中国农业气象 ›› 2021, Vol. 42 ›› Issue (01): 24-33.doi: 10.3969/j.issn.1000-6362.2021.01.003

• 农业生态环境栏目 • 上一篇    下一篇

自然状态下栓皮栎人工林空气负离子浓度与相对湿度的关系

施光耀,桑玉强,张劲松,孟平,蔡露露,裴松义   

  1. 1.中国林业科学研究院林业研究所/国家林业局林木培育重点实验室,北京 100091;2.南京林业大学南方现代林业协同创新中心,南京 210037; 3.河南农业大学,郑州 450002; 4.河南省地球物理空间信息研究院,郑州 450016; 5.国有建平县黑水机械化林场,朝阳 122000
  • 收稿日期:2021-01-17 出版日期:2021-01-20 发布日期:2021-01-17
  • 通讯作者: 张劲松,研究员,研究方向为林业气象,E-mail:zhangjs@caf.ac.cn E-mail:zhangjs@caf.ac.cn
  • 作者简介:施光耀,E-mail:shiguangyao01@163.com
  • 基金资助:
    中央级公益性科研院所基本科研业务费专项资金项目(CAFYBB2018ZA002)

Relationship between Negative Air Ion and Relative Humidity in Quercus variabilis Plantation under Natural Conditions

SHI Guang-yao, SANG Yu-qiang, ZHANG Jin-song, MENG Ping, CAI Lu-lu, PEI Song-yi   

  1. 1. Research Institute of Forestry, Chinese Academy of Forestry/Key Laboratory of Tree Breeding and Cultivation, State Forestry Administration, Beijing 100091, China; 2. Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037; 3. Henan Agricultural University, Zhengzhou 450002; 4. Henan Geophysical Space Information Research Institute, Zhengzhou 450016; 5. State Owned Jianping County Heishui Mechanized Forest Farm, Chaoyang 122000
  • Received:2021-01-17 Online:2021-01-20 Published:2021-01-17

摘要: 空气负离子是衡量一个地区空气清洁度的重要指标,对人体的心理和生理机能具有重要的促进作用。随着森林生态旅游的兴起,空气负离子的发生过程及影响机制已成为生物气象、森林生态和森林康养等相关领域的研究热点。本研究以华北低丘山地栓皮栎人工林为试验对象,在2018年和2019年6−9月森林植被叶面积相对不变期间,定位观测获取人工林冠层空气负离子及微气象参数,采用Python软件筛选出光合有效辐射约为零,温度、风速及颗粒物浓度相对不变条件下的观测数据,分析空气湿度(RH)对空气负离子浓度(NAI)的影响特征,建立基于空气相对湿度的预测模型,并对模型进行检验。结果表明,在不同空气相对湿度范围内,空气负离子浓度随空气湿度的升高呈现三种变化趋势,在空气相对湿度35%~55%范围内,空气负离子浓度相对稳定,二者呈稳定常数关系;在相对湿度55%~75%范围内,空气负离子浓度迅速上升,二者呈线性递增关系;在相对湿度75%~95%范围内,空气负离子浓度适度下降,二者呈线性递减关系。在此基础上,构建了空气负离子浓度与空气相对湿度的分段拟合方程,3个湿度区间分别为NAI=729;NAI=9.396RH+198.994,决定系数(R2)为0.807(P<0.01);以及NAI=−4.849RH+1232.992,决定系数(R2)为0.642(P<0.01)。各拟合函数的预测值与实测值均不存在显著差异,均方根误差(RMSE)分别为6.175、7.091、8.213,而RH在55%~75%和75%~95%范围内决定系数(R2)分别为0.806、0.836,模型的模拟精度高且均方根误差较小。说明构建的分段拟合函数能够准确反映空气相对湿度对空气负离子浓度的影响,可为进一步深入研究空气负离子对气候变化的响应机制提供基础依据。

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