The Complex Nonlinear Coupling Causal Patterns Between PM2.5 and Meteorological Factors in Tibetan Plateau: A Case Study in Xining
PM2.5 pollution influences the population health and people’s daily life. Because meteorological factors are main factor affecting the formation of PM2.5, the interaction between PM2.5 and meteorological factors needs to be better understood, both for air quality management and for PM2.5...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9591570/ |
| _version_ | 1818424657667162112 |
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| author | Zhixiao Zou Changxiu Cheng Shi Shen |
| author_facet | Zhixiao Zou Changxiu Cheng Shi Shen |
| author_sort | Zhixiao Zou |
| collection | DOAJ |
| description | PM2.5 pollution influences the population health and people’s daily life. Because meteorological factors are main factor affecting the formation of PM2.5, the interaction between PM2.5 and meteorological factors needs to be better understood, both for air quality management and for PM2.5 projection. Here, we use a nonlinear state space method called the convergent cross mapping method to identify the complex coupling patterns between PM2.5 and meteorological factors in a plateau city: Xining. The results prove that PM2.5-meteorological coupling patterns change with seasons and PM2.5-meteorological coupling patterns are fixed in spring, autumn and winter. There is no fixed pattern in summer. In spring, there is a negative unidirectional effect from precipitation to PM2.5 and a negative bidirectional effect between relative humidity and PM2.5. In autumn, there are some negative bidirectional effects between PM2.5 and relative humidity, precipitation, and air pressure, while solar radiation has a positive bidirectional effect on PM2.5. In winter, there are negative bidirectional couplings between PM2.5 and wind speed and temperature and a positive bidirectional coupling between relative humidity and PM2.5. Furthermore, relative humidity is a consistent driving factor affecting PM2.5. Most of the time, air quality managers may alleviate PM2.5 by increasing relative humidity. Thus, the results provide a meteorological means for improving air quality in plateau cities. |
| first_indexed | 2024-12-14T14:01:32Z |
| format | Article |
| id | doaj.art-fd4a22bd628c4325afa39620aa2f0ab8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T14:01:32Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-fd4a22bd628c4325afa39620aa2f0ab82022-12-21T22:58:43ZengIEEEIEEE Access2169-35362021-01-01915037315038210.1109/ACCESS.2021.31234559591570The Complex Nonlinear Coupling Causal Patterns Between PM2.5 and Meteorological Factors in Tibetan Plateau: A Case Study in XiningZhixiao Zou0Changxiu Cheng1Shi Shen2https://orcid.org/0000-0001-9126-229XKey Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing, ChinaKey Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing, ChinaCenter for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaPM2.5 pollution influences the population health and people’s daily life. Because meteorological factors are main factor affecting the formation of PM2.5, the interaction between PM2.5 and meteorological factors needs to be better understood, both for air quality management and for PM2.5 projection. Here, we use a nonlinear state space method called the convergent cross mapping method to identify the complex coupling patterns between PM2.5 and meteorological factors in a plateau city: Xining. The results prove that PM2.5-meteorological coupling patterns change with seasons and PM2.5-meteorological coupling patterns are fixed in spring, autumn and winter. There is no fixed pattern in summer. In spring, there is a negative unidirectional effect from precipitation to PM2.5 and a negative bidirectional effect between relative humidity and PM2.5. In autumn, there are some negative bidirectional effects between PM2.5 and relative humidity, precipitation, and air pressure, while solar radiation has a positive bidirectional effect on PM2.5. In winter, there are negative bidirectional couplings between PM2.5 and wind speed and temperature and a positive bidirectional coupling between relative humidity and PM2.5. Furthermore, relative humidity is a consistent driving factor affecting PM2.5. Most of the time, air quality managers may alleviate PM2.5 by increasing relative humidity. Thus, the results provide a meteorological means for improving air quality in plateau cities.https://ieeexplore.ieee.org/document/9591570/Nonlinear state spacecouplingPM2.5meteorological factorsplateau cities |
| spellingShingle | Zhixiao Zou Changxiu Cheng Shi Shen The Complex Nonlinear Coupling Causal Patterns Between PM2.5 and Meteorological Factors in Tibetan Plateau: A Case Study in Xining IEEE Access Nonlinear state space coupling PM2.5 meteorological factors plateau cities |
| title | The Complex Nonlinear Coupling Causal Patterns Between PM2.5 and Meteorological Factors in Tibetan Plateau: A Case Study in Xining |
| title_full | The Complex Nonlinear Coupling Causal Patterns Between PM2.5 and Meteorological Factors in Tibetan Plateau: A Case Study in Xining |
| title_fullStr | The Complex Nonlinear Coupling Causal Patterns Between PM2.5 and Meteorological Factors in Tibetan Plateau: A Case Study in Xining |
| title_full_unstemmed | The Complex Nonlinear Coupling Causal Patterns Between PM2.5 and Meteorological Factors in Tibetan Plateau: A Case Study in Xining |
| title_short | The Complex Nonlinear Coupling Causal Patterns Between PM2.5 and Meteorological Factors in Tibetan Plateau: A Case Study in Xining |
| title_sort | complex nonlinear coupling causal patterns between pm2 5 and meteorological factors in tibetan plateau a case study in xining |
| topic | Nonlinear state space coupling PM2.5 meteorological factors plateau cities |
| url | https://ieeexplore.ieee.org/document/9591570/ |
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