The next generation of low-cost personal air quality sensors for quantitative exposure monitoring
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...
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Copernicus Publications
2014-10-01
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Series: | Atmospheric Measurement Techniques |
Online Access: | http://www.atmos-meas-tech.net/7/3325/2014/amt-7-3325-2014.pdf |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
R. Piedrahita Y. Xiang N. Masson J. Ortega A. Collier Y. Jiang K. Li R. P. Dick Q. Lv M. Hannigan L. Shang |
spellingShingle |
R. Piedrahita Y. Xiang N. Masson J. Ortega A. Collier Y. Jiang K. Li R. P. Dick Q. Lv M. Hannigan L. Shang The next generation of low-cost personal air quality sensors for quantitative exposure monitoring Atmospheric Measurement Techniques |
author_facet |
R. Piedrahita Y. Xiang N. Masson J. Ortega A. Collier Y. Jiang K. Li R. P. Dick Q. Lv M. Hannigan L. Shang |
author_sort |
R. Piedrahita |
title |
The next generation of low-cost personal air quality sensors for quantitative exposure monitoring |
title_short |
The next generation of low-cost personal air quality sensors for quantitative exposure monitoring |
title_full |
The next generation of low-cost personal air quality sensors for quantitative exposure monitoring |
title_fullStr |
The next generation of low-cost personal air quality sensors for quantitative exposure monitoring |
title_full_unstemmed |
The next generation of low-cost personal air quality sensors for quantitative exposure monitoring |
title_sort |
next generation of low-cost personal air quality sensors for quantitative exposure monitoring |
publisher |
Copernicus Publications |
series |
Atmospheric Measurement Techniques |
issn |
1867-1381 1867-8548 |
publishDate |
2014-10-01 |
description |
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,
O<sub>3</sub>, NO<sub>2</sub>, and total VOCs, and NDIR sensors used to measure CO<sub>2</sub>.
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.
<br><br>
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.
<br><br>
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
O<sub>3</sub>, 6.4–8.4 ppb for NO<sub>2</sub>, 0.28–0.44 ppm for CO, and 16.8 ppm
for CO<sub>2</sub>. Median signal to noise (S / N) ratios for the M-Pod sensors
were higher than the regulatory instruments: for NO<sub>2</sub>, 3.6 compared to
23.4; for O<sub>3</sub>, 1.4 compared to 1.6; for CO, 1.1 compared to 10.0; and for
CO<sub>2</sub>, 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.
<br><br>
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.
<br><br>
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<sup>−1</sup>), and higher for O<sub>3</sub> (−2.6 to
2.0 ppb day<sup>−1</sup>), NO<sub>2</sub> (−1.56 to 0.51 ppb day<sup>−1</sup>), and
CO<sub>2</sub> (−4.2 to 3.1 ppm day<sup>−1</sup>). Overall, the user study confirmed
the utility of the M-Pod as a low-cost tool to assess personal exposure. |
url |
http://www.atmos-meas-tech.net/7/3325/2014/amt-7-3325-2014.pdf |
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doaj-74cc46a4d283491cbab3adeb548694e42020-11-24T22:58:21ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482014-10-017103325333610.5194/amt-7-3325-2014The next generation of low-cost personal air quality sensors for quantitative exposure monitoringR. Piedrahita0Y. Xiang1N. Masson2J. Ortega3A. Collier4Y. Jiang5K. Li6R. P. Dick7Q. Lv8M. Hannigan9L. Shang10University of Colorado Boulder, Department of Mechanical Engineering, 427 UCB, 1111 Engineering Drive, Boulder, CO 80309, USAUniversity of Michigan, Department of Electrical Engineering and Computer Science, 2417-E EECS, 1301 Beal Avenue, Ann Arbor, MI 48109, USAUniversity of Colorado Boulder, Department of Mechanical Engineering, 427 UCB, 1111 Engineering Drive, Boulder, CO 80309, USAUniversity of Colorado Boulder, Department of Mechanical Engineering, 427 UCB, 1111 Engineering Drive, Boulder, CO 80309, USAUniversity of Colorado Boulder, Department of Mechanical Engineering, 427 UCB, 1111 Engineering Drive, Boulder, CO 80309, USAUniversity of Colorado Boulder, Department of Computer Science, 1045 Regent Drive, Boulder, CO 80309, USAUniversity of Colorado Boulder, Department of Electrical Engineering, 425 UCB, 1111 Engineering Drive, Boulder, CO 80309, USAUniversity of Michigan, Department of Electrical Engineering and Computer Science, 2417-E EECS, 1301 Beal Avenue, Ann Arbor, MI 48109, USAUniversity of Colorado Boulder, Department of Computer Science, 1045 Regent Drive, Boulder, CO 80309, USAUniversity of Colorado Boulder, Department of Mechanical Engineering, 427 UCB, 1111 Engineering Drive, Boulder, CO 80309, USAUniversity of Colorado Boulder, Department of Electrical Engineering, 425 UCB, 1111 Engineering Drive, Boulder, CO 80309, USAAdvances 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, O<sub>3</sub>, NO<sub>2</sub>, and total VOCs, and NDIR sensors used to measure CO<sub>2</sub>. 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. <br><br> 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. <br><br> 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 O<sub>3</sub>, 6.4–8.4 ppb for NO<sub>2</sub>, 0.28–0.44 ppm for CO, and 16.8 ppm for CO<sub>2</sub>. Median signal to noise (S / N) ratios for the M-Pod sensors were higher than the regulatory instruments: for NO<sub>2</sub>, 3.6 compared to 23.4; for O<sub>3</sub>, 1.4 compared to 1.6; for CO, 1.1 compared to 10.0; and for CO<sub>2</sub>, 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. <br><br> 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. <br><br> 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<sup>−1</sup>), and higher for O<sub>3</sub> (−2.6 to 2.0 ppb day<sup>−1</sup>), NO<sub>2</sub> (−1.56 to 0.51 ppb day<sup>−1</sup>), and CO<sub>2</sub> (−4.2 to 3.1 ppm day<sup>−1</sup>). Overall, the user study confirmed the utility of the M-Pod as a low-cost tool to assess personal exposure.http://www.atmos-meas-tech.net/7/3325/2014/amt-7-3325-2014.pdf |