A Practical Way to ISO/GUM Measurement Uncertainty for Analytical Assays Including In-House Validation Data

González, A. Gustavo; Herrador, M. Ángeles; GarcíaAsuero, Agustín; Martín, Julia

doi:10.5772/intechopen.72048

Cited by 7 publications

(8 citation statements)

References 176 publications

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“…It has already been shown that measurement uncertainties, linked to the chemical analysis of the PMF input species (Tables S4 and S5), played a major contribution to the overall uncertainty (Pachon et al, 2010). Here, we tried to consider all the possible sources of uncertainties from the chemical analysis procedures, notably applying a GUM (Guide to the Expression of Uncertainty in Measurement) approach for most of the organic markers (González et al, 2018;White, 2008). In the end, about 90 % of the total estimated uncertainty was associated to the measurement uncertainty and the PMF model uncertainty, estimated following the recommendations of Brown et al (2015), accounted only for 10%.…”

Section: Comparison Of the Poa Factorsmentioning

confidence: 99%

Comparison of five methodologies to apportion organic aerosol sources during a PM pollution event

Srivastava

Daellenbach

Zhang

et al. 2021

Science of The Total Environment

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This study presents a comparison of five methodologies to apportion primary (POA) and secondary organic aerosol (SOA) sources from measurements performed in the Paris region (France) during a highly processed PM pollution event. POA fractions, estimated from EC-tracer method and positive matrix factorization (PMF) analyses, conducted on measurements from PM10 filters, aerosol chemical speciation monitor (ACSM) and offline aerosol mass spectrometry (AMS), were all comparable (2.2 -3.7 µg m -3 as primary organic carbon (POC)). Associated relative uncertainties (measurement + model) on POC estimations ranged from 8 to 50%. The best apportionment of primary traffic OA was achieved using key markers (EC and 1-nitropyrene) in the chemical speciation-based PMF showing more pronounced rush-hour peaks and greater correlation with NOx than other traffic related POC factors. All biomass burning-related factors were in good agreement, with a typical diel profile and a night-time increase linked to residential heating. If PMF applied to ACSM data showed good agreement with other PMF outputs corrected from dust-related factors (coarse PM), discrepancies were observed between individual POA factors (traffic, biomass burning) and directly comparable SOA factors and highly oxidized OA.Similar secondary organic carbon (SOC) concentrations (3.3 ± 0.1 µg m -3 ) were obtained from all approaches, except the SOA-tracer method (1.8 µg m -3 ). Associated uncertainties ranged from 14 to 52% with larger uncertainties obtained for PMF-chemical data, EC-and SOA-tracer methods. This latter significantly underestimated total SOA loadings, even including biomass burning SOA, due to missing SOA classes and precursors. None of the approaches was able to identify the formation mechanisms and/or precursors responsible for the highly oxidized SOA fraction associated with nitrate-and/or sulfate-rich aerosols (35% of OA). We recommend the use of a 3 combination of different methodologies to apportion the POC/SOC concentrations/contributions to get the highest level of confidence in the estimates obtained.

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Section: Comparison Of the Poa Factorsmentioning

confidence: 99%

Comparison of five methodologies to apportion organic aerosol sources during a PM pollution event

Srivastava

Daellenbach

Zhang

et al. 2021

Science of The Total Environment

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“…The values of LHV were also used to calculate the percentage uncertainties of the fuel blends; these percentage uncertainties were considered using those for several instruments, such as the digital balancing scale, digital stopwatch, smoke meter, and gas analyzer, as reported in Table . The overall experimental uncertainty was computed using eq . , .25ex2ex infix= uncertainty of false{ false( normalCO false) 2 + false( normalCO 2 false) 2 + false( normalO 2 false) 2 + false( normalNO normalx false) 2 + false( normalHC false) 2 + false( normalEGT false) 2 + false( normalSmoke false) 2 } infix= uncertainty of false{ false( 0.01 false) 2 + false( 0.19 false) 2 + false( 0.01 false) 2 + false( 0.05 false) 2 + false( 0.22 false) 2 + false( 2.01 false) 2 + false( 0.002 false) 2 } infix= prefix± 2.03 %…”

Section: Methodsmentioning

confidence: 99%

Effects of Blended Diesel–Biodiesel Fuel on Emissions of a Common Rail Direct Injection Diesel Engine with Different Exhaust Gas Recirculation Rates

Sethin,

Oo,

Thawornprasert

et al. 2024

ACS Omega

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The aim of the study was to investigate the exhaust gas emissions and fuel consumption of a common rail direct injection (CRDI) diesel engine using mixed diesel (B10) and biodiesel (B20− B100) fuels. The study's primary objective was to determine the effects of blended diesel-biodiesel fuel on CRDI emissions based on different exhaust gas recirculation (EGR) rates, including carbon monoxide (CO), carbon dioxide (CO 2 ), oxygen (O 2 ), nitrogen oxide (NO x ), and hydrocarbon (HC) emissions, smoke opacity, exhaust gas temperature, and fuel consumption. The CRDI experiments involved adjusting the different engine speeds (1400−3000 rpm) and EGR rates (0 and 12.5%), which were analyzed to determine their impact on these parameters for both blended diesel and biodiesel fuels. The results showed that under the conditions of no EGR (0%), the CO and HC emissions and the smoke opacity were lower than those with a 12.5% EGR rate for all fuel types and all cases. With 12.5% EGR rate, the O 2 emissions and the EGT of the CRDI diesel engine decreased, which resulted in significantly lower NO x emissions because of EGR into the combustion chamber. For the maximum engine speed of 3000 rpm and with no EGR, the CO and HC emissions and the smoke opacity were lower than those with a 12.5% EGR rate for all fuel types. With a 12.5% EGR rate at 3000 rpm, the O 2 emissions and the exhaust gas temperature were reduced by 0.07% and 2.27%, respectively, and the NO x emissions were reduced by 2.54%. However, with EGR, the CO and HC emissions and the smoke opacity increased by 7.70%, 18.61%, and 0.4%, respectively. Furthermore, the fuel consumption of pure biodiesel (B100) at 3000 rpm with a 12.5% EGR rate was reduced by 2.81% compared to that with a 0% EGR rate. Because the temperature in the combustion chamber is high enough for the engine to run, the EGR reuses a portion of the exhaust gases and can help to minimize the quantity of fuel in the combustion chamber. As a suggestion based on these observations, biodiesel fuel should not exceed B80 because the viscosity and density of fuel that are too high may affect the fuel injection system, both the injectors, and the pressure pump, causing the injectors to be unable to work correctly. These findings can contribute to the development of strategies and technologies for reducing emissions and improving fuel efficiency in CRDI diesel engines.

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“…As part of the local preliminary testing process, an uncertainty budget for the jig was developed from guidelines in IAEA TECDOC 1585 and uncertainty budgets that have previously been included in brachytherapy literature [21,22]. TECDOC 1585 [23] provides guidance to standards laboratories regarding the assessment and reporting of uncertainty in measurements with reference to the ISO/IEC Guide 98-3:2008, colloquially referred to as the ISO-GUM method [23,24]. The TECDOC 1585 document provides an example of uncertainty analysis for RAKR calibrations which was used as a template to begin the uncertainty analysis used in this study [23].…”

Section: Design and Fabrication Of 3d Printed Jigmentioning

confidence: 99%

“…TECDOC 1585 [23] provides guidance to standards laboratories regarding the assessment and reporting of uncertainty in measurements with reference to the ISO/IEC Guide 98-3:2008, colloquially referred to as the ISO-GUM method [23,24]. The TECDOC 1585 document provides an example of uncertainty analysis for RAKR calibrations which was used as a template to begin the uncertainty analysis used in this study [23]. Sources of uncertainty were initially broken down with reference to the IAEA in-air RAKR formalism described in the next section (see equation 1).…”

Section: Design and Fabrication Of 3d Printed Jigmentioning

confidence: 99%

3D printed brachytherapy jig for Reference Air Kerma Rate calibration

et al. 2021

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3D printing in modern radiotherapy provides creative autonomy which can be a valuable tool for use in brachytherapy source calibration. Radiotherapy centres may verify their brachytherapy source strength with a calibrated Farmer chamber. For this purpose, a jig was designed, 3D printed and commissioned for in-air source strength calibration. Measurements on four afterloaders with varied equipment and environments were completed. A full uncertainty budget was developed and measurements with the in-air jig were consistently within 3% of the certificate source strength. By creating a jig that is able to be customised to multiple catheter sizes and cylindrical chamber designs, centres can be provided with the option of independently checking their source strength with ease and for little cost.

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A Practical Way to ISO/GUM Measurement Uncertainty for Analytical Assays Including In-House Validation Data

Cited by 7 publications

References 176 publications

Comparison of five methodologies to apportion organic aerosol sources during a PM pollution event

Comparison of five methodologies to apportion organic aerosol sources during a PM pollution event

Effects of Blended Diesel–Biodiesel Fuel on Emissions of a Common Rail Direct Injection Diesel Engine with Different Exhaust Gas Recirculation Rates

3D printed brachytherapy jig for Reference Air Kerma Rate calibration

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