不确定数据条件下的生命周期评价及其应用

针对产品生命周期评价中普遍存在着的不确定性数据问题，提出基于概率统计的方法，进行生命周期评价以及重要环境影响清单参数的识别与灵敏度分析。结合水源中央空调系统，建立了统计平均意义下的污染排放清单，运用MonteCarlo模拟获得了以均值219．746和方差9．4243所表征的空调机系统生命周期环境影响的概率分布。进一步通过K—S检验与灵敏度分析，识别出10个具有重要环境影响贡献的清单参数及其中4个对环境影响分布较敏感的参数。以概率分布代替固定数值可以反映产品环境绩效的统计信息，能够有效地用于不确定数据条件下的产品生命周期评价。     

Inclusion of the variability of diffuse pollutions in LCA for agriculture: the case of slurry application techniques

LCAs (life cycle assessments) are often based on average data to produce a generic evaluation of a good or service. However, ignoring variability and induced uncertainty of LCA results reduces their significance, especially when dealing with agricultural processes that present high natural fluctuations. The objective of the study was to explore the robustness of LCA results when accounting for variable emissions data, illustrated by the case of slurry application techniques. Four application techniques were compared: band spreading, broadcast spreading, harrowing after surface application and direct injection.On the basis of the normalisation results, acidification, eutrophication and global warming potentials were selected. To estimate field nitrogen emissions, an original approach was developed based on relative nitrogen loss factors for each technique from a literature review. The calculated field emissions from different soil and climate conditions were considered equally probable and were propagated into a range of LCA result using the Monte Carlo method. Injection and harrowing both showed reduced acidification and eutrophication potentials compared to band spreading and broadcast spreading but had larger global warming potentials, which could be particularly important with injection. Harrowing consequently appeared as the best compromise. Despite the large range of LCA results, robust conclusions could be drawn. To achieve a more refined comparison between the techniques, the use of process-based models in contrasted situations is suggested.     

Sensitivity of key factors and uncertainties in health risk assessment of benzene pollutant

Predicting long-term potential human health risks from contaminants in the multimedia environment requires the use of models. However,there is uncertainty associated with these predictions of many parameters which can be represented by ranges or probability distributions rather than single value.Based on a case study with information from an actual site contaminated with benzene,this study describes the application of MMSOILS model to predict health risk and distributions of those predictions generated using Monte Carlo techniques.A sensitivity analysis was performed to evaluate which of the random variables are most important in producing the predicted distributions of health risks.The sensitivity analysis shows that the predicted distributions can be accurately reproduced using a small subset of the random variables.The practical implication of this analysis is the ability to distinguish between important versus unimportant random variables in terms of their sensitivity to selected endpoints.This directly translates into a reduction in data collection and modeling effort.It was demonstrated that how correlation coefficient could be used to evaluate contributions to overall uncertainty from each parameter.The integrated uncertainty analysis shows that although drinking groundwater risk is similar with inhalation air risk,uncertainties of total risk come dominantly from drinking groundwater route.Most percent of the variance of total risk comes from four random variables.     

Uncertainty analysis in life cycle inventory. Application to the production of electricity with French coal power plants

Uncertainty assessment in LCAs is an important aspect for decision-makers to judge the significance of differences in product or process options.Stochastic models (e.g., Monte Carlo) are tools used in Life Cycle Inventory (LCI) to compute the uncertainty of cumulative emissions and resource requirements. However, one main problem when applying such models is the large number of unit processes that make up a product system. In this paper a combination of qualitative and quantitative approaches in uncertainty assessment is proposed for an efficient assessment of uncertainty. The qualitative assessment of data quality relies on data quality indicators, whereas the quantitative assessment uses Monte Carlo simulation.The effort to select accurate probability functions concentrates on data with a significant contribution to the cumulative results¹ and/or with a high uncertainty. The probability function of selected data is estimated using different techniques, depending on the amount of information available.Some results are presented by applying the method on selected French coal-based electricity.     

基于不确定度和敏感度分析的LCA数据质量评估与控制方法

通过提出定量评估并控制LCA数据质量的系统化方法(称为CLCD-Q方法),从LCA案例的原始数据和清单数据算法开始评估不确定度;然后通过两次蒙特卡罗模拟,先后得出单元过程清单数据及LCA结果的不确定度;最后结合敏感度分析,辨识出LCA模型中具有高不确定度和高敏感度的关键数据,从而指出控制和改进数据质量的关键点.结果发现,上述方法可在eBalance软件和CLCD数据库中实现.同时,对中国电网电力生命周期的示例研究表明,上述方法将传统的LCA数据质量评估延伸到了原始数据层面,从而为数据收集过程中的原始数据与算法选择提供了直接的支持,同时也可以针对数据质量不达标的LCA结果,指出最有效的改进方向.     

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.     

The use of Monte Carlo methods in factor analysis

Monte Carlo techniques are introduced in target transformation factor analysis (TTFA), in combination with the concept of the principal factor model, in order to account for local variances in the data set and to estimate the uncertainties in the obtained source profiles. The new method is validated using several types of artificial data sets. It was found that application of the Monte Carlo method leads to a significant improvement of the accuracy of the derived source profiles in comparison with standard TTFA. From the introduction of (known) error sources to the artificial data sets it was found that the source-profile reproduction quality is optimal if the magnitudes of the Monte Carlo variations are chosen equal to the magnitudes of the introduced errors.     

Application of Monte Carlo-assisted factor analysis to large sets of environmental pollution data

Target Transformation Factor Analysis (TTFA) has been applied to large data sets of trace element concentrations in lichens with the aim to resolve sources of environmental pollution. The concept of the principal factor model is used in order to account for local variances in the data set. Monte Carlo techniques are used to estimate the uncertainties in the obtained source profiles. It was found that the Monte Carlo method offers a new approach to the problem of the determination of the number of factors to be used in the analysis.     

健康风险评价中的不确定性分析

介绍了健康风险评价过程中存在的不确定性问题及其评价模型 ,讨论了致癌物质和非致癌物质的评价方法中的不确定性。最后列举了不确定性分析的方法 ,即蒙特卡罗法、泰勒简化法、概率树法和专家判断法。     

中国人为源VOC排放清单不确定性研究

针对采用"排放因子法"构建的中国人为源VOC排放清单,开展了不确定性研究工作.基于排放清单输入信息的准确性和可靠性,构建了活动水平和排放因子的不确定度评估系统,获得了清单输入信息概率密度分布函数.利用Monte Carlo模型将输入信息的不确定度传递到清单的输出值,计算了2005年我国人为源VOC排放概率密度分布函数....     

Uncertainties related to the identification of the marginal energy technology in consequential life cycle assessments

When performing life cycle assessment (LCA) assumptions regarding the energy use are often decisive for the outcome. In this paper, current approaches of identifying marginal electricity and heat technologies for consequential LCAs are challenged. The identification of marginal energy technologies is examined from three angles: The marginal electricity technology is identified in Danish historical and potential future energy systems. The methods of identifying and using marginal electricity and heat technologies in key LCA studies are analysed. Finally, the differences in applying energy system analysis and assuming one marginal technology are illustrated, using waste incineration with energy substitution as a case. The main problem with the current approach is the use of one single marginal technology. It is recommended to use fundamentally different affected technologies and identify these in several possible and fundamentally different future scenarios. If possible, the affected technologies should also be identified based on energy system analyses considering the technical characteristics of the technologies involved. Some results in this paper may be applicable to other affected technologies than energy.     

基于云证据理论的地铁盾构施工临近建筑物变形安全控制预警决策方法研究

为实现对地铁盾构施工临近建筑物变形安全控制的预警,结合工程实践和标准法规构建了包括监测项目和巡视项目共7个指标的评价体系,并将其划分为一般风险、显著风险、高风险和严重风险4个等级;基于监测信息和专家评价值确定指标特征值,采用云模型构建指标基本可信度分配,基于D-S证据理论对指标进行多层融合,利用蒙特卡洛模拟对建筑物变形影响因素进行敏感性分析;以武汉轨道交通7号线土建工程第3标段金银湖村5栋建筑物为例,对地铁盾构施工临近建筑物变形的安全警情状态进行评价。评价结果表明:4栋建筑物处于显著风险等级,1栋建筑物为一般风险等级,并结合蒙特卡洛模拟对风险最大的建筑物的监测点的影响因素进行敏感性分析,可为建筑物变形安全控制提供决策建议。     

蒙特卡洛分析在氯气泄漏事故环境风险评价中的应用研究

气体泄漏事故环境风险系统中存在的不确定性问题影响了定量环境风险评价结果的科学性和实用性.因此,本研究采用蒙特卡洛分析方法对氯气泄漏事故风险系统中的6个风险变量进行分析,并进一步对气体泄漏过程进行仿真模拟,得到模拟结果及其概率分布.同时,将蒙特卡洛分析结果、毒性剂量反应指标和风险受体信息在地理信息系统中进行综合分析,以个...     

基于不确定性分析的太湖水体多环芳烃的生态风险评价

利用概率法[蒙特卡罗抽样(monte carlo sampling,MCS)和拉丁超立方抽样(latin hypercube sampling,LHS)]、区间分析法、模糊数法和方差传递等不确定性处理方法,分析了太湖水体ΣPAH8生态风险的不确定性,量化了不确定性因素影响的太湖水体ΣPAH8的生态风险.结果表明,基于概率理论的MCS和LHS模拟结果为太湖水体ΣPAH8危害商值的概率分布,危害商值的平均值分别为0.37和0.35,90%的置信区间分别为(0.000 18,0.89)和(0.000 17,0.92),超过临界值1的概率分别为9.71%和9.68%,敏感性分析表明毒性数据对ΣPAH8商值概率分布的影响较大;区间分析结果表明太湖水体ΣPAH8危害商值范围为0.000 17～0.99;模糊数计算得到可信度为0.9对应的太湖水体ΣPAH8危害商值的区间值为(0.001 5,0.016 3);方差传递结果表明太湖水体ΣPAH8的危害商值在90%置信水平下的置信区间为(0.000 16,0.88),各种不确定性分析均表明PAHs的生态风险较低.通过各种方法比较,基于概率理论的不确定性处理技术更适合太湖水体PAHs的生态风险评价,可为水体有机污染物的风险管理和控制提供科学依据.     

基于CMAQ模型的随机响应曲面不确定性传递分析方法实现与评价

针对蒙特卡洛法在复杂环境模型进行不确定性传递分析时对计算机和时间资源需求巨大的缺点,本文引进快速高效的随机响应曲面法,并将其成功应用于CMAQ区域空气质量模型的不确定性传递分析,建立了基于CMAQ区域空气质量模型的不确定性分析概念框架.采用2阶和3阶随机响应曲面法,研究了排放清单不确定性对臭氧模拟结果的影响,并与1000次蒙特卡洛模拟结果进行对比.结果表明:3种模拟情景下臭氧浓度的均值几乎相同,模拟结果的概率分布曲线也基本一致,而采用随机响应曲面法可以极大节省模拟所需时间,提高计算效率,显示随机响应曲面法具有在复杂大气环境模型中进行不确定性传递分析的潜在价值.     

Stochastic trajectory models for turbulent diffusion: Monte Carlo process versus Markov chains

Turbulent diffusion of passive scalars and particles is often simulated with either a Monte Carlo process or a Markov chain. Knowledge of the velocity correlation generated by either of these stochastic trajectory models is essential to their application. The velocity correlation for Monte Carlo process and Markov chain was studied analytically and numerically. A general relationship was developed between the Lagrangian velocity correlation and the probability density function for the time steps in a Monte Carlo process. The velocity correlation was found to be independent of the fluid velocity probability density function, but to be related to the time-step probability density function. For a Monte Carlo process with a constant time step, the velocity correlation is a triangle function; and the integral time scale is equal to one-half of the time-step length. When the time step was chosen randomly with an exponential pdf distribution, the resulting velocity correlation was an exponential function. Other time-step probability density functions, such as a uniform distribution and a half-Gaussian distribution, were also tested.A Markov chain, which presumes one-step memory, has a piecewise linear velocity correlation function with a finite time step. For a Markov chain with a short time step, only an exponential velocity correlation function can be realized. Thus, a Monte Carlo process with random time steps is more versatile than a Markov chain. Direct numerical calculation of the velocity correlation verified the analytical results.A new model which combines the ideas of the Monte Carlo process and the Markov chain was developed. By examining the long-time mean square dispersion, we found an exact solution for the Lagrangian integral time scale of the new model in terms of the intercorrelation parameter and the mean and the variance of the time steps. Using this new model, we can generate any velocity correlation, including one with a negative tail. Two approximate solutions that give upper and lower bounds for the Lagrangian velocity correlation are proposed.     

The role and implementation of LCA within life cycle management at Alcan

Life cycle management (LCM) aims at expanding the scope of the environmental management system of a company to address the up- and downstream impacts associated with the activities of its suppliers and customers. It is based on a perspective that focuses on products and the corresponding processes in addition to facilities and production sites. Therefore, the life cycle assessment (LCA) methodology plays a central role in implementing LCM. At Alcan, one of the world's leading producers of aluminum materials and products, as well as composite components and packaging solutions, LCA as a core element of LCM is being used for a variety of applications. In order to achieve the objectives of LCM and to ensure efficient decision support, the LCAs are performed in a simplified mode. Simplifications predominantly concern up- and downstream processes outside of Alcan's direct control as well as impact assessment procedures, the reuse of internal life cycle inventory analysis modules, and the aggregation and presentation of the results for top-management and other internal decision-makers. A recently completed LCA from the automotive sector demonstrates the ongoing implementation of LCA at Alcan.     

城市总体规划环境影响评价方法

开发不同类型规划的环境影响评价技术和方法是当前国内的热点研究领域之一.围绕城市总体规划特点,在分析识别规划经济增长和用地布局方案中存在的多种不确定性的基础上,以产业和用地为基本评估单元,综合运用蒙特卡罗随机采样技术和HSY算法,并将其与地理信息系统进行整合,建立了基于结构与空间不确定性分析的城市规划环评方法和系统评估模型.     

北京市燃气工业锅炉CO2排放因子及不确定性分析

降低碳排放评估的不确定性对于减排政策选择和成本效益分析十分重要,基于北京市227个天然气锅炉样本检测数据,分析影响CO₂排放因子的关键参数特征,评估北京市天然气锅炉本地化CO₂排放因子,采用蒙特卡洛模型对排放因子的不确定性进行分析,并将其与IPCC、国家清单、城市清单等同类排放因子进行了比较.结果表明:北京市天然气锅炉CO₂排放因子为2.052 kg/m3,90%概率分布范围为1.982~2.086 kg/m3;基于热值的CO₂排放因子推荐值为55.829 kg/GJ,90%概率分布范围为55.788~55.908 kg/GJ,排放因子的不确定范围为-3.59%~1.57%.北京市天然气锅炉CO₂排放因子稍低于IPCC2006国家温室气体清单指南推荐缺省值（0.5%）.天然气低位热值对排放因子的影响最大,其方差贡献率达94%,而单位热值含碳量和氧化率二者的方差贡献率仅占6%.通过北京市本地实测数据和蒙特卡洛模型模拟,给出了天然气锅炉排放因子及概率分布的范围,提高了评估精度,有助于改进北京市温室气体排放清单活动水平的数据收集工作,指导并降低天然气工业锅炉CO₂排放因子不确定性.      

Life cycle inventory for thermal treatment of waste solvent from chemical industry: a multi-input allocation model

Waste solvent incineration is an important issue in life cycle assessments (LCAs) dealing with chemical products and/or processes. Nearly all chemical products and processes involve organic solvents, and incineration is often the favoured solution to waste solvent treatment as it can deal with a large variety of solvent types and quantities. At present, there are no generic models for waste solvent incineration which allow integrating this technology as a unit process in LCA. As waste solvents as a rule are incinerated as a mixture of several solvents, an allocation problem occurs: measurements of the consumption of ancillaries and energy carriers, and of emission of pollutants and generation of co-products, always refer to the mixture of waste solvents. However, in LCAs usually waste specific data is needed. To solve this problem we developed a multi-input allocation model of the incineration process. A comprehensive case study on a waste solvent incineration plant from chemical industry provided the necessary data. The results from the multi-input allocation modelling are consumption and emission factors which facilitate the calculation of solvent specific life cycle inventory results.