Importance evaluation based on random forest algorithms: Insights into the relationship between negative air ions variability and environmental factors in urban green spaces

Abstract

Negative air ions (NAIs) exert positive effects on human health. Urban green spaces produce NAIs and perform valuable ecological functions; this phenomenon has attracted much attention. However, NAIs in urban green spaces are influenced by many factors, leading to extremely large variability in their concentrations and complicating their measurement. Therefore, we collected observational data on NAI concentrations (NAICs), as well as on other environmental factors for one year in Shanghai City Park. We then used this data to construct an indicator of NAI variability (NAIV); we understand NAIV to be dependent upon NAIC, and study of the derivative can better reflect the driving force and dominant factors of the original function. Based on a preliminary investigation of correlation, and on a multiple linear regression analysis, we used a random forest algorithm to evaluate the influence of various factors that affect the variability of NAIs. The results show that "water factors, " whose main contribution is humidity, exert the most influence, followed by "phenology factors, " whose main contribution is temperature, and "particulate factors, " whose main contribution is PM2.5. High humidity, high temperature, and low PM2.5 concentration enrich NAI generation and extend their lifetimes, thus helping to maintain them within a relatively stable range. In this study, the main driving forces that govern NAI changes were shown to be humidity, temperature and particulate matter. Our results may help to deepen our understanding of NAI characteristics and applications in urban green spaces.