Bottom estimating is high uncertainity

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Trueness Closeness of agreement between the average of an infinite number of replicate measured quantity values and a reference quantity value. It follows that the true value of a measured quantity cannot be exactly known either.

High uncertainity estimating is Bottom

This assumption is fundamental to the MU Bottoj. The MU concept also assumes that if the bias of a procedure is known, then steps are taken to minimise it, e. However, because the bias value cannot be known exactly, an uncertainty will be associated with such a correction. Thus, in the MU concept, a measurement result can comprise two uncertainties i that associated with a bias correction uBiasuncertaonity ii the uncertainty due to random effects imprecision, uImp. Both these uncertainties are expressed as SDs which, when combined together, provide the combined standard uncertainty for the procedure uProc.

Thus the MU approach considers a single measurement result to be the best estimate of the measured quantity, and centred on this the combined standard uncertainty provides an interval of values within which the estimatiny value of the measured estimtaing is believed to lie, with a stated coverage probability. For example, a bigh glucose concentration of 5. The GUM describes a bottom-up approach to estimating MU, by which an uncertainty budget for a given measurement procedure Bottom estimating is high uncertainity assembled by estimatijg all potential sources of uncertainty e. The contributing uncertainties are combined in a mathematical model that best represents their interactions in the measurement process.

The combining calculation yields the estimated combined standard measurement uncertainty for the whole procedure uc. Extimating simple example is the estimation of the concentration of glucose c by weighing it w into a known volume of water v. The GUM bottom-up approach can quickly become unwieldy and mathematically complex. Fortunately, clinical biochemistry measurement methods employ quality control QC materials to estimate and monitor whole procedure imprecision, so QC data can be used to estimate the contribution of random effects uImp to the measurement uncertainty of the whole procedure uProcwith the assumption that the measurand behaves identically in both patient samples and quality control material.

If a procedure has been adjusted for bias, then the uncertainty associated with the correction uBias may need to be combined with uImp to estimate uProc. This decision depends on the magnitude of ubias relative to uImp. The t test can be used to objectively assess the relative significance of ubias, or sometimes a subjective decision is made, e. If uImp varies significantly across the reportable range, more than one estimate may be required. This top-down approach is generally recognised as a direct estimate of the combined standard uncertainty of the whole procedure uc using the GUM approach.

Estimating MU in the routine biochemistry laboratory The effort and cost of estimating MU should be commensurate with the clinical quality of measurement required. MU Goals It is important that the MU for a given measurement procedure falls within clinically acceptable limits, so that results are of appropriate quality and reliability for patient management. Depending on analyte physiology, specimen type and clinical use of results, MU goals may be based on biological variation, expert group recommendations, or professional opinion. It should be noted that MU goals cannot always be met due to performance limitations of available routine technology.

Defining the Measurand A measurand is the quantity intended to be measured and should be well defined. This is straightforward for analytes that are chemically well characterised, such as sodium or urea, but may be difficult when the measured quantity is method dependent e. In such cases the measurand is procedure-dependent and measurand definition should include sufficient detail e. Measurand definition includes the system containing the component analyte of interest, e. The measurand description must also identify the kind-of-quantity being examined e. Uncertainty can be associated with the measurand due to: Imprecision of Measurement Because patient results are compared over time with clinical decision limits or previous results, it is essential to estimate imprecision across as many unavoidable standard operating procedure variables as possible, e.

The intermediate imprecision uImpexpressed as SD, is an estimate of the uncertainty due to the random effects of the whole procedure over time.

Aboard are several other to tag uncertainties in save mining Table 4. In the drowned several others, there has been a personal effort to understand and long the series of methane emissions from ms and unnecessary gas authorities, and much has been lacking about the top dollar sources.

Bias of Measurement The GUM approach assumes that if the bias of a procedure has been estimated, usually by replicate measurements of a reference material, then it is minimised by re-calibration or by a correction factor. See also discussion in Chapter 3. Modified from Spokas et al. Because of these deficiencies, the Committee classifies the estimates of landfill emission uncertainties under a low confidence level Figure 4. Coal Mining In active underground mines, methane emission measurements from degasification systems are relatively easy to monitor because degasification systems are localized, and the measurements are usually performed at the surface.

Therefore, the uncertainty of emission estimates for degasification systems is low. Emission estimates from the ventilation systems carry larger uncertainty. These measurements are taken in mines to ensure safety by controlling the percentage of methane in the ventilation air. Mine Safety and Health Administration. The monitoring devices can be handheld or machine-mounted sensors. As long as the equipment is functioning well and the measurements are taken frequently, methane emission estimates are reliable. However, the position of the sensor with respect to the cross section of the entry and how the measurements are taken can be critical. This is due to methane layering in low-velocity areas and variable airflow rates in high-velocity entries.

For ventilation systems, methane emissions are calculated based on the flow rate and the methane content in the ventilation air. Both measurements rely on underground observations in entries connected to the ventilation shafts. Since the emission estimate relies on individual measurements in the entries rather than the total output on the surface, accurate reporting can be obtained by visiting and measuring all entries. Usually, there are no measurements conducted at the surface to confirm the values obtained underground, yet such measurements are needed to verify the current techniques. Further, methane emissions from mines change based on atmospheric pressure variations, and so continuous measurements are warranted.

Therefore, a set of measurements that miss a major atmospheric change in pressure and temperature e. Even for current measurements in underground mines, differences in annual emissions of 10 percent have been reported Mutmansky and Wang, For surface mines, although the production data are relatively accurate, uncertainties are present in the gas content data and the emission factor. Moreover, shallow coals, which are not a target for coal-bed methane CBM because of their low gas content, usually have limited gas content data; thus the basin gas content average may be far from accurate.

Most gas content data uncertajnity from coal occurring bigh than 76 m, which is typically gassier than shallow coals. If these gas content values are used, the values would overestimate the gas content and consequently the emissions. Emission factors may vary as ubcertainity, depending on specifics of the mining method and coal and overburden handling in the mine, Boftom their impact is likely lower than that of variation in gas content. As mentioned in Chapter uncsrtainitythe current emission factor to calculate emissions from coal is a factor of 1.

A follow-up study of Canadian emissions King, applied emission factors categorized by mine type, coal basin, and coal rank using mine-specific data and also suggested increasing gas content data by 50 percent to account for the emissions from unmined strata. Adding data about coal rank and mine-specific factors would decrease the uncertainty. Post-mining emission factors currently assume For comparison, a 20 percent factor is used in Australian methodology, after data by Williams et al. EPA uncertainty analysis for active coal mining EPA, b indicates that, for example, inmethane emissions from coal mines were estimated to be between 2.

The uncertainties for abandoned underground mines are related to uncertainties in generating a decline curve which, in turn, depends on 1 coal methane adsorption isotherms, 2 coal permeability which determined methane flow capacityand 3 pressure at abandonment.

Bpttom adsorption isotherms can vary within the same coal seams and the same mines Mastalerz et al. Isotherms are often not available for a specific coal or mine, necessitating the use of an unceratinity of some dstimating coal mine that may not be representative. Coal permeability is estimatinng parameter bigh is not often available for the specific coal estimatiny mine. Improved monitoring for abandoned mines could include pressure buildup and methane fstimating measurements at the surface. There are several ways to reduce uncertainties in coal mining Table 4. However, because the number of both surface and underground mines is well documented and there is good understanding of coal production, the Committee classifies the estimates of both active and abandoned mining uncertainties under a high confidence level Figure 4.

Unaccounted-for Sources of Emissions In addition to the uncertainties of the sources that are listed in the inventories, bottom-up emission inventories also have uncertainties due to emission source categories that are missing from the compiled estimates see Chapter 2. Both known unaccounted-for sources and previously unrecognized emission sources can be quantified and revealed for previously unknown sources by integrating top-down and bottom-up assessments. For example, Kort et al. Later analyses suggested that the dominant source was microbial coal-bed methane from active mining e.

In another example, by combining bottom-up and top-down approaches to partition emissions in an urban setting of Indianapolis, Lamb et al. Identifying and quantifying unaccounted-for emissions through multiscale measurement campaigns is critical to addressing uncertainties in emission inventories and to improving understanding of methane emissions in general. Overall Source Category Conclusions Based on the source category—specific discussion about uncertainties, the Committee concludes that Each source category has a wide range of uncertainties for methane emission estimates: These uncertainties and approaches for addressing uncertainties are summarized in Table 4.

Sparse Atmospheric Monitoring Observations As described in Chapter 3methods for measuring atmospheric methane using high-quality, globally distributed networks have uncertainties of 1. However, the coverage of these measurement networks is sparse, and sampling is weekly or even less frequently at many sites.

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