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This paper presents the results from a coordinated approach for atmospheric investigation, exploring synergies between different techniques. A wide range of instruments have been used during an intensive measurement period both from ground (lidar, sunphotometer, aethalometer and Aerosol Chemical Speciation Monitor) and airborne (aerodynamic particle sizer, the Picarro gas analyzer and the NO2 CAPS analyzer) in 2016 over Magurele, 6 km South West of Bucharest.

A Python code was developed to automatically retrieve the aerosol type (and its predominant component in the mixture) from EARLINET’s 3 backscatter and 2 extinction data. The typing relies on Artificial Neural Networks which are trained to identify the most probable aerosol type from a set of mean-layer intensive optical parameters. This paper presents the use and limitations of the code with respect to the quality of the inputed lidar profiles, as well as with the assumptions made in the aerosol model.

The study goal is to assess the chemical and optical properties of aerosols present in the lofted layers and at the ground. The biomass burning aerosols were evaluated in low level layers from multi-wavelength lidar measurements, while chemical composition at ground was assessed using an Aerosol Chemical Speciation Monitor (ACSM) and an Aethalometer. Classification of aerosol type and specific organic markers were used to explore the potential to sense the particles from the same origin at ground base and on profiles.

Particulate Matter (PM) has become a major concern in terms of human health and climate impact. In particular, the source apportionment (SA) of organic aerosols (OA) present in submicron particles (PM1) has gained relevance as an atmospheric research field due to the diversity and complexity of its primary sources and secondary formation processes. Moreover, relatively simple but robust instruments such as the Aerosol Chemical Speciation Monitor (ACSM) are now widely available for the near real-time online determination of the composition of the non34 refractory PM1. One of the most used tools for SA purposes is the source-receptor Positive Matrix Factorization (PMF) model. Even though the recently developed rolling PMF technique has already been used for OA SA on ACSM datasets, no study has assessed its added value concerning the more common seasonal PMF method from a practical approach yet. In this paper, both techniques were applied to a synthetic dataset and to nine European ACSM datasets in order to spot the main output discrepancies between methods. The main advantage of the synthetic dataset approach was that the methods' outputs could be compared to the expected 'true' values, i.e. the original synthetic dataset values. This approach revealed similar apportionment results amongst methods, but differing with respect to the truth, although the rolling PMF profile adaptability feature has been proven advantageous. Also, these results highlighted the impact of the profile anchor on the solution. In the multi-site study, while differences were generally not significant when considering year-long periods, their importance grew towards shorter time spans, as in intra-month or intra-day cycles. Rolling PMF performed better than seasonal PMF globally for the ambient datasets investigated here as far correlation with external measurements is concerned, especially in periods between seasons. The results of this comparison also support rolling PMF benefits even though output discrepancies with seasonal PMF were scarce. Altogether, the results of this study provide solid evidence of the robustness of both methods and on the overall efficiency of the recently-proposed rolling PMF approach. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for aerosol typing from multiwavelength lidar data, based on Artificial Neural Networks. The aerosol model used to simulate optical properties for the training of the network is described. The algorithm is tested on real observations from ESA-CALIPSO database.

The aim of this study is to characterize aerosols from both chemical and optical point of view and to explore the conditions to sense the same particles in elevated layers and at the ground. Three days of continuous measurements using a multi-wavelength depolarization lidar(RALI) and a C-ToF-AMS aerosol mass spectrometer are analyzed. The presence of smoke particles was assessed in low level layers from RALI measurements. Chemical composition of submicronic volatile/semi-volatile aerosols at ground level was monitored by the CTOF AMS Several episodes of biomass burning aerosols have been identified by both techniques due to the presence of specific markers (f60, linear particle depolarization ratio, Ängström exponent).

Chemical composition variations of aerosols during summer and winter seasons have been investigated using an Aerosol Mass Spectrometer (AMS) at Magurele, a city near Bucharest, Romania. Proportions of chemical species, concentration time series, mass range distribution and aerodynamic size distribution of aerosols have been analyzed. AMS measurements proved that the organics, nitrate, sulphate and ammonium, with small amounts of chloride were the main species at ground level during the investigated period. Important differences have been noticed between hot and cold seasons related to the total submicronic aerosol concentration and composition. The submicronic aerosols loadings are almost two times higher in summer in comparison with wintertime. Proportions of the species are evenly distributed during wintertime. One accumulation peak can be depicted with diameter between 450 to 600 nm. The fine particle mode consisted typically of organics, sulphate and ammonium during summertime, with size distribution centered at about 400-500 nm. [ABSTRACT FROM AUTHOR]

In this paper we present results from the first European (EUR) and North Atlantic (NAT) volcanic ash exercise (VOLCEX) which includes all the warning phases associated as alerting, reactive and proactive phases. Because volcanic ash represents one of the most serious natural hazards that could affect aviation safety, the measurement campaign was focused on improving the reaction of relevant authorities in case of airspace contamination with volcanic ash and, particularly, to assess the current operational procedures and information flows. Organized in the frame of the International Civil Aviation Organization - EUR/NAT Office in Paris, the VOLCEX measurements in Romania were coordinated by the Romanian Atmospheric 3D research Observatory (RADO) and Romanian Air Traffic Services (ROMATSA). The results from lidar measurements from Bucharest, Iasi, Cluj-Napoca-Napoca and Timisoara are presented, emphasizing the dynamics of the aerosol layers and the estimation of particle mass concentrations in the free troposphere. [ABSTRACT FROM AUTHOR]

This study focuses on the local scale behavior of SO2 emissions resulting from lignite-combustion thermal power plants. The investigations aimed at monitoring the SO2 emissions and imissions over a two week period in September 2010. One of the largest power plants from Eastern Europe was chosen for monitoring using different optoelectronic techniques. Rovinari power plant (44°54'45"N - 23°09'44"E) is one of the largest electricity suppliers of Romania, having 4 groups of 330 MW each, with a total installed capacity of 1420 MW and is located in the Rovinari-Motru coal mining area This power plant along with similar facilities located in south-west of Romania is responsible for local and regional pollution problems concerning acid rain, formation of fine particulate matter, smog etc. Emissions from the stacks of Rovinari power plant were monitored with a UV camera that uses the strong absorption of SO2 molecules between 280-320 nm. Images with UV camera were collected from distances ranging between 1 and 2 km. Immisions measurements were made simultaneously with a HORIBA SO2 analyzer for continuous monitoring of atmospheric SO2 imissions using UV fluorescence method. Within the two weeks of measurements the SO2 immisions hourly limit of 350 µg/m³ was exceeded two times, the highest measured concentration of 590 µg/m³ occurring during stable meteorological conditions (very low wind speeds) inhibiting dispersion and favoring the accumulation of SO2 on a local scale. At the stacks, SO2 concentrations were constantly over 1000 ppm*m during the analyzed days. [ABSTRACT FROM AUTHOR]