Articles | Volume 19, issue 9
https://doi.org/10.5194/amt-19-2923-2026
© Author(s) 2026. 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-19-2923-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 May 2026
Research article |
| 04 May 2026
| 04 May 2026
Evaluating machine learning model performance in a two-step colocation process for TVOC and BTEX sensor calibration
Caroline Frischmon
CORRESPONDING AUTHOR
Department of Mechanical Engineering, University of Colorado Boulder, 1111 Engineering Drive, Boulder, CO, 80309, USA
Jack Porter
South Coast Air Quality Monitoring District, 21865 Copley Drive, Diamond Bar, CA, 91765, USA
Ethan Balagopalan
South Coast Air Quality Monitoring District, 21865 Copley Drive, Diamond Bar, CA, 91765, USA
William Senga
South Coast Air Quality Monitoring District, 21865 Copley Drive, Diamond Bar, CA, 91765, USA
Jill Johnston
Department of Environmental and Occupational Health, University of California Irvine, 856 Health Sciences Quad, Irvine, CA, 92697, USA
Michael Hannigan
Department of Mechanical Engineering, University of Colorado Boulder, 1111 Engineering Drive, Boulder, CO, 80309, USA
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Short summary
We implemented a two-step colocation strategy to improve the transferability of sensor calibration models to field conditions, particularly for total volatile organic compound (TVOC) and benzene, toluene, ethylbenzene, and xylene (BTEX) sensors. In our comparison of various calibration models, we found that they generally performed well even as they tended to overpredict baseline concentrations and underpredict peaks. This work provides important insights on TVOC and BTEX sensor calibration.
We implemented a two-step colocation strategy to improve the transferability of sensor...
