Many attempts have been made to quantify the relationship between the amount of persistent organic pollutants sequestered by passive air sampling devices and their actual concentrations in ambient air. However, this information may not be necessary for some applications. In this study, two sets of 30 ten-year-long time series of simultaneous passive and high-volume active air sampling carried out at the Košetice observatory in the Czech Republic were used for a comparison of temporal trends. Fifteen polyaromatic hydrocarbons, seven polychlorinated biphenyls and eight organochlorine pesticides were investigated. In most cases, a good agreement was observed between the trends derived from passive and active monitoring with the exception of several compounds obviously affected by sampling artifacts. Two sampling artifacts were observed: breakthrough of high-volume sampler filters for penta- and hexachlorobenzene and semiquantitative values for PAHs with a high molecular weight. It has been suggested before that annually aggregated results of passive air monitoring may be used directly for the assessment of the long-term behavior of these compounds. The extensive set of long-term data used in this study allowed us to confirm this finding and to demonstrate that it is also possible to derive temporal trends and the compounds' half-lives in air from the passive-sampling time series.

A 6-month air sampling survey was conducted at 26 sites across the African continent with the aim to establish baseline information on contamination of ambient air with persistent organic pollutants (POPs) as a reference for future monitoring programs in the region. Sampling sites included continental, rural and urban backgrounds, agricultural and industrial sites as well as waste and obsolete pesticide dumps. Concentrations of polycyclic aromatic hydrocarbons, polychlorinated dibenzo-p-dioxins and furans, polychlorinated biphenyls and organochlorine pesticides were low at most of the rural background sites, but they raise some concern in big cities. The large temporal variability in the pesticide concentrations suggested seasonal application of gamma-HCHs and endosulfans; levels of p,p'-DDT were often much higher than those of p,p'-DDE and indicated recent application of DDT.

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Passive air sampling of semivolatile organic compounds (SVOCs) is a relatively inexpensive method that facilitates extensive campaigns with numerous sampling sites. An important question in the design of passive-sampling networks concerns the number and location of samplers. We investigate this question with the example of 17 SVOCs sampled at 14 background sites across the Czech Republic. More than 200 time series (length 5-11 years) were used to characterize SVOC levels and trends in air between 2003 and 2015. Six polychlorinated biphenyls (PCBs), 6 polyaromatic hydrocarbons (PAHs), and 5 organochlorine pesticides (OCPs) at 14 sites were assessed using data from the MONET passive sampling network. Significant decreases were found for most PCBs and OCPs whereas hexachlorobenzene (HCB) and most PAHs showed (mostly insignificant) increases. Spatial variability was rather low for PCBs and OCPs except for dichlorodiphenyltrichloroethane (DDT) and rather high for PAHs. The variability of the SVOC levels and trends depends on characteristics of the sites including their remoteness, landscape, population, and pollution sources. The sites can be grouped in distinct clusters, which helps to identify similar and, thereby, potentially redundant sites. This information is useful when monitoring networks need to be optimized regarding the location and number of sites.

Urban, suburban and rural background air samples were collected in southern Ghana in 2008 employing polyurethane foam disc passive air samplers (PAS). PAS were analysed for organochlorine pesticides (OCPs), namely hexachlorocyclohexanes (α-, β-, γ- and δ-hexachlorocyclohexane), dichlorodiphenyltrichloroethane including metabolites (o,p'- and p,p'-DDT, DDE and DDD), hexachlorobenzene, pentachlorobenzene, aldrin, dieldrin, endrins (endrin, endrin aldehyde and endrin ketone), isodrin, heptachlors (heptachlor, heptachlor epoxide A and heptachlor epoxide B), chlordanes (α-, β-chlordane, oxychlordane and trans-nonachlor), endosulfans (α- and β-endosulfan and endosulfan sulphate), methoxychlor and mirex using a gas chromatograph coupled to a mass spectrometer. The levels of OCPs ranged for the individual pesticides from below limit of quantification to 750 pg m(-3) (for α-endosulfan), and current agricultural application seemed to be the main primary source of most abundant pesticides. Re-volatilization of previously used pesticides from contaminated soils could not be ruled out either as potential secondary source of contamination, especially in warm and dry seasons and periods of intensive agricultural activities. Higher atmospheric concentrations were observed in November and December during the dry season compared to lower concentrations observed in June, July and August when the country experiences heavy rains. The highest seasonal variation was observed for currently used pesticides as α-endosulfan. A p,p'-DDT/p,p'-DDE ratio suggested recent inputs of fresh technical DDT.

Knowledge of the appropriate learning styles in which students approach the study supports the effectiveness of the teaching process. There is international research that explores the factors that influence student learning styles or students' preferences. The results of some research based on the similar methodologies are inconsistent. The aim of this research under the conditions of Czech tertiary education was to verify what factors students´ learning style preferences in the subject Marketing depend on. The method of questioning based on quantitative research was used. 132 students of University of Economics, Prague and of College of Polytechnics Jihlava were involved in the research. The questions were formulated in a way to be able to define the learning style and whether students were aware of their sensory preferences. The model was based on the VARK model and the learning style according to motivation and intent. A chi-square test of independence was used for verification. The preferences of a deep problem-based learning style prevail. Learning styles preferences depend on factors related to the practical preparation of the students and the difficulty of the subject. Keywords: learning style, VARK model, deep problem-based learning style, Czech tertiary education, subject Marketing.