Estimation and forecasting of PM10 air pollution in Ankara via time series and harmonic regressions

Y Akdi,E Gölveren,Y Okkaoğlu,M E Yücel

doi:10.1007/s13762-020-02705-0

Abstract

In this study, monthly particulate matter (PM10) values in Ankara (39.9334° N, 32.8597° E) from January 1993 to December 2017 are examined. The PM10 are those thoracic particles whose aerodynamic diameter is less than 10 μm (micrometers), and it is of critical health importance due to the penetrability to the lower airways. As an alternative to classical unit root tests, a unit root test primarily based on periodograms is introduced owing to its advantages over alternatives. After examining the stationarity of the series through periodogram-based test as well as its standard rivals, periodic components in the series are examined and it is observed that the series has both periodic and seasonal components. These components are modeled, using the inherent dynamics of a time series alone, within a trigonometric harmonic regression setup, eventually yielding the forecast values for 2018 that turns out to be superior to those obtained by means of ARIMA (autoregressive integrated moving average). This is a striking result since the modeling framework requires no assumptions, no parameter estimations except for the variance of the white noise series, no simulations of the power of tests, no adjustments of test statistics with respect to sample size and no preliminary work as to independent variable which is simply time, i.e., the period of forecast.

Highlights

Air pollution can be defined as the phenomenon that various substances that need to be found in the air are out of the specified limits and the substances that should not be in the air are found at a hazardous rate to humans, plants, animalsEditorial responsibility: M
Even in the absence of other variables, which might be of concern under another analytical scheme, our work shows that a better forecasting performance has been viable
Among the studies intended to determine the relationship between pollutants and meteorological parameters and to determine the key causes of air pollution, Çiçek et al (2004) use the stepwise regression to exhibit the relationship between SO2, PM10, NO, NO2, carbon monoxide (CO) values and meteorological parameters such as the temperature, wind speed and relative humidity for the period of November 2001–April 2002 in Sıhhiye District of Ankara

Summary

Introduction

Air pollution can be defined as the phenomenon that various substances that need to be found in the air are out of the specified limits and the substances that should not be in the air are found at a hazardous rate to humans, plants, animals. The population in Ankara reached from 3,889,199 in 2000 to 4,771,716 in 2010 and 5,445,026 in 2017, where 94.3% of city population live in the centrum (TurkStat 2018) Such an increase in population and urbanization calls for a number of environmental issues. With regard to different particulate matters, PM2.5 could have been considered in our analysis. We could not entail these in our analysis due to the inavailability of data with regard to sources of PM10 Against this background, in this study, we propose an appropriate time series model for the monthly P M10 amounts observed in Ankara from January 1993 to December 2017 and generate a set of forecasts for future.

Literature

Materials and methods

Results and discussion