Detrended cross-correlation analysis of urban traffic congestion and NO2 concentrations in Chengdu

Abstract

In urban congested road sections, usually there exhibits elevated exhaust emissions due to longer idling and more frequent acceleration of vehicles. Using detrended cross-correlation analysis (DCCA), the relationship between air pollution and traffic congestion in the urban area of Chengdu was investigated. In order for a better quantification of the congested condition in a relatively large spatial region, a new measure, i.e., the congestion length (CL), is developed, extracted, and estimated using the Google Real-Time Traffic Maps and GIS technology. Relationships between the hourly average congestion length (HACL) and NO2 concentrations in the urban area of Chengdu from 12 May to 17 May, 2013 were analyzed. A high long-term cross-correlation between HACL and NO2 was observed, implying the ambient NO2 concentration fluctuations are positively cross-correlated with urban traffic congestion in the form of a power function. However, the ambient NO2 concentration did not respond immediately to the change of road traffic due to a relatively slow and lagged photochemical reaction process. A time lagged cross-correlation was further analyzed and showed that the time lag could be as large as 10h. These findings can be used for improving air quality forecasting accuracy by taking into account the time lags in correlation between emissions and air quality.