Air pollution is a global health issue. While “reference” monitors used to measure pollution levels for compliance with legislative limits provide high quality, robust data, the equipment is highly expensive and requires significant expertise for maintenance, and therefore spatial resolution of measurements is difficult to achieve. Furthermore, such equipment is not usually suitable for deployment in the developing world. Therefore, low-cost solutions to monitor air pollution need to be investigated to evaluate the extent to which they can provide valid data to study this global problem, by increasing the granularity of the data available.
The key pitfall of the use of low cost sensors is the quality of the data, which may be susceptible to drift over time, interference from other pollutants and climate conditions, differing performance in differing environments, and lack of reproducibility between sensor units. Therefore, there is a pressing need to understand the performance of these sensors in a range of conditions and how data may be used and processed to maintain sufficient robustness.
The principal aim of my project is to investigate the potential uses of low-cost air quality sensors to produce reliable data for different use-case scenarios, for example regulation, source identification, awareness-raising. The eventual aim is to develop our understanding of these sensors and their outputs to deploy low-cost sensor boxes in a city-wide air quality monitoring network. The project is highly multidisciplinary, involving electronics, sensor technology, computing, chemistry, health, and public engagement.