Automated data scanning for dense networks of low-cost air quality instruments: Detection and differentiation of instrumental error and local to regional scale environmental abnormalities

Recent improvements in low-cost air quality instrumentation make deployment of dense networks of sensors possible. However, the shear volume of data from these networks means that traditional methods for data quality control and data analysis are no longer viable. We propose a real-time data scanning routine that detects local and regional variability within the data sets. This can be used to differentiate errors resulting from instrument malfunction or calibration drifts from natural (environmentally driven) regional changes in ambient concentrations. Our case study considered hourly-averaged ozone data from Texas and from two networks in Vancouver. We used 7 and 28 days of data for the algorithm initialisation with simulated and real instrumental changes. The algorithm output can be used as part of a limited resource maintenance schedule for sensor networks, and to improve understanding of air quality processes and their relation to environmental and public health data.