Harmonization of Uncertainties of X-Ray Fluorescence Data for PM2.5 Air Filter Analysis
| Autor: | Paul Duda, R. K. M. Jayanty, William Gutknecht, Joann Rice, Andrea McWilliams, Robert B. Kellogg, James Flanagan, Richard H. Sarver |
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| Rok vydání: | 2010 |
| Předmět: |
Chemical speciation
Attenuation Instrumentation Environmental engineering Spectrometry X-Ray Emission X-ray fluorescence Sampling (statistics) Air Pollutants Occupational Filter (signal processing) Management Monitoring Policy and Law United States Data Interpretation Statistical Calibration Environmental science Particulate Matter Particle Size United States Environmental Protection Agency Waste Management and Disposal Algorithms Filtration Environmental Monitoring Air filter Remote sensing |
| Zdroj: | Journal of the Air & Waste Management Association. 60:184-194 |
| ISSN: | 2162-2906 1096-2247 |
| Popis: | The U.S. Environmental Protection Agency (EPA)'s PM2.5 Chemical Speciation Network (CSN) and the Interagency Monitoring of Protected Visual Environments (IMPROVE) network use X-ray fluorescence (XRF) analysis to quantify trace elements in samples of fine particles less than 2.5 microns in aerodynamic diameter (PM2.5). Methods for calculating uncertainty values for XRF results vary considerably among laboratories and instrument makes and models. To support certain types of modeling and data analysis, uncertainty estimates are required that are consistent within and between monitoring programs, and that are independent of the laboratories that performed the analyses and the analytical instrumentation used. The goal of this work was to develop a consensus model for uncertainties associated with XRF analysis of PM2.5 filter samples. The following important components of uncertainty are included in the model described herein: variability in peak area, calibration, field sampling, and attenuation of X-ray intensity for light elements. This paper includes a detailed analysis of how attenuation uncertainties for light elements are derived. For the remaining uncertainty components included in the model, an approach and recommendations are presented to ensure that laboratories performing this type of analysis can use similar equations and parameterizations. By applying this uniform approach, it is illustrated how the uncertainties reported by the CSN and IMPROVE network laboratories can be brought into very good agreement. The proposed method is best applied at the time of data generation, but retrospective estimation of uncertainties in existing data-sets is also possible. This paper serves to document the equations used for calculating the uncertainties in speciated PM2.5 data currently being posted on EPA's Air Quality System database for the PM2.5 CSN program. |
| Databáze: | OpenAIRE |
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