Performance evaluation of MOMA – a remote network calibration technique for PM _2.5 and PM ₁₀ sensors

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.