| 论文题目： | Air pollution in Germany: Spatio-temporal variations and their driving factors based on continuous data from 2008 to 2018 |
|---|---|
| 第一作者： | Liu Xiansheng; Hadiatullah Hadiatullah; Tai Pengfei; Xu Yanling; Zhang Xun etc. |
| 联系作者： | |
| 发表年度： | 2021 |
| 摘 要： | This study analyzed long-term observational data of particulate matter (PM2.5, PM10) variability, gaseous pollutants (CO, NO2, NOx, SO2, and O-3), 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 (R 2 = 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. (C) 2021 Elsevier Ltd. All rights reserved. |
| 英文摘要： | This study analyzed long-term observational data of particulate matter (PM2.5, PM10) variability, gaseous pollutants (CO, NO2, NOx, SO2, and O-3), 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 (R 2 = 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. (C) 2021 Elsevier Ltd. All rights reserved. |
| 刊物名称： | ENVIRONMENTAL POLLUTION |
| 全文链接： | |
| 论文类别： | SCI |
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