Hierarchical network design for nitrogen dioxide measurement in urban environments, part 1: proxy selection
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 conce...
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Veröffentlicht in: | arXiv.org 2019-11 |
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Sprache: | eng |
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Zusammenfassung: | 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. |
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ISSN: | 2331-8422 |
DOI: | 10.48550/arxiv.1911.03137 |