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 co-locating 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. Noticing this development, Hagler et al. warned that some systems may use predictor variables for calibration in such a way that a line is crossed from justifiable and empirical correction of a known artifact to a method that is essentially a predictive statistical model. In addition, the processing steps that are carried out along the way are often not clearly communicated. 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. Adding to the observations and recommendations made by Hagler et al. (2018)(2), we have further noticed that there is substantial and consistent confusion within both the scientific community and the interested public regarding the amount and type of processing applied to sensor data, and at what point derived data can be considered to have lost a meaningful link to quantitative traceability. The relevance of this issue to air quality sensors is significant since in most countries air quality targets and standards are set out in primary legislation and measured attainment of those targets has demanding traceability requirements. Clarity regarding the level of sensor data processing is important for evaluation of sensor technology, as well as correct use and interpretation of its data. 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.(1) We have adapted these levels and suggest a sequence of processing levels for data from low-cost air quality sensor systems (Table 1).

SCHNEIDER Philip;  BARTANOVA Alena;  CASTELL Nuria;  DAUGE Franck R.;  GERBOLES Michel;  HAGLER G.;  HUEGLIN Christoph;  JONES Roderic L.;  KAHN Sean;  LEWIS Alastair C.;  MIJLING Bas;  MÜLLER Michael;  PENZA Michele;  SPINELLE Laurent;  STACEY Brian;  VOGT Matthias;  WESSELING Joost;  WILLIAMS Ron; 

2019-08-26

AMER CHEMICAL SOC

JRC117395

0013-936X (online),   

https://publications.jrc.ec.europa.eu/repository/handle/JRC117395,   

10.1021/acs.est.9b03950 (online),