Modeling Temporal Variation of Particulate Matter Concentration at Three Different Locations of Delhi
Debopam Rakshit, Arkaprava Roy, Koushik Atta, Saju Adhikary, . Vishwanath
International Journal of Environment and Climate Change · pp. 1831–1839 · Published 31 Aug 2022
10.9734/ijecc/2022/v12i1131191Abstract
Aims: To model the concentration variation of PM2.5 and PM10 in selected locations of Delhi. Study Design: ARFIMA-GARCH model. Place and Duration of Study: The study was conducted by using daily (24 hour interval) data of PM2.5 and PM10 concentration from three air quality monitoring stations of Delhi namely, Narela, Okhla Phase II and Pusa. Methodology: The ARFIMA model is applied as the mean model and the GARCH model as the variance model. Results: The selected series are stationary and exhibit the presence of long memory in the mean structure. Due to the presence of long memory in mean, the ARFIMA model is applied. The residual series have conditional heteroscedasticity. Hence, the GARCH model is applied as a variance model. The fitted models are validated using RMSE, MAE and MAPE. Conclusion: The concentration variation of PM2.5 and PM10 followed long memory process in mean structure. ARFIMA-GARCH model satisfactorily explained the variation of concentration.
References (13)
- 1 Distribution of the Estimators for Autoregressive Time Series With a Unit Root [DOI]
- 2 An Analysis of Variance Test for Normality (Complete Samples) [DOI]
- 3 Generalized autoregressive conditional heteroskedasticity [DOI]
- 4 Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation [DOI]
- 5 Testing for a Unit Root in Time Series Regression [DOI]
- 6 AN INTRODUCTION TO LONG‐MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING [DOI]
- 7 THE ESTIMATION AND APPLICATION OF LONG MEMORY TIME SERIES MODELS [DOI]
- 8 Fractional Differencing [DOI]
- 9 Global mortality from outdoor fine particle pollution generated by fossil fuel combustion: Results from GEOS-Chem [DOI]
- 10 Health and economic impact of air pollution in the states of India: the Global Burden of Disease Study 2019 [DOI]
- 11 Health impacts of fine particles under climate change mitigation, air quality control, and demographic change in India [DOI]
- 12 Exploring short term spatio-temporal pattern of PM2.5 and PM10 and their relationship with meteorological parameters during COVID-19 in Delhi [DOI]
- 13 Journal of the American Statistical Association [DOI]
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