Date of Award

Spring 6-15-2024

Document Type

Undergraduate Honors Thesis

Degree Name

B.S. in Chemistry

Organizational Unit

College of Natural Science and Mathematics, Chemistry and Biochemistry

First Advisor

J. Alex Huffman


Atmospheric chemistry, Air quality, Regional data


Science surrounding the use of low-cost sensors (LCS) to monitor air quality is rapidly expanding to satisfy the need to fill in regional air quality data gaps. This project evaluated the suitability of two types of LCS: Modulair-PM and PurpleAir (SD and Flex models) as efficient means to measure airborne particulate matter. The study involved physical installation of nine co-located sensors at a suburban site in Denver, connectivity troubleshooting as necessary, and data analysis/modeling of data over multiple months (19 weeks). PM data was compared between individual sensors of the same type, as well as across the two different sensor models, and used to draw conclusions about air quality trends at the field site. Comparisons between sensors were generally in good agreement (R2 values of 0.98, 0.99, 1.00), but an additive bias was observed for several sensors, highlighting the importance of calibration of these types of units. The project concluded with installation of the PurpleAir units at a field site at the Kennedy Mountain Campus of the University of Denver. Results from the project create a base level of understanding of LCS functionality to be expanded upon in future project applications.

Copyright Date


Copyright Statement / License for Reuse

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Olivia Wuttke


Received from author

File Format



English (eng)


21 pgs

File Size

1.1 MB