Assessing the Utility of Low-Cost Particulate Matter Sensors over a 12-Week Period in the Cuyama Valley of California
Abstract
:1. Introduction
2. Methodology
3. Results and Discussion
3.1. Cuyama Aerosol Environment
3.2. Precision
3.3. Accuracy Comparison to BAM
3.4. Sampling Orientation
3.5. Size Distribution
3.6. Meteorology and Size Distribution Influence
3.7. Data Recovery
3.8. Drift of OPC-N2 Sensor
3.9. Early Detection
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Lelieveld, J.; Evans, J.S.; Fnais, M.; Giannadaki, D.; Pozzer, A. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 2015, 525, 367–371. [Google Scholar] [CrossRef] [PubMed]
- World Health Organization. Ambient Air Pollution: A Global Assessment of Exposure and Burden of Disease; WHO Press: Geneva, Switzerland, 2016; pp. 23–37. ISBN 978-92-4-151135-3. [Google Scholar]
- Schlesinger, R.B.; Kunzli, N.; Hidy, G.M.; Gotschi, T.; Jerrett, M. The health relevance of ambient particulate matter characteristics: Coherence of toxicological and epidemiological inferences. Inhal. Toxicol. 2006, 18, 95–125. [Google Scholar] [CrossRef] [PubMed]
- EPA. NAAQS Table. Available online: https://www.epa.gov/criteria-air-pollutants/naaqs-table (accessed on 20 June 2017).
- Kumar, P.; Morawska, L.; Martani, C.; Biskos, G.; Neophytou, M.; Di Sabatino, S.; Bell, M.; Norford, L.; Britter, R. The rise of low-cost sensing for managing air pollution in cities. Environ. Int. 2015, 75, 199–205. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lewis, A.; Edwards, P. Validate personal air-pollution sensors. Nature 2016, 535, 29–31. [Google Scholar] [CrossRef] [PubMed]
- Hall, E.S.; Kaushik, S.M.; Vanderpool, R.W.; Duvall, R.M.; Beaver, M.R.; Long, R.W.; Solomon, P.A. Integrating sensor monitoring technology into the current air pollution regulatory support paradigm: Practical considerations. Am. J. Environ. Eng. 2014, 4, 147–154. [Google Scholar]
- Jiao, W.; Hagler, G.; Williams, R.; Sharpe, R.; Brown, R.; Garver, D.; Judge, R.; Caudill, M.; Rickard, J.; Davis, M.; et al. Community Air Sensor Network (CAIRSENSE) project: Evaluation of low-cost sensor performance in a suburban environment in the southeastern United States. Atmos. Meas. Tech. 2016, 9, 5281–5292. [Google Scholar] [CrossRef]
- Snyder, E.G.; Watkins, T.H.; Solomon, P.A.; Thoma, E.D.; Williams, R.W.; Hagler, G.S.W.; Shelow, D.; Hindin, D.A.; Kilaru, V.J.; Preuss, P.W. The changing paradigm of air pollution monitoring. Environ. Sci. Technol. 2013, 47, 11369–11377. [Google Scholar] [CrossRef] [PubMed]
- Nieuwenhuijsen, M.J.; Donaire-Gonzalez, D.; Rivas, I.; de Castro, M.; Cirach, M.; Hoek, G.; Seto, E.; Jerrett, M.; Sunyer, J. Variability in and agreement between modeled and personal continuously measured black carbon levels using novel smartphone and sensor technologies. Environ. Sci. Technol. 2015, 49, 2977–2982. [Google Scholar] [CrossRef] [PubMed]
- Piedrahita, R.; Xiang, Y.; Masson, N.; Ortega, J.; Collier, A.; Jiang, Y.; Li, K.; Dick, R.P.; Lv, Q.; Hannigan, M.; Shang, L. The next generation of low-cost personal air quality sensors for quantitative exposure monitoring. Atmos. Meas. Tech. 2014, 7, 3325–3336. [Google Scholar] [CrossRef] [Green Version]
- Gao, M.; Cao, J.; Seto, E. A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2.5 in Xi’an, China. Environ. Pollut. 2015, 199, 56–65. [Google Scholar] [CrossRef] [PubMed]
- Mead, M.I.; Popoola, O.A.M.; Stewart, G.B.; Landshoff, P.; Calleja, M.; Hayes, M.; Baldovi, J.J.; McLeod, M.W.; Hodgson, T.F.; Dicks, J.; et al. The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks. Atmos. Environ. 2013, 70, 186–203. [Google Scholar] [CrossRef]
- Jovašević-Stojanović, M.; Bartonova, A.; Topalović, D.; Lazović, I.; Pokrić, B.; Ristovski, Z. On the use of small and cheaper sensors and devices for indicative citizen-based monitoring of respirable particulate matter. Environ. Pollut. 2015, 206, 696–704. [Google Scholar] [CrossRef] [PubMed]
- Hinds, W.C. Aerosol Technology: Properties, Behavior, and Measurement of Airborne Particles, 2nd ed.; Wiley-Interscience: New York, NY, USA, 1999; ISBN 978-0-471-19410-1. [Google Scholar]
- Aircasting. Available online: http://aircasting.org/about (accessed on 15 May 2017).
- Alphasense Ltd. User Manual: OPC-N2 Optical Particle Counter. 072–0300, Issue 3; Alphasense Ltd.: Braintree, UK, 2015. [Google Scholar]
- GRIMM Aerosol Technik GmbH & Co. GRIMM Portable Aerosol Spectrometer, Datasheet. 11-R; GRIMM Aerosol Technik GmbH & Co. KG: Ainring, Germany, 2016. [Google Scholar]
- EPA. List of Designated Reference and Equivalent Methods. Available online: https://www3.epa.gov/ttnamti1/files/ambient/criteria/AMTIC%20List%20Dec%202016-2.pdf (accessed on 20 June 2017).
- EPA. Standard Operating Procedure for the Continuous Measurement of Particulate Matter. Available online: https://www3.epa.gov/ttnamti1/files/ambient/pm25/sop_project/905505_BAM_SOP_Draft_Final_Oct09.pdf (accessed on 20 June 2017).
- Castellani, B.; Morini, E.; Filipponi, M.; Nicolini, A.; Palombo, M.; Cotana, F.; Rossi, F. Comparative analysis of monitoring devices for particulate content in exhaust gases. Sustainability 2014, 6, 4287–4307. [Google Scholar] [CrossRef]
- Sousan, S.; Koehler, K.; Hallett, L.; Peters, T.M. Evaluation of the Alphasense optical particle counter (OPC-N2) and the Grimm portable aerosol spectrometer (PAS-1.108). Aerosol Sci. Technol. 2016, 50, 1–14. [Google Scholar] [CrossRef]
Instrument | Sampling Orientation |
---|---|
BAM-1020 | Omnidirectional |
GRIMM 11-R | Omnidirectional |
OPC-N2 A | North/Omnidirectional * |
OPC-N2 B | North |
OPC-N2 C | South |
AirBeam A | North |
AirBeam B | North |
AirBeam C | South |
X Axis Instrument | Y Axis Instrument | R2 | Linear Regression | Number of Measurements | Time Period |
---|---|---|---|---|---|
AirBeam A | AirBeam B | 0.99 | y = 0.96x + 0.55 | 1995 | full |
AirBeam A | AirBeam C | 0.98 | y = 0.85x – 0.28 | 1995 | full |
AirBeam B | AirBeam C | 0.95 | y = 0.87x – 0.62 | 1998 | full |
OPC-N2 A | OPC-N2 B | 0.84 | y = 0.71x + 3.54 | 1647 | full |
OPC-N2 A | OPC-N2 B | 0.81 | y = 1.00x + 1.95 | 842 | 14 April–1 June |
OPC-N2 A | OPC-N2 B | 0.91 | y = 0.66x + 3.45 | 805 | 1 June–7 July |
OPC-N2 A | OPC-N2 C | 0.85 | y = 0.57x + 1.72 | 1629 | full |
OPC-N2 B | OPC-N2 C | 0.79 | y = 0.78x − 0.82 | 1725 | full |
X Axis Instrument | Y Axis Instrument | R2 | Linear Regression | Number of Measurements | Time Period |
---|---|---|---|---|---|
BAM | AirBeam A | 0.25 | y = 0.06x + 5.52 | 1995 | full |
BAM | AirBeam B | 0.21 | y = 0.05x + 5.98 | 1997 | full |
BAM | AirBeam C | 0.33 | y = 0.06x + 4.15 | 1998 | full |
BAM | OPC-N2 A | 0.76 | y = 0.22x + 1.76 | 1764 | full |
BAM | OPC-N2 A | 0.53 | y = 0.21x + 2.71 | 939 | 14 April–1 June |
BAM | OPC-N2 A | 0.81 | y = 0.23x + 0.32 | 825 | 1 June–7 July |
BAM | OPC-N2 B | 0.67 | y = 0.16x + 4.62 | 1799 | full |
BAM | OPC-N2 C | 0.61 | y = 0.13x + 2.47 | 1776 | full |
X Axis Instrument | Y Axis Instrument | Pollutant | R2 | Linear Regression | Number of Measurements |
---|---|---|---|---|---|
GRIMM 11-R | BAM | PM10 | 0.91 | y = 0.86x + 6.52 | 1753 |
GRIMM 11-R | OPC-N2 A | PM10 | 0.84 | y = 0.20x + 2.83 | 1526 |
GRIMM 11-R | OPC-N2 B | PM10 | 0.81 | y = 0.14x + 5.36 | 1626 |
GRIMM 11-R | OPC-N2 C | PM10 | 0.81 | y = 0.12x + 2.84 | 1594 |
GRIMM 11-R | OPC-N2 A | PM2.5 | 0.43 | y = 0.15x + 1.92 | 1521 |
GRIMM 11-R | OPC-N2 B | PM2.5 | 0.41 | y = 0.16x + 3.51 | 1625 |
GRIMM 11-R | OPC-N2 C | PM2.5 | 0.40 | y = 0.13x + 1.99 | 1590 |
GRIMM 11-R | OPC-N2 A | PM1 | 0.39 | y = 0.28x + 0.29 | 1417 |
GRIMM 11-R | OPC-N2 B | PM1 | 0.45 | y = 0.41x + 2.08 | 1623 |
GRIMM 11-R | OPC-N2 C | PM1 | 0.38 | y = 0.28x + 0.97 | 1475 |
GRIMM 11-R | AirBeam A | PM2.5 | 0.66 | y = 0.40x + 4.33 | 1753 |
GRIMM 11-R | AirBeam B | PM2.5 | 0.62 | y = 0.36x + 4.91 | 1755 |
GRIMM 11-R | AirBeam C | PM2.5 | 0.71 | y = 0.37x + 3.13 | 1755 |
Sensor | Number of Possible Samples | Number of Samples Recovered | % Recovery 1-min | % Recovery 1-h |
---|---|---|---|---|
OPC-N2 A | 120,181 | 105,934 * | 88.1 | 88.7 |
OPC-N2 B | 120,181 | 107,613 * | 89.5 | 95.4 |
OPC-N2 C | 120,181 | 106,204 * | 88.3 | 92.4 |
AirBeam A | 120,181 | 119,548 | 99.5 | 100.0 |
AirBeam B | 120,181 | 119,689 | 99.6 | 100.0 |
AirBeam C | 120,181 | 119,689 | 99.6 | 100.0 |
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Mukherjee, A.; Stanton, L.G.; Graham, A.R.; Roberts, P.T. Assessing the Utility of Low-Cost Particulate Matter Sensors over a 12-Week Period in the Cuyama Valley of California. Sensors 2017, 17, 1805. https://doi.org/10.3390/s17081805
Mukherjee A, Stanton LG, Graham AR, Roberts PT. Assessing the Utility of Low-Cost Particulate Matter Sensors over a 12-Week Period in the Cuyama Valley of California. Sensors. 2017; 17(8):1805. https://doi.org/10.3390/s17081805
Chicago/Turabian StyleMukherjee, Anondo, Levi G. Stanton, Ashley R. Graham, and Paul T. Roberts. 2017. "Assessing the Utility of Low-Cost Particulate Matter Sensors over a 12-Week Period in the Cuyama Valley of California" Sensors 17, no. 8: 1805. https://doi.org/10.3390/s17081805
APA StyleMukherjee, A., Stanton, L. G., Graham, A. R., & Roberts, P. T. (2017). Assessing the Utility of Low-Cost Particulate Matter Sensors over a 12-Week Period in the Cuyama Valley of California. Sensors, 17(8), 1805. https://doi.org/10.3390/s17081805