Mia Pohjola

The Spatial and Temporal Variation of Measured Urban PM10 and PM2.5 in the Helsinki Metropolitan Area

We have studied particulate matter (PM) concentrations,PM10 and PM2.5, measured in an urban air qualitymonitoring network in the Helsinki Metropolitan Area during1997–1999. The data includes PM10 concentrationsmeasured at five locations (two urban traffic, one suburbantraffic, one urban background and one regional backgroundsite) and PM2.5 concentrations measured at twolocations (urban traffic and urban background sites). Theconcentrations of PM10 show a clear diurnal variation,as well as a spatial variation within the area. Bycontrast, both the spatial and temporal variation of thePM2.5 concentrations was moderate. We have analysedthe evolution of urban PM concentrations in terms of therelevant meteorological parameters in the course of oneselected peak pollution episode during 21–31 March, 1998.The meteorological variables considered included wind speedand direction, ambient temperature, precipitation, relativehumidity, atmospheric pressure at the ground level,atmospheric stability and mixing height. The elevated PMconcentrations during the 1998 March episode were clearlyrelated to conditions of high atmospheric pressure,relatively low ambient temperatures and low wind speeds inpredominantly stable atmospheric conditions. The resultsprovide indirect evidence indicating that the PM10concentrations originate mainly from local vehiculartraffic (direct emissions and resuspension), while thePM2.5 concentrations are mostly of regionally andlong-range transported origin.

Estimating domestic wood burning emissions of particulate matter in two Nordic cities by combining ambient air observations with receptor and dispersion models.

The major emission source of primary PM2 (5) in many Nordic countries is wood burning for domestic heating Though direct measurements of wood burning emissions are possible under controlled conditions, emission inventories for urban scale domestic heating are difficult to calculate and remain uncertain As an alternative method for estimating these emissions, this paper makes use of ambient air measurements chemical analysis of filter samples receptor models, dispersion models, and simple inverse modelling methods to infer the emission strengths A comparison of dispersion models with receptor models indicates that the dispersion models tend to overestimate the contribution from wood burning The inverse modelling results are found to agree with those from the receptor modelling Though both the receptor and inverse modelling point to an overestimation of the wood burning emissions of PM2 (5), it is not possible to assign this solely to errors in the emissions inventory as a dispersion model error can be significant It is recommended to improve plume rise and urban canopy meteorological descriptions in the dispersion models before these models are of sufficient quality to allow quantitative assessments of emission inventories

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.