Comparing spatial and temporal variabilities between the Vaisala AQT530 monitor and reference measurements

Abstract. Low-cost gas and particle sensors can enhance the spatial coverage of Air Quality (AQ) monitoring networks in urban settings. While their accuracy is insufficient to replace reference instruments, they may still capture spatial differences among different stations, as well as temporal trends and month-to-month variabilities at a specific location. To assess this, we conducted a 19-month study using two Vaisala AQ Transmitters-Monitors (Model AQT530), collocated with reference-grade instruments, at two AQ stations in Nicosia: an urban traffic and an urban background station. These two stations are ideal for the needs of this study considering that the reference measurements carried out there exhibit statistically significant spatial and temporal differences in pollutant concentrations when analysed over the entire period and on a monthly basis. The AQT530 air quality monitor employs Low-Cost Sensors (LCSs) for gaseous pollutants (i.e., CO, NO2, NO and O3) and particulate matter (PM). Tests of the performance of the two AQT530 monitors during an initial period when those were collocated at the urban traffic station revealed high unit-to-unit agreements for the CO, NO and PM10, and good to moderate for the NO2, O3 and PM2.5 measurements. The CO and PM10 LCS measurements also effectively captured concentration differences between the two stations when averaged over the entire study period or monthly, with some exceptions for specific months. These LCSs successfully detected spatial concentration differences (i.e., monthly, daily and hourly) as long as those were above a certain threshold. Overall, the CO and PM sensors successfully tracked month-to-month trends over the entire study period, similarly to reference instruments, whereas NO2, NO, and O3 sensors struggled due to environmental sensitivities. Despite this, all sensors identified statistically significant month-to-month variations at the same station, with the PM2.5 measurements showing the strongest agreement with reference data.