Source-Receptor and Inverse Modelling to quantify urban PARTiculate emissions (SRIMPART)
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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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    http://oecd.metastore.ingenta.com/content/3809501ec001.pdf
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  • http://www.keepeek.com/Digital-Asset-Management/oecd/environment/source-receptor-and-inverse-modelling-to-quantify-urban-particulate-emissions-srimpart/abstract_9789289332415-1-en
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Author(s):
Nordic Council of Ministers

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One of the major emission sources of primary PM2.5 in Nordic countries such as Norway, Sweden, Denmark and Finland is wood burning for domestic heating. In Norway alone it is estimated that 80% of PM2.5 is emitted through this source. Though direct measurements of wood burning emissions are possible under controlled conditions, emission inventories for domestic heating are difficult to calculate. Emissions vary from stove to stove as well as wood type, wood condition and burning habits. The consumption rate of wood burning is also strongly dependent on meteorological as well as societal conditions. As a result the uncertainty in wood burning emission inventories used in dispersion modelling is considered to be quite high.