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Source-Receptor and Inverse Modelling to quantify urban PARTiculate emissions (SRIMPART)

image of Source-Receptor and Inverse Modelling to quantify urban PARTiculate emissions (SRIMPART)

Airborne particulate matter (PM) is considered to be a significant health risk for humans. Yet, concentration levels in much of Europe still remain high. One of the major emission sources of primary PM2.5 (airborne particle matter with a diameter < 2.5 m) in Nordic countries is wood burning due to domestic heating. Unfortunately, emission inventories for wood burning are difficult to determine and there is a large uncertainty in the impact of these emissions on air quality. In SRIMPART we have applied independent methods to assess the contribution of wood burning to the total PM2.5 concentrations in three Nordic cities (Oslo, Lycksele and Helsinki). These methods include receptor modelling, based on chemical analysis of filter samples, and inverse modelling using dispersion models. The results show that estimates of emissions based on wood consumption or based on the methods applied in SRIMPART have a similar level of uncertainty and so it is not possible to categorically state which is the most correct. However, both methods do agree within their respective uncertainties and this provides support that the long term average emissions from wood burning are correct to within a factor of two.

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Inverse modelling using multiple linear regression

Multiple Linear Regression (MLR), as described in section 3.4, of the modelled source contributions was applied to the observed total PM2.5 concentrations for the three cities of Oslo, Lycksele and Helsinki. The application was not limited in Oslo or Lycksele to just the filter data used in the receptor modelling but was also applied to other available data. The results of the inverse modelling results are described here.

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