|Student:||Vera van Zoest|
|Timeline:||September 2015 - 31 August 2019|
Air quality is of major importance for human health. To better understand its importance and to study its effects on human health, there is a need for modelling and mapping air quality. Modelling and mapping of air quality is however difficult as monitoring of air quality is often not done at a fine spatial and temporal resolution. Often one sensor is used to estimate exposure of all inhabitants of a city, or yearly averages are used when the spatial variation of the urban area is taken into account.
In the city of Eindhoven, the Netherlands, an innovative air quality measurement network is established to monitor air quality at urban scale using low-cost sensors. 35 sensors are installed in the city which continuously measure air pollutant concentrations. These sensors measure particulate matter (PM) in different size classes (PM10, PM2.5, PM1), ultrafine particles (UFPs), ozone (O3), and nitrogen dioxide (NO2). The data quality of low-cost sensor networks and the uncertainty in the models and maps using the data of such sensor networks are largely unknown. When more knowledge is obtained about the spatial data quality and uncertainty of air quality models and maps using low-cost sensors, these maps can be used to study the influence of air pollutant exposure on health effects such as asthma.
This study addresses the following research questions:
- what is the quality of the data retrieved from low-cost air quality sensors for modelling and mapping air quality in an urban area?
- what type of regression models and interpolation methods can be used for mapping air quality?
- how can uncertainty in the air quality maps be quantified, visualized, and communicated?
- how can air quality maps be used to determine the influence of air pollutant exposure on asthma-related symptoms?
This project is funded by STW Technology Foundation under the title “Development of an Automatic system for Mapping Air quality risks in Space and Time” (DAMAST).