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

Liu X

Liu X(1), Hadiatullah H(2), Tai P(3), Xu Y(4), Zhang X(5), Schnelle-Kreis J(6), Schloter-Hai B(6), Zimmermann R(1).
Author information:
(1)Joint Mass Spectrometry Center, Cooperation Group Comprehensive Molecular Analytics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany; Joint Mass Spectrometry Center, Chair of Analytical Chemistry, University of Rostock, 18059, Rostock, Germany.
(2)School of Pharmaceutical Science and Technology, Tianjin University. 300072, Tianjin, China.
(3)School of Geography and Tourism, Qufu Normal University, 276826, Rizhao, China.
(4)College of Plant Health and Medicine, Qingdao Agricultural University, 266109, Qingdao, China.
(5)Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, 100048, Beijing, China; Key Laboratory of Resources Utilization and Environmental Remediation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 100101, Beijing, China. Electronic address: [Email]
(6)Joint Mass Spectrometry Center, Cooperation Group Comprehensive Molecular Analytics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.

https://dx.doi.org/10.1016/j.envpol.2021.116732

This study analyzed long-term observational data of particulate matter (PM2.5, PM10) variability, gaseous pollutants (CO, NO2, NOX, SO2, and O3), 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 PM2.5 and PM10 concentrations were analyzed by Back Propagation Neural Network model, showing that CO and temperature had the greater correlations with PM2.5 and PM10. Following that, PM2.5 and PM10 show a strong positive correlation (R2 = 0.96, p < 0.01), suggesting that the reduction of PM2.5 is essential for reducing PM pollution and enhancing air quality in Germany. Based on typical PM10/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 PM10. Taken together, exploring the prediction methods and analyzing the characteristic variation of pollutants will contribute an essential implication for air quality control in Germany.