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The Netherlands is considered one of the hotspot areas in Europe with high concentrations of particulate matter (PM) and may not be able to meet all standards for PM 2.5 in time with current legislation (Matthijsen et al., 2009). To improve our understanding of the composition, distribution and origin of PM 2.5 in the ambient air an intensive one-year measurement campaign (from August 2007 to September 2008) was performed at five locations in the Netherlands. The five sites consist of three rural background sites, one urban background site and one curbside site. We have applied source apportionment using Positive Matrix Factorization (EPA-PMF) on the pooled data from the five sites to identify and quantify the most relevant source contributions and their spatial variability to PM 2.5 in the Netherlands. The results of this study are compared to a full mass closure analysis of the data. Using EPA-PMF we could identify seven unique sources for the PM 2.5 fraction: nitrate-rich secondary aerosol, sulphate-rich secondary aerosol, traffic and resuspended road dust, industrial (metal) activities/incineration, sea spray, crustal material and residual oil combustion. Wind directional analysis was used to determine the possible locations of the identified sources. On the five locations secondary inorganic aerosol (SIA) is responsible for the largest contribution. The contribution of SIA to the total PM 2.5 mass is largely constant at all used sites. This indicates these sources are common sources which behave like area sources and affects each site. The largest contribution of the traffic and resuspended road dust profile was found at the curbside site. Using combined data from five measurement sites provides focus on the common sources (e.g. SIA) affecting all locations.

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

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Globally, NO2 and PM2.5 declined while O3 increased during strict lockdown periods.