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当前位置: 首页 > 文章 > 海南省主要景区负氧离子浓度分布及预报方程研究 安徽农业科学 2016,44 (29) 189-191+208
Position: Home > Articles > Study on the Distribution of Negative Oxygen Ion Concentration and Forecast Equation of Main Scenic Spots in Hainan Province Journal of Anhui Agricultural Sciences 2016,44 (29) 189-191+208

海南省主要景区负氧离子浓度分布及预报方程研究

作  者：
赵蕾;吴坤悌;陈明
单  位：
海南省气象服务中心
关键词：
负氧离子;变化特征;相关分析;逐步回归;海南省
摘  要：
[目的]研究海南省主要景区负氧离子浓度分布及预报方程。[方法]利用2010年2月—2011年12月海南省5个景区负氧离子浓度观测点实况资料,分析其时空变化规律及与气象要素的相关特征,并采用逐步回归方法建立负氧离子浓度预测模型。[结果]空气负氧离子浓度有明显的日变化,夜间高、白天低,最大值出现在早晨,最小值出现在中午,白天空气负氧离子浓度变化波动较大,夜晚变化平缓。负氧离子浓度与气温呈负相关,与相对湿度呈正相关。5个测点负氧离子浓度的逐步回归方程中,海口火山口和三亚南山预报模型通过了回归方程和回归系数检验,负氧离子预测模型预报能力较好。[结论]该研究可为旅游气象服务提供理论依据。
译  名：
Study on the Distribution of Negative Oxygen Ion Concentration and Forecast Equation of Main Scenic Spots in Hainan Province
作  者：
ZHAO Lei;WU Kun-ti;CHEN Ming;Hainan Meteorological Service Center;
关键词：
Negative oxygen ion;;Variation features;;Correlation analysis;;Stepwise regression;;Hainan Province
摘  要：
[Objective] The aim was to study the distribution of negative oxygen ion concentration and forecast equation of main scenic spots in Hainan Province. [Method]Based on the observation data of negative oxygen ion concentration in five scenic spots in Hainan Province from Feb.in 2010 to Dec. in 2011,the temporal and spatial variation and its correlation with meteorological factors were analyzed,the stepwise regression equation was adopted to establish forecast model of negative oxygen ion concentration. [Result]The concentration of air negative oxygen ion varied within a day,the maximum value appeared in the morning and the minimum appeared at noon,the concentration of negative oxygen ion fluctuated significantly during daytime while changed gently at night. Negative oxygen ion concentration was negatively correlated with air temperature,and was positively correlated with relative humidity. The forecast model of Haikou Huoshankou and Sanya Nanshan were tested by regression equation and regression coefficient,and the ability of negative oxygen ion prediction model was good. [Conclusion] The study can provide theoretical basis for tourism meteorological service.

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