文献类型：期刊文献

英文题名：Negative Air Ion (NAI) Dynamics over Zhejiang Province, China, Based on Multivariate Remote Sensing Products

作者：Tao, Sichen[1,2] Sun, Zongchen[1] Lin, Xingwen[1] Zhang, Zhenzhen[1] Wu, Chaofan[1] Zhang, Zhaoyang[1] Zhou, Benzhi[3,4] Zhao, Zhen[1] Cao, Chenchen[1] Guan, Xinyu[1] Zhuang, Qianjin[5] Wen, Qingqing[5] Xu, Yuling[6]

第一作者：Tao, Sichen

通信作者：Zhang, ZZ[1]

机构：[1]Zhejiang Normal Univ, Coll Geog & Environm Sci, Jinhua 321004, Peoples R China;[2]Ningbo Educ Bur, Zhenhai Luotuo Middle Sch, Ningbo 315202, Peoples R China;[3]Chinese Acad Forestry, Res Inst Subtrop Forestry, Hangzhou 311400, Peoples R China;[4]Natl Forestry & Grassland Adm, Qianjiangyuan Forest Ecosyst Res Satat, Hangzhou 311400, Peoples R China;[5]Nanshan Prov Nat Reserve Management Ctr, Jinhua 321000, Peoples R China;[6]Zhejiang Jinhua Ecol & Environm Monitoring Ctr, Jinhua 321000, Peoples R China

年份：2023

卷号：15

期号：3

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20230713592987);Scopus(收录号：2-s2.0-85147977441);WOS:【SCI-EXPANDED(收录号:WOS:000931026200001)】；

基金：This research was funded by the “Jinhua Science and Technology Research Program, grant numbers 2022-4-036, 2021-4-341“ and the “Department of Science and Technology of Zhejiang Province in China, “Pioneer” and “Bellwethers” R & D projects, grant number 2022C03119”.

语种：英文

外文关键词：negative air ions; Random Forest; bio-geophysical parameters; spatial-temporal distributions; multivariate remote sensing data

摘要：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-2 of 0.878 for model training, and R-2 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(-3)), 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.