Comparison of continuous and filter based mass measurements in Mexico City

Vega, Elizabeth; Reyes, Elizabeth; Wellens, Ann; Sanchez, G.; Chow, Judith C.; Watson, John G.

doi:10.1016/s1352-2310(03)00216-4

Cited by 13 publications

(15 citation statements)

References 20 publications

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“…Thus, there are significant differences between the PM 10 means and medians of two methods. This observation agrees with other studies (Allen et al, 1997;Park et al, 2006;Rizzo et al, 2003;Vega et al, 2003). Also, for PM 2.5 , statistical tests show the two methods had significantly different means (P < 0.05).…”

Section: Teom Versus Gravimetric Methodssupporting

confidence: 92%

“…The most frequently identified cause of difference in PM mass concentration measurements by filter-based and TEOM methods is the loss of semivolatile mass and PM-bound moisture due to heating of the TEOM sampling stream to 50 C (Allen et al, 1997;Grover et al, 2005;Jerez et al, 2006;Vega et al, 2003). Therefore, to correct for possible loss of particulate NH 4 NO 3 by heating, the concentration of particulate NH 4 NO 3 was used to adjust TEOM measurements.…”

Section: Teom Versus Gravimetric Methodsmentioning

confidence: 99%

“…Immediately following this study, a series of articles, responses, and rebuttals were published to discuss and debate the pros and cons of the TEOM method for various applications (Allen, 1998;Patashnick, 1998aPatashnick, , 1998b. Since then, more studies reported that the TEOM gave lower PM mass concentration measurements as compared with filter-based PM samplers (Ayers et al, 1999;Charrona et al, 2003;Jaques et al, 2004;Lee et al, 2005;Price et al, 2003;Rizzo et al, 2003;Vega et al, 2003). Two plausible reasons discussed extensively from these studies are (1) losses of particulate materials (semivolatile PM and particlebound water) in the TEOM due to its internal heating mechanism; and (2) negative and positive artifacts associated with changes in air mass composition (Allen et al, 1997) or thermal instability of the TEOM system (Page et al, 2007;Patashnick, 1998a).…”

Section: Introductionmentioning

confidence: 99%

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Field evaluation of particulate matter measurements using tapered element oscillating microbalance in a layer house

Wang-Li

Liu

et al. 2012

Journal of the Air & Waste Management Association

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The tapered element oscillating microbalance (TEOM) is one type of continuous ambient particulate matter (PM) monitor. Adsorption and desorption of moisture and semivolatile species may cause positive or negative artifacts in TEOM PM mass measurement. The objective of this field study was to investigate possible uncertainties associated with TEOM measurements in the poultry operation environment. For comparisons of TEOM with filter-based gravimetric method, four instruments (TEOM-PM 10 , low-volume PM 10 sampler, TEOM-PM 2.5 , and PM 2.5 speciation sampler) were collocated and tested inside a poultry house for PM 2.5 and PM 10 (PM with aerodynamic equivalent diameter 2.5 and 10 m, respectively) measurements. Fifteen sets of 24-hr PM 10 concentrations and 13 sets of 24-hr PM 2.5 measurements were obtained. Results indicate that compared with filter-based gravimetric method, TEOM gave significantly lower values of both PM 10 and PM 2.5 mass concentrations. For PM 10 , the average ratio of TEOM to the gravimetric method was 0.936. For PM 2.5 , the average ratio of TEOM to the gravimetric method was 0.738. Particulate matter in the poultry houses possibly contains semivolatile compounds and moisture due to high levels of relative humidity (RH) and gas pollutants. The internal heating mechanism of the TEOM may cause losses in mass through volatilization. To investigate the effects of TEOM settings on concentration measurements, the heaters of two identical TEOMs were set at 50 C, 30 C, or no heating at all. They were collocated and tested for total suspended particle (TSP), PM 10 , and PM 2.5 measurements in layer house for 6 weeks. For all TSP, PM 10 , and PM 2.5 measurements, the internal TEOM temperature setting had a significant effect (P < 0.05). Significantly higher PM mass concentrations were measured at lower temperature settings. The effects of environmental (i.e., temperature, RH, NH 3 and CO 2 concentrations) and instrumental (i.e., filter loading and noise) parameters on PM measurements were also assessed using regression analysis.Implications: Because of its potential health and environmental effects, particulate matter (PM) emissions from animal feeding operations (AFOs) have been a great concern to the public and to the regulatory agencies. The tapered element oscillating microbalance (TEOM) PM monitor has been was adapted for continuous PM measurements in some AFO air quality studies. This study investigated possible uncertainties associated with TEOM measurements in an egg production environment. It was discovered that there was a significant bias in TEOM measurements of PM 10 as compared with federal reference method. Internal temperature settings of a TEOM have significant impact on its PM measurement.

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Section: Teom Versus Gravimetric Methodssupporting

confidence: 92%

Section: Teom Versus Gravimetric Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Field evaluation of particulate matter measurements using tapered element oscillating microbalance in a layer house

Wang-Li

Liu

et al. 2012

Journal of the Air & Waste Management Association

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“…7, the regression of TEOM on FRM produces a curve that is not too far from the actual calibration curve, because the FRM (used as the independent variable) has substantially smaller imprecision than the TEOM at this temperature. The typical approach used by the EPA and others Bortnick et al, 2002;Vega et al, 2003;Kashuba and Scheff, 2008), however, is to use the regression of the FRM on the TEOM. This regression curve is substantially different from the actual calibration curve (and the other regression curve).…”

Section: Comparison Of the Sem Calibration With Ordinary Regressionmentioning

confidence: 99%

“…In these cases, ordinary regression produces two contradictory curves, depending on which device is used as the independent variable in the model, both of which are biased away from the correct calibration curve (Bollen, 1989). Nevertheless, numerous PM 2.5 or PM 10 method comparison studies (e.g., Allen et al, 1997;Ayers et al, 1999;Chung et al, 2001;Charron et al, 2004;Schwab et al, 2006;Zhu et al, 2007) have been based on ordinary regression, and a number of studies (e.g., Bortnick et al, 2002;Vega et al, 2003;Kashuba and Scheff, 2008) comparing TEOM and filter-based (e.g., FRM) measurements have used the less-precise TEOM as the independent variable, exacerbating the problem. In fact, the U.S. Environmental Protection Agency (EPA) guidelines for relating FRM and continuous PM 2.5 measurements specify the use of ordinary linear regression with the presumably less-precise continuous measurement device used as the independent variable.…”

Section: Introductionmentioning

confidence: 99%

Using structural equation modeling to construct calibration equations relating PM2.5 mass concentration samplers to the federal reference method sampler

Bilonick

Connell

Talbott

et al. 2015

Atmospheric Environment

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h i g h l i g h t sCollocated PM 2.5 mass concentration samplers were calibrated using linked structural equation models (SEMs). Bias and imprecision were determined for FRM, TEOM, SFS, SASS, and IMPROVE samplers. SEMs were stratified to describe the temperature dependency of TEOM samplers. TEOM bias relative to FRM followed a well-defined sigmoidal temperature dependency. FRM was most precise while TEOM imprecision decreased with increasing temperature. a b s t r a c tThe objective of this study was to remove systematic bias among fine particulate matter (PM 2.5 ) 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 PM 2.5 exposure estimation as a result of including data from 97 different PM 2.5 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 PM 2.5 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 imprecis...

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Measuring Sediment Transport by Wind

King

Bellamy

2022

Treatise on Geomorphology

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Comparison of continuous and filter based mass measurements in Mexico City

Cited by 13 publications

References 20 publications

Field evaluation of particulate matter measurements using tapered element oscillating microbalance in a layer house

Field evaluation of particulate matter measurements using tapered element oscillating microbalance in a layer house

Using structural equation modeling to construct calibration equations relating PM2.5 mass concentration samplers to the federal reference method sampler

Measuring Sediment Transport by Wind

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