Road with moving cars

High levels of significance found in the correlation of air quality data with traffic and weather data

Hawa Dawa has completed a study on the impact of traffic on air quality in a town in southern Germany.

The team collected data on air quality and traffic volume in the field and then correlated these values in-house. The aim of the study was to investigate the diverse effects of traffic and weather on air quality levels and pollution behaviour. A further hypothesis tested as part of the study was the extent to which speed limits or speed recommendations have an positive influence on air quality.

To investigate the relationship between the number of vehicles, their respective speed, weather factors (wind, temperature and humidity) and the concentration of air pollutants (specifically, nitrogen dioxide and particulate matter PM10), traffic monitoring devices and Hawa Dawa Sentience air measuring devices were placed alongside one another at multiple locations across the town. The team then carried out sophisticated simulation and modelling on the raw data to analyse the potential effect of modifying speed limits.

The results of this in-field study demonstrate that that accuracy of statements on the various influences on air quality increases significantly the more live data points collected. Pure simulations and modelling that omit a sufficient amount of actual data are based on a high number of assumptions, are highly complex and therefore have a much higher risk of significant inaccuracies. The study also showed that a change in the speed limit from 50 km/h to 30 km/h does not necessarily lead directly to lower air pollution levels. Rather there are many site-specific variables; environmental or physical influences that should additionally be taken into account when planning air quality improvement measures.

Matthew Fullerton, Head of Software Development and co-founder of Hawa Dawa, adds: “this extensive project and our detailed analysis demonstrates how vital it is to collect actual data on site and to correlate and consider a range of potentially influential factors. This is the only way to create a valid decision-making basis for designing and implementing planned measures“.