Analysis And Research On The Spatio-temporal Relationship Between PM_2.5 Concentration Value And Energy Consumption In China

China's economy is developing rapidly,and PM_2.5 pollution is becoming more and more serious.High-concentration PM_2.5.5 pollution not only pollutes the ecological environment,but also seriously affects people's production and life,even endangers people's health.In view of this,revealing the spatial distribution of PM_2.5 pollution,mastering the development law of PM_2.5 pollution,and further reducing PM_2.5 pollution need to be resolved.Energy consumption is closely related to economic development.It can promote sustained economic and social development,but excessive and unreasonable energy consumption will also bring corresponding environmental pollution problems,which will affect human production and life.In order to further explore how the total energy consumption affects PM_2.5 concentration value,the study uses the annual average PM_2.5 concentration value of the prefecture-level and above cities and the total energy consumption as data to explore the spatio-temporal relationship.The study is basing on the China's annual average PM_2.5 concentration image data provided by Dalhousie University in Canada from 2000 to 2017,and analyzed the temporal and spatial distribution and evolution of the annual average PM_2.5concentration from different angles;based on the night-time lighting data from 1992 to2017 and the total energy consumption data?2000-2017?provided by the China Energy Statistical Yearbook,based on the use of night light data to simulate the total energy consumption on the pixel scale of China,the total energy consumption was analyzed from different perspectives.Starting from the prefecture-level and above cities in my country,the spatiao-temporal relationship between the average annual PM_2.5concentration value and the total energy consumption was explored by using geographic weighted regression model?GWR?.The results of the study show that:?1?The annual average PM_2.5 concentration in China increased significantly from 2000 to2017?p<0.05?.Among the eight major economic regions,the average annual PM_2.5concentration in the northern coastal region,the middle reaches of the Yangtze River,and the eastern coastal region was relatively higher than other five economic regions;high concentrations of PM_2.5 are mainly distributed the harsh climate zones such as the Taklimakan Desert and the Qaidam Basin Desert,which are highly affected by natural factors,and the central and eastern regions with frequent human activities.?2?On the grid image of the simulation of the total energy consumption of cities at the prefecture level and above,the maximum pixel value shows a trend of increasing year by year;Standard differential level shows that the total energy consumption of China in 2000-2017 is low in most regions.The areas with high total consumption levels are mainly distributed in the northern coastal part of the eight economic zones,the eastern coastal part of the city and the southern coastal part of the city.?3?The effect of geographic weighted regression model?GWR?is better than that of ordinary least squares regression model?OLS?.Geographically weighted regression model?GWR?results show that there are differences in the spatial distribution of the interpretation ability?fitness R²?and local coefficients???of urban models at different prefecture levels and above,but in the Haixi Mongolian Tibetan Autonomous and Yushu and Tibetan Autonomous Prefecture of Qinghai Province in the northwestern region have better interpretation and prediction capabilities.