Experimental Setup and Machine Learning-Based Prediction Model for Electro-Cyclone Filter Efficiency: Filtering of Ship Particulate Matter Emission
Ship emissions significantly impact air quality, particularly in coastal and port regions, contributing to elevated concentrations of PM<sub>2.5</sub>, and PM<sub>10</sub>, with varying effects observed across different locations. This study investigates the effectiveness of...
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2025-01-01
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| author | Aleksandr Šabanovič Jonas Matijošius Dragan Marinković Aleksandras Chlebnikovas Donatas Gurauskis Johannes H. Gutheil Artūras Kilikevičius |
| author_facet | Aleksandr Šabanovič Jonas Matijošius Dragan Marinković Aleksandras Chlebnikovas Donatas Gurauskis Johannes H. Gutheil Artūras Kilikevičius |
| author_sort | Aleksandr Šabanovič |
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| description | Ship emissions significantly impact air quality, particularly in coastal and port regions, contributing to elevated concentrations of PM<sub>2.5</sub>, and PM<sub>10</sub>, with varying effects observed across different locations. This study investigates the effectiveness of emission control policies, inland and port-specific contributions to air pollution, and the health risks posed by particulate matter (PM). A regression discontinuity model at Ningbo Port revealed that ship activities show moderate PM<sub>2.5</sub> and PM<sub>10</sub> variations. In Busan Port, container ships accounted for the majority of emissions, with social costs from pollutants estimated at USD 31.55 million annually. Inland shipping near the Yangtze River demonstrated significant PM contributions, emphasizing regional impacts. Health risks from PM<sub>2.5</sub>, a major global toxic pollutant, were highlighted, with links to respiratory, cardiovascular, and cognitive disorders. Advances in air purification technologies, including hybrid electrostatic filtration systems, have shown promising efficiency in removing submicron particles and toxic gases, reducing energy costs. In this paper, a random forest machine learning model developed to predict particulate concentrations post-cleaning demonstrated robust performance (MAE = 0.49 P/cm<sup>3</sup>, <i>R</i><sup>2</sup> = 0.97). These findings underscore the critical need for stringent emission controls, innovative filtration systems, and comprehensive monitoring to mitigate the environmental and health impacts of ship emissions. |
| format | Article |
| id | doaj-art-8359a815bc364df2ab664e01bc16c2cf |
| institution | Kabale University |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-8359a815bc364df2ab664e01bc16c2cf2025-01-24T13:22:02ZengMDPI AGAtmosphere2073-44332025-01-0116110310.3390/atmos16010103Experimental Setup and Machine Learning-Based Prediction Model for Electro-Cyclone Filter Efficiency: Filtering of Ship Particulate Matter EmissionAleksandr Šabanovič0Jonas Matijošius1Dragan Marinković2Aleksandras Chlebnikovas3Donatas Gurauskis4Johannes H. Gutheil5Artūras Kilikevičius6Department of Mechanical and Material Engineering, Faculty of Mechanics, Vilnius Gediminas Technical University-VILNIUS TECH, Plytinės st. 25, LT-10105 Vilnius, LithuaniaMechanical Science Institute, Vilnius Gediminas Technical University-VILNIUS TECH, Plytinės st. 25, LT-10105 Vilnius, LithuaniaMechanical Science Institute, Vilnius Gediminas Technical University-VILNIUS TECH, Plytinės st. 25, LT-10105 Vilnius, LithuaniaMechanical Science Institute, Vilnius Gediminas Technical University-VILNIUS TECH, Plytinės st. 25, LT-10105 Vilnius, LithuaniaMechanical Science Institute, Vilnius Gediminas Technical University-VILNIUS TECH, Plytinės st. 25, LT-10105 Vilnius, LithuaniaInstitute of Particle Process Engineering, Rheinland-Pfälzische Technische Universität (RPTU), Gottlieb-Daimler-Straße 44, D-67663 Kaiserslautern, GermanyMechanical Science Institute, Vilnius Gediminas Technical University-VILNIUS TECH, Plytinės st. 25, LT-10105 Vilnius, LithuaniaShip emissions significantly impact air quality, particularly in coastal and port regions, contributing to elevated concentrations of PM<sub>2.5</sub>, and PM<sub>10</sub>, with varying effects observed across different locations. This study investigates the effectiveness of emission control policies, inland and port-specific contributions to air pollution, and the health risks posed by particulate matter (PM). A regression discontinuity model at Ningbo Port revealed that ship activities show moderate PM<sub>2.5</sub> and PM<sub>10</sub> variations. In Busan Port, container ships accounted for the majority of emissions, with social costs from pollutants estimated at USD 31.55 million annually. Inland shipping near the Yangtze River demonstrated significant PM contributions, emphasizing regional impacts. Health risks from PM<sub>2.5</sub>, a major global toxic pollutant, were highlighted, with links to respiratory, cardiovascular, and cognitive disorders. Advances in air purification technologies, including hybrid electrostatic filtration systems, have shown promising efficiency in removing submicron particles and toxic gases, reducing energy costs. In this paper, a random forest machine learning model developed to predict particulate concentrations post-cleaning demonstrated robust performance (MAE = 0.49 P/cm<sup>3</sup>, <i>R</i><sup>2</sup> = 0.97). These findings underscore the critical need for stringent emission controls, innovative filtration systems, and comprehensive monitoring to mitigate the environmental and health impacts of ship emissions.https://www.mdpi.com/2073-4433/16/1/103ship emissionsair qualityparticulate matteremission control policieshealth risks of PMhybrid filtration systems |
| spellingShingle | Aleksandr Šabanovič Jonas Matijošius Dragan Marinković Aleksandras Chlebnikovas Donatas Gurauskis Johannes H. Gutheil Artūras Kilikevičius Experimental Setup and Machine Learning-Based Prediction Model for Electro-Cyclone Filter Efficiency: Filtering of Ship Particulate Matter Emission Atmosphere ship emissions air quality particulate matter emission control policies health risks of PM hybrid filtration systems |
| title | Experimental Setup and Machine Learning-Based Prediction Model for Electro-Cyclone Filter Efficiency: Filtering of Ship Particulate Matter Emission |
| title_full | Experimental Setup and Machine Learning-Based Prediction Model for Electro-Cyclone Filter Efficiency: Filtering of Ship Particulate Matter Emission |
| title_fullStr | Experimental Setup and Machine Learning-Based Prediction Model for Electro-Cyclone Filter Efficiency: Filtering of Ship Particulate Matter Emission |
| title_full_unstemmed | Experimental Setup and Machine Learning-Based Prediction Model for Electro-Cyclone Filter Efficiency: Filtering of Ship Particulate Matter Emission |
| title_short | Experimental Setup and Machine Learning-Based Prediction Model for Electro-Cyclone Filter Efficiency: Filtering of Ship Particulate Matter Emission |
| title_sort | experimental setup and machine learning based prediction model for electro cyclone filter efficiency filtering of ship particulate matter emission |
| topic | ship emissions air quality particulate matter emission control policies health risks of PM hybrid filtration systems |
| url | https://www.mdpi.com/2073-4433/16/1/103 |
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