The bioclimatology of thermal stress over Europe between 1979 and 2019 was analysed using the Universal Thermal Climate Index (UTCI) derived from ERA5‐HEAT reanalysis. The bioclimatology of different European regions was assessed using Köppen–Geiger climate classification. The annual number of hours with heat stress (UTCI > 32°C) increased significantly during the study period for all the analysed Köppen–Geiger climate subclasses, showing also a clear increase towards southern Europe. The highest percentage of hours (20% of all hours) with cold stress (UTCI < −13°C) occur over northern Europe. A significant increasing trend (>0.05 hr·year⁻¹) in the number of hours with heat stress was observed for 23 out of 32 analysed European cities representative for the Köppen–Geiger climate subclasses. For these cities not only the number of hours with heat stress has increased but also the heat stress is more persistent, while the number of cases and the persistence of the periods with cold stress have decreased over the last four decades. The UTCI values showed a statistically significant increase between 0.6 and 3.2°C for all the analysed cities over the study period reflecting the rising of global mean temperatures.

On the morning of 23 March 2018, an unusual phenomenon was observed over Romania where the southeastern part of the country was covered in a fresh-layer of orange snow. The event was extensively reported in mass-media and social-media and raised questions about the origin and the possible impact of the orange snow. Even if this type of events, intrusions of Saharan dust, have been reported before in Romania, and in Europe in general, their occurrence during negative temperature conditions is very rare. Saharan dust intrusion occurs over Europe mainly during spring and, in general, is not accompanied by snow at low altitudes. In this article, for the first time, the synoptic-scale conditions leading to the Saharan dust intrusion over Romania and the chemical and physical properties of the deposited dust particles in a snow layer were analyzed. The Saharan dust event affected a permanent atmospheric measurement research infrastructure located southwest of Bucharest, the capital city of Romania. In-situ and remote sensing measurements conducted at this research infrastructure allowed the identification of the dust source as the north Sahara. The source was confirmed by the elemental ratios of the main components (e.g., Al, Ca, Mg, Fe, K). For example, the (Ca+Mg)/Fe ratio of 1.39 was characteristic for the north Sahara. The dust morphology and the minerals were analyzed by scanning electron microscopy with energy disperse X-ray spectrometry (SEM/EDX). The size distribution of the particle geometric diameter showed that they are centred on 1 $\mathsf{\mu }$ m, but larger particles up to 40 $\mathsf{\mu }$ m are also present. To visualize the minerals, an approach was developed which emphasized the presence of the calcite, quartz or clay minerals. The optical parameters of dust were measured by re-suspending the particles. Values of the optical parameters (i.e., asymmetry parameter at 550 nm was 0.604, single scattering albedo was 0.84–0.89) were similar to those measured for Saharan dust intrusions over the Iberian Peninsula. Also, the non-refractory particles found in the dust-contaminated snow layer were analyzed, indicating the presence of HULIS-like compounds, most probably advected from the Mediterranean sea.

The satellite based monitoring initiative for regional air quality (SAMIRA) initiative was set up to demonstrate the exploitation of existing satellite data for monitoring regional and urban scale air quality. The project was carried out between May 2016 and December 2019 and focused on aerosol optical depth (AOD), particulate matter (PM), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂). SAMIRA was built around several research tasks: 1. The spinning enhanced visible and infrared imager (SEVIRI) AOD optimal estimation algorithm was improved and geographically extended from Poland to Romania, the Czech Republic and Southern Norway. A near real-time retrieval was implemented and is currently operational. Correlation coefficients of 0.61 and 0.62 were found between SEVIRI AOD and ground-based sun-photometer for Romania and Poland, respectively. 2. A retrieval for ground-level concentrations of PM_2.5 was implemented using the SEVIRI AOD in combination with WRF-Chem output. For representative sites a correlation of 0.56 and 0.49 between satellite-based PM_2.5 and in situ PM_2.5 was found for Poland and the Czech Republic, respectively. 3. An operational algorithm for data fusion was extended to make use of various satellite-based air quality products (NO₂, SO₂, AOD, PM_2.5 and PM₁₀). For the Czech Republic inclusion of satellite data improved mapping of NO₂ in rural areas and on an annual basis in urban background areas. It slightly improved mapping of rural and urban background SO₂. The use of satellites based AOD or PM_2.5 improved mapping results for PM_2.5 and PM₁₀. 4. A geostatistical downscaling algorithm for satellite-based air quality products was developed to bridge the gap towards urban-scale applications. Initial testing using synthetic data was followed by applying the algorithm to OMI NO₂ data with a direct comparison against high-resolution TROPOMI NO₂ as a reference, thus allowing for a quantitative assessment of the algorithm performance and demonstrating significant accuracy improvements after downscaling. We can conclude that SAMIRA demonstrated the added value of using satellite data for regional- and urban-scale air quality monitoring.

Data collected over a period of 18 months (December 2019–May 2021) at the Bucharest–Măgurele Cloudnet station were analysed for the first time to determine the macrophysical and microphysical cloud properties over this site. A total number of 1,327,680 vertical profiles containing the target classification based on the Cloudnet algorithm were analysed, of which 1,077,858 profiles contained hydrometeors. The highest number of profiles with hydrometeors (>60%) was recorded in December 2020, with hydrometeors being observed mainly below 5 km. Above 5 km, the frequency of occurrence of hydrometeors was less than <20%. Based on the initial Cloudnet target classification, a cloud classification scheme was implemented. Clouds were more frequently observed during winter compared with other seasons (45% of all profiles). Ice clouds were the most frequent type of cloud (468,463 profiles) during the study period, followed by mixed phases (220,280 profiles) and mixed phased precipitable clouds (164,868 profiles). The geometrical thickness varied from a median value of 244 m for liquid clouds during summer to 3362 m for mix phased precipitable clouds during spring.

The changes in the characteristics of heatwaves over Romania have been analyzed using the excess heat factor calculated for two climate change scenarios (RCP4.5 and RCP8.5) from the EURO-CORDEX project. The changes were evaluated for the near future (2021–2050) using the historical period (1971–2000) as reference. The frequency of occurrence and the duration of heatwaves is projected to increase for both climate scenarios in particular over southern Romania. In this region, the percentage of change in the near future for the number of heatwaves is between 50 and 60% for the RCP4.5 scenario and 60–80% for the RCP8.5 scenario. Also for the same region, the duration of heatwaves will increase by 30–50% for the RCP4.5 scenario and 60–80% for the RCP8.5 scenario. These results indicate that the human exposure to heatwaves will increase in Romania in the near future. To increase awareness on heatwaves and their impact, we propose a series of immediate actions that include (1) improving the communication of the impact of heatwaves, (2) identification of the regions where the population is more vulnerable to heatwaves, and (3) better understanding of the mortality and morbidity associate with heatwaves in Romania.

Pollen allergies are a cause of much suffering for an increasing number of individuals. Current pollen monitoring techniques are lacking due to their reliance on manual counting and classification of pollen by human technicians. In this study, we present a neural network architecture capable of distinguishing pollen species using data from an automated particle measurement device. This work presents an improvement over the current state of the art in the task of automated pollen classification, using fluorescence spectrum data of aerosol particles. We obtained a relative reduction in the error rate of over 48%, from 27% to 14%, for one of the datasets, with similar improvements for the other analyzed datasets. We also use a novel approach for doing hyperparameter tuning for multiple input networks.