Spatiotemporal Characteristics and Influencing Factors of PM<sub>2.5</sub> Levels in Lianyungang: Insights from a Multidimensional Analysis
The rapid industrialization and urbanization in China have exacerbated air pollution, particularly PM<sub>2.5</sub>, posing significant threats to public health. This study focused on Lianyungang, an industrial city, to analyze the spatiotemporal variations in PM<sub>2.5</sub>...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2072-4292/16/23/4495 |
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| author | Xue Li Haihong He Dewei Wang Wenli Qiao Chunli Liu Yiming Sun Lulu Li Shuting Han Guozhen Zha |
| author_facet | Xue Li Haihong He Dewei Wang Wenli Qiao Chunli Liu Yiming Sun Lulu Li Shuting Han Guozhen Zha |
| author_sort | Xue Li |
| collection | DOAJ |
| description | The rapid industrialization and urbanization in China have exacerbated air pollution, particularly PM<sub>2.5</sub>, posing significant threats to public health. This study focused on Lianyungang, an industrial city, to analyze the spatiotemporal variations in PM<sub>2.5</sub> concentrations from 2000 to 2023 and identify the influencing factors. Utilizing high-resolution PM<sub>2.5</sub> data from the ChinaHighPM<sub>2.5</sub> dataset and ERA5 meteorological data, the study employed Empirical Orthogonal Function (EOF) analysis to capture spatial variability and the Bayesian Estimator of Abrupt Change Seasonal and Trend (BEAST) to assess long-term trends and abrupt changes. The key findings include a marked seasonal pattern, with higher PM<sub>2.5</sub> levels during the winter months and lower concentrations in the summer, primarily driven by temperature, humidity, and precipitation. A significant decline in PM<sub>2.5</sub> levels was observed after 2014, following the implementation of pollution control measures. The study underscores the importance of continued environmental regulation and green technology adoption in mitigating air pollution in rapidly industrializing cities. This research provides a comprehensive analysis of PM<sub>2.5</sub> trends and highlights the critical role of natural and human factors, contributing valuable insights for policymakers and researchers aiming to improve air quality. |
| format | Article |
| id | doaj-art-2f88a5930c5643829bf9cfaf9889a842 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-2f88a5930c5643829bf9cfaf9889a8422024-12-13T16:31:03ZengMDPI AGRemote Sensing2072-42922024-11-011623449510.3390/rs16234495Spatiotemporal Characteristics and Influencing Factors of PM<sub>2.5</sub> Levels in Lianyungang: Insights from a Multidimensional AnalysisXue Li0Haihong He1Dewei Wang2Wenli Qiao3Chunli Liu4Yiming Sun5Lulu Li6Shuting Han7Guozhen Zha8School of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaSchool of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaLianyungang Branch of Jiangsu Hydrology and Water Resource Survey Bureau, Lianyungang 222004, ChinaJiangsu Institute of Marine Resources Development, Jiangsu Ocean University, Lianyungang 222005, ChinaMarine College, Shandong University, Weihai 264209, ChinaSchool of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaSchool of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, ChinaSchool of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, ChinaSchool of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, ChinaThe rapid industrialization and urbanization in China have exacerbated air pollution, particularly PM<sub>2.5</sub>, posing significant threats to public health. This study focused on Lianyungang, an industrial city, to analyze the spatiotemporal variations in PM<sub>2.5</sub> concentrations from 2000 to 2023 and identify the influencing factors. Utilizing high-resolution PM<sub>2.5</sub> data from the ChinaHighPM<sub>2.5</sub> dataset and ERA5 meteorological data, the study employed Empirical Orthogonal Function (EOF) analysis to capture spatial variability and the Bayesian Estimator of Abrupt Change Seasonal and Trend (BEAST) to assess long-term trends and abrupt changes. The key findings include a marked seasonal pattern, with higher PM<sub>2.5</sub> levels during the winter months and lower concentrations in the summer, primarily driven by temperature, humidity, and precipitation. A significant decline in PM<sub>2.5</sub> levels was observed after 2014, following the implementation of pollution control measures. The study underscores the importance of continued environmental regulation and green technology adoption in mitigating air pollution in rapidly industrializing cities. This research provides a comprehensive analysis of PM<sub>2.5</sub> trends and highlights the critical role of natural and human factors, contributing valuable insights for policymakers and researchers aiming to improve air quality.https://www.mdpi.com/2072-4292/16/23/4495PM<sub>2.5</sub>Lianyungangspatiotemporal variationsChinaHighPM<sub>2.5</sub>EOF |
| spellingShingle | Xue Li Haihong He Dewei Wang Wenli Qiao Chunli Liu Yiming Sun Lulu Li Shuting Han Guozhen Zha Spatiotemporal Characteristics and Influencing Factors of PM<sub>2.5</sub> Levels in Lianyungang: Insights from a Multidimensional Analysis Remote Sensing PM<sub>2.5</sub> Lianyungang spatiotemporal variations ChinaHighPM<sub>2.5</sub> EOF |
| title | Spatiotemporal Characteristics and Influencing Factors of PM<sub>2.5</sub> Levels in Lianyungang: Insights from a Multidimensional Analysis |
| title_full | Spatiotemporal Characteristics and Influencing Factors of PM<sub>2.5</sub> Levels in Lianyungang: Insights from a Multidimensional Analysis |
| title_fullStr | Spatiotemporal Characteristics and Influencing Factors of PM<sub>2.5</sub> Levels in Lianyungang: Insights from a Multidimensional Analysis |
| title_full_unstemmed | Spatiotemporal Characteristics and Influencing Factors of PM<sub>2.5</sub> Levels in Lianyungang: Insights from a Multidimensional Analysis |
| title_short | Spatiotemporal Characteristics and Influencing Factors of PM<sub>2.5</sub> Levels in Lianyungang: Insights from a Multidimensional Analysis |
| title_sort | spatiotemporal characteristics and influencing factors of pm sub 2 5 sub levels in lianyungang insights from a multidimensional analysis |
| topic | PM<sub>2.5</sub> Lianyungang spatiotemporal variations ChinaHighPM<sub>2.5</sub> EOF |
| url | https://www.mdpi.com/2072-4292/16/23/4495 |
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