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.
Highlights
In recent years, air quality has attracted widespread concern due to its rapid deterioration
In this paper, we proposed a novel method of causal identification based on compressive sensing of air pollutants using urban big data
The compression model was developed to compress spatiotemporal correlation data representing the amount of air pollutants in Beijing and its surrounding cites
Summary
Air quality has attracted widespread concern due to its rapid deterioration. Simona et al [9] proposed a mobile air pollution monitoring framework coupled together with a data-driven modelling approach for predicting the air quality inside urban areas, at human breathing level. Due to the availability of air quality big data collected by wireless sensors deployed in different regions, it has become possible to analyze the causalities of pollutants among surrounding cities. As can be seen in Fig., it is difficult to predict which urban air pollutants have a significant impact on Beijing’s air quality based on the original data. We will utilize Granger causality with compressive sensing to further explore the impact of pollutants in other cities on Beijing’s air quality
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