On average, we spend around 90% of the time in indoor environments. Indoor Air Quality (IAQ) has been receiving increased attention from the environmental bodies, local authorities and citizens as it is becoming clearer that poor IAQ has public health implications. Therefore, monitoring of indoor environment and involving citizens becomes crucial to enhance IAQ and managing their indoor environments by raising awareness – a goal of many Citizen Science (CS) projects. In this work, we present a use case of IAQ monitoring in a European project with a focus on Smart Cities with citizen engagement and involvement. It is well known that the cost of Air Quality (AQ) monitoring stations, which are often stationary, and generally produce reliable, and high-quality data is a non-starter for CS projects as cost prohibits the scaling of deployment and citizen involvement. On the other hand, it is widely assumed that low-cost devices for AQ, although available in abundance, often produce low-quality data, putting the credibility of basing any analysis on low-cost sensors. There is an increasing number of research efforts that look at how to ascertain the data quality of such sensors so that they could still be used reliably, often to provide indicative readings, and for analytics. In this work, we present data science-based techniques that have been utilised for selecting low-cost sensors based on their data quality indicators, and an integrated visualisation system that utilises structure data for IAQ to support multi-city trials in a CS project. The sensors are selected after analysing their consistency over a period by applying different approaches such as statistical analysis and graphical plots.
|Title of host publication||2021 IEEE 7th World Forum on Internet of Things (WF-IoT)|
|Place of Publication||Piscataway, NJ|
|Number of pages||6|
|Publication status||Published - 13 Nov 2021|