1887

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.

English

.

Results of the receptor modelling

The results of the receptor modelling studies are presented here for two case study cities (Oslo and Lyksele). The focus is on the results of the wood burning contribution but other contributions will also be discussed. The main emphasis here is on the chemical profiles and total source contributions determined by the various methods. A number of results are reported based on different receptor models and on different assumptions (users) when applying the models. This "ensemble" of models and users is used, along with uncertainty estimates from the individual receptor models, to indicate the uncertainty of the receptor modelling results.

English

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error