| Literature DB >> 32350319 |
Guangfei Yang1, Yuhong Liu2, Xianneng Li2.
Abstract
In recent years, ozone (O3) pollution in China has shown a worsening trend. Due to the vast territory of China, O3 pollution is a widespread and complex problem. It is vital to understand the current spatiotemporal distribution of O3 pollution in China. In this study, we collected hourly data on O3 concentrations in 338 cities from January 1, 2016, to February 28, 2019, to analyze O3 pollution in China from a spatiotemporal perspective. The spatial analysis showed that the O3 concentrations exceeded the limit in seven geographical regions of China to some extent, with more serious pollution in North, East, and Central China. The O3 concentrations in the eastern areas were usually higher than those in the western areas. The temporal analysis showed seasonal variations in O3 concentration, with the highest O3 concentration in the summer and the lowest in the winter. The weekend effect, which occurs in other countries (such as the USA), was found only in some cities in China. We also found that the highest O3 concentration usually occurred in the afternoon and the lowest was in the early morning. The comprehensive analysis in this paper could improve our understanding of the severity of O3 pollution in China.Entities:
Year: 2020 PMID: 32350319 PMCID: PMC7190652 DOI: 10.1038/s41598-020-64111-3
Source DB: PubMed Journal: Sci Rep ISSN: 2045-2322 Impact factor: 4.379
The O3 concentration limits of the NAAQS in China and the WHO (unit: μg/m3).
| NAAQS | Who | |||
|---|---|---|---|---|
| Grade 1 | Grade 2 | Air Quality Guidelines (AQG) | Interim target 1 | High level |
| 100 | 160 | 100 | 160 | 240 |
Figure 1The spatial distribution of the 90th percentile of the maximum daily 8-hour average of the urban O3 concentration in 338 cities in China in 2016 (a), 2017 (b), and 2018 (c). The maps were generated in ArcGIS10.2, URL: http://www.esrichina-bj.cn/softwareproduct/ArcGIS/.
The top 10 cities with the highest 90th percentile of the maximum daily 8-hour average urban O3 concentration in 2016-2018 in China.
| 2016 | 2017 | 2018 |
|---|---|---|
1. Beijing (Beijing Municipality) 2. Tai’an (Shandong Province) 3. Dezhou (Shandong Province) 4. Hengshui (Hebei Province) 5. Dongying (Shandong Province) 6. Wuxi (Jiangsu Province) 7. Jinzhou (Liaoning Province) 8. Jinan (Shandong Province) 9. Panjin (Liaoning Province) 10. Heze (Shandong Province) | 1. Linfen (Shanxi Province) 2. Baoding (Hebei Province) 3. Jincheng (Shanxi Province) 4. Anyang (Henan Province) 5. Jiaozuo (Henan Province) 6. Tai’an (Shandong Province) 7. Xingtai (Hebei Province) 8. Langfang (Hebei Province) 9. Tangshan (Hebei Province) 10. Luoyang (Henan Province) | 1. Baoding (Hebei Province) 2. Jinan (Shandong Province) 3. Liaocheng (Shandong Province) 4. Binzhou (Shandong Province) 5. Jincheng (Shanxi Province) 6. Dezhou (Shandong Province) 7. Shijiazhuang (Hebei Province) 8. Xingtai (Hebei Province) 9. Cangzhou (Hebei Province) 10. Handan (Hebei Province) |
Figure 2The over-standard rate of O3 concentration in 338 Chinese cities. The results of the seven geographical regions are also displayed.
Figure 3The box plots of the annual O3 concentrations of 338 cities in China in 2016–2018.
Figure 4The annual over-standard rate of the O3 concentrations in 338 Chinese cities in 2016-2018.
Figure 5The average O3 concentrations of the 338 cities of China during 2016, 2017 and 2018 (a) and during spring (b), summer (c), autumn (d), and winter (e). The maps were generated in ArcGIS10.2, URL: http://www.esrichina-bj.cn/softwareproduct/ArcGIS/.
Figure 6Monthly variation in the maximum daily 8-hour average concentration of O3 in seven geographical regions and in all cities during 2016-2018.
Figure 7Weekly variation in the maximum daily 8-hour average concentration of O3 in seven geographical regions and three urban agglomerations in China during 2016-2018.
Figure 8Weekly variation in the maximum daily 8-hour average concentration of O3 during four seasons in seven geographical regions during 2016-2018.
Figure 9Daily variation in the maximum daily 8-hour average concentration of O3 in seven geographical regions and three urban agglomerations during 2016-2018. (This figure was created by using matplotlib, a Python 2D plotting library, URL:https://matplotlib.org).
Figure 10Diurnal and nocturnal variation in the average hourly concentration of O3 in seven geographical regions and three urban agglomerations during 2016-2018.
Figure 11The regional division of China into seven geographical regions and three urban agglomerations. The map was generated in ArcGIS10.2, URL: http://www.esrichina-bj.cn/softwareproduct/ArcGIS/.