Understanding the source components captured by the Purple Air Network

10 January 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


PM2.5 has been linked to numerous pollution-mediated adverse health effects and their monitoring is key for taking preventative and mitigative measures. Accurate measurements of PM2.5 concentrations are available at EPA sites, but such data lacks spatial resolution due to a limited number of monitoring locations. In recent years the deployment of low-cost sensor networks has opened up the possibility of acquiring air quality data at a high spatiotemporal resolution. However, the sensitivity, noise, and accuracy of data acquired by low-cost sensors remain a concern. Here, we studied PM2.5 measurements made from EPA and Purple Air (PA) sensor networks in the Chicago area to understand the parameters influencing the performance characteristics of the low-cost sensor network. Using time series decomposition of PM2.5 data into short-term and baseline components using Kolmogorov–Zurbenko (KZ) filter and analysis of the extracted frequency signals, we determine that PA sensor data is more sensitive to meteorological conditions than anthropogenic activities in both short-term, and baseline components. We hypothesize that the low-cost sensor networks may have different sensitivity to aerosol from different sources and hence care must be taken in their calibrations and in their use for evaluating the impact of air quality mitigation policies.


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