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Nearly two years of SODAR measurements in Hannover, Northern Germany have been analysed for the frequency of occurrence of low-level jets and their properties. Characteristic properties such as the height of the jet core above ground, maximum wind speed, and wind shear underneath the jet core have been derived from the data set. The occurrence of these jets, which appear in a bit more than 20 % of all nights, is correlated with the occurrence of typical large-scale weather patterns (“Großwetterlagen”). Maximum wind speed and height of the jet are positively correlated to each other and both increase with increasing geostrophic forcing. The evaluations further show that low-level jet wind speeds tend to develop until the bulk shear underneath the jet core reaches a critical threshold of about 0.08 s−1. Further increase in speed of the jet is then assumed to be linked to a growing height of the jet core above ground keeping the shear at its critical value.

A lot of emission reduction measures to improve the air quality in Beijing were performed during the Olympic Summer Games in 2008, which has cut down coarse particles largely. But high air pollution has become more frequent (Du et al., 2014; Gao et al., 2014). In order to find out the long-term characteristics of airborne PM in different seasons, a continuous oneyear daily mean PM sampling from June 2010 till June 2011 was performed on the ground at the campus of the China University of Geosciences, Beijing (CUGB). To discriminate the composition of PM2.5 source attribution, particle characteristics and external impacts on the PM levels were investigated. Two sequential High-Volume operated to collect PM4 samples. The inorganic elements, inorganic water-soluble ions, EC and OC as well as Levoglucosan, eleven hopane substances and eleven PAHs of PM were analysed from PM samples by PEDXRF, ICP-MS, IC, thermal/optical carbon analyser and in situ derivatisation direct thermal desorption gas chromatography time-offlight mass spectrometry (IDTD-GC-TOFMS), respectively. Positive matrix factorization (version 3.0, U.S. EPA) on the basis of the chemical composition analyses and back trajectory cluster analyses were combined together to perform source apportionment. The results show that the main sources of particles during haze are different from season to season (Figure 1): secondary inorganic ions formation and biomass burning for summer and autumn haze, coal combustion for winter haze and mineral dust emissions for spring haze. Sources of PM during clear days were dominated by mineral dust emissions and traffic while haze was characterized by secondary inorganic ions formation during the whole year. High air pollution was found to be always accompanied by southerly air flows (industry and cities are about 100 km away), high relative humidity, low mixing layer height and low wind speed, i.e. stagnant weather conditions (Liu et al., 2014).

As particulate matter (PM) impacts human health, knowledge about its composition, exposure and source apportionment is required. A study of the urban atmosphere in the case of Augsburg, Germany, during winter (31 January–12 March 2010) is thus presented here. Investigations were performed on the basis of aerosol mass spectrometry and further air pollutants and meteorological measurements, including mixing layer height. Organic matter was separated by source apportionment of PM1 with positive matrix factorization (PMF) in three factors: OOA – oxygenated organic aerosol (secondary organic factor), HOA – hydrocarbon-like organic aerosol (traffic factor or primary organic factor) and WCOA – wood combustion organic aerosol (wood combustion factor), which extend the information from black carbon (BC) measurements. PMF was also applied to the particle size distribution (PSD) data of PM2.5 to determine different source profiles and we assigned them to the particle sources: nucleation aerosol, fresh traffic aerosol, aged traffic aerosol, stationary combustion aerosol and secondary aerosol. Ten different temporal phases were identified on the basis of weather characteristics and aerosol composition and used for correlations of all air pollutants and meteorological parameters. While source apportionment from both organic PM composition and PSD agree and show that the main emission sources of PM exposure are road traffic as well as stationary and wood combustion, secondary aerosol factor concentrations are very often the highest ones. The hierarchical clustering analysis with the Ward method of cross-correlations of each air pollutant and PM component and of the correlations of each pollutant with all meteorological parameters provided two clusters: "secondary pollutants of PM1 and fine particles" and "primary pollutants (including CO and benzene) and accumulation mode particles". The dominant meteorological influences on pollutant concentrations are wind speed and mixing layer height which are coupled with a certain wind direction. The compounds of the cluster "secondary pollutants and fine particles" show a negative correlation with absolute humidity, i.e., low concentrations during high absolute humidity and vice versa. The PM10 limit value exceedances originated not only from the emissions but also in combination with specific meteorological conditions. NC3-10 (number concentration of nucleation mode particles) and NC10-30 (Aitken mode particles), i.e., ultrafine particles and the fresh traffic aerosol, are only weakly dependent on meteorological parameters and thus are driven by emissions. The results of this case study provide information about chemical composition and causes of PM exposure during winter time in urban air pollution.