to estimate container ship emissions Onboard EA [22] A method evaluating emissions in detailed Onboard EA [23] Precise identification of carbon emission hotspot areas and ship types in ports Onboard D [19] Monitor the SO2 concentration in ship exhausts Imaging S [26] Estimate SO2, CO2, and NO concentrations of exhaust gas by imaging methods Imaging NN [27] Investigating the impacts of satellite fire observation accuracy on the top-down nitrogen oxide emission estimation in northeastern Asia. RS D [21] Develop a marine diesel SOX online detection system RS H [28] Remotely detect sulfur contents in fuel oil used by ships in ECA RS S [24] Detect NO2 pollution plumes from ships with the TROPOMI/S5P satellite sensors.…”

“…Here, 𝑀 = |𝐼|. When 𝑥 𝑖𝑗𝑘 = 1, the constraint is 𝑢 𝑖𝑘 + 1 ≤ 𝑢 𝑗𝑘 , namely, the orders of visiting the ships 𝑖, 𝑗. Constraint (17) and Constraint (18) constrain the ranges of the variables.…”

Improving the accuracy of ship emission inventories based on LSTM-CNN model

“…Monitoring the emissions of pollutants from ships in port areas has a crucial impact on local environmental protection [1] . However, after reviewing the literature, it was found that most studies only roughly estimate ship pollutant emissions as part of the pollution sources in port cities [2] , without considering the accuracy of predicting optimized ship pollutant emissions. Current research on ship pollutants mainly focuses on utilizing Automatic Identification System (AIS) data, which provides real-time ship navigation information, and then using the Ship Traffic Emission Assessment Model (STEAM) developed by the Finnish Meteorological Institute (FMI) to calculate emissions [3] .…”