Articles | Volume 12, issue 9
Atmos. Meas. Tech., 12, 5161–5181, 2019
https://doi.org/10.5194/amt-12-5161-2019
Atmos. Meas. Tech., 12, 5161–5181, 2019
https://doi.org/10.5194/amt-12-5161-2019

Research article 26 Sep 2019

Research article | 26 Sep 2019

Gaussian process regression model for dynamically calibrating and surveilling a wireless low-cost particulate matter sensor network in Delhi

Tongshu Zheng et al.

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Cited articles

Austin, E., Novosselov, I., Seto, E., and Yost, M. G.: Laboratory evaluation of the Shinyei PPD42NS low-cost particulate matter sensor, PLoS One, 10, 1–17, https://doi.org/10.1371/journal.pone.0137789, 2015. 
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Byrd, R. H., Lu, P., Nocedal, J., and Zhu, C.: A limited memory algorithm for bound constrained optimization, available at: http://users.iems.northwestern.edu/~nocedal/PDFfiles/limited.pdf (last access: 10 December 2018), 1994. 
CPCB: Air quality monitoring, emission inventory, and source apportionment studies for Delhi, available at: http://cpcb.nic.in/cpcbold/Delhi.pdf, (last access: 10 December 2018), 2009. 
Crilley, L. R., Shaw, M., Pound, R., Kramer, L. J., Price, R., Young, S., Lewis, A. C., and Pope, F. D.: Evaluation of a low-cost optical particle counter (Alphasense OPC-N2) for ambient air monitoring, Atmos. Meas. Tech., 11, 709–720, https://doi.org/10.5194/amt-11-709-2018, 2018. 
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Short summary
Here we present a simultaneous Gaussian process regression (GPR) and linear regression pipeline to calibrate and monitor dense wireless low-cost particulate matter sensor networks (WLPMSNs) on the fly by using all available reference monitors across an area. Our approach can achieve an overall 30 % prediction error at a 24 h scale, can differentiate malfunctioning nodes, and track drift. Our solution can substantially reduce manual labor for managing WLPMSNs and prolong their lifetimes.