4.1. Impact of the Measures on PM10 Mass Concentration
We investigated whether changes in PM10
levels after the introduction of a truck transit ban through the city area and the implementation of the first stage of the LEZ in Munich could be detected by analysis of emission data on PM10
mass concentration collected at an urban background and a street monitoring site. The comparison of the PM10
mass concentrations (adjusted for exposure at the reference station, wind direction, day of the week, time of the day and public holidays and calculated separately for summer and winter seasons in a semiparametric model with first-order autoregressive errors) showed a large relative decrease of PM10
levels at the street site (13.0%, p
-value: <0.001), whereas the relative decrease observed at the urban background monitoring site was smaller (4.5%, p
-value: <0.001). The decrease of PM10
mass concentration predicted in Munich by dispersion modeling ranges between 2% and 10 % depending on the monitoring sites and the active stage of the LEZ [40
]. The maximal reduction (up to 10 %) was predicted only for the third stage of the LEZ. The changes of PM10
concentrations detected at the street site in our study are larger than the reductions predicted a priori and are also larger than those observed in the most other German cities [16
In general, the implementation of LEZ could influence the composition of the car fleet as well as the traffic intensity. The percentage of registered vehicles without any badge (Euro 1 or less) decreased during the time period 2007–2010 from 9.2% to 2.5% for passenger cars and from 31.5% to 24.1% for trucks, respectively. In the same time the percentage of vehicles with green badge increased from 78.4% to 89.6% for passenger cars and from 19.0% to 36.1% for trucks, respectively. Those changes are especially pronounced between the years 2007 and 2008, it means immediately before the implementation of the LEZ in Munich [41
]. Such an extraordinary modernization of vehicle fleet in the city towards low-emission cars was reported also for Berlin [28
]. Note that there is only information about the in Munich registered vehicles; no such information is available about the car fleet composition in flowing traffic in the city. Regarding the flowing traffic it can be assumed that the older vehicles are less often in use compared to the newer vehicles.
For the dispersion modelling, it was assumed that the traffic intensity remained constant over the time period 2007–2010. However, the analyses presented here are not only considering the impact of the LEZ alone, but also the additional impact of the transit ban for all trucks. The transit ban for trucks could affect the PM10 levels even to a larger extent than the LEZ, which operated in the analyzed period in the first stage only. It leads not solely to a reduction of particles emitted by vehicle exhaust, but also to a reduction of particles originated from tyre and brake wear or dust re-suspension. Due to the ban on driving for trucks on Sunday, the effect for Sunday can be directly ascribed to the implementation of the LEZ. A similar pattern was found for Saturday, but only at Prinzregentenstrasse. This lead us to the assumption that in winter, the vehicle fleet on weekends in the city and on Sundays in the urban background was the same before and after the introduction of the LEZ, whereas this was not the case during the summer season.
In the previous study estimating the LEZ impact in Munich, a slightly weaker effect of 12.3% relative reduction of PM10
mass concentration at Prinzregentenstrasse was found [25
]. The analysis was based on the comparison of relative PM10
concentration changes by a reference station. However, such analysis of the quotient between the specific monitoring station and the reference station neglects the uncertainty of the measurements at the reference station. Further regression analyses on the ratio as used in the previous study revealed a comparably poor model fit (data not shown). This is also denoted by the strong deviation of the estimation of the intercept from 1 in our analysis. For comparison, we also analyzed the same period as described in Cyrys et al.
] by use of the model applied in our study. We only found negligible (statistically insignificant) changes of the PM10
mass concentration (Prinzregenstrasse −1.05%, p
-value: 0.855; Lothstrasse: 2.42%, p
-value: 0.499), as it was similarly found by Morfeld et al.
]. For further comparisons of the approach used in our study with other modelling approaches we refer to the “comparison of different modeling approaches” section in the Supplemental Material
The results of our study are not directly comparable to the results obtained for other measures of traffic reduction, which were already introduced in some European cities. We analyzed here the common effect of the implementation of LEZ and transit ban for trucks in Munich and we are aware that such combination is not that common. Even if we might be able to evaluate the effects of the LEZ and transit ban for trucks separately, the comparison with other cites might remain difficult, as the regulations and areas of LEZ’s differ from city to city. The following discussion should compare rather roughly the range of the effects observed for different measures across Europe. Several studies analyzed the impact of congestion charging in London [17
]. Atkinson et al.
] observed reductions in PM10
only at the background monitor. The study conducted by Beevers and Carslaw [18
] indicated that NOx
emissions have been reduced by about 12% in the charging zone, whereas the study of Tonne et al.
] showed that the congestion charge schema led to only modest reductions in air pollutant concentrations across Greater London, but greater reductions in the charging zone. Ellison et al.
] found that the LEZ in London had a significant effect on the composition of the vehicle fleet in London and reduced the PM10
Johansson and colleagues [43
] assessed the effect of traffic congestion in Stockholm and concluded that the annual average NOx
concentrations along the most densely trafficked streets would be lower by up to 12% and 7%, respectively. Note that the effects in the studies of Beevers and Carslaw [18
], Tonne et al.
] and Johansson et al.
] were analyzed by dispersion modelling combined with regression calculation and were not verified by air quality measurements. PM2.5
concentrations in Copenhagen were reduced by 5% after the introduction of the LEZ [19
]. Unadjusted mean pollutants concentrations were lower after the implementation of the LEZ in five Dutch cities; the reduction in PM2.5
levels was larger at urban streets (31%) than in the suburban background (20%) [22
The public debate is often focused solely on PM10
concentrations (as this parameter is currently regulated) without taking into account that only the toxic fraction of PM10
causes adverse human health effects [2
]. Due to combustion processes particles originating from traffic exhibit a higher toxicity than particles from other sources; especially diesel-engine vehicles, which produce about 12% of the mean PM10
exposure of the German population [44
], emit these more toxic particles.
Hence, the effectiveness of LEZ could be analyzed more precisely if Black Smoke (as marker for diesel soot) or the organic fraction of particles would be measured in ambient air instead of total PM10
]. Unfortunately, in Germany no routine measurements of Black Smoke concentrations in ambient air are conducted. Quadir and colleagues [45
] reported recently significant lower concentrations for elemental carbon and some of particulate organic compounds after the introduction of the LEZ in Munich (the data were collected during special monitoring campaigns and not routinely by the monitoring network). Source apportionment analysis showed a reduction of traffic factor contribution by 60% after the implementation of the LEZ [45
]. Also in Berlin the concentration of soot particles decreased 2010 by 52% compared with 2007 [29
]. As climatic conditions in the years 2008–2010 were adverse when compared to 2007, the authors attribute these results to the reduced traffic soot emissions. Currently, a debate about the pollution through PM2.5
emerges, but the data material is insufficient because the monitoring of PM2.5
is only at its early stages.
In addition to dispersion modelling, estimating the expected changes of PM10 mass concentration in the ambient air, our analysis evaluates the effects of the measures by analysis of the measured PM10 values. However, the limitation of this strategy is, that also long-term changes of PM10, which could not be explained by the included predictors, especially by the PM10 levels of the reference station (for example changes in heating habits), are completely attributed to the LEZ effect.