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The concept of uncertainty in an isotopic analysis is not uniform in the scientific community worldwide and can compromise the risk of false compliance assessment applied to carbon isotopic analyses in natural gas exploratory evaluation. In this work, we demonstrated a way to calculate one of the main sources of this uncertainty, which is underestimated in most studies focusing on gas analysis: the δ13C calculation itself is primarily based on the raw analytical data. The carbon isotopic composition of methane, ethane, propane, and CO2 was measured. After a detailed mathematical treatment, the corresponding expanded uncertainties for each analyte were calculated. Next, for the systematic isotopic characterization of the two gas standards, we calculated the standard uncertainty, intermediary precision, combined standard uncertainty, and finally, the expanded uncertainty for methane, ethane, propane, and CO2. We have found an expanded uncertainty value of 1.8‰ for all compounds, except for propane, where a value of 1.6‰ was obtained. The expanded uncertainty values calculated with the approach shown in this study reveal that the error arising from the application of delta calculation algorithms cannot be neglected, and the obtained values are higher than 0.5‰, usually considered as the accepted uncertainty associated with the GC-IRMS analyses. Finally, based on the use of uncertainty information to evaluate the risk of false compliance, the lower and upper acceptance limits for the carbon isotopic analysis of methane in natural gas are calculated, considering the exploratory limits between -55‰ and -50‰: (i) for the underestimated current uncertainty of 0.5‰, the lower and upper acceptance limits, respectively, are -54.6‰ and -50.4‰; and (ii) for the proposed realistic uncertainty of 1.8‰, the lower and upper acceptance limits would be more restrictive; i.e., -53.5‰ and -51.5‰, respectively.

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Sulfur-containing compounds are naturally found in crude oil, and they can be partially removed during the refining process. The wide use of fossil fuels has a significant contribution to sulfur emissions into the atmosphere, and Governments are striving to reduce the amount of the fuels by environmental regulations. The reduction of sulfur levels in diesel and other transportation fuels is beneficial from economic and environmental points, but meeting this standard represents a major operational and economic challenge for the oil and gas industry. Quantitative measurement of the sulfur amount must be taken along the oil refining chains guided by standards of measurement and recommended analytical methods such as various American Society for Testing and Materials methods (ASTM D2622, ASTM D5453, ASTM D7039, and ASTM D7220). Advancement in the refining processes and environmental regulations also require reliable measurements and well-defined criteria for compliance assessment. This work presented a brief review of the ASTM Standards used in the laboratories of the Brazilian oil and gas industry to determine the total sulfur content in fuels. We also presented an approach based on the reproducibility of the measurement methods and the guard band concept to evaluate the conformity statement.

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This study aimed to introduce non-parametric tests and guard bands to assess the compliance of some river water properties with Brazilian environmental regulations. Due to the heterogeneity of the measurands pH, Biochemical Oxygen Demand (BOD), manganese molar concentration, and Escherichia coli, which could be wrongly treated as outliers, as well as the non-Gaussian data, robust methods were used to calculate the measurement uncertainty. Next, based on guard bands, the compliance assessment was evaluated using this previous uncertainty information. For these four measurands, partial overlaps between their uncertainties and the specification limit could generate doubts about compliance. The non-parametric approach for calculating the uncertainty connected to the guard bands concept classified pH and BOD as "conform", with a risk to the consumer of up to 4.0% and 4.9%, respectively; in contrast, manganese molar concentration and Escherichia coli were "not conform", with a risk to the consumer of up to 25% and 7.4%, respectively. The methodology proposed was satisfactory because it considered the natural heterogeneity of data with non-Gaussian behavior instead of wrongly excluding outliers. In an unprecedented way, two connected statistical approaches shed light on the measurement uncertainty in compliance assessment of water analysis.

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The low-quality of automotive fuels may lead to the generation of pollutants harmful to both environmental and human health. The quality evaluation of automotive fuels requires a multiparameter conformity assessment, which may lead to an increased total risk of false conformity decisions even if all parameters comply with the acceptance limits. Thus, the aim of this work was to propose the establishment of multivariate acceptance limits in order to ensure a reduced total risk of false conformity decisions applied to automotive fuels analysis. Particular and total (consumers' and/or producers') risks were estimated using frequentist (specific) and Bayesian (global) approaches. Multivariate acceptance limits were estimated using Monte Carlo method, adopting an appropriate multivariate coverage factor (k') defined using MS Excel Solver function. The definition of multivariate acceptance limits ensures a total risk below the maximum admissible risk (typically 5%) and was successfully employed in the conformity assessment of automotive fuels (diesel and gasoline). The employment of the multivariate acceptance limits may be useful in the conformity assessment of several multiparameter products.

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Chemical analysts use analytical blanks in their analyses, but seldom is this source of uncertainty evaluated. Generally, there is great confusion. Although the numerical value of the blank, in some situations, can be negligible, its source of uncertainty cannot be. This article discusses the uncertainty contribution of the analytical blank using a numerical example of the copper content in waters by flame atomic absorption spectrometry. The results indicate that the uncertainties of the analytical blank can contribute up to 50% when the blank sample is considered in this analysis, confirming its high impact. This effect can be primarily observed where the analyte concentration approaches the lower range of the analytical curve. Even so, the blank is not always computed. Therefore, the relevance of the analytical blank can be confirmed by uncertainty evaluation.

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