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

Abstract 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...

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Autores principales:	Hone-Jay Chu, Muhammad Zeeshan Ali, Yu-Chen He
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e38da898768745cdb73dad6712c677da
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id	oai:doaj.org-article:e38da898768745cdb73dad6712c677da
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spelling	oai:doaj.org-article:e38da898768745cdb73dad6712c677da2021-12-02T13:33:59ZSpatial calibration and PM2.5 mapping of low-cost air quality sensors10.1038/s41598-020-79064-w2045-2322https://doaj.org/article/e38da898768745cdb73dad6712c677da2020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79064-whttps://doaj.org/toc/2045-2322Abstract 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.Hone-Jay ChuMuhammad Zeeshan AliYu-Chen HeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hone-Jay Chu Muhammad Zeeshan Ali Yu-Chen He Spatial calibration and PM2.5 mapping of low-cost air quality sensors
description	Abstract 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.
format	article
author	Hone-Jay Chu Muhammad Zeeshan Ali Yu-Chen He
author_facet	Hone-Jay Chu Muhammad Zeeshan Ali Yu-Chen He
author_sort	Hone-Jay Chu
title	Spatial calibration and PM2.5 mapping of low-cost air quality sensors
title_short	Spatial calibration and PM2.5 mapping of low-cost air quality sensors
title_full	Spatial calibration and PM2.5 mapping of low-cost air quality sensors
title_fullStr	Spatial calibration and PM2.5 mapping of low-cost air quality sensors
title_full_unstemmed	Spatial calibration and PM2.5 mapping of low-cost air quality sensors
title_sort	spatial calibration and pm2.5 mapping of low-cost air quality sensors
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/e38da898768745cdb73dad6712c677da
work_keys_str_mv	AT honejaychu spatialcalibrationandpm25mappingoflowcostairqualitysensors AT muhammadzeeshanali spatialcalibrationandpm25mappingoflowcostairqualitysensors AT yuchenhe spatialcalibrationandpm25mappingoflowcostairqualitysensors
_version_	1718392827930476544

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

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