Toward a Unified Terminology of Processing Levels for Low-Cost Air-Quality Sensors

Low-cost sensor systems for measuring air quality have received widespread scientific and media attention over recent years. It has become an established technical methodology to improve the data quality of such sensor systems by colocating them at traditional air quality monitoring stations equipped with reference instrumentation and field-calibrating individual units using various statistical techniques. Methods range from (multi)linear regression to more complex statistical techniques, often using additional predictor variables such as air temperature or relative humidity, and occasionally data not actually measured by the sensor system itself (e.g., station observations or model output). Most of these techniques improve the level of agreement between sensor-derived data and reference data, in many cases eliminating issues such as chemical interferences and sensor-to-sensor variability. It is not always clear, however, the extent to which the data arising from such processing are still a true and independent measurement by the sensor system, or some blend of secondary data and model prediction. The current lack of governmental or third-party standards for low-cost sensor performance and occasional lack of distinction between sensors and sensor systems further complicates data processing.

To address this challenge we propose a unified terminology of processing levels for low-cost air quality sensor systems. A strict sequence of processing levels is already common practice in satellite remote sensing, where it has been in wide use across multiple agencies for decades. We have adapted these levels and suggest a sequence of processing levels for data from low-cost air quality sensor systems.