摘要
利用2019年3月~2020年2月温州地区空气负氧离子浓度资料,分析其分布特征及与气象因素的相关性,采用多元回归方法建立负氧离子浓度预测模型。结果表明:泰顺的年均负氧离子浓度最高,其次为大罗山,这两个站均为高山站;平阳、洞头、乐清的负氧离子浓度较低,其中平阳最低;永嘉、温州、瑞安、文成、苍南的负氧离子浓度较为接近。乐清、大罗山、永嘉、平阳、瑞安负氧离子浓度以冬季最高,春季最低;温州、泰顺、洞头负氧离子浓度以夏季最高,春季最低;文成负氧离子浓度以冬季最高,夏季最低;苍南负氧离子浓度以秋季最高,春季最低。大罗山和苍南夜间负氧离子浓度较高,白天负氧离子浓度较低,最大值出现在早晨时段,最小值出现在中午时段,可选择在早晨开展康养旅游;泰顺和文成中午时段负氧离子浓度较高,早晨和傍晚负氧离子浓度较低,可选择在中午开展康养旅游。负氧离子浓度与雨量、风速呈正相关,与气温、湿度呈负相关。负氧离子预测模型预报能力较好,预测模型的建立实现了温州地区空气负氧离子浓度的定量化预报,对生态旅游气象服务有重要意义。
Based on the oxygen anion concentration data from March 2019 to February 2020 in Wenzhou, its distribution and the relationship with meteorological elements were analyzed. A forecasting model of oxygen anion concentration was established by a multiple regression analysis method. The results show that the annual average oxygen anion concentration in Taishun is the highest, followed by Daluoshan, and the two stations are both located in high mountain area;the oxygen anion concentrations in Pingyang, Dongtou and Yueqing are low, and the concentration in Pingyang is the lowest;the oxygen anion concentrations in Yongjia, Wenzhou urban area, Ruian, Wencheng and Cangnan are similar. The oxygen anion concentrations in Yueqing, Daluoshan, Yongjia, Pingyang and Ruian are the highest in winter and the lowest in spring;the oxygen anion concentrations in Wenzhou urban area, Taishun and Dongtou are the highest in summer and the lowest in spring;the oxygen anion concentration in Wencheng is the highest in winter and the lowest in summer;the oxygen anion concentration in Cangnan is the highest in autumn and the lowest in spring. The oxygen anion concentrations in Daluoshan and Cangnan are higher at night and lower in the daytime, with the maximum value appeared in the morning, and the minimum value appeared at noon, so the health tourism can be carried out in the morning in such areas;the oxygen anion concentrations in Taishun and Wencheng are higher at noon and lower in the morning or evening, so the health tourism can be carried out at noon in such areas. The oxygen anion concentration is in a positive correlation with rainfall and wind speed, and in a negative correlation with air temperature and humidity. The model could quantitatively predict the oxygen anion concentration in Wenzhou, which is important to ecological tourism meteorology service.
出处
《自然科学》
2020年第6期569-580,共12页
Open Journal of Nature Science
关键词
负氧离子浓度
气象因素
预测模型
Oxygen Anion Concentration
Meteorological Elements
Forecasting Model
