Source apportionment and spatial variability of PM2.5 using measurements at five sites in the Netherlands

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 PM2.5 in time with current legislation (Matthijsen et al., 2009). To improve our understanding of the composition, distribution and origin of PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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.

► Seven unique sources for the PM2.5 fraction could be identified.
► SIA has the largest and fairly constant contribution to the total PM2.5 mass on all sites.
► Highest contribution of the traffic related sources on the kerbside location.
► Wind directional analysis corresponds with the expected locations of sources.
► Combining the data focuses on common sources, instead of unique (local) sources.