Mixture Regression for Clustering Atmospheric-Sounding Data: A Study of the Relationship between Temperature Inversions and PM10 Concentrations

Abstract

Temperature inversions prevent the mixing of air near the surface with the air higher in the atmosphere, contributing to high concentrations of air pollutants. Inversions can be identified by sampling temperature data at different heights, usually done with radiosondes. In our study, we propose using the SMIXS clustering algorithm to cluster radiosonde temperature data as longitudinal data into clusters with distinct temperature profile shapes. We clustered 8 years of early morning radiosonde data from Ljubljana, Slovenia, into 15 clusters and investigated their relationship to PM10 pollution. The results show that high PM10 concentrations (above 50 g/m3, which is the daily limit value) are associated with early morning temperature inversions. The highest concentrations are typical for winter days with the strongest temperature inversions (temperature difference of 5 ∘C or more in the inversion layer) while the lowest concentrations (about 10 g/m3) are typical for days with no early morning temperature inversion. Days with very strong temperature inversions are quite rare. We show that clustering temperature profiles into a distinct number of clusters adds to the interpretability of radiosonde data. It simplifies the characterization of temperature inversions, their frequency, occurrence, and their impact on PM10 concentrations.