关键词: 空气负离子, 空气湿度, 估算模型, 气象因子

Abstract: Negative air ion is an important indicator of measuring air cleanliness in an area, and it plays an important role in promoting the psychological and physiological functions of the human body. With the rise of forest eco-tourism, the produce process and influence mechanism of negative air ion have become research hotspots in related fields such as biometeorology, forest ecology, and forest health. In this study, the Quercus variabilis plantation in the hilly area of North China was taken as the experimental object. The negative air ions and micrometeorological parameters of the canopy were obtained by positioning observation under the condition of relatively constant leaf area of forest from June to September in 2018 and 2019, respectively. Python software was used to screen out the observation data under the condition that the photosynthetically active radiation is about zero and the temperature, wind speed, and pollutant concentration were relatively constant. The impact of relative air humidity on negative air ions was analyzed. The results show that negative air ion present three changing trends with the increase of air humidity, which is relatively stable within the range from 35% to 55% of relative air humidity; rapidly increase within the range from 55% to 75% of relative air humidity, represents a linearly increasing relationship; moderately decrease within the range of 75% to 95% of relative air humidity, represents a linear decreasing relationship. Based on this, the piecewise fitting equations of negative air ion and air relative humidity are constructed as NAI=729 (RH35%−55%); NAI=9.396RH+198.994 (RH55%−75%), and the coefficient of determination (R2) is 0.807 (P<0.01); NAI=−4.849RH+1232.992 (RH75%−95%), and the coefficient of determination (R2) is 0.642 (P<0.01). There is no found a significant difference between the measured value and predicted value of the constructed piecewise fitting function through the analysis and comparison. The root means square error (RMSE) is 6.175, 7.091, and 8.213, respectively, while the coefficient of determination (R2) is 0.806 and 0.836 within RH55%−75% and RH75%−95%, respectively. The accuracy of the model is high and the root means square error is small. Therefore, the piecewise fitting function constructed in this study can accurately reflect the impact of air humidity on negative air ion, thereby providing a working foundation for further research on the response mechanism of negative air ion to meteorological changes.

Key words: Negative air ions, Air humidity, Estimation model, Meteorological factor