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

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