DOI: 10.1016/j.atmosenv.2014.12.046
Scopus记录号: 2-s2.0-84920880177
论文题名: Using structural equation modeling to construct calibration equations relating PM2.5 mass concentration samplers to the federal reference method sampler
作者: Bilonick R ; A ; , Connell D ; P ; , Talbott E ; O ; , Rager J ; R ; , Xue T
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 103 起始页码: 365
结束页码: 377
语种: 英语
英文关键词: Calibration
; Federal reference method
; PM2.5
; TEOM
Scopus关键词: Air quality
; Calibration
; Diseases
; Errors
; Particles (particulate matter)
; Regression analysis
; Urban growth
; Air quality measurements
; Federal reference methods
; Fine particulate matter
; Simple linear regression
; Structural equation modeling
; Tapered element oscillating microbalances
; Temperature dependencies
; TEOM
; Random errors
; air quality
; calibration
; concentration (composition)
; data set
; epidemiology
; health risk
; measurement method
; monitoring
; nonlinearity
; numerical model
; particulate matter
; speciation (chemistry)
; temperature effect
; air quality
; analytic method
; Article
; calibration
; controlled study
; environmental temperature
; federal reference method
; maximum likelihood method
; measurement error
; particulate matter
; priority journal
; quality control
; sampler
; structural equation modeling
; tapered element oscillating microbalance
; Pennsylvania
; Pittsburgh
; United States
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: The objective of this study was to remove systematic bias among fine particulate matter (PM2.5) mass concentration measurements made by different types of samplers used in the Pittsburgh Aerosol Research and Inhalation Epidemiology Study (PARIES). PARIES is a retrospective epidemiology study that aims to provide a comprehensive analysis of the associations between air quality and human health effects in the Pittsburgh, Pennsylvania, region from 1999 to 2008. Calibration was needed in order to minimize the amount of systematic error in PM2.5 exposure estimation as a result of including data from 97 different PM2.5 samplers at 47 monitoring sites. Ordinary regression often has been used for calibrating air quality measurements from pairs of measurement devices; however, this is only appropriate when one of the two devices (the "independent" variable) is free from random error, which is rarely the case. A group of methods known as "errors-in-variables" (e.g., Deming regression, reduced major axis regression) has been developed to handle calibration between two devices when both are subject to random error, but these methods require information on the relative sizes of the random errors for each device, which typically cannot be obtained from the observed data. When data from more than two devices (or repeats of the same device) are available, the additional information is not used to inform the calibration. A more general approach that often has been overlooked is the use of a measurement error structural equation model (SEM) that allows the simultaneous comparison of three or more devices (or repeats). The theoretical underpinnings of all of these approaches to calibration are described, and the pros and cons of each are discussed. In particular, it is shown that both ordinary regression (when used for calibration) and Deming regression are particular examples of SEMs but with substantial deficiencies. To illustrate the use of SEMs, the 7865 daily average PM2.5 mass concentration measurements made by seven collocated samplers at an urban monitoring site in Pittsburgh, Pennsylvania, were used. These samplers, which included three federal reference method (FRM) samplers, three speciation samplers, and a tapered element oscillating microbalance (TEOM), operated at various times during the 10-year PARIES study period. Because TEOM measurements are known to depend on temperature, the constructed SEM provided calibration equations relating the TEOM to the FRM and speciation samplers as a function of ambient temperature. It was shown that TEOM imprecision and TEOM bias (relative to the FRM) both decreased as temperature increased. It also was shown that the temperature dependency for bias was non-linear and followed a sigmoidal (logistic) pattern. The speciation samplers exhibited only small bias relative to the FRM samplers, although the FRM samplers were shown to be substantially more precise than both the TEOM and the speciation samplers. Comparison of the SEM results to pairwise simple linear regression results showed that the regression results can differ substantially from the correctly-derived calibration equations, especially if the less-precise device is used as the independent variable in the regression. © 2014 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82052
Appears in Collections: 气候变化事实与影响
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作者单位: University of Pittsburgh School of Medicine, Pittsburgh, PA, United States; CONSOL Energy Inc., Research and Development, South Park, PA, United States; University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, United States
Recommended Citation:
Bilonick R,A,, Connell D,et al. Using structural equation modeling to construct calibration equations relating PM2.5 mass concentration samplers to the federal reference method sampler[J]. Atmospheric Environment,2015-01-01,103