Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 13
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.
Collection:
https://doi.org/10.5194/amt-18-3147-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 13
Research article
 | 
15 Jul 2025
Research article |  | 15 Jul 2025

Improving the quantification of peak concentrations for air quality sensors via data weighting

Caroline Frischmon, Jonathan Silberstein, Annamarie Guth, Erick Mattson, Jack Porter, and Michael Hannigan

Related authors

Evaluating machine learning model performance in a two-step colocation process for 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
Short summary

Cited articles

Barkjohn, K. K., Gantt, B., and Clements, A. L.: Development and application of a United States-wide correction for PM2.5 data collected with the PurpleAir sensor, Atmos. Meas. Tech., 14, 4617–4637, https://doi.org/10.5194/amt-14-4617-2021, 2021. a
Bi, J., Wildani, A., Chang, H. H., and Liu, Y.: Incorporating low-cost sensor measurements into high-resolution PM2.5 modeling at a large spatial scale, Environ. Sci. Technol., 54, 2152–2162, 2020. a
Bigi, A., Mueller, M., Grange, S. K., Ghermandi, G., and Hueglin, C.: Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech., 11, 3717–3735, https://doi.org/10.5194/amt-11-3717-2018, 2018. a
Casey, J. G. and Hannigan, M. P.: Testing the performance of field calibration techniques for low-cost gas sensors in new deployment locations: across a county line and across Colorado, Atmos. Meas. Tech., 11, 6351–6378, https://doi.org/10.5194/amt-11-6351-2018, 2018. a
Casey, J. G., Collier-Oxandale, A., and Hannigan, M.: Performance of artificial neural networks and linear models to quantify 4 trace gas species in an oil and gas production region with low-cost sensors, Sensor. Actuat. B-Chem., 283, 504–514, 2019. a, b
More articles (37)
Download
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.
Read more
Share
Collection
Low-cost sensors for the measurement of atmospheric...