Modelling urban particulate matter in RAINS

While the findings of City-Delta 2 are not yet available, a concept has been developed on how to assess with the RAINS model levels of particulate matter in European urban areas. Obviously, this work is in progress, and might change if counter-evidence emerges in the coming months.

Also for urban areas, RAINS will use annual mean concentrations of PM2.5 at urban background stations to quantify impacts on life expectancy. With this focus, the concept is based on the analysis of available observational data – supported by the findings of City-Delta 1 – that a large fraction of PM2.5 in urban background air originates from long-range transport. Conceptually, this fraction will be calculated by the European scale Eulerian EMEP model for the 50*50 km grid cell in which a city is located. RAINS will then assess the extra contribution from urban sources. Preliminary analysis of monitoring data shows a clear relation between the magnitude of this urban signal and the emission density within the city. An example for the UK finds a rather consistent linear relation between observed annual mean PM10 levels at the various monitoring stations and the emission densities in the 5*5 km box around each station (Figure 5.15). Correlations are best if emission densities are averaged over 5*5 km rather than over 1*1, 3*3 or 10*10 km, and if only emissions from road transport are considered. As a sideline, it is interesting to note that such relations emerge also for roadside sites, though obviously with a steeper slope.

PM10concentration as a function of emission density, primary PM10from UK road transport (2001)

0 5 10 15 20 25 30 35 40

0 1 2 3 4 5

PM₁₀emission density, t km^-2yr^-1 5 km x 5 km

[PM10],ugm-3

Roadside sites Urban sites Rural sites

PM10concentration as a function of emission density, primary PM10from UK road transport (2001)

0 5 10 15 20 25 30 35 40

0 1 2 3 4 5

PM₁₀emission density, t km^-2yr^-1 5 km x 5 km

[PM10],ugm-3

Roadside sites Urban sites Rural sites

Figure 5.15: Relation between measured PM10 concentrations within London and the emission density of PM10 from road transport

The influence of traffic emissions on the urban signal of PM2.5 is supported by monitoring results from the Austrian AUPHEP project (Puxbaum et al., 2003). This project measured PM daily over 12 months at twin sites in and around Vienna and provides a chemical analysis of the various size fractions. As shown in Figure 5.16, the higher PM2.5 concentrations in the city can mainly be found for black carbon, organic carbon and ammonium sulphate. While the ammonium sulphate increase in the city remains to be explained in the absence of major SO2 sources in Vienna, the other components are a clear fingerprint of traffic-related emission sources. No significant differences between the urban and rural site were found for the other chemical species.

Urban Impact PM2.5

Figure 5.16: Urban impacts of PM2.5 in Vienna June 1999-May 2000, by chemical composition (i.e., difference between the PM2.5 concentrations measured at the urban and the rural monitoring sites).

Source: Puxbaum et al., 2003

For comparison, Figure 5.17 shows the same analysis for the coarse fraction of PM, i.e., PM10-PM2.5. There is a strong signal of chemically undetermined species in the winter, presumably mineral dust from roads (gravel).

It is also interesting to note that PM2.5 concentrations in Vienna have been greater than those at the pristine rural site (annual mean of 18 µg/m³) by between 1.5 µg/m³ in the summer and up to 6 µg/m³ in the winter, with an annual average difference of about 4 µg/m³ . This confirms the importance of long-range transport also within the urban areas.

Urban Impact PMC

Figure 5.17: Urban impacts of the coarse PM (PM10-PM2.5) fraction in Vienna June 1999-May 2000, by chemical composition (i.e., difference between the PMc concentrations measured at the urban and the rural monitoring sites). Source: Puxbaum et al., 2003

For the RAINS model it is planned to derive a relationship between emission densities within cities (possibly limited to transport sources) and the urban increment according to Figure 5.15. Work is underway to collect chemical and size-resolved monitoring data for additional cities. It is conceivable that additional factors need to be taken into account, such as the total size of a city or meteorological or topographic factors. City-Delta model results will help to test this hypothesis for various cities in Europe and derive a representative function.

With this function, regional scale PM concentrations as computed from the EMEP model can then be adjusted for urban areas, taking into account the emission densities, which are influenced, inter alia, by the amount of traffic emissions and thus by the level of emission controls applied to transport sources. While there are no consistent and reliable emission inventories available for the more than 200 cities in Europe, it is expected that the information available in RAINS, i.e., population data for each city, city areas, characteristic emission factors for transport sources, fleet composition, application of emission control measures, etc. should help to construct appropriate surrogates for this calculation.

5.4 Uncertainties

As explained above, many aspects load any estimate of health impacts of particles with significant uncertainties. For quantification of the health-relevant air quality changes resulting from emission changes, the general imperfections of dispersion modelling for fine particles cannot be eliminated in the near future, and additional uncertainties originate from lack of solid understanding of all emission sources.

While the specific approach for uncertainty treatment within the integrated assessment model can only be designed once the model approach has been ultimately decided (i.e., after all results from the EMEP dispersion model and City-Delta are finally available), preparatory actions have been taken to derive quantified estimates of the uncertainties of the various elements in the model chain. City-Delta by its design provides, inter alia, information about the extent of agreement and disagreement among the available state-of-the art urban dispersion models.