Random Forest Algorithm for the Relationship between Negative Air Ions and Environmental Factors in an Urban Park

作     者：Miao, Si Zhang, Xuyi Han, Yujie Sun, Wen Liu, Chunjiang Yin, Shan 

作者机构：Shanghai Jiao Tong Univ Sch Agr & Biol 800 Dongchuan Rd Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Res Ctr Low Carbon Agr 800 Dongchuan Rd Shanghai 200240 Peoples R China Natl Forestry & Grassland Adm Shanghai Urban Forest Ecosyst Res Stn 800 Dongchuan Rd Shanghai 200240 Peoples R China Minist Agr & Rural Affairs Key Lab Urban Agr 800 Dongchuan Rd Shanghai 200240 Peoples R China Shanghai Forest Stn 1053-7 Hutai Rd Shanghai 200072 Peoples R China 

出 版 物：《ATMOSPHERE》 (大气层)

年 卷 期：2018年第9卷第12期

页      面：463-463页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：National Key R&D Program of China [2017YFC0505501] National Natural Science Foundation of China Shanghai Landscaping and City Appearance Administrative Bureau [G171206] 

主　　题：negative air ion environmental influencing factor random forest algorithm effects ranking 

摘      要：Negative air ions (NAIs) are a natural component of air and have a positive impact on the health of urban residents. Few studies have focused on the relationship between NAI concentration (NAIC) in the urban atmosphere and environmental factors, such as meteorological factors and air pollutants. Therefore, we established observation points in Zhongshan Park in downtown Shanghai, China, and continuously measured and recorded changes in NAIC for one year. We also monitored nine meteorological factors and six atmospheric pollutants. Through correlation analysis and multiple linear regression analysis, the key factors influencing NAIC were screened, and the effects of those factors on NAIC were explored using the random forest algorithm. The results show that NAIC is most sensitive to humidity, followed by radiation and temperature, and finally to PM2.5. Humidity is the most critical factor, primarily because it directly affects the formation of NAIs from both the environment and vegetation. Furthermore, our results reveal that the mechanisms through which NAIC is influenced by the same factor varies seasonally. We analyzed the relationship between NAIC in an urban atmosphere and environmental factors by using big data, which is a new method for studying the relationships between NAIs and environmental factors. Our results indicate potential explanations for the mechanisms underlying NAI response to various environmental factors.