Indicator factsheet TERM 2003 04 EEA-31 — Transport contribution to air quality · 2017. 8. 26. · Indicator factsheet TERM 2003 04 EEA-31 — Transport contribution to air quality

Indicator factsheet

TERM 2003 04 EEA-31 — Transport contribution to air quality Road transport is the largest contributor to NOx emissions in Europe and the second

largest for PM10. The data analysed from selected stations in major urban agglomerations indicate that both mean and maximum values of NO2 concentrations at road traffic stations appear to remain stable during the period 1999–2001 and are accompanied by a similar tendency in the background concentrations. For PM10 the situation appears more complex, since mean values at road traffic stations appear to remain stable, whereas the maximum values vary considerably over the same period. Both mean and maximum urban background PM10 values remain stable. Overall the decrease in emissions does not appear to have a 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: Mean and maximum values of annual averages of NO2 for traffic and urban background stations

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mean-traff ic max.-traff ic mean-backgd max-backgd

Figure 2: Mean and maximum values of annual averages of PM10 for traffic and urban background stations

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mean-traffic max.-traffic mean-backgd max-backgd

NB: Station pairs from capital cities were preferred, but when not available the next largest city for which data were available was chosen. Since the available data (stations) vary from year to year (see Tables 2 and 3) only the years 1999–2001 were chosen in order to ensure a consistent dataset across these years. Meteorological data are 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. Source: AirBase.

Results and assessment Policy relevance Most of the high NO2 and PM10 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 SO2, NO2, PM10 and O3. Limits have been set at levels that should prevent or reduce harmful effects on health and ecosystems.

For the protection of human health, the NO2 limit value of 200µg/m3 1h average and 40µg/m3 annual average has been set in Council Directive 1999/30/EC. The limit value is to be met by 1 January 2010. The same directive also sets limit values for SO2 and PM10, 125µg/m3 24-hour average and 50µg/m3 24-hour average respectively, to be met by 1 January 2005 (see also Signals 2002 AP11-14). For PM10 there is also an annual average limit value, which is set at 40µg/m3.

The road transport sector is one of the main contributors to air pollutant issues. The contribution to tropospheric ozone formation precursors (2) (TOFP) in 2001 was 39 % in the EEA-18, 31 % in the AC-9 (3) and 28 % in the CC-3 countries. Regarding the emission of primary and secondary PM10 inorganic pollutants contributing to the formation of particulates (4), in 2001, 28 % was attributed to road transport in EEA-18 countries, 16 % in AC-9 and 10 % in CC-3 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 2003 03, EEA, 2003).

Environmental context Short-term exposure to NO2 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. Nitrogen oxides (NOx) 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 contribute to the formation of ground-level (tropospheric) O3. Exposure to periods of a few days of high O3 concentration can have adverse health effects, in particular inflammatory responses and reduction in lung function. Exposure to moderate O3 concentrations for longer periods may lead to a reduction in lung function in young children. Its main action being on the respiratory functions, SO2 is directly toxic to humans. 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 NOx emissions, since directives concerning vehicles’ fuel quality (including sulphur levels, see European Commission, 2003) have had a significant impact on the reduction of SO2. Finally, fine particles have adverse effects on human health and can be responsible and/or contribute to a number of respiratory problems.

Assessment NOxNOx emissions from road transport decreased by 28 % in EEA-18, 33 % in AC-9 and 15 % CC-3 countries between 1990 and 2001 (see Table 6). This was mainly due to the introduction of catalysers on new cars. Increasing road travel and an increasing number of vehicles (see TERM 2003 32, EEA 2003) has partly offset reductions achieved by emission abatement. Even though emissions have decreased, in 2001 road transport still contributed 45 % to the total emissions in EEA-18 countries, 34 % in AC-9 countries and 27 % in CC-3 countries (see Table 7). The difference between average yearly traffic and background concentrations (see Figure 3) varies greatly from city to city, but overall higher values are observed at the traffic stations, indicating that traffic contributes significantly to NO2 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 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

(1) OJ L 296, 21.11.1996, pp. 55–63. (2) CH4, CO, NMVOC, NOx. (3) Data from Malta were not available. (4) SO2, NOx and NH3.

should be chosen, but this could not be realised as data availability was limited. Overall, the interannual variation of the concentrations shows a decrease in the background concentrations and an increase in the traffic concentrations (see Table 2).

Figure 3: Difference between NO2 traffic and urban background average yearly values

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1996 1997 1998 1999 2000 2001

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AT, WIEN BE, LIEGE CZ, PRAHA 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 were 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 were used as no alternative was available. See Table 1 for details.

Source: AirBase.

In Oslo and Liège, the background and traffic concentrations are very close. In 2000, higher background concentrations are observed in Oslo (background contribution increased), whereas in 2001, higher background concentrations are observed in Liège due to a slight decrease in the traffic concentrations.

In general, the differences between traffic and urban background are very large in some cases (reaching up to 52 µg/m3 in London in 2000), especially in comparison with the annual limit value of 40 µg/m3. The traffic background difference between 2000 and 2001 appears to be rather stable, even decreasing in some cities (with the exception of Berlin where an increase is observed), indicating a possible overall decrease in the NO2 traffic concentrations, since the urban background concentrations appear to be rather stable from year to year (see Figure 1). Overall, the annual limit value is exceeded at traffic stations in almost all cities (see Table 2) and to the largest extent in London, Rome and Kraków.

The daily variation of NO2 concentrations across selected traffic and background stations (Figure 4) indicates significantly higher concentrations at traffic compared with 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 suffers round the clock congestion, or only during office hours like Helsinki and Tallin which have very pronounced peak hours. Overall, the hourly limit value is not exceeded.

Finally in Figure 5, the ‘Sunday effect’ for NO2 (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. Again it was the traffic stations in Berlin, London and Rome that measured the highest concentrations.

Figure 4: Diurnal variation of NO2 concentrations in 2001 for selected traffic and urban

background station pairs

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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 were used as no alternative was available. See Table 1 for details.

Source: AirBase.

Figure 5: Weekly variation of NO2 concentrations in 2001 for selected traffic and urban background station pairs

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EE, TALLINN, Viru T FI, HELSINKI, Kallio 2 B FI, HELSINKI, Toolo T


IT, ROMA, P.zza E.Fermi T GB, LONDON, London N. Kensington B GB, LONDON, London Marylebone Road T


Source: AirBase.

PM10Primary and secondary emissions of PM10 decreased by 29 % in EEA-18, 35 % in AC-9 and 14 % in CC-3 countries between 1990 and 2000 (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 2001, 28 % in EEA-18, 16 % in AC-9 and 10 % CC-3 countries (see Table 7) and the overall increase in road travel and number of vehicles (see TERM 2003 32, EEA 2003) has partly offset reductions achieved by emission abatement.

Data availability for urban areas across Europe is limited, since it only became mandatory to monitor PM10 concentrations from 2001 onwards. The difference between average yearly traffic and background concentrations (see Figure 6) varies from city to city, though not as much as for NO2. Higher values are almost always observed at traffic stations, indicating that traffic contributes significantly to PM10 concentrations in urban areas. The yearly variation of PM10 (see Table 3 and Figure 6) shows a notable increase in the traffic concentrations for Kraków, Berlin and Stockholm between 2000 and 2001, whereas during the same period a notable increase in the background concentrations is only observed in Oslo. Overall, in 2001 the annual limit value was exceeded at traffic stations in Kraków, Athens, Rome and Stockholm.

Figure 6: Difference between PM10 traffic and urban background average yearly values

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CZ, PRAHA EE, TALLINN FI, HELSINKI DE, BERLIN IT, ROMA NO, OSLO PL, KRAKOW 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 were used as no alternative was available. See Table 1 for details.

Source: AirBase.

The daily variation of PM10 concentrations across traffic and background stations (Figure 7) shows that significantly higher concentrations are observed at traffic compared with background stations, verifying the influence of road traffic. The two peaks related to the traffic peak hours are again observed, though, similarly to NO2, in London and Berlin increased traffic is observed throughout the day (see explanation in NO2 assessment).

In Figure 8, the ‘Sunday effect’ for PM10 (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.

Figure 7: Diurnal variation of PM10 concentrations in 2001 for selected traffic and urban background station pairs

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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


Source: AirBase.

Figure 8: Weekly variation of PM10 concentrations in 2001 for selected traffic and urban background station pairs

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CZ, PRAHA, Pha2-Riegrovy sady B CZ, PRAHA, Pha1-nam- Republiky TEE, TALLINN, Iismoe B EE, TALLINN, Viru TFI, HELSINKI, Kallio 2 B FI, HELSINKI, Toolo TDE, BERLIN, Buch B DE, BERLIN, Charlottenburg-Lerschpfad TIT, ROMA, Villa Ada B IT, ROMA, P.zza E.Fermi TGB, LONDON, London N. Kensington B GB, LONDON, London Marylebone Road T


Source: AirBase.

SO2Road transport emissions of sulphur dioxide fell by 82 % between 1990 and 2001 in EEA-18, 72 % in AC-10 and 11 % in CC-3 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 2003 32, EEA 2003). However, SO2 emissions from road transport only represent 2 % of the total emissions in all EEA-31 (5) countries (see Table 7).

The difference between traffic and background concentrations (Figure 9) shows a decreasing tendency, mainly due to decreasing traffic concentrations (see Table 4). For Athens and Bratislava the background concentrations are higher than the traffic influence, possibly due to the influence of other sources. Between 2000 and 2001 both traffic and background concentrations are reduced in Athens (see Table 4), whereas in Bratislava an increase in the background concentration is observed. An increase in both traffic and background concentrations is also observed in Lisbon.

Figure 9: Difference between SO2 traffic and urban background average yearly values

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AT, WIEN CZ, PRAHA EE, TALLINN DE, BERLIN GR, ATHENS HU, BUDAPEST

IT, ROMA LV, RIGA LT, VILNIUS 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 were 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 were used as no alternative was available. See Table 1 for details.

Source: AirBase.

The daily variation of hourly SO2 concentrations across traffic and background stations (Figure 10) shows that hourly concentrations are well bellow the hourly limit value (350 µg/m3) and generally the difference between the two station types is very small, strengthening the conclusion that the traffic influence on SO2 levels is relatively insignificant. Furthermore, the two peaks as observed for PM10 and NO2 are not pronounced, again indicating a small traffic influence in the concentrations observed.

In Figure 11, 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 PM10 or NO2. Overall the average daily values are significantly lower than the limit value (125 µg/m3).

(5) Malta is not included.

Figure 10: Diurnal variation of SO2 concentrations in 2001 for selected traffic and urban


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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 were used as no alternative was available. See Table 1 for details.

Source: AirBase.

Figure 11: Weekly variation of SO2 concentrations in 2001 for selected traffic and urban background station pairs

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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


Source: AirBase.

O3 Concentrations of O3 across Europe depend on ozone precursor emissions, however the relationships are highly non-linear. Emissions of TOFP fell between 1990 and 2001 by 41 % in EEA-18, 40 % in AC-10 and 8 % in CC-3 countries (see Table 6), but transport volumes increased, partly off-setting the effect of the emission reductions (see TERM 2003 32, EEA 2003).

The difference between traffic and background concentrations (Figure 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 O3 generation. Maximum concentrations generally occur downwind of the source areas of the precursor emissions. This is observed in all cities in 2001, apart from Rome where higher O3 values are observed for the traffic station. Overall, the yearly variation (see Table 5) indicates that a complex situation is observed across Europe, since between 2000 and 2001 certain stations indicate a downward trend, whereas others show increasing values. This has much to do with the non-linearity between O3 precursor gases and their actual contribution to O3 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 O3 traffic and urban background average yearly values

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1996 1997 1998 1999 2000 2001

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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 were used as no alternative was available. See Table 1 for details,

Source: AirBase.

Figure 13: Diurnal variation of O3 concentrations in 2001 for selected traffic and urban


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EE, TALLINN, Iismoe B EE, TALLINN, Viru T FI, HELSINKI, Toolo T


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 were used as no alternative was available. See Table 1 for details.

Source: AirBase.

CO

Although in 2001 road transport contributed significantly to total emissions, 52 % in EEA-18, 33 % in AC-10 and 31 % in CC-3 countries (see Table 6), CO traffic and background concentrations showed very low values compared with the limit value (running eight-hour average concentration of 10mg/m3 in 2005), 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 1990 and 2001 where a reduction of 50 % in EEA-18 and AC-10 was observed. However, this reduction was only 2 % in CC-3 countries but then concentration data for these countries were not available in AirBase.

References AirBase (developed and maintained by ETC/Air and climate change) is available at http://air-climate.eionet.eu.int/databases/airbase.html

European Commission, 2003. Directive 2003/17/EC of the European Parliament and of the Council of 3 March 2003 amending Directive 98/70/EC relating to the quality of petrol and diesel fuels.

EEA, 2003, Air pollution in Europe 1990–2000, European Environment Agency, Copenhagen, Denmark (forthcoming).

EEA–ETC/ACC, 2003, Manipulated data based on 2003 update of Member States’ data reported to UNECE/CLRTAP/EMEP. Base data are available on the EMEP website (http://webdab.emep.int/).

http://air-climate.eionet.eu.int/databases/aibase.htmlhttp://air-climate.eionet.eu.int/databases/aibase.htmlhttp://webdab.emep.int/

Data Table 1: Data availability for the traffic and urban background stations considered in the analysis

Type NO2 SO2 PM10 O31996 1997 1998 1999 2000 2001 1996 1997 1998 1999 2000 2001 1996 1997 1998 1999 2000 2001 1996 1997 1998 1999 2000 2001

AT, WIEN, Wien Stephansplatz B 33% 99% 98% 98% 100% 100% 100% 99% 99% 98% 99% 100% 100% 100% 98% 98% 100% 100%AT, WIEN, Wien Rinnbekstrasse T 99% 99% 98% 98% 99% 100% 98% 98% 99% 97% 99% 100%

BE, LIEGE, 43R223 - JEMEPPE B - - 47% 60% 11% 92% - 96% 99% 56% - 98%

96% 94% 96% 96% 98% 77%99% 99% 96% 100% 100% 99%

- - - - - -BE, LIEGE, 43R201 - LIEGE T 84% 89% 90% 92% 87% 91% 84% 89% 90% 94% 93% 96% - 32% 89% 94% 93% 80%

CZ, PRAHA, Pha2-Riegrovy sady B 96% 95% 98% 98% 96% 97% 96% - - - 97% 95% - - - - - -CZ, PRAHA, Pha1-nam- Republiky T 97% 97% 99% 99% 99% 99% 98% - - - 99% 99% 29% 98% 96% 100% 79% 95%

DK, COPENHAGEN, Copenhagen/1259 B - - 97% 53% - -DK, COPENHAGEN, Copenhagen/1257 T 61% 95% 97% 95% 99% 95% - - 97% 97% - 96%

EE, TALLINN, Iismoe B - - - - - 84% - - - - - 84% - - - - - 84%EE, TALLINN, Viru T - 92% 94% 93% 95% 94% - - - - - 94% - 98% 88% 64% 94% 94%

FI, HELSINKI, Kallio 2 B - - - 98% 99% 97% - - - - - - - - - 99% 99% 98% - - - - 98% 99%FI, HELSINKI, Toolo T 95% 99% 99% 99% 99% 99% - 96% 96% - - - 91% 97% 98% 99% 99% 99% 98% 100% 100% 95% 100% 98

DE, BERLIN, Buch B 99% 99% 98% 100% 98% 93% 100% 100% 100% 100% 98% 95% - - - 98% 99% 96% 100% 100% 96% 85% 98% 95%DE, BERLIN, Charlottenburg-Lerschpfa T 97% 99% 95% 99% 95% 93% 100% 99% 95% 99% 95% 93% - - - - 96% 96% 97% 99% 96% 99% 96% 88%

GR, ATHENS, NeaSmyrni B - 96% - 85% 82% 82% 92% - 98% - 93% 92% 92% - - - - - - 97% - 94% 96% 92%GR_ATHENS_Marussi T - 44% - 64% 61% 99% - 84% - 90% 35% 89% - - - - - 99% - 79% - 89% 87% 97%

HU, BUDAPEST, Laborc utca B - 97% - - - - - 97% - - - - - 93% - - - -HU, BUDAPEST, Szina tir T - 99% - - - - - 89% - - - -

IT, ROMA, Villa Ada B - - - 91% 92% - - - - 86% 61% 84% - - - 40% 73% 88% - - - 95% 95% 93%IT, ROMA, P.ZZA E.FERMI T - - - - - 89% - - - - - 91% - - - - - 84% - - - - - 92%

LV, RIGA, Riga Kengarags-2 B - - - - 93% 80% - - - - 88% 73% - - - - 84% 70%LV, RIGA, Riga Centrs T - - - - 35% 80% - - - - 35% 85% - - - - 33% 68%

LT, VILNIUS, Vilnius 21A B - - 99% 99% - - - - 100% 99% - - - - - 95% - -LT, VILNIUS, Vilnius 13A T - 95% 99% 96% - - - 92% 99% 97% - - - 76% 96% 97% - -

NO, OSLO, Nordahl Brunsgate B 25% - 25% 50% 15% - - 25% 47% 49% 39% 24%NO, OSLO, Kirkeveien T 23% - 25% 46% 50% 49% - - 25% 46% 49% 49%

PL, KRAKOW, KrakProk B - - - 91% 73% 83% - - 64% 91% 84% 99%PL, KRAKOW, KrakKras T - 93% 93% 94% 84% 90% - - - - - 95%

PT, LISBOA, Beato B - 98% 99% 99% 91% 99% - 96% 99% 98% 92% 99% - - 30% 99% 91% 99%PT, LISBOA, Entrecampos T - 100% 98% 99% 93% 85% - 99% 87% 100% 93% 84% - 100% 96% 96% 93% 80% - 91% 97% 99% 85% 86%

SK, BRATISLAVA, Mamateyova B 53% 87% 89% 97% 91% 96% 95% - 100% 96% 100% 98% - 89% 94% - 82% 98%SK, BRATISLAVA, Trnavske myto T 99% 89% 67% 95% 97% 96% 96% 100% 96% 96% 99% 93% - - - 96% 93% 96%

SE, STOCKHOLM, Södermalm B - - - 98% 98% 98% - - - 83% 93% 91% - - - 97% 96% 97%SE_STOCKHOLM_Hornsgatan T - - - 99% 99% 99% - - - 71% 99% 93%

GB, LONDON, LONDON N. KENSING

72% 96% 94% 98% 97% - - - - - - 72%

- - - - - 84%- - - - - 95%

- 89% 94% 99% 99% 100%- 90% 98% 98% 85% 99%

T B 72% 99% 99% 97% 96% 96% 74% 99% 99% 99% 96% 97% 75% 97% 98% 99% 96% 96% 75% 98% 98% 99% 95% 97%GB, LONDON, LONDON MARYLEBON T - 39% 98% 93% 96% 94% - 36% 94% 96% 96% 85% - 45% 98% 95% 99% 89% - 44% 87% 96% 99% 96%

Station

Source: AirBase. 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: FS_Transport_contribution_to_AQ_TERM_2003_03_1stDrft_5dec03.xls

Table 2: Average yearly NO2 values

Type NO2 Station 1996 1997 1998 1999 2000 2001

AT, Vienna, Wien Stephansplatz B 40 45 36 29 29 30 AT, Vienna, Wien Rinnbekstrasse T 45 44 41 42 43 44 BE, Liège, 43R223 — Jemeppe B N/A N/A 36 37 35 38 BE, Liège, 43R201 — Liège T 39 37 39 39 36 35 CZ, Prague, Pha2-Riegrovy sady B 37 36 32 34 32 34 CZ, Prague, Pha1-nam- Republiky T 47 48 42 41 39 42 DK, Copenhagen, Copenhagen/1259 B N/A N/A N/A N/A N/A N/A DK, Copenhagen, Copenhagen/1257 T 46 43 42 47 42 40 EE, Tallinn, Iismoe B N/A N/A N/A N/A N/A 12 EE, Tallinn, Viru T N/A 37 33 30 29 36 FI, Helsinki, Kallio 2 B N/A N/A N/A 26 23 24 FI, Helsinki, Toolo T 41 36 38 39 35 36 DE, Berlin, Buch B 15 16 18 19 17 15 DE, Berlin, Charlottenburg-Lerschpfad T 58 49 38 44 41 46 GR, Athens, NeaSmyrni B N/A 50 N/A 52 53 45 GR, Athens, Marussi T N/A 36 N/A 31 35 35 HU, Budapest, Laborc utca B N/A 54 N/A N/A N/A N/A HU, budapest, Szina tir T N/A 52 N/A N/A N/A N/A IT, Rome, Villa Ada B N/A N/A N/A 20 42 39 IT, Rome, P.zza E.Fermi T N/A N/A N/A N/A N/A 89 LV, Riga, Riga Kengarags-2 B N/A N/A N/A N/A 25 26 LV, Riga, Riga Centrs T N/A N/A N/A N/A 58 43 LT, Vilnius, Vilnius 21A B N/A N/A 16 12 N/A N/A LT, Vilnius, Vilnius 13A T N/A 35 24 27 N/A N/A NO, Oslo, Nordahl Brunsgate B 45 N/A 30 38 43 N/A NO, Oslo, Kirkeveien T 51 N/A 33 44 38 42 PL, Kraków, KrakProk B N/A N/A N/A 35 30 28 PL, Kraów, KrakKras T N/A 68 63 71 74 66 PT, Lisbon, Beato B N/A 25 27 24 17 20 PT, Lisbon, Entrecampos T N/A 29 32 35 39 27 SK, Bratislava, Mamateyova B 21 36 33 32 29 39 SK, Bratislava, Trnavske myto T 51 34 38 58 48 45 SE, Stockholm, Södermalm B N/A N/A N/A 19 19 17 SE, Stockholm, Hornsgatan T N/A N/A N/A 58 51 49 UK, London, London N. Kensington B 44 50 45 46 40 42 UK, London, London Marylebone Road T N/A 95 92 91 93 84

Table 3: Average yearly PM10 values

Type PM10 Station 1996 1997 1998 1999 2000 2001

AT, Vienna, Wien Stephansplatz B N/A N/A N/A N/A N/A N/A AT, Vienna, Wien Rinnbekstrasse T N/A N/A N/A N/A N/A N/A BE, Liège, 43R223 — Jemeppe B N/A 32 29 30 29 N/A BE, Liège, 43R201 — Liege T N/A N/A N/A N/A N/A N/A CZ, Prague, Pha2-Riegrovy sady B 42 35 28 31 31 30 CZ, Prague, Pha1-nam- Republiky T 74 60 29 31 39 36 DK, Copenhagen, Copenhagen/1259 B N/A N/A N/A N/A N/A N/A DK, Copenhagen, Copenhagen/1257 T N/A N/A N/A N/A N/A 34 EE, Tallinn, Iismoe B N/A N/A N/A N/A N/A 18 EE, Tallinn, Viru T N/A N/A N/A N/A N/A 30 FI, Helsinki, Kallio 2 B N/A N/A N/A 16 15 16 FI, Helsinki, Toolo T 26 23 25 22 22 23 DE, Berlin, Buch B N/A N/A N/A 23 23 21 DE, Berlin, Charlottenburg-Lerschpfad T N/A N/A N/A N/A 32 35 GR, Athens, NeaSmyrni B 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 HU, Budapest, Laborc utca B N/A N/A N/A N/A N/A N/A HU, Budapest, Szina tir T N/A N/A N/A N/A N/A N/A IT, Rome, Villa Ada B N/A N/A N/A 19 31 29 IT, Rome, P.zza E.Fermi T N/A N/A N/A N/A N/A 49 LV, Riga, Riga Kengarags-2 B 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 LT, Vilnius, Vilnius 21A B N/A N/A N/A 29 N/A N/A LT, Vilnius, Vilnius 13A T N/A N/A N/A N/A N/A N/A NO, Oslo, Nordahl Brunsgate B N/A 18 23 18 17 25 NO, Oslo, Kirkeveien T N/A N/A 30 26 29 28 PL, Kraków, KrakProk B N/A 39 48 31 30 26 PL, Kraków, KrakKras T N/A 72 68 50 37 43 PT, Lisbon, Beato B N/A N/A N/A N/A N/A N/A PT, Lisbon, Entrecampos T N/A 35 36 33 36 36 SK, Bratislava, Mamateyova B N/A N/A N/A N/A N/A N/A SK, Bratislava, Trnavske myto T N/A N/A N/A 39 39 36 SE, Stockholm, Södermalm B N/A N/A N/A N/A N/A N/A SE, Stockholm, Hornsgatan T N/A N/A N/A 30 39 51 UK, London, London N. Kensington B 23 24 20 21 20 20 UK, London, London Marylebone Road T N/A 39 32 35 37 34

Table 4: Average yearly SO2 values

Type SO2 Station 1996 1997 1998 1999 2000 2001

AT, Vienna, Wien Stephansplatz B 17 14 10 5 7 6 AT, Vienna, Wien Rinnbekstrasse T 21 15 13 8 6 6 BE, Liège, 43R223 — Jemeppe B N/A N/A N/A N/A N/A N/A BE, Liège, 43R201 — Liège T 17 12 13 12 11 11 CZ, Prague, Pha2-Riegrovy sady B 40 N/A N/A N/A 9 7 CZ, Prague, Pha1-nam- Republiky T 52 N/A N/A N/A 12 9 DK, Copenhagen, Copenhagen/1259 B N/A N/A N/A N/A N/A N/A DK, Copenhagen, Copenhagen/1257 T 9 5 4 4 3 EE, Tallinn, Iismoe B N/A N/A N/A N/A N/A 2 EE, Tallinn, Viru T N/A N/A N/A N/A N/A 3 FI, Helsinki, Kallio 2 B 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 DE, Berlin, Buch B 12 7 5 4 4 3 DE, Berlin, Charlottenburg-Lerschpfad T 21 15 11 10 8 8 GR, Athens, NeaSmyrni B N/A 26 N/A 17 17 12 GR, Athens, Marussi T N/A 16 N/A 15 11 9 HU, Budapest, Laborc utca B N/A 37 N/A N/A N/A N/A HU, Budapest, Szina tir T N/A 51 N/A N/A N/A N/A IT, Rome, Villa Ada B N/A N/A N/A 2 1 2 IT, Rome, P.zza E.Fermi T N/A N/A N/A N/A N/A 8 LV, Riga, Riga Kengarags-2 B N/A N/A N/A N/A 6 5 LV, Riga, Riga Centrs T N/A N/A N/A N/A 15 11 LT, Vilnius, Vilnius 21A B N/A N/A 9 6 N/A N/A LT, Vilnius, Vilnius 13A T N/A 9 6 7 N/A N/A NO, Oslo, Nordahl Brunsgate B 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 PL, Kraków, KrakProk B N/A N/A N/A N/A N/A N/A PL, Kraków, KrakKras T N/A N/A N/A N/A N/A 23 PT, Lisbon, Beato B N/A 6 3 1 1 3 PT, Lisbon, Entrecampos T N/A 19 15 13 6 8 SK, Bratislava, Mamateyova B 34 20 31 15 14 16 SK, Bratislava, Trnavske myto T 26 21 17 18 12 11 SE, Stockholm, Södermalm B N/A N/A N/A 3 2 3 SE, Stockholm, Hornsgatan T N/A N/A N/A N/A N/A N/A UK, London, London N. Kensington B 12 12 8 7 6 6 UK, London, London Marylebone Road T 39 23 18 13 15 12

Table 5: Average yearly O3 values

Type O3 Station 1996 1997 1998 1999 2000 2001

AT, Vienna, Wien Stephansplatz B 41 43 49 45 49 45 AT, Vienna, Wien Rinnbekstrasse T N/A N/A N/A N/A N/A N/A BE, Liège, 43R223 — Jemeppe B N/A N/A N/A N/A N/A N/A BE, Liège, 43R201 — Liège T 25 35 41 36 43 CZ, Prague, Pha2-Riegrovy sady B N/A N/A N/A N/A N/A N/A CZ, Prague, Pha1-nam- Republiky T 15 29 41 36 35 30 DK, Copenhagen, Copenhagen/1259 B N/A N/A 47 55 N/A N/A DK, Copenhagen, Copenhagen/1257 T N/A N/A 33 33 N/A 35 EE, Tallinn, Iismoe B N/A N/A N/A N/A N/A 61 EE, Tallinn, Viru T N/A 34 26 26 24 39 FI, Helsinki, Kallio 2 B N/A N/A N/A N/A 45 46 FI, Helsinki, Toolo T 35 37 36 40 38 39 DE, Berlin, Buch B 47 46 49 45 45 47 DE, Berlin, Charlottenburg-Lerschpfad T 19 19 23 25 27 30 GR, Athens, NeaSmyrni B N/A 59 N/A 53 57 57 GR, Athens, Marussi T N/A 61 N/A 76 68 49 HU, Budapest, Laborc utca B N/A 50 N/A N/A N/A N/A HU, Budapest, Szina tir T N/A N/A N/A N/A N/A N/A IT, Rome, Villa Ada B N/A N/A N/A 39 40 40 IT, Rome, P.zza E.Fermi T N/A N/A N/A N/A N/A 48 LV, Riga, Riga Kengarags-2 B N/A N/A N/A N/A 53 56 LV, Riga, Riga Centrs T N/A N/A N/A N/A 96 51 LT, Vilnius, Vilnius 21A B N/A N/A N/A N/A N/A N/A LT, Vilnius, Vilnius 13A T N/A 37 17 28 N/A N/A NO, Oslo, Nordahl Brunsgate B 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 PL, Kraków, KrakProk B N/A N/A 50 43 40 37 PL, Kraków, KrakKras T N/A N/A N/A N/A N/A N/A PT, Lisbon, Beato B N/A N/A 38 37 45 50 PT, Lisbon, Entrecampos T N/A 13 20 23 25 30 SK, Bratislava, Mamateyova B N/A 30 30 N/A 57 40 SK, Bratislava, Trnavske myto T N/A N/A N/A N/A N/A N/A SE, Stockholm, Södermalm B N/A N/A N/A 52 47 49 SE, Stockholm, Hornsgatan T N/A N/A N/A N/A N/A N/A UK, London, London N. Kensington B 30 25 30 35 33 34 UK, London, London Marylebone Road T N/A 11 12 13 13 14

Table 6: Change in emissions from the road transport sector, 1990–2001 (%) PFP NOx SO2 TOFP CO

Austria 0 – 2 – 26 – 30 – 49 Belgium – 8 – 6 – 68 – 14 – 24 Denmark – 31 – 31 – 94 – 39 – 35 Finland – 47 – 52 – 96 – 45 – 21 France – 37 – 36 – 83 – 50 – 62 Germany – 34 – 35 – 77 – 58 – 66 Greece – 1 8 – 89 13 6 Ireland 16 21 – 71 – 17 – 29 Italy – 28 – 26 – 89 – 31 – 37 Luxembourg – 26 – 28 – 15 – 35 – 35 Netherlands – 40 – 39 – 85 – 43 – 47 Portugal 62 70 – 55 41 – 3 Spain – 5 3 – 72 – 17 – 40 Sweden – 33 – 37 – 92 – 47 – 51 United Kingdom – 40 – 39 – 95 – 51 – 57 European Union – 29 – 28 – 82 – 41 – 50 Iceland 2 195 2 133 0 270 97 Liechtenstein – 44 – 47 – 67 – 55 – 45 Norway – 37 – 38 – 84 – 46 – 54 EEA-18 – 29 – 28 – 82 – 41 – 50 Cyprus – 4 0 – 12 0 0 Czech Republic – 24 – 27 – 30 – 18 3 Estonia – 69 – 67 – 96 – 79 – 81 Hungary – 16 – 12 – 91 – 21 – 21 Latvia – 21 – 24 132 – 47 – 58 Lithuania – 49 – 47 – 85 – 63 – 79 Poland – 44 – 40 – 75 – 47 – 58 Slovakia – 22 – 22 – 74 – 21 – 20 Slovenia – 6 – 5 – 34 – 9 – 17 AC-10 (no Malta) – 35 – 33 – 72 – 40 – 50 Bulgaria – 60 – 60 – 72 – 58 – 49 Romania – 51 – 54 – 40 – 42 – 41 Turkey 13 15 0 23 29 CC-3 – 14 – 15 – 11 – 8 – 2

NB: For Iceland, no data were available for 1990 and 1995 was used instead. Source: EEA/ETC-ACC, 2003.

Table 7: Road transport contribution to total emissions in 2001 (%)

PFP (6) NOx SO2 TOFP CO Austria 34 49 8 30 25 Belgium 29 45 3 39 41 Denmark 25 35 1 36 51 Finland 25 34 0 36 53 France 27 49 4 37 37 Germany 31 52 3 38 48 Greece 16 34 0 50 69 Ireland 18 40 1 43 76 Italy 32 51 2 52 67 Luxembourg 26 39 11 44 63 Netherlands 27 38 2 38 56 Portugal 31 51 2 41 65 Spain 22 39 1 30 48 Sweden 32 42 1 35 51 United Kingdom 30 46 0 39 62 European Union 28 46 2 39 52 Iceland 16 24 1 39 94 Liechtenstein 43 62 10 42 74 Norway 17 21 2 17 47 EEA-18 28 45 2 39 52 Cyprus 24 46 14 62 97 Czech Republic 19 31 1 30 42 Estonia 12 39 1 35 40 Hungary 23 55 0 50 74 Latvia 31 50 12 39 30 Lithuania 21 51 2 36 40 Poland 12 26 2 24 20 Slovakia 15 34 1 33 41 Slovenia 30 61 3 55 91 AC-10 (no Malta) 16 34 2 31 33 Bulgaria 7 33 0 31 36 Romania 2 7 0 10 14 Turkey 16 33 3 37 41 CC-3 10 27 2 28 31

Source: EEA/ETC-ACC, 2003.

(6) Particulate formation precursors.

Metadata Technical information 1 Data source: AirBase data (http://air-climate.eionet.eu.int/databases/airview.html and http://air-

climate.eionet.eu.int/databases/AirBaseXML.html). National total and sectoral emissions officially reported to the UNECE/CLRTAP/EMEP, update 2003.

2. Description of data: National air quality measurement data reported to AirBase database under framework of European Council Decision 97/101/EC and Comission Decision 2001/752/EC.

3. Spatial coverage: As much as EEA-31 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, so they could not be used.

Ireland: background station only measures PM10 daily values, i.e. not sufficient for this analysis.

Luxembourg: no traffic or background data available.

Spain: no background data available.

The data missing from AC-10 are:

Slovenia: no traffic station.

Malta: no data available.

Cyprus: no data available.

Poland: no data available for Warsaw, so the urban area of Kraków was studied instead.

Hungary: only 1997 data were available and used where appropriate.

The data missing from CC-3:

Bulgaria: There was no traffic station available

Romania: Only daily average data were available

Turkey: No data were available

4. Temporal coverage: 1996–2001 was attempted, however data are not always available (see Table 1).

5. Methodology: Annual country data submissions to AirBase (at ETC/ACC website) 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 were 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: Interpolation to derive annual total emission data from one country when data are missing between two different years. If the reported data are missing 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 (see TERM 2003 03).

Qualitative information 7. Strengths and weaknesses: Strength: officially reported data by the countries to AirBase are used. Weakness: data reported across countries vary 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 factsheet for each graph separately. The data quality cannot be commented upon, since data are reported by the individual countries, but data availability is sometimes low and does not allow for

http://air-climate.eionet.eu.int/databases/airview.htmlhttp://air-climate.eionet.eu.int/databases/AirBaseXML.htmlhttp://air-climate.eionet.eu.int/databases/AirBaseXML.html

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 Time-series

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 NOx, CO, PM10, SO2. In this analysis NO2 has been used instead of NOx due to the lack of available data. When looking at traffic contribution, it would be more appropriate to study NOx data, so improvement in this direction would also be necessary. Furthermore, as scientific evidence indicates that PM2.5 forms the largest part of the PM10 measured at traffic stations, it should become obligatory for the Member States to measure PM2.5 in urban agglomerations, namely at traffic stations.

Other data

Meteorological data are 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 are requested by AirBase, but following the EoI decision, it is not obligatory for the Member States to deliver these data. It is important that Member States submit this type of data, as this would greatly help with this type of analysis.

Spatial coverage

Based on measurements, spatial coverage of EEA-31 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.

Documents

Indicator factsheet TERM 2003 04 EEA-31 — Transport contribution to air quality · 2017. 8. 26. · Indicator factsheet TERM 2003 04 EEA-31 — Transport contribution to air quality