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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 12 Feb 2019

Research article | 12 Feb 2019

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

Evaluating and Improving the Reliability of Gas-Phase Sensor System Calibrations Across New Locations for Ambient Measurements and Personal Exposure Monitoring

Sharad Vikram1, Ashley Collier-Oxandale2, Michael Ostertag1, Massimiliano Menarini1, Camron Chermak1, Sanjoy Dasgupta1, Tajana Rosing1, Michael Hannigan2, and William G. Griswold1 Sharad Vikram et al.
  • 1Department of Computer Science & Engineering, University of California, San Diego, USA
  • 2Environmental Engineering Program, University of Colorado, Boulder, USA

Abstract. Advances in ambient environmental monitoring technologies are enabling concerned communities and citizens to collect data to better understand their local environment and potential exposures. These mobile, low-cost tools make it possible to collect data with increased temporal and spatial resolution providing data on a large scale with unprecedented levels of detail. This type of data has the potential to empower people to make personal decisions about their exposure and support the development of local strategies for reducing pollution and improving health outcomes.

However, calibration of these low-cost instruments has been a challenge. Often, a sensor package is calibrated via field calibration. This involves colocating the sensor package with a high-quality reference instrument for an extended period and then applying machine learning or other model fitting technique such as multiple-linear regression to develop a calibration model for converting raw sensor signals to pollutant concentrations. Although this method helps to correct for the effects of ambient conditions (e.g., temperature) and cross-sensitivities with non-target pollutants, there is a growing body of evidence that calibration models can overfit to a given location or set of environmental conditions on account of the incidental correlation between pollutant levels and environmental conditions, including diurnal cycles. As a result, a sensor package trained at a field site may provide less reliable data when moved, or transferred, to a different location. This is a potential concern for applications seeking to perform monitoring away from regulatory monitoring sites, such as personal mobile monitoring or high-resolution monitoring of a neighborhood.

We performed experiments confirming that transferability is indeed a problem and show that it can be improved by collecting data from multiple regulatory sites and building a calibration model that leverages data from a more diverse dataset. We deployed three sensor packages to each of three sites with reference monitors (nine packages total) and then rotated the sensor packages through the sites over time. Two sites were in San Diego, CA, with a third outside of Bakersfield, CA, offering varying environmental conditions, general air quality composition, and pollutant concentrations. When compared to prior single-site calibration, the multi-site approach exhibits better model transferability for a range of modeling approaches. Our experiments also reveal that random forest is especially prone to overfitting, and confirms prior results that transfer is a significant source of both bias and standard error. Bias dominated in our experiments, suggesting that transferability might be easily increased by detecting and correcting for bias.

Also, given that many monitoring applications involve the deployment of many sensor packages based on the same sensing technology, there is an opportunity to leverage the availability of multiple sensors at multiple sites during calibration. We contribute a new neural network architecture model termed split-NN that splits the model into two-stages, in which the first stage corrects for sensor-to-sensor variation and the second stage uses the combined data of all the sensors to build a model for a single sensor package. The split-NN modeling approach outperforms multiple linear regression, traditional 2- and 4-layer neural network, and random forest models.

Sharad Vikram et al.
Interactive discussion
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Sharad Vikram et al.
Sharad Vikram et al.
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Publications Copernicus
Short summary
Low-cost air quality sensors are enabling people to collect data to better understand their local environment and potential exposures. However, there is some concern regarding how reliable the calibrations of these sensors are in new and different environments. To explore this issue, our team co-located sensors at three different sites with high-quality monitoring instruments to compare to. We explored the transferability of calibration models as well as approaches to improve reliability.
Low-cost air quality sensors are enabling people to collect data to better understand their...