Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China
With the continuous progress of human production and life, air quality has become the focus of attention. In this paper, Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces were taken as the study area, where there are 58 air quality monitoring stations from which daily and monthly data ar...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-02-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | http://www.mdpi.com/2220-9964/6/2/52 |
| _version_ | 1819291869169844224 |
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| author | Zhichen Yu Shaobo Zhong Chaolin Wang Yongsheng Yang Guannan Yao Quanyi Huang |
| author_facet | Zhichen Yu Shaobo Zhong Chaolin Wang Yongsheng Yang Guannan Yao Quanyi Huang |
| author_sort | Zhichen Yu |
| collection | DOAJ |
| description | With the continuous progress of human production and life, air quality has become the focus of attention. In this paper, Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces were taken as the study area, where there are 58 air quality monitoring stations from which daily and monthly data are obtained. Firstly, the temporal characteristics of the air quality index (AQI) are explored. Then, the spatial distribution of the AQI is mapped by the inverse distance weighted (IDW) method, the ordinary kriging (OK) method and the Bayesian maximum entropy (BME) method. Additionally, cross-validation is utilized to evaluate the mapping results of these methods with two indexes: mean absolute error and root mean square interpolation error. Furthermore, the correlation analysis of meteorological factors, including precipitation anomaly percentage, precipitation, mean wind speed, average temperature, average water vapor pressure and average relative humidity, potentially affecting the AQI was carried out on both daily and monthly scales. In the study area and period, AQI shows a clear periodicity, although overall, it has a downward trend. The peak of AQI appeared in November, December and January. BME interpolation has a higher accuracy than OK. IDW has the maximum error. Overall, the AQI of winter (November), spring (February) is much worse than summer (May) and autumn (August). Additionally, the air quality has improved during the study period. The most polluted areas of air quality are concentrated in Beijing, the southern part of Tianjin, the central-southern part of Hebei, the central-northern part of Henan and the western part of Shandong. The average wind speed and average relative humidity have real correlation with AQI. The effect of meteorological factors such as wind, precipitation and humidity on AQI is putative to have temporal lag to different extents. AQI of cities with poor air quality will fluctuate greater than that of others when weather changes and has higher correlation with meteorological factors. |
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| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-44025441149941ce9874b8c3b7c70fdc2022-12-21T17:16:44ZengMDPI AGISPRS International Journal of Geo-Information2220-99642017-02-01625210.3390/ijgi6020052ijgi6020052Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern ChinaZhichen Yu0Shaobo Zhong1Chaolin Wang2Yongsheng Yang3Guannan Yao4Quanyi Huang5Institute of Public Safety Research/Department of Engineering Physics, Tsinghua University, Beijing 100084, ChinaInstitute of Public Safety Research/Department of Engineering Physics, Tsinghua University, Beijing 100084, ChinaInstitute of Public Safety Research/Department of Engineering Physics, Tsinghua University, Beijing 100084, ChinaInstitute of Public Safety Research/Department of Engineering Physics, Tsinghua University, Beijing 100084, ChinaInstitute of Public Safety Research/Department of Engineering Physics, Tsinghua University, Beijing 100084, ChinaInstitute of Public Safety Research/Department of Engineering Physics, Tsinghua University, Beijing 100084, ChinaWith the continuous progress of human production and life, air quality has become the focus of attention. In this paper, Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces were taken as the study area, where there are 58 air quality monitoring stations from which daily and monthly data are obtained. Firstly, the temporal characteristics of the air quality index (AQI) are explored. Then, the spatial distribution of the AQI is mapped by the inverse distance weighted (IDW) method, the ordinary kriging (OK) method and the Bayesian maximum entropy (BME) method. Additionally, cross-validation is utilized to evaluate the mapping results of these methods with two indexes: mean absolute error and root mean square interpolation error. Furthermore, the correlation analysis of meteorological factors, including precipitation anomaly percentage, precipitation, mean wind speed, average temperature, average water vapor pressure and average relative humidity, potentially affecting the AQI was carried out on both daily and monthly scales. In the study area and period, AQI shows a clear periodicity, although overall, it has a downward trend. The peak of AQI appeared in November, December and January. BME interpolation has a higher accuracy than OK. IDW has the maximum error. Overall, the AQI of winter (November), spring (February) is much worse than summer (May) and autumn (August). Additionally, the air quality has improved during the study period. The most polluted areas of air quality are concentrated in Beijing, the southern part of Tianjin, the central-southern part of Hebei, the central-northern part of Henan and the western part of Shandong. The average wind speed and average relative humidity have real correlation with AQI. The effect of meteorological factors such as wind, precipitation and humidity on AQI is putative to have temporal lag to different extents. AQI of cities with poor air quality will fluctuate greater than that of others when weather changes and has higher correlation with meteorological factors.http://www.mdpi.com/2220-9964/6/2/52air quality indexcorrelation analysisinverse distance weightingkriging methodBayesian maximum entropy |
| spellingShingle | Zhichen Yu Shaobo Zhong Chaolin Wang Yongsheng Yang Guannan Yao Quanyi Huang Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China ISPRS International Journal of Geo-Information air quality index correlation analysis inverse distance weighting kriging method Bayesian maximum entropy |
| title | Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China |
| title_full | Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China |
| title_fullStr | Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China |
| title_full_unstemmed | Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China |
| title_short | Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China |
| title_sort | mapping comparison and meteorological correlation analysis of the air quality index in mid eastern china |
| topic | air quality index correlation analysis inverse distance weighting kriging method Bayesian maximum entropy |
| url | http://www.mdpi.com/2220-9964/6/2/52 |
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