Spatiotemporal Distribution Characteristics and Influencing Factors of Negative Air Ion (NAI) Concentrations in Yunnan
Abstract Negative air ions (NAIs) can directly reflect air quality, promote human physical and mental health, and possess significant ecological and practical value. To comprehensively investigate the spatiotemporal variation characteristics and influencing factors of negative air ion (NAI) concentr...
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| Format: | Article |
| Language: | English |
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Springer
2025-08-01
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| Series: | Aerosol and Air Quality Research |
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| Online Access: | https://doi.org/10.1007/s44408-025-00054-6 |
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| _version_ | 1849226575186231296 |
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| author | Xiaodong Dou Shengfang Hou Yinghua Shen Qiyang Peng Huajun Liu Qi Yi Yujie Liu |
| author_facet | Xiaodong Dou Shengfang Hou Yinghua Shen Qiyang Peng Huajun Liu Qi Yi Yujie Liu |
| author_sort | Xiaodong Dou |
| collection | DOAJ |
| description | Abstract Negative air ions (NAIs) can directly reflect air quality, promote human physical and mental health, and possess significant ecological and practical value. To comprehensively investigate the spatiotemporal variation characteristics and influencing factors of negative air ion (NAI) concentrations in Yunnan Province, based on one year of NAI concentration data from 88 automated monitoring stations across Yunnan, combined with meteorological data, pollutant levels (PM2.5, SO2, NO2), vegetation coverage (NDVI), and topographic data, this paper clarified for the first time the temporal variation characteristics of NAI concentration in Yunnan Province and its influencing factors. Stepwise linear regression and random forest models were employed to construct NAI concentration estimation models suitable for Yunnan Province and compare the model predictive performance, revealed the NAI concentration distribution characteristics of Yunnan Province from the temporal and spatial scales for the first time. The results show that NAI concentrations in Yunnan Province has a significant temporal distribution pattern, presenting “dual-peak-dual-trough” and a “single-peak-single-trough” characteristics on the hourly and monthly scales respectively, and the concentration in the rainy season is generally higher than that in the dry season. In terms of influencing factors, NAI is affected by multiple factors. NAI concentrations were positively correlated with Precipitation and NDVI and negatively correlated with PM2.5 and NO2. PM2.5 was identified as the primary nonlinear influencing factor, with variable importance ranked as: PM2.5 > precipitation > elevation > temperature > humidity > NDVI > SO2 > NO2. In terms of spatial distribution, NAI concentrations showed a "topography-dominated + mountain range demarcation" characteristic, with the highest concentrations in the Hengduan Mountains of northwest Yunnan. East and north of the Ailao Mountains exhibited lower concentrations, transitioning to higher levels westward and southward, forming a distinct gradient across the mountain ranges. This study systematically revealed the spatiotemporal distribution characteristics of NAI concentrations in Yunnan Province for the first time, enriched the understanding of the changing patterns and influencing factors of NAI, and provided substantial reference for regional ecological civilization construction and sustainable development. Graphical Abstract |
| format | Article |
| id | doaj-art-653502ddc6f14c0391bc09e2ac5a4736 |
| institution | Kabale University |
| issn | 1680-8584 2071-1409 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Aerosol and Air Quality Research |
| spelling | doaj-art-653502ddc6f14c0391bc09e2ac5a47362025-08-24T11:13:15ZengSpringerAerosol and Air Quality Research1680-85842071-14092025-08-0125911410.1007/s44408-025-00054-6Spatiotemporal Distribution Characteristics and Influencing Factors of Negative Air Ion (NAI) Concentrations in YunnanXiaodong Dou0Shengfang Hou1Yinghua Shen2Qiyang Peng3Huajun Liu4Qi Yi5Yujie Liu6Yunnan Meteorological Service CenterSchool of Earth Sciences, Yunnan UniversityYunnan Meteorological Service CenterYunnan Meteorological Service CenterSchool of Earth Sciences, Yunnan UniversitySchool of Earth Sciences, Yunnan UniversitySchool of Earth Sciences, Yunnan UniversityAbstract Negative air ions (NAIs) can directly reflect air quality, promote human physical and mental health, and possess significant ecological and practical value. To comprehensively investigate the spatiotemporal variation characteristics and influencing factors of negative air ion (NAI) concentrations in Yunnan Province, based on one year of NAI concentration data from 88 automated monitoring stations across Yunnan, combined with meteorological data, pollutant levels (PM2.5, SO2, NO2), vegetation coverage (NDVI), and topographic data, this paper clarified for the first time the temporal variation characteristics of NAI concentration in Yunnan Province and its influencing factors. Stepwise linear regression and random forest models were employed to construct NAI concentration estimation models suitable for Yunnan Province and compare the model predictive performance, revealed the NAI concentration distribution characteristics of Yunnan Province from the temporal and spatial scales for the first time. The results show that NAI concentrations in Yunnan Province has a significant temporal distribution pattern, presenting “dual-peak-dual-trough” and a “single-peak-single-trough” characteristics on the hourly and monthly scales respectively, and the concentration in the rainy season is generally higher than that in the dry season. In terms of influencing factors, NAI is affected by multiple factors. NAI concentrations were positively correlated with Precipitation and NDVI and negatively correlated with PM2.5 and NO2. PM2.5 was identified as the primary nonlinear influencing factor, with variable importance ranked as: PM2.5 > precipitation > elevation > temperature > humidity > NDVI > SO2 > NO2. In terms of spatial distribution, NAI concentrations showed a "topography-dominated + mountain range demarcation" characteristic, with the highest concentrations in the Hengduan Mountains of northwest Yunnan. East and north of the Ailao Mountains exhibited lower concentrations, transitioning to higher levels westward and southward, forming a distinct gradient across the mountain ranges. This study systematically revealed the spatiotemporal distribution characteristics of NAI concentrations in Yunnan Province for the first time, enriched the understanding of the changing patterns and influencing factors of NAI, and provided substantial reference for regional ecological civilization construction and sustainable development. Graphical Abstracthttps://doi.org/10.1007/s44408-025-00054-6Negative air ionsSpatial and temporal dynamicsEnvironmental factorsRandom forest modelStepwise linear regression |
| spellingShingle | Xiaodong Dou Shengfang Hou Yinghua Shen Qiyang Peng Huajun Liu Qi Yi Yujie Liu Spatiotemporal Distribution Characteristics and Influencing Factors of Negative Air Ion (NAI) Concentrations in Yunnan Aerosol and Air Quality Research Negative air ions Spatial and temporal dynamics Environmental factors Random forest model Stepwise linear regression |
| title | Spatiotemporal Distribution Characteristics and Influencing Factors of Negative Air Ion (NAI) Concentrations in Yunnan |
| title_full | Spatiotemporal Distribution Characteristics and Influencing Factors of Negative Air Ion (NAI) Concentrations in Yunnan |
| title_fullStr | Spatiotemporal Distribution Characteristics and Influencing Factors of Negative Air Ion (NAI) Concentrations in Yunnan |
| title_full_unstemmed | Spatiotemporal Distribution Characteristics and Influencing Factors of Negative Air Ion (NAI) Concentrations in Yunnan |
| title_short | Spatiotemporal Distribution Characteristics and Influencing Factors of Negative Air Ion (NAI) Concentrations in Yunnan |
| title_sort | spatiotemporal distribution characteristics and influencing factors of negative air ion nai concentrations in yunnan |
| topic | Negative air ions Spatial and temporal dynamics Environmental factors Random forest model Stepwise linear regression |
| url | https://doi.org/10.1007/s44408-025-00054-6 |
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