水中阴离子表面活性剂的测量不确定度评定

简要介绍了测量不确定度的概念，运用《测量不确定度评定与表示》的技术规范，通过对阴离子表面活性剂的测定过程，分析了影响阴离子表面活性剂测量不确定度的因素，给出了相对标准不确定度分量，并具体阐明了测量不确定度的评定步骤，得出测量扩展不确定度的结果。     

地下水矿化度测定的不确定度评定

分析质量法测量地下水矿化度的测量不确定度来源，评定了地下水矿化度的测量不确定度，在各不确定度中，称量引入的不确定度较大。     

滴定法测定的不确定度评定——以EDTA法测定水中钙镁为例

用测量不确定度表示检测结果是当前国际上约定做法，然而如何对测量结果的不确定度进行合理评定，一直是困扰检测实验室的一个难题。作者依据测量不确定度的评定原则，通过实例，简要地阐述了滴定法测量不确定度评定方法，对环境检测领域测量不确定度的评定具有借鉴意义。     

离子选择电极分析法测定标样中氟化物的不确定度评定

通过对离子选择电极法测定标样中氟化物的过程研究，分析了该方法测量不确定度的来源，给出了相对不确定度分量，得出测量扩展不确定度的结果。     

水中氯化物测定的不确定度评定

根据硝酸银滴定法测定水中氯化物的含量，分析了该方法测量不确定度的来源，评定了水中氯化物的测量不确定度，在各不确定度中，以标准溶液配制与样品分析时滴定消耗的硝酸银体积引入的不确定度较大。     

水质铜测定的不确定度评定

根据火焰原子吸收分光光度法测定水中铜的含量，分析了测量不确定度的主要来源，即标准曲线不确定度、标准溶液不确定度、测量重复性不确定度。计算得到水中铜的测定结果的合成不确定度为0．098mg／L，扩展不确定度为0．196mg/L。     

原子吸收法测定水中铁的不确定度分析

通过对原子吸收法测定水中铁的不确定度的分析，找出了导致不确定度的因素。对测量不确定度进行计算和评定，结果表明，影响其测量不确定度的主要因素是标准曲线精密度以及仪器漂移。     

测量试样中的氮氧化物含量不确定度评定

以实际监测数据为例，详细描述测量试样中的NOx含量不确定度评定方法，包括不确定度源的分析，A类标准不确定度评定、B类标准不确定度评定、合成标准不确定度和扩展不确定度等，对不确定度的分量作了详尽的分析和计算。     

重铬酸钾法测定水中COD的不确定度分析

建立了重铬酸钾法测定水中化学需氧量的相对合成标准不确定度的数学模式,应用一个较高浓度的实例,对B类不确定度的分量作了详尽的分析和计算,得出测量扩展不确定度的结果。     

水中总磷测量的不确定度评定

采用钼酸铵分光光度法测定水中总磷的含量，分析该法测量不确定度的来源，评定水中总磷的测量不确定度。     

Uncertainty associated with the gravimetric measurement of particulate matter concentration in ambient air

This work applied a propagation of uncertainty method to typical total suspended particulate (TSP) sampling apparatus in order to estimate the overall measurement uncertainty. The objectives of this study were to estimate the uncertainty for three TSP samplers, develop an uncertainty budget, and determine the sensitivity of the total uncertainty to environmental parameters. The samplers evaluated were the TAMU High Volume TSP Sampler at a nominal volumetric flow rate of 1.42 m³ min^–1 (50 CFM), the TAMU Low Volume TSP Sampler at a nominal volumetric flow rate of 17 L min^–1 (0.6 CFM) and the EPA TSP Sampler at the nominal volumetric flow rates of 1.1 and 1.7 m³ min^–1 (39 and 60 CFM). Under nominal operating conditions the overall measurement uncertainty was found to vary from 6.1 x 10^–6 g m^–3 to 18.0 x 10^–6 g m^–3, which represented an uncertainty of 1.7% to 5.2% of the measurement. Analysis of the uncertainty budget determined that three of the instrument parameters contributed significantly to the overall uncertainty: the uncertainty in the pressure drop measurement across the orifice meter during both calibration and testing and the uncertainty of the airflow standard used during calibration of the orifice meter. Five environmental parameters occurring during field measurements were considered for their effect on overall uncertainty: ambient TSP concentration, volumetric airflow rate, ambient temperature, ambient pressure, and ambient relative humidity. Of these, only ambient TSP concentration and volumetric airflow rate were found to have a strong effect on the overall uncertainty. The technique described in this paper can be applied to other measurement systems and is especially useful where there are no methods available to generate these values empirically.

Implications:?This work addresses measurement uncertainty of TSP samplers used in ambient conditions. Estimation of uncertainty in gravimetric measurements is of particular interest, since as ambient particulate matter (PM) concentrations approach regulatory limits, the uncertainty of the measurement is essential in determining the sample size and the probability of type II errors in hypothesis testing. This is an important factor in determining if ambient PM concentrations exceed regulatory limits. The technique described in this paper can be applied to other measurement systems and is especially useful where there are no methods available to generate these values empirically.     

An uncertainty analysis of mean flow velocity measurements used to quantify emissions from stationary sources

Point velocity measurements conducted by traversing a Pitot tube across the cross section of a flow conduit continue to be the standard practice for evaluating the accuracy of continuous flow-monitoring devices. Such velocity traverses were conducted in the exhaust duct of a reduced-scale analog of a stationary source, and mean flow velocity was computed using several common integration techniques. Sources of random and systematic measurement uncertainty were identified and applied in the uncertainty analysis. When applicable, the minimum requirements of the standard test methods were used to estimate measurement uncertainty due to random sources. Estimates of the systematic measurement uncertainty due to discretized measurements of the asymmetric flow field were determined by simulating point velocity traverse measurements in a flow distribution generated using computational fluid dynamics. For the evaluated flow system, estimates of relative expanded uncertainty for the mean flow velocity ranged from ±1.4% to ±9.3% and depended on the number of measurement locations and the method of integration.

Implications:Accurate flow measurements in smokestacks are critical for quantifying the levels of greenhouse gas emissions from fossil-fuel-burning power plants, the largest emitters of carbon dioxide. A systematic uncertainty analysis is necessary to evaluate the accuracy of these measurements. This study demonstrates such an analysis and its application to identify specific measurement components and procedures needing focused attention to improve the accuracy of mean flow velocity measurements in smokestacks.     

Estimation of measurement uncertainty associated to the determination of pesticide residues: a case study

In the analytical analysis the measurement uncertainty is a quantitative indicator of the confidence describing the range around a reported or experimental result within which the true value can be expected. Several approaches can be used to estimate the measurement uncertainty associated to the analysis of pesticide residues: a) the top-down, the estimation can be referred to default values; b) the bottom-up the estimation is related to the uncertainty sources. Concerning the bottom-up approach, the following contributions have been investigated: weight of sample, calibration solutions, final volume of sample and intermediate repeatability studies. The commodity/residue combination selected in this study was celery/tau-fluvalinate pesticide. Tau-fluvalinate is a broad-spectrum insecticide in the pyrethroid class of pesticides. The Maximum Residue Limit (MRL) of tau-fluvalinate in celery has been set at 0.01 mg/kg. The tau- Fluvalinate showed two chromatographic peaks. Since the individual standards are not available, the two peaks were integrated separately and the instrumental responses were added. The total residue was calculated on the basis of resulted peaks. The present work aims to compare the uncertainty estimated by experimental data using repeated analysis (n = 12) of a real sample and a spiked sample. The relative expanded uncertainty for two data set, incurred and spiked, was 22 % and 20 %, respectively. No differences were observed from repeated determinations of real samples and spiked samples.     

Small-scale variability of metals in soil and composite sampling

Soil pollution data is also strongly scattering at small scale. Sampling of composite samples, therefore, is recommended for pollution assessment. Different statistical methods are available to provide information about the accuracy of the sampling process. Autocorrelation and variogram analysis can be applied to investigate spatial relationships. Analysis of variance is a useful method for homogeneity testing. The main source of the total measurement uncertainty is the uncertainty arising from sampling. The sample mass required for analysis can also be estimated using an analysis of variance. The number of increments to be taken for a composite sample can be estimated by means of simple statistical formulae. Analytical results of composite samples obtained from different fusion procedures of increments can be compared by means of multiple mean comparison. The applicability of statistical methods and their advantages are demonstrated for a case study investigating metals in soil at a very small spatial scale. The paper describes important statistical tools for the quantitative assessment of the sampling process. Detailed results clearly depend on the purpose of sampling, the spatial scale of the object under investigation and the specific case study, and have to be determined for each particular case.     

PM2.5 speciation trends network: evaluation of whole-system uncertainties using data from sites with collocated samplers

The objectives of this paper are to contrast the relative variability of replicate laboratory measurements of selected chemical components of fine particulate matter (PM) with total variability from collocated measurements and to compare the magnitudes of the uncertainties determined from collocated sampler data with those currently being provided to U.S. Environmental Protection Agency (EPA)'s Air Quality System (AQS) database by RTI International (RTI). Pointwise uncertainty values are needed for modeling and data analysis and should include all the random errors affecting each data point. Total uncertainty can be decomposed into two primary components: analytical measurement uncertainty and sampling uncertainty. Analytical measurement uncertainties are relatively easy to calculate from routine quality control (QC) data. Sampling uncertainties, on the other hand, are comparatively difficult to measure. In this paper, the authors describe data from collocated samplers to provide a snapshot of whole-system uncertainty for several important chemical species. The components of uncertainty were evaluated for key species from each of the analytical methods employed by the PM2.5 Speciation Trends Network (STN) program: gravimetry, ion chromatography (IC), X-ray fluorescence (XRF), and thermal-optical analysis for organic carbon and elemental carbon. The results show that the laboratory measurement uncertainties are typically very small compared with uncertainties calculated from the differences between samples collected from collocated samplers. These differences are attributable to the "field" components uncertainty, which may include contamination and/or losses during shipping, handling, and sampling, as well as other distortions of the concentration level due to flow and sample volume variations. Uncertainties calculated from the collocation results were found to be generally similar to the uncertainties currently being loaded into EPA's AQS system, with some exceptions described below.     

Uncertainty estimation in the analysis of pesticide residues in olive oil using data from proficiency tests

In this work we report the results for estimating the measurement uncertainty (MU) following up the application of two different approaches, relatively the top-down procedure, by using proficiency test data. We have focused the estimation on the olive oil matrix. We used the analytical data obtained from five selected editions of the Proficiency Tests (PTs, from 2007 to 2011) on pesticide residues in olive oil to estimate the MU. These PTs have been organized by Istituto Superiore di Sanità annually in cooperation with International Olive Council (IOC) since 1997. The number of participants in each trial ranged from 10 to 43. We used a total of 34 pesticide results. The expanded uncertainty U _(c) was calculated using a covering factor k = 2 for a confidence interval of 95%. In the approach 1, the within–laboratory reproducibility standard deviation is combined with estimates of the method and laboratory bias using PTs data. In the approach 2, the way of estimating the MU is based only on the bias that the laboratory has obtained participating in a sufficient number of the IOC proficiency tests. Comparing the relative expanded uncertainty based on these different approaches we notice values quite constant and close, from 42% to 48%. Moreover, these calculated expanded uncertainties are less than the default value of 50% (corresponding to a 95% confidence level), adopted from European guidance document SANCO based on the fit-for-purpose relative standard deviation (FFP-RSD).     

Estimate of the uncertainty in measurement for the determination of mercury in seafood by TDA AAS

An approach for the estimate of the uncertainty in measurement considering the individual sources related to the different steps of the method under evaluation as well as the uncertainties estimated from the validation data for the determination of mercury in seafood by using thermal decomposition/amalgamation atomic absorption spectrometry (TDA AAS) is proposed. The considered method has been fully optimized and validated in an official laboratory of the Ministry of Agriculture, Livestock and Food Supply of Brazil, in order to comply with national and international food regulations and quality assurance. The referred method has been accredited under the ISO/IEC 17025 norm since 2010. The approach of the present work in order to reach the aim of estimating of the uncertainty in measurement was based on six sources of uncertainty for mercury determination in seafood by TDA AAS, following the validation process, which were: Linear least square regression, Repeatability, Intermediate precision, Correction factor of the analytical curve, Sample mass, and Standard reference solution. Those that most influenced the uncertainty in measurement were sample weight, repeatability, intermediate precision and calibration curve. The obtained result for the estimate of uncertainty in measurement in the present work reached a value of 13.39%, which complies with the European Regulation EC 836/2011. This figure represents a very realistic estimate of the routine conditions, since it fairly encompasses the dispersion obtained from the value attributed to the sample and the value measured by the laboratory analysts. From this outcome, it is possible to infer that the validation data (based on calibration curve, recovery and precision), together with the variation on sample mass, can offer a proper estimate of uncertainty in measurement.