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

Hone Jay Chu, Muhammad Zeeshan Ali, Yu Chen He

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


The data quality of low-cost sensors has received considerable attention and has also led to PM2.5 warnings. However, the calibration of low-cost sensor measurements in an environment with high relative humidity is critical. This study proposes an efficient calibration and mapping approach based on real-time spatial model. The study carried out spatial calibration, which automatically collected measurements of low-cost sensors and the regulatory stations, and investigated the spatial varying pattern of the calibrated low-cost sensor data. The low-cost PM2.5 sensors are spatially calibrated based on reference-grade measurements at regulatory stations. Results showed that the proposed spatial regression approach can explain the variability of the biases from the low-cost sensors with an R-square value of 0.94. The spatial calibration and mapping algorithm can improve the bias and decrease to 39% of the RMSE when compared to the nonspatial calibration model. This spatial calibration and real-time mapping approach provide a useful way for local communities and governmental agencies to adjust the consistency of the sensor network for improved air quality monitoring and assessment.

Original languageEnglish
Article number22079
JournalScientific reports
Issue number1
Publication statusPublished - 2020 Dec

All Science Journal Classification (ASJC) codes

  • General


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