Characterizing PM₂.₅ Data in Low-Cost Air Quality Sensors
Pham, K., Shams, S., Payne, K., Nicewonger, W.. (2026). Characterizing PM₂.₅ Data in Low-Cost Air Quality Sensors. , doi:https://doi.org/10.5065/0999-0f83
| Title | Characterizing PM₂.₅ Data in Low-Cost Air Quality Sensors |
|---|---|
| Genre | Manuscript |
| Author(s) | Kaylena Pham, Shima Shams, Kathryn Payne, William Nicewonger |
| Abstract | Short- and long-term exposure to fine particulate matter, specifically particles with a diameter below 2.5 μm (PM₂.₅) is attributed to severe human health impacts. Conventional air quality monitoring for PM₂.₅ is limited on their spatial resolution and costs, often requiring high-cost instrumentation and time-intensive work to record observations. Low-cost air quality sensors offer the ability to measure air pollutants at higher network density compared to conventional methods. 3D-Printed Automatic Weather Station (3D-PAWS) is a low-cost weather and environmental monitoring system that seeks to provide accessible and reliable environmental observations. Given the potentials of 3D-PAWS Air Quality Sensors (3D-PAWS AQ), we find that they have an important role in measuring PM₂.₅. In this work, we characterize the 3D-PAWS AQ through analyzing its biases and uncertainties to improve its usability. Our study area focuses on Athens, Georgia; Boulder, Colorado; and Lee Park, Virginia, where we evaluate 3D-PAWS data collocated with air quality data sourced from EPA reference-grade air quality monitors (Teledyne 640x) for comparison. We first processed and cleaned our instrument data to smoothen and reduce noise in our dataset. Afterwards, we applied a regression analysis to calibrate the data with respect to the reference data. We find that 3D-PAWS is capable of capturing features that are reflected in reference monitors and a calibration process can be applied to mitigate biases. Our results emphasize the importance in evaluating the accuracy of low-cost sensors to improve PM₂.₅ monitoring across varied environmental regimes. |
| Publication Title | |
| Publication Date | Aug 1, 2026 |
| Publisher's Version of Record | https://doi.org/10.5065/0999-0f83 |
| OpenSky Citable URL | https://n2t.net/ark:/85065/d76t0s7x |
| OpenSky Listing | View on OpenSky |
| RAL Affiliations | NSAP |