北京市道路防护林带内外PM2.5质量浓度特征及与气象要素的相关性

以北京市奥林匹克森林公园北侧的北五环路防护林带为研究对象,通过在林带外及林带内布设5处PM2.5质量浓度监测点进行3个月的定位监测,分析北京市道路防护林带内外PM2.5质量浓度特征,并通过建立PM2.5质量浓度与风速、温度、相对湿度等气象要素的相关关系,分析气象要素对PM2.5质量浓度的相关性.结果表明：1）在时间变化规律上,林带内PM2.5质量浓度08：00和12：00相对较高,其他时间段相对较低,18：00后质量浓度又开始增加,林带内PM2.5质量浓度在日变化规律上呈单峰或双峰变化,林带内外PM2.5质量浓度的日变化规律基本一致;2）在空间变化规律上,林带内PM2.5质量浓度水平比林带外相对较高,距北五环路40 m处PM2.5质量浓度值相对其他布置点2、20、30和60m处都高;3）林带内PM2.5质量浓度与风速、温度呈负相关关系,与相对湿度、气压呈正相关关系;4）林带内PM2.5质量浓度与温度的关系可以用幂函数来描述.

Correlation analysis between PM2.5 mass concentration characteristics and meteorological elements inside and outside the shelterbelts for typical roads in Beijing

The shelter forest belts adjacent to the north fifth ring road in the northern part of the Beijing Olympic forest park were chosen as the research object,five monitoring points were set up inside and outside the shelterbelts to locate and monitor for three months.The temporal and spatial variation of PM2.5 mass concentration was analyzed inside and outside the shelterbelts at the same time,and the correlations were established among PM2.5 mass concentration and wind speed,temperature,relative humidity and other weather conditions,for a purpose of analyzing the effect of various meteorological factors on PM2.5 mass concentration.The results showed that：1） as for the temporal variation,PM2.5 mass concentration inside the shelterbelts presented a single-peak or bimodal distribution （peaks at 08：00 and 12：00,respectively,and the mass concentration was lower at other time）,and increased significantly only about 18：00 p.m..There was roughly a similar pattern of diurnal variation of PM2.5 mass concentration inside and outside the shelterbelts.2） The PM2.5 mass concentration inside the shelterbelts was higher than outside,and that at 40 m away from north five ring road inside the shettbelts was relatively higher than other locations.3） The mass concentrations of PM2.5 is significantly and negatively correlated with temperature and wind speed,but positively correlated with air pressure as well as relative humidity;the correlation between PM2.5 mass concentration and temperature can be expressed by a power function.