Spatiotemporal Distribution of Air Pollution Characteristics in Jiangsu Province, China

Abstract

Following the deepening of climate change and the increasing industrialization in recent years, the problem of air pollution in cities has become increasingly prominent. Based on the data of air pollutants and meteorological elements in Jiangsu Province, China (2013–2017), we analyze the spatiotemporal characteristics of air pollution. The results show that the air-quality index (AQI) in Jiangsu Province decreased from 2013 to 2017 and the highest AQI is in winter and the lowest in the summer, while its values in coastal cities of Jiangsu are less than those of inland cities. For the temporal distribution of primary pollutants, PM2.5, PM10, SO2, NO2, and CO present the same trend under seasonal and monthly time scales, i.e., winter is higher and summer is lower; however, the other secondary pollutant, O3, presents opposite characteristics under the same time scale: it has higher concentration levels in summer and lower in winter. For the spatial distribution, PM2.5 and PM10 are in good concord: the higher values are found in the west of Jiangsu Province and lower in the east. For the spatial distribution of NO2, this presents higher concentrations in south and lower concentrations in north according to the position of Yangzi River, while the distribution of O3 concentration is opposite to that of NO2. The meteorological elements selected are related to air pollution, the AQI is significantly negatively correlated with monthly temperature (including average, minimum, and maximum temperatures), monthly average water vapor pressure, monthly precipitation, and monthly sunshine duration; the correlation coefficients are –0.852, –0.846, –0.850, –0.797, –0.727, and –0.599, respectively. As far as the relationships between air pollutions are concerned, there is a significant positive correlation between AQI, PM2.5, PM10, SO2, and NO2, while O3 is remarkably negatively correlated with other pollutants and AQI. The most prominent correlations are distinguished into two groups: one is AQI, PM2.5, and PM10, with correlation coefficients of 0.876 and 0.966, and the other is SO2, NO2, with correlation coefficient of about 0.9.