Intra- and inter-city variability of PM2.5 concentrations in Greece as determined with a low-cost sensor network

Abstract

Measurements of PM2.5 concentrations in five major Greek cities over a two-year period using calibrated low-cost sensor-based particulate matter (PM) monitors (Purple Air PA-II) were combined with local meteorological parameters, synoptic patterns and air mass residence time models to investigate the factors controlling PM2.5 spatiotemporal variability over continental Greece. Fourteen sensors nodes in Athens, Patras, Ioannina, Xanthi, and Thermi (in the Metropolitan Area of Thessaloniki) were selected out of more than 100 of a countrywide network for detailed analysis. The cities have populations ranging from 65k to 3M inhabitants and cover different latitudes along the South-North axis. High correlations between the daily average PM2.5 levels were observed among all sites, indicating strong intra- and inter-city covariance of concentrations, both in cold and warm periods. Higher PM2.5 concentrations in all cities during the cold period were primarily associated with low temperatures and stagnant anticyclonic conditions, favoring the entrapment of residential heating emissions from biomass burning. Anticyclonic conditions were also connected to an increased frequency of PM2.5 episodes, exceeding the updated daily guideline value (15 μg m−3) of the World Health Organization (WHO). During the warm period, nearly uniform PM2.5 levels were encountered across continental Greece, independently of their population size. This uniformity strongly suggests the importance of long-range transport and regional secondary aerosol formation for PM2.5 during this period. Peak concentrations were associated mainly with regional northern air flows over Greece and the Balkan Peninsula. The use of the measurements from dense air quality sensor networks, provided that a robust calibration protocol and continuous data quality assurance practices are followed, appears to be an efficient tool to gain insights on the levels and variability of PM2.5 concentrations, underpinning the characterization of spatial and seasonal particularities and supporting real-time public information and warning.