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HERO ID
7459203
Reference Type
Journal Article
Title
Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore
Author(s)
Datta, A; Saha, A; Zamora, ML; Buehler, C; Hao, L; Xiong, F; Gentner, DR; Koehler, K
Year
2020
Is Peer Reviewed?
1
Journal
Atmospheric Environment
ISSN:
1352-2310
EISSN:
1873-2844
Volume
242
Page Numbers
117761
Language
English
PMID
32922146
DOI
10.1016/j.atmosenv.2020.117761
Web of Science Id
WOS:000576804200003
URL
https://www.sciencedirect.com/science/article/pii/S1352231020304933
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Abstract
Low-cost air pollution monitors are increasingly being deployed to enrich knowledge about ambient air-pollution at high spatial and temporal resolutions. However, unlike regulatory-grade (FEM or FRM) instruments, universal quality standards for low-cost sensors are yet to be established and their data quality varies widely. This mandates thorough evaluation and calibration before any responsible use of such data. This study presents evaluation and field-calibration of the PM2.5 data from a network of low-cost monitors currently operating in Baltimore, MD, which has only one regulatory PM2.5 monitoring site within city limits. Co-location analysis at this regulatory site in Oldtown, Baltimore revealed high variability and significant overestimation of PM2.5 levels by the raw data from these monitors. Universal laboratory corrections reduced the bias in the data, but only partially mitigated the high variability. Eight months of field co-location data at Oldtown were used to develop a gain-offset calibration model, recast as a multiple linear regression. The statistical model offered substantial improvement in prediction quality over the raw or lab-corrected data. The results were robust to the choice of the low-cost monitor used for field-calibration, as well as to different seasonal choices of training period. The raw, lab-corrected and statistically-calibrated data were evaluated for a period of two months following the training period. The statistical model had the highest agreement with the reference data, producing a 24-h average root-mean-square-error (RMSE) of around 2 μgm−3. To assess transferability of the calibration equations to other monitors in the network, a cross-site evaluation was conducted at a second co-location site in suburban Essex, MD. The statistically calibrated data once again produced the lowest RMSE. The calibrated PM2.5 readings from the monitors in the low-cost network provided insights into the intra-urban spatiotemporal variations of PM2.5 in Baltimore.
Keywords
Baltimore; Field colocation; Gain-offset model; Linear regression; Low-cost monitors
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