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Activity Number:
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74
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Type:
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Contributed
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Date/Time:
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Sunday, August 2, 2009 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Physical and Engineering Sciences
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| Abstract - #305728 |
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Title:
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A Bayesian Model for Assessment of Sulfur in Diesel Fuel at Ultra-Low Levels
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Author(s):
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William F. Guthrie*+ and W. Robert Kelly
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Companies:
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National Institute of Standards and Technology and National Institute of Standards and Technology
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Address:
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100 Bureau Drive, Gaithersburg, MD, 20899-8980,
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Keywords:
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Bayesian analysis ; uncertainty assessment ; background correction ; log-normal distribution ; isotope-dilution inductively-coupled plasma mass spectrometry ; detection limits
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Abstract:
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New regulations now limit the sulfur concentration in diesel fuel to less than 15 mg/kg and should provide $150 billion in health and environmental benefits annually. However, ensuring that the measurement systems for fuel production and regulation are in control requires reference standards with lower sulfur levels than ever before. One difficulty in developing such standards is the need to correct the measurements for relatively large and variable amounts of background sulfur. This talk describes a Bayesian model for the assessment of sulfur in diesel fuel via mass spectrometry that uses the three-parameter log-normal distribution to describe background variation. Results from this model have lower uncertainty and more closely follow known physical constraints than the results from models previously used for standards with higher sulfur levels.
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