@article{92b0bea0d6394b9ea5b9596a471402e2,
title = "Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore",
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, PM",
author = "Abhirup Datta and Arkajyoti Saha and Zamora, {Misti Levy} and Colby Buehler and Lei Hao and Fulizi Xiong and Gentner, {Drew R.} and Kirsten Koehler",
note = "Funding Information: This publication was developed under Assistance Agreement no. RD835871 awarded by the U.S. Environmental Protection Agency to Yale University. It has not been formally reviewed by the Environmental Protection Agency (EPA). The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Agency. The EPA does not endorse any products or commercial services mentioned in this publication. A.D. and A.S. was supported by the Johns Hopkins Bloomberg American Health Initiative Spark Award. A.D. is supported by National Science Foundation DMS-1915803. C.B. is supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE1752134. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. M.L.Z. was also supported by the National Institute of Environmental Health Sciences of the National Institutes of Health under awards number 1K23ES029985-01 and K99ES029116. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors thank the Maryland Department of the Environment Air and Radiation Management Administration for allowing collocation of the sensors with their instruments at the downtown Baltimore site. D.R.G. and F.X. would like to thank HKF Technology for their support. Funding Information: This publication was developed under Assistance Agreement no. RD835871 awarded by the U.S. Environmental Protection Agency to Yale University. It has not been formally reviewed by the Environmental Protection Agency (EPA). The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Agency. The EPA does not endorse any products or commercial services mentioned in this publication. A.D. and A.S. was supported by the Johns Hopkins Bloomberg American Health Initiative Spark Award . A.D. is supported by National Science Foundation DMS-1915803. C.B. is supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE1752134 . Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation . M.L.Z. was also supported by the National Institute of Environmental Health Sciences of the National Institutes of Health under awards number 1K23ES029985-01 and K99ES029116 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors thank the Maryland Department of the Environment Air and Radiation Management Administration for allowing collocation of the sensors with their instruments at the downtown Baltimore site. D.R.G., and F.X. would like to thank HKF Technology for their support. Publisher Copyright: {\textcopyright} 2020 Elsevier Ltd",
year = "2020",
month = dec,
day = "1",
doi = "10.1016/j.atmosenv.2020.117761",
language = "English (US)",
volume = "242",
journal = "Atmospheric Environment",
issn = "1352-2310",
publisher = "Elsevier Limited",
}