河流为人类社会的发展做出了巨大贡献,陆域的生产生活活动也给河流带来了不可忽视的影响,因此研究两者交互区域的风险评估并确定优先保护区域,对于促进两个系统健康发展具有十分重要的意义.本文从系统论角度出发,充分考虑了河流与陆域两个系统之间的联系与相互作用,分别对两个系统选取不同指标,河流环境系统主要考虑水体的水文、水质、生物、河岸带状况以及压力等指标;陆域环境系统主要考虑社会压力指标、现有状态指标和恢复潜力指标,并将两个系统的评价结果带入风险评估矩阵对交互区域所处风险等级进行评价,并对云南省昆明市市内盘龙江河段和老运粮河河段进行了实例验证与分析,得到较为符合实际的评估结果,为今后该地区水体污染治理方向提供了一定的依据.

基于区间模糊线性规划方法,综合考虑区域人均GDP、人均水资源量和人均污水排放量,构建区域适度人口规划模型,并以青海省西宁市为研究案例开展研究。结果表明,分别在低、中、高满意度情景下,2020年西宁市适度人口为别为[282.16,324.00],[241.94,282.88]和[220.32,261.93]万,同时发现,可利用水资源总量不足将是限制区域人口发展的主要因素。

Abstract Energy system contains multiple uncertainties, and it is hard to express all its uncertainties by only one method. In order to solve this problem, an interval-fuzzy possibilistic programming (IFPP) method was developed based on the interval parameter programming (IPP), the fuzzy possibilistic programming (FPP) and fuzzy expected value equation within a general optimization framework. In this model, uncertainties presented in terms of crisp intervals and fuzzy-boundary intervals in both the objective function and constraints can be effectively addressed, and decision maker can choose the credibility degree of constraints based on his preference. The method was applied to optimize China energy management system with \CO2\ emission constraint, in which a \CO2\ emission coefficient model was employed to estimate the \CO2\ emission of each province. The study set two \CO2\ emission scenarios to analyze China energy system planning. The optimization results showed the approach could be used for generating a series of optimization schemes under multiple credibility levels, ensuring the energy system could meet the society demand, considering a proper balance between expected energy system costs and risks of violating the constraints of \CO2\ emission. Strengthening the \CO2\ emission constraint suggests the increasing of non-fossil energy generation and a higher system costs.

Electric power system involves different fields and disciplines which addressed the economic system, energy system, and environment system. Inner uncertainty of this compound system would be an inevitable problem. Therefore, an inexact multistage fuzzy-stochastic programming (IMFSP) was developed for regional electric power system management constrained by environmental quality. A model which concluded interval-parameter programming, multistage stochastic programming, and fuzzy probability distribution was built to reflect the uncertain information and dynamic variation in the case study, and the scenarios under different credibility degrees were considered. For all scenarios under consideration, corrective actions were allowed to be taken dynamically in accordance with the pre-regulated policies and the uncertainties in reality. The results suggest that the methodology is applicable to handle the uncertainty of regional electric power management systems and help the decision makers to establish an effective development plan.

Abstract Economic restructuring, water resources management, population planning and environmental protection are subjects to inner uncertainties of a compound system with objectives which are competitive alternatives. Optimization model and water quality model are usually used to solve problems in a certain aspect. To overcome the uncertainty and coupling in reginal planning management, an interval fuzzy program combined with water quality model for regional planning and management has been developed to obtain the absolutely “optimal” solution in this study. The model is a hybrid methodology of interval parameter programming (IPP), fuzzy programing (FP), and a general one-dimensional water quality model. The method extends on the traditional interval parameter fuzzy programming method by integrating water quality model into the optimization framework. Meanwhile, as an abstract concept, water resources carrying capacity has been transformed into specific and calculable index. Besides, unlike many of the past studies about water resource management, population as a significant factor has been considered. The results suggested that the methodology was applicable for reflecting the complexities of the regional planning and management systems within the planning period. The government policy makers could establish effective industrial structure, water resources utilization patterns and population planning, and to better understand the tradeoffs among economic, water resources, population and environmental objectives.

为解决现有污水处理技术优选方法的片面性、主观性和不确定性,本研究以环境、社会和经济效益最大化为目标,采用区间两阶段随机规划的方法,构建模型测算区域新增废水的最优处理方法和处理量,并应用于滇池盘龙江流域,结果表明在废水排放量较大的区域,多种污水处理技术的组合要优于选择任意一种处理技术.其中,厌氧-缺氧-好氧工艺由于运营和投资成本低,在处理规模较大时,具有明显优势,而间歇式循环延时曝气活性污泥法则适用于废水排放规模较小的城镇,其污染物去除效果要优于厌氧-缺氧-好氧工艺和氧化沟法.

目前湖泊营养盐浓度级别评价常采用的平均值法忽视了变量的不确定性.本文据此提出一种基于正态分布总体均值假设检验的级别评价方法,并结合我国地表水环境质量标准(GB3838—2002)对营养盐浓度的分类,提出了简便快捷的图示法.结果发现,该方法能体现营养盐浓度变量的不确定性,融合先验级别假设,将决策风险定量化,可提供较为广阔的决策空间.同时,采用该方法对滇池外海观音山监测断面氨氮浓度的水质级别进行评价,发现先验级别假设、样本容量和决策偏好对评价结果有显著影响;与平均值法的比较发现,假设检验法具有更小的错误概率(例如,样本容量>13时,风险<0.21),评价结果具有更高的可信度,验证了假设检验法的适用性.

水质达标评价是流域水污染防治决策的科学基础,但水质变量的不确定性在传统的达标评价中常被忽略并由此产生决策风险.二项分布检验法可定量表征水质变量不确定性导致的决策风险,其结果更具鲁棒性.基于二项分布检验法,纳入风险偏好和利益权衡,提出了水质达标评价的决策框架.以海河流域为研究对象,对其2014年7月22日—2015年7月14日的周监测数据进行达标评价分析,对比了不同方法的决策风险,发现平均值法会导致很大的取伪错误概率,超标比例法会导致很大的弃真错误概率;二项分布检验法的2类错误概率均可控制在相对较低水平,决策风险最小,可为决策者提供较大的决策空间.案例研究表明,二项分布检验法在水质达标评价中具有适用性和灵活性.

Abstract Economic restructuring, energy planning and environmental protection are subject to inherent uncertainties in a compound system with competing decision objectives. Therefore, an inexact multi-objective programming model for regional economy-energy-environment system management has been developed to obtain absolutely “optimal” solutions. Under two comparative scenarios, three subsystems, six industries, four types of energy, and three kinds of air pollution were considered in an optimization model, and a net system benefit and trade-off analysis between subsystems was conducted. The methods of interval-parameter programming and multi-objective programming were incorporated into the model to tackle the uncertainties and complexities reflected in the case study. The model results indicated that the developed model could provide effective linkages among the economy-energy-environment systems and offer decision makers great insight into the reliability tradeoffs for the adjustment of the existing management policy.

Abstract This research developed an integrated simulation-optimization method (ISOM). This model incorporated eutrophication modeling, water resource allocation and trophic status assessment within a general modeling framework. In ISOM, the simulation effort [i.e. environmental fluid dynamics code (EFDC)] was used to forecast the concentration of water quality variables to evaluate the lake trophic status under various conditions, while the optimization studies were used to identify the optimal water transfer strategies from a number of alternatives. To solve the model, a surrogate-based genetic algorithm (GA) was proposed in which the support vector regression (SVR) was used to create a set of easy-to-use and rapid-response surrogates for identifying the functional relationships between water transfer and lake trophic status. By replacing the EFDC and the corresponding trophic state index (TSI) equations with the surrogates, the computation efficiency could be improved. The developed ISOM was applied to the inter-basin water transfer management of the Niulanjiang-Dianchi Water Transfer Project (NDWTP) to support the eutrophication restoration of Lake Dianchi. Optimal water transfer schemes for three different remediation durations were generated from the model. The results demonstrated that NDWTP could exert a positive influence on the ecology and environment of Lake Dianchi, and that the trophic level for the Lake Dianchi could be effectively mitigated through the adoption of optimal water transfer schemes.

Abstract Phosphorus (P) is viewed as one limiting factor for phytoplankton growth in freshwater lakes. Simple budget models are very efficient for cross-lakes comparisons, while neglecting key distinction between algal P and other forms. Here, a phosphorus budget model was developed to balance between process resolution and cross-system applicability, in which lake total phosphorus (TP) was divided into algal-bound P and other fractions. The model was tested for six lakes on the Yunnan Plateau, China and the Markov Chain Monte Carlo (MCMC) algorithm of Bayesian hierarchical inference was employed for parameters estimation. The model results showed that (a) both algal species composition and P loading are key factors that influence the efficiency of converting phosphorus into algal P; (b) variability of the settling velocity of non-algal P and algal P decreases with increasing TP concentrations, representing a lower capacity for restoration; and (c) settling velocity declined exponentially with the increase of trophic state index, indicating a potential rapid rise of P removal rates during eutrophication restoration. Two conceptual models were then proposed to identify the prior countermeasures for eutrophication restoration in the lakes: (a) for Conceptual Model II, e.g. Lake Lugu, increasing the physical settling of phosphorus should be given priority to; (b) for Conceptual Model I, including the other five lakes, increasing the biological settling of phosphorus should be paid extra attention.

Abstract Sensitivity analysis is a primary approach used in mathematical modeling to identify important factors that control the response dynamics in a model. In this paper, we applied the Morris sensitivity analysis method to identify the important factors governing the dynamics in a complex 3-dimensional water quality model. The water quality model was developed using the Environmental fluid dynamics code (EFDC) to simulate the fate and transport of nutrients and algal dynamics in Lake Dianchi, one of the most polluted large lakes in China. The analysis focused on the response of four water quality constituents, including chlorophyll-a, dissolved oxygen, total nitrogen, and total phosphorus, to 47 parameters and 7 external driving forces. We used Morris sensitivity analysis with different sample sizes and factor perturbation ranges to study the sensitivity with regard to different output metrics of the water quality model, and we analyzed the consistency between different sensitivity scenarios. In addition to the analysis with aggregate outputs, a spatiotemporal variability analysis was performed to understand the spatial heterogeneity and temporal distribution of sensitivities. Our results indicated that it is important to consider multiple characteristics in a sensitivity analysis, and we have identified a robust set of sensitive factors in the water quality model that will be useful for systematic model parameter identification and uncertainty analysis.

To enhance the effectiveness of watershed load reduction decision making, the Risk Explicit Interval Linear Programming (REILP) approach was developed in previous studies to address decision risks and system returns. However, REILP lacks the capability to analyze the tradeoff between risks in the objective function and constraints. Therefore, a refined REILP model is proposed in this study to further enhance the decision support capability of the REILP approach for optimal watershed load reduction. By introducing a tradeoff factor ($\alpha$) into the total risk function, the refined REILP can lead to different compromises between risks associated with the objective functions and the constraints. The proposed model was illustrated using a case study that deals with uncertainty-based optimal load reduction decision making for Lake Qionghai Watershed, China. A risk tradeoff curve with different values of $\alpha$ was presented to decision makers as a more flexible platform to support decision formulation. The results of the standard and refined REILP model were compared under 11 aspiration levels. The results demonstrate that, by applying the refined REILP, it is possible to obtain solutions that preserve the same constraint risk as that in the standard REILP but with lower objective risk, which can provide more effective guidance for decision makers.

Abstract Human activities are the main drivers of alterations of regional N cycles. With increasing population and economic development, human-induced N inputs are expected to continue to increase in the future, especially in many regions of developing countries. Because N sources vary substantially at different temporal and spatial scales and stages of economic development, it is of great importance for environmental managers to be able to simulate the dynamics of N inputs to a specific region of interest. Based on the concept of net anthropogenic N inputs (NANI), a quasi-mass-balance method, a system dynamics model simulating regional N inputs (NANI-SD) is developed and presented here for the first time. The NANI-SD model evaluates how much new N from anthropogenic activities is introduced to the whole basin, providing a simple but effective way to examine human influences on regional N cycles. Our application of the NANI-SD model to the Lake Dianchi basin in China shows that human-induced N inputs will continue to increase under current trends of development. Scenarios focused on lowering population growth rate and banning crop production were not effective in achieving long-term reductions in N inputs because their impacts were compensated by the increases in croplands and food imports, respectively. However, adjusting diet patterns and limiting livestock numbers within the basin were shown to be highly effective in controlling regional N inputs without compromising environmental sustainability of food imported regions. There was a significant trade-off between N self-sufficiency and N inputs to the region, posing the issue of “pollution transfer” as the regions of livestock production providing animal products to the Lake Dianchi basin could suffer from locally intensified levels of N pollution introduced while producing those animal N products. The positive relationship between NANI and the proportion of animal-based protein in food indicates that reducing meat consumption could be an effective way of controlling local N inputs without sacrificing food sovereignty. NANI to the basin could also be reduced by recycling N in human and livestock wastes, but its capacity to reduce NANI is limited and projected to diminish with time.

Rapid urbanization and population growth have resulted in worldwide serious water shortage and environmental deterioration. It is then essential for efficient and feasible allocation of scarce water and environment resources to the competing users. Due to inherent uncertainties, decision making for resources allocation is vulnerable to failure. The scheme feasibility can be evaluated by reliability, representing the failure probability. A progressive reliability-oriented multi-objective (PROMO) optimal decision-making procedure is proposed in this study to deal with problems with numerous reliability objectives. Dimensionality of the objectives is reduced by a top-down hierarchical reliability analysis (HRA) process combining optimization with evaluation. Pareto solutions of the reformulated model, representing alternative schemes non-dominated with each other, are generated by a metalmodel-based optimization algorithm. Evaluation and identification of Pareto solutions are conducted by multi-criteria decision analysis (MCDA). The PROMO procedure is demonstrated for a case study on industrial structure transformation under strict constraints of water resources and total environmental emissions amounts in Guangzhou City, South China. The Pareto front reveals tradeoffs between economic returns of the industries and system reliability. For different reliability preference scenarios, the Pareto solutions are ranked and the top-rated one was recommended for implementation. The model results indicate that the PROMO procedure is effective for model solving and scheme selection of uncertainty-based multi-objective decision making.

In this study, interval-parameter programming, two-stage stochastic programming (TSP), and conditional value-at-risk (CVaR) were incorporated into a general optimization framework, leading to an interval-parameter CVaR-based two-stage programming (ICTP) method. The ICTP method had several advantages: (i) its objective function simultaneously took expected cost and risk cost into consideration, and also used discrete random variables and discrete intervals to reflect uncertain properties; (ii) it quantitatively evaluated the right tail of distributions of random variables which could better calculate the risk of violated environmental standards; (iii) it was useful for helping decision makers to analyze the trade-offs between cost and risk; and (iv) it was effective to penalize the second-stage costs, as well as to capture the notion of risk in stochastic programming. The developed model was applied to sulfur dioxide abatement in an air quality management system. The results indicated that the ICTP method could be used for generating a series of air quality management schemes under different risk-aversion levels, for identifying desired air quality management strategies for decision makers, and for considering a proper balance between system economy and environmental quality.

Abstract In this research, an interval-fuzzy possibilistic programming (IFPP) method was developed by integrating interval parameter programming (IPP), fuzzy possibilistic programming (FPP), and a fuzzy expected value equation within a general optimization framework. The developed IFPP method can not only effectively address uncertainties presented in terms of crisp intervals and fuzzy-boundary intervals in both the objective function and constraints, but it can also improve the traditional fuzzy mathematical programming by choosing the credibility degree of constraints based on the decision maker’s preference and avoiding complicated intermediate models with high computational efficiency. The developed method was applied to identify optimal placements for best management practices (BMPs) to control nutrient pollution in the Baoxianghe River watershed in China, in which a GIS-aided export coefficient model (ECM) was employed to estimate the phosphorus loads from a nonpoint source (NPS). The optimization results showed that the hybrid approach could be used to generate a series of implementation levels for BMPs under multiple credibility levels, ensuring that the NPS phosphorus loads discharged into rivers reduce to an allowable level and considering a proper balance between expected system costs and risks of violating the constraints. Relaxing the sub-basin discharge permits suggests a global discharge permit for the entire watershed, which may allow managers to shift BMP implementation among sub-basins to meet the overall discharge permit at a lower cost.

In this research, a large-scale inexact optimization method was developed for the conjunctive use management of a watershed-lake water distribution system. The modeling framework has the advantages in taking into account the water balance of multi-reservoirs, satisfying the municipal industrial and agricultural water demands in the multi-period context, reflecting the relationship among multi-reservoirs, multiple water related projects, and maintaining the operational rules of certain lake levels. Moreover, such a method can also handle the uncertainty expressed as fuzzy membership functions through integrating the fuzzy credibility chance-constrained programming. The developed method was applied to the conjunctive use management of water resources in the lake Dianchi watershed, China. Cost-effective water allocation schemes for the groundwater project and the water transfer project, and optimal operational rules for the lake and multiple reservoirs were successfully obtained. Also, the annual water balance of the watershed-lake system and the system cost of the conjunctive water use were investigated and analyzed under multiple credibility levels of meeting the water demands for municipal, industrial and agricultural users.

构建了基于正交设计-水质模拟-方案评价的污染物总量控制方案的方法体系,并应用于滇池流域.选择TN、TP作为总量控制指标,将滇池流域划分为6个子区单元,通过设计5水平(0%,5%,10%,15%,20%)6因素的正交试验表,得到25种不同削减方案;耦合EFDC模型模拟得到25种不同削减情景条件下湖泊水质的TN、TP浓度分布,计算各方案综合营养状态指数(TSI),筛选符合中度富营养化标准(60

随着我国有机污染物控制力度不断加大,氮磷营养盐逐步凸显为流域环境质量改善与经济社会发展的重要约束因子.如何以流域氮磷环境承载力为约束,实现经济社会优化发展已成为我国流域环境管理面临的主要难题.传统的环境承载力研究局限于流域内部的自然资源约束和经济社会优化,忽略了流域内外物质交换对承载力的影响,结果往往以牺牲外部可持续性为代价.基于开放系统视角,本研究提出可持续性约束下的流域氮磷环境承载力概念与测度方法,认为流域氮磷环境承载力是指在流域内外氮磷营养盐交换平衡的前提下,流域水环境氮磷含量达标情况下所能承载的最大人口规模.以氮磷污染严重的滇池流域为例,构建了氮磷环境承载力优化模型,得到滇池湖体氮磷达标和流域内外氮磷交换平衡约束下流域的最大人口承载能力为538万人,相对于现状2010年可提升32%;此外,外流域调水、氮磷循环利用率和污水外排等措施对承载力有显著影响.