Visual analysis of haze evolution and correlation in Beijing

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 PM 2.5 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 PM 2.5 , 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 PM 2.5 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. Graphical abstract