Determination of the background pollution in the Eastern Mediterranean applying a statistical clustering technique

Abstract

The knowledge of the background air pollution is essential in order to implement abatement strategies for improving air quality. In the present study, a statistical clustering algorithm based on Hidden Markov Models (HMMs) was used to determine the background concentrations of PM10 and O3 at a coastal monitoring site in the Eastern Mediterranean by analyzing an eight-year period of air quality data, from 2011 to 2018. Time series of the deseasonalized daily average concentrations of PM10 and O3 and their diurnal amplitudes were applied to the model to identify the unobserved patterns or hidden states that define the pollution states of the two mentioned air pollutants. The pollution state characterized by the lowest daily average concentration and diurnal amplitudes defined the background pollution level and had the main interest in the current work. The findings revealed that during the studied period, 59% of the total daily PM10 values and 67% of the O3 values corresponded to the background pollution state and the average background concentrations were 14.3 ± 5.2 μg/m3 for PM10 and 48.4 ± 8.2 ppb for O3 respectively. In the case of PM10, anthropogenic activities and Saharan dust events, which define different pollution states, contribute in a decisive way to the PM10 load in the region and play a significant role in the observed annual PM10 levels. Additionally, in the case of O3, anthropogenic activities and regional pollution affect the background levels of the region mainly during summer months. The proposed method is a flexible tool with great potential for estimating the background levels of air pollutants at various monitoring stations.