Analyse des Web-Controlling in der Automobilindustrie (German Edition)

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Bruns, Stephan B. Entscheidungstheoretische Analyse und empirische Evidenz Betreuer: Prof. Freytag Kauffeld-Monz, Martina : Knowledge transfer and trust building in regional systems of innovation Betreuer: Prof.

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Beispielsweise kann bei sog. Im Falle der Nutzung von speziellen Mikrocontrollern werden sog. Durch sukzessives, heuristisches Vorgehen s. Die praktische Umsetzung, mit leittechnischen Methoden in die betriebliche Prozessebene vorzudringen, wird detailliert und anschaulich beschrieben. Als einfaches Kriterium bietet es sich an, die Abweichungen vom Mittelwert zu betrachten, die einen prozentual zum Messbereich bzw. Im oberen Teil der Abbildung ist das Zustandsraummodell des zu regelnden Systems dargestellt.

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Witt Frenzel Baudisch, Alexander : Product innovation, consumer heterogeneity, and market growth. A theoretical discussion and empirical analysis Betreuer: Prof. If we had not have determined separate effects for the two seasons, the reduction of the PM 10 concentration by the measures at Prinzregentenstrasse would have been estimated with Figure 3 shows the temporal patterns of the modeled PM 10 concentrations at Prinzregentenstrasse and Lothstrasse for the periods with and without measures adjusted for PM 10 concentration at the reference station, wind direction and public holidays.

Temporal variability of PM 10 levels occurred between the seasons, the weekdays and times of day. The concentrations were higher in the winter months. The morning and afternoon rush hour peaks during the working days were clearly visible especially at Prinzregentenstrasse. The morning peak in the summer months was more clearly separated from the afternoon peak as in the winter season.

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The efficiency of the measures depended on the time of the day see also Supplemental Material, Figure S2 and followed a diurnal pattern. Due to the implemented measures the PM 10 burden was stronger reduced during hours with higher relative and absolute PM 10 mass concentration, i. The rush hour peaks themselves were reduced and there seemed to be lesser spillover from the morning to the afternoon.

Furthermore, the improving of air quality during the nights on workdays was faster at the street site. The effect of the measures vanishes during night-time of the first days of the working week. Modeled hourly concentrations of PM 10 at Prinzregentenstrasse first and second chart and Lothstrasse third, fourth chart adjusted for PM 10 at the reference station, wind direction and public holidays. The mean daily effects of both measures stratified by season and week day are shown in Figure 4 for each day of the week separately.

In the summer season, the effect of the measures for each day of the week was at the street site stronger than in winter, whereas this tendency is not observed at the background site. On Sunday a strong seasonal dependency of the effect was observed: the measures were only effective in summer. The effects of the linearly modeled confounding covariates are displayed in Supplemental Material, Table S1.

The logarithmic values of the reference station had a significant, additive effect of log 1.

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The model for the measurements at Prinzregentenstrasse explained The shape of the smooth effect of wind direction indicates decreased PM 10 levels, if the wind blew from the South or West at Lothstrasse and if the wind blew from North or East at Prinzregentenstrasse data not shown. We investigated whether changes in PM 10 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 PM 10 mass concentration collected at an urban background and a street monitoring site.

The comparison of the PM 10 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 PM 10 levels at the street site The changes of PM 10 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 , 23 , 24 , 26 , 27 , 28 , 29 ].

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 — from 9. In the same time the percentage of vehicles with green badge increased from Those changes are especially pronounced between the years and , 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 — 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 PM 10 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 The analysis was based on the comparison of relative PM 10 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.

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. 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 , 18 , 42 ].

Atkinson et al. Ellison et al. Note that the effects in the studies of Beevers and Carslaw [ 18 ], Tonne et al. Unadjusted mean pollutants concentrations were lower after the implementation of the LEZ in five Dutch cities; the reduction in PM 2. The public debate is often focused solely on PM 10 concentrations as this parameter is currently regulated without taking into account that only the toxic fraction of PM 10 causes adverse human health effects [ 2 , 3 , 16 ]. 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 PM 10 concentration [ 16 ].

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.

As climatic conditions in the years — were adverse when compared to , the authors attribute these results to the reduced traffic soot emissions. Currently, a debate about the pollution through PM 2. In addition to dispersion modelling, estimating the expected changes of PM 10 mass concentration in the ambient air, our analysis evaluates the effects of the measures by analysis of the measured PM 10 values.

However, the limitation of this strategy is, that also long-term changes of PM 10 , which could not be explained by the included predictors, especially by the PM 10 levels of the reference station for example changes in heating habits , are completely attributed to the LEZ effect. The second objective of this study was the examination of the seasonal and diurnal variation of the detected air quality improvements. The extent of the PM 10 reduction at the background sites was largely similar in both seasons. In the urban background, exhaust particles represent a smaller fraction of fine particles compared to street site and the composition of particles varies less between winter and summer.

On the contrary the difference between the two seasons was more pronounced at the street site, where the overall reduction of PM 10 mass concentrations was considerable larger as at the background site.

At this site, the reduction of PM 10 levels due to the measures was larger during the summer season and smaller during the winter season. In winter, additional particle sources such as domestic heating, wood combustion or combustion of other fossil fuels contribute significantly to the PM 10 mass concentrations in the ambient air.

Also the contribution of re-suspended dust to fine particles concentration in the ambient air increases in winter due to the application of road salt for deicing. In addition, the generation of secondary aerosols such as nitrate or sulfate is more intensive in winter. Consequently, exhaust particles represent a smaller fraction of the fine particles in winter than in summer.

Therefore, the measures regulating only the exhaust particles became less effective in the winter period.

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In addition, adverse meteorological conditions leading to increased PM 10 levels from local mobile as well as stationary sources are occurring more frequently in the winter season. Our analysis suggests that in such episodes the influence of the implemented measures regulating the car exhaust is limited and that air quality is dominated by other unaffected mobile and stationary sources. The reduced rush hour peaks may indicate that a larger proportion of old cars no longer accessed the city during midday.

Alternatively, the contribution of aged particles from the morning rush hours during day and night time might be shifted due to the reduction in diesel particles. Further, the temporal varying effect of the measures could be caused by the same reason as the differences between winter and summer season.

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One has to keep in mind that the morning and afternoon rush hour peaks seem to be more separated in summer not due to differences in traffic flow between summer and winter, but as a result of the combination of increased traffic intensity and increased solar radiation in summer. In our study we evaluated the effectiveness of two measures a truck transit ban through the city area and implementation of LEZ on the reduction of PM 10 mass concentrations in the ambient air in Munich, Germany.

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The analysis of the routinely collected PM 10 mass concentrations data by a semiparametric regression model showed statistically significant reduction of PM 10 levels at a monitoring site located in the direct vicinity of a highly frequented road and to a lesser extent at an monitoring site located in the urban background. The statistical regression modeling was essential to identify the size of the effect. The magnitude of the effect at the street site was larger in summer season; smaller seasonal variation was observed at the urban background site.