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. Following that, PM and PM show a strong positive correlation (R = 0.96, p < 0.01), suggesting that the reduction of PM is essential for reducing PM pollution and enhancing air quality in Germany. Based on typical PM/CO ratios obtained under ideal weather conditions, it is conducive to roughly estimate the contribution of natural sources. In winter, the earth's crust contributed about 20.1% to PM. Taken together, exploring the prediction methods and analyzing the characteristic variation of pollutants will contribute an essential implication for air quality control in Germany.
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| http://dx.doi.org/10.1016/j.envpol.2021.116732 | DOI Listing |
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