Temporal and Spatial Patterns of China’s Main Air Pollutants: Years 2014 and 2015
China faces unprecedented air pollution today. In this study, a database (SO2, NO2, CO, O3, PM2.5 (particulate matter with aerodynamic diameter less than 2.5 μm), and PM10 (particulate matter with aerodynamic diameter less than 10 μm) was developed from recordings in 188 cities across China in 2014...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-07-01
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| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/8/8/137 |
| _version_ | 1811269039374204928 |
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| author | Tiancai Zhou Jian Sun Huan Yu |
| author_facet | Tiancai Zhou Jian Sun Huan Yu |
| author_sort | Tiancai Zhou |
| collection | DOAJ |
| description | China faces unprecedented air pollution today. In this study, a database (SO2, NO2, CO, O3, PM2.5 (particulate matter with aerodynamic diameter less than 2.5 μm), and PM10 (particulate matter with aerodynamic diameter less than 10 μm) was developed from recordings in 188 cities across China in 2014 and 2015 to explore the spatial-temporal characteristics, relationships among atmospheric contaminations, and variations in these contaminants. Across China, the results indicated that the average monthly concentrations of air pollutants were higher from November to February than in other months. Further, the spatial patterns of air pollutants showed that the most polluted areas were located in Shandong, Henan, and Shanxi provinces, and the Beijing-Tianjin-Hebei region. In addition, the average daily concentrations of air pollutants were also higher in spring and winter, and significant relationships between the principal air pollutants (negative for O3 and positive for the others) were found. Finally, the results of a generalized additive model (GAM) indicated that the concentrations of PM10 and O3 fluctuate dynamically; there was a consistent increase in CO and NO2, and PM2.5 and SO2 showed a sharply decreasing trend. To minimize air pollution, open biomass burning should be prohibited, the energy efficiency of coal should be improved, and the full use of clean fuels (nuclear, wind, and solar energy) for municipal heating should be encouraged from November to February. Consequently, an optimized program of urban development should be highlighted. |
| first_indexed | 2024-04-12T21:34:48Z |
| format | Article |
| id | doaj.art-a0539d49ce814b8e9301b3b53f7e9667 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-04-12T21:34:48Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-a0539d49ce814b8e9301b3b53f7e96672022-12-22T03:15:57ZengMDPI AGAtmosphere2073-44332017-07-018813710.3390/atmos8080137atmos8080137Temporal and Spatial Patterns of China’s Main Air Pollutants: Years 2014 and 2015Tiancai Zhou0Jian Sun1Huan Yu2College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaCollege of Earth Sciences, Chengdu University of Technology, Chengdu 610059, ChinaChina faces unprecedented air pollution today. In this study, a database (SO2, NO2, CO, O3, PM2.5 (particulate matter with aerodynamic diameter less than 2.5 μm), and PM10 (particulate matter with aerodynamic diameter less than 10 μm) was developed from recordings in 188 cities across China in 2014 and 2015 to explore the spatial-temporal characteristics, relationships among atmospheric contaminations, and variations in these contaminants. Across China, the results indicated that the average monthly concentrations of air pollutants were higher from November to February than in other months. Further, the spatial patterns of air pollutants showed that the most polluted areas were located in Shandong, Henan, and Shanxi provinces, and the Beijing-Tianjin-Hebei region. In addition, the average daily concentrations of air pollutants were also higher in spring and winter, and significant relationships between the principal air pollutants (negative for O3 and positive for the others) were found. Finally, the results of a generalized additive model (GAM) indicated that the concentrations of PM10 and O3 fluctuate dynamically; there was a consistent increase in CO and NO2, and PM2.5 and SO2 showed a sharply decreasing trend. To minimize air pollution, open biomass burning should be prohibited, the energy efficiency of coal should be improved, and the full use of clean fuels (nuclear, wind, and solar energy) for municipal heating should be encouraged from November to February. Consequently, an optimized program of urban development should be highlighted.https://www.mdpi.com/2073-4433/8/8/137air pollutantsdynamicsspatial patternsgeneralized additive modelpolicy recommendations |
| spellingShingle | Tiancai Zhou Jian Sun Huan Yu Temporal and Spatial Patterns of China’s Main Air Pollutants: Years 2014 and 2015 Atmosphere air pollutants dynamics spatial patterns generalized additive model policy recommendations |
| title | Temporal and Spatial Patterns of China’s Main Air Pollutants: Years 2014 and 2015 |
| title_full | Temporal and Spatial Patterns of China’s Main Air Pollutants: Years 2014 and 2015 |
| title_fullStr | Temporal and Spatial Patterns of China’s Main Air Pollutants: Years 2014 and 2015 |
| title_full_unstemmed | Temporal and Spatial Patterns of China’s Main Air Pollutants: Years 2014 and 2015 |
| title_short | Temporal and Spatial Patterns of China’s Main Air Pollutants: Years 2014 and 2015 |
| title_sort | temporal and spatial patterns of china s main air pollutants years 2014 and 2015 |
| topic | air pollutants dynamics spatial patterns generalized additive model policy recommendations |
| url | https://www.mdpi.com/2073-4433/8/8/137 |
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