Analysis Of Multiscale Trend Sample Entropy Based On Air Quality Time Series In Hunan Province

This paper presents two types of information entropy describing the complexity and consistency of non-stationary time series,Multiscale Trend Sample Entropy（MTSE）and Multiscale Cross Trend Sample Entropy（MCTSE）.In order to accurately prevent and control air pollution,the dynamic characteristics of air quality time series within and between 14cities and prefectures in Hunan are studied by using these two methods.The AQI,PM_2.5and SO₂sequences of 14 cities and prefectures in Hunan Province were studied with MTSE method.It is found that the complexity of the three sequences increases first and then decreases as the scale increases,and the decline rate is slower.Based on the cluster analysis of the above MTSE entropy results,the change trends of these three air quality indicators are found generally similar in different seasons.Using MCTSE,the consistency of AQI and the concentrations of six air pollutants in four seasons between 14 cities and prefectures in Hunan Province was studied.It is found that the consistency of particulate pollutants PM_2.5and AQI is the greatest,especially in winter,and O₃pollution is serious only in summer;the consistency of PM₁₀and AQI is second only to PM_2.5,but the new indicator has obviously replaced the old one as the main pollutant.The trend of AQI change between neighboring cities is basically the same,indicating that the air quality between neighboring cities has a linkage effect.The similarity of PM_2.5,PM₁₀and O₃sequences between the two cities is significantly higher than other pollutants,and the similarity of these pollutants shows a stronger trend in winter and autumn than in spring and summer.It provides a theoretical basis for finding sources of pollution that affect the air quality of 14 cities and prefectures and jointly controlling pollution,and finally provides air pollution prevention suggestions and feasible countermeasures for these cities.