紫黑色作物花色苷实验室定量检测方法研究

为了寻求快速、经济地测定自然界中紫黑色作物总花色苷含量的方法,利用酸性乙醇对黑元麦、茄子、葡萄3种不同作物进行花色苷提取,并采用pH示差法和差减法2种不同的实验室方法对3种样品总花色苷含量进行留样分析再测定。结果表明,黑元麦、葡萄采用pH示差法和差减法这2种测定方法的结果精确度和准确性没有显著性差异,且样品进行留样再检测时发现,2种样品测定结果之差的绝对值均小于测定结果质量不确定度,可以说明黑元麦、葡萄花色苷实验室定量检测pH示差法和差减法均适用。茄子采用pH示差法测定值相对标准偏差为2.20%,精确度明显好于差减法;从质控图看,茄子检出值出现1次连续4个点上升的情况,说明茄子皮利用差减法测定结果准确性差;从留样再检测结果可以看出,利用差减法测定2次结果之差的绝对值为0.023 mg/g,小于测定结果质量不确定度0.011 mg/g,说明茄子花色苷pH示差法检测结果可信赖,差减法检测存在问题。     

电感耦合等离子体发射光谱法测量土壤中有效锌含量的不确定度评估

对电感耦合等离子体发射光谱法(ICP-OES)测定土壤有效锌含量的不确定度进行评定,分析了整个检测过程产生不确定度的来源,对称样量、浸提液体积、标准系列溶液配制、线性标准曲线拟合、测量重复性等产生的不确定度分量进行计算,量化给出扩展不确定度。待测土壤中有效锌含量最终结果表示:w(Zn)=(1.12±0.10)mg/kg,包含因子k=2,置信概率为95%。测量过程中,标准溶液制备所产生的不确定度最大。因此,在ICP-OES法测定土壤样品有效锌时应足够重视标准溶液制备与曲线拟合过程,以减小测量不确定度。本文研究结果为控制ICP-OES法测定土壤有效锌数据质量提供了理论依据。     

Uncertain and multi-objective programming models for crop planting structure optimization

Crop planting structure optimization is a significant way to increase agricultural economic benefits and improve agricultural water management. The complexities of fluctuating stream conditions, varying economic profits, and uncertainties and errors in estimated modeling parameters, as well as the complexities among economic, social, natural resources and environmental aspects, have led to the necessity of developing optimization models for crop planting structure which consider uncertainty and multi-objectives elements. In this study, three single-objective programming models under uncertainty for crop planting structure optimization were developed, including an interval linear programming model, an inexact fuzzy chance-constrained programming (IFCCP) model and an inexact fuzzy linear programming (IFLP) model. Each of the three models takes grayness into account. Moreover, the IFCCP model considers fuzzy uncertainty of parameters/variables and stochastic characteristics of constraints, while the IFLP model takes into account the fuzzy uncertainty of both constraints and objective functions. To satisfy the sustainable development of crop planting structure planning, a fuzzy-optimization-theory-based fuzzy linear multi-objective programming model was developed, which is capable of reflecting both uncertainties and multi-objective. In addition, a multi-objective fractional programming model for crop structure optimization was also developed to quantitatively express the multi-objective in one optimization model with the numerator representing maximum economic benefits and the denominator representing minimum crop planting area allocation. These models better reflect actual situations, considering the uncertainties and multi-objectives of crop planting structure optimization systems. The five models developed were then applied to a real case study in Minqin County, north-west China. The advantages, the applicable conditions and the solution methods of each model are expounded. Detailed analysis of results of each model and their comparisons demonstrate the feasibility and applicability of the models developed, therefore decision makers can choose the appropriate model when making decisions.     

考虑风险价值的不确定性水资源优化配置

保障区域农业用水的可持续发展,对水资源进行优化配置至关重要。由于粮食主产区水资源配置过程中存在许多不确定性因素,在追求最小用水成本时,也存在着较大的风险,因此该文以三江平原涵盖的七台河、佳木斯、双鸭山、鹤岗和鸡西5个重要粮食主产区为研究区域,以区间两阶段随机规划模型为基础,引入风险偏好,构建地表水和地下水优化配置模型。结果表明,双鸭山和鸡西的配水过程中地表水缺水量很大,主要开采利用地下水;佳木斯作为粮食生产面积较大的行政区,需要外来水进行补给;七台河和鹤岗的种植面积较小,综合考虑引水成本和粮食收益,引用较少的外来水来降低成本;最后得出不同来水水平下,各种风险偏好下水资源优化配置的最小成本的变化规律,即在低来水水平下,用水成本从344.2×108~355.4×108元增加到411.5×108~430.7×108元;在高来水水平下,总用水成本从422.5×108~435.3×108元降低到351.7×108~365.3×108元;在中来水水平时,总用水成本则呈现出先增加后减少的规律。该模型兼有区间两阶段和风险价值模型的特点,综合衡量成本和风险,可有效节约用水成本,并能增强水资源系统规避风险的能力,并以佳木斯2014年实际用水为例,计算得到相对误差在15%以内,较为真实地反映水资源优化配置过程中的不确定性和风险性,为提高水资源利用效率和区域水资源规划管理提供依据。     

固相萃取-气相色谱方法测定水中毒死蜱含量的不确定度评定

本文建立了利用固相萃取-气相色谱法测定水中毒死蜱含量的方法,通过数学模型分析并计算了检测过程中的不确定度分量,并计算出相对扩展不确定度。     

多种数据源下栖息地模型及预测结果的比较

由于多来源的海洋环境数据常以不同时间、空间分辨率呈现,并具有不同的误差,因此,有必要分析数据源的差异是否会对研究结果产生显著影响,是否会影响基于不同数据源估计的模型对其他数据的适用性。为此,本研究利用多个网站提供的叶绿素浓度与海表水温数据,采用线性回归与随机检验方法,分析了不同数据源对栖息地模型构建及其预测效果的影响。研究结果表明,不同数据源的数据之间常存在系统性偏差,从而使得模型参数的估计具有显著性差异,该模型不适合于其他数据源的数据;多源环境数据间的离散性反映数据存在随机误差,环境数据的随机误差将使模型结果具有随机性,因此本研究建议定量分析模型结果的不确定性,以使模型结果得到科学应用。     

Spatial distribution of δ~2H and δ~18O values in the hydrologic cycle of the Nile Basin

Existing δ2H and δ18O values for precipitation and surface water in the Nile Basin were used to analyze precipitation inputs and the influence of evaporation on the isotopic signal of the Nile River and its tributaries. The goal of the data analysis was to better understand basin processes that influence seasonal streamflow for the source waters of the Nile River, because climate and hydrologic models have continued to produce high uncertainty in the prediction of precipitation and streamflow in the Nile Basin. An evaluation of differences in precipitation δ2H and δ18O values through linear regression and distribution analysis indicate variation by region and season in the isotopic signal of precipitation across the Nile Basin. The White Nile Basin receives precipitation with a more depleted isotopic signal compared to the Blue Nile Basin. The hot temperatures of the Sahelian spring produce a greater evaporation signal in the precipitation isotope distribution compared to precipitation in the Sahara/Mediterranean region, which can be influenced by storms moving in from the Mediterranean Sea. The larger evaporative effect is reversed for the two regions in summer because of the cooling of the Sahel from inflow of Indian Ocean monsoon moisture that predominantly influences the climate of the Blue Nile Basin. The regional precipitation isotopic signals convey to each region's streamflow, which is further modified by additional evaporation according to the local climate. Isotope ratios for White Nile streamflow are significantly altered by evaporation in the Sudd, but this isotopic signal is minimized for streamflow in the Nile River during the winter, spring and summer seasons because of the flow dominance of the Blue Nile. During fall, the contribution from the White Nile may exceed that of the Blue Nile, and the heavier isotopic signal of the White Nile becomes apparent. The variation in climatic conditions of the Nile River Basin provides a means of identifying mechanistic processes through changes in isotope ratios of hydrogen and oxygen, which have utility for separating precipitation origin and the effect of evaporation during seasonal periods. The existing isotope record for precipitation and streamflow in the Nile Basin can be used to evaluate predicted streamflow in the Nile River from a changing climate that is expected to induce further changes in precipitation patterns across the Nile Basin.     

Characteristics and issues related to regional-scale modeling of nitrogen flows

This paper summarizes the characteristics of regional-scale nitrogen (N) flow models. The regional scale is generally considered to be an area that ranges from more than 10 km² to the size of a continent. Parameterization is the key process in creating a regional-scale model. During parameterization, transfer functions that reflect the controlling factors must be created at the target scale because the influence of different factors will change with the size of the scale. Watersheds are the most useful unit for evaluating overall N discharge; however, regional activity data is most often available for municipal units. Thus, municipal units must be reaggregated into watershed units. A longer time period is desirable to normalize seasonal and annual variations at regional scales. Parameters that influence N flow must match the investigated spatial and temporal scales. Given the need to use a range of parameters that vary in terms of the quality of the data, models exhibit inevitable uncertainties. Quantification of the uncertainties and verification of the estimated results are required. Error propagation, the Monte Carlo simulation method and maximum and minimum values have been used to obtain different threshold values of uncertainty. To verify regional-scale N flow models, the following five approaches have been used or proposed: (1) calibration of the model by detailed monitoring at multiple sites, (2) verification of the most important process of the extrapolation mechanisms, (3) verification of the N budget, paying particular attention to water quality, (4) comparison with the results quantified by different models, (5) comparison with aerial or satellite image analysis. As regional-scale modeling of N flow will become more important in the future, it is important to develop models than can accurately estimate N dynamics at this scale.