干湿球法测量相对湿度的测量不确定度讨论

对干湿球法测量相对湿度时测量不确定度的数学模型、测量不确定度的各分量进行了讨论与分析,并结合实测数据进行了计算。     

水中氨氮测量不确定度的评定

通过对测定水中氨氮测量不确定度的全面分析.找出影响不确定度的因素,对不确定度进行评估,给出了该分析项目的测量不确定度,如实反映了测量的置信度和准确度.     

液相色谱法测定空气中苯并[a]芘的不确定度评估

根据《化学分析中不确定度的评估指南》提供的不确定度分析思路,对液相色谱法测定空气中苯并[a]芘的不确定度进行了评估,分析了影响测量不确定度的各个因素,对测量结果的不确定度进行评定和表述。     

火焰原子吸收法测定水中锰的不确定度分析

测量不确定度是表征合理地赋予被测量值的分散性,与测量结果相联系的参数。通过火焰原子吸收法对水中锰测定不确定度进行分析,确定锰测量不确定度最主要来源是工作曲线。通过合理选择不确定度分量,建立有效的质量控制程序,优化评定过程,达到对不确定度合理分析的目的。     

火焰原子吸收法测定水中锰的不确定度分析

测量不确定度是表征合理地赋予被测量值的分散性,与测量结果相联系的参数.通过火焰原子吸收法对水中锰测定不确定度进行分析,确定锰测量不确定度最主要来源是工作曲线.通过合理选择不确定度分量,建立有效的质量控制程序,优化评定过程,达到时不确定度合理分析的目的.     

分光光度计测定水中总磷的不确定度评定

对GB/T11893-1989<水质总磷的测定分光光度法>中分光光度法测定水中总磷浓度的测量不确定度进行评定.根据JJF1059-1999<测量不确定度评定与表示>技术规范的要求,分析了该测量过程的测量不确定度来源,建立了数学模型,对测量不确定度的各个分量进行计算,最后合成求出总磷浓度的标准不确定度和扩展不确定度.     

环境地表γ辐射剂量率的测量不确定度评定

依据JJF1059.1-2012《测量不确定度评定与表示》方法和原理,对环境地表γ辐射剂量率测量不确定度进行了评定:建立数学模型,分析测量不确定度的来源,得出相对扩展不确定度。     

水中高锰酸盐指数测定不确定度的评定

合理评定测量结果的不确定度是分析实验室必须重视的问题。通过酸性高锰酸钾氧化法测定水中高锰酸盐指数的实例,确立高锰酸盐指数测量的不确定度数学模型。讨论了高锰酸盐指数测定值不确定度的各种因素,对各不确定度分量进行分析和量化,求得其扩展不确定度。结果表明,影响其测量不确定度的主要因素是测量熏复性。在高锰酸盐指数值为4.17 mg/L的水样测定中,扩展不确定度为0.08 mg/L。     

自配标准溶液不确定度的评定

测量不确定度（uncertainty of measurement）是表征合理地赋予被测量之值的分散性,与测量结果相联系的参数。不确定度一词指可疑程度,广义而言,测量不确定度是对测量结果正确性的可疑程度。标准溶液作为一种标准物质,有许多优点,比如使用方便、溶液均匀、量值准确等。在环境监测分析中经常要使用标准溶液,在配制过程产生不确定度,而标准溶液的不确定度直接影响检测结果的不确定度,因此,对自配标准溶液的测量不确定度进行合理评定,显得尤为重要。配制标准溶液时的合成不确定度与称量的溶质质量、物质的纯度、配制体积、环境温度的不确定度等有关,充分分析不确定度的来源上,得出其扩展不确定度。并以亚硝酸盐标准溶液为例,全面分析了影响标准溶液不确定度的因素,给出了不确定度评定的具体过程和方法。     

水样中氯化物测量不确定度的评定

目的对硝酸银滴定法测定水中氯化物含量的不确定度的来源及其对测量不确定度的影响进行分析。方法根据《测量不确定度评定与表示》JJF1059-1999对《生活饮用水标准检验方法》(GB/T 5750-2006)中氯化物测定的硝酸银滴定法的测量不确定度进行分析评定。结果按数学模型计算水样中氯化物浓度为25.2mg/L,水样中氯化物测定结果的扩展不确定度为0.6mg/L,结果表达为(25.2±0.6)mg/L。结论水样中氯化物含量测定的测量不确定度影响中,以分析滴定中消耗硝酸银标准溶液的体积引入的不确定度最大,其次为配制NaC l标准使用溶液引入的不确定度。     

Uncertainty analysis of emission estimates in the RAINS integrated assessment model

Uncertainty is a critical issue for all models that attempt to quantify the necessary emission reductions that are required to meet environmental quality targets. This paper discusses a methodology specifically developed to analyse the uncertainties in the emission estimates with the regional air pollution information and simulation (RAINS) integrated assessment model, considering the uncertainties in the model parameters themselves. Overall, it was found that a typical range of uncertainties for modeled national emissions of sulfur dioxide, nitrogen oxides and ammonia in Europe lies between 10 and 30%. In general, the uncertainties are strongly dependent on the potential for error compensation. This compensation potential is larger (and uncertainties are smaller) if calculated emissions are composed of a larger number of equal-sized source categories, where the errors in input parameters are not correlated with each other. Thus, estimates of the national total emissions are generally more certain than estimates of sectoral emissions. A sensitivity analysis with respect to the uncertainty in input parameters showed that the actual uncertainties are critically influenced by the specific situation (pollutant, year, country). However, the emission factor is an important contributor to the uncertainty in estimates of historical emissions, while uncertainty in the activity data dominates the future estimates.     

A taxonomy for regulatory uncertainty—application to the European Emission Trading Scheme

Research on environmental uncertainty typically distinguishes between different sources of uncertainty according to the different environments that a company interacts with. The regulatory environment and associated uncertainties become especially important when investigating the subject of pollution reduction. Many researchers have started to investigate the effects of environmental regulation on innovation, however, only little is known about the role of regulatory uncertainty in this context. We provide a taxonomy of regulatory uncertainties by building on the Miles and Snow scale of environmental uncertainty. We apply this taxonomy to uncertainties in current climate policy and test it based on a survey among companies that are subject to the European Emission Trading Scheme (EU ETS), a flexible mechanism of the Kyoto-Protocol. Our results show that the EU ETS creates uncertainties in all categories of our taxonomy and suggest further research to better understand how different regulatory uncertainties affect companies’ business decisions.     

Identification of key issues for further investigation in improving the reliability of life-cycle assessments

The interative nature of life-cycle assessment (LCA) means that more details are looked for until a certain level of reliability has been achieved. This paper is concerned with the identification of key issues for further investigation in such an iterative procedure. Key issues in this context are defined as those aspects of an LCA which need more detailed research to arrive at a solid conclusion. The main concept in the context of finding key issues is the study of the propagation of uncertainties in underlying data. The structured procedure of LCA can be described in mathematical terms, so that standard mathematical techniques for the study of the propagation of uncertainties can be employed. The influence of uncertainties in input data on uncertainties in output data can be calculated, and the main source of the resulting uncertainties can be identified. The result of the analysis is a list of prioritized key issues for more detailed research and more accurate data.     

The role of uncertainty in climate change adaptation strategies—A Danish water management example

We propose a generic framework to characterize climate change adaptation uncertainty according to three dimensions: level, source and nature. Our framework is different, and in this respect more comprehensive, than the present UN Intergovernmental Panel on Climate Change (IPCC) approach and could be used to address concerns that the IPCC approach is oversimplified. We have studied the role of uncertainty in climate change adaptation planning using examples from four Danish water related sectors. The dominating sources of uncertainty differ greatly among issues; most uncertainties on impacts are epistemic (reducible) by nature but uncertainties on adaptation measures are complex, with ambiguity often being added to impact uncertainties. Strategies to deal with uncertainty in climate change adaptation should reflect the nature of the uncertainty sources and how they interact with risk level and decision making: (i) epistemic uncertainties can be reduced by gaining more knowledge; (ii) uncertainties related to ambiguity can be reduced by dialogue and knowledge sharing between the different stakeholders; and (iii) aleatory uncertainty is, by its nature, non-reducible. The uncertainty cascade includes many sources and their propagation through technical and socio-economic models may add substantially to prediction uncertainties, but they may also cancel each other. Thus, even large uncertainties may have small consequences for decision making, because multiple sources of information provide sufficient knowledge to justify action in climate change adaptation.     

Uncertainties in stormwater runoff data collection from a small urban catchment, Southeast China

Monitoring data are often used to identify stormwater runoff characteristics and in stormwater runoff modelling without consideration of their inherent uncertainties. Integrated with discrete sample analysis and error propagation analysis, this study attempted to quantify the uncertainties of discrete chemical oxygen demand (COD), total suspended solids (TSS) concentration, stormwater flowrate, stormwater event volumes, COD event mean concentration (EMC), and COD event loads in terms of flow measurement, sample collection, storage and laboratory analysis. The results showed that the uncertainties due to sample collection, storage and laboratory analysis of COD from stormwater runoff are 13.99%, 19.48% and 12.28%. Meanwhile, flow measurement uncertainty was 12.82%, and the sample collection uncertainty of TSS from stormwater runoff was 31.63%. Based on the law of propagation of uncertainties, the uncertainties regarding event flow volume, COD EMC and COD event loads were quantified as 7.03%, 10.26% and 18.47%.     

The elephant in the room – A comparative study of uncertainties in carbon offsets

The clean development mechanism (CDM) is a flexible mechanism under the Kyoto Protocol, which makes it possible for developed countries to offset their emissions of greenhouse gases through investing in climate change mitigation projects in developing countries. When the mitigation benefit of a CDM project is quantified, measurable uncertainties arise that can be minimised using established statistical methods. In addition, some unmeasurable uncertainties arise, such as the rebound effect of demand-side energy efficiency projects. Many project types related to land use, land-use change and forestry (LULUCF) have been excluded from the CDM in part because of the high degree of statistical uncertainty in measurements of the carbon sink and risk of non-permanence. However, recent discussions within the United Nations Framework Convention on Climate Change (UNFCCC) have opened up for the possibility of including more LULUCF activities in the future. In the light of this discussion, we highlight different aspects of uncertainties in LULUCF projects (e.g. the risk of non-permanence and the size of the carbon sink) in relation to other CDM project categories such as renewables and demand-side energy efficiency. We quantify the uncertainties, compare the magnitudes of the uncertainties in different project categories and conclude that uncertainties could be just as significant in CDM project categories such as renewables as in LULUCF projects. The CDM is a useful way of including and engaging developing countries in climate change mitigation and could be a good source of financial support for LULUCF mitigation activities. Given their enormous mitigation potential, we argue that additional LULUCF activities should be included in the CDM and other future climate policy instruments. Furthermore, we note that Nationally Appropriate Mitigation Actions (NAMAs) are currently being submitted to the UNFCCC by developing countries. Unfortunately, the under-representation of LULUCF in comparison to its potential is evident in the NAMAs submitted so far, just as it has been in the CDM. Capacity building under the CDM may influence NAMAs and there is a risk of transferring the view on uncertainties to NAMAs.     

温室气体排放总量计算的不确定性及对清洁发展机制的影响

系统分析了温室气体排放总量计算中存在的不确定性，阐述了这些不确定性对未来清洁发展机制合作的影响指出CO2应该是未来清洁发展机制合作减排的主要温室气体，这也符合中国的能源利用特点。     

总有机碳不确定度评价

根据燃烧氧化非分散红外吸收法(NDIR),用TOC测定水中的中有机碳(TOC),并且分析了主要的测量不确定度来源,即工作曲线不确定度、标注溶液不确定度、测量重复性不确定度和仪器分辨率不确定度,分别量化后加以合成绩的总有机碳的测量不确定度.     

基于Monte Carlo方法的污染场地风险评价及不确定性研究

风险评价结果的不确定性直接影响风险管理者的管理和决策,为定量研究污染场地评价过程的不确定性,在系统分析污染场地危害产生过程的基础上,构建了污染场地暴露过程评价的概念模型;提出用概率分布函数表征场地污染参数的不确定性,采用基于过程的污染物运移数值模拟模型以减小模型不确定性的影响,用Monte Carlo方法评估参数不确定性对暴露浓度不确定性的贡献,进而形成暴露点污染物浓度的概率分布函数.在此基础上,基于剂量-效应模型,分别采用暴露点浓度的5%、50%和95%置信区间上限值表示乐观情况下,正常情况下以及最不利情况下的暴露浓度,计算敏感人群的健康风险.研究选择国内西南地区某铬渣污染场地进行案例分析,结果表明,在最乐观情况下六价铬和总铬的非致癌危害商分别是8.98和1.02,正常情况下分别是30.57和2.72;最不利情况下分别是77.95和7.11.研究结果表明该方法能较好的表征各参数不确定性影响下的最终风险,为污染场地的修复和后续管理提供决策支持.