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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

We present a management and data correction framework for low-cost electrochemical sensors for nitrogen dioxide (NO2) deployed within a hierarchical network of low-cost and regulatorygrade instruments. The framework is founded on the idea that it is possible in a suitably configured network to identify a source of reliable 'proxy' data for each sensor site that has a similar probability distribution of measurement values over a suitable time period. Previous work successfully applied these ideas to a sensor system with a simple linear 2-parameter (slope and offset) response, with parameters estimated by moment matching site and proxy data distributions. However, applying these ideas to electrochemical sensors for NO2 presents significant additional difficulties for which we demonstrate solutions. The three NO2 sensor response parameters (offset, ozone (O3) response slope, and NO2 response slope) are known to vary significantly as a consequence of ambient humidity and temperature variations. Here we demonstrate that these response parameters can be estimated by minimising the Kullback-Leibler divergence between sensor-estimated and proxy NO2 distributions over a 3-day b Present address: Trustpower, 108 Durham St, Tauranga, New Zealand 1 window. We then estimate an additional offset term by using co-location data. This offset term is dependent on climate and spatially correlated and can thus be projected across the network.Co-location data also estimates the time-, space-and concentration-dependent error distribution between sensors and regulatory-grade instruments. Robust O3 measurements are obtained using a semiconducting oxide-based instrument, previously described. We show how the parameter variations can be used to indicate both sensor failure and failure of the proxy assumption. With these procedures, we demonstrate measurement at nine different locations across two regions of Southern California over seven months with average root mean square error ± 7.2 ppb (range over locations 4 -11 ppb) without calibration other than the remote proxy comparison. We apply the procedures to a network of 56 sensors distributed across the Inland Empire and Los Angeles County regions. The results show large variations in NO2 concentration taking place on short time-and distance scales across the region. These spatiotemporal NO2 variations were not captured by the more sparsely distributed regulatory network of air monitoring stations demonstrating the need for reliable data from dense networks of monitors to supplement the existing regulatory networks.

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