Validation of PM2.5 Concentrations Based on Finnish Emission—Source-Receptor Scenario Model

Abstract

Atmospheric fine particulate matter (PM2.5) is a major health risk in both developing and developed countries. Health impact assessments utilize often air quality models, consisting of emission and atmospheric dispersion and meteorological models. For policy purposes, there is often a need to assess the air quality impact of large number of alternative emission reduction measures. For such assessments at high spatial resolution for regional scale domains, e.g. the area of a whole country, simplified linear source-receptor relationships can be used to substitute more laborious atmospheric models. In this study we compared PM2.5 concentrations calculated with our policy analysis emission model with available measurement data. The PM2.5 concentrations were modelled using the Finnish Regional Emission Scenario (FRES) model coupled with source-receptor matrices at various resolutions. The measurement data for comparisons were taken from several monitoring stations across Finland, and represented different site types i.e. rural and urban background and traffic dominated environments. In general the model overestimated the PM2.5 concentrations in urban locations and underestimated in rural stations. One possible reason for the overestimation is that emissions from some sectors may have inaccurate spatial disaggregation. Especially the use of population density as a spatial proxy for the distribution of emissions often poorly represents the polluting activity and results in too high modelled concentrations in densely populated areas. In rural regions the omission of sea traffic emissions and natural sources might explain some of the underestimation. The results highlight the importance of the quality of the emission data used as input in dispersion modelling and the need for reliable spatial representation of emissions in the model.

Questions and Answers

Session 2, Talk 2.7, Ville-Veikko Paunu

Questioner: Peter Viane

Question: Why is wood burning missing from your analysis? Road resuspension are normally associated with the coarse fraction.

Answer: Wood burning is an important source of PM2.5. We have done a lot of work on wood combustion emissions, so we know there is more work to be done in other sectors in which we know some overestimation is done. These sectors were looked into more closely in this study. While most particles emitted by resuspension fall within the coarse fraction, resuspension still contributes to the PM2.5 concentrations as well.

Questioner: Richard Derwent

Question: Have you performed any evaluation of the PM input into Finland by long-range transport from the GAINS model? My understanding is that this model does not include organic aerosol. This may be a major cause of your rural PM2.5 underestimation.

Answer: We haven’t performed such an evaluation. The GAINS model includes primary organic carbon and VOC’s, but not as speciated.

Questioner: Johannes Bieser

Question: On what scientific basis did you decide to reduce these particular emission sectors? Did you validate your assumptions e.g. by looking at fingerprints in the speciated PM2.5 composition? Could your revised emission estimate lead to the right results for the wrong reasons? (E.g. uncertainties in meteorological parameters like PBL height could also explain the observed bias.)

Answer: We did not have speciated PM2.5 measurement results in this study. However, it would be really interesting to have them and get an idea of the source division of the PM2.5 in each measurement point. The choosing of sectors was based on our knowledge of sources which have thought overestimations in our model, as expressed in the presentation and the extended abstract. Of course, these sources don’t explain all of the difference between measured and calculated concentrations. The revisions done for these sectors in this study were rather crude, and more sophisticated revisions are planned for the near future. In other words, revisions of these sectors will not be based on this study, but more comprehensive evaluation of our emission model.