A DFA-based bivariate regression model for estimating the dependence of PM2.5 among neighbouring cities

Abstract

On the basis of detrended fluctuation analysis (DFA), we propose a new bivariate linear regression model. This new model provides estimators of multi-scale regression coefficients to measure the dependence between variables and corresponding variables of interest with multi-scales. Numerical tests are performed to illustrate that the proposed DFA-bsaed regression estimators are capable of accurately depicting the dependence between the variables of interest and can be used to identify different dependence at different time scales. We apply this model to analyze the PM2.5 series of three adjacent cities (Beijing, Tianjin, and Baoding) in Northern China. The estimated regression coefficients confirmed the dependence of PM2.5 among the three cities and illustrated that each city has different influence on the others at different seasons and at different time scales. Two statistics based on the scale-dependent t-statistic and the partial detrended cross-correlation coefficient are used to demonstrate the significance of the dependence. Three new scale-dependent evaluation indices show that the new DFA-based bivariate regression model can provide rich information on studied variables.

Research on Performance Control of Intercalated Melt-blown Nonwoven Materials

“…With the acceleration of global industrialization, the air environment is deteriorating rapidly due to factors such as automobile and industrial exhaust. This trend has had a detrimental impact on public health, with air pollution becoming an increasingly pressing issue [1]. In China, particulate matter is the primary pollutant affecting urban air quality.…”