Reply on RC2

Abstract

Ambient fine particulate matter (PM2.5) pollution is a major health risk. Networks of low-cost sensors (LCS) are increasingly being used to understand local air pollution variation. However, measurements from LCS have uncertainties which can act as a potential barrier for effective decision-making. LCS data thus need to be calibrated to obtain better quality PM2.5 estimates. In order to develop correction factors, LCS are typically co-located with gold-standard reference monitors. A calibration equation is then developed that relates the raw output of the LCS as closely as possible to measurements from the reference monitor. This calibration algorithm is then typically transferred to measurements from monitors in the network. Calibration algorithms tend to be evaluated based on their performance at co-location sites. It is often implicitly assumed that the conditions at the relatively sparse co-location sites are representative of the LCS network, overall. Little work has been done to explicitly evaluate the sensitivity of the LCS network hotspot detection, and spatial and temporal PM2.5 trends to the correction method applied. This paper provides a first look at how transferable different calibration methods are using a dense network of Love My Air LCS monitors in Denver. It offers a series of transferability metrics that can be applied to other networks and offers suggestions for which calibration method would be most useful for different end goals. Finally, it develops a set of best practice suggestions on calibrating LCS networks.