  The development of low-cost sensors and novel calibration algorithms offer
new opportunities to supplement existing regulatory networks to measure air
pollutants at a high spatial resolution and at hourly and sub-hourly
timescales. We use a random forest model on data from a network of low-cost
sensors to describe the effect of land use features on local-scale air quality,
extend this model to describe the hourly-scale variation of air quality at high
spatial resolution, and show that deviations from the model can be used to
identify particular conditions and locations where air quality differs from the
expected land-use effect. The conditions and locations under which deviations
were detected conform to expectations based on general experience.
