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Spatial calibration and PM2.5 mapping of low-cost air quality sensors

Environmental Studies and Forestry

Spatial calibration and PM2.5 mapping of low-cost air quality sensors

H. Chu, M. Z. Ali, et al.

This study from Hone-Jay Chu, Muhammad Zeeshan Ali, and Yu-Chen He presents an innovative spatial calibration and mapping method for low-cost PM2.5 sensors, effectively tackling measurement discrepancies in humid conditions. The proposed spatial regression model significantly reduces bias and RMSE, enhancing air quality monitoring for communities and agencies.

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Playback language: English
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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