二氧化钛对地下水中砷硅的吸附及再生回用

二氧化钛(TiO_2)材料作为性能优异的吸附材料被广泛应用于地下水砷(As)的去除中.结果表明,地下水中共存硅离子(Si)会占据TiO_2的吸附位点,从而影响As的吸附及TiO_2材料的再生回用.本文通过同步辐射扩展X射线吸收精细结构(EXAFS)研究了Si对As微观吸附机制的影响,表明Si的存在不会影响As在TiO_2上的吸附构型.衰减全反射傅里叶变换红外光谱(ATR-FTIR)原位研究表明Si在TiO_2表面形成Si单体、低聚体和多聚体,从而竞争As的吸附位点,同时增加TiO_2再生的难度.为了实现TiO_2材料的高效再生,本研究进一步考察了氟化钠(NaF)对TiO_2表面Si的脱附效果,发现NaF可以有效地洗脱Si,再生后的TiO_2吸附性能稳定.ATR-FTIR光谱原位分析发现,NaF的加入可有效脱附TiO_2表面的Si单体和多聚体.当利用NaF和Na OH共同洗脱TiO_2表面的As和Si时,3次循环中As的脱附率为86.8%~100.3%,Si的脱附率为67.9%~82.0%.本研究为地下水砷硅共吸附材料的再生提供了一种有效方法.     

Hydrolyzed Al(Ⅲ) clusters: Speciation stability of nano-Al13

Pure nano-Al13 and aggregates at various concentrations were prepared to examine the particle size effect of coagulation with inorganic polymer fiocculant. The property and stability of various species formed were characterized using Infrared, 27Al-NMR,photo correlation spectroscopy (PCS), and Ferron assay. Results showed that concentration and temperature exhibited different roles on the stability of Al13. The quantity of Alb species analyzed by ferron assay in the initial aging period corresponded well with that of Al13, which has been confirmed in a dimension range of 1-2 nm by PCS. Al13 solutions at high concentrations (0.5-2.11 mol/L)were observed to undergo further aggregation with aging. The aggregates with a wide particle size distribution would contribute to the disappeared/decreased Al13 basis on the 27Al-NMR spectrum, whereas a part of Al13 would still remain as Alb. At low concentrations,Al13 solution was quite stable at normal temperature, but lost its stability quickly when heating to 90°C.     

杨树人工林碳循环对淹水的响应

本文采用涡度相关方法对安徽怀宁杨树人工林的碳通量进行监测,得到微气象数据和通量数据。通过坐标旋转、密度校正和数据插补处理提高数据质量。选取2005年9月淹水期间的数据。分六个阶段进行分析。得到以下结果：（1）在半小时尺度上,在淹水期表观量子效率α随着土壤含水量SWC上升而降低;在未淹水期最大。为-0．0023μmolphoton^-1;退水期最低,为-0．00122mgCO2μmolphoton^-1,退水后恢复到-0,00168mgCO2μmolphoton^-1;α在淹水中2期降低速率最大,在整个淹水期间α的恢复速率是大于降低速率的。（2）生态系统总初级生产力（GEP）在淹水后不断降低．耒淹水期为8．0gCm^-2day^-1．退水后为4．7gCm^-2day^-1,在淹水中2期降幅是最大的达到了16％。（3）生态系统呼吸（Reco）在完全被水淹的第一天降到最低,仅为2．2gCm^-2day^-1;由于温度的影响其变化规律比较复杂。在淹水中2期的变化幅度是最大的;退水后Reco为4．2gCm^-2day^-1恢复到来淹水前的87％。（4）淹水期净生态系统CO2交换量（NEE）的变化表现为先上升后下降的趋势,在淹水中1期最大为-4．2gCm^-2day^-1。退水后最低。为0．6gCm^-2day^-1是未淹水前的17％。     

New policies to deal with climate change and other drivers impacting on resilience to flooding in urban areas: the CORFU approach

In the context of urban flood management, resilience is equal to resisting, recovering, reflecting and responding. The variety of causes of flooding and their consequences underpin the need for increased and internationally coordinated efforts to enhance technologies and policies for dealing with floods. This paper addresses this issue and presents some novel research ideas related to resilience to flooding in urban areas, which are under development within the EU FP7 project ‘Collaborative research on flood resilience in urban areas’ (CORFU). The approach adopted in this project aims to quantify the cost-effectiveness of resilience measures and integrative and adaptable flood management plans for different scenarios of relevant drivers: urban development, socio-economic trends and climate changes. It is believed that the way in which the different models are being put together, combined with the variability of conditions in case study areas in Asia and in Europe, will ultimately enable more scientifically sound policies for the management of the consequences of urban flooding.     

Adsorptive removal of hydrophobic organic compounds by carbonaceous adsorbents: A comparative study of waste-polymer-based, coal-based activated carbon, and carbon nanotubes

Adsorption of the hydrophobic organic compounds (HOCs) trichloroethylene (TCE), 1,3-dichlorobenzene (DCB), 1,3-dinitrobenzene (DNB) and γ-hexachlorocyclohexane (HCH) on five different carbonaceous materials was compared. The adsorbents included three polymer-based activated carbons, one coal-based activated carbon (F400) and multiwalled carbon nanotubes (MWNT). The polymerbased activated carbons were prepared using KOH activation from waste polymers: polyvinyl chloride (PVC), polyethyleneterephthalate (PET) and tire rubber (TR). Compared with F400 and MWNT, activated carbons derived from PVC and PET exhibited fast adsorption kinetics and high adsorption capacity toward the HOCs, attributed to their extremely large hydrophobic surface area (2700 m²/g) and highly mesoporous structures. Adsorption of small-sized TCE was stronger on the tire-rubber-based carbon and F400 resulting from the pore-filling effect. In contrast, due to the molecular sieving effect, their adsorption on HCH was lower. MWNT exhibited the lowest adsorption capacity toward HOCs because of its low surface area and characteristic of aggregating in aqueous solution.     

Risk,vulnerability, and pragmatic inevitability: the conflict–disaster nexus and urban governance in Johannesburg,South Africa

This paper spotlights post-disaster relief provision in Johannesburg, South Africa, following the floods of 2016 in a bid to explore how local government and non-governmental actors in the country conceive of compounding vulnerability and conflict within urban disaster governance. It reveals the diverse strategies employed to navigate violent conflict during the cyclical occurrence of disaster and reconstruction that the predominantly migrant population experiences in the Setswetla informal settlement, adjacent to the Alexandra township in northern Johannesburg. Rendered visible in moments of disaster and recovery are the spatial politics and multidimensional nature of conflict. These phenomena unfold across various levels of urban governance and in the affected community and effectively construct a disaster citizenship that makes risk reduction and community cohesion impossible in the eyes of disaster managers. This research, based on a set of expert interviews, integrates conflict and disaster studies to shed light on how the conflict–disaster interface materialises, and is operationalised, in an urban setting.     

磁性离子印迹聚合物的制备及其对水中Pb(Ⅱ)的吸附

以铅离子为模板离子、乙二醇二甲基丙烯酸酯和偶氮二异丁腈为交联剂和引发剂、稀盐酸为洗脱剂,采用微波辅助反相乳液悬浮聚合法,制备了磁性离子印迹聚合物(MIIP),通过SEM、FTIR、XRD和BET技术对其进行了表征,并将其用于水中Pb(Ⅱ)的吸附.在初始质量浓度60 mg/L、溶液pH 6、吸附温度303 K、吸附时间6...     

Streamflow Hydrology Estimate Using Machine Learning (SHEM)

Continuity and accuracy of near real‐time streamflow gauge (streamgage) data are critical for flood forecasting, assessing imminent risk, and implementing flood mitigation activities. Without these data, decision makers and first responders are limited in their ability to effectively allocate resources, implement evacuations to save lives, and reduce property losses. The Streamflow Hydrology Estimate using Machine Learning (SHEM) is a new predictive model for providing accurate and timely proxy streamflow data for inoperative streamgages. SHEM relies on machine learning (“training”) to process and interpret large volumes (“big data”) of historic complex hydrologic information. Continually updated with real‐time streamflow data, the model constructs a virtual dataset index of correlations and groups (clusters) of relationship correlations between selected streamgages in a watershed and under differing flow conditions. Using these datasets, SHEM interpolates estimated discharge and time data for any indexed streamgage that stops transmitting data. These estimates are continuously tested, scored, and revised using multiple regression analysis processes and methodologies. The SHEM model was tested in Idaho and Washington in four diverse watersheds, and the model's estimates were then compared to the actual recorded data for the same time period. Results from all watersheds revealed a high correlation, validating both the degree of accuracy and reliability of the model.     

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real‐time decisions given that flood conditions change rapidly. This study's objective is to build real‐time data‐driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics‐based model that estimates errors in the flow predictions from a physics‐based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short‐term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics‐based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser‐based Web application, enabling ease of use by both technical and nontechnical personnel.     

River Channel Geometry and Rating Curve Estimation Using Height above the Nearest Drainage

River channel geometry is an important input to hydraulic and hydrologic models. Traditional approaches to quantify river geometry have involved surveyed river cross sections, which cannot be extended to ungaged basins. In this paper, we describe a method for developing a synthetic rating curve to relate flow to water level in a stream reach based on reach‐averaged channel geometry properties developed using the Height above Nearest Drainage (HAND) method. HAND uses a digital elevation model (DEM) of the terrain and computes the elevation difference between each land surface cell and the stream bed cell to which it drains. Taking increments in water level in the stream, HAND defines the inundation zone and a water depth grid within this zone, and the channel characteristics are defined from this water depth grid. We apply our method to the Blanco River (Texas) and the Tar River (North Carolina) using 10‐m terrain data from the United States Geological Survey (USGS) 3D Elevation Program (3DEP) dataset. We evaluate the method's performance by comparing the reach‐average stage‐river geometry relationships and rating curves to those from calibrated Hydrologic Engineering Center's River Analysis System (HEC‐RAS) models and USGS gage observations. The results demonstrate that after some adjustment, the river geometry information and rating curves derived from HAND using national‐coverage datasets are comparable to those obtained from hydraulic models or gage measurements. We evaluate the inundation extent and show our approach is able to capture the majority of the Federal Emergency Management Agency (FEMA) 100‐year floodplain.