A Bayesian-Optimized Surrogate Model Integrating Deep Learning Algorithms for Correcting PurpleAir Sensor Measurements

A Bayesian-Optimized Surrogate Model Integrating Deep Learning Algorithms for Correcting PurpleAir Sensor Measurements

Lowcost sensors are widely used for air quality monitoring due to their affordability, portability and easy maintenance. However, the performance of such sensors, such as PurpleAir Sensors (PAS), is often affected by changes in environmental (e.g., temperature and humidity) or emission conditions, a...

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Bibliographic Details
Main Authors: Masrur Ahmed, Jing Kong, Ningbo Jiang, Hiep Nguyen Duc, Praveen Puppala, Merched Azzi, Matthew Riley, Xavier Barthelemy
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Atmosphere
Subjects:
machine learning (ML)
deep learning (DL)
BaySurcls
PurpleAir sensor (PAS)
PM2.5 pollution
data correction
Online Access:https://www.mdpi.com/2073-4433/15/12/1535
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