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| Titel |
The next generation of low-cost personal air quality sensors for quantitative exposure monitoring |
| VerfasserIn |
R. Piedrahita, Y. Xiang, N. Masson, J. Ortega, A. Collier, Y. Jiang, K. Li, R. P. Dick, Q. Lv, M. Hannigan, L. Shang |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 7, no. 10 ; Nr. 7, no. 10 (2014-10-07), S.3325-3336 |
| Datensatznummer |
250115921
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| Publikation (Nr.) |
 copernicus.org/amt-7-3325-2014.pdf |
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| Zusammenfassung |
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. |
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