1 Calibrating Networks of Low-1 Cost Air Quality Sensors

20 Ambient fine particulate matter (PM2.5) pollution is a major health risk. Networks of low-cost 21 sensors (LCS) are increasingly being used to understand local air pollution variation. However, 22 measurements from LCS have uncertainties which can act as a potential barrier for effective 23 decision-making. LCS data thus need to be calibrated to obtain better quality PM2.5 estimates. In 24 order to develop correction factors, LCS are typically co-located with gold-standard reference 25 monitors. A calibration equation is then developed that relates the raw output of the LCS as closely 26 as possible to measurements from the reference monitor. This calibration algorithm is then 27 typically transferred to measurements from monitors in the network. Calibration algorithms tend to 28 be evaluated based on their performance at co-location sites. It is often implicitly assumed that the 29 conditions at the relatively sparse co-location sites are representative of the LCS network, overall. 30 Little work has been done to explicitly evaluate the sensitivity of the LCS network hotspot 31 detection, and spatial and temporal PM2.5 trends to the correction method applied. This paper 32 provides a first look at how transferable different calibration methods are using a dense network of 33 Love My Air LCS monitors in Denver. It offers a series of transferability metrics that can be 34 applied to other networks and offers suggestions for which calibration method would be most 35 useful for different end goals. Finally, it develops a set of best practice suggestions on calibrating 36 LCS networks. 37 38 https://doi.org/10.5194/amt-2022-65 Preprint. Discussion started: 8 March 2022 c © Author(s) 2022. CC BY 4.0 License.