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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 regulatory-grade 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. 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 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. We show how the parameter variations can be used to indicate both sensor failure and failure of the proxy assumption. We apply the procedures to a network of 56 sensors distributed across the Inland Empire and Los Angeles County regions, demonstrating the need for reliable data from dense networks of monitors to supplement the existing regulatory networks., Comment: 40 pages inclusive of supporting information. 13 figure in main text; 15 figure in SI

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., Comment: 26 pages, 11 figures

Previous studies have shown that a hierarchical network comprising a number of compliant reference stations and a much larger number of low-cost sensors can deliver reliable air quality data at high temporal and spatial resolution for ozone at neighbourhood scales. Key to this framework is the concept of a proxy: a reliable (regulatory) data source whose results have sufficient statistical similarity over some period of time to those from any given low-cost measurement site. This enables the low-cost instruments to be calibrated remotely, avoiding the need for costly on-site calibration of dense networks. This paper assesses the suitability of this method for local air pollutants such as nitrogen dioxide which show large temporal and spatial variability in concentration. The proxy technique is evaluated using the data from the network of regulatory air monitoring stations measuring nitrogen dioxide in Southern California to avoid errors introduced by low-cost instrument performance. Proxies chosen based on land use similarity signalled typically less than 0.1 percent false alarms. Although poor proxy performance was observed when the local geography was unusual (a semi-enclosed valley) in this instance the closest neighbour station proved to be an appropriate alternative. The method also struggled when wind speeds were low and very local sources presumably dominated the concentration patterns. Overall, we demonstrate that the technique can be applied to nitrogen dioxide, and that appropriate proxies can be found even within a spatially sparse network of stations in a region with large spatio-temporal variation in concentration., Comment: 24 pages, 7 figures, supplementary information appended to main text

Plausibility of data from networks of low-cost measurement devices is a growing and important contentious issue. Informal networks of low-cost devices have particularly come to prominence for air quality monitoring. The contentious point is the believability of data without regular on-site calibration since that is a specialist task and the costs very quickly become very much larger than the cost of installation in the first place. This article suggests that approaches to the problem that involve appropriate use of independent information have the potential to resolve the contention. Ideas are illustrated particularly with reference to low-cost sensor networks for air quality measurement., Comment: 14 pages

We demonstrate how a hierarchical network comprising a number of compliant reference stations and a much larger number of low-cost sensors can deliver reliable high temporal-resolution ozone data at neighbourhood scales. The framework, demonstrated originally for a smaller scale regional network deployed in the Lower Fraser Valley, BC was tested and refined using two much more extensive networks of gas-sensitive semiconductor-based (GSS) sensors deployed at neighbourhood scales in Los Angeles: one of ~20 and one of ~45 GSS ozone sensors. Of these, ten sensors were co-located with different regulatory measurement stations, allowing a rigorous test of the accuracy of the algorithms used for off-site calibration and adjustment of low cost sensors. The method is based on adjusting the gain and offset of the low-cost sensor to match the first two moments of the probability distribution of the sensor result to that of a proxy: a calibrated independent measurement (usually derived from regulatory monitors) whose probability distribution evaluated over a time that emphasizes diurnal variations is similar to that at the test location. The regulatory measurement station physically closest to the low-cost sensor was a good proxy for most sites. The algorithms developed were successful in detecting and correcting sensor drift, and in identifying locations where geographical features resulted in significantly different patterns of ozone variation due to the relative dominance of different dispersion, emission and chemical processes. The entire network results show very large variations in ozone concentration that take place on short time- and distance scales across the Los-Angeles region. Such patterns were not captured by the more sparsely distributed stations of the existing regulatory network and demonstrate the need for reliable data from dense networks of monitors., Comment: 28 pages, 12 figures, Supplementray information appended has 14 pages