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

Abstract

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, R2 of 0.878 for model training, and R2 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.

Temporal Dynamics of Negative Air Ion Concentrations in Nanjing Tulou Scenic Area

“…However, August had the highest monthly concentrations, primarily because August's weather was favorable-high air temperature and relative humidity are ideal for producing NAIs [28]. Conversely, human gatherings and activities can disrupt the generation and dispersion of NAIs, leading to irregular fluctuations in NAI concentrations [19,28].…”

“…A correlation between NAI concentrations and these meteorological factors (Figure 8) during this period resulted in a noteworthy decrease in NAI concentrations compared to other seasons. Reduced population flow decreases pollutant emissions, promoting smoother NAI generation and dispersion, potentially leading to higher concentrations [3,15,19]. Lin et al (2022) correlated tourism climate comfort with passenger flow using meteorological data from 2017 to 2019.…”

“…NAI concentrations were influenced by the combination of multiple factors, including meteorological, environmental, and vegetation types; thus, different regions exhibit various in NAI concentrations. Most previous studies have used portable recorders and semi-fixed monitoring devices, which may offer limited data sample sizes [6,19] and lacks continuous recording, making it challenging to explore the daily variations in NAI concentrations. Therefore, adopting long-term on-site monitoring could provide a better understanding of the dynamic distribution of NAI concentrations.…”

Relative Humidity Dominances in Negative Air Ion Concentration: Insights from One–Year Measurements of Urban Forests and Natural Forests

“…The sources and influencing factors of NAIs are intricate and variable, constrained by environmental factors such as climatic conditions [12], vegetation types [11], and geographical conditions [13,14]. NAIs can derive from diverse natural electrical phenomena including lightning, thunderstorms, and plant leaf tips [15,16].…”