AN OPTIMIZATION MODEL FOR EFFICIENT MANAGEMENT OF URBAN WATER RESOURCES1

ABSTRACT: A mathematical programming model is proposed to determine economically efficient urban water resource allocation and pricing policy by maximizing the sum of the consumer and producer surplus. The optimization of this nonlinear problem is accomplished by the use of linear programming algorithm. The feasibility of using recycled water for municipal purposes is examined in a planning context. The impact of higher water quality discharge standards on pricing and allocation of water is analyzed and the attractiveness of water reuse option is demonstrated.     

A NONLINEAR MULTILEVEL MODEL FOR REGIONAL WATER RESOURCES PLANNING1

ABSTRACT: A nonlinear multilevel transportation model is developed to study large-scale allocations in a water resources system. The model uses a modified transportation matrix formulated with nonlinear cost functions as the basic subregional model and the goal coordination method for multilevel decomposition and optimization of the overall regional system. The model is applied to projected water requirements for Salt Lake County in 1985. Sources of water supply - surface water, ground water, import water, and reuse of reclaimed wastewater on a restricted basis - are available to satisfy water requirements for municipal, industrial, and agricultural sectors in four subregions. The conjugate gradient projection method is used to optimize the first level subregional models having cost functions of the form of C = aX^b, and the second level problem is solved using the conjugate gradient method.     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

危险品运输网络优化设计研究综述

为分析当今反恐新形势下的危险品运输网络优化设计问题的研究现状,系统总结国内外关于一般场景和恐怖袭击威胁2种情况下危险品运输网络优化设计研究的主要模型和方法,梳理两者现有的研究内容,并讨论其共性及恐怖袭击威胁情景下的研究的新特点。结果发现:目前关于解决危险品运输网络优化设计问题模型的鲁棒性研究较为缺乏,尤其是模型在更为复杂和不确定性更强的恐怖袭击情景中适用性不强;危险品运输的鲁棒优化模型即使在突发事件条件下,也可以使决策者能够作出相对满意的决策;鲁棒性危险品运输网络能够规避风险扰动,可用来防止在恐怖袭击中因危险品车辆爆炸等造成更大的危害。     

Livelihoods,conflict and aid programming: is the evidence base good enough?

In conflict‐affected situations, aid‐funded livelihood interventions are often tasked with a dual imperative: to generate material welfare benefits and to contribute to peacebuilding outcomes. There may be some logic to such a transformative agenda, but does the reality square with the rhetoric? Through a review of the effectiveness of a range of livelihood promotion interventions—from job creation to microfinance—this paper finds that high quality empirical evidence is hard to come by in conflict‐affected situations. Many evaluations appear to conflate outputs with impacts and numerous studies fail to include adequate information on their methodologies and datasets, making it difficult to appraise the reliability of their conclusions. Given the primary purpose of this literature—to provide policy guidance on effective ways to promote livelihoods—this silence is particularly concerning. As such, there is a strong case to be made for a restrained and nuanced handling of such interventions in conflict‐affected settings.     

Aggregating high-priority landscape areas to the parcel level: an easement implementation tool

Landscape characteristics and parcel ownership information are often collected on different spatial scales leading to difficulties in implementing land use plans at the parcel level. This study provides a method for aggregating highly resolute landscape planning information to the parcel level. Our parcel prioritization model directly incorporates a Land Trust's conservation goals in the form of a compromise programming model. We then demonstrate the use of our approach for implementation decisions, including parcel selection under a budget constraint and the estimation of a total conservation budget necessary to meet specific conservation goals. We found that these cost constraints significantly alter the composition of the 'best' parcels for conservation and can also provide guidance for implementation. The model's results were integral to a local Land Trust's ability to further define and achieve their goals.     

An integrated fuzzy-stochastic modeling approach for risk assessment of groundwater contamination

An integrated fuzzy-stochastic risk assessment (IFSRA) approach was developed in this study to systematically quantify both probabilistic and fuzzy uncertainties associated with site conditions, environmental guidelines, and health impact criteria. The contaminant concentrations in groundwater predicted from a numerical model were associated with probabilistic uncertainties due to the randomness in modeling input parameters, while the consequences of contaminant concentrations violating relevant environmental quality guidelines and health evaluation criteria were linked with fuzzy uncertainties. The contaminant of interest in this study was xylene. The environmental quality guideline was divided into three different strictness categories: "loose", "medium" and "strict". The environmental-guideline-based risk (ER) and health risk (HR) due to xylene ingestion were systematically examined to obtain the general risk levels through a fuzzy rule base. The ER and HR risk levels were divided into five categories of "low", "low-to-medium", "medium", "medium-to-high" and "high", respectively. The general risk levels included six categories ranging from "low" to "very high". The fuzzy membership functions of the related fuzzy events and the fuzzy rule base were established based on a questionnaire survey. Thus the IFSRA integrated fuzzy logic, expert involvement, and stochastic simulation within a general framework. The robustness of the modeling processes was enhanced through the effective reflection of the two types of uncertainties as compared with the conventional risk assessment approaches. The developed IFSRA was applied to a petroleum-contaminated groundwater system in western Canada. Three scenarios with different environmental quality guidelines were analyzed, and reasonable results were obtained. The risk assessment approach developed in this study offers a unique tool for systematically quantifying various uncertainties in contaminated site management, and it also provides more realistic support for remediation-related decisions.     

基于水质模拟的不确定条件下两阶段随机水资源规划模型

针对流域内不同企业的水资源分配及企业生产污染排放导致的水环境问题,运用区间两阶段随机规划的方法,耦合区间两阶段模型（ITSP）和区间水质模型（IS-P）,建立不确定两阶段随机水质-水量耦合规划模型（ITSP-SP）.该模型以流域内系统利益最大为目标函数,模拟了流域内各个企业的水量分配及排污过程中河道水质变化,并在保证河流水质达标前提下优化预计分配水量,调整企业生产规模.通过模型运算得到区间解,为管理者提供了多样的决策方案.并且,该模型充分考虑不确定因素对系统利益的影响,能够有效的规避系统决策失误及方案缺失现象.     

基于不同电力需求的中国减污降碳协同增效路径

当前我国同时面临改善生态环境质量和实现碳达峰碳中和两大战略任务,协同推进减污降碳已成为我国经济社会发展全面绿色转型的必然选择,电力部门在转型过程中将发挥重要作用.面向不同的电力需求情景,构建低成本实现碳达峰、碳中和的多目标模型,求解得出减污降碳协同增效最优路径方案.结果表明,在如期实现碳达峰、碳中和目标的前提下,减污和降碳协同性较好,两者协同控制可高效助力低碳转型的实现;优化电力部门发电结构是实现减污降碳协同增效的关键措施,研究期火电占比不断下降,清洁电力占比超过92.5%;不同电力需求下二氧化碳和主要大气污染物排放量有明显差异,其中二氧化碳排放量受电力需求影响最大,低电力需求、中等电力需求和高电力需求情景下峰值二氧化碳排放量分别为94.16亿、 104.09亿和107.46亿t,主要污染物二氧化硫、氮氧化物和颗粒物的排放同样表现出在低电力需求、中等电力需求和高电力需求情景下依次递增的趋势.电力需求的提高仅增加了电力部门内部发电结构调整的压力,未影响到其他部门的产量和活动水平,即电力需求导致的电力部门减排压力未表现出部门间传递的趋势.