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。     

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。     

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。     

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。     

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。     

气相色谱-质谱法测定水中挥发性有机物的测量不确定度评定

根据测量不确定度评定与表示理论，采用气相色谱一质谱法测定水中挥发性有机物。以氯乙烯为例，通过计算和评定，得出当氯乙烯的测量结果为4．99μg／L时，取包含因子k=2（约95％置信概率），扩展不确定度U=0．96μg／L。该不确定度评定方法在实际工作中具有较强的实用价值。     

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。     

低本底α、β仪器测量放射性核素的不确定度分析

针对所分析项目最后的结果计算公式，分析得到公式中每个参数所需要的实验步骤，以及算得参数的各项因子，找出每个实验步骤和各项因子中所带入的A类和B类不确定度，最后将这些参数中的不确定度合成。     

The influence on partial order ranking from input parameter uncertainty. Definition of a robustness parameter

The method of partial order ranking has been used within the environmental area for a variety of purposes as an attractive way of handling complex information. However, the environmental data are often associated with a significant degree of uncertainty. In this investigation the general nature of the influence from data uncertainty on the partial order ranking is analyzed. A Monte Carlo type analysis is performed in which a series of randomly formed data are used to test the influence of data uncertainty. The partial order ranking is interpreted, where the results are transferred to a one-dimensional ranking scale taking into account that not all elements are ranked with the same certainty. A simple general robustness parameter (E) in form of the expected number of comparisons for each ranking element is defined and correlated to the uncertainty analysis results. A simple equation relates E to the number of elements and the number of parameters, respectively. The magnitude of the ranking uncertainty is shown to increase rapidly when the E value decreases below 4-5 comparisons per element. When the E value exceeds 5 the ranking uncertainty becomes nearly constant and independent on the actual E value.     

Methods for quantifying variability and uncertainty in AP-42 emission factors: case studies for natural gas-fueled engines

Quantitative methods for characterizing variability and uncertainty were applied to case studies of oxides of nitrogen and total organic carbon emission factors for lean-burn natural gas-fueled internal combustion engines. Parametric probability distributions were fit to represent inter-engine variability in specific emission factors. Bootstrap simulation was used to quantify uncertainty in the fitted cumulative distribution function and in the mean emission factor. Some methodological challenges were encountered in analyzing the data. For example, in one instance, five data points were available, with each data point representing a different market share. Therefore, an approach was developed in which parametric distributions were fitted to population-weighted data. The uncertainty in mean emission factors ranges from as little as approximately +/-10% to as much as -90 to +180%. The wide range of uncertainty in some emission factors emphasizes the importance of recognizing and accounting for uncertainty in emissions estimates. The skewness in some uncertainty estimates illustrates the importance of using numerical simulation approaches that do not impose restrictive symmetry assumptions on the confidence interval for the mean. In this paper, the quantitative method, the analysis results, and key findings are presented.     

Methods for Quantifying Variability and Uncertainty in AP-42 Emission Factors: Case Studies for Natural Gas-Fueled Engines

Abstract

Quantitative methods for characterizing variability and uncertainty were applied to case studies of oxides of nitrogen and total organic carbon emission factors for lean-burn natural gas-fueled internal combustion engines. Parametric probability distributions were fit to represent inter-engine variability in specific emission factors. Bootstrap simulation was used to quantify uncertainty in the fitted cumulative distribution function and in the mean emission factor. Some methodological challenges were encountered in analyzing the data. For example, in one instance, five data points were available, with each data point representing a different market share. Therefore, an approach was developed in which parametric distributions were fitted to population-weighted data. The uncertainty in mean emission factors ranges from as little as ~±10% to as much as -90 to 21+180%. The wide range of uncertainty in some emission factors emphasizes the importance of recognizing and accounting for uncertainty in emissions estimates. The skewness in some uncertainty estimates illustrates the importance of using numerical simulation approaches that do not impose restrictive symmetry assumptions on the confidence interval for the mean. In this paper, the quantitative method, the analysis results, and key findings are presented.     

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。     

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.     

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.     

Assessing the uncertainty associated with national greenhouse gas emission inventories:: a case study for Austria

The uncertainty associated with the Austrian Greenhouse Gas emission inventory has been determined for the gases CO₂, CH₄ and N₂O and for the overall greenhouse potential. Expert interviews were conducted to obtain uncertainties in inventory input data. Based on these interviews, error distributions were developed and combined using Monte-Carlo analysis. Results for all sources and gases combined indicate an overall uncertainty between 10.5% and 12% depending on the base year considered. Excluding emissions and the uncertainty associated with forest sinks and natural sources, overall uncertainty decreased by 2% points. The mere ‘random error’, which is considered the level of uncertainty to be achieved with the current methodology (excluding all systematic errors) is 5% points lower. Detailed evaluation shows that much of the overall uncertainty derives from a lack of understanding the processes associated with N₂O emissions from soils. Other important contributors to GHG emission uncertainties are CH₄ from landfills and forests as CO₂ sinks. The uncertainty of the trend has been determined at near 5% points, with solid waste production (landfills) having the strongest contribution. Theoretical considerations do not permit a decrease of the trend uncertainty—even when forest sinks are not considered—below 3% points.     

Development of probabilistic emission inventories of air toxics for Jacksonville, Florida, USA

Probabilistic emission inventories were developed for 1,3-butadiene, mercury (Hg), arsenic (As), benzene, formaldehyde, and lead for Jacksonville, FL. To quantify inter-unit variability in empirical emission factor data, the Maximum Likelihood Estimation (MLE) method or the Method of Matching Moments was used to fit parametric distributions. For data sets that contain nondetected measurements, a method based upon MLE was used for parameter estimation. To quantify the uncertainty in urban air toxic emission factors, parametric bootstrap simulation and empirical bootstrap simulation were applied to uncensored and censored data, respectively. The probabilistic emission inventories were developed based on the product of the uncertainties in the emission factors and in the activity factors. The uncertainties in the urban air toxics emission inventories range from as small as -25 to +30% for Hg to as large as -83 to +243% for As. The key sources of uncertainty in the emission inventory for each toxic are identified based upon sensitivity analysis. Typically, uncertainty in the inventory of a given pollutant can be attributed primarily to a small number of source categories. Priorities for improving the inventories and for refining the probabilistic analysis are discussed.     

Performance Audits of EPA Protocol Gases and Inspection and Maintenance Calibration Gases

Air pollution abatement policies must be based on quantitative information on current and future emissions of pollutants. As emission projections uncertainties are inevitable and traditional statistical treatments of uncertainty are highly time/resources consuming, a simplified methodology for nonstatistical uncertainty estimation based on sensitivity analysis is presented in this work. The methodology was applied to the “with measures” scenario for Spain, concretely over the 12 highest emitting sectors regarding greenhouse gas and air pollutants emissions. Examples of methodology application for two important sectors (power plants, and agriculture and livestock) are shown and explained in depth. Uncertainty bands were obtained up to 2020 by modifying the driving factors of the 12 selected sectors and the methodology was tested against a recomputed emission trend in a low economic-growth perspective and official figures for 2010, showing a very good performance.

Implications:?A solid understanding and quantification of uncertainties related to atmospheric emission inventories and projections provide useful information for policy negotiations. However, as many of those uncertainties are irreducible, there is an interest on how they could be managed in order to derive robust policy conclusions. Taking this into account, a method developed to use sensitivity analysis as a source of information to derive nonstatistical uncertainty bands for emission projections is presented and applied to Spain. This method simplifies uncertainty assessment and allows other countries to take advantage of their sensitivity analyses.