基于Landsat 8遥感数据的天津市地表温度反演

以Landsat 8遥感数据为数据源,进行天津市地表温度反演研究。首先采用单通道算法反演地表温度,并利用均值标准差法进行温度分级。然后建立不同温度等级面积比例的估算模型。再通过随机样点,从不同温度等级和土地覆盖类型2个角度,分别建立并比较不同类样点的地表温度与各指数的拟合模型。结果表明:次高温区域面积比例与人口密度、人均GDP都具有较高的决定系数;地表温度与NDVI、BAEM的二元线性回归决定系数高于地表温度与单一指数的决定系数;将样点分类后,低温点与MNDBI的决定系数高于其他温度等级样点,水域和植被样点与各种指数的决定系数高于其他地物类型样点。     

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.     

珠江口表层水中多环芳烃的分布特征及健康风险评估

分别于2015年2、5、8、11月在珠江八大入海口采集表层水体样品,应用固相萃取富集法对该区域表层水体中16种USEPA优控多环芳烃(PAHs)的时空分布特征进行分析,并利用终生致癌风险增量模型(ILCR)对该区域的饮水健康风险进行评价。结果表明:珠江口4个季度所采集的水样中,∑15PAHs的浓度范围为18.0~50.3 ng/L,含量处于中等水平。其中7种强致癌性∑7PAHs的浓度范围为1.53~3.73 ng/L,占∑15PAHs的5.89%~11.1%,∑15PAHs和∑7PAHs在枯水期(2、11月)样品中明显高于丰水期(5、8月)。就组成特征而言,各采样点PAHs以3、4环为主。珠江口表层水中非致癌类PAHs的危害商数值为0.99×10~(-5)~2.73×10~(-5),远低于USEPA规定的阈值(1);致癌类PAHs产生的健康风险为6.50×10~(-8)~2.37×10~(-7),其中Ba P导致的饮水途径健康风险最高,所有点位致癌类PAHs的健康风险均低于USEPA推荐的对致癌物质最大可接受风险水平(10~(-6)),表明珠江口表层水中PAHs尚不具备严重的致癌风险,但是仍然存在潜在的健康风险,需要重点控制和管理。     

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.     

DRYING UP OF THE YELLOW RIVER:A RESPONSE TO GLOBAL CLIMATIC CHANGE

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人工神经网络在预测深基坑周边地表沉降变形中的应用研究

深基坑开挖引起的周边地表变形预测是一个复杂非线性问题,引起地表沉降的影响因素很多,各因素之间呈高度的非线性关系。传统的基坑用边地表沉降变形预测方法存在着一定的局限性,其预测精度有待提高,而人工神经网络是一种多元非线性动力学系统,可以灵活方便地对多成因的复杂未知系统进行高度建模,实现全面考虑各种主要影响因素的深基坑周边地表沉降变形预测。本文介绍了误差反向传播(BP)网络模型的结构、学习过程及其算法的改进,径向基函数(RBF)网络模型的结构及其学习过程;分析了影响深基坑开挖周边土体沉降变形的主要影响因素;以25个基坑工程的地表沉降实测资料为训练样本,建立了11个输入影响因素的BP神经网络模型和RBF神经网络模型,通过对样本的学习训练过程及对5个检验样本的预测精度,说明了人工神经网络用于预测基坑周边地表沉降的可行性和准确性。     

基于改进遗传算法的河流水污染源反演方法

针对河流水污染应急响应过程中污染源排放历史迟知、未知的问题,结合多种群遗传算法和自适应遗传算法,利用一维河流水质模型和水质监测数据,研究建立了基于改进遗传算法的河流水污染定量源反演方法,实现了对河流污染源排放历史的识别与重构.将该方法应用于美国特拉基河流的3个不同流量下的示踪剂实验中,对示踪剂排放历史进行定量源反演分析.结果表明：IGA算法对高、中、低不同流量下的3次示踪剂实验均可以很好的重构和识别示踪剂排放历史,对于实际河流水污染源反演分析的误差均在可接受范围内.IGA算法在河流水污染源反演分析中具有一定的可靠性和稳定性,可为河流水污染精准溯源与治理提供科学的技术支撑.     

沱江沉积物中有机氯农药分布特征及其与藻类有机质的关系和生态风险评估

使用气相色谱-质谱联用仪分析了长江支流沱江流域48个表层沉积物中有机氯农药（organochlorine pesticides,OCPs）的残留水平,探讨了其分布和组成特征及其与总有机碳（total organic carbon,TOC）、藻类有机质之间的关系,以及评估其生态风险.结果表明沱江流域表层沉积物中OCPs的总含量为3.17~127 ng·g^-1,其中六六六（hexachlorocyclohexane,HCHs）类农药的含量为2.83~86.0 ng·g^-1,滴滴涕（dichlorodiphenyltrichloroethane,DDTs）类农药的含量为0.340~40.9 ng·g^-1.OCPs空间分布特点为：上游 < 中游 < 下游 < 支流.HCHs和DDTs的组成成分分析表明,沱江流域存在林丹输入的现象,主要来自于历史残留,这与绝大多数流域的DDTs的输入状况相似.OCPs含量与TOC、藻类有机质含量之间存在着极显著的正相关性关系,表明藻类有机质在TOC对沉积物中的OCPs分配中起更为重要的作用.生态风险评估表明,沱江流域表层沉积物的有机氯农药存在较大的生态风险,可能对河流的底栖生物及生态环境造成明显的影响.