Effect of user interpretation on uncertainty estimates: examples from the air-to-milk transfer of radiocesium

An important source of uncertainty in predictions of numerical simulation codes of environmental transport processes arises from the assumptions made by the user when interpreting the model and the scenario to be assessed. This type of uncertainty was examined systematically in this study and was compared with uncertainty due to varying parameter values in a code. Three terrestrial food chain codes that are driven by deposition of radionuclides from the atmosphere were used by up to ten participants to predict total deposition of ¹³⁷Cs and concentrations on pasture and in milk for two release scenarios. Collective uncertainty among the predictions of the ten users for concentrations in milk calculated for one scenario by one code was a factor of 2000, while the largest individual uncertainty was 20 times lower. Choice of parameter values contributed most to user-induced uncertainty, followed by scenario interpretation. Due to the significant disparity in predictions, it is recommended that assessments should not be carried out alone by a single code user.     

Development of quantitative estimates of uncertainty in environmental risk assessments when the scientific data base is inadequate

The reasons for developing quantitative estimates of uncertainty in environmental risk assessments are discussed along with a method for developing them which involves scientific judgement. In the situation considered here the regulatory needs are ahead of the science, which makes the development of the estimates on uncertainty more difficult, but not impossible. Quantitative estimates for all uncertainties involved in the estimation of risk resulting from exposure to volatile organic compounds (VOCs) in drinking water are developed and tabulated. By far the largest contribution to the uncertainty in the risk estimates for VOCs in drinking water are due to uncertainty in the extrapolation of the dose-response curve to low levels. The uncertainty due to extrapolation is on the order of 10⁴ and 10⁶. Other components of the analysis may contribute uncertainties of a few orders of magnitude. In general the largest uncertainties are in the toxicological data base and the manipulation of it needed to estimate risk. The data base and manipulations needed to estimate exposure due to VOCs in drinking water were at the more an order to magnitudes in uncertainty.     

Examination of model uncertainty and parameter interaction in a global carbon cycling model (GLOCO)

Simulation models play an important role in understanding the causes and consequences of climate change. In order to make full use of these models, it is necessary to establish the magnitude and sources of uncertainty associated with their predictions. This information can be used to achieve a better understanding of the simulated systems, to increase the reliability of model predictions, to guide field surveys and laboratory experiments, and to define realistic values that should be used in scientific, economic, and political discussions of future conditions. In this paper, a new tree-structured density estimation technique that extends the ability of Monte Carlo-based analyses to explore parameter interactions and uncertainty in complex environmental models was applied. The application of the technique is demonstrated using the GLOCO global carbon cycle model. The paper demonstrates that there are numerous distinct parameter combinations that can meet fairly stringent calibration criteria, and they are concentrated in relatively small subsets of the parameter space. These different subsets can be viewed as representing different ecological systems that achieve the same calibration or performance goals in fundamentally different ways. It is also shown that the simulated responses of these systems to future environmental change can lead to different conclusions regarding the interaction between factors affecting environmental processes, such as the growth of vegetation. Together, these results show how the tree-structured density estimation technique can be applied to gain a broader understanding of model performance and of ecosystem responses to change.     

Sources of uncertainty in model predictions: lessons learned from the IAEA Forest and Fruit Working Group model intercomparisons

The International Atomic Energy Agency (IAEA), through the BIOMASS program, has provided a unique international forum for assessing the relative contribution of different sources of uncertainty associated with environmental modeling. The methodology and guidance for dealing with parameter uncertainty have been fairly well developed and quantitative tools such as Monte-Carlo modeling are often recommended. The issue of model uncertainty is still rarely addressed in practical applications and the use of several alternative models to derive a range of model outputs (similar to what was done in IAEA model intercomparisons) is one of a few available techniques. This paper addresses the often overlooked issue of what we call 'modeler uncertainty,' i.e., differences in problem formulation, model implementation and parameter selection originating from subjective interpretation of the problem at hand. This study uses results from the Fruit and Forest Working Groups created under the BIOMASS program (BIOsphere Modeling and ASSessment). The greatest uncertainty was found to result from modelers' interpretation of scenarios and approximations made by modelers. In scenarios that were unclear for modelers, the initial differences in model predictions were as high as seven orders of magnitude. Only after several meetings and discussions about specific assumptions did the differences in predictions by various models merge. Our study shows that the parameter uncertainty (as evaluated by a probabilistic Monte-Carlo assessment) may have contributed over one order of magnitude to the overall modeling uncertainty. The final model predictions ranged between one and three orders of magnitude, depending on the specific scenario. This study illustrates the importance of problem formulation and implementation of an analytic-deliberative process in fate and transport modeling and risk characterization.     

REDD+项目中不确定性对森林碳减排量的影响研究

减少砍伐和退化所致排放(REDD+)项目旨在通过采取各种政策方法和积极的激励措施,以帮助发展中国家减少砍伐和森林退化,同时还包括森林保护、森林可持续经营以及增加森林碳信用。在REDD+项目实施过程中,对于碳减排量的准确监测与度量是一个非常重要的步骤,而不确定性问题的普遍存在将可能对碳减排量产生影响。本文通过建立一个包含REDD+项目开发商和代理人两个博弈者的动态博弈模型,通过对动态博弈模型的仿真研究,分析了不确定性对利益相关者收益以及森林碳减排量的影响。研究结果表明,不确定性会使得森林碳减排量增加,以弥补因不确定性所导致的低估量。同时,不确定性的增加也会导致碳排放量的补偿支付价格增长。由于不确定性只会对代理人补贴产生影响,而不会影响开发商补贴,因而向开发商提高补贴更能促进利益相关者的利润增加。并且不确定性越大效果越明显,因此在未来向开发商而非代理人提供补贴才是政策制定者的最佳选择。由于REDD+项目中除了能给开发商和代理人带来收益之外,还以可以增进全社会环境和生态福利水平,而这种外部收益在"森林碳信用"的市场价格中并未得到完全体现,因此需要政策制定者为REDD+项目提供经济激励以弥补这种外部收益。政策制定者选用补贴模式时应优先考虑基于开发商补贴建立公平有效的激励机制。同时,为保证资金被完全用于REDD+的项目之中,并被负责任地按照预期目标使用,因此需要建立能满足国际标准的良好财政治理机制以预防腐败和欺诈,促进REDD+项目的成功实施。     

EPA's SHEDS-multimedia model: Children's cumulative pyrethroid exposure estimates and evaluation against NHANES biomarker data

The U.S. EPA's SHEDS-Multimedia model was applied to enhance the understanding of children's exposures and doses to multiple pyrethroid pesticides, including major contributing chemicals and pathways. This paper presents combined dietary and residential exposure estimates and cumulative doses for seven commonly used pyrethroids, and comparisons of model evaluation results with NHANES biomarker data for 3-PBA and DCCA metabolites. Model input distributions were fit to publicly available pesticide usage survey data, NHANES, and other studies, then SHEDS-Multimedia was applied to estimate total pyrethroid exposures and doses for 3–5 year olds for one year variability simulations. For dose estimations we used a pharmacokinetic model and two approaches for simulating dermal absorption. SHEDS-Multimedia predictions compared well to NHANES biomarker data: ratios of 3-PBA observed data to SHEDS-Multimedia modeled results were 0.88, 0.51, 0.54 and 1.02 for mean, median, 95th, and 99th percentiles, respectively; for DCCA, the ratios were 0.82, 0.53, 0.56, and 0.94. Modeled time-averaged cumulative absorbed dose of the seven pyrethroids was 3.1 nmol/day (versus 8.4 nmol/day for adults) in the general population (residential pyrethroid use and non-use homes) and 6.7 nmol/day (versus 10.5 nmol/day for adults) in the simulated residential pyrethroid use population. For the general population, contributions to modeled cumulative dose by chemical were permethrin (60%), cypermethrin (22%), and cyfluthrin (16%); for residential use homes, contributions were cypermethrin (49%), permethrin (29%), and cyfluthrin (17%). The primary exposure route for 3–5 year olds in the simulated residential use population was non-dietary ingestion exposure; whereas for the simulated general population, dietary exposure was the primary exposure route. Below the 95th percentile, the major exposure pathway was dietary for the general population; non-dietary ingestion was the major pathway starting below the 70th percentile for the residential use population. The new dermal absorption methodology considering surface loading had some impact, but did not change the order of key pathways.     

Multi-group approximation, scattering and calibration coefficients, uncertainty estimates and detection limits of a NaI(Tl)-based gamma spectrometry set-up for low-level activity analysis

This paper describes a quantitative radioactivity analysis method especially suitable for environmental samples with low-level activity. The method, consisting of a multi-group approximation based on total absorption and Compton spectra of gamma rays, is coherently formalized and a computer algorithm thereof designed to analyze low-level activity NaI(Tl) gamma ray spectra of environmental samples. Milk powder from 1988 was used as the example case. Included is a special analysis on the uncertainty estimation. Gamma sensitiveness is defined and numerically evaluated. The results reproduced the calibration data well, attesting to the reliability of the method. The special analysis shows that the uncertainty of the assessed activity is tied to that of the calibration activity data. More than 77% of measured 1461-keV photons of ⁴⁰K were counted in the range of clearly lower energies. Pile-up of single line photons (¹³⁷Cs) looks negligible compared to that of a two-line cascade (¹³⁴Cs). The detection limit varies with radionuclide and spectrum region and is related to the gamma sensitiveness of the detection system. The best detection limit always lies in a spectrum region holding a line of the radionuclide and the highest sensitiveness. The most radioactive milk powder sample showed a activity concentration of 21 ± 1 Bq g⁻¹for ¹³⁷Cs, 323 ± 13 Bq g⁻¹ for ⁴⁰K and no ¹³⁴Cs.     

Soil organic carbon dynamics of croplands in European Russia: estimates from the “model of humus balance”

The Model of Humus Balance was used to estimate the influence of climate effects and changing agricultural practices on carbon (C) levels in soddy–podzolic soils in the Russian Federation for the years 2000–2050. The model was linked with a spatial database containing soil, climate and farming management layers for identification of spatial change of C sequestration potential. Analysis of relationships between C, soil texture and climate indicated that compared with a business-as-usual scenario, adaptation measures could increase the number of polygons storing soil organic carbon (SOC) by 2010–2020. The rate of possible C loss is sensitive to the different climate scenarios, with a maximum potential for SOC accumulation expected in 2030–2040, thereafter decreasing to 2050. The effect is most pronounced for the arid part of the study area under the emission scenario with the highest rate of increase in atmospheric CO₂ concentration, supporting findings from the dynamic SOC model, RothC. C sequestration during the study period was permanent for clay and clay loam soils with a C content of more than 2%, suggesting that C sequestration should be focused on highly fertile, fine-textured soils. We also show that spatial heterogeneity of soil texture can be a source of uncertainty for estimates of SOC dynamics at the regional scale. Figures in color are available at     

A dynamic model for assessing radiological consequences of routine releases in the Loire river: parameterisation and uncertainty/sensitivity analysis

A dynamic model for assessing the transfer of several radionuclides ((58)Co, (60)Co, (110 m)Ag, (134)Cs, (137)Cs, (54)Mn and (131)I) in a food-chain was applied on the Loire river, where 14 nuclear power plants situated on five different sites operate. The model considers the following potential exposure pathways: (i) transfer of radionuclides through the aquatic food chain and the subsequent internal exposure of humans due to ingestion of contaminated water and/or fish; (ii) use of river water for agricultural purposes (irrigation), transfer of radionuclides through the terrestrial food chain and the subsequent internal exposure of humans due to ingestion of contaminated foodstuffs; (iii) internal exposure due to inhalation of dust originating from resuspension of contaminated soil particles; (iv) external exposure from radionuclides present in the river or deposited on the river sediments or the soil. For each of the parameters introduced in this model, a probability density function, allowing further uncertainty and sensitivity analysis, was proposed. Uncertainty/sensitivity analysis were performed to: (i) compare calculations to empirical data; (ii) determine a confidence interval for the mean annual dose to critical groups; and (iii) identify the parameters responsible for the uncertainty and subsequent research priorities.     

The impact of climate change and its uncertainty on carbon storage in Switzerland

Projected future climate change will alter carbon storage in forests, which is of pivotal importance for the national carbon balance of most countries. Yet, national-scale assessments are largely lacking. We evaluated climate impacts on vegetation and soil carbon storage for Swiss forests using a dynamic vegetation model. We considered three novel climate scenarios, each featuring a quantification of the inherent uncertainty of the underlying climate models. We evaluated which regions of Switzerland would benefit or lose in terms of carbon storage under different climates, and which abiotic factors determine these patterns. The simulation results showed that the prospective carbon storage ability of forests depends on the current climate, the severity of the change, and the time required for new species to establish. Regions already prone to drought and heat waves under current climate will likely experience a decrease in carbon stocks under prospective ‘extreme’ climate change, while carbon storage in forests close to the upper treeline will increase markedly. Interestingly, when climate change is severe, species shifts can result in increases in carbon stocks, but when there is only slight climate change, climate conditions may reduce growth of extant species while not allowing for species shifts, thus leading to decreases in carbon stocks.     

Areas of uncertainty in comparing risks of energy systems

In the past, the safety of technology has mainly been ensured by deterministic criteria that were based on engineering principles and on experience. Recently, however, it has become necessary to include risk assessment into safety policy. This need arises due to the order of magnitude of possible health and environmental impact from normal operation or accidents, as well as the complexity of modern systems that no longer allow for intuitive understanding of possible accident sequences. Assessment of risks of technical installations is subject to large uncertainties that are caused by lack of data and inadequate quantification and evaluation methods. This paper reviews the main areas of uncertainties with regard to their importance for decisionmaking. Uncertainties are an inherent part of any risk assessment result and can never be avoided. It is thus important to pay particular attention to them including limitations of methods, sensitivity to assumptions about parameters, and site-specific considerations. All this information must be described in great detail so that it can be integrated into the decisionmaking process. If applied in this context, risk assessment is a useful tool for identifying major contributors to risk and effective actions of risk management.     

Importance of climate uncertainty for projections of forest ecosystem services

Regional Environmental Change - Mountain forests provide a wide range of ecosystem services (ES, e.g., timber production, protection from natural hazards, maintaining biodiversity) and are...     

The influence of uncertainty and location-specific conditions on the environmental prioritisation of human pharmaceuticals in Europe

The selection of priority APIs (Active Pharmaceutical Ingredients) can benefit from a spatially explicit approach, since an API might exceed the threshold of environmental concern in one location, while staying below that same threshold in another. However, such a spatially explicit approach is relatively data intensive and subject to parameter uncertainty due to limited data. This raises the question to what extent a spatially explicit approach for the environmental prioritisation of APIs remains worthwhile when accounting for uncertainty in parameter settings. We show here that the inclusion of spatially explicit information enables a more efficient environmental prioritisation of APIs in Europe, compared with a non-spatial EU-wide approach, also under uncertain conditions. In a case study with nine antibiotics, uncertainty distributions of the PAF (Potentially Affected Fraction) of aquatic species were calculated in 100 1 100 km² environmental grid cells throughout Europe, and used for the selection of priority APIs. Two APIs have median PAF values that exceed a threshold PAF of 1% in at least one environmental grid cell in Europe, i.e., oxytetracycline and erythromycin. At a tenfold lower threshold PAF (i.e., 0.1%), two additional APIs would be selected, i.e., cefuroxime and ciprofloxacin. However, in 94% of the environmental grid cells in Europe, no APIs exceed either of the thresholds. This illustrates the advantage of following a location-specific approach in the prioritisation of APIs. This added value remains when accounting for uncertainty in parameter settings, i.e., if the 95th percentile of the PAF instead of its median value is compared with the threshold. In 96% of the environmental grid cells, the location-specific approach still enables a reduction of the selection of priority APIs of at least 50%, compared with a EU-wide prioritisation.     

Scenario-model-parameter: a new method of cumulative risk uncertainty analysis

The recently developed concepts of aggregate risk and cumulative risk rectify two limitations associated with the classical risk assessment paradigm established in the early 1980s. Aggregate exposure denotes the amount of one pollutant available at the biological exchange boundaries from multiple routes of exposure. Cumulative risk assessment is defined as an assessment of risk from the accumulation of a common toxic effect from all routes of exposure to multiple chemicals sharing a common mechanism of toxicity. Thus, cumulative risk constitutes an improvement over the classical risk paradigm, which treats exposures from multiple routes as independent events associated with each specific route. Risk assessors formulate complex models and identify many realistic scenarios of exposure that enable them to estimate risks from exposures to multiple pollutants and multiple routes. The increase in complexity of the risk assessment process is likely to increase risk uncertainty. Despite evidence that scenario and model uncertainty contribute to the overall uncertainty of cumulative risk estimates, present uncertainty analysis of risk estimates accounts only for parameter uncertainty and excludes model and scenario uncertainties. This paper provides a synopsis of the risk assessment evolution and associated uncertainty analysis methods. This evolution leads to the concept of the scenario-model-parameter (SW) cumulative risk uncertainty analysis method. The SMP uncertainty analysis is a multiple step procedure that assesses uncertainty associated with the use of judiciously selected scenarios and models of exposure and risk. Ultimately, the SMP uncertainty analysis method compares risk uncertainty estimates determined using all three sources of uncertainty with conventional risk uncertainty estimates obtained using only the parameter source. An example of applying the SMP uncertainty analysis to cumulative risk estimates from exposures to two pesticides indicates that inclusion of scenario and model sources.     

Procedures for estimation of modelling uncertainty in air quality assessment

The main objectives of this work focus, firstly, on a review of the current existent methodologies to estimate air quality modelling uncertainty, and, secondly, in the preparation of guidelines for modelling uncertainty estimation, which can be used by local and regional authorities responsible for air quality management. From the application exercise, it was concluded that it is possible to define a subset of statistical parameters able to reproduce the general uncertainties estimation. Concerning the quality indicators defined by EU directives, the results show that the legislated uncertainty estimation measures are ambiguous and inadequate in several aspects, mainly in what concerns the error measures for hourly and daily indicators based on the highest observed concentration. A relative error at the percentile correspondent to the allowed number of exceedances of the limit value was suggested and tested, showing that is a more robust and appropriate parameter for model performance evaluation.     

黄河流域县域经济密度测算及空间分异研究

县域经济是国民经济的基本单元,科学评估县域经济的空间分异、影响因素和形成机理,可以为区域经济分析和区域发展政策制定提供科学决策依据。本文以黄河流域504个县域为研究单元,采用熵权灰色模型、Jenks最佳分类法和Kernel分析等方法,基于人均、地均组成的经济密度综合指数构建经济密度评价指标体系和经济密度综合评价模型,从空间分布特征、空间集聚状态两个维度对2000年、2014年黄河流域县域经济密度的空间分异的特征状态和影响因素进行研究。研究结果表明:(1)流域经济密度的空间分异主要受人均密度系统影响,地均密度系统只是强化或减弱人均系统的分异强度。(2)黄河流域东部与中西部之间的经济密度差异明显。东部经济密度水平高,且形成了以郑州、济南为中心的高级别集聚状态,中东部以西安为中心形成了次一级集聚状态,而中西部经济密度整体水平低且仅有少量的弱集聚核心和集聚影响区。(3)流域中西部内部经济空间分异也发生变化。内蒙古、河南的经济密度水平较高,而其他省区的水平相对较弱;流域中西部高水平、中高水平经济密度区域及实力较强的集聚核心和扩展轴带分布在内蒙古中部、河南中部、陕西南部、山西中部,其他省区的集聚核心和扩展轴带较弱。     

The financial additionality and viability of CDM projects allowing for uncertainty

Financial additionality and viability for Clean Development Mechanism (CDM) projects is commonly demonstrated through deterministic internal rate of return (IRR) benchmark analysis, supplemented with a sensitivity analysis. This method is vulnerable to assumptions on cash flows (including that from carbon credits) and is unable to differentiate grades of separation from the benchmark. This paper examines the financial additionality test and viability in the presence of cash flow uncertainty, where IRR becomes a random variable. A case example project involving wind power is exampled. It is seen that the boundaries between acceptance and rejection as a CDM project based on financial additionality and viability tests become blurred, leading to possible alternative conclusions. Comment is given on false positives and false negatives, in terms of acceptance and rejection of projects.     

A probabilistic approach to obtaining limiting estimates of radionuclide concentration in biota

The US Department of Energy has developed a graded approach for evaluating radiation doses to biota. Limiting concentrations of radionuclides in water, soil, and sediment were derived for twenty-three radionuclides. Four organism types (aquatic animals, riparian animals, terrestrial animals, and terrestrial plants) were selected as the basis for method development. While environmental transfer data needed for deriving biota tissue concentrations are available for aquatic animals and terrestrial plants, less information is available for terrestrial and riparian organisms. Two methods were applied and examined for their ability to provide estimates of organism:soil or organism:water concentration factors in lieu of measured data. The kinetic/allometric approach combined with a parameter uncertainty analysis provides a needed method to estimate concentration factors across multiple species with limited input data.     

Uncertainty of block estimates introduced by mis-allocation of point samples: on the example of spatial indoor Radon data

The European indoor Radon map which is currently under production is based on gridded data supplied by the contributing countries. Each grid node represents the arithmetic mean (among other statistics) of the individual measurements within 10 × 10 km², called cells, pixels or blocks, which are aligned to a common metric coordinate system. During work the question emerged, if uncertainty in the geo-referencing of individual data might affect the aggregated “block” statistics to an extent that the statistics have an unpredictably high additional uncertainty, which makes them unusable.