Visual analysis of haze evolution and correlation in Beijing View Full Text


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Article Info

DATE

2019-02

AUTHORS

Wenting Zhang, Yinqiao Wang, Qiong Zeng, Yunhai Wang, Guoning Chen, Tao Niu, Changhe Tu, Yi Chen

ABSTRACT

Haze is a hazardous atmospheric phenomenon that threatens human health and leads to severe economic problems. A number of weather factors are relevant to the emergence and evolvement of haze. In this paper, we present a visual analytics system for haze study, including its evolution and correlations to a number of weather factors. Specifically, we introduce a haze event detection algorithm based on common haze identification rules in meteorology using the PM2.5 concentration data. We develop a comparative visualization to consistently overview trends of scalar variables and wind directions, in which wind patterns are extracted via clustering streamlines at user-given sampling time. To study the correlation between wind and PM2.5, we decompose time steps into time intervals according to the temporal similarity of streamlines. Additionally, we develop a 1D function dissimilarity measurement to study the temporal correlation between PM2.5 concentrations and relevant weather factors, such as wind strength, relatively humidity and planetary boundary layer. Furthermore, we employ particle advection using pathline computation within the wind field to locate the origins and destinations of particles seeded in user-interested areas. We applied our system to study of a number of hazes occurring in January 2013 in Beijing, (China). Interpretations and evaluations from domain experts demonstrate the effectiveness of our system in facilitating haze study. More... »

PAGES

1-16

References to SciGraph publications

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URI

http://scigraph.springernature.com/pub.10.1007/s12650-018-0516-0

DOI

http://dx.doi.org/10.1007/s12650-018-0516-0

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45 schema:description Haze is a hazardous atmospheric phenomenon that threatens human health and leads to severe economic problems. A number of weather factors are relevant to the emergence and evolvement of haze. In this paper, we present a visual analytics system for haze study, including its evolution and correlations to a number of weather factors. Specifically, we introduce a haze event detection algorithm based on common haze identification rules in meteorology using the PM2.5 concentration data. We develop a comparative visualization to consistently overview trends of scalar variables and wind directions, in which wind patterns are extracted via clustering streamlines at user-given sampling time. To study the correlation between wind and PM2.5, we decompose time steps into time intervals according to the temporal similarity of streamlines. Additionally, we develop a 1D function dissimilarity measurement to study the temporal correlation between PM2.5 concentrations and relevant weather factors, such as wind strength, relatively humidity and planetary boundary layer. Furthermore, we employ particle advection using pathline computation within the wind field to locate the origins and destinations of particles seeded in user-interested areas. We applied our system to study of a number of hazes occurring in January 2013 in Beijing, (China). Interpretations and evaluations from domain experts demonstrate the effectiveness of our system in facilitating haze study.
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