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1 Fact sheet - meta-info cover page ETC/ACC IP24 Task number Task Title ETC task leader Task 1.3 Contribution to TERM Liana Kalognomou Indicator Indicator title: EEA 31 Transport contribution to air quality ETC/ACC Indicator ID TERM 24 4 Authors Name Organisation Lead Author: Liana Kalognomou LHTEE Co-authors: - Status & Deadlines Status (mark with X) Planned deadline (IP24) (Fill all out at start of drafting process) Date of delivery First Draft, for approval by EEA X 7.24 Working draft only in 24 Final Draft, for approval by EEA X Nov 4 5 Nov 4 Final Version, Approved by EEA X (cntry rev. deadline 26nov4 3 Feb 5 Deliver to our EIONET-CIRCLE Interest Group "ETC/ACC Consortium": Internal ETC/ACC Review (by ETC-members and data source owners) Date Modificated by Organisation Reason for modification 11/7/27 1

2 Indicator fact sheet TERM 24 4 EEA31 Transport contribution to Air Quality Road transport is the largest contributor to NO x emissions in Europe and the second largest for PM. The data analysed from selected stations in major urban agglomerations, indicate that both mean and maximum values of NO 2 concentrations at road traffic stations appear to remain relatively stable (trend is smaller than the statistical uncertainty on estimate) during the period and are accompanied by a similar tendency in the background concentrations. For PM the situation appears more complex, since although mean and maximum values at road traffic and background stations appear to remain relatively stable during the period , both mean and maximum values at both station types show a large increase in 22. This is particularly due to the large increase in the concentrations measured in Krakow and Prague, although an increase is also observed in other cities across Europe. Overall the decrease in emissions does not appear to have a statistically significant influence on the air quality and the increase in the number of vehicles is off-setting the technological and fuel quality improvements. Figure 1: NO 2 mean and maximum values of annual averages for traffic and urban background stations mean-traffic max-traffic mean-backgd max-backgd Figure 2: PM mean and maximum values of annual averages for traffic and urban background stations mean-traffic max-traffic mean-backgd max-backgd 11/7/27 2

3 NB: Station pairs from capital cities were preferred, but when not available the next largest city for which data was available was chosen. Since the available data (stations) vary from year to year (see tables 2 and 3) only the years were chosen in order to ensure a consistent dataset across these years (only stations with complete data for these years were chosen, with the exception of Riga for which data for 1999 was not available and Lisbon traffic station for which NO 2 traffic station data was not available for 22). Meteorological data is not available, so meteorologically induced changes cannot be analysed. The traffic stations were selected according to their yearly data availability and their completeness as regards hourly data availability. For certain years and stations the data availability was low, however these were used as no alternative was available. See table 1 for details. For further details on data availability issues, please refer to the metadata section. 11/7/27 3

4 Results and assessment Policy relevance Most of the high NO 2 and PM concentration levels observed in the majority of urban agglomerations are caused by transport and especially road transport. It is important to quantify this sector s contribution since much of the European population lives and works in such agglomerations and hence this indicator is relevant information for the Clean Air for Europe (CAFE) programme. Policy context Following the Air Quality Framework Directive (96/62/EC 1 ), a number of limit values have been set for the atmospheric concentrations of main pollutants, including SO 2, NO 2, PM and O 3. Limits have been set at levels that should prevent or reduce harmful effects on health and ecosystems. For the protection of human health, the NO 2 limit value of 2μg/m 3 1h average and 4μg/m 3 annual average has been set in Council Directive 1999/3/EC. The limit value is to be met by 1 January 2. The same Directive also sets limit values for SO 2 and PM, 125μg/m 3 24-hour average and 5μg/m 3 24-hour average respectively, to be met by 1 January 25 (see also Signals 23 AP11-14). For PM there is also an annual average limit value, which is set at 4μg/m 3. The road transport sector is one of the main contributors to air pollutant issues. The contribution to tropospheric ozone formation precursors 2 (TOFP) in 22 was 35% in the EEA31 3, 39% in the EU9 4, 37% in the EU15 and 25% in the CC4 countries. Regarding the emission of primary and secondary PM inorganic pollutants contributing to the formation of particulates 5, in 22 23% was attributed to road transport in EEA31 countries, 16% in EU9, 27% in EU15 and 12% in CC4 countries. It is expected that the share of the transport sector in national total emissions will increase in the coming years, hence also the concentrations observed and caused by this sector (see also TERM 24 3, EEA, 23). Environmental context Short-term exposure to NO 2 is associated with reduced lung function and airway responsiveness and increased reactivity to natural allergens. Long-term exposure is associated with increased risk of respiratory infection in children. NO x also play an important role in a number of important environmental pollution problems, such as acidification, eutrophication and photochemical smog (see Signals AP3b and AP5b). High peak levels result mainly from traffic emissions. Furthermore, total nitrogen oxides (NO x ) contribute to the formation of ground-level (tropospheric) O 3. Exposure to periods of a few days of high O 3 concentration can have adverse health effects, in particular inflammatory responses and reduction in lung function. Exposure to moderate O 3 concentrations for longer periods may lead to a reduction in lung function in young children. SO 2 is directly toxic to humans, its main action being on the respiratory functions. Indirectly, it affects human health as it is converted to sulphate in the form of fine particulate matter (see Signals indicator AP12). In recent years, the road transport sector is particularly responsible for NO x emissions, since directives concerning vehicles fuel quality (including sulphur levels, see European Commission, 23) have had a significant impact on the reduction of SO 2. Finally, fine particles have adverse effects on human health and can be responsible and/or contribute to a number of respiratory problems. Assessment NO x NO x emissions from road transport decreased by 33% in EEA31, EU9 and EU15 and 21% CC4 countries between 199 and 22 (see table 6). This was mainly due to the introduction of catalysers on new cars. Increasing road travel and increasing number of vehicles (see TERM 24 32, EEA 23) has partly offset reductions achieved by emission abatement. Even though emissions have decreased, in 22 road transport still contributed 4% to the total emissions in EEA31 countries, 37% in EU9 countries, 44% in EU15 and 25% in CC4 countries (see table 7). The difference between average yearly traffic and background concentrations (see fig. 3) varies greatly from city to city, but overall higher values are observed at the traffic stations, indicating that traffic contributes significantly to NO 2 concentrations in urban areas. Higher traffic concentrations are systematically not observed for the city of Athens, leading to the conclusion that perhaps significant sources, other than traffic, are 1 OJ L 296, , pp CH 4, CO, NMVOC, NO x 3 Excluding Malta, but including Croatia 4 Data from Malta was not available 5 SO 2, NO x and NH 3 11/7/27 4

5 also present. It could also be the case that the location of the station is such that traffic significantly influences the concentrations measured at the background station, in which case other stations should be chosen, but this could not be realised as data availability was limited. Overall, the interannual variation of the difference between traffic and background values shows a decreasing tendency, mainly due to an increase in the traffic values (see fig. 3 and table 2). Figure 3: Difference between NO 2 traffic and urban background average yearly values AT, WIEN BE, LIEGE CZ, PRAHA DK, COPENHAGEN EE, TALLINN FI, HELSINKI DE, BERLIN GR, ATHENS HU, BUDAPEST IT, ROMA LV, RIGA LT, VILNIUS NO, OSLO PL, KRAKOW PT, LISBOA SK, BRATISLAVA SE, STOCKHOLM GB, LONDON NB: Station pairs from capital cities were preferred, but when not available the next largest city for which data was available was chosen. The traffic and background stations in each city were selected according to their yearly data availability and their completeness as regards hourly data availability. For certain years and stations the data availability was low, however these In Oslo and Liege, the background and traffic concentrations were very close until 2, though thereafter for Liege the difference appears to be stabilised with background concentrations slightly higher than traffic (see fig. 3, 21 and 22) and for Oslo a significant decrease in the background levels combined with a slight decrease in traffic concentrations has lead to a positive difference between traffic and urban background (see fig. 3, 22). Overall, the differences between traffic and urban background are very large in some cases (reaching 3 3 up to 52 μg/m in London in 2), especially in comparison to the annual limit value of 4 μg/m. In 3 22 the largest difference was observed in Rome (49 μg/m ). Although the traffic-background difference between 2 and 21 appeared to be rather stable, even decreasing in some cities, this changed between 21 and 22. The biggest changed was observed in Bratislava, where a slight decrease in the background concentration was combined with a large increase in the traffic 3 concentration (17 μg/m ). In Berlin, the increase observed between 21 and 22 continued at approximately the same pace (due to the significant increase in the traffic concentration), followed also by an increase in traffic concentration in Stockholm. A slight increase in traffic concentration was also observed in Riga. This increase in traffic concentration in various cities across Europe is also apparent in fig. 1 where it is also clear that urban background concentration appears to be rather stable from year to year. Overall, the annual limit value is exceeded at traffic stations in almost all cities (see table 2) and the largest exceedances are observed in London, Rome, Paris and Krakow. The daily variation of NO 2 concentrations across selected traffic and background stations (fig. 4) indicates significantly higher concentrations at traffic compared to background stations, verifying the influence of road traffic. Another indication of the significant influence of traffic on the concentrations measured are the two peaks observed during the day, corresponding to the peak traffic hours which vary from city to city depending on the office and shopping hours. These appear more or less pronounced, depending on whether the city centre (where the traffic stations are located) attracts traffic throughout the day (e.g. London and Berlin) and suffer round the clock congestion, or only 11/7/27 5

6 during office hours like Prague, Helsinki and Tallin which have pronounced peak hours. Overall, exceedances of the hourly limit value are not observed. Finally in fig. 5, the Sunday effect for NO 2 (lower concentrations due to less traffic) is present in most background stations, but significant in traffic stations and also the concentrations observed over the week are generally lower at background stations, verifying once more the traffic influence. Once again it was the traffic stations in Berlin, London and Rome that measured the highest concentrations. Figure 4: Diurnal variation of NO 2 concentrations in 22 for selected traffic and urban background station pairs CZ, PRAHA, Pha2-Riegrovy sady B CZ, PRAHA, Pha1-nam- Republiky T EE, TALLINN, Iismoe B EE, TALLINN, Viru T FI, HELSINKI, Kallio 2 B FI, HELSINKI, Toolo T DE, BERLIN, Buch B DE, BERLIN, Charlottenburg-Lerschpfad T IT, ROMA, Villa Ada B IT, ROMA, P.zza E.Fermi T GB, LONDON, London N. Kensington B GB, LONDON, London Marylebone Road T NB: The selected station pairs were chosen according to their yearly data availability, their completeness as regards hourly data availability and the number of pollutants that were recorded. For certain stations the data availability was low, however these Figure 5: 9 8 Weekly variation of NO 2 concentrations in 22 for selected traffic and urban background station pairs Sa S M T W Th F CZ, PRAHA, Pha2-Riegrovy sady B CZ, PRAHA, Pha1-nam- Republiky T EE, TALLINN, Iismoe B EE, TALLINN, Viru T FI, HELSINKI, Kallio 2 B FI, HELSINKI, Toolo T DE, BERLIN, Buch B DE, BERLIN, Charlottenburg-Lerschpfad T IT, ROMA, Villa Ada B IT, ROMA, P.zza E.Fermi T GB, LONDON, London N. Kensington B GB, LONDON, London Marylebone Road T 11/7/27 6

7 NB: The selected station pairs were chosen according to their yearly data availability, their completeness as regards hourly data availability and the number of pollutants that were recorded. For certain stations the data availability was low, however these PM Primary and secondary emissions of PM decreased by 33% in EEA31, 36% in EU9, 34% in EU15 and 2% in CC4 countries between 199 and 22 (see table 6). The emission reductions were mainly due to abatement measures including fuel switching and the increased penetration of catalytic converters, since the application of abatement techniques to reduce precursor emissions often reduces the primary particle emissions also. However, road transport still contributes significantly to total primary and secondary emissions in 22, 23% in EEA31, 16% in EU9, 27% in EU15 and 12% CC4 countries (see table 7) and the overall increase in road travel and number of vehicles (see TERM 24 32, EEA 23) has partly offset reductions achieved by emission abatement. Data availability for urban areas across Europe is limited, since it only became mandatory to monitor PM concentrations from 21 onwards. The difference between average yearly traffic and background concentrations (see fig. 6) varies from city to city, though not as much as for NO 2. Higher values are almost always observed at traffic stations, indicating that traffic contributes significantly to PM concentrations in urban areas. The yearly variation of PM (see table 3 and fig. 6) shows a notable increase in the traffic concentrations for Krakow, Rome, Tallinn, Berlin, Prague, and Athens between 21 and 22, whereas during the same period a notable increase in the background concentrations is only observed in Krakow and Prague. Overall, in 22 the annual limit value of 4 3 μg/m was exceeded at traffic stations across a number of large cities in Europe. Figure 6: Difference between PM traffic and urban background average yearly values CZ, PRAHA EE, TALLINN FI, HELSINKI DE, BERLIN IT, ROMA NO, OSLO PL, KRAKOW SK, BRATISLAVA GB, LONDON NB: The traffic and background stations in each city were selected according to their yearly data availability and their completeness as regards hourly data availability. For certain years and stations the data availability was low, however these The daily variation of PM concentrations across traffic and background stations (fig. 7) shows that significantly higher concentrations are observed at traffic compared to background stations, verifying the influence of road traffic. The two peaks related to the traffic peak hours are again observed, though, similarly to NO 2, in London and Berlin increased traffic is observed throughout the day (see explanation in NO 2 assessment). In fig. 8, the Sunday effect for PM (lower concentrations due to less traffic) is present at background stations but significant at traffic stations and also the concentrations observed over the week are lower at background stations, verifying once more the traffic influence. The daily limit value is exceeded on most weekdays in Rome, Athens and Stockholm (see accompanying excel sheet for details). 11/7/27 7

8 Figure 7: Diurnal variation of PM concentrations in 22 for selected traffic and urban background station pairs FI, HELSINKI, Kallio 2 B DE, BERLIN, Buch B IT, ROMA, Villa Ada B GB, LONDON, London N. Kensington B FI, HELSINKI, Toolo T DE, BERLIN, Charlottenburg-Lerschpfad T IT, ROMA, P.zza E.Fermi T GB, LONDON, London Marylebone Road T NB: The selected station pairs were chosen according to their yearly data availability, their completeness as regards hourly data availability and the number of pollutants that were recorded. For certain stations the data availability was low, however these Figure 8: 6 Weekly variation of PM concentrations in 22 for selected traffic and urban background station pairs Sa S M T W Th F CZ, PRAHA, Pha2-Riegrovy sady B CZ, PRAHA, Pha1-nam- Republiky T EE, TALLINN, Iismoe B EE, TALLINN, Viru T FI, HELSINKI, Kallio 2 B FI, HELSINKI, Toolo T DE, BERLIN, Buch B DE, BERLIN, Charlottenburg-Lerschpfad T IT, ROMA, Villa Ada B IT, ROMA, P.zza E.Fermi T GB, LONDON, London N. Kensington B GB, LONDON, London Marylebone Road T NB: The selected station pairs were chosen according to their yearly data availability, their completeness as regards hourly data availability and the number of pollutants that were recorded. For certain stations the data availability was low, however these 11/7/27 8

9 SO 2 Road transport emissions of sulphur dioxide have been reduced by 76% between 199 and 22 in EEA31, 73% in EU9, 88% in EU15 and 11% in CC4 countries (see table 6). The emission reductions were mainly due to the considerable reductions in the sulphur content of automotive fuels over the period, which appears to have had an effect on the concentrations observed, despite the increasing traffic volumes (see TERM 24 32, EEA 23). However, in 22 SO2 emissions from road transport only represent 2% of the total emissions in all EEA31 countries (see table 7). The difference between traffic and background concentrations (fig. 9) shows a decreasing tendency, mainly due to decreasing traffic concentrations (see table 4). For Athens and Bratislava the background concentrations are significantly higher than the traffic influence, possibly due to the influence of other sources. Between 21 and 22 in Athens, Bratislava and Lisbon there was a slight increase in the background concentration and a significant decrease in the traffic concentration (see table 4). Figure 9: 3 Difference between SO 2 traffic and urban background average yearly values AT, WIEN BE, LIEGE CZ, PRAHA EE, TALLINN DE, BERLIN GR, ATHENS HU, BUDAPEST IT, ROMA LV, RIGA LT, VILNIUS PL, KRAKOW PT, LISBOA SK, BRATISLAVA GB, LONDON NB: Station pairs from capital cities were preferred, but when not available the next largest city for which data was available was chosen. The traffic and background stations in each city were selected according to their yearly data availability and their completeness as regards hourly data availability. For certain years and stations the data availability was low, however these The daily variation of hourly SO 2 concentrations across traffic and background stations (figure ) 3 shows that hourly concentrations are well bellow the hourly limit value (35 μg/m ) and generally the difference between the two station types is very small, strengthening the conclusion that the traffic influence on SO 2 levels is relatively insignificant. Furthermore, the two peaks as observed for PM and NO 2 are not pronounced, again indicating a small traffic influence in the concentrations observed. However, the situation in Rome and London appears to be different, where significantly higher concentrations are observed at traffic stations and peaks in the diurnal pattern appear at peak traffic hours. In fig. 11, in most cities lower concentrations are observed on Sundays, but also during other days of the week, hence the lower concentrations cannot be attributed to less traffic. Furthermore the reduction observed is relatively lower than that of PM or NO2. Overall the average daily values are 3 significantly lower than the limit value of 125 μg/m. 11/7/27 9

10 Figure : Diurnal variation of SO 2 concentrations in 22 for selected traffic and urban background station pairs CZ, PRAHA, Pha2-Riegrovy sady B CZ, PRAHA, Pha1-nam- Republiky T EE, TALLINN, Iismoe B EE, TALLINN, Viru T DE, BERLIN, Buch B DE, BERLIN, Charlottenburg-Lerschpfad T IT, ROMA, Villa Ada B IT, ROMA, P.zza E.Fermi T GB, LONDON, London N. Kensington B GB, LONDON, London Marylebone Road T NB: The selected station pairs were chosen according to their yearly data availability, their completeness as regards hourly data availability and the number of pollutants that were recorded. For certain stations the data availability was low, however these Figure 11: Weekly variation of SO 2 concentrations in 22 for selected traffic and urban background station pairs Sa S M T W Th F CZ, PRAHA, Pha2-Riegrovy sady B CZ, PRAHA, Pha1-nam- Republiky T EE, TALLINN, Iismoe B EE, TALLINN, Viru T DE, BERLIN, Buch B DE, BERLIN, Charlottenburg-Lerschpfad T IT, ROMA, Villa Ada B IT, ROMA, P.zza E.Fermi T GB, LONDON, London N. Kensington B GB, LONDON, London Marylebone Road T NB: The selected station pairs were chosen according to their yearly data availability, their completeness as regards hourly data availability and the number of pollutants that were recorded. For certain stations the data availability was low, however these 11/7/27

11 O 3 O 3 concentrations across Europe depend on ozone precursor emissions, however the relationship is highly non-linear. Emissions of TOFP have been reduced between 199 and 22 by 43% in EEA31, 27% in EU9, 48% in EU15 and 16% in CC4 countries (see table 6), but transport volumes have increased partly off-setting the effect of the emission reductions (see TERM 24 32, EEA 23). The difference between traffic and background concentrations (fig. 12) indicates that higher ozone concentrations are observed at background stations, which is to be expected since close to sources NO from emissions may react with O3 to form NO2 thus reducing O3 locally, whereas outside the urban core the NO sink becomes less important and a more balanced ratio between ozone precursors often leads to O 3 generation. Maximum concentrations generally occur downwind of the source areas of the precursor emissions. Higher concentrations at background stations are observed in all cities in 22. Overall, the yearly variation (see table 5) indicates that a complex situation is observed across Europe, since between 21 and 22 certain stations indicate a downward trend, whereas others show increasing values. This has much to do with the non-linearity between O 3 precursor gases and their actual contribution to O 3 concentrations and the particular meteorological conditions prevailing in the area of study, since the recirculation of air masses may cause the polluted (ozone rich) air to reside in the region for a number of days. Figure 12: Difference between O 3 traffic and urban background average yearly values DK, COPENHAGEN EE, TALLINN FI, HELSINKI DE, BERLIN GR, ATHENS IT, ROMA LV, RIGA PT, LISBOA GB, LONDON NB: The traffic and background stations in each city were selected according to their yearly data availability and their completeness as regards hourly data availability. For certain years and stations the data availability was low, however these 11/7/27 11

12 Figure 13: Diurnal variation of O 3 concentrations in 22 for selected traffic and urban background station pairs EE, TALLINN, Iismoe B FI, HELSINKI, Kallio 2 B DE, BERLIN, Buch B IT, ROMA, Villa Ada B GB, LONDON, London N. Kensington B EE, TALLINN, Viru T FI, HELSINKI, Toolo T DE, BERLIN, Charlottenburg-Lerschpfad T IT, ROMA, P.zza E.Fermi T GB, LONDON, London Marylebone Road T NB: The selected station pairs were chosen according to their yearly data availability, their completeness as regards hourly data availability and the number of pollutants that were recorded. For certain stations the data availability was low, however these CO Although in 22 road transport contributes significantly to total emissions, 43% in EEA31, 31% in EU9, 49% in EU15 and 29% in CC4 countries (see table 6), CO traffic and background concentrations showed very low values compared to the limit value (running 8-hour average concentration of mg/m 3 in 25), hence it was not considered necessary to include this pollutant in the analysis. Moreover, a further reduction in CO emissions is expected, judging from the trend between 199 and 22 where a reduction of 53% in EEA31 was observed. However this reduction was less in CC4 countries (17%) but concentration data for these countries were not available in AirBase. References AirBase (developed and maintained by ETC/Air and Climate Change) is available at de Leeuw, F.A.A.M., 22, A set of emission indicators for long-range transboundary air pollution. Environmental Science & Policy, 5, European Commission, 23, Directive 23/17/EC of the European Parliament and of the Council of 3 March 23 amending Directive 98/7/EC relating to the quality of petrol and diesel fuels. EEA, 23, Air Pollution in Europe 199-2, ΕΕΑ Topic Report No 4, European Environment Agency, Copenhagen, Denmark, 23 EEA ETC/ACC, 24, Manipulated data based on 24 update of Member States data reported to UNECE/CLRTAP/EMEP. Base data are available on the EMEP web site ( 11/7/27 12

13 Data Table 1: Data availability for the traffic and urban background stations considered in the analysis Station Type NO2 SO2 PM O AT, WIEN, Wien Stephansplatz B 33% 99% 98% 98% % % % % 99% 99% 98% 99% % % % % 98% 98% % % % AT, WIEN, Wien Rinnbekstrasse T 99% 99% 98% 98% 99% % % 98% 98% 99% 97% 99% % % BE, LIEGE, 43R223 - Jemeppe B % 6% 11% 92% 93% % - 96% 99% 56% - 98% 98% BE, LIEGE, 43R21 - Liege T 84% 89% 9% 92% 87% 91% 42% 84% 89% 9% 94% 93% 96% 96% % 89% 94% 93% 8% 93% CZ, PRAHA, Pha2-Riegrovy sady B 96% 95% 98% 98% 96% 97% 96% 96% % 95% 96% 96% 94% 96% 96% 98% 77% 94% CZ, PRAHA, Pha1-nam- Republiky T 97% 97% 99% 99% 99% 99% 99% 98% % 99% 99% 99% 99% 96% % % 99% 99% 29% 98% 96% % 79% 95% 96% DK, COPENHAGEN, Copenhagen/1259 B % % % 53% DK, COPENHAGEN, Copenhagen/1257 T 61% 95% 97% 95% 99% 95% 99% 72% 96% 94% 98% 97% % 89% % 97% - 96% 99% EE, TALLINN, Iismoe B % % % % % % % % EE, TALLINN, Viru T - 92% 94% 93% 95% 94% 97% % 97% % 98% - 98% 88% 64% 94% 94% 97% FI, HELSINKI, Kallio 2 B % 99% 97% 98% % 99% 98% 99% % 99% 98% FI, HELSINKI, Toolo T 95% 99% 99% 99% 99% 99% 97% - 96% 96% % 97% 98% 99% 99% 99% 99% 98% % % 95% % FR, PARIS, 18eme B % 99% 78% 91% FR, PARIS, Champs Elysees T % 98% 86% 97% DE, BERLIN, Buch B 99% 99% 98% % 98% 93% 99% % % % % 98% 95% % 99% 96% 98% % % 96% 85% 98% 95% % DE, BERLIN, Charlottenburg-Lerschpfad T 97% 99% 95% 99% 95% 93% 99% % 99% 95% 99% 95% 93% 99% % 96% 98% 97% 99% 96% 99% 96% 88% 95% GR, ATHENS, NeaSmyrni B - 96% - 85% 82% 82% 89% 92% - 98% - 93% 92% 88% 92% % - 94% 96% 92% 85% GR_ATHENS_Marussi T - 44% - 64% 61% 99% 98% - 84% - 9% 35% 89% 97% % 43% - 79% - 89% 87% 97% 98% HU, BUDAPEST, Laborc utca B - 97% % % HU, BUDAPEST, Széna tér T - 99% % IT, ROMA, Villa Ada B % 92% - 92% % 61% 84% 94% % 73% 88% 88% % 95% 93% 94% IT, ROMA, P.zza E.Fermi T % 93% % 94% % 96% % 92% LV, RIGA, Riga Kengarags-2 B % 8% 83% % 73% 69% % 7% 72% LV, RIGA, Riga Centrs T % 8% 75% % 85% 91% % % 68% 88% LT, VILNIUS, Vilnius 21A B % 99% % 99% % LT, VILNIUS, Vilnius 13A T - 95% 99% 96% % 99% 97% % 96% 97% NO, OSLO, Nordahl Brunsgate B 25% - 25% 5% 15% % 47% 49% 39% 24% 24% NO, OSLO, Kirkeveien T 23% - 25% 46% 5% 49% 99% % 46% 49% 49% 99% PL, KRAKOW, KrakProk B % 73% 83% 46% % - 89% 94% 99% 99% % 39% % 91% 84% 99% - PL, KRAKOW, KrakKras T - 93% 93% 94% 84% 9% 96% % 93% - 9% 98% 98% 85% 99% 9% PT, LISBOA, Beato B - 98% 99% 99% 91% 99% 95% - 96% 99% 98% 92% 99% 98% % 99% 91% 99% 97% PT, LISBOA, Entrecampos T - % 98% 99% 93% 85% % 87% % 93% 84% 81% - % 96% 96% 93% 8% 79% - 91% 97% 99% 85% 86% - SK, BRATISLAVA, Mamateyova B 53% 87% 89% 97% 91% 96% 97% 95% - % 96% % 98% 96% % - 89% 94% - 82% 98% 98% SK, BRATISLAVA, Trnavske myto T 99% 89% 67% 95% 97% 96% 99% 96% % 96% 96% 99% 93% 99% % 93% 96% 96% SE, STOCKHOLM, Södermalm B % 98% 98% 96% % 93% 91% 93% % 96% 97% 95% SE_STOCKHOLM_Hornsgatan T % 99% 99% 98% % 99% 93% 94% GB, LONDON, N. Kensington B 72% 99% 99% 97% 96% 96% 98% 74% 99% 99% 99% 96% 97% 99% 75% 97% 98% 99% 96% 96% 98% - 98% 98% 99% 95% 97% 99% GB, LONDON, Marylebone Rd T - 39% 98% 93% 96% 94% 98% - 36% 94% 96% 96% 85% 96% - 45% 98% 95% 99% 89% 98% - 44% 87% 96% 99% 96% 97%, 24 NB: The data in red indicate that hourly data were not available and so average daily data were used to calculate the average yearly values. File: TERM_24_4_EEA31_Transport_contribution_to_AQ_Final_523.xls 11/7/27 13

14 Table 2: Average yearly NO 2 values Station Type NO AT, WIEN, Wien Stephansplatz B AT, WIEN, Wien Rinnbekstrasse T BE, LIEGE, 43R223 - Jemeppe B N/A N/A BE, LIEGE, 43R21 - Liege T CZ, PRAHA, Pha2-Riegrovy sady B CZ, PRAHA, Pha1-nam- Republiky T DK, COPENHAGEN, Copenhagen/1259 B N/A N/A N/A N/A N/A N/A 2 DK, COPENHAGEN, Copenhagen/1257 T EE, TALLINN, Iismoe B N/A N/A N/A N/A N/A EE, TALLINN, Viru T N/A FI, HELSINKI, Kallio 2 B N/A N/A N/A FI, HELSINKI, Toolo T FR, PARIS, 18eme B N/A N/A N/A FR, PARIS, Champs Elysees T N/A N/A N/A DE, BERLIN, Buch B DE, BERLIN, Charlottenburg-Lerschpfad T GR, ATHENS, NeaSmyrni B N/A 5 N/A GR, ATHENS, Marussi T N/A 36 N/A HU, BUDAPEST, Laborc utca B N/A 54 N/A N/A N/A N/A N/A HU, BUDAPEST, Széna tér T N/A 52 N/A N/A N/A N/A N/A IT, ROMA, Villa Ada B N/A N/A N/A IT, ROMA, P.zza E.Fermi T N/A N/A N/A N/A N/A LV, RIGA, Riga Kengarags-2 B N/A N/A N/A N/A LV, RIGA, Riga Centrs T N/A N/A N/A N/A LT, VILNIUS, Vilnius 21A B N/A N/A N/A N/A N/A LT, VILNIUS, Vilnius 13A T N/A N/A N/A N/A NO, OSLO, Nordahl Brunsgate B 45 N/A N/A 21 NO, OSLO, Kirkeveien T 51 N/A PL, KRAKOW, KrakProk B N/A N/A N/A PL, KRAKOW, KrakKras T N/A PT, LISBOA, Beato B N/A PT, LISBOA, Entrecampos T N/A N/A SK, BRATISLAVA, Mamateyova B SK, BRATISLAVA, Trnavske myto T SE, STOCKHOLM, Södermalm B N/A N/A N/A SE, STOCKHOLM, Hornsgatan T N/A N/A N/A GB, LONDON, London N. Kensington B GB, LONDON, London Marylebone Road T N/A , 24 11/7/27 14

15 Table 3: Average yearly PM values Station Type PM AT, WIEN, Wien Stephansplatz B N/A N/A N/A N/A N/A N/A N/A AT, WIEN, Wien Rinnbekstrasse T N/A N/A N/A N/A N/A N/A N/A BE, LIEGE, 43R223 - Jemeppe B N/A N/A 42 BE, LIEGE, 43R21 - Liege T N/A N/A N/A N/A N/A N/A N/A CZ, PRAHA, Pha2-Riegrovy sady B CZ, PRAHA, Pha1-nam- Republiky T DK, COPENHAGEN, Copenhagen/1259 B N/A N/A N/A N/A N/A N/A 25 DK, COPENHAGEN, Copenhagen/1257 T N/A N/A N/A N/A N/A EE, TALLINN, Iismoe B N/A N/A N/A N/A N/A EE, TALLINN, Viru T N/A N/A N/A N/A N/A 3 41 FI, HELSINKI, Kallio 2 B N/A N/A N/A FI, HELSINKI, Toolo T DE, BERLIN, Buch B N/A N/A N/A DE, BERLIN, Charlottenburg-Lerschpfad T N/A N/A N/A N/A GR, ATHENS, NeaSmyrni B N/A N/A N/A N/A N/A N/A N/A GR, ATHENS, Marussi T N/A N/A N/A N/A N/A 55 7 HU, BUDAPEST, Laborc utca B N/A N/A N/A N/A N/A N/A N/A HU, BUDAPEST, Széna tér T N/A N/A N/A N/A N/A N/A N/A IT, ROMA, Villa Ada B N/A N/A N/A IT, ROMA, P.zza E.Fermi T N/A N/A N/A N/A N/A LV, RIGA, Riga Kengarags-2 B N/A N/A N/A N/A N/A N/A N/A LV, RIGA, Riga Centrs T N/A N/A N/A N/A N/A N/A 58 LT, VILNIUS, Vilnius 21A B N/A N/A N/A 29 N/A N/A N/A LT, VILNIUS, Vilnius 13A T N/A N/A N/A N/A N/A N/A N/A NO, OSLO, Nordahl Brunsgate B N/A NO, OSLO, Kirkeveien T N/A N/A PL, KRAKOW, KrakProk B N/A PL, KRAKOW, KrakKras T N/A PT, LISBOA, Beato B N/A N/A N/A N/A N/A N/A N/A PT, LISBOA, Entrecampos T N/A SK, BRATISLAVA, Mamateyova B N/A N/A N/A N/A N/A N/A 31 SK, BRATISLAVA, Trnavske myto T N/A N/A N/A SE, STOCKHOLM, Södermalm B N/A N/A N/A N/A N/A N/A N/A SE, STOCKHOLM, Hornsgatan T N/A N/A N/A GB, LONDON, London N. Kensington B GB, LONDON, London Marylebone Road T N/A , 24 11/7/27 15

16 Table 4: Average yearly SO 2 values Station Type SO AT, WIEN, Wien Stephansplatz B AT, WIEN, Wien Rinnbekstrasse T BE, LIEGE, 43R223 - Jemeppe B N/A N/A N/A N/A N/A N/A 11 BE, LIEGE, 43R21 - Liege T CZ, PRAHA, Pha2-Riegrovy sady B 4 N/A N/A N/A CZ, PRAHA, Pha1-nam- Republiky T 52 N/A N/A N/A DK, COPENHAGEN, Copenhagen/1259 B N/A N/A N/A N/A N/A N/A N/A DK, COPENHAGEN, Copenhagen/1257 T N/A N/A EE, TALLINN, Iismoe B N/A N/A N/A N/A N/A 2 1 EE, TALLINN, Viru T N/A N/A N/A N/A N/A 3 2 FI, HELSINKI, Kallio 2 B N/A N/A N/A N/A N/A N/A N/A FI, HELSINKI, Toolo T N/A 4 4 N/A N/A N/A N/A DE, BERLIN, Buch B N/A DE, BERLIN, Charlottenburg-Lerschpfad T GR, ATHENS, NeaSmyrni B N/A 26 N/A GR, ATHENS, Marussi T N/A 16 N/A HU, BUDAPEST, Laborc utca B N/A 37 N/A N/A N/A N/A N/A HU, BUDAPEST, Széna tér T N/A 51 N/A N/A N/A N/A N/A IT, ROMA, Villa Ada B N/A N/A N/A IT, ROMA, P.zza E.Fermi T N/A N/A N/A N/A N/A 8 7 LV, RIGA, Riga Kengarags-2 B N/A N/A N/A N/A LV, RIGA, Riga Centrs T N/A N/A N/A N/A LT, VILNIUS, Vilnius 21A B N/A N/A 9 6 N/A N/A N/A LT, VILNIUS, Vilnius 13A T N/A N/A N/A N/A NO, OSLO, Nordahl Brunsgate B N/A N/A N/A N/A N/A N/A N/A NO, OSLO, Kirkeveien T N/A N/A N/A N/A N/A N/A N/A PL, KRAKOW, KrakProk B N/A N/A N/A N/A N/A N/A 11 PL, KRAKOW, KrakKras T N/A N/A N/A N/A N/A PT, LISBOA, Beato B N/A PT, LISBOA, Entrecampos T N/A SK, BRATISLAVA, Mamateyova B SK, BRATISLAVA, Trnavske myto T SE, STOCKHOLM, Södermalm B N/A N/A N/A SE, STOCKHOLM, Hornsgatan T N/A N/A N/A N/A N/A N/A N/A GB, LONDON, London N. Kensington B GB, LONDON, London Marylebone Road T , 24 11/7/27 16

17 Table 5: Average yearly O 3 values Station Type O AT, WIEN, Wien Stephansplatz B AT, WIEN, Wien Rinnbekstrasse T N/A N/A N/A N/A N/A N/A N/A BE, LIEGE, 43R223 - Jemeppe B N/A N/A N/A N/A N/A N/A N/A BE, LIEGE, 43R21 - Liege T CZ, PRAHA, Pha2-Riegrovy sady B N/A N/A N/A N/A N/A N/A N/A CZ, PRAHA, Pha1-nam- Republiky T DK, COPENHAGEN, Copenhagen/1259 B N/A N/A N/A N/A N/A DK, COPENHAGEN, Copenhagen/1257 T N/A N/A N/A EE, TALLINN, Iismoe B N/A N/A N/A N/A N/A EE, TALLINN, Viru T N/A FI, HELSINKI, Kallio 2 B N/A N/A N/A N/A FI, HELSINKI, Toolo T DE, BERLIN, Buch B DE, BERLIN, Charlottenburg-Lerschpfad T GR, ATHENS, NeaSmyrni B N/A 59 N/A GR, ATHENS, Marussi T N/A 61 N/A HU, BUDAPEST, Laborc utca B N/A 5 N/A N/A N/A N/A N/A HU, BUDAPEST, Széna tér T N/A N/A N/A N/A N/A N/A N/A IT, ROMA, Villa Ada B N/A N/A N/A IT, ROMA, P.zza E.Fermi T N/A N/A N/A N/A N/A LV, RIGA, Riga Kengarags-2 B N/A N/A N/A N/A LV, RIGA, Riga Centrs T N/A N/A N/A N/A LT, VILNIUS, Vilnius 21A B N/A N/A N/A N/A N/A N/A N/A LT, VILNIUS, Vilnius 13A T N/A N/A N/A N/A NO, OSLO, Nordahl Brunsgate B N/A N/A N/A N/A N/A N/A N/A NO, OSLO, Kirkeveien T N/A N/A N/A N/A N/A N/A N/A PL, KRAKOW, KrakProk B N/A N/A N/A PL, KRAKOW, KrakKras T N/A N/A N/A N/A N/A N/A N/A PT, LISBOA, Beato B N/A N/A PT, LISBOA, Entrecampos T N/A N/A SK, BRATISLAVA, Mamateyova B N/A 3 3 N/A SK, BRATISLAVA, Trnavske myto T N/A N/A N/A N/A N/A N/A N/A SE, STOCKHOLM, Södermalm B N/A N/A N/A SE, STOCKHOLM, Hornsgatan T N/A N/A N/A N/A N/A N/A N/A GB, LONDON, London N. Kensington B GB, LONDON, London Marylebone Road T N/A , 24 11/7/27 17

18 Table 6: Change in emissions from the road transport sector PFP 6 Nox SO2 TOFP CO Austria 3% % -48% -3% -55% Belgium -21% -19% -75% -29% -37% Denmark -32% -33% -94% -42% -39% Finland -5% -56% -96% -48% -25% France -41% -41% -83% -55% -68% Germany -47% -48% -97% -67% -71% Greece -2% 8% -89% 13% 6% Ireland % 14% -72% -27% -37% Italy -25% -22% -9% -32% -41% Luxembourg -22% -28% -15% -35% -35% Netherlands -36% -36% -91% -42% -49% Portugal 37% 45% -68% 7% -16% Spain -3% 1% -78% -22% -46% Sweden -37% -41% -92% -51% -55% United Kingdom -45% -45% -95% -58% -64% EU15-34% -33% -88% -48% -56% Cyprus -5% 1% -15% -3% -1% Czech Republic -4% -38% -31% -32% -16% Estonia -71% -68% -95% -8% -83% Hungary -% -6% -9% -17% -21% Latvia -22% -24% 133% -46% -6% Lithuania -47% -44% -85% -61% -79% Poland -43% -39% -75% -15% -58% Slovakia -12% -12% -72% -13% -3% Slovenia -11% -7% -82% -12% -22% EU9 (no Malta) -36% -33% -73% -27% -51% EU24 (no Malta) -34% -33% -84% -45% -55% Iceland 1322% 2133% % 27% 97% Liechtenstein -6% -63% -8% -61% -39% Norway -41% -42% -85% -5% -57% EFTA3-34% -34% -78% -43% -52% Bulgaria -61% -6% -72% -57% -49% Croatia 16% 21% -21% -15% -51% Romania -53% -54% -4% -42% -41% Turkey 1% 1% % 8% 14% CC4-2% -21% -11% -16% -17% EEA32 (no Malta, including Croatia) -33% -33% -76% -43% -53% Source: EEA/ETC-ACC, 24 6 The weighting of each pollutant can be found in de Leeuw, F.A.A.M. (22). 11/7/27 18

19 Table 7: Road transport contribution to total emissions in 22 PFP Nox SO2 TOFP CO Austria 38% 52% 6% 33% 24% Belgium 29% 45% 3% 35% 37% Denmark 24% 33% 1% 34% 49% Finland 24% 34% % 34% 51% France 26% 48% 4% 35% 34% Germany 29% 44% % 32% 44% Greece 16% 34% % 5% 69% Ireland 19% 38% 2% 39% 74% Italy 34% 53% 2% 49% 65% Luxembourg 32% 39% 11% 42% 63% Netherlands 32% 42% 2% 41% 57% Portugal 25% 42% 2% 36% 57% Spain 2% 37% 1% 29% 45% Sweden 3% 4% 1% 33% 49% United Kingdom 3% 45% % 37% 59% EU15 27% 44% 1% 37% 49% Cyprus 24% 45% 14% 58% 97% Czech Republic 18% 28% 2% 27% 41% Estonia 11% 36% 1% 31% 36% Hungary 26% 61% % 54% 7% Latvia 35% 5% 13% 34% 29% Lithuania 26% 58% 2% 38% 42% Poland 12% 3% 2% 41% 19% Slovakia 21% 39% 1% 37% 49% Slovenia 26% 57% 1% 47% 59% EU9 (no Malta) 16% 37% 2% 39% 31% EU24 (no Malta) 25% 43% 2% 37% 46% Iceland 16% 24% 1% 39% 94% Liechtenstein 43% 62% % 42% 74% Norway 16% 21% 2% 17% 45% EFTA3 16% 21% 2% 19% 49% Bulgaria 7% 29% % 28% 31% Croatia 29% 44% 9% 45% 48% Romania 2% 7% % 9% 14% Turkey 18% 28% 5% 32% 36% CC4 12% 25% 3% 25% 29% EEA32 (no Malta, including Croatia) 23% 4% 2% 35% 43% Source: EEA/ETC-ACC, 24 11/7/27 19

20 Metadata Technical information 1 Data source: AirBase data ( and National total and sectoral emissions officially reported to the UNECE/CLRTAP/ EMEP, update Description of data: National air quality measurement data reported to AirBase database under framework of the European Council Decision 97/1/EC and Comission Decision 21/752/EC. 3. Spatial coverage: As much as EEA32 as possible. The data missing from EU-15 are: France: The stations in AirBase are characterised (traffic, background), but for the traffic stations it is not indicated where (in which city) these are located. However after contacting Dominique Gombert working for the AirParif station network, it was possible to acquire the information needed to select the traffic station needed for this analysis. Ireland: The data available is not sufficient for this analysis. The only background station in AirBase measures only PM daily average data (no hourly or any other pollutant data), and only 21 and 22 data are available, and even then 22 data are incomplete (no August-November data). Luxembourg: Only traffic station data is available. Spain: There are no background station data in the major cities. The data missing from EU- are: Slovenia: The data available is not sufficient for this analysis as the cities have either only traffic or only background stations. Malta: only background station data is available. Cyprus: No data is available. Poland: No traffic station data is available for Warsaw, so the urban area of Krakow was studied instead. Hungary: Only 1997 data were available and used where appropriate. The data missing from CC4: Bulgaria: There is no traffic station available. Romania: The data available is not sufficient for this analysis as the cities have either only traffic or only background stations. Turkey: No data was available Croatia: No data was available. 4. Temporal coverage: was attempted, however data is not always available (see table 1). 5. Methodology: Annual country data submissions to AirBase (at ETC/ACC web site) were downloaded from the database and manipulated. 6. Methodology of manipulation: Concentrations: The average diurnal variation was obtained by averaging each hour of the hourly data available at the selected measurement station. Average weekly variation was obtained by averaging the daily average for each day of the week (hourly or average daily data where used, depending on data availability) at the selected measurement station. Average yearly data was obtained from average hourly or average daily data, whichever was available at the selected measurement station (see table 1 for details). For all of the above, data gaps were not filled in. Emissions: Annual country data submissions to the CLRTAP have been used. ETC-ACC gap-filling methodology (see TERM 24 3 factsheet for details): to allow trend analysis, where countries have not reported data for one, or several years, data has been interpolated to derive annual emission when data is missing between two different years. If the reported data is missing 11/7/27 2

21 either at the beginning or at the end of the time series period, the emission value has been considered to equal the first (or last) reported emission value. It is recognised that the use of gap-filling can potentially lead to artificial trends, but it is considered unavoidable if a comprehensive and comparable set of emissions data for European countries is required for policy analysis purposes. Qualitative information 7. Strengths and weaknesses: Strength: officially reported data by the countries to AirBase is used. Weakness: data reported across countries varies in quantity. The station characterisation (urban background or traffic) is difficult to compare across countries. 8. Reliability, accuracy, robustness, uncertainty: The uncertainties are discussed in the indicator fact sheet for each graph separately. The data quality cannot be commented upon, since it data reported by the individual countries, but data availability is sometimes low and does not allow for robust conclusions/intercomparisons. See also table 1. Main problem is the lack of data and not the actual quality of the data available. 9. Overall scoring (1 3, 1 = no major problems, 3 = major reservations): 2 Relevancy: 1 Accuracy: 2 Comparability over time: 3 Comparability over space: 3 Further work required Timeseries Countries should improve data availability in AirBase, in terms of the yearly coverage. A suggestion would be that for stations that have recently been included in AirBase, also the past data could be uploaded, if available. Pollutants In terms of the pollutant coverage, all countries should ensure that there is at least one station of each type in AirBase, for the largest urban agglomeration and measuring all basic pollutants such as NO x, CO, PM, SO 2. In this analysis NO 2 has been used instead of NO x due to the lack of available data. When looking at traffic contribution, it would be more appropriate to study NO x data, so improvement in this direction would also be necessary. Furthermore, as scientific evidence indicates that PM 2.5 form the largest part of the PM measured at traffic stations, it should become obligatory for the member states to measure PM 2.5 in urban agglomerations, namely at traffic stations. Other data Meteorological data is needed in order to estimate the meteorologically induced variation in the concentrations observed. Member States should be encouraged to submit such data to a relevant database, perhaps AirBase could be extended to include such information also, as it is always needed in an air quality analysis. Station information in AirBase It is not sufficient to just know the station type but rather it is necessary to understand the geometry of the area the station is located in and also where within the urban area (in relation to major roads and significant industrial sources) it is located. Such data is requested by AirBase, but following the EoI decision, it is not obligatory for the MS to deliver this data. It is important that MS submit this type of data, as this would greatly help with this type of analysis. Spatial coverage Based on measurements, spatial coverage of EEA-32 is not possible. This can only be done with the assistance of models. In order to draw conclusions for the urban areas as a whole (and not station specific conclusions) using measurements only, adequate coverage in terms of number of stations is necessary. This is not the case for the urban areas at present. Other transport modes Based on available measurements, only road transport can be considered in this analysis. 11/7/27 21