About AAQR

Aims and Scope

Articles online
For contributors
Call for Papers
Guideline for the
Special Issue Proposal
Subscription
Information

Advertising

Contact Us
 
Search for  in   Search  Advanced search  

 

Volume 16, No. 9, September 2016, Pages 2302-2313 PDF(3.97 MB)  
doi: 10.4209/aaqr.2016.04.0139   

Long-Range Correlations in Air Quality Time Series: Effect of Differencing and Shuffling

Asha B. Chelani

Air Pollution Control Division, National Environmental Engineering Research Institute (CSIR-NEERI), Nagpur 440020, India

 

Highlights
  • Comparison of three methods of long-range correlation in air quality time series.
  • Effect of differencing and shuffling on long-range correlation property.
  • Distortion of long-range correlation property after statistical transformations.
  • DFA and power spectral density analysis showed almost similar results.
  • Persistence is influenced by short-range correlations in the AQI time series.

Abstract

 

Long-range correlations in the air quality index (AQI) are analysed using rescaled range analysis (R/S), detrended fluctuation analysis (DFA) and power spectral density analysis. Air quality index in five cities of India is considered for this purpose. Statistical transformations such as differencing and shuffling have been carried out to examine the effect of temporal correlations on long-range correlation property of the time series. All three methods indicated the presence of persistence in original AQI time series. After differencing, long-range correlation property is, however, observed to be distorted. R/S analysis did not show the similar results as DFA and power spectral density analysis. Shuffled time series is shown to possess persistence as in the original one by using R/S analysis, whereas other two methods showed random behaviour at most of the locations. This suggests that the persistence property is largely influenced by short-range correlations in the AQI time series. The incorporation of this information can enhance the performance of the models to forecast the air quality. The similarity in the results of DFA and power spectral density analysis suggests that both methods can be relied more than R/S analysis in studying the persistence property of the time series.

 

 

Keywords: Long-range correlations; Differencing; Shuffling; Air quality index.

 

 

Copyright © 2009-2014 AAQR All right reserved.