Innovative Spatial-Temporal Network Modeling and Analysis Method of Air Quality

Abstract

Air quality system is characterized by dynamism, dependency, and complexity. Scientifically representing the internal structure of air mass distribution and its relationship to reveal the dynamic evolution of air quality is the key to solve the air pollution problem. This paper abstracts the air quality system into the complex network innovatively by synthesizing spatial and temporal factors influencing air quality status. Based on quantifying the regional dynamic interconnection and interaction, our modeling approach is proposed to mine the relationship of different regions. First, the dynamic time-varying nature of air pollutant concentration is essential to get the interaction frequency of local air quality in the time dimension. The time correlation analysis of air quality nodes is conducted by calculating the time correlation matrix to construct the air quality network topology. Second, spatial distance and wind are the main factors influencing the diffusion of pollutants, which is used to characterize spatial homogeneity and heterogeneity. By computing the spatial correlation matrix, the spatial interaction intensity is quantified. Then, air quality spatiotemporal model is established by integrating the temporal and spatial correlation. Finally, based on the air quality spatiotemporal network model, community detecting algorithms are used to mine the local similarity and regional interaction. We evaluated our model with extensive experiments based on real data. The results show that our model is dynamic, reliable, and scalable. Utilizing the characteristics of the complex network community, our approach reflects the local and propagating characteristics of air quality and lays the foundation for air pollution prevention and further prediction.

Keywords

• Air quality system
• the complex network
• dynamic model
• data mining