For four winters, the city of Stuttgart had to regularly issue particulate matter warnings when limit values threatened to be exceeded due to the weather conditions. The city and state then called on the population to avoid using their cars in the city as much as possible. In April 2020, Stuttgart was able to discontinue the particulate matter warning after the permitted number of limit values violations for particulate matter had been observed for two years in a row.
However, the maximum values for nitrogen dioxide recorded by the measuring stations in the city area are regularly exceeded by a considerable margin. For this reason, the clean air plan continues to apply for the state capital. It includes a year-round traffic ban in the Stuttgart environmental zone for all diesel vehicles with the exhaust emission standard Euro 4 and worse. Since July 2020, a driving ban has applied in the Stuttgart valley basin even for Euro 5 diesel vehicles.
However, a closer look at the situation shows that within this "small environmental zone" some areas are completely acceptable in terms of air quality: They reach values that are below 20 μg/m³ – with a particulate matter limit value of 50 μg/m³. In contrast, there are highly critical areas outside the environmental zone with values of 100 μg/m³ and more. Blanket driving bans therefore only shift the problem. The emissions and associated air pollution then arise in the surrounding area. The alternative to blanket driving bans is to keep traffic flowing at a reduced level of environmental impact.
The situation room in Stuttgart was only able to draw on insufficient data to assess the situation. The blanket particulate matter warning, for example, was based solely on an atmospheric forecast from Germany's National Meteorological Service (Deutscher Wetterdienst). With the help of accurate nationwide data delivered in real time and at high frequency, more precise forecasts can be made, enabling more targeted measures to be taken against pollutant emissions. Up to now, the recording of the actual environmental impact on German cities has been based on data from the few measuring points of Germany's Federal Environmental Protection Agency (Umweltbundesamt). Further IoT-based sensor technology in combination with simulation models helps to generate a more precise picture of the actual emission of pollutants and associated environmental impact. These models are only complete when satellite or weather forecast data are taken into account.
A dashboard is among the data-based tools used to support decision-making in traffic management. It displays air quality at a granular level, shows possible limit value violations, and simulates various action scenarios. The dashboard clearly shows the traffic control center all relevant components, such as measured values, high traffic volumes, and weather conditions. An integrated AI-based forecast of developments enables the timely initiation of appropriate measures to prevent pollutants from exceeding limit values. The information system also determines what effects certain traffic control measures achieve over time and analyses the various available control instruments for their appropriateness. Intelligent traffic system control and speed adjustment keep traffic flowing, reduce pollution, and increase acceptance among road users. Before/after analyses show those responsible which measures have which effect.
The automotive industry can also help reduce pollutant emissions. One way to obtain a clear picture of emissions data, for example, is to use on-board units that can even be retrofitted in vehicles. Complete systems such as Low Carbon Mobility Management (LCMM) can be used to control logistics fleets. An app helps drivers develop an environmentally oriented driving style. A cloud backend with dashboards enables fleet operators to optimally control their fleets by means of up-to-date position determination of the respective vehicle. DHL and DB Schenker use the patented system in Europe and China and have been able to reduce emissions from their vehicles by an average of 20 percent and fuel costs by 15 percent.