环境科学
污染物
微粒
空间分布
大气科学
污染
空气污染
空气污染物
空间变异性
空气质量指数
自然地理学
气象学
地理
化学
地质学
统计
有机化学
生物
遥感
数学
生态学
作者
Youru Yao,Cheng He,Shiyin Li,Wei Ma,Li Shu,Qi Yu,Na Mi,Jia Yu,Wei Wang,Li Yin,Zhang Yong
标识
DOI:10.1016/j.scitotenv.2019.01.026
摘要
Characteristics of the spatial and temporal distribution of air pollutants may reveal the cause of air pollution, especially for large regions where the anthropogenic pollutant emission is concentrated. This study addresses this issue by focusing on Shandong province, which has the highest air pollutant emissions in China. First, the spatial and temporal variation characteristics of the observed concentrations of conventional pollutants are analyzed in detail. The most prominent indicator of the problem (PM2.5), was selected as the key analytical object. On the spatial scale, the Multivariate Moran model was used to identify factors affecting the spatial distribution of PM2.5. On the time scale, wavelet analysis was used to explore the fluctuation characteristics of PM2.5 at different time periods. Results show that there are significant regional differences in pollutant concentration within Shandong province. The concentration of particulate matter and gaseous pollutants in western and northern Shandong is significantly higher than eastern Shandong. The average concentrations of PM2.5, PM10, SO2 and NO2 were highest in winter and lowest in summer, whereas concentration of O3 peaked in summer. For PM2.5, the annual mean concentration has a significant spatial correlation with SO2 emission, GDP per capita, population density and energy consumption per unit of GDP; in addition, the correlation between different regions and various indices is different. On the time scale, the fluctuation energy of PM2.5 concentrated in Dezhou and Liaocheng is the strongest on December 18 and 19, 2015. The inversion temperature has a strong influence on the daily variation of PM2.5 concentration. The formation and evolution of atmospheric pollution, therefore, can be explored by combining the temporal and spatial distribution of pollutants, providing a comprehensive analytical method for atmospheric pollution in different regions.
科研通智能强力驱动
Strongly Powered by AbleSci AI