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In this study, AERONET (Aerosol Robotic Network) and EARLINET (European Aerosol Research Lidar Network) data from 17 collocated lidar and sun photometer stations were used to characterize the optical properties of aerosol and their types for the 2008–2018 period in various regions of Europe. The analysis was done on six cluster domains defined using circulation types around each station and their common circulation features. As concluded from the lidar photometer measurements, the typical aerosol particles observed during 2008–2018 over Europe were medium-sized, medium absorbing particles with low spectral dependence. The highest mean values for the lidar ratio at 532 nm were recorded over Northeastern Europe and were associated with Smoke particles, while the lowest mean values for the Angstrom exponent were identified over the Southwest cluster and were associated with Dust and Marine particles. Smoke (37%) and Continental (25%) aerosol types were the predominant aerosol types in Europe, followed by Continental Polluted (17%), Dust (10%), and Marine/Cloud (10%) types. The seasonal variability was insignificant at the continental scale, showing a small increase in the percentage of Smoke during spring and a small increase of Dust during autumn. The aerosol optical depth (AOD) slightly decreased with time, while the Angstrom exponent oscillated between “hot and smoky” years (2011–2015) on the one hand and “dusty” years (2008–2010) and “wet” years (2017–2018) on the other hand. The high variability from year to year showed that aerosol transport in the troposphere became more and more important in the overall balance of the columnar aerosol load.

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Pollutants' data, meteorological data and medical data feeds the relational database with information from vulnerable urban areas (i.e., Targoviste and Ploiesti), which will help the running of algorithms between air quality, meteorology and health effects and later use of forecasted outputs to forecast health effects. Several computerbased tools were developed to facilitate the population of the database with speci ic data from various sources. One of these tools allows the automatic capturing of pollutants' concentrations from web-based of icial sources. The relational database structure integrates the ields for the required variables in the attributed data tables (PM 2.5 and carried compounds/metals sub-database, meteorological sub-database and medical sub-database). The main criteria in selecting the respiratory illnesses that are linked to atmospheric pollution for children are the wheezing. The medical database contains as main ields: the number of wheezing episodes, number of asthma attacks (with hospitalization), the response to inhalation medication, medication controller, eosinophil count, serum level of E immunoglobins (lgE), and residential address and school/kindergarten address of the children. The presented database structure and adjacent tools are expected to improve the current monitoring methodology of air pollutants, mainly respirable dusts, and their content in various compounds in correlation with children's health.

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