Future Air Quality in Danish cities 5. Urban Background Levels5.1 Urban Vehicle Emission Inventory The Urban Background Model (UBM) is used to predict urban background air quality levels in Copenhagen. Apart from data about the regional background described in the previous chapter, the UBM model also requires inputs about urban emissions on a 2 x 2 km2 grid. The Urban Emission Model (UBE) is used to estimate these emissions. 5.1 Urban Vehicle Emission InventoryThe urban emissions are depended on the development in traffic on the urban road network and in vehicle emission factors. The development in emission factors are described in chapter 3. Development in Urban Traffic For each grid cell, the Urban Emission Model requires traffic loads and vehicle composition on fire road types: local roads, traffic roads, arterial roads and motorways. Traffic Loads An analysis of the development in traffic loads in the city centre of Copenhagen shows that traffic loads have been constant during 1960-1994 with minor decreases and increases (Jensen 1997). The Municipality of Copenhagen has found similar results for 1970-1998 with a minor increasing trend since 1993 (Municipality of Copenhagen 1998). The geographic variation in traffic development has been uneven since traffic loads have increased by about 20% over the borders of the municipality and decreased 10% over the borders of the city centre. The regional roads within the municipality have had an increase of about 40% and other roads an decrease of about 15% (Municipality of Copenhagen 1997). The development in traffic loads has been characterised by stagnation in the city centre, increase on urban arterial roads and on the regional roads. The Municipality of Copenhagen has carried out traffic forecast for 1992-2010 based on a traffic model for the Greater Copenhagen Area (HTM) and a traffic model (ØTM) developed for evaluating the impact of a new major development area in Copenhagen (Ørestad) (Municipality of Copenhagen 1997). Based on these traffic models the municipality assumes a 10 per cent traffic increase during 1992-2010 on the road network and the increase is expected to be on regional roads. The regional roads have been identified (Municipality of Copenhagen 1999) and traffic increases on regional roads and other roads have been estimated based on the km travelled on these two road types assuming a traffic increase of 10 per cent on the entire road network during 1995-2010. Traffic increases for 2000 and 2005 have been estimated by interpolation, and traffic loads have been assumed to be constant after 2010. The increase on regional roads is 17 per cent from 1995-2010. The assumed development in traffic loads is given in Table 5.1. Table 5.1
Traffic Composition The Danish Road Directorate has carried out a forecast of the development in national km travelled broken down on different vehicle categories for 1997-2016 (Danish Road Directorate 1998). The analysis showed very small changes in traffic composition, therefore, the future vehicle composition on the urban road network is assumed to similar to 1995. Penetration of Catalyst Vehicles The number of catalyst vehicles has a major impact on emissions. The Danish Road Directorate was requested to supply data on the future penetration of catalyst vehicles based on km travelled which is given in Table 5.2 Table 5.2
Cold Starts The number of vehicles with cold engines has also a major impact on emissions. The assumed development in km travelled with cold engines based on data from COPERT III is given in Table 5.3 Table 5.3
Diurnal Traffic Variation Diurnal traffic variations are assumed to be similar for all scenario years since no data is available to establish trends. Development in Urban Emissions The development in urban emissions is estimated using the Urban Emission Model based on the traffic input outlined above and emission factors given in chapter 3. In Table 5.4 the total emissions for all grids are given for the different scenario years. NOx and CO emissions are estimated to decrease by a factor of 7 and benzene by a factor of 10 from 1995 to 2020. The sharp decrease in emissions is a result of stringent emission standards, decrease in cold starts times and penetration of catalyst vehicles which greatly counterbalance the assumed 10% increase in traffic. Table 5.4 [Look here] 5.2 Validation of UBM Predictions for 1995The Urban background Model (UBM) is used to predict urban background concentrations based on input from the regional background levels produced by the DEM model and urban emissions produced by the UBE model. Benzene A method has been set up to estimate benzene concentrations in the regional background as input for the UBM model since the DEM model does not predict benzene levels in the regional background. Benzene measurements have only been carried out at street level (Jagtvej in Copenhagen). An analysis of measurements shows that the ratio between benzene (ppb) and CO (ppm) was 4.0 before 1996 and 1.6 in 1999. Therefore, the benzene levels in the urban background are estimated based on the ratio of 4.0 for 1995 and 1.6 for scenario years 2000-2020 assuming that these ratios also are valid for the urban background. The ratio decreases due to a shift from 3 to 1 per cent of benzene in gasoline. To estimate the regional background levels of benzene the UBM model was run with the assumption that the UBM model gives a perfect prediction of measurements, whereby, the regional levels are measured urban background levels minus modelled urban background concentrations. In this way, the average ratio between the regional and urban background was established as Bnz_reg = Bnz_urban*0.36. That is, on average the regional background levels of benzene are 64% less that urban background levels of benzene. All in all, regional and urban background levels of benzene are ratios of urban background levels of CO. Annual Means In Table 5.5 the annual means predicted by the UBM model is compared with measurements
at the Copenhagen urban background station. Table 5.5
The UBM model underestimates NOx, NO2 and CO air quality levels, and overestimates ozone levels. Observed benzene levels are actually modelled and therefore equivalent to modelled benzene levels. Seasonal Variation Ozone The predicted seasonal variation of ozone is compared to measurements in Figure 5.2. There is generally a good agreement between modelled and observed levels although levels are overestimated in February due to too high predictions by the DEM model. It is also seen that urban background levels of ozone are highly dependent on the regional levels. Urban background levels are slightly lower than regional levels because urban NOx emission deplete urban ozone levels. Figure 5.1 [Look here] NOx and NO2 The predicted seasonal variation of NOx and NO2 is compared to measurements in Figure 5.2. There is generally a good agreement between modelled and observed levels although levels are generally underestimated. Figure 5.2 [Look here] CO and Benzene The predicted seasonal variation of CO and benzene is compared to measurements in Figure 5.3. There is generally a good agreement between modelled and observed levels although CO levels are underestimated. Figure 5.3 [Look here] Diurnal Variation Ozone The predicted diurnal variation of ozone is compared to measurements at the Copenhagen urban background station in Figure 5.4. There is generally a good agreement between modelled and measured levels although predicted levels are overestimated during the evening and night. Figure 5.4 [Look here] NOx and NO2 The predicted diurnal variation of NOx and NO2 is compared to measurements at the Copenhagen urban background station in Figure 5.5. There is generally a good agreement between modelled and measured levels although predicted levels are generally underestimated. Figure 5.5 [Look here] CO and Benzene The predicted diurnal variation of CO and benzene is compared to measurements at the Copenhagen urban background station in Figure 5.6. There is generally a good agreement between modelled and measured CO levels although predicted levels are generally underestimated. There is also a general good agreement between modelled and measured benzene levels although benzene levels are underestimated during night and overestimated during afternoon rush hours. Figure 5.6 [Look here] Underestimation by COPERT III The general underestimation of NOx and NO2 (and therefore overestimation of ozone) may be due to too low vehicle emission factors for NOx. The general underestimation of CO may also be due to underestimation of vehicle emissions for CO. Emission factors were based on COPERT III that may be too low for Danish conditions because a validation of the Urban Background Model was carried out with much better agreement between modelled and observed levels using emission factors based on Danish studies (Jensen 1992, 1995; Krawack 1991) and fitting of emission factors to obtain better agreement with measurements. The validation study is published in Berkowicz (1999). The main difference is the these emission factors have about a factor 2 higher CO values for passenger cars without catalysts and also about at factor 2 higher NOx values for lorries. A possible underestimation of emission factors by COPERT III is further investigated in the next chapter. 5.3 Future Urban Background Air QualityCopenhagen Urban Background Table 5.1 sums up the UBM model runs for the difference scenario years for the future urban background air quality in Copenhagen. The table gives the predicted development in annual levels in ppb/ppm, µg/m3/mg/m3 and as an index. The index is defined as the levels in scenario years divided by the levels in 1995. Future predicted levels are also given for the urban background station in Copenhagen using observed levels from 1995 as a base and the index for the development. Table 5.6
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