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.



Discussion and conclusions

This study has implemented receptor, dispersion and inverse modelling techniques in order to derive source contributions and emissions strengths related to domestic wood burning. Four Nordic cities were included in the study, these being Oslo (Norway), Helsinki (Finland), and Lycksele and Gävle (Sweden). A range of receptor and dispersion models have been used in the study. The results show that dispersion models applied in Oslo and Helsinki overestimate the wood burning contribution compared to receptor modelling. The use of multiple users and receptor models shows significant variation between receptor modelling results, but the variation is less sensitive for the wood burning contributions than it is to other sources. The study has also shown that inverse modelling techniques, based on modelled source contributions and measured PM2.5 concentrations, give similar results to the receptor models.


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