The aim of this study is to assess the distribution of dust over the Mediterranean region, with a special focus on the territory of Romania. Two parameters are analyzed—Dust Load (DL) and Aerosol Optical Depth (AOD), the data is obtained from the dust forecast model BSC-DREAM8b v2.0, for the period between December 2015 and February 2019. The main geographical features of dust occurrence in the Mediterranean region are presented at the monthly and annual scale. The results show that, for Romania, the dust load is high from February to June, when it reaches its annual maximum. The atmospheric circulation inducing intense dust events over Romania have also been assessed using an objective classification method. A key element for the dust transport from the Sahara toward South-Eastern Europe is represented by the development of a deep cyclone South of Italy, following thereafter a North-East path towards the Balkan peninsula. The results at the regional scale are analyzed in connection with the aerosol optical properties at the local scale (e.g., aerosol optical depth at 440 nm, Absorption Ångström Exponent and Scattering Ångström Exponent at 440 nm and 675 nm, respectively) retrieved from the Aerosol Robotic Network (AERONET-NASA) for Romania, using data from ACTRIS-RO monitoring sites from Iași, Cluj–Napoca, and Bucharest. The differences between the forecast model and the observational data are also explored. Our results also show that the contribution of the natural mineral dust to air pollution in Romania is small, representing not more than 10% of all kinds of aerosols detected over the observation points from the ACTRIS-RO network.

The monitoring and evaluation of air quality is a topic of great global interest as, with the decline of air quality, there are negative effects on human health and ecosystems. Thus, the purpose of this paper was to evaluate the air quality over 11 years, in the period 2011–2021, in four cities in Romania, reported as most polluted, namely, Brasov, Cluj-Napoca, Iasi, and Timisoara. Pollutants of interest included arsenic, carbon monoxide, and PM_2.5. The measured concentrations of the selected pollutants were collected from the National Environmental Protection Agency public reports. The database considered the daily measurements for the selected pollutants, from three monitoring stations in each city so that the air quality and trends for the last 11 years and impact assessment could be developed. Therefore, the input data were statistically analyzed to identify the trends of air quality, and then, on this basis, the environmental impacts and risks and health hazards were quantified. High concentrations of PM_2.5 were recorded for Iasi city, while for Timisoara city, significant concentrations of arsenic were reported. The results regarding the air quality aggregate index, air pollution index, and health hazard index were in the regular range, but in the case of sensitive, vulnerable targets such as children, they were triple compared to adults. The results show that the alert threshold value for PM_2.5 was exceeded every year in all four cities, while in the case of Timisoara city, the arsenic air pollution proved to be at a significant level with a major risk for human health.

The article is aimed at presenting a semi-empirical model coded and computed in the programming language Python, which utilizes data gathered with a standard biaxial elastic lidar platform in order to calculate the altitude profiles of the structure coefficients of the atmospheric refraction index $C_N^2\left(z\right)$ and other associated turbulence parameters. Additionally, the model can be used to calculate the PBL (Planetary Boundary Layer) height, and other parameters typically employed in the field of astronomy. Solving the Fernard–Klett inversion by correlating sun-photometer data obtained through our AERONET site with lidar data, it can yield the atmospheric extinction and backscatter profiles $\alpha \left(z\right)$ and $\beta \left(z\right)$ , and thus obtain the atmospheric optical depth. Finally, several theoretical notions of interest that utilize the solved parameters are presented, such as approximated relations between $C_N^2\left(z\right)$ and the atmospheric temperature profile $T\left(z\right)$ , and between the scintillation of backscattered lidar signal and the average wind speed profile $U\left(z\right)$ . These obtained profiles and parameters also have several environmental applications that are connected directly and indirectly to human health and well-being, ranging from understanding the transport of aerosols in the atmosphere and minimizing the errors in measuring it, to predicting extreme, and potentially-damaging, meteorological events.

The aim of this study is to evaluate the role of the weather conditions and local climate on the temporal and spatial variability of particulate matters (PM 10) in Iași city which is facing major pollution problems in the recent years. Daily data from 4 monitoring stations of Environmental Protection Agency-Iași–for main weather parameters and particulate matters – and the temperature from an inner temperature and relative humidity observation network inside the city were used for a three year study (2013-2015). Linear correlation, composite analysis and multiple regression are the main statistical methods applied in the analysis. In brief, the most important meteorological parameters enhancing air pollution in Iași seem to be represented by thermal inversions developing in the region strongly related to local climate conditions. The Pearson correlation coefficient (stronger than -0.40) between PM10 and thermal gradient, the difference in the PM10 concentration exceeding 20 μg/m³ between strong thermal inversions and unstable conditions and the leading role of thermal gradients in multiple regression are the main indicators of the great role of thermal inversion in generating and sustaining pollution conditions in this area. The maximum concentrations of PM10 occur in May and March, gathering more than 30% of the days for the entire year. Complementary studies were taken into account in order to analyse the aerosol optical properties retrieved from Aerosol Robotic Network (AERONET-NASA).