| 23 Sep 2025
Evaluation and Calibration of Clarity Node S Low-Cost Sensors in Lubbock, Texas
Abstract. Aerosol particles, also known as Particulate Matter (PM), have a profound impact on human health, air quality, the weather, and climate. PM can be measured using a variety of measuring techniques and instruments, notably reference-grade instruments and Low-Cost Sensors (LCS). Although Low-Cost Sensors allow for a higher resolution network, some have accuracy issues and reliability when compared to reference-grade units, which prompts the need to develop a calibration. This work, which is part of the Lubbock Environmental Action Plan (LEAP) for Communities, aims to provide information on air quality levels across the city of Lubbock using Clarity Node S sensors. In this study, which is the first step of the work, an evaluation and calibration of four Clarity Node S sensors was performed. The Clarity Node S sensors were selected for this project due to the sensors' ability to operate without a power or Wi-Fi source. Good agreement was found between the sensors when they were collocated with each other from March to May 2024 on the Aerosol Research Observation Station (AEROS). Next, one LEAP unit was collocated at AEROS with a reference unit, and different calibration tests were performed for the three PM concentrations measured by the Clarity units (PM1, PM2.5, and PM10, particles with diameters <1, 2.5, and 10 µm, respectively). The selected calibration was developed and implemented for all four LEAP units. The calibrated LEAP units were then collocated near two different reference units for a duration of eight months (July 2024 to February 2025), and a comparison was performed. While one reference unit showed a good agreement with three LEAP units, the other reference units were very different from the collected LEAP unit.
This study assess the performance of 4 Clarity Node S sensors collocated with/near an EDM and BAM FEM monitor for PM1, PM2.5, and PM10. They provide a good summary of their study site and instrumentation used, and a detailed description of their correction process. However, I fear their correction model is heavily overfit to the EDM data and needs to be re-evaluated. There is also a concern over the seemingly lack of pre-treatment (QA/QC) for the LCS data – it is well established that these monitors need to have erroneous values removed prior to correcting, typically through comparing the data from replicated internal sensors (ie. https://doi.org/10.5194/amt-15-3315-2022), and this likely explains the relatively low correlation of the raw PM2.5 data.
The authors do not mention splitting their data into testing/training sets like what Clarity Co. did for the correction they provided– without this there is a very high risk of overfitting, and the presented statistics will be biased. In addition, the EMD observations were included in the correction model, which will result in overfitting and a risk of the EDM observations dominating the corrected value. This is especially visible in Figure 7, where the corrected data nearly perfectly follows the EDM timeseries, in contrast to Figure 9 where the BAM monitor was used instead of the EDM. Due to the overfitting resulting from a lack of train/test splitting and the incestuous inclusion of the EDM data in the correction, I have serious concerns over the efficacy and transferability of the correction presented. The authors must split their data properly and seriously reconsider the inclusion of EDM data within the regression terms for this to be statistically sound.
I was expecting a discussion or conclusions section, but following the results there is just a summary section that repeats the methods and key findings. It could be helpful for the reader to have the large results section parsed into results/discussion/conclusion as is normally done.
See below for specific line-by-line comments and suggestions.
Specific comments: