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.     

圆形烟道烟尘采样点位置确定之正误

采样点位置的确定是烟尘测试中的一项基础工作。在全国沿用10多年的确定圆形圆形烟道烟尘采样点位置的计算表中的数据,经验算有60％有误;原来以烟道中心为基准的计算公式虽然正确,但实用性差。为了进一步完善烟气测试理论,提高监测质量,提出以烟道内壁为基准的新计算公式。运用新、旧2种计算公式得到一致的计算结果—新计算表,进一步证实了原计算表中的错误。     

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。     

Multi-level screening of a proposed hazardous waste treatment and disposal facility site--a case study

A comprehensive environmental assessment (EA) is a pre-requisite before the site for developing a hazardous waste treatment and disposal facility (HWTDF) is selected. However, the resource limitations, especially for developing countries, often dictatethat the detailed EA be restricted to those sites, which are constraint free and have low hazard potentials. Thus, a preliminary screening exercise for assessing the suitability ofsite for developing the HWTDF needs to be carried out to avoid huge costs involved in detailed EA. While screening a HWTDF site,various factors such as present land use, ecologically sensitiveareas, geology and hydrogeology of the area, the quality and quantity of wastes, engineered safeguards, and the operationalprocedures that need to be adopted, are required to be addressed. In this paper, a multi-level screening criteria employing RemoteSensing, Constraint Mapping, Groundwater Pollution Potential Index (DRASTIC Index), and the Site Ranking was used to assess the suitability of a proposed site for the development of HWTDF.The study helped to identify various constraints at the proposedsite and their significance on the development of the HWTDF.     

夜间建筑施工噪声扰民监测方法探讨

以一家夜间施工工地为例 ,通过对现有夜间建筑施工噪声测量方法及国家和深圳经济特区有关噪声污染定义的分析 ,阐述了夜间施工工地噪声扰民监测方法