Uncertainty analysis of predicted disturbance from off-road vehicular traffic in complex landscapes at fort hood

The US Army Engineering Research Development Center (ERDC) uses a modified form of the Revised Universal Soil Loss Equation (RUSLE) to estimate spatially explicit rates of soil erosion by water across military training facilities. One modification involves the RUSLE support practice factor (P factor), which is used to account for the effect of disturbance by human activities on erosion rates. Since disturbance from off-road military vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) is used to predict the distribution of P factor values across a training facility. This research analyzes the uncertainty in this model's disturbance predictions for the Fort Hood training facility in order to determine both the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty. This analysis shows that a three-category vegetation map used by the disturbance model was the greatest source of prediction uncertainty, especially for the map categories shrub and tree. In areas mapped as grass, modeling error (uncertainty associated with the model parameter estimates) was the largest uncertainty source. These results indicate that the use of a high-quality vegetation map that is periodically updated to reflect current vegetation distributions, would produce the greatest reductions in disturbance prediction uncertainty.     

Modeling human-induced climatic change: A summary for environmental managers

The rapid increase in atmospheric concentrations of greenhouse gases has caused concern because of their potential to alter the earth's radiation budget and disrupt current climate patterns While there are many uncertainties associated with use of general circulation models (GCMs), GCMs are currently the best available technology to project changes in climate associated with elevated gas concentrations. Results indicate increases in global temperature and changes in global precipitation patterns are likely as a result of doubled CO₂. GCMs are not reliable for use at the regional scale because local scale processes and geography are not taken into account. Comparison of results from five GCMs in three regions of the United States indicate high variability across regions and among models depending on season and climate variable. Statistical methods of scaling model output and nesting finer resolution models in global models are two techniques that may improve projections. Despite the many limitations in GCMs, they are useful tools to explore climate-earth system dynamics when used in conjunction with water resource and ecosystem models. A variety of water resource models showed significant alteration of regional hydrology when run with both GCM-generated and hypothetical climate scenarios, regardless of region or model complexity. Similarly, ecological models demonstrate the sensitivity of ecosystem production, nutrient dynamics, and distribution to changes in climate and CO₂ levels. We recommend the use of GCM-based scenarios in conjunction with water resource and ecosystem models to guide environmental management and policy in a “no-regrets” framework or as part of a precautionary approach to natural resource protection.     

大流量孔口流量计的校准、数据处理及误差分析

用罗兹流量计校准孔口流量计,用温度和大气压力对孔口流量计的负压进行修正,使孔口流量计的流量和压力修正项间的关系为一直线,得到孔口流量计的标准曲线并评价其精度。探讨了孔口流量计流量不确定度的主要来源,为精度控制提供了理论依据。      

中国环境风险评价体系的完善:来自美国的经验和启示

分析总结了美国在比较风险评价研究、环境风险评价指南、环境风险评价过程中的不确定性分析和风险交流等方面的经验,并结合我国环境风险评价现状和存在问题,提出,开展比较风险评价研究,完善环境风险评价指南体系,减小环境风险评价的不确定性和建立有效可行的环境风险交流体系的建议,从而完善我国环境风险评价体系,提高环境风险评价结果的真实性和可靠性。     

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.     

Toward Integrative Management Advice of Water Quality, Oil Spills, and Fishery in the Gulf of Finland: A Bayesian Approach

Understanding and managing ecosystems affected by several anthropogenic stressors require methods that enable analyzing the joint effects of different factors in one framework. Further, as scientific knowledge about natural systems is loaded with uncertainty, it is essential that analyses are based on a probabilistic approach. We describe in this article about building a Bayesian decision model, which includes three stressors present in the Gulf of Finland. The outcome of the integrative model is a set of probability distributions for future nutrient concentrations, herring stock biomass, and achieving the water quality targets set by HELCOM Baltic Sea Action Plan. These distributions can then be used to derive the probability of reaching the management targets for each alternative combination of management actions.     

Interval-parameter Fuzzy-stochastic Semi-infinite Mixed-integer Linear Programming for Waste Management under Uncertainty

An interval-parameter fuzzy-stochastic semi-infinite mixed-integer linear programming (IFSSIP) method is developed for waste management under uncertainties. The IFSSIP method integrates the fuzzy programming, chance-constrained programming, integer programming and interval semi-infinite programming within a general optimization framework. The model is applied to a waste management system with three disposal facilities, three municipalities, and three periods. Compared with the previous methods, IFSSIP have two major advantages. One is that it can help generate solutions for the stable ranges of the decision variables and objective function value under fuzzy satisfaction degree and different levels of probability of violating constraints, which are informative and flexible for solution users to interpret/justify. The other is that IFSSIP can not only handle uncertainties through constructing fuzzy and random parameter, but also reflect dynamic features of the system conditions through interval function of time over the planning horizon. By comparing IFSSIP with interval-parameter mixed-integer linear semi-infinite programming and parametric programming, the IFSSIP method is more reasonable than others.     

石墨炉原子吸收法测定土壤中镉的不确定度评定

评定了石墨炉原子吸收法测定土壤样品中镉的测量不确定度。在测定过程中,对样品称量、水分测定、样品消解、样品定容、标准溶液配制、标准曲线拟合等影响不确定度的分量进行了评定。按照《测量不确定度评定与表示》(JJF 1059.1—2012)的规定进行合成,给出了扩展不确定度,镉含量结果的表示式为(0.115±0.015)mg/kg,k=2。     

固体吸附/热脱附-气相色谱法测定环境空气中苯系物的不确定度评定

建立固体吸附/热脱附-气相色谱法测定环境空气中苯系物的不确定度评定方法,分析测定过程中不确定度的来源,进行各不确定度分量的评估,并给出合成相对标准不确定度和扩展不确定度。     

Estimate of the uncertainty in measurement for the determination of mercury in seafood by TDA AAS

An approach for the estimate of the uncertainty in measurement considering the individual sources related to the different steps of the method under evaluation as well as the uncertainties estimated from the validation data for the determination of mercury in seafood by using thermal decomposition/amalgamation atomic absorption spectrometry (TDA AAS) is proposed. The considered method has been fully optimized and validated in an official laboratory of the Ministry of Agriculture, Livestock and Food Supply of Brazil, in order to comply with national and international food regulations and quality assurance. The referred method has been accredited under the ISO/IEC 17025 norm since 2010. The approach of the present work in order to reach the aim of estimating of the uncertainty in measurement was based on six sources of uncertainty for mercury determination in seafood by TDA AAS, following the validation process, which were: Linear least square regression, Repeatability, Intermediate precision, Correction factor of the analytical curve, Sample mass, and Standard reference solution. Those that most influenced the uncertainty in measurement were sample weight, repeatability, intermediate precision and calibration curve. The obtained result for the estimate of uncertainty in measurement in the present work reached a value of 13.39%, which complies with the European Regulation EC 836/2011. This figure represents a very realistic estimate of the routine conditions, since it fairly encompasses the dispersion obtained from the value attributed to the sample and the value measured by the laboratory analysts. From this outcome, it is possible to infer that the validation data (based on calibration curve, recovery and precision), together with the variation on sample mass, can offer a proper estimate of uncertainty in measurement.