What can we learn from nested IoT low‐cost sensor networks for air quality? A case study of PM2.5 in Birmingham, UK

Abstract

AbstractLow‐cost sensing and the Internet of Things (IoT), present new possibilities for unconventional monitoring of environmental parameters. This paper describes a series of intersecting networks of particulate matter sensors that were deployed across the Birmingham conurbation for a 12‐month period. The networks consisted of a combination of commercially available sensors and University developed sensors. Data from these networks were assimilated with data from a third‐party Zephyr deployment, along with the DEFRA AURN network, which was hosted on an open‐source online platform. This nesting of sensor networks allowed for new insights into sensor performance, including the accuracy of a large network to detect regional concentrations and the number of sensors needed for effective monitoring beyond indicative measurements. After comprehensive data validation steps, the sensors were shown to perform well during co‐location with reference instrumentation (exhibiting slopes of 0.74–1.3). The sensors demonstrated good capability of detecting temporal patterns of regional PM2.5 with the mean of the entire sensor network recording an annual mean PM2.5 concentration within 0.2 μgm−3 of the regulatory network annual mean observation. Network‐derived statistics for estimating urban background concentrations compared to a reference site increase in‐line with the number of sensors available, however when assessing this for near‐source concentrations the importance of sensor location rather than the number of sensors is highlighted. Overall, the network provided novel insights into local concentrations, detecting similar hotspots to those identified by a high‐resolution model. The increased spatial coverage afforded by the sensor network has the potential to support higher resolution evaluation of models and provide unprecedented spatial evidence for air pollution management interventions.