Optimizing Air Quality Monitoring: Comparative Analysis of Linear Regression and Machine Learning in Low-Cost Sensor Calibration

Optimizing Air Quality Monitoring: Comparative Analysis of Linear Regression and Machine Learning in Low-Cost Sensor Calibration

Abstract Background Low-cost sensors (LCS) are widely used for air quality monitoring, but their accuracy depends on proper calibration. This study compares linear regression (LR) and machine learning (ML) techniques, particularly random forest (RF), to determine optimal calibration strategies. Obje...

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Bibliographic Details
Main Authors: Runcheng Fang, Scott Collingwood, Yue Zhang, Joseph B. Stanford, Christina Porucznik, Darrah Sleeth
Format: Article
Language:English
Published: Springer 2025-04-01
Series:Aerosol and Air Quality Research
Subjects:
Air quality monitoring
Sensor calibration
Linear regression
Machine learning
Low-cost sensors
Online Access:https://doi.org/10.1007/s44408-025-00009-x
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https://doi.org/10.1007/s44408-025-00009-x

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