Spectral analysis approach for assessing the accuracy of low-cost air quality sensor network data

Abstract

Extensive monitoring of particulate matter (PM) smaller than 2.5 µm, i.e., PM2.5, is critical for understanding changes in local air quality due to policy measures. With the emergence of low-cost air quality sensor networks, high spatiotemporal measurements of air quality are now possible. However, the sensitivity, noise, and accuracy of field data from such networks are not fully understood. In this study, we use spectral analysis of a 2-year data record of PM2.5 from both the Environmental Protection Agency (EPA) and PurpleAir (PA), a low-cost sensor network, to identify the contributions of individual periodic sources to local air quality in Chicago. We find that sources with time periods of 4, 8, 12, and 24 h have significant but varying relative contributions to the data for both networks. Further analysis reveals that the 8 and 12 h sources are traffic-related and photochemistry-driven, respectively, and that the contributions of both these sources are significantly lower in the PA data than in the EPA data. The presence of distinct peaks in the power spectrum analysis highlights recurring patterns in the air quality data; however, the underlying factors contributing to these peaks require further investigation and validation. We also use a correction model that accounts for the contribution of relative humidity and temperature, and we observe that the PA temporal components can be made to match those of the EPA over the medium and long term but not over the short term. Thus, standard approaches to improve the accuracy of low-cost sensor network data will not result in unbiased measurements. The strong source dependence of low-cost sensor network measurements demands exceptional care in the analysis of ambient data from these networks, particularly when used to evaluate and drive air quality policies.