Questions and Answers

Here you will find answers to some of the most frequently asked questions in regards to the NEMO project

NEMO technologies target High emitting vehicles in the traffic flow. How do we know, that this is an effective approach?

Opus RSE and CIEMAT carried out studies in Madrid using remote sensing technology to identify high emitters. A vehicle was categorized as a high emitter if it was among the top 2% most polluting vehicles). The study showed that a quite a small share of vehicles, vehicles categorized as High Emitters is responsible for a large share of air pollution caused by traffic.

For a vehicle to be categorized as a Emitter it must be among the top 2% most polluting vehicles at least once and among the top 20% most polluting vehicles at least twice. Further at least 70% of the times a vehicle’s pollution level was measured it must be among the top 20% most polluting vehicles. Since emissions from vehicles can vary, this is to avoid a vehicle being falsely identified as a high emitter based on a singular event of high emissions from a vehicle that normally performs well.

Using this method, just 3,7-5,3% of the vehicles were identified as High Emitters, depending on the pollutant. These vehicles were found to be responsible for 17,8-41,0% of the total pollution from traffic.

Thus, targeting and excludingthe few High emitters has great potential to improve the air quality in urban areas, for the benefits of all citizens.



How do we differentiate between noise caused by faulty/obsolete vehicles and noise caused by driving conditions or road characteristics?

The relation between noise and driving conditions is a complex matter. But the noise might be simplified to be a function of a vehicle’s speed, acceleration, engine speed and engine load. If the monitoring system can differentiate between these parameters. So for example, a reference noise level can be calculated for each vehicle category (e.g., passenger cars, heavy vehicles, powered two-wheelers) under specific driving conditions and under specific external conditions such as the road characteristics.

With the system set up at a specific site, it collects measurements of each passing vehicle. As more and more data points are collected the system can estimate the relation between the driving conditions and the noise level and establish a reference noise level for all kinds of vehicles in all kinds of driving conditions.

As the system is “trained” it gets better and better at identifying outliers, meaning vehicles that make more noise than it should under the specific driving conditions at that specific site.