Characteristic and Spatiotemporal Variation of Air Pollution in Northern China Based on Correlation Analysis and Clustering Analysis of Five Air Pollutants. Issue 8 (22nd April 2020) - Tian, Dayong; Fan, Junhui; Jin, Hangbiao; Mao, Haichen; Geng, Dan; Hou, Shaogang; Zhang, Peng; Zhang, Yifeng
Abstract: Air quality in Northern China has become a global hot spot issue due to a series of air pollution events in the recent years. In this study, five representative air pollutants (PM2.5, SO2, NO2, CO, and O3 ) were employed to reveal the spatial and temporal distribution of air pollution in Northern China. Periodic decline in PM2.5, SO2, CO, and NO2 from 2016 to 2018 indicated that air pollution control measures have achieved desired results. In addition, PM2.5 was significantly positively correlated with SO2, CO, and NO2 ( p  < 0.001), and O3 had negative correlations with the other four pollutants. Furthermore, the heavy pollution phenomenon in Shijiazhuang, Anyang, Xingtai, and Handan was attributed to their industrial structures (e.g., steel industry) and geographical location based on clustering analysis. Contrary to above four pollutants, the annual average concentrations of O3 increased in all the nine cities (1.4–35.9%) from 2016 to 2018. It is necessary to plan mitigation strategy for O3 based on further investigation of source and formation mechanism of O3 . Plain Language Summary: Air pollution in Northern China has attracted many attentions due to continuous air pollution issues in the recent years. However, few studies focused on air pollution in the Beijing‐Tianjin‐Hebei transmission corridor. In this study, the spatiotemporal variation of air pollutants in nine typical cities was investigated. Pearson correlation analysis and clustering analysis were employed to analyze their relationship and reveal the pollution characteristic and the factors affecting the air pollution. This study provided an insight for the prevention and control of air pollution in Northern China. Key Points: Four pollutants (PM2.5, SO2, CO, and NO2 ) declined and O3 concentrations increased in the nine cities from 2016 to 2018 PM2.5 was positively correlated with SO2, CO, and NO2, while O3 had negative correlations with the other four pollutants Clustering analysis shows the air pollution in the cities is mainly related to their industrial structures besides geographical location