Negative Air Ion (NAI) Dynamics over Zhejiang Province, China, Based on Multivariate Remote Sensing Products
Negative air ions (NAIs), which are known as the “air vitamin”, have been widely used as a measure of air cleanness. Field observation provides an alternative way to record site-level NAIs. However, these observations fail to capture the regional distribution of NAIs due to the limited number of sit...
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| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/3/738 |
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| author | Sichen Tao Zongchen Sun Xingwen Lin Zhenzhen Zhang Chaofan Wu Zhaoyang Zhang Benzhi Zhou Zhen Zhao Chenchen Cao Xinyu Guan Qianjin Zhuang Qingqing Wen Yuling Xu |
| author_facet | Sichen Tao Zongchen Sun Xingwen Lin Zhenzhen Zhang Chaofan Wu Zhaoyang Zhang Benzhi Zhou Zhen Zhao Chenchen Cao Xinyu Guan Qianjin Zhuang Qingqing Wen Yuling Xu |
| author_sort | Sichen Tao |
| collection | DOAJ |
| description | Negative air ions (NAIs), which are known as the “air vitamin”, have been widely used as a measure of air cleanness. Field observation provides an alternative way to record site-level NAIs. However, these observations fail to capture the regional distribution of NAIs due to the limited number of sites. In this study, satellite-based bio-geophysical parameters from the climate, topography, air quality, vegetation, and anthropogenic intensity were used to estimate the daily NAIs with the Random Forest model (RF). In situ NAI observations over Zhejiang Province, China were incorporated into the model. Daily NAIs were averaged to capture the spatio-temporal distribution. The results showed that (1) the RF algorithm performed better than traditional regression analysis and the common BP neural network to generate regional NAIs at a spatial scale of 500 m over the larger scale, with an RMSE of 258.62, R<sup>2</sup> of 0.878 for model training, and R<sup>2</sup> of 0.732 for model testing; (2) in the variable importance measures (VIM) analysis, 87.96% of the NAI variance was caused by the elevation, aspect, slope, surface temperature, solar-induced chlorophyll fluorescence (SIF), relative humidity (RH), and the concentration of carbon monoxide (CO), while path analysis indicated that SIF was one of the most important factors affecting NAI concentration across the whole region; (3) NAI concentrations in 87.16% of the region were classified above grade III (>500 ions cm<sup>−3</sup>), which was able to meet the needs of human health maintenance; (4) the highest NAI concentration was distributed over the southwest of the Zhejiang Province, where forest land dominates. The lowest NAI concentration was mostly found in the northeast regions, where urban areas are well-developed; and (5) among different land types, the NAI concentrations were ranked as forest land > water bodies > barren > grassland > croplands > urban and built-up. Among different seasons, summer and winter have the highest and lowest NAIs, respectively. Our study provided a substantial reference for ecosystem services assessment in Zhejiang Province. |
| first_indexed | 2024-03-11T09:27:19Z |
| format | Article |
| id | doaj.art-82e78c314db544b3925e4da53cff1f37 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T09:27:19Z |
| publishDate | 2023-01-01 |
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| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-82e78c314db544b3925e4da53cff1f372023-11-16T17:53:32ZengMDPI AGRemote Sensing2072-42922023-01-0115373810.3390/rs15030738Negative Air Ion (NAI) Dynamics over Zhejiang Province, China, Based on Multivariate Remote Sensing ProductsSichen Tao0Zongchen Sun1Xingwen Lin2Zhenzhen Zhang3Chaofan Wu4Zhaoyang Zhang5Benzhi Zhou6Zhen Zhao7Chenchen Cao8Xinyu Guan9Qianjin Zhuang10Qingqing Wen11Yuling Xu12College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, ChinaResearch Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou 311400, ChinaCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, ChinaNanshan Provincial Nature Reserve Management Center, Jinhua 321000, ChinaNanshan Provincial Nature Reserve Management Center, Jinhua 321000, ChinaZhejiang Jinhua Ecological and Environmental Monitoring Center, Jinhua 321000, ChinaNegative air ions (NAIs), which are known as the “air vitamin”, have been widely used as a measure of air cleanness. Field observation provides an alternative way to record site-level NAIs. However, these observations fail to capture the regional distribution of NAIs due to the limited number of sites. In this study, satellite-based bio-geophysical parameters from the climate, topography, air quality, vegetation, and anthropogenic intensity were used to estimate the daily NAIs with the Random Forest model (RF). In situ NAI observations over Zhejiang Province, China were incorporated into the model. Daily NAIs were averaged to capture the spatio-temporal distribution. The results showed that (1) the RF algorithm performed better than traditional regression analysis and the common BP neural network to generate regional NAIs at a spatial scale of 500 m over the larger scale, with an RMSE of 258.62, R<sup>2</sup> of 0.878 for model training, and R<sup>2</sup> of 0.732 for model testing; (2) in the variable importance measures (VIM) analysis, 87.96% of the NAI variance was caused by the elevation, aspect, slope, surface temperature, solar-induced chlorophyll fluorescence (SIF), relative humidity (RH), and the concentration of carbon monoxide (CO), while path analysis indicated that SIF was one of the most important factors affecting NAI concentration across the whole region; (3) NAI concentrations in 87.16% of the region were classified above grade III (>500 ions cm<sup>−3</sup>), which was able to meet the needs of human health maintenance; (4) the highest NAI concentration was distributed over the southwest of the Zhejiang Province, where forest land dominates. The lowest NAI concentration was mostly found in the northeast regions, where urban areas are well-developed; and (5) among different land types, the NAI concentrations were ranked as forest land > water bodies > barren > grassland > croplands > urban and built-up. Among different seasons, summer and winter have the highest and lowest NAIs, respectively. Our study provided a substantial reference for ecosystem services assessment in Zhejiang Province.https://www.mdpi.com/2072-4292/15/3/738negative air ionsRandom Forestbio-geophysical parametersspatial–temporal distributionsmultivariate remote sensing data |
| spellingShingle | Sichen Tao Zongchen Sun Xingwen Lin Zhenzhen Zhang Chaofan Wu Zhaoyang Zhang Benzhi Zhou Zhen Zhao Chenchen Cao Xinyu Guan Qianjin Zhuang Qingqing Wen Yuling Xu Negative Air Ion (NAI) Dynamics over Zhejiang Province, China, Based on Multivariate Remote Sensing Products Remote Sensing negative air ions Random Forest bio-geophysical parameters spatial–temporal distributions multivariate remote sensing data |
| title | Negative Air Ion (NAI) Dynamics over Zhejiang Province, China, Based on Multivariate Remote Sensing Products |
| title_full | Negative Air Ion (NAI) Dynamics over Zhejiang Province, China, Based on Multivariate Remote Sensing Products |
| title_fullStr | Negative Air Ion (NAI) Dynamics over Zhejiang Province, China, Based on Multivariate Remote Sensing Products |
| title_full_unstemmed | Negative Air Ion (NAI) Dynamics over Zhejiang Province, China, Based on Multivariate Remote Sensing Products |
| title_short | Negative Air Ion (NAI) Dynamics over Zhejiang Province, China, Based on Multivariate Remote Sensing Products |
| title_sort | negative air ion nai dynamics over zhejiang province china based on multivariate remote sensing products |
| topic | negative air ions Random Forest bio-geophysical parameters spatial–temporal distributions multivariate remote sensing data |
| url | https://www.mdpi.com/2072-4292/15/3/738 |
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