摘要
基于2018—2020年湖北省11个城市空气质量监测数据以及气象数据,采用固定效应和面板分位数回归方法,探讨了AQI的变化特征及其与污染物浓度和气象因素之间的关系。结果表明:污染物中PM_(2.5)、PM_(10)、NO_(2)和O_(3)与AQI均呈正相关,且分位数较高时,影响作用较大;SO_(2)与AQI呈负相关,且在任何分位点下影响均显著;CO对AQI具有双重影响,低分位点呈负相关,而高分位点呈正相关。气象因素中,平均气压、相对湿度、最大风速和风向与AQI呈负相关,其中风向在0.1~0.5分位点下影响较为显著,大型蒸发量在各分位点下与AQI均呈显著正相关,日照时长仅在0.1和0.25分位点下对AQI显著的正向影响。
Based on the air quality monitoring data and meteorological data of eleven cities in Hubei Province from 2018 to 2020,fixed effects and panel quantile regression methods were used to explore the changing characteristics of AQI and its relationship with pollutant concentrations and meteorological factors.The results show that:PM_(2.5),PM_(10),NO_(2) and O_(3) of pollutants were positively correlated with AQI,and the effect is greater when the quantile is higher,SO_(2) is negatively correlated with AQI and has a significant impact at any quantile,CO has a dual effect on AQI,with low quantile points being negatively correlated and high quantile points being positively correlated;Among meteorological factors,average air pressure,relative humidity,maximum wind speed,and wind direction are negative⁃ly correlated with AQI.Wind direction has a significant influence at the 0.1 to 0.5 quantile,and large-scale evapora⁃tion is significantly positively correlated with AQI at each quantile.The duration of sunshine only had a significant positive effect on the AQI at the 0.1 and 0.25 quantile.
作者
姚美
YAO Mei(School of Science,Hubei Univ.of Tech.,Wuhan 430068,China)
出处
《安徽农学通报》
2022年第2期147-151,181,共6页
Anhui Agricultural Science Bulletin
关键词
AQI
气象因素
分位数回归
面板数据
AQI
Meteorological factor
Quantile regression
Panel data
