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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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https://doi.org/10.5194/amt-2017-260
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
09 Aug 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).
Closing the gap on lower cost air quality monitoring: machine learning calibration models to improve low-cost sensor performance
Naomi Zimmerman1, Albert A. Presto1, Sriniwasa P. N. Kumar1, Jason Gu2, Aliaksei Hauryliuk1, Ellis S. Robinson1, Allen L. Robinson1, and Ramachandran Subramanian1 1Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, 15213, USA
2Sensevere LLC, Pittsburgh, 15222, USA
Abstract. Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to areas where limited monitoring exists. However, low-cost sensors are frequently sensitive to environmental conditions and pollutant cross-sensitivities, which have historically been poorly addressed by laboratory calibrations, limiting their utility for monitoring. In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP) sensor package, which measures CO, NO2, O3, and CO2. We explored three methods: 1) laboratory univariate linear regression, 2) empirical multivariate linear regression and 3) machine-learning based calibration models using random forests (RF). Calibration models were developed for 19 RAMP monitors using training and testing windows spanning August 2016 through February 2017 in Pittsburgh, PA. The random forest models matched (CO) or significantly outperformed (NO2, CO2, O3) the other calibration models, and their accuracy and precision was robust over time for testing windows of up to 16 weeks. Following calibration, average mean absolute error on the testing dataset from the random forest models was 38 ppb for CO (14 % relative error), 10 ppm for CO2 (2 % relative error), 3.5 ppb for NO2 (29 % relative error) and 3.4 ppb for O3 (15 % relative error), and Pearson r versus the reference monitors exceeded 0.8 for most units. Model performance is explored in detail, including a quantification of model variable importance, accuracy across different concentration ranges, and performance in a range of monitoring contexts including the National Ambient Air Quality Standards (NAAQS), and the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. A key strength of the RF approach is that it accounts for pollutant cross sensitivities. This highlights the importance of developing multipollutant sensor packages (as opposed to single pollutant monitors); we determined this is especially critical for NO2 and CO2. The evaluation reveals that only the RF-calibrated sensors meet the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. We also demonstrate that the RF model calibrated sensors could detect differences in NO2 concentrations between a near-road site and a suburban site less than 1.5 km away. From this study, we conclude that combining RF models with the RAMP monitors appears to be a very promising approach to address the poor performance that has plagued low cost air quality sensors.

Citation: Zimmerman, N., Presto, A. A., Kumar, S. P. N., Gu, J., Hauryliuk, A., Robinson, E. S., Robinson, A. L., and Subramanian, R.: Closing the gap on lower cost air quality monitoring: machine learning calibration models to improve low-cost sensor performance, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2017-260, in review, 2017.
Naomi Zimmerman et al.
Naomi Zimmerman et al.
Naomi Zimmerman et al.

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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.
Low-cost sensors promise neighborhood-scale air quality monitoring, but have been plagued by...
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