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Atmosphere 2016, 7(3), 35; doi:10.3390/atmos7030035

A Visualization Approach to Air Pollution Data Exploration—A Case Study of Air Quality Index (PM2.5) in Beijing, China

1,2
,
1,2,* and 1,2,3,4
1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Luoyu Road 129, Wuhan 430079, China
2
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Luoyu Road 129, Wuhan 430079, China
3
School of Remote Sensing and Information Engineering, Wuhan University, Luoyu Road 129, Wuhan 430079, China
4
Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Wuhan 430068, China
*
Author to whom correspondence should be addressed.
Academic Editor: Robert W. Talbot
Received: 24 November 2015 / Revised: 17 February 2016 / Accepted: 24 February 2016 / Published: 29 February 2016
View Full-Text   |   Download PDF [4068 KB, uploaded 29 February 2016]   |  

Abstract

In recent years, frequent occurrences of significant air pollution events in China have routinely caused panic and are a major topic of discussion by the public and air pollution experts in government and academia. Therefore, this study proposed an efficient visualization method to represent directly, quickly, and clearly the spatio-temporal information contained in air pollution data. Data quality check and cleansing during a preliminary visual analysis is presented in tabular form, heat matrix, or line chart, upon which hypotheses can be deduced. Further visualizations were designed to verify the hypotheses and obtain useful findings. This method was tested and validated in a year-long case study of the air quality index (AQI of PM2.5) in Beijing, China. We found that PM2.5, PM10, and NO2 may be emitted by the same sources, and strong winds may accelerate the spread of pollutants. The average concentration of PM2.5 in Beijing was greater than the AQI value of 50 over the six-year study period. Furthermore, arable lands exhibited considerably higher concentrations of air pollutants than vegetation-covered areas. The findings of this study showed that our visualization method is intuitive and reliable through data quality checking and information sharing with multi-perspective air pollution graphs. This method allows the data to be easily understood by the public and inspire or aid further studies in other fields. View Full-Text
Keywords: air pollution; PM2.5; visual exploration; spatio-temporal data analysis air pollution; PM2.5; visual exploration; spatio-temporal data analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, H.; Fan, H.; Mao, F. A Visualization Approach to Air Pollution Data Exploration—A Case Study of Air Quality Index (PM2.5) in Beijing, China. Atmosphere 2016, 7, 35.

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