A Network of Low-Cost Air Quality Sensors and Its Use for Mapping Urban Air Quality

Recent rapid technological advances in sensor technology have resulted in a wide variety of small and low-cost microsensors with significant potential for measuring air pollutants. In this contribution, we evaluate the performance of a commercially available low-cost sensor platform for air quality and show how the data from a network of such devices can be used for high-resolution mapping of urban air quality. Our results indicate that the sensor platforms are subject to a significant sensor-to-sensor variability as well as strong dependencies on environmental conditions. A field calibration of all individual sensor devices by co-locating them with an air quality monitoring station equipped with reference instrumentation is thus required for obtaining the best possible results. We further demonstrate that, despite relatively low accuracy at the individual sensor level, a methodology based on geostatistical data fusion is capable of merging the information from the sensor network with model information in such a way that we can obtain realistic and frequently updated maps of urban air quality. We show that exploiting the “swarm knowledge” of the entire network of sensors is capable of extracting useful information from the data even though individual sensors are subject to significant uncertainty.

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