Concentration distribution and cluster analysis of PM2.5 based on lognormal cloud model

Abstract

Air pollution has seriously affected people’s life and health, and the monitoring and analysis of air pollutant concentration has gradually attracted the attention of the whole society. However, among many data analysis methods, cloud model is favored by many scholars because of its good ability to transform between quantitative data and qualitative data. By analyzing the distribution characteristics of PM2.5 concentration, it is found that the normal distribution and the log-normal distribution can describe the distribution of PM2.5 concentration well, in this paper, normal cloud model and lognormal cloud model are used to study the distribution of PM2.5 concentration. Firstly, the PM2.5 data of different time and space dimensions were analyzed by using normal cloud model, and it was found that PM2.5 pollutants can be divided into low concentration and middle and high concentration by heuristic Gauss Cloud transformation, at the same time, it is found that the normal cloud model of these two concepts can fit the original distribution of data well. Secondly, the distribution of PM2.5 concentration is analyzed by using the log-normal distribution, and it is found that the lognormal distribution can describe the global distribution of PM2.5 concentration better than the normal distribution, in this paper, the logarithmic normal cloud model is used to simulate the concentration distribution of PM2.5 pollutants, and the results show that the logarithmic normal cloud model is more effective than normal cloud model. Finally, by comparing the ambiguity of normal cloud model and lognormal cloud model, it is found that PM2.5 concentration does not always obey a single distribution in local time period, that is, the distribution of PM2.5 concentration is different in different time period.