Analysis Of Pm2.5 Concentration Distribution Characteristics And Influencing Factors In Different Regions Of China

“Smog”has always been one of the most concerned topics of the people.In recent years,the air quality has been changing rapidly.The explosion of PM2.5 has become the primary environmental problem faced by most cities in China.It is an important factor affecting human health and visibility.Therefore,environmental protection is an urgent task.Nowadays,China has gradually established an environmental monitoring system,but the large amount of data generated by environmental monitoring stations has not been fully utilized.When the air quality has not been fundamentally improved,historical data has been used to analyze the factors affecting PM2.5 concentration.It is very meaningful to help the public reasonably avoid pollution and help the government to provide reasonable suggestions.This article mainly explores the main factors that affect the PM2.5 concentration in different regions of China.Through modeling,it explores the impact of factors such as macro,micro,and regional differences on PM2.5 concentration,and makes reasonable suggestions for further formulating air pollution control programs.First,this article selects PM2.5 monitoring data from 103 cities in 7 provinces across the country from 2017 to 2019 for research.Descriptive statistical analysis was performed on the data in time and space,and the spatial and temporal distribution characteristics of PM2.5 data were discussed.The results show that:from the perspective of time distribution,the air quality in 7 provinces is improving year by year,and the average annual air quality is“good“and the number of days is increasing year by year;PM2.5 concentration is also affected by seasonal and day-night changes,and the overall concentration is high in winter and low in summer,Early morning concentration is higher than that of night.From the perspective of spatial distribution,the overall air quality of Hebei Province is the worst among the seven provinces,and the air quality of Yunnan Province is the best.The results of spatial autocorrelation analysis showed that 40 out of 103 cities had significant correlations with PM2.5concentrations in neighboring cities.The analysis of the influencing factors based on the Pearson correlation coefficient shows thatSO₂?NO₂?CO?PM10can promote the increase of PM2.5 concentration,O₃ and humidity and temperature can inhibit the increase of PM2.5 concentration.Secondly,in order to analyze the influencing factors of PM2.5 concentration more comprehensively,a layered linear zero model and a complete model with micro influencing factors?gas pollutants,meteorological elements?and macro influencing factors?economic and industrial factors?as explanatory variables are established.The fitting degree of the hierarchical linear model and the ordinary linear regression model is compared and analyzed to highlight the advantages of the hierarchical linear model.Finally,a spatial panel model with macro-influencing factors as independent variables is established,and an empirical analysis of its influence on PM2.5concentration in terms of geographical distribution is carried out.The prediction results of the two methods,spatial panel model and OLS regression model,are compared and analyzed.The results show that the fitting effect of the spatial panel model is better than the OLS regression model.