Over the past few years there has been an enormous surge in interest in the application of low-cost sensors to measurements of air pollutants by educators, citizen scientists and members of various groups interested in air pollution levels in their own communities.  This interest has stemmed from a combination of factors that include:

1. Development and marketing of low-cost electrochemical sensors for gas-phase species
2. Application of low-cost optical particle counters (OPCs), originally developed for monitoring of HVAC systems, to measurements of ambient particle density and inferred mass concentrations
3. Introduction of the hobbyist microcontroller circuit boards such as the Arduino board
4. Recent advances in mobile phone app technology that provides for easy display and mapping of data

As a result, dozens of groups, including many university groups, NGOs and small companies, have developed sensor-based devices for measurements of a wide variety of air pollutants.  Such devices typically purport to measure various combinations of PM (PM1, PM2.5 & PM10), CO, CO₂, O₃, NO, NO₂, SO₂, H₂S, VOCs and black carbon.  The US EPA has responded to the growing public interest in sensors by developing an Air Sensor Toolbox for Citizen Scientists with an abundance of information about available sensors, how to use them and how to interpret the measurements.  Programs to evaluate sensors have been established by both the US EPA (Feinberg et al., 2018; Jiao et al., 2016) and by the Air Quality Sensor Performance Evaluation Center (AQ-SPEC) of California’s South Coast Air Quality Management District.
​

A majority of the ~50 commercially available sensor packages tested by the EPA and SCAQMD have performed very poorly, with coefficients of determination (R2) values of 0.5 or less, and several sensors having R2 values of ~0.0 (i.e., “random noise generators”).  The 2B Tech Personal Ozone Monitor (POM) had the best performance in these independent tests by SCAQMD with measured R2 values of 0.99 in the lab and 1.00 in the field when compared with FEM reference instruments.  Of course, the POM is a miniaturized instrument (and a US EPA Federal Equivalent Method) – not a sensor.  These independent tests have in some cases led to sensor improvements, and newer versions of some sensor packages have performed better in retesting. Those sensor packages that make use of multiple sensors calibrated using multivariate methods upon co-location with reference methods in the real atmospheric environment perform the best, it is now generally agreed that for most sensors lab calibrations simply do not work; for accurate measurements sensors must be frequently calibrated in the field by sampling real ambient air and compared with co-located reference instruments.

References

Apte, J.S., Messier, K.P., Gani, S., Brauer, M., Kirchstetter, T.W., Lunden, M.M., Marshall, J.D., Portier, C.J., Vermeulen, R.C.H. and Hamburg, S.P. (2017) High-resolution air pollution mapping with Google Street View cars: Exploiting big data, Environmental Science & Technology, 51, 6999–7008.  Direct Link

J.A. Ellenburg, C.J. Williford, S.L. Rodriguez, P.C. Andersen, A.A. Turnipseed, C.A. Ennis, K.A. Basman, J.M. Hatz, J.C. Prince, D.H. Meyers, D.J. Kopala, M.J. Samon, K.J. Jaspers, B.J. Latham, B.J. Carpenter and J.W. Birks (2019) Global Ozone (GO3) Project and AQTreks:  Use of evolving technologies by students and citizen scientists to monitor air pollutants, Atmospheric Environment: X, 4, 100,048, 1-16 (2019).  Direct Link

Feinberg, S., Williams, R., Hagler, G.S.W., Rickard, J., Brown, R., Garver, D., Harshfield, G., Stauffer, P., Mattson, E., Judge, R. and Garvey, S. (2018) Long-term evaluation of air sensor technology under ambient conditions in Denver, Colorado, Atmospheric Measurement Techniques, 11, 4605–4615.  Direct Link

Hagan, D.H., Isaacman-VanWertz, G., Franklin, J. P., Wallace, L.M.M., Kocar, B.D., Heald, C.L. and Kroll, J.H. (2018) Calibration and assessment of electrochemical air quality sensors by co-location with regulatory-grade instruments, Atmospheric Measurement Techniques, 11, 315–328.  Direct Link
​
Jiao, W., Hagler, G., Williams, R., Sharpe, R., Brown, R., Garver, D., Judge, R., Caudill, M., Rickard, J., Davis, M., Weinstock, L., Zimmer-Dauphinee, S. and Buckley, K. (2016) Community Air Sensor Network, ​(CAIRSENSE) project: Evaluation of low-cost sensor performance in a suburban environment in the southeastern United States, Atmospheric Measurement Techniques, 9, 5281–5292.  Direct Link

Zheng, T., Bergin, M.H., Johnson, K.K., Tripathi, S.N., Shirodkar, S., Landis, R. Sutaria, R. D.E. Carlson (2018) Field evaluation of low-cost particulate matter sensors in high-and low-concentration environments, Atmospheric Measurement Techniques, 11, 4823-4846.  Direct Link