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Comparison of Winter Biomass Burning Source Contribution at National Atmospheric Observatory Košetice Based on AMS and Aethalometer Data.
Schwarz, Jaroslav ; Vodička, Petr ; Zíková, Naděžda ; Mbengue, Saliou ; Šerfözö, Norbert ; Pokorná, Petra ; Makeš, Otakar ; Ždímal, Vladimír
Although atmospheric aerosol concentrations exhibit decreasing trend in last decades, the contribution of aerosol emitted by biomass combustion is opposite due to increasing wood combustion used for residential heating. Previous works determined that the share of aerosol of biomass burning origin was up to 50 % in winter. In this work, the data from aerosol mass spectrometer (AMS) and Positive Matrix Factorization (PMF) are used to elucidate biomass combustion aerosol impact at National Atmospheric Observatory Košetice (NAOK) and the results are compared with simple aethalometer model approach.
Fulltext: content.csg -  PDF
Plný tet: SKMBT_C22019110512051 - PDF
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Study of Wet Deposition of Atmospheric Aerosol using Horizontal Hydrometeors.
Zíková, Naděžda ; Pokorná, Petra ; Pešice, Petr ; Sedlák, Pavel ; Ždímal, Vladimír
Processes between atmospheric aerosol (AA) and clouds, source of large uncertainty in weather and climate changes estimations, were described on fogs at Milešovka, meteorological observatory of the Institute of Atmospheric Physics. For the description of the AA properties, online measurement of outdoor particle number size distribution (PNSD) in the size range 10 nm – 20 μm was conducted using SMPS and APS spectrometers. The sampling system consisted of a heated whole air inlet, and PM2.5 sampling head, being switched by an automatic valve. From the difference between PNSD sampled by whole air inlet and by PM2.5 inlet, PNSD of activated particles (aPNSD) was calculated. The aPNSDs differ with hydrometeor type and depend on air mass history, with a stronger influence on freezing fog AA.
Fulltext: content.csg - PDF
Plný tet: SKMBT_C22019110512030 - PDF
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NMR Aerosolomics as a Tool to Distinguish Various Types of Aerosol Samples.
Horník, Štěpán ; Schwarz, Jaroslav ; Ždímal, Vladimír ; Sýkora, Jan
In the recent study, the summer and winter aerosol samples were analyzed using NMR aerosolomics approach. The samples were collected in Prague-Suchdol during summer 2008 and winter 2009 in two different particle size fractions - PM2.5 and PM 10. Around 50 compounds were identified in each aerosol spectrum owing to the comprehensive library. The profile of 86 identified compounds, which were identified in the samples altogether, served as an input data for statistical analysis. Multivariate statistical analysis clearly discriminates the two groups studied. Furthermore, it is possible to determine the most significant compounds.
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NMR Aerosolomics as a Tool to Distinguish Various Types of Aerosol Samples.
Horník, Štěpán ; Schwarz, Jaroslav ; Ždímal, Vladimír ; Sýkora, Jan
In the recent study, the summer and winter aerosol samples were analyzed using NMR aerosolomics approach. The samples were collected in Prague-Suchdol during summer 2008 and winter 2009 in two different particle size fractions - PM2.5 and PM 10. Around 50 compounds were identified in each aerosol spectrum owing to the comprehensive library. The profile of 86 identified compounds, which were identified in the samples altogether, served as an input data for statistical analysis. Multivariate statistical analysis clearly discriminates the two groups studied. Furthermore, it is possible to determine the most significant compounds.
Fulltext: content.csg - PDF
Plný tet: SKMBT_C22019041208492 - PDF
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Cloud Processing of Atmospheric Aerosol Spectra.
Zíková, Naděžda ; Pokorná, Petra ; Pešice, Petr ; Sedlák, Pavel ; Ždímal, Vladimír
Atmospheric aerosol (AA), and its influence on the cloud formation, lifetime and other properties, remains the most uncertain (with low confidence level) element in the IPCC radiative forcing estimations (Stocker et al., 2013). The AA, however, is influenced by the cloud processing as well (Collett et al., 2008., Zíková and Ždímal, 2016). Cloud processing of AA (and vice versa) can be described on fogs, or on low clouds present at a suitable station. An example of such a station is Milešovka, where fog is present for almost 55 % of the time (Fišák et al., 2009), giving a great opportunity to explore the changes in the particle size distributions due to the cloud processing.
Fulltext: content.csg - PDF
Plný tet: SKMBT_C22018110212450 - PDF
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