The concentration of negative air ions (NAIs) is an important indicator of air quality. Here, we analyzed the distribution patterns of negative air ion (NAI) concentrations at different time scales using statistical methods; then described the contribution of meteorological factors of the different season to the concentration of NAIs using correlation analysis and regression analysis; and finally made the outlook for the trends of NAI concentrations in the prospective using the auto regressive integrated moving average (ARIMA) models. The dataset of NAI concentrations and meteorological factors measured at the fixed stations in the Mountain Wuyi National Park were obtained from the Fujian Provincial Meteorological Bureau. The study showed that NAI concentrations were correlated with relative humidity spanning all seasons. Water was an important factor affecting the distribution of NAI concentrations in different time series. Compared with other ARIMA models, the outlook value of the ARIMA (0,1, 1) model was closer to the original data and the errors were smaller. This article provided a unique perspective on the study of the distribution of negative air oxygen ions over time series.

Precipitation (PRE) is an essential factor that affects the negative air ions (NAIs) concentrations. However, the mechanism of NAIs concentrations and their influencing factors on rainy and non-rainy days remains unclear. Here, we used hourly data of NAIs concentrations and meteorological data in 2019 to analyze the distribution of NAIs concentrations and its influencing factors on rainy and non-rainy days in the Wuyi Mountain National Park (WMNP) of China, which was listed as a World Cultural and Natural Heritage Site in 1999. The results indicated that the NAIs concentrations on rainy days were significantly higher than on non-rainy days. However, the NAIs concentrations on rainy days were slightly higher than on the first and second days after rainy days. Then, the NAIs concentrations were significantly reduced on the third day and after that. Thus, rainy days lead to a 2-day lag in the smooth reduction of NAIs on non-rainy days after rainy days. NAIs concentrations were significantly correlated with the relative humidity (RHU) on both rainy and non-rainy days. By analyzing the meteorological factors on NAIs for ranking the feature importance scores on rainy and non-rainy days, PRE was ranked first on rainy days, and sea level pressure (PRS_Sea) and temperature (TEM) were ranked first and second on non-rainy days, respectively. Based on the univariate linear regression model (ULRM), NAIs concentrations responded strongly (higher absolute slope values) to RHU on rainy days and to pressure (PRS), visibility (VIS), water vapor pressure (VAP), TEM, and ground surface temperature (GST) on non-rainy days. The results highlight the importance of PRE in the lag time of NAIs concentrations on rainy and non-rainy days.