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。     

Evaluating the impact of uncertainty and variability on the value chain optimization of a forest biomass power plant using Monte Carlo Simulation

Mathematical programming models have been used to optimize the design and management of forest bioenergy supply chains. A deterministic mathematical model is beneficial for making optimum decisions; however, its applicability to real-world problems may be limited because it does not capture all the complexities, including uncertainties in the parameters, in the supply chain. In this paper, a combination of Monte Carlo Simulation and optimization model is used to evaluate the impact of uncertainty in biomass quality, availability and cost, and electricity prices on the supply chain of a forest biomass power plant. The optimization model is a deterministic mixed integer non-linear model with monthly time steps over a 1-year planning horizon. Variability in biomass quality, i.e. moisture content (MC) and higher heating value (HHV), based on the historical data of a real case study is studied in detail and fitted probability distributions are used in the model, while for electricity prices different scenarios are considered. The results show that the impact of variability in the MC on profit is higher than that of uncertainty in HHV. It is observed that the annual profit ranges between $13.3 million and $17.9 million in the presence of all possible uncertainties while its average is $15.5 million. Uncertainty in biomass availability and cost and electricity price results in the risks of having annual profit of less than $14 million and low monthly storage levels.     

中国国际贸易隐含碳文献比较研究

贸易中的隐含碳流动对中国的二氧化碳排放具有重要影响。从现有的文献来看,尽管出现了大量的针对中国贸易中隐含碳的相关研究,但由于数据及方法的不同,结果之间存在较大差异,很大程度上影响了研究结果的科学性和应用价值。本文对现有的中国国际贸易隐含碳文献进行了比较研究,结果发现:不同研究得出了相对一致的结论,但在隐含碳的具体数量上差异巨大。定性来看,中国是一个隐含碳净出口国,净出口量呈现出逐年增加的趋势。定量来看,1997-2007年,中国隐含碳出口量从3.1-8.8亿t增长到17.2-30.2亿t二氧化碳;隐含碳进口量从1.0-7.0亿t增长到5.8-16.5亿t二氧化碳;隐含碳净出口量从1.8-7.3亿t增长到11.4-22.6亿t二氧化碳;隐含碳净出口量占国内总排放量的比重由5%-20%增长到17%-30%。贸易中隐含碳核算结果的不确定性主要来自三个方面:核算方法产生的不确定性、数据融合产生的不确定性以及原始数据引入的不确定性。总体来看,多区域投入产出模型的研究结果相对稳定,可重复性较高;从数据精度和数据可获得性方面考虑,40个左右的部门分类可以取得较为理想的结果:把中国划分为多个区域研究国际贸易中的隐含碳在理论上更加可靠。国家气候变化对外谈判和国内减排目标的实现都需要对碳排放进行精确管理。在未来的研究中,应重视隐含碳核算的不确定性研究,逐步提高隐含碳核算的精度,更好地服务于国家的气候变化政策。     

Analysis of uncertainty consideration in environmental assessment: an empirical study of Canadian EA practice

Identifying and communicating uncertainty is core to effective environmental assessment (EA). This study evaluates the extent to which uncertainties are considered and addressed in Canadian EA practice. We reviewed the environmental protection plans, follow-up programs, and panel reports (where applicable) of 12 EAs between 1995 and 2012. The types of uncertainties and levels of disclosure varied greatly. When uncertainties were acknowledged, practitioners adopted five different approaches to address them. However, uncertainties were never discussed or addressed in depth. We found a lack of suitable terminology and consistency in how uncertainties are disclosed, reflecting the need for explicit guidance, and we present recommendations for improvement. Canadian Environmental Impact Statements are not as transparent with respect to uncertainties as they should be, and uncertainties in EA need to be better considered and communicated.     

Irrigation Water Allocation Using an Inexact Two‐Stage Quadratic Programming with Fuzzy Input under Climate Change

Agricultural irrigation accounts for nearly 70% of the total water use around the world. Uncertainties and climate change together exacerbate the complexity of optimal allocation of water resources for irrigation. An interval‐fuzzy two‐stage stochastic quadratic programming model is developed for determining the plans for water allocation for irrigation with maximum benefits. The model is shown to be applicable when inputs are expressed as discrete, fuzzy or random. In order to reflect the effect of marginal utility on benefit and cost, the model can also deal with nonlinearities in the objective function. Results from applying the model to a case study in the middle reaches of the Heihe River basin, China, show schemes for water allocation for irrigation of different crops in every month of the crop growth period under various flow levels are effective for achieving high economic benefits. Different climate change scenarios are used to analyze the impact of changing water requirement and water availability on irrigation water allocation. The proposed model can aid the decision maker in formulating desired irrigation water management policies in the wake of uncertainties and changing environment.     

Implications of different population model structures for management of threatened plants

Population viability analysis (PVA) is a reliable tool for ranking management options for a range of species despite parameter uncertainty. No one has yet investigated whether this holds true for model uncertainty for species with complex life histories and for responses to multiple threats. We tested whether a range of model structures yielded similar rankings of management and threat scenarios for 2 plant species with complex postfire responses. We examined 2 contrasting species from different plant functional types: an obligate seeding shrub and a facultative resprouting shrub. We exposed each to altered fire regimes and an additional, species‐specific threat. Long‐term demographic data sets were used to construct an individual‐based model (IBM), a complex stage‐based model, and a simple matrix model that subsumes all life stages into 2 or 3 stages. Agreement across models was good under some scenarios and poor under others. Results from the simple and complex matrix models were more similar to each other than to the IBM. Results were robust across models when dominant threats are considered but were less so for smaller effects. Robustness also broke down as the scenarios deviated from baseline conditions, likely the result of a number of factors related to the complexity of the species’ life history and how it was represented in a model. Although PVA can be an invaluable tool for integrating data and understanding species’ responses to threats and management strategies, this is best achieved in the context of decision support for adaptive management alongside multiple lines of evidence and expert critique of model construction and output.     

Fine‐resolution conservation planning with limited climate‐change information

Climate‐change induced uncertainties in future spatial patterns of conservation‐related outcomes make it difficult to implement standard conservation‐planning paradigms. A recent study translates Markowitz's risk‐diversification strategy from finance to conservation settings, enabling conservation agents to use this diversification strategy for allocating conservation and restoration investments across space to minimize the risk associated with such uncertainty. However, this method is information intensive and requires a large number of forecasts of ecological outcomes associated with possible climate‐change scenarios for carrying out fine‐resolution conservation planning. We developed a technique for iterative, spatial portfolio analysis that can be used to allocate scarce conservation resources across a desired level of subregions in a planning landscape in the absence of a sufficient number of ecological forecasts. We applied our technique to the Prairie Pothole Region in central North America. A lack of sufficient future climate information prevented attainment of the most efficient risk‐return conservation outcomes in the Prairie Pothole Region. The difference in expected conservation returns between conservation planning with limited climate‐change information and full climate‐change information was as large as 30% for the Prairie Pothole Region even when the most efficient iterative approach was used. However, our iterative approach allowed finer resolution portfolio allocation with limited climate‐change forecasts such that the best possible risk‐return combinations were obtained. With our most efficient iterative approach, the expected loss in conservation outcomes owing to limited climate‐change information could be reduced by 17% relative to other iterative approaches.     

Implications of Conceptual Channel Representation on SWAT Streamflow and Sediment Modeling

Hydrologic modeling outputs are influenced by how a watershed system is represented. Channel routing is a typical example of the mathematical conceptualization of watershed landscape and processes in hydrologic modeling. We investigated the sensitivity of accuracy, equifinality, and uncertainty of Soil and Water Assessment Tool (SWAT) modeling to channel dimensions to demonstrate how a conceptual representation of a watershed system affects streamflow and sediment modeling. Results showed the amount of uncertainty and equifinality strongly responded to channel dimensions. On the other hand, the model performance did not significantly vary with the changes in the channel representation due to the degree of freedom allowed by the conceptual nature of hydrologic modeling in the parameter calibration. Such findings demonstrated good modeling performance statistics do not necessarily mean small output uncertainty, and partial improvements in the watershed representation may neither increase modeling accuracy nor reduce uncertainty. We also showed the equifinality and uncertainty of hydrologic modeling are case‐dependent rather than specific to models or regions, suggesting great caution should be used when attempting to transfer uncertainty analysis results to other modeling studies, especially for ungauged watersheds. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Environmental cost of using poor decision metrics to prioritize environmental projects

Conservation decision makers commonly use project‐scoring metrics that are inconsistent with theory on optimal ranking of projects. As a result, there may often be a loss of environmental benefits. We estimated the magnitudes of these losses for various metrics that deviate from theory in ways that are common in practice. These metrics included cases where relevant variables were omitted from the benefits metric, project costs were omitted, and benefits were calculated using a faulty functional form. We estimated distributions of parameters from 129 environmental projects from Australia, New Zealand, and Italy for which detailed analyses had been completed previously. The cost of using poor prioritization metrics (in terms of lost environmental values) was often high—up to 80% in the scenarios we examined. The cost in percentage terms was greater when the budget was smaller. The most costly errors were omitting information about environmental values (up to 31% loss of environmental values), omitting project costs (up to 35% loss), omitting the effectiveness of management actions (up to 9% loss), and using a weighted‐additive decision metric for variables that should be multiplied (up to 23% loss). The latter 3 are errors that occur commonly in real‐world decision metrics, in combination often reducing potential benefits from conservation investments by 30–50%. Uncertainty about parameter values also reduced the benefits from investments in conservation projects but often not by as much as faulty prioritization metrics.     

How certain are we about the certainty-equivalent long term social discount rate?

Theoretical arguments for using a term structure of social discount rates (SDR) that declines with the time horizon have influenced government guidelines in the US and Europe. The certainty equivalent discount rate that often underpins this guidance embodies uncertainty in the primitives of the SDR, such as growth. For distant time horizons the probability distributions of these primitives are ambiguous and the certainty equivalent itself is uncertain. Yet, if a limited set of characteristics of the unknown probability distributions can be agreed upon, ‘sharp’ upper and lower bounds can be defined for the certainty-equivalent SDR. Unfortunately, even with considerable agreement on these features, these bounds are widely spread for horizons beyond 75 years. So while estimates of the present value of intergenerational impacts, including the social cost of carbon, can be bounded in the presence of this ambiguity, they typically remain so imprecise as to provide little practical guidance.