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

Abstract

Birmingham is a city within the West Midlands region of the United Kingdom. In June 2021, coinciding with the introduction of the Clean Air Zone by Birmingham City Council (BCC), multiple low-cost IoT sensor networks for air pollution were deployed across the city by both the University of Birmingham and BCC. Low-cost sensor networks are growing in popularity due to their lower costs compared to regulatory instruments (£10’s-£1000’s per unit compared to £10,000+ per unit) and the reduced need for specialised staff allow for deployments at greater spatial scales (1-3).  Although such low-cost sensing is often associated with uncertainty, the measurement of PM2.5 optical particle counters have been generally shown to perform well, giving indicative insight into concentrations following calibrations and corrections for external influence such as humidity (4-7). One common problem with sensor networks is they tend to be isolated and unopen deployments, deployed and maintained by an interested party with the focus of their own monitoring goal. To tackle this, Birmingham Urban Observatory was an online platform created and used by researchers at the University of Birmingham to host and share open access meteorological and air pollution data from low-cost sensor deployments. Whilst hosting and displaying data from two of their own deployments of air quality sensors (Zephyrs by Earthsense and AltasensePM: an in-house designed PM sensor), the platform also pulled data from the DEFRA AURN sites and collaborated with local government to pull data from their own low-cost sensor network. The result was a real-time view of environmental data produced from a series of nested arrays of sensors.This poster presents findings from this combined low-cost network, considering the successes and pitfalls of the low-cost monitoring network alongside insight into regional and local PM2.5 concentrations. Colocations against reference instruments within the network demonstrate good performance of the low-cost sensors after calibration and data validation but the project experienced challenges in deploying the network and sensor reliability. Low-cost sensor data generally gives novel insight into spatial analysis of PM2.5 across the city and this is presented alongside other experiences of deploying and using sensor networks for air quality.1 Lewis et al., (2016) https://doi.org/10.1039/C5FD00201J2 Chong and Kumar. (2003) doi: 10.1109/JPROC.2003.8149183 Snyder et al., (2013) https://doi.org/10.1021/es40226024 Magi et al., (2020) https://doi.org/10.1080/02786826.2019.16199155 Crilley et al., (2018) https://doi.org/10.5194/amt-11-709-20186 Cowell et al., (2022) https://doi.org/10.3389/fenvs.2021.7984857 Cowell et al., (2022) https://doi.org/10.1039/D2EA00124A