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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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Atmos. Meas. Tech., 7, 3325-3336, 2014
https://doi.org/10.5194/amt-7-3325-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
07 Oct 2014
The next generation of low-cost personal air quality sensors for quantitative exposure monitoring
R. Piedrahita1, Y. Xiang4, N. Masson1, J. Ortega1, A. Collier1, Y. Jiang2, K. Li3, R. P. Dick4, Q. Lv2, M. Hannigan1, and L. Shang3 1University of Colorado Boulder, Department of Mechanical Engineering, 427 UCB, 1111 Engineering Drive, Boulder, CO 80309, USA
2University of Colorado Boulder, Department of Computer Science, 1045 Regent Drive, Boulder, CO 80309, USA
3University of Colorado Boulder, Department of Electrical Engineering, 425 UCB, 1111 Engineering Drive, Boulder, CO 80309, USA
4University of Michigan, Department of Electrical Engineering and Computer Science, 2417-E EECS, 1301 Beal Avenue, Ann Arbor, MI 48109, USA
Abstract. Advances in embedded systems and low-cost gas sensors are enabling a new wave of low-cost air quality monitoring tools. Our team has been engaged in the development of low-cost, wearable, air quality monitors (M-Pods) using the Arduino platform. These M-Pods house two types of sensors – commercially available metal oxide semiconductor (MOx) sensors used to measure CO, O3, NO2, and total VOCs, and NDIR sensors used to measure CO2. The MOx sensors are low in cost and show high sensitivity near ambient levels; however they display non-linear output signals and have cross-sensitivity effects. Thus, a quantification system was developed to convert the MOx sensor signals into concentrations.

We conducted two types of validation studies – first, deployments at a regulatory monitoring station in Denver, Colorado, and second, a user study. In the two deployments (at the regulatory monitoring station), M-Pod concentrations were determined using collocation calibrations and laboratory calibration techniques. M-Pods were placed near regulatory monitors to derive calibration function coefficients using the regulatory monitors as the standard. The form of the calibration function was derived based on laboratory experiments. We discuss various techniques used to estimate measurement uncertainties.

The deployments revealed that collocation calibrations provide more accurate concentration estimates than laboratory calibrations. During collocation calibrations, median standard errors ranged between 4.0–6.1 ppb for O3, 6.4–8.4 ppb for NO2, 0.28–0.44 ppm for CO, and 16.8 ppm for CO2. Median signal to noise (S / N) ratios for the M-Pod sensors were higher than the regulatory instruments: for NO2, 3.6 compared to 23.4; for O3, 1.4 compared to 1.6; for CO, 1.1 compared to 10.0; and for CO2, 42.2 compared to 300–500. By contrast, lab calibrations added bias and made it difficult to cover the necessary range of environmental conditions to obtain a good calibration.

A separate user study was also conducted to assess uncertainty estimates and sensor variability. In this study, 9 M-Pods were calibrated via collocation multiple times over 4 weeks, and sensor drift was analyzed, with the result being a calibration function that included baseline drift. Three pairs of M-Pods were deployed, while users individually carried the other three.

The user study suggested that inter-M-Pod variability between paired units was on the same order as calibration uncertainty; however, it is difficult to make conclusions about the actual personal exposure levels due to the level of user engagement. The user study provided real-world sensor drift data, showing limited CO drift (under −0.05 ppm day−1), and higher for O3 (−2.6 to 2.0 ppb day−1), NO2 (−1.56 to 0.51 ppb day−1), and CO2 (−4.2 to 3.1 ppm day−1). Overall, the user study confirmed the utility of the M-Pod as a low-cost tool to assess personal exposure.


Citation: Piedrahita, R., Xiang, Y., Masson, N., Ortega, J., Collier, A., Jiang, Y., Li, K., Dick, R. P., Lv, Q., Hannigan, M., and Shang, L.: The next generation of low-cost personal air quality sensors for quantitative exposure monitoring, Atmos. Meas. Tech., 7, 3325-3336, https://doi.org/10.5194/amt-7-3325-2014, 2014.
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