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。     

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。     

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。     

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。     

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。     

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。     

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。     

水中放射性核素铯-137测量的不确定度评估

放射性核素利用化学载带、浓缩后进行测量,化学前处理步骤比较复杂,通过分析测量实验中的不确定度和分析计算公式中的有关参数,了解到分析水中放射性核素铯-137时,主要受到样品测量、仪器探测效率、样品化学回收率和样品取样体积等方面的不确定度因素的影响。     

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。     

Quantification of emission factor uncertainty

Emissions factors are important for estimating and characterizing emissions from sources of air pollution. There is no quantitative indication of uncertainty for these emission factors, most factors do not have an adequate data set to compute uncertainty, and it is very difficult to locate the data for those that do. The objectives are to compare the current emission factors of Electric Generating Unit NOx sources with currently available continuous emission monitoring data, develop quantitative uncertainty indicators for the Environmental Protection Agency (EPA) data quality rated emission factors, and determine the possible ranges of uncertainty associated with EPA's data quality rating of emission factors. EPA's data letter rating represents a general indication of the robustness of the emission factor and is assigned based on the estimated reliability of the tests used to develop the factor and on the quantity and representativeness of the data. Different sources and pollutants that have the same robustness in the measured emission factor and in the representativeness of the measured values are assumed to have a similar quantifiable uncertainty. For the purposes of comparison, we assume that the emission factor estimates from source categories with the same letter rating have enough robustness and consistency that we can quantify the uncertainty of these common emission factors based on the qualitative indication of data quality which is known for almost all factors. The results showed that EPA's current emission factor values for NOx emissions from combustion sources were found to be reasonably representative for some sources; however AP-42 values should be updated for over half of the sources to reflect current data. The quantified uncertainty ranges were found to be 25-62% for A rated emission factors, 45-75% for B rated emission factors, 60-82% for C rated emission factors, and 69-86% for D rated emission factors, and 82-92% for E rated emission factors.     

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.     

Geographical scenario uncertainty in generic fate and exposure factors of toxic pollutants for life-cycle impact assessment

In environmental life-cycle assessments (LCA), fate and exposure factors account for the general fate and exposure properties of chemicals under generic environmental conditions by means of 'evaluative' multi-media fate and exposure box models. To assess the effect of using different generic environmental conditions, fate and exposure factors of chemicals emitted under typical conditions of (1).Western Europe, (2). Australia and (3). the United States of America were compared with the multi-media fate and exposure box model USES-LCA. Comparing the results of the three evaluative environments, it was found that the uncertainty in fate and exposure factors for ecosystems and humans due to choice of an evaluative environment, as represented by the ratio of the 97.5th and 50th percentile, is between a factor 2 and 10. Particularly, fate and exposure factors of emissions causing effects in fresh water ecosystems and effects on human health have relatively high uncertainty. This uncertainty is mainly caused by the continental difference in the average soil erosion rate, the dimensions of the fresh water and agricultural soil compartment, and the fraction of drinking water coming from ground water.     

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.     

Development of Probabilistic Emission Inventories of Air Toxics for Jacksonville,Florida

Abstract

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.