Articles | Volume 18, issue 13
https://doi.org/10.5194/amt-18-3147-2025
© Author(s) 2025. 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-18-3147-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Jul 2025
Research article |
| 15 Jul 2025
| 15 Jul 2025
Improving the quantification of peak concentrations for air quality sensors via data weighting
Caroline Frischmon
CORRESPONDING AUTHOR
Department of Mechanical Engineering, University of Colorado Boulder, 1111 Engineering Drive, Boulder, CO 80309, USA
Jonathan Silberstein
Department of Mechanical Engineering, University of Colorado Boulder, 1111 Engineering Drive, Boulder, CO 80309, USA
Annamarie Guth
Department of Mechanical Engineering, University of Colorado Boulder, 1111 Engineering Drive, Boulder, CO 80309, USA
Erick Mattson
Colorado Department of Public Health and Environment, 4300 Cherry Creek Drive South, Glendale, CO 80246, USA
Jack Porter
South Coast Air Quality Monitoring District, 21865 Copley Drive Diamond Bar, CA 91765, USA
Michael Hannigan
Department of Mechanical Engineering, University of Colorado Boulder, 1111 Engineering Drive, Boulder, CO 80309, USA
Related authors
Caroline Frischmon, Jack Porter, Ethan Balagopalan, William Senga, Jill Johnston, and Michael Hannigan
EGUsphere, https://doi.org/10.5194/egusphere-2025-4697, https://doi.org/10.5194/egusphere-2025-4697, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
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.
Caroline Frischmon, Jack Porter, Ethan Balagopalan, William Senga, Jill Johnston, and Michael Hannigan
EGUsphere, https://doi.org/10.5194/egusphere-2025-4697, https://doi.org/10.5194/egusphere-2025-4697, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
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.
Kevin J. Sanchez, Bo Zhang, Hongyu Liu, Matthew D. Brown, Ewan C. Crosbie, Francesca Gallo, Johnathan W. Hair, Chris A. Hostetler, Carolyn E. Jordan, Claire E. Robinson, Amy Jo Scarino, Taylor J. Shingler, Michael A. Shook, Kenneth L. Thornhill, Elizabeth B. Wiggins, Edward L. Winstead, Luke D. Ziemba, Georges Saliba, Savannah L. Lewis, Lynn M. Russell, Patricia K. Quinn, Timothy S. Bates, Jack Porter, Thomas G. Bell, Peter Gaube, Eric S. Saltzman, Michael J. Behrenfeld, and Richard H. Moore
Atmos. Chem. Phys., 22, 2795–2815, https://doi.org/10.5194/acp-22-2795-2022, https://doi.org/10.5194/acp-22-2795-2022, 2022
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Atmospheric particle concentrations impact clouds, which strongly impact the amount of sunlight reflected back into space and the overall climate. Measurements of particles over the ocean are rare and expensive to collect, so models are necessary to fill in the gaps by simulating both particle and clouds. However, some measurements are needed to test the accuracy of the models. Here, we measure changes in particles in different weather conditions, which are ideal for comparison with models.
Kevin J. Sanchez, Bo Zhang, Hongyu Liu, Georges Saliba, Chia-Li Chen, Savannah L. Lewis, Lynn M. Russell, Michael A. Shook, Ewan C. Crosbie, Luke D. Ziemba, Matthew D. Brown, Taylor J. Shingler, Claire E. Robinson, Elizabeth B. Wiggins, Kenneth L. Thornhill, Edward L. Winstead, Carolyn Jordan, Patricia K. Quinn, Timothy S. Bates, Jack Porter, Thomas G. Bell, Eric S. Saltzman, Michael J. Behrenfeld, and Richard H. Moore
Atmos. Chem. Phys., 21, 831–851, https://doi.org/10.5194/acp-21-831-2021, https://doi.org/10.5194/acp-21-831-2021, 2021
Short summary
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Models describing atmospheric airflow were combined with satellite measurements representative of marine phytoplankton and other meteorological variables. These combined variables were compared to measured aerosol to identify upwind influences on aerosol concentrations. Results indicate that phytoplankton production rates upwind impact the aerosol mass. Also, results suggest that the condensation of mass onto short-lived large sea spray particles may be a significant sink of aerosol mass.
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Short summary
Air quality sensors often underpredict peak concentrations, which is a major issue in applications such as emission event detection. Here, we detail a novel approach involving data weighting to improve quantification of these peak values. To demonstrate its effectiveness, we applied data weighting to carbon monoxide, methane, and volatile organic compound sensor data. This work broadens our ability to use air sensors in contexts where accurate quantification of peak concentrations is essential.
Air quality sensors often underpredict peak concentrations, which is a major issue in...
