Time series analysis of the interdependence among air pollutants

Abstract

A statistical time series analysis was applied to study the interdependence between the primary and secondary pollutants in the Taipei area. Estimations using the vector autoregression model (VAR) indicate that 2 and 4 h time lags are sufficient to represent the observed values at two stations studied. The impulse response functions and variance decompositions of NO, NO₂ and O₃ were derived using the vector moving average representations to examine the significance of one species on others. Influences of photochemistry and transport processes on these air pollutants at different locations were evaluated from the results. This technique may provide a simple tool for preliminary assessment of pollution problems.