A Data-Driven Approach to Identify Major Air Pollutants in Shanghai Port Area and Their Contributing Factors
Air pollution is a growing concern in metropolitan areas worldwide, and Shanghai, as one of the world’s busiest ports, faces significant challenges in local air pollution control. Assessing the contribution of a specific port to air pollution is essential for effective environmental management and p...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/12/2/288 |
| _version_ | 1797297824850771968 |
|---|---|
| author | Xing-Zhou Li Zhong-Ren Peng Qingyan Fu Qian Wang Jun Pan Hongdi He |
| author_facet | Xing-Zhou Li Zhong-Ren Peng Qingyan Fu Qian Wang Jun Pan Hongdi He |
| author_sort | Xing-Zhou Li |
| collection | DOAJ |
| description | Air pollution is a growing concern in metropolitan areas worldwide, and Shanghai, as one of the world’s busiest ports, faces significant challenges in local air pollution control. Assessing the contribution of a specific port to air pollution is essential for effective environmental management and public health improvement, making the analysis of air pollution contributions at a selected port in Shanghai a pertinent research focus. This study aims to delve into the distribution patterns of atmospheric pollutants in port areas and their influencing factors, utilizing a data-driven approach to unveil the relationship between pollution sources and dispersion. Through a comparative analysis of pollution levels in the port’s interior, surrounding regions, and urban area concentrations, we ascertain that carbon monoxide (CO) and nitric oxide (NO) are the primary pollutants in the port, with concentrations significantly exceeding those of the surrounding areas and urban area levels. These two pollutants exhibit an hourly pattern, with lower levels during the day and higher concentrations at night. Employing a random forest model, this study quantitatively analyzes the contribution rates of different factors to pollutant concentrations. The results indicate that NO concentration is primarily influenced by operational intensity and wind speed, while CO concentration is mainly affected by meteorological factors. Further, an orthogonal experiment reveals that maintaining daily operational vehicle numbers within 5000 effectively controls NO pollution, especially at low wind speeds. Additionally, humidity and temperature exhibit similar trends in influencing NO and CO, with heightened pollution occurring within the range of 75% to 90% humidity and 6 °C to 10 °C temperature. Severe pollution accumulates under stagnant wind conditions with wind speeds below 0.2 m/s. The results help to explore the underlying mechanisms of port pollution further and use machine learning for early pollution prediction, aiding timely warnings and emission reduction strategy formulation. |
| first_indexed | 2024-03-07T22:25:58Z |
| format | Article |
| id | doaj.art-a73be5e8a6ca4864a82a663a58ca5dd0 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-07T22:25:58Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-a73be5e8a6ca4864a82a663a58ca5dd02024-02-23T15:23:12ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-02-0112228810.3390/jmse12020288A Data-Driven Approach to Identify Major Air Pollutants in Shanghai Port Area and Their Contributing FactorsXing-Zhou Li0Zhong-Ren Peng1Qingyan Fu2Qian Wang3Jun Pan4Hongdi He5School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaInternational Center for Adaptation Planning and Design, College of Design, Construction and Planning, University of Florida, Florida, FL 32611-5706, USAShanghai Environment Monitor Center, Shanghai 200235, ChinaShanghai Environment Monitor Center, Shanghai 200235, ChinaShanghai Environment Monitor Center, Shanghai 200235, ChinaSchool of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaAir pollution is a growing concern in metropolitan areas worldwide, and Shanghai, as one of the world’s busiest ports, faces significant challenges in local air pollution control. Assessing the contribution of a specific port to air pollution is essential for effective environmental management and public health improvement, making the analysis of air pollution contributions at a selected port in Shanghai a pertinent research focus. This study aims to delve into the distribution patterns of atmospheric pollutants in port areas and their influencing factors, utilizing a data-driven approach to unveil the relationship between pollution sources and dispersion. Through a comparative analysis of pollution levels in the port’s interior, surrounding regions, and urban area concentrations, we ascertain that carbon monoxide (CO) and nitric oxide (NO) are the primary pollutants in the port, with concentrations significantly exceeding those of the surrounding areas and urban area levels. These two pollutants exhibit an hourly pattern, with lower levels during the day and higher concentrations at night. Employing a random forest model, this study quantitatively analyzes the contribution rates of different factors to pollutant concentrations. The results indicate that NO concentration is primarily influenced by operational intensity and wind speed, while CO concentration is mainly affected by meteorological factors. Further, an orthogonal experiment reveals that maintaining daily operational vehicle numbers within 5000 effectively controls NO pollution, especially at low wind speeds. Additionally, humidity and temperature exhibit similar trends in influencing NO and CO, with heightened pollution occurring within the range of 75% to 90% humidity and 6 °C to 10 °C temperature. Severe pollution accumulates under stagnant wind conditions with wind speeds below 0.2 m/s. The results help to explore the underlying mechanisms of port pollution further and use machine learning for early pollution prediction, aiding timely warnings and emission reduction strategy formulation.https://www.mdpi.com/2077-1312/12/2/288port air pollutionnitric oxidecarbon monoxidemeteorological factorscontributing factorspollution control strategies |
| spellingShingle | Xing-Zhou Li Zhong-Ren Peng Qingyan Fu Qian Wang Jun Pan Hongdi He A Data-Driven Approach to Identify Major Air Pollutants in Shanghai Port Area and Their Contributing Factors Journal of Marine Science and Engineering port air pollution nitric oxide carbon monoxide meteorological factors contributing factors pollution control strategies |
| title | A Data-Driven Approach to Identify Major Air Pollutants in Shanghai Port Area and Their Contributing Factors |
| title_full | A Data-Driven Approach to Identify Major Air Pollutants in Shanghai Port Area and Their Contributing Factors |
| title_fullStr | A Data-Driven Approach to Identify Major Air Pollutants in Shanghai Port Area and Their Contributing Factors |
| title_full_unstemmed | A Data-Driven Approach to Identify Major Air Pollutants in Shanghai Port Area and Their Contributing Factors |
| title_short | A Data-Driven Approach to Identify Major Air Pollutants in Shanghai Port Area and Their Contributing Factors |
| title_sort | data driven approach to identify major air pollutants in shanghai port area and their contributing factors |
| topic | port air pollution nitric oxide carbon monoxide meteorological factors contributing factors pollution control strategies |
| url | https://www.mdpi.com/2077-1312/12/2/288 |
| work_keys_str_mv | AT xingzhouli adatadrivenapproachtoidentifymajorairpollutantsinshanghaiportareaandtheircontributingfactors AT zhongrenpeng adatadrivenapproachtoidentifymajorairpollutantsinshanghaiportareaandtheircontributingfactors AT qingyanfu adatadrivenapproachtoidentifymajorairpollutantsinshanghaiportareaandtheircontributingfactors AT qianwang adatadrivenapproachtoidentifymajorairpollutantsinshanghaiportareaandtheircontributingfactors AT junpan adatadrivenapproachtoidentifymajorairpollutantsinshanghaiportareaandtheircontributingfactors AT hongdihe adatadrivenapproachtoidentifymajorairpollutantsinshanghaiportareaandtheircontributingfactors AT xingzhouli datadrivenapproachtoidentifymajorairpollutantsinshanghaiportareaandtheircontributingfactors AT zhongrenpeng datadrivenapproachtoidentifymajorairpollutantsinshanghaiportareaandtheircontributingfactors AT qingyanfu datadrivenapproachtoidentifymajorairpollutantsinshanghaiportareaandtheircontributingfactors AT qianwang datadrivenapproachtoidentifymajorairpollutantsinshanghaiportareaandtheircontributingfactors AT junpan datadrivenapproachtoidentifymajorairpollutantsinshanghaiportareaandtheircontributingfactors AT hongdihe datadrivenapproachtoidentifymajorairpollutantsinshanghaiportareaandtheircontributingfactors |