Aerosol composition and properties variation at the ground and over the column under different air masses advection in South Italy

Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering

In this study, we present an aerosol classification technique based on measurements of a double monochromator Brewer spectrophotometer during the period 1998–2017 in Thessaloniki, Greece. A machine learning clustering procedure was applied based on the Mahalanobis distance metric. The classification process utilizes the UV Single Scattering Albedo (SSA) at 340 nm and the Extinction Angstrom Exponent (EAE) at 320–360 nm that are obtained from the spectrophotometer. The analysis is supported by measurements from a CIMEL sunphotometer that were deployed in order to establish the training dataset of Brewer measurements. By applying the Mahalanobis distance algorithm to the Brewer timeseries, we automatically assigned measurements in one of the following clusters: Fine Non Absorbing Mixtures (FNA): 64.7%, Black Carbon Mixtures (BC): 17.4%, Dust Mixtures (DUST): 8.1%, and Mixed: 9.8%. We examined the clustering potential of the algorithm by reclassifying the training dataset and comparing it with the original one and also by using manually classified cases. The typing score of the Mahalanobis algorithm is high for all predominant clusters FNA: 77.0%, BC: 63.9%, and DUST: 80.3% when compared with the training dataset. We obtained high scores as well FNA: 100.0%, BC: 66.7%, and DUST: 83.3% when comparing it with the manually classified dataset. The flags obtained here were applied in the timeseries of the Aerosol Optical Depth (AOD) at 340 nm of the Brewer and the CIMEL in order to compare between the two and also stress the future impact of the proposed clustering technique in climatological studies of the station.

Short period PM2.5 prediction based on multivariate linear regression model

A multivariate linear regression model was proposed to achieve short period prediction of PM_2.5 (fine particles with an aerodynamic diameter of 2.5 μm or less). The main parameters for the proposed model included data on aerosol optical depth (AOD) obtained through remote sensing, meteorological factors from ground monitoring (wind velocity, temperature, and relative humidity), and other gaseous pollutants (SO₂, NO₂, CO, and O₃). Beijing City was selected as a typical region for the case study. Data on the aforementioned variables for the city throughout 2015 were used to construct two regression models, which were discriminated by annual and seasonal data, respectively. The results indicated that the regression model based on annual data had (R² = 0.766) goodness-of-fit and (R² = 0.875) cross-validity. However, the regression models based on seasonal data for spring and winter were more effective, achieving 0.852 and 0.874 goodness-of-fit, respectively. Model uncertainties were also given, with the view of laying the foundation for further study.