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.2020 Jan 5;17(1):360.
doi: 10.3390/ijerph17010360.

Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis

Affiliations

Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis

Yu-Ting Bai et al. Int J Environ Res Public Health..

Abstract

Pollutant analysis and pollution source tracing are critical issues in air quality management, in which correlation analysis is important for pollutant relation modeling. A dynamic correlation analysis method was proposed to meet the real-time requirement in atmospheric management. Firstly, the spatio-temporal analysis framework was designed, in which the process of data monitoring, correlation calculation, and result presentation were defined. Secondly, the core correlation calculation method was improved with an adaptive data truncation and grey relational analysis. Thirdly, based on the general framework and correlation calculation, the whole algorithm was proposed for various analysis tasks in time and space, providing the data basis for ranking and decision on pollutant effects. Finally, experiments were conducted with the practical data monitored in an industrial park of Hebei Province, China. The different pollutants in multiple monitoring stations were analyzed crosswise. The dynamic features of the results were obtained to present the variational correlation degrees from the proposed and contrast methods. The results proved that the proposed dynamic correlation analysis could quickly acquire atmospheric pollution information. Moreover, it can help to deduce the influence relation of pollutants in multiple locations.

Keywords: air pollution management; correlation degree; pollutant source tracing; spatio-temporal analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Framework of spatio-temporal correlation analysis on atmospheric pollutants.
Figure 2
Figure 2
Flow chart of dynamic spatio-temporal correlation algorithm.
Figure 3
Figure 3
Distribution of air monitoring points. HS: HengShui station.
Figure 4
Figure 4
Correlation degree between PM2.5 and PM10, CO, temperature, humidity. Temperature and humidity are abbreviated as Tem and Hum, respectively.
Figure 5
Figure 5
Correlation degrees by different methods.
Figure 6
Figure 6
Correlation degree deviation between dynamic and static methods.
Figure 6
Figure 6
Correlation degree deviation between dynamic and static methods.
Figure 7
Figure 7
Cross-correlation degree of any two monitoring points at 4 moments.
Figure 7
Figure 7
Cross-correlation degree of any two monitoring points at 4 moments.
Figure 8
Figure 8
Correlation degrees between any two points.
Figure 9
Figure 9
Correlation degrees between two points by contrast methods (data of July 2016).
Figure 10
Figure 10
Correlation degrees of variable and point crosswise.
Figure 10
Figure 10
Correlation degrees of variable and point crosswise.
Figure 11
Figure 11
Correlation degree deviation between dynamic and static methods of data in July 2016.
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