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Abstract
This study proposes a spatial calibration and mapping approach for low-cost PM2.5 sensors, addressing inconsistencies between low-cost sensor measurements and regulatory stations, especially in high humidity environments. A spatial regression model is used, improving bias and RMSE compared to nonspatial methods. The approach enhances air quality monitoring and assessment for communities and agencies.
Publisher
Scientific Reports
Published On
Dec 16, 2020
Authors
Hone-Jay Chu, Muhammad Zeeshan Ali, Yu-Chen He
Tags
PM2.5 sensors
spatial calibration
air quality monitoring
humidity
spatial regression
environmental assessment

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