摘要
气象因素对城市空气污染具有重要影响。分析不同季节PM10质量浓度变化与气象因子之间的关系,建立模型进行颗粒物污染预测,可以为污染物治理提供科学依据。为了解兰州市PM10污染特征,2011年1月─2011年12月对兰州市可吸入颗粒物(PM10)进行了为期1年的监测,并利用监测数据和同期气象观测数据,分析了PM10的质量浓度与气象因素之间的相关性。结果表明:PM10的质量浓度与温度呈现负相关关系,温度越高,PM10质量浓度越低。当风向为NW和NNW时,PM10污染相对较轻;而当风向为NE和ENE时,PM10污染比较严重。兰州市属于典型的河谷城市,四面环山,气流闭塞,风速过小导致城区大气污染物不利于向城区外扩散。PM10的质量浓度与气压呈正相关,兰州市冬季气压较高,PM10质量浓度较大;夏季气压较低,PM10质量浓度较低。降水能够对环境空气中污染物起到清除和冲刷作用,对可吸入颗粒物去除作用显著。PM10在无降水日的平均质量浓度为263.47μg·m-3,所有降水日的PM10平均质量浓度为171.71μg·m-3,比无降水日降低34.83%。
Meteorological effect and relationship between the mass concentration of PM10 can provide scientific information for pollution control. To understand the characteristics of PM10 pollution in Lanzhou city, the PM10 mass concentrations are monitored from January 2011 to December 2011. Based on the data of PM10 and meteorological data from Jan. 2011 to Dec. 2011 in Lanzhou, the influence of meteorological conditions on concentration levels of PM10 was investigated using correlation analysis. The results show that PM10 concentrations were significantly negative correlated with temperature; the higher the temperature, the lower the concentration of PM10 would be. Wind direction for NW and NNW, PM10 pollution was relatively light; the wind of NE and ENE, PM10 pollution was more severe. Wind direction for NE and ENE, PM10 pollution was more severe. Lanzhou is a typical valley city and the wind speed is too mild leading to urban atmospheric pollutants to spread outside the city. The concentrations of PM10 and air pressure were positively correlated. Precipitation could remove and wash PM10 and the effect was very significant. The average concentration of PM10 was 263.47 μg·m-3 in the days of no rain and the average concentration was 171.71 μg·m-3 in the days of rain. The mass concentration of PM10 in the days of rain was 34.83% lower than that of no rains days.
出处
《生态环境学报》
CSCD
北大核心
2016年第1期99-102,共4页
Ecology and Environmental Sciences
基金
甘肃省科技攻关项目(1204FKCA130)
半干旱气候变化教育部重点实验室(兰州大学)开放课题项目
关键词
可吸入颗粒物
质量浓度水平
气象因素
相关性分析
PM10
mass concentration level
meteorological conditions
correlation analysis
