Estimation of daily surface ozone using periodic and stochastic modeling in Chennai region

Abstract

This study deals with the modeling and forecasting of surface ozone time series in an urban area. First, an analysis of the systematic components (periodicity and stochastic components) was performed. Subsequently, prediction model for the daily surface ozone series was developed. In the recent past years there was no permanent measurement of surface ozone data in this site, so we measured surface ozone during period from June 2011 to September 2012 at the urban site Chennai the capital of Tamil Nadu, India. Daily cumulative ozone data series was obtained by using hourly instantaneous data. The data series is free of trend were found using Mann-Kendall test. The periodicity of ozone data was analyzed using Fourier Transform method. Stochastic components of ozone data are assumed as residues between observed ozone data and values computed from periodic model. Stochastic model presented in this research is basically a 3^rd order autoregressive model. The developed models were validated using correlation coefficient between the model predicted values and observed values. This result suggests that this approach is a promising way to forecast daily surface ozone with sufficient accuracy.