Mining Key Stations by Constructing the Air Quality Spatial-Temporal Propagation Network

Abstract

Deteriorated air quality is a significant hazard to human health and a crucial problem in numerous countries. Existing research has extensively investigated the air quality states and future trends, but neglects the analysis of the time-space relevance behavior and the pollutant propagation relation of air quality. Based on the theory of complex network, this paper characterizes the interaction process between different sites, constructs the air quality propagation network and mines the key stations of the whole region. Firstly, pollutant monitoring data from the Beijing-Tianjin-Hebei region are used for analysis, monitoring stations are abstracted into network nodes, and edge sets are constructed using pollutant paths obtained from spatial and temporal reachability calculation. By accumulating the effect of the paths of the propagation network at different timestamps, the weight of the network during the propagation process is assigned for reflecting the interactive degree of stations. Secondly, the PageRank algorithm evaluates the importance of nodes in the propagation network to find the influential pollution area. Key stations considering both the quantity and quality of propagation relations are then obtained. Experiment results demonstrate the relevance of the node rank distribution and the real pollution condition and verify the reliability of the network and the node ranking algorithm. This work provides a reliable theoretical guidance on the timely prevention and control of air pollution, and gives a basis of the location selection of monitoring stations.

Keywords

• Air pollution
• pollutant control
• complex networks
• ranking algorithm
• data mining