SPATIOTEMPORAL AGGREGATION CHANGE PATTERN OF PM2.5 AND O3 CONCENTRATION IN FUJIAN PROVINCE,CHINA
Based on the daily average concentration data of PM2.5 and O3 from environmental monitoring stations in Fujian Province from 2017 to 2021,the change characteristics and correlation of PM2.5 and O3 concentrations in different time scales were discussed,and the potential spatial correlation of PM2.5 and O3 pollution was explored by univariate and bivariate spatial autocorrelation analysis methods.The results showed that the annual average concentrations of PM2.5 and O3 in Fujian Province showed an overall downward trend in the past five years,and the trend of annual average concentration change was relatively synchronous.The high monthly average concentration of PM2.5 appeared in January to February,and November to December,and the low value appeared in June to August.The monthly variation showed a"U"distribution with high points at both ends and a low point in the middle.The high monthly average concentration of O3 appeared in April to May and September to October,the low value appeared in January and December,and the monthly variation showed a bimodal"M"distribution.The concentrations of PM2.5 and O3 were positively correlated from January to October,and negatively correlated from November to December.Under different concentrations of PM2.5 pollution,the change in O3 concentration was different.When the concentration of PM2.5 was less than 45 μg/m3,there was a positive correlation between PM2.5 and O3 concentration,while when the concentration of PM2.5 was more than 45 μg/m3,there was a negative correlation between them.There was a spatial positive autocorrelation between the average annual and seasonal concentrations of PM2.5 and O3,the L-L clustering area was located in the northwest,mainly distributed in Longyan,Sanming,and Nanping,and the H-H clustering area was located in the southeast,mainly distributed in Fuzhou,Putian,Xiamen and Zhangzhou.The spatial distribution of the annual and the seasonal average concentration of PM2.5 and O3 had obvious clustering and similarity.
NETL
NSTL
万方数据
