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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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