Modeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodology

In this study, monthly particulate matter (PM2.5) of Paris for the period between January 2000 and December 2019 is investigated by utilizing a periodogram-based time series methodology. The main contribution of the study is modeling the PM2.5 of Paris by extracting the information purely from the examined time series data, where proposed model implicitly captures the effects of other factors, as all their periodic and seasonal effects reside in the air pollution data. Periodicity can be defined as the patterns embedded in the data other than seasonality, and it is crucial to understand the underlying periodic dynamics of air pollutants to better fight pollution. The method we use successfully captures and accounts for the periodicities, which could otherwise be mixed with seasonality under an alternative methodology. Upon the unit root test based on periodograms, it is revealed that the investigated data has periodicities of 1 year and 20 years, so harmonic regression is utilized as an alternative to Box-Jenkins methodology. As the harmonic regression displayed a better performance both in and out-of-sample forecasts, it can be considered as a powerful alternative to model and forecast time series with a periodic structure.

Automated summary

The study investigated monthly PM2.5 data in Paris from January 2000 to December 2019 using a periodogram-based time series methodology. The proposed model captured the periodicities in the air pollution data, revealing cyclic patterns of 1 year and 20 years. Harmonic regression was found to be a superior alternative to Box-Jenkins methodology for modeling and forecasting time series with periodic structures.