火焰原子吸收法测定土壤镍结果的不确定度评定

根据《土壤和沉积物铜、锌、铅、镍、铬的测定火焰原子吸收分光光度法HJ 491-2019》测定某土壤样品镍的含量,分析测试过程中不确定度的来源,并对样品测定结果进行不确定度的评定。测得样品中镍的含量为38. 7mg/kg,扩展不确定度为4. 30mg/kg (k=2)。采用新标准首次建立了火焰原子吸收法测定土壤镍含量的不确定度评定方法,得到标准曲线的配制和拟合过程、测量重复性是土壤镍含量不确定度的主要影响因素;为采取措施降低不确定度提供参考,为定量评价测量的质量提供帮助,为影响规范限度的符合性提供依据。     

Top-down法评定水溶性油田化学剂中金属元素的测量不确定度

采用Top-down技术评定微波消解-电感耦合等离子体质谱法测定水溶性油田化学剂样品中砷、铅、镉、铬含量的测量不确定度。对实验数据偏倚和精密度受控情况进行验证后,以实验内稳定样品的重复测定结果计算实验室内期间相对标准偏差（SP,rel）和实验室内重复性相对标准偏差（Sr,rel）,以能力验证结果计算检 测过程偏倚及实验室间精密度相对标准偏差（Sbias,rel）,据此评定测量不确定度,从而反映测定方法和实验室的 精密度控制情况。微波消解 电感耦合等离子体质谱法测定水溶性油田化学剂样品中砷、铅、镉、铬含量的扩展 不确定度分别为17.7％,26.1％,18.7％,19.8％。Top-down精密度法评定测量不确定度操作简便,可反映实验室分析人员操作、仪器重复性和环境条件等因素对不确定度的干扰,避免了繁复的分量计算和模型建立过 程,但Top-down法不能识别分析测试过程中对测量不确定度有较大影响的关键分量。Sbias,rel对扩展不确定度的贡献最大,实验室可通过能力验证结果数据评估检测过程精密度控制情况。     

电源适配器平均效率的测量不确定度评定

对符合GB 20943-2013定义的电源适配器平均效率的测量不确定度进行了实例评定,示范了如何处理评定过程中复杂的非线性数学模型问题,阐述了获取电源适配器平均效率合成标准不确定度的过程和方法,进而确定电源适配器平均效率测量结果的扩展不确定度。     

水样品中挥发酚的测量不确定度的评定方法

根据环境监测的要求,中国石油集团安全环保技术研究院实验室在出具数据时要对测量结果进行测量不确定度评定。用实例建立了分光光度法测定水样品中挥发酚的合成标准不确定度的数学模式,它由质量的标准测量不确定度和体积的标准测量不确定度组成;并对这两部分标准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结果。     

工业废水中化学需氧量检测的不确定度评定

文章通过对工业废水中化学需氧量检测过程中不确定度来源进行详细的分析和鉴别,确定出影响因素的不确定度分量,从而计算出合成标准不确定度和扩展不确定度。不确定度的评定有利于分析人员在分析过程中对可能产生误差的环节加以特别的关注。     

XRF标准样品的不确定度分析

本文采用熔融法制作含有镉(Cd)、汞(Hg)、溴(Br)、铬(Cr)、铅(Pb)等目标元素的标准样品,对定值、均匀性、稳定性引起的不确定度分量进行了分析和计算,并计算了标准样品的扩展不确定度。     

关于不确定度评定中两个问题的讨论

通过对环境监测仪器分析不确定度评定中，标准不确定度和相对标准不确定度的单位来源以及关于相对标准不确定度两种计算方法的讨论，得出标准不确定度是有量纲的、相对标准不确定度是无量纲的；最大允许误差较易获得，用这种方法计算不确定度有很好的可靠性。相对标准偏差计算不确定度则结果更好。     

废水氨氮测量不确定度的评定

采用纳氏试剂法测定废水中氨氮含量,其不确定度来源于标准曲线、取样体积和样品的重复测定,通过对测定过程涉及到的标准储备液、标准曲线绘制、标准曲线拟合、取样体积、移液管等因素进行分析,确定其产生的不确定度,进而提高测量的准确度。实验结果表明,通过对废水中氨氮不确定度的评定,能够准确反映实验过程中产生的不确定度的所有来源。     

原子荧光法测定含油废水中砷不确定度评定

对原子荧光法测定含油废水砷含量过程中引入的不确定度进行分析,采用线性回归标准曲线法测定,利用被测物质浓度C和相应的荧光强度值I的线性关系,建立标准曲线回归方程,并对影响含油废水中砷测量结果不确定度的来源包括砷标准使用溶液浓度、标准曲线、测量重复性、样品预处理过程等进行分析和评定,认为标准曲线绘制、测量重复性、标准使用溶液浓度是影响测定结果的主要因素。     

连续流动分析法测定水中总氮的不确定度评定

在实验室分析基础上,对连续流动分析法测定水中总氮过程的不确定度进行评定。本文建立了数学模型,对不确定度来源进行了分析,并计算了不确定度分量、合成不确定度及扩展不确定度,最后给出了总氮测量结果的标准表示方法。     

Propagation of uncertainties in soil and pesticide properties to pesticide leaching

In the new Dutch decision tree for the evaluation of pesticide leaching to groundwater, spatially distributed soil data are used by the GeoPEARL model to calculate the 90th percentile of the spatial cumulative distribution function of the leaching concentration in the area of potential usage (SP90). Until now it was not known to what extent uncertainties in soil and pesticide properties propagate to spatially aggregated parameters like the SP90. A study was performed to quantify the uncertainties in soil and pesticide properties and to analyze their contribution to the uncertainty in SP90. First, uncertainties in the soil and pesticide properties were quantified. Next, a regular grid sample of points covering the whole of the agricultural area in the Netherlands was randomly selected. At the grid nodes, realizations from the probability distributions of the uncertain inputs were generated and used as input to a Monte Carlo uncertainty propagation analysis. The analysis showed that the uncertainty concerning the SP90 is 10 times smaller than the uncertainty about the leaching concentration at individual point locations. The parameters that contribute most to the uncertainty about the SP90 are, however, the same as the parameters that contribute most to uncertainty about the leaching concentration at individual point locations (e.g., the transformation half-life in soil and the coefficient of sorption on organic matter). Taking uncertainties in soil and pesticide properties into account further leads to a systematic increase of the predicted SP90. The important implication for pesticide regulation is that the leaching concentration is systematically underestimated when these uncertainties are ignored.     

还原-偶氮光度法测定废水中硝基苯的不确定度评估

以还原-偶氮光度法测定了废水中硝基苯的浓度,分析了测量过程中4个主要部分的不确定度。样品中硝基苯的浓度为0.500μg/mL,取包含因子k=2（95%置信概率）,扩展不确定度为U=0.032μg/mL,结果可表示为：（0.500±0.032）μg/mL。此方法测定硝基苯的不确定度的评析,有利于更准确地表示测量结果。     

Uncertainty assessment of the model RICEWQ in northern Italy

Model predictions are often seriously affected by uncertainties arising from many sources. Ignoring the uncertainty associated with model predictions may result in misleading interpretations when the model is used by a decision-maker for risk assessment. In this paper, an analysis of uncertainty was performed to estimate the uncertainty of model predictions and to screen out crucial variables using a Monte Carlo stochastic approach and a number of statistical methods, including ANOVA and stepwise multiple regression. The model studied was RICEWQ (Version 1.6.1), which was used to forecast pesticide fate in paddy fields. The results demonstrated that the paddy runoff concentration predicted by RICEWQ was in agreement with field measurements and the model can be applied to simulate pesticide fate at field scale. Model uncertainty was acceptable, runoff predictions conformed to a log-normal distribution with a short right tail, and predictions were reliable at field scale due to the narrow spread of uncertainty distribution. The main contribution of input variables to model uncertainty resulted from spatial (sediment-water partition coefficient and mixing depth to allow direct partitioning to bed) and management (time and rate of application) parameters, and weather conditions. Therefore, these crucial parameters should be carefully parameterized or precisely determined in each site-specific paddy field before the application of the model, since small errors of these parameters may induce large uncertainty of model outputs.     

不确定条件下矿产资源的最优开采

通过一个连续时间的随机动态规划模型,探讨了市场需求、资源存量的不确定性以及勘探活动对矿产资源价格和开采速度的影响,并给出了随机条件下Hotelling法则的表达形式.模型结果显示:不确定性对资源价格变化速度的影响主要取决于开采成本,如果相对于资源存量来说开采成本是不变的或者开采的规模成本是不变的,则不确定性对资源价格的变化速度没有影响.反之,如果开采的规模成本是递增的,则不确定性会加速资源价格的变化.此外,不确定性的存在将加快资源的开采速度.至于勘探活动,它一方面降低了地质条件的不确定性,另一方面增加了资源的存量,所以勘探活动降低了资源价格和开采速度的变化率,减少了不确定性对资源开采的影响.     

COMPUTING THE RISKS ASSOCIATED WITH WASTELOAD) ALLOCATION MODELING1

ABSTRACT: The risks associated with a traditional wasteload allocation (WLA) analysis were quantified with data from a recent study of the Upper Trinity River (Texas). Risk is define here as the probability of failing to meet an established in-stream water quality standard. The QUAL-TX dissolved oxygen (DO) water quality model was modified to a Monte Carlo framework. Flow augmentation coding was also modified to allow an exact match to be computed between the predicted and an established DO concentration standard, thereby providing an avenue for linking input parameter uncertainty to the assignment of a wasteload permit (allowable mass loading rate). Monte Carlo simulation techniques were employed to propagate input parameter uncertainties, typically encountered during WLA analysis, to the computed effluent five-day carbonaceous biochemical oxygen demand requirements for a single major wastewater treatment plant (WWTP). The risk of failing to meet an established in-stream DO criterion may be as high as 96 percent. The uncertainty associated with estimation of the future total Kjeldahl nitrogen concentration for a single tributary was found to have the greatest impact on the determination of allowable WWTP loadings.     

测定水样中化学需氧量的不确定度

对化学需氧量测定的不确定度进行合理评定,应充分考虑测定过程中的不确定度来源。建立了重铬酸钾法测定水样中化学需氧量不确定度的数学模型,对组成化学需氧量的各个因子的不确定度分量进行了详尽的分析和计算,得出了本次样品CODCr的测定结果为72.2mg/L,扩展不确定度为3.8mg/L。     

空气质量自动监测中测量不确定度的应用

根据测量原理建立数学模型,分析各种不确定分量的来源,评定标准不确定度,确定合成不确定度和扩展不确定度。通过不确定影响分量的分析,找出最大不确定分量,重点控制其分量,可保证测量的准确性和精度,也可通过重新评估显著性不确定分量,找出方法存在的不足和问题,提出控制不确定分量的步骤和方法,改善测量方法和手段提高测量准确性和精度。     

Prediction and uncertainty source analysis of the spatial and temporal disturbance from off-road vehicular traffic in a complex ecosystem

Vehicle use during military training activities results in soil disturbance and vegetation loss. The capacity of lands to sustain training is a function of the sensitivity of lands to vehicle use and the pattern of land use. The sensitivity of land to vehicle use has been extensively studied. Less well understood are the spatial patterns of vehicle disturbance. Since disturbance from off-road vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) was used to predict the pattern of vehicle disturbance across a training facility. An uncertainty analysis of the model predictions assessed the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty.For the most part, this analysis showed that mapping and modeling process errors contributed more than 95% of the total uncertainty of predicted disturbance, while satellite imagery error contributed less than 5% of the uncertainty. When the total uncertainty was larger than a threshold, modeling error contributed 60% to 90% of the prediction uncertainty. Otherwise, mapping error contributed about 10% to 50% of the total uncertainty. These uncertainty sources were further partitioned spatially based on other sources of uncertainties associated with vehicle moment, landscape characterization, satellite imagery, etc.