Application of Kolmogorov-Zurbenko filter to quantify the long-term meteorological and emission impacts on air quality

Abstract

High concentrations of Particulate Matter (PM) have become a major problem in Turkey because of its economic development over the past decades, as well as its geographical proximity to natural dust source areas. In this study, PM10 data (for the period of 2010-2020) of 36 ground-based stations in 12 metropolitan cities over different regions in Turkey was used and Kolmogorov-Zurbenko (KZ) filter was implemented to decompose the data into its temporal components. A stepwise Multiple Linear Regression (MLR) model was developed to establish relationships between PM10 concentrations and a set of meteorological variables for each city to quantify the long-term meteorological as well as emission impacts on changes and trends in air quality. In this study analysis has revealed that out of twelve major cities in Turkey, only three of them has PM10 levels below or around 45 mu gmi 3,which is 24 h Air Quality Guideline (AQG) level identified by WHO (WHO, 2021). Overall, over the selected period, long-term change in PM10 concentration is negative for 10 out of 12 cities, ranging between i 39.6 mu gmi 3 and i 1.2 mu gmi 3. Only Adana and Kayseri have a positive long-term change ranging between 1.0 mu gmi 3 and 3.1 mu gmi 3. Long-term change in meteorology related component (chi LTmet) is relatively constant, hence, long-term change in PM10 concentration (chi LT) is heavily influenced by emission related component (chi LT emis). As emission impact reduces over time, there is a decrease in PM10 levels in most of the cities. This finding is further supported by reductions in national emissions data.