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Discussion papers | Copyright
https://doi.org/10.5194/amt-2018-216
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 08 Aug 2018

Research article | 08 Aug 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring

Carl Malings1, Rebecca Tanzer1, Aliaksei Hauryliuk1, Sriniwasa P. N. Kumar1, Naomi Zimmerman2, Levent B. Kara3, Albert A. Presto1, and R. Subramanian1 Carl Malings et al.
  • 1Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213, USA
  • 2Department of Mechanical Engineering, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada
  • 3Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213, USA

Abstract. Assessing the intra-city spatial distribution and temporal variability of air quality can be facilitated by a dense network of monitoring stations. However, the cost of implementing such a network can be prohibitive if traditional high-quality, expensive monitoring systems are used. To this end, the Real-time Affordable Multi-Pollutant (RAMP) monitor has been developed, which can measure up to five gases including the criteria pollutant gases carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3), along with temperature and relative humidity. This study compares various algorithms to calibrate the RAMP measurements including linear and quadratic regression, clustering, neural networks, Gaussian processes, and random forests. Using data collected by more than sixty RAMP monitors over periods ranging up to eighteen months, it was found that quadratic regression models or a hybrid of random forest and linear models tend to be the most effective calibration models overall. In specific cases, other types of models can have comparable or even superior performance. Furthermore, generalized calibration models may be used instead of individual models with only a small reduction in overall performance. For long-term deployments, it is recommended that new models be developed each year, due to the noticeable change in performance when models for one year were used for processing data collected in the subsequent year. This makes annually-developed generalized calibration models even more useful since only a subset of deployed monitors are needed to build these models. These results will help guide future efforts in the calibration and use of low-cost sensor systems worldwide.

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Supplementary Data for "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" C. Malings, R. Tanzer, A. Hauryliuk, S. P. N. Kumar, N. Zimmerman, L. B. Kara, A. A. Presto, and R. Subramanian https://doi.org/10.5281/zenodo.1302030

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This paper compares several methods for calibrating data from low-cost air quality monitors to reflect the concentrations of various gaseous pollutants in the atmosphere, identifying the best-performing approaches. With these calibrations methods, such monitors can be used to gather information on air quality at a higher spatial resolution than is possible using traditional technologies, and can be deployed to areas (e.g. developing countries) where there are no existing monitor networks.
This paper compares several methods for calibrating data from low-cost air quality monitors to...
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