Articles | Volume 11, issue 1
Atmos. Meas. Tech., 11, 291–313, 2018
https://doi.org/10.5194/amt-11-291-2018
Atmos. Meas. Tech., 11, 291–313, 2018
https://doi.org/10.5194/amt-11-291-2018
Research article
 | Highlight paper
15 Jan 2018
Research article  | Highlight paper | 15 Jan 2018

A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring

Naomi Zimmerman et al.

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Cited articles

Air Quality England: Air Pollution Report, 1st January to 31st December 2016, Cambridge Parker Street (Site ID: CAM 1), 1–4, available at: http://www.airqualityengland.co.uk/site/statistics?site_id=CAM1 (last access: 22 June 2017), 2015.
Bart, M., Williams, D. E., Ainslie, B., McKendry, I., Salmond, J., Grange, S. K., Alavi-Shoshtari, M., Steyn, D., and Henshaw, G. S.: High density ozone monitoring using gas sensitive semi-conductor sensors in the lower Fraser valley, British Columbia, Environ. Sci. Technol., 48, 3970–3977, https://doi.org/10.1021/es404610t, 2014.
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Castell, N., Dauge, F. R., Schneider, P., Vogt, M., Lerner, U., Fishbain, B., Broday, D., and Bartonova, A.: Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?, Environ. Int., 99, 293–302, https://doi.org/10.1016/j.envint.2016.12.007, 2017.
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Short summary
Low-cost sensors promise neighborhood-scale air quality monitoring but have been plagued by inconsistent performance for precision, accuracy, and drift. CMU and SenSevere collaborated to develop the RAMP, which uses electrochemical sensors. We present a machine learning algorithm that overcomes previous performance issues and meets US EPA's data quality recommendations for personal exposure for NO2 and tougher "supplemental monitoring" standards for CO & ozone across 19 RAMPs for several months.