ORCID
https://orcid.org/0009-0002-2770-6751
Date of Award
Spring 2025
Language
English
Embargo Period
4-30-2026
Document Type
Master's Thesis
Degree Name
Master of Science (MS)
College/School/Department
Department of Atmospheric and Environmental Sciences
Program
Atmospheric Science
First Advisor
Scott Miller
Second Advisor
Cheng-Hsuan Lu
Committee Members
Scott Miller, Cheng-Hsuan Lu
Keywords
low-cost sensor, air quality, calibration, mesonet
Subject Categories
Other Chemical Engineering | Other Environmental Sciences
Abstract
The University at Albany designed and manufactured 59 low-cost air quality sensor packages to continuously measure PM₂.₅, CO, O₃, NO₂, and NO at 38 New York State Mesonet (NYSM) sites located in the New York City Metropolitan Area. Prior to use for monitoring, low-cost sensors require calibration to correct for environmental sensitivities. Calibration models can be developed using data collected from co-location periods in which low-cost sensors are installed at sites with Federal Reference Methods and/or Federal Equivalent Methods instruments. In this study, packages were periodically co-located (calibrated) for 18 to 162 days at the New York State Department of Environmental Conservation Queens College site. Two types of calibration models, a multiple linear regression (MLR) and a Random Forest-MLR hybrid model, were initially developed using the “traditional” calibration approach in which individual models are trained for each sensor and package (i.e., 59 CO models). Both models produced accurate concentrations during short-term evaluation (< 2 months), with the hybrid model improving accuracy at low concentrations for O₃, PM₂.₅, NO₂, and NO. Extending evaluation over a 1-year period to reflect realistic network operations revealed drift reducing data quality for NO₂ and O₃, with O₃ developing a negative bias only 3 months after calibration. Periodically recalibrating packages was expensive and ineffective at improving model performance, with most packages failing to be recalibrated prior to the onset of NO₂ and O₃ drift. To supplement the need for recalibration, the Network Calibration Algorithm was developed by training a single MLR ( CO) or hybrid model (O₃, PM₂.₅, NO₂, NO) per pollutant using 16 months of continuous co-located data from two packages permanently deployed at the calibration site. The algorithm successfully increased long-term accuracy and stability, enabling long-term deployments of low-cost sensors without the need for recalibration.
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Recommended Citation
Hojeily, Ellie, "Calibration of a Low-Cost Air Quality Sensor Package Integrated Into the New York State Mesonet" (2025). Electronic Theses & Dissertations (2024 - present). 153.
https://scholarsarchive.library.albany.edu/etd/153