Preprints
https://doi.org/10.5194/egusphere-2023-969
https://doi.org/10.5194/egusphere-2023-969
30 May 2023
 | 30 May 2023

Performance evaluation of MOMA – a remote network calibration technique for PM2.5 and PM10 sensors

Lena Francesca Weissert, Geoff Steven Henshaw, David Edward Williams, Brandon Feenstra, Randy Lam, Ashley Collier-Oxandale, Vasileios Papapostolou, and Andrea Polidori

Abstract. We evaluate the potential of using a previously developed remote calibration framework we name MOMA to improve the data quality in PM sensors deployed in hierarchical networks. MOMA assumes that a network of reference instruments can be used as ‘proxies’ to calibrate the sensors given that the probability distribution of the data at the proxy site is similar to that at a sensor site. We use the reference network to test the suitability of proxies selected based on distance versus proxies selected based on land use similarity. The performance of MOMA for PM sensors is tested with sensors collocated with reference instruments across three Southern California regions, representing a range of land uses, topography, and meteorology, and calibrated against a distant proxy reference. We compare two calibration approaches, one where calibration parameters get calculated and applied at monthly intervals and one which uses a drift detection framework for calibration. We demonstrate that MOMA improves the accuracy of the data when compared against the collocated reference data. The improvement was more visible for PM10 and when using the drift detection approach. We also highlight that sensor drift was associated with variations in particle composition rather than instrumental factors explaining the better performance of the drift detection approach if wind conditions and associated PM sources varied within a month.

Lena Francesca Weissert et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Lena Francesca Weissert et al.

Lena Francesca Weissert et al.

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Short summary
We apply a previously developed remote calibration framework to a network of PM sensors deployed in the Southern California regions. Our results show that a remote calibration can improve the accuracy of PM data, which was particularly visible for PM10. We highlight that sensor drift was mostly due to differences in particle composition rather than monitor operational factors. Thus, PM sensors may require frequent calibration if PM sources vary with different wind conditions or seasons.