Causal Identification Based on Compressive Sensing of Air Pollutants Using Urban Big Data

Abstract

This study addresses the causal identification of air pollutants from surrounding cities affecting Beijing's air quality. A novel compressive sensing causality analysis (CS-Causality) method, which combines Granger causality analysis (GCA) and maximum correntropy criterion (MCC), is presented for efficient identification of the air pollutant causality between Beijing and surrounding cities. Firstly, taking the spatiotemporal correlation into consideration, the original data is mapped into low-dimensional space. Valid information is then obtained based on compressive sensing (CS), which can greatly reduce the dimensions of the data, thus decreasing the amount of data analysis required. Secondly, to analyze the causal relations, GCA, represented by the prediction from one time series to another, is extended to rule out 'Non-Granger' causes of air pollutants in Beijing originating from its surrounding cities. Thirdly, the greatest impact on Beijing's air quality is confirmed based on MCC. Finally, the accuracy of these results is verified using the transfer entropy.