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.
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.
the Creative Commons Attribution 4.0 License.
A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring
Naomi Zimmerman
Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Albert A. Presto
Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Sriniwasa P. N. Kumar
Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Jason Gu
Sensevere LLC, Pittsburgh, PA 15222, USA
Aliaksei Hauryliuk
Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Ellis S. Robinson
Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Allen L. Robinson
Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
R. Subramanian
Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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Discussed (final revised paper)
Discussed (preprint)
Latest update: 03 Nov 2024
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.
Low-cost sensors promise neighborhood-scale air quality monitoring but have been plagued by...