Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 11, issue 1
Articles | Volume 11, issue 1
https://doi.org/10.5194/amt-11-291-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-11-291-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 1
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, Albert A. Presto, Sriniwasa P. N. Kumar, Jason Gu, Aliaksei Hauryliuk, Ellis S. Robinson, Allen L. Robinson, and R. Subramanian

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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.
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