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Industries in close proximity to residential districts are a historical phenomenon of urban development frequently causing air quality problem in European cities. Mladá Boleslav, the long-time centre of the automobile industry in the Czech Republic, is typical example of urban development. However, this air pollution source reconnaissance study found a source of currently increasing importance for the European cities, wood burning. Receptor modelling, time series of organic tracers for wood and coal burning, small-scale multiple-site PM 10 monitoring and mobile PM 10 measurement were combined to identify sources of fine (PM 0.15-1.15 ) and coarse (PM 1.15-10 ) particles in a residential district of Mladá Boleslav in winter 2013. The receptor model was applied to hourly concentrations of organic and elemental carbon in fine and 27 elements in fine and coarse aerosol particles at a receptor site. Multiple-site measurements with PM 10 monitors showed no statistically significant differences among the monitors. Thus, the source apportionment derived from the central site data should apply to the entire residential district. Campaign average PM 10 (33.9 μgm −3 ) consisted of 88% fine particles. Wood burning (49%), coal combustion (34%), traffic (16%), and industry (1%) were identified as the fine particle sources while combined wood burning and coal dust (80%), road dust with salt (14%), and abrasion of car brakes (6%) were identified the coarse particle sources. The large contributions of wood and coal combustion were surprising for this residential district that is a block of flats with district heating. High correlations were observed between the wood combustion contributions and the levoglucosan and mannosan concentrations. The homohopane index of 0.05 indicated lignite combustion. Peak concentrations in excess of 500 μgm −3 of PM 10 recorded during mobile measurements along with visible plumes from home heating, support the source apportionment of the fine particle mass. [ABSTRACT FROM AUTHOR]

The bilinear receptor model positive matrix factorization (PMF) was used to apportion particulate matter with an aerodynamic diameter of 1–10 μm (PM1–10) sources in a village, Březno, situated in an industrial region of northern Bohemia in Central Europe. The receptor model analyzed the data sets of 90- and 60-min integrations of PM1–10mass concentrations and elemental composition for 27 elements. The 14-day sampling campaigns were conducted in the village in summer 2008 and winter 2010. Also, to ensure seasonal and regional representativeness of the data sets recorded in the village, the spatial-temporal variability of the 24-hr PM10and PM1–10within 2008–2010 in winter and summer across the multiple sites was evaluated. There were statistically significant interseasonal differences of the 24-hr PM data, but not intrasummer or intrawinter differences of the 24-hr PM1–10data across the multiple sites. PMF resolved seven sources of PM1–10. They were high-temperature coal combustion; combustion in local heating boilers; marine aerosol; mineral dust; primary biological/wood burning; road dust, car brakes; and gypsum. The main summer factors were assigned to mineral dust (38.2%) and primary biological/wood burning (33.1%). In winter, combustion factors dominated (80%) contribution to PM1–10. The conditional probability function (CPF) helped to identified local sources of PM1–10. The source of marine aerosol from the North Sea and English Channel was indicated by the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT). [ABSTRACT FROM PUBLISHER]

The performance and the uncertainty of receptor models (RMs) were assessed in intercomparison exercises employing real-world and synthetic input datasets. To that end, the results obtained by different practitioners using ten different RMs were compared with a reference. In order to explain the differences in the performances and uncertainties of the different approaches, the apportioned mass, the number of sources, the chemical profiles, the contribution-to-species and the time trends of the sources were all evaluated using the methodology described in Belis et al. (2015). In this study, 87% of the 344 source contribution estimates (SCEs) reported by participants in 47 different source apportionment model results met the 50% standard uncertainty quality objective established for the performance test. In addition, 68% of the SCE uncertainties reported in the results were coherent with the analytical uncertainties in the input data. The most used models, EPA-PMF v.3, PMF2 and EPA-CMB 8.2, presented quite satisfactory performances in the estimation of SCEs while unconstrained models, that do not account for the uncertainty in the input data (e.g. APCS and FA-MLRA), showed below average performance. Sources with well-defined chemical profiles and seasonal time trends, that make appreciable contributions (>10%), were those better quantified by the models while those with contributions to the PM mass close to 1% represented a challenge. The results of the assessment indicate that RMs are capable of estimating the contribution of the major pollution source categories over a given time window with a level of accuracy that is in line with the needs of air quality management. [ABSTRACT FROM AUTHOR]