Value assignment and uncertainty evaluation for anion and single-element reference solutions incorporating historical information.

The National Institute of Standards and Technology, which is the national metrology institute of the USA, assigns certified values to the mass fractions of individual elements in single-element solutions, and to the mass fractions of anions in anion solutions, based on gravimetric preparations and instrumental methods of analysis. The instrumental method currently is high-performance inductively coupled plasma optical emission spectroscopy for the single-element solutions, and ion chromatography for the anion solutions. The uncertainty associated with each certified value comprises method-specific components, a component reflecting potential long-term instability that may affect the certified mass fraction during the useful lifetime of the solutions, and a component from between-method differences. Lately, the latter has been evaluated based only on the measurement results for the reference material being certified. The new procedure described in this contribution blends historical information about between-method differences for similar solutions produced previously, with the between-method difference observed when a new material is characterized. This blending procedure is justified because, with only rare exceptions, the same preparation and measurement methods have been used historically: in the course of almost 40 years for the preparation methods, and of 20 years for the instrumental methods. Also, the certified values of mass fraction, and the associated uncertainties, have been very similar, and the chemistry of the solutions also is closely comparable within each series of materials. If the new procedure will be applied to future SRM lots of single-element or anion solutions routinely, then it is expected that it will yield relative expanded uncertainties that are about 20 % smaller than the procedure for uncertainty evaluation currently in use, and that it will do so for the large majority of the solutions. However, more consequential than any reduction in uncertainty, is the improvement in the quality of the uncertainty evaluations that derives from incorporating the rich historical information about between-method differences and about the stability of the solutions over their expected lifetimes. The particular values listed for several existing SRMs are given merely as retrospective illustrations of the application of the new method, not to suggest that the certified values or their associated uncertainties should be revised.

How measurement science can improve confidence in research results.

The current push for rigor and reproducibility is driven by a desire for confidence in research results. Here, we suggest a framework for a systematic process, based on consensus principles of measurement science, to guide researchers and reviewers in assessing, documenting, and mitigating the sources of uncertainty in a study. All study results have associated ambiguities that are not always clarified by simply establishing reproducibility. By explicitly considering sources of uncertainty, noting aspects of the experimental system that are difficult to characterize quantitatively, and proposing alternative interpretations, the researcher provides information that enhances comparability and reproducibility.

Coverage Intervals.

Since coverage intervals are widely used expressions of measurement uncertainty, this contribution reviews coverage intervals as defined in the Guide to the Expression of Uncertainty in Measurement (GUM), and compares them against the principal types of probabilistic intervals that are commonly used in applied statistics and in measurement science. Although formally identical to conventional confidence intervals for means, the GUM interprets coverage intervals more as if they were Bayesian credible intervals, or tolerance intervals. We focus, in particular, on a common misunderstanding about the intervals derived from the results of the Monte Carlo method of the GUM Supplement 1 (GUM-S1), and offer a novel interpretation for these intervals that we believe will foster realistic expectations about what they can deliver, and how and when they can be useful in practice.

Validation of Single Particle ICP-MS for Routine Measurements of Nanoparticle Size and Number Size Distribution.

Single particle inductively coupled plasma-mass spectrometry (spICP-MS) is an emerging technique capable of simultaneously measuring nanoparticle size and number concentration of metal-containing nanoparticles (NPs) at environmental levels. single particle ICP-MS will become an established measurement method once the metrological quality of the measurement results it produces have been proven incontrovertibly. This Article presents the first validation of spICP-MS capabilities for measuring mean NP size and number size distribution of gold nanoparticles (AuNPs). The validation is achieved by (i) calibration based on the consensus value for particle size derived from six different sizing techniques applied to National Institute of Standards and Technology (NIST) Reference Material (RM) 8013; (ii) comparison with high-resolution scanning electron microscopy (HR-SEM) used as a reference method, which is linked to the International System of Units (SI) through a calibration standard characterized by the NIST metrological atomic force microscope; and (iii) evaluation of the uncertainty associated with the measurement of the mean particle size to enable comparison of the spICP-MS and HR-SEM methods. After establishing HR-SEM and spICP-MS measurement protocols, both methods were used to characterize commercial AuNP suspensions of three different sizes (30, 60, and 100 nm) with four different coatings and surface charge at pH 7. Single particle ICP-MS measurements (corroborated by HR-SEM) revealed the existence of two distinct subpopulations of particles in the number size distributions for four of the 60 nm commercial suspensions, a fact that was not apparent in the measurement results supplied by the vendor using transmission electron microscopy. This finding illustrates the utility of spICP-MS for routine characterization of commercial AuNP suspensions regardless of size or coating.

CCQM-K121 - Monoterpenes in Nitrogen at 2.5 nmol mol-1 Final Report.

The growing awareness of climate change, and continuing concerns regarding tropospheric and stratospheric chemistry, will require future measurements and standards for compounds linked to these issues. To globally monitor and control the emissions of these species in the atmosphere, it is necessary to demonstrate measurement equivalence at the highest levels of accuracy for assigned values of standards. This report describes the results of a key comparison for several important monoterpene species, which are relevant to atmospheric chemistry and climate. The comparison samples include α-pinene, 3-carene, R-limonene and 1,8-cineole in a nitrogen matrix gas, at a 2.5 nmol mol-1 amount-of-substance fraction. The objective of this key comparison is to evaluate the participants' capabilities to measure trace-level monoterpenes using their own calibration techniques.

Bayesian estimation in random effects meta-analysis using a non-informative prior.

Pooling information from multiple, independent studies (meta-analysis) adds great value to medical research. Random effects models are widely used for this purpose. However, there are many different ways of estimating model parameters, and the choice of estimation procedure may be influential upon the conclusions of the meta-analysis. In this paper, we describe a recently proposed Bayesian estimation procedure and compare it with a profile likelihood method and with the DerSimonian-Laird and Mandel-Paule estimators including the Knapp-Hartung correction. The Bayesian procedure uses a non-informative prior for the overall mean and the between-study standard deviation that is determined by the Berger and Bernardo reference prior principle. The comparison of these procedures focuses on the frequentist properties of interval estimates for the overall mean. The results of our simulation study reveal that the Bayesian approach is a promising alternative producing more accurate interval estimates than those three conventional procedures for meta-analysis. The Bayesian procedure is also illustrated using three examples of meta-analysis involving real data. Copyright © 2016 John Wiley & Sons, Ltd.

Development of a Northern Continental Air Standard Reference Material.

The National Institute of Standards and Technology (NIST) recently began to develop standard mixtures of greenhouse gases as part of a broad program mandated by the 2009 United States Congress to support research in climate change. To this end, NIST developed suites of gravimetrically assigned primary standard mixtures (PSMs) comprising carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) in a dry-natural air balance at ambient mole fraction levels. In parallel, the National Oceanic and Atmospheric Administration (NOAA) in Boulder, Colorado, charged 30 aluminum gas cylinders with northern hemisphere air at Niwot Ridge, Colorado. These mixtures, which constitute NIST Standard Reference Material (SRM) 1720 Northern Continental Air, were certified by NIST for ambient mole fractions of CO2, CH4, and N2O relative to NIST PSMs. NOAA-assigned values are also provided as information in support of the World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) Program for CO2, CH4, and N2O, since NOAA serves as the WMO Central Calibration Laboratory (CCL) for CO2, CH4, and N2O. Relative expanded uncertainties at the 95% confidence interval are <±0.06% of the certified values for CO2 and N2O and <0.2% for CH4, which represents the smallest relative uncertainties specified to date for a gaseous SRM produced by NIST. Agreement between the NOAA (WMO/GAW) and NIST values based on their respective calibration standards suites is within 0.05%, 0.13%, and 0.06% for CO2, CH4, and N2O, respectively. This collaborative development effort also represents the first of its kind for a gaseous SRM developed by NIST.

Development of a southern oceanic air standard reference material.

In 2009, the United States Congress charged the National Institute of Standards and Technology (NIST) with supporting climate change research. As part of this effort, the Gas Sensing Metrology Group at NIST began developing new gas standard mixtures for greenhouse gas mixtures relevant to atmospheric measurements. Suites of gravimetrically prepared primary standard mixtures (PSMs) were prepared at ambient concentration levels for carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) in a dry-air balance. In parallel, 30 gas cylinders were filled, by the National Institute of Water and Atmospheric Research (NIWA) in Wellington, New Zealand, to high pressure from pristine southern oceanic air at Baring Head, New Zealand, and shipped to NIST. Using spectroscopic instrumentation, NIST analyzed the 30 cylinder samples for mole fractions of CO2, CH4, and N2O. Certified values were assigned to these mixtures by calibrating the instrumentation with the PSM suites that were recently developed at NIST. These mixtures became NIST Standard Reference Material (SRM) 1721 Southern Oceanic Air and are certified for ambient mole fraction, the first of their kind for NIST. The relative expanded uncertainties corresponding to coverage intervals with 95% probability are no larger than 0.06% of the certified values, representing the smallest uncertainties to date ever assigned to an NIST gas SRM.

Post hoc interlaboratory comparison of single particle ICP-MS size measurements of NIST gold nanoparticle reference materials.

Single particle inductively coupled plasma-mass spectrometry (spICP-MS) is an emerging technique that enables simultaneous measurement of nanoparticle size and number quantification of metal-containing nanoparticles at realistic environmental exposure concentrations. Such measurements are needed to understand the potential environmental and human health risks of nanoparticles. Before spICP-MS can be considered a mature methodology, additional work is needed to standardize this technique including an assessment of the reliability and variability of size distribution measurements and the transferability of the technique among laboratories. This paper presents the first post hoc interlaboratory comparison study of the spICP-MS technique. Measurement results provided by six expert laboratories for two National Institute of Standards and Technology (NIST) gold nanoparticle reference materials (RM 8012 and RM 8013) were employed. The general agreement in particle size between spICP-MS measurements and measurements by six reference techniques demonstrates the reliability of spICP-MS and validates its sizing capability. However, the precision of the spICP-MS measurement was better for the larger 60 nm gold nanoparticles and evaluation of spICP-MS precision indicates substantial variability among laboratories, with lower variability between operators within laboratories. Global particle number concentration and Au mass concentration recovery were quantitative for RM 8013 but significantly lower and with a greater variability for RM 8012. Statistical analysis did not suggest an optimal dwell time, because this parameter did not significantly affect either the measured mean particle size or the ability to count nanoparticles. Finally, the spICP-MS data were often best fit with several single non-Gaussian distributions or mixtures of Gaussian distributions, rather than the more frequently used normal or log-normal distributions.

Direct measurements show decreasing methane emissions from natural gas local distribution systems in the United States.

Fugitive losses from natural gas distribution systems are a significant source of anthropogenic methane. Here, we report on a national sampling program to measure methane emissions from 13 urban distribution systems across the U.S. Emission factors were derived from direct measurements at 230 underground pipeline leaks and 229 metering and regulating facilities using stratified random sampling. When these new emission factors are combined with estimates for customer meters, maintenance, and upsets, and current pipeline miles and numbers of facilities, the total estimate is 393 Gg/yr with a 95% upper confidence limit of 854 Gg/yr (0.10% to 0.22% of the methane delivered nationwide). This fraction includes emissions from city gates to the customer meter, but does not include other urban sources or those downstream of customer meters. The upper confidence limit accounts for the skewed distribution of measurements, where a few large emitters accounted for most of the emissions. This emission estimate is 36% to 70% less than the 2011 EPA inventory, (based largely on 1990s emission data), and reflects significant upgrades at metering and regulating stations, improvements in leak detection and maintenance activities, as well as potential effects from differences in methodologies between the two studies.

Calibration and uncertainty assessment for certified reference gas mixtures.

The weighted least squares method to build an analysis function described in ISO 6143, Gas analysis--Comparison methods for determining and checking the composition of calibration gas mixtures, is modified to take into account the typically small number of instrumental readings that are obtained for each primary standard gas mixture used in calibration. The theoretical basis for this modification is explained, and its superior performance is illustrated in a simulation study built around a concrete example, using real data. The corresponding uncertainty assessment is obtained by application of a Monte Carlo method consistent with the guidance in the Supplement 1 to the Guide to the expression of uncertainty in measurement, which avoids the need for two successive applications of the linearizing approximation of the conventional method for uncertainty propagation. The three main steps that NIST currently uses to certify a reference gas mixture (homogeneity study, calibration, and assignment of value and uncertainty assessment), are described and illustrated using data pertaining to an actual standard reference material.

Invited Article: Concepts and tools for the evaluation of measurement uncertainty.

Measurements involve comparisons of measured values with reference values traceable to measurement standards and are made to support decision-making. While the conventional definition of measurement focuses on quantitative properties (including ordinal properties), we adopt a broader view and entertain the possibility of regarding qualitative properties also as legitimate targets for measurement. A measurement result comprises the following: (i) a value that has been assigned to a property based on information derived from an experiment or computation, possibly also including information derived from other sources, and (ii) a characterization of the margin of doubt that remains about the true value of the property after taking that information into account. Measurement uncertainty is this margin of doubt, and it can be characterized by a probability distribution on the set of possible values of the property of interest. Mathematical or statistical models enable the quantification of measurement uncertainty and underlie the varied collection of methods available for uncertainty evaluation. Some of these methods have been in use for over a century (for example, as introduced by Gauss for the combination of mutually inconsistent observations or for the propagation of "errors"), while others are of fairly recent vintage (for example, Monte Carlo methods including those that involve Markov Chain Monte Carlo sampling). This contribution reviews the concepts, models, methods, and computations that are commonly used for the evaluation of measurement uncertainty, and illustrates their application in realistic examples drawn from multiple areas of science and technology, aiming to serve as a general, widely accessible reference.

Standardizing CT lung density measure across scanner manufacturers.

PURPOSE: Computed Tomography (CT) imaging of the lung, reported in Hounsfield Units (HU), can be parameterized as a quantitative image biomarker for the diagnosis and monitoring of lung density changes due to emphysema, a type of chronic obstructive pulmonary disease (COPD). CT lung density metrics are global measurements based on lung CT number histograms, and are typically a quantity specifying either the percentage of voxels with CT numbers below a threshold, or a single CT number below which a fixed relative lung volume, nth percentile, falls. To reduce variability in the density metrics specified by CT attenuation, the Quantitative Imaging Biomarkers Alliance (QIBA) Lung Density Committee has organized efforts to conduct phantom studies in a variety of scanner models to establish a baseline for assessing the variations in patient studies that can be attributed to scanner calibration and measurement uncertainty. METHODS: Data were obtained from a phantom study on CT scanners from four manufacturers with several protocols at various tube potential voltage (kVp) and exposure settings. Free from biological variation, these phantom studies provide an assessment of the accuracy and precision of the density metrics across platforms solely due to machine calibration and uncertainty of the reference materials. The phantom used in this study has three foam density references in the lung density region, which, after calibration against a suite of Standard Reference Materials (SRM) foams with certified physical density, establishes a HU-electron density relationship for each machine-protocol. We devised a 5-step calibration procedure combined with a simplified physical model that enabled the standardization of the CT numbers reported across a total of 22 scanner-protocol settings to a single energy (chosen at 80 keV). A standard deviation was calculated for overall CT numbers for each density, as well as by scanner and other variables, as a measure of the variability, before and after the standardization. In addition, a linear mixed-effects model was used to assess the heterogeneity across scanners, and the 95% confidence interval of the mean CT number was evaluated before and after the standardization. RESULTS: We show that after applying the standardization procedures to the phantom data, the instrumental reproducibility of the CT density measurement of the reference foams improved by more than 65%, as measured by the standard deviation of the overall mean CT number. Using the lung foam that did not participate in the calibration as a test case, a mixed effects model analysis shows that the 95% confidence intervals are [-862.0 HU, -851.3 HU] before standardization, and [-859.0 HU, -853.7 HU] after standardization to 80 keV. This is in general agreement with the expected CT number value at 80 keV of -855.9 HU with 95% CI of [-857.4 HU, -854.5 HU] based on the calibration and the uncertainty in the SRM certified density. CONCLUSIONS: This study provides a quantitative assessment of the variations expected in CT lung density measures attributed to non-biological sources such as scanner calibration and scanner x-ray spectrum and filtration. By removing scanner-protocol dependence from the measured CT numbers, higher accuracy and reproducibility of quantitative CT measures were attainable. The standardization procedures developed in study may be explored for possible application in CT lung density clinical data.

Value Assignment of Vitamin D Metabolites in Vitamin D Standardization Program Serum Samples.

Assay variability has been cited as an obstacle to establishing optimal vitamin D exposure. As part of the Vitamin D Standardization Program (VDSP) effort to standardize the measurement of total 25-hydroxyvitamin D [25(OH)D], the value assignment of total 25(OH)D in 50 single-donor serum samples was performed using two isotope-dilution LC with tandem MS methods. Both methods are recognized as reference measurement procedures (RMPs) by the Joint Committee for Traceability in Laboratory Medicine. These samples and their assigned values serve as the foundation for several aspects of the VDSP. To our knowledge, this is the first time that two RMPs have been used to assign 25(OH)D values to such a large number of serum samples.

Comparison of TCDD and PCB CYP1A induction sensitivities in fresh hepatocytes from human donors, sprague-dawley rats, and rhesus monkeys and HepG2 cells.

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) and related chemicals induce cytochrome P450 1A (CYP1A) gene expression and, at sufficient exposures, cause toxicity. Human health risks from such exposures are typically estimated from animal studies. We tested whether animal models predict human sensitivity by characterizing CYP1A gene expression in cultures of fresh hepatocytes from human donors, rats, and rhesus monkeys and HepG2 human hepatoma cells. We exposed the cells to three aryl hydrocarbon receptor (AhR) ligands of current environmental interest and measured 7-ethoxyresorufin-O-deethylase (EROD) activity and concentrations of CYP1A1 and CYP1A2 mRNA. We found that human cells are about 10-1000 times less sensitive to TCDD, 3,3',4,4',5-pentachlorobiphenyl (PCB 126), and Aroclor 1254 than rat and monkey cells, that relative potencies among these chemicals are different across species, and that gene expression thresholds exist for these chemicals. Newly calculated rat-human interspecies relative potency factors for PCB 126 were more than 100 times lower than the current rodent-derived value. We propose that human-derived values be used to improve the accuracy of estimates of human health risks.

Use of statistical tools to evaluate the reductive dechlorination of high levels of TCE in microcosm studies.

A large, multi-laboratory microcosm study was performed to select amendments for supporting reductive dechlorination of high levels of trichloroethylene (TCE) found at an industrial site in the United Kingdom (UK) containing dense non-aqueous phase liquid (DNAPL) TCE. The study was designed as a fractional factorial experiment involving 177 bottles distributed between four industrial laboratories and was used to assess the impact of six electron donors, bioaugmentation, addition of supplemental nutrients, and two TCE levels (0.57 and 1.90 mM or 75 and 250 mg/L in the aqueous phase) on TCE dechlorination. Performance was assessed based on the concentration changes of TCE and reductive dechlorination degradation products. The chemical data was evaluated using analysis of variance (ANOVA) and survival analysis techniques to determine both main effects and important interactions for all the experimental variables during the 203-day study. The statistically based design and analysis provided powerful tools that aided decision-making for field application of this technology. The analysis showed that emulsified vegetable oil (EVO), lactate, and methanol were the most effective electron donors, promoting rapid and complete dechlorination of TCE to ethene. Bioaugmentation and nutrient addition also had a statistically significant positive impact on TCE dechlorination. In addition, the microbial community was measured using phospholipid fatty acid analysis (PLFA) for quantification of total biomass and characterization of the community structure and quantitative polymerase chain reaction (qPCR) for enumeration of Dehalococcoides organisms (Dhc) and the vinyl chloride reductase (vcrA) gene. The highest increase in levels of total biomass and Dhc was observed in the EVO microcosms, which correlated well with the dechlorination results.