Modeling Temporal Variation of Particulate Matter Concentration at Three Different Locations of Delhi
Debopam Rakshit
Discipline of Agricultural Statistics, ICAR-Indian Agricultural Research Institute, India.
Arkaprava Roy *
Division of Soil Science and Agricultural Chemistry, ICAR-Indian Agricultural Research Institute, India.
Koushik Atta
Department of Plant Physiology, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, Nadia, West Bengal-741252, India.
Saju Adhikary
Department of Agronomy, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, Nadia, West Bengal-741252, India.
. Vishwanath
Division of Soil Science and Agricultural Chemistry, ICAR-Indian Agricultural Research Institute, India.
*Author to whom correspondence should be addressed.
Abstract
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.
Keywords: ARIMA, GARCH, particulate matter, pollution, time series, volatility