Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference-grade performance.

Particulate Matter (PM) low-cost sensors (LCS) present a cost-effective opportunity to improve the spatiotemporal resolution of airborne PM data. Previous studies focused on PM-LCS-reported hourly data and identified, without fully addressing, their limitations. However, PM-LCS provide measurements at finer temporal resolutions. Furthermore, government bodies have developed certifications to accompany new uses of these sensors, but these certifications have shortcomings. To address these knowledge gaps, PM-LCS of two models, 8 Sensirion SPS30 and 8 Plantower PMS5003, were collocated for one year with a Fidas 200S, MCERTS-certified PM monitor and were characterised at 2 min resolution, enabling replication of certification processes, and highlighting their limitations and improvements. Robust linear models using sensor-reported particle number concentrations and relative humidity, coupled with 2-week biannual calibration campaigns, achieved reference-grade performance, at median PM2.5 background concentration of 5.5 µg/m3, demonstrating that, with careful calibration, PM-LCS may cost-effectively supplement reference equipment in multi-nodes networks with fine spatiotemporality.

Author Correction: Long-term field comparison of multiple low-cost particulate matter sensors in an outdoor urban environment.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Long-term field comparison of multiple low-cost particulate matter sensors in an outdoor urban environment.

Exposure to ambient particulate matter (PM) air pollution is a leading risk factor for morbidity and mortality, associated with up to 8.9 million deaths/year worldwide. Measurement of personal exposure to PM is hindered by poor spatial resolution of monitoring networks. Low-cost PM sensors may improve monitoring resolution in a cost-effective manner but there are doubts regarding data reliability. PM sensor boxes were constructed using four low-cost PM micro-sensor models. Three boxes were deployed at each of two schools in Southampton, UK, for around one year and sensor performance was analysed. Comparison of sensor readings with a nearby background station showed moderate to good correlation (0.61 < r < 0.88, p < 0.0001), but indicated that low-cost sensor performance varies with different PM sources and background concentrations, and to a lesser extent relative humidity and temperature. This may have implications for their potential use in different locations. Data also indicates that these sensors can track short-lived events of pollution, especially in conjunction with wind data. We conclude that, with appropriate consideration of potential confounding factors, low-cost PM sensors may be suitable for PM monitoring where reference-standard equipment is not available or feasible, and that they may be useful in studying spatially localised airborne PM concentrations.

City Scale Particulate Matter Monitoring Using LoRaWAN Based Air Quality IoT Devices.

Air Quality (AQ) is a very topical issue for many cities and has a direct impact on citizen health. The AQ of a large UK city is being investigated using low-cost Particulate Matter (PM) sensors, and the results obtained by these sensors have been compared with government operated AQ stations. In the first pilot deployment, six AQ Internet of Things (IoT) devices have been designed and built, each with four different low-cost PM sensors, and they have been deployed at two locations within the city. These devices are equipped with LoRaWAN wireless network transceivers to test city scale Low-Power Wide Area Network (LPWAN) coverage. The study concludes that (i) the physical device developed can operate at a city scale; (ii) some low-cost PM sensors are viable for monitoring AQ and for detecting PM trends; (iii) LoRaWAN is suitable for city scale sensor coverage where connectivity is an issue. Based on the findings from this first pilot project, a larger LoRaWAN enabled AQ sensor network is being deployed across the city of Southampton in the UK.