原子荧光法测定污泥中总砷含量的不确定度评定

对采用原子荧光法测定污泥中总砷过程中引入的不确定度进行分析,找出了影响测定结果的因素,并对各不确定度分量及合量进行了评定。测量不确定度主要来源于试样称量、标准溶液配制、工作曲线拟合、污泥含水率、各种玻璃量器的使用及测量重复性。其中样品预处理、样品消解液中总砷的质量浓度及污泥含水率所产生的不确定度较大。这对进一步优化试验方法,提高检测结果的准确性有重要意义。     

适应性环境管理模式在大型赛事筹备中 的应用（下） ——应用实例

将适应性环境管理的模式应用于深圳第26届世界大学生夏季运动会生态环境保障工作,对生态环境保障总目标层层分解落实,由第三方评估机构对任务逐项跟踪管理,定期评估各部门绩效,根据外部条件和内部因素的变化对工作方案和阶段性目标进行动态调整,最大限度地降低了环境管理工作的不确定性。经过六个月的实践应用,大运赛事期间深圳市生态环境保障工作取得了十分显著的成效。     

Integrating soil carbon cycling with that of nitrogen and phosphorus in the watershed model SWAT: Theory and model testing

In this paper we describe and test a sub-model that integrates the cycling of carbon (C), nitrogen (N) and phosphorus (P) in the Soil Water Assessment Tool (SWAT) watershed model. The core of the sub-model is a multi-layer, one-pool soil organic carbon (S_C) algorithm, in which the decomposition rate of S_C and input rate to S_C (through decomposition and humification of residues) depend on the current size of S_C. The organic N and P fluxes are coupled to that of C and depend on the available mineral N and P, and the C:N and N:P ratios of the decomposing pools. Tillage explicitly affects the soil organic matter turnover rate through tool-specific coefficients. Unlike most models, the turnover of soil organic matter does not follow first order kinetics. Each soil layer has a specific maximum capacity to accumulate C or C saturation (S_x) that depends on texture and controls the turnover rate. It is shown in an analytical solution that S_x is a parameter with major influence in the model C dynamics. Testing with a 65-yr data set from the dryland wheat growing region in Oregon shows that the model adequately simulates the S_C dynamics in the topsoil (top 0.3 m) for three different treatments. Three key model parameters, the optimal decomposition and humification rates and a factor controlling the effect of soil moisture and temperature on the decomposition rate, showed low uncertainty as determined by generalized likelihood uncertainty estimation. Nonetheless, the parameter set that provided accurate simulations in the topsoil tended to overestimate S_C in the subsoil, suggesting that a mechanism that expresses at depth might not be represented in the current sub-model structure. The explicit integration of C, N, and P fluxes allows for a more cohesive simulation of nutrient cycling in the SWAT model. The sub-model has to be tested in forestland and rangeland in addition to agricultural land, and in diverse soils with extreme properties such high or low pH, an organic horizon, or volcanic soils.     

风险分析基本方法探讨

面对未来不利事件的不确定性,对其情景的认识只能进行风险分析。人们研究出了许多的风险分析方法,但基本方法只有5种:发生概率计算法、暴露评价法、危险辨识法、期望值计算法和经验合成法。它们的共同点是:(1)明确具体风险内涵,框定风险问题涉及的系统;(2)涉及风险源、影响和后果;(3)进行不确定意义下的量化分析。由此我们总结出风险分析基本原理,并确认了自然灾害风险分析基本原理和自然灾害风险分析的基本模式。     

自然灾害风险分析的基本原理

介绍了风险研究的基本内容,探讨了自然灾害风险分析的基本原理。从风险的本质和自然灾害的具体表现可以看出,自然灾害风险的分析,依赖于一整套贯穿于致灾因子到灾害影响的、能前后相联的模型。本文对各个环节的模型提出了一些原则性的建议。     

Sensitivity Analyses of Spatial Population Viability Analysis Models for Species at Risk and Habitat Conservation Planning

Abstract:  Population viability analysis (PVA) is an effective framework for modeling species- and habitat-recovery efforts, but uncertainty in parameter estimates and model structure can lead to unreliable predictions. Integrating complex and often uncertain information into spatial PVA models requires that comprehensive sensitivity analyses be applied to explore the influence of spatial and nonspatial parameters on model predictions. We reviewed 87 analyses of spatial demographic PVA models of plants and animals to identify common approaches to sensitivity analysis in recent publications. In contrast to best practices recommended in the broader modeling community, sensitivity analyses of spatial PVAs were typically ad hoc, inconsistent, and difficult to compare. Most studies applied local approaches to sensitivity analyses, but few varied multiple parameters simultaneously. A lack of standards for sensitivity analysis and reporting in spatial PVAs has the potential to compromise the ability to learn collectively from PVA results, accurately interpret results in cases where model relationships include nonlinearities and interactions, prioritize monitoring and management actions, and ensure conservation-planning decisions are robust to uncertainties in spatial and nonspatial parameters. Our review underscores the need to develop tools for global sensitivity analysis and apply these to spatial PVA.     

生命周期清单分析的数据质量评价

生命周期清单分析(LCI)数据质量的分析方法可概括为两类:采用诸如地理代表性、数据年代或数据获取方式等一系列指标来表示;根据不确定性来综合反映LCI质量.笔者在分析了这两类方法各自所存在缺陷的基础上,提出了将这两者相结合的评价方法:采用5个独立的反映数据质量的指标,根据系统各单元各数据属性对各指标从1～5进行打分,形成数据的质量指标向量元素.根据数据质量向量元素的算术平均在总指标范围中所占的百分数将质量指标向量转化为对应的综合数据质量指标(DQI),继而根据DQI可得出每个数据的随机分布,以便进行清单结果的不确定性随机模拟.最后将方法应用于钢铁生产生命周期清单数据中.      

多云天气异戊二烯排放通量的计算

根据2002和2003年夏季内蒙古草原的实测资料,发现多云天气下,云通过可见光辐射对异戊二烯排放带来明显影响.研究和提高云量较大条件下异戊二烯排放通量计算的准确性是非常必要和重要的,它将有助于全面了解和准确估算不同天气、所有时段异戊二烯的排放通量.研究表明,对于云量≥6的时段,可以将经验公式中的大气质量(m)取为1,并使用原计算系数来计算异戊二烯的排放通量,其计算值与观测值相对偏差的平均值可降为72%,该方法可以明显降低多云天气下排放通量计算值的不确定性.      

环境监测用氮气中多组分VOCs标准物质的研制

介绍了采用称量法制备瓶装1μmol/mol氮气中42个组分挥发性有机物(VOCs)标准物质的研制方法。建立了选择离子模式,气相色谱-质谱联用的分析方法,对目标组分在气瓶中的长期稳定性进行了考察。所选择的42种目标组分完全满足中国环境保护标准《环境空气挥发性有机物的测定吸附管采样-热脱附/气相色谱-质谱法》(HJ 644—2013)和美国环保署《使用特殊处理的采样罐/气相色谱仪检测环境大气中的挥发性有机物》(EPA TO—14A)这2个方法标准中所规定的环境空气中挥发性有机有害成分的监测要求。将研制的气体标准物质与中国计量科学研究院(NIM)和英国国家物理实验室(NPL)分别进行了比对测试,取得了良好的比对结果与国际等效度。结果表明,1μmol/mol氮气中42种组分VOCs标准物质的有效期为一年,相对扩展不确定度为5.0%(包含因子k=2),并取得国家标准物质证书GBW(E)062231。     

Factors affecting the quantitative uncertainty of the estimated short-term intake. Part I—Calculation methods

ABSTRACT

The calculation of the combined uncertainty of the international estimated short-term intake (IESTI) of ethephon residues in apples is shown as an example. The ethephon residues in apples were reported by the Joint FAO (Food and Agriculture Organization of the United Nations)/WHO (World Health Organization) Meeting on Pesticide Residues (JMPR). The apple consumption data were taken from the IESTI (international short-term intake) calculation template used by the JMPR. The IESTI was calculated with the currently used method (case 2a) and a proposed one recommended by the EFSA (European Food Safety Authority)/RIVM (Dutch National Institute for Public Health) Scientific Workshop co-sponsored by FAO and WHO. In this example, the ratio of IESTI_proposed/IESTI_current and their combined relative uncertainty are about 2.8, and 1.7, respectively. The larger IESTI and uncertainty obtained with the proposed equation are the consequence of calculation only with the large portion (LP) instead of its combination with unit mass, and the MRL instead of the highest residue (HR). The LP is the major contributor to the combined uncertainty. Both the calculated IESTI and its combined uncertainty depend on the actual food – pesticide residue combination, and should be calculated for each case.