气候变化领域集成服务门户(CCPortal): Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore

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DOI	10.1016/j.atmosenv.2020.117761
	Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore
	Datta A.; Saha A.; Zamora M.L.; Buehler C.; Hao L.; Xiong F.; Gentner D.R.; Koehler K.
发表日期	2020
ISSN	1352-2310
卷号	242
英文摘要	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. © 2020 Elsevier Ltd
关键词	Baltimore Field colocation Gain-offset model Linear regression Low-cost monitors PM2.5
语种	英语
scopus关键词	Air quality; Linear regression; Location; Mean square error; Ambient air pollution; Calibration equations; Multiple linear regressions; Prediction quality; Root mean square errors; Spatial and temporal resolutions; Spatio-temporal variation; Statistical modeling; Costs; atmospheric pollution; calibration; data quality; field method; particulate matter; spatiotemporal analysis; urban area; article; calibration; Maryland; prediction; sensitivity analysis; Baltimore; Maryland; United States
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/248916
作者单位	Department of Biostatistics, Johns Hopkins University, Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD 21205, United States; Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD 21205, United States; SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale University, New Haven, CT, United States; Department of Chemical & Environmental Engineering, Yale University, School of Engineering and Applied Science, New Haven, CT 06511, United States
推荐引用方式 GB/T 7714	Datta A.,Saha A.,Zamora M.L.,et al. Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore[J],2020,242.
APA	Datta A..,Saha A..,Zamora M.L..,Buehler C..,Hao L..,...&Koehler K..(2020).Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore.ATMOSPHERIC ENVIRONMENT,242.
MLA	Datta A.,et al."Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore".ATMOSPHERIC ENVIRONMENT 242(2020).