Mitigating Impact of Devils Lake Flooding on the Sheyenne River Sulfate Concentration

Devils Lake is a terminal lake located in northeast North Dakota. Because of its glacial origin and accumulated salts from evaporation, the lake has a high concentration of sulfate compared to the surrounding water bodies. From 1993 to 2011, Devils Lake water levels rose by ~10 m, which flooded surrounding communities and increased the chance of an overspill to the Sheyenne River. To control the flooding, the State of North Dakota constructed two outlets to pump the lake water to the river. However, the pumped water has raised concerns about of water quality degradation and potential flooding risk of the Sheyenne River. To investigate these perceived impacts, a Soil and Water Assessment Tool (SWAT) model was developed for the Sheyenne River and it was linked to a coupled SWAT and CE‐QUAL‐W2 model that was developed for Devils Lake in a previous study. While the current outlet schedule has attempted to maintain the total river discharge within the confines of a two‐year flood (36 m³/s), our simulation from 2012 to 2018 revealed that the diversion increased the Sheyenne River sulfate concentration from an average of 125 to >750 mg/L. Furthermore, a conceptual optimization model was developed with a goal of better preserving the water quality of the Sheyenne River while effectively mitigating the flooding of Devils Lake. The optimal solution provides a “win–win” outlet management that maintains the efficiency of the outlets while reducing the Sheyenne River sulfate concentration to ≤600 mg/L.     

What matters for work engagement? A diary study on resources and the benefits of selective optimization with compensation for state work engagement

This diary study addresses the benefits of employees' daily use of selective optimization with compensation (SOC) for state work engagement. We hypothesized that day‐level SOC not only directly fosters work engagement but that SOC also reveals its beneficial effects for work engagement in interaction with both external and internal resources. Specifically, we proposed SOC substitutes for job control, role clarity, and state of being recovered, thus helping employees manage low daily levels of these resources. We tested our hypotheses with a sample of 138 employees who completed two daily surveys over a total of 545 workdays. Results of multilevel analyses revealed that SOC benefits work engagement in both proposed ways. First, day‐level SOC was positively related to state work engagement. Additionally, day‐level role clarity and state of being recovered predicted state work engagement, but day‐level job control did not. Second, SOC benefitted state work engagement by offsetting low levels of role clarity and being recovered, and by boosting job control in their respective relationships with work engagement. The results suggest that by using SOC at work, employees can actively enhance their own work engagement on a given workday. This knowledge provides promising starting points for the development of interventions.     

城市给水管网优化设计研究

研究了几种典型的城市给水管网优化设计数学模型,并给出了求解方法,指出在给水管网系统的优化设计中进一步完善广义简约(GRG)算法的必要性.     

COMPARISON OF STOCHASTIC AND DETERMINISTIC DYNAMIC PROGRAMMING FOR RESERVOIR OPERATING RULE GENERATION1

ABSTRACT: Two dynamic programming models — one deterministic and one stochastic — that may be used to generate reservoir operating rules are compared. The deterministic model (DPR) consists of an algorithm that cycles through three components: a dynamic program, a regression analysis, and a simulation. In this model, the correlation between the general operating rules, defined by the regression analysis and evaluated in the simulation, and the optimal deterministic operation defined by the dynamic program is increased through an iterative process. The stochastic dynamic program (SDP) describes streamflows with a discrete lag-one Markov process. To test the usefulness of both models in generating reservoir operating rules, real-time reservoir operation simulation models are constructed for three hydrologically different sites. The rules generated by DPR and SDP are then applied in the operation simulation model and their performance is evaluated. For the test cases, the DPR generated rules are more effective in the operation of medium to very large reservoirs and the SDP generated rules are more effective for the operation of small reservoirs.     

基于边介数的省域生态廊道构建方法优化

我国城市建设和生态保护工作均对土地资源有大量需求,二者之间的矛盾在经济优先发展区表现尤为明显.为了有效地改善生态环境,管控土地利用并引导其变化发展,需要建设具备不可替代特征的省域生态廊道.最小累积阻力模型（minimum cumulative resistance,MCR）是识别生态廊道最常用、有效的模型,但在应用于省域尺度时,MCR模型识别的潜在廊道路由存在冗余的问题.因此,通过引入网络科学中的边介数指数（edge-betweenness）对MCR模型进行优化,计算潜在廊道路由的边介数指数值,选取出其中最为重要和简明的结构来连通生态源地,即提取潜在路由中的骨干路由（backbone route）和关键战略点（key strategic point）作为不可替代的结构来指导省域生态廊道建设.将优化后的MCR模型应用于广东省,构建了全长5 493 km的省域生态廊道,其中包含生态源地20处,关键战略点11个,骨干生态廊道29条.骨干路由与关键战略点构成的不可替代省域生态廊道（irreplaceable provincial corridor）能够实现"廊道数量和占地面积最少、连通性基本不变"的目标.研究显示,边介数能够对潜在路由进行优化筛选,识别出维护省域生态安全的关键结构;不可替代生态廊道能够指导省域生态规划和土地空间的发展利用,并为更高水平的生态安全环境提供了演进的基础;同时也为土地资源紧张的地区提供了建设生态廊道的参考与依据.      

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

B-P网络应用于地下水水质监测点优选

以地下水水质评价分级标准作为训练样本,构造B-P网络模型对其进行训练,用训练好的B-P网络对某地的地下水水质监测点进行评判、优选。并与其它方法的结果进行比较,结果表明,B-P网络用于环境测点优选不仅原理直观,而且具有较好的客观性和实用性。     

A Metamodeling Approach for Integrated Assessment of Air Quality Policies

A method is proposed to build integrated models (also called Metamodels) aimed at quantifying the economic efficiency of air quality policies. This Metamodeling approach is based on the coupling of two complementary models, that operate at different scales in space and time, and which represent the economic and the physical and chemical processes, respectively. The joint consideration of the physico-chemical and techno-economic structure of the pollution control problems permits a comprehensive evaluation of air pollution abatement strategies. The motivating pollution control problems include urban-regional air quality management through efficient energy and traffic control policies. A pilot study, exploiting data collected in the Geneva canton (Switzerland), is used to demonstrate the potential of the approach.     

和田市土地利用结构变化研究

我国正处在城市化急速发展的时期。城市从数量、规模等各方面都在膨胀，这给城市土地利用带来了一系列问题。本文在综合分析和田市城市土地利用现状和未来发展要求的基础上，为解决人类在高强度经济活动中，特别是在农村经济建设和城乡建设快速发展的条件下。针对如何解决土地资源的合理利用、环境恶化及持续发展之间的矛盾。提出了和田市未来合理开发利用土地资源、产业结构的调整、城市功能区的布局和进行城市建设等方面可行性方案。     

基于GIS的海湾陆源污染排海总量控制的空间优化分配方法研究——以大连湾为例

开发了基于GIS的海湾陆源污染排海总量控制的空间优化分配的方法 .并在“大连湾数据库”的支持下 ,以大连湾为例 ,研究了陆源污染排海总量控制的空间优化分配问题 .为环境空间优化决策提供依据