Air pollution in Germany: Spatio-temporal variations and their driving factors based on continuous data from 2008 to 2018.

This study analyzed long-term observational data of particulate matter (PM, PM) variability, gaseous pollutants (CO, NO, NO, SO, and O), and meteorological factors in 412 fixed monitoring stations from January 2008 to December 2018 in Germany. Based on Hurst index analysis, the trend of atmospheric pollutants in Germany was stable during the research period. The relative correlations of gaseous pollutants and meteorological factors on PM and PM concentrations were analyzed by Back Propagation Neural Network model, showing that CO and temperature had the greater correlations with PM and PM.