Information Network Topologies for Enhanced Local Adaptive Management

We examined the principal effects of different information network topologies for local adaptive management of natural resources. We used computerized agents with adaptive decision algorithms with the following three fundamental constraints: (1) Complete understanding of the processes maintaining the natural resource can never be achieved, (2) agents can only learn by experimentation and information sharing, and (3) memory is limited. The agents were given the task to manage a system that had two states: one that provided high utility returns (desired) and one that provided low returns (undesired). In addition, the threshold between the states was close to the optimal return of the desired state. We found that networks of low to moderate link densities significantly increased the resilience of the utility returns. Networks of high link densities contributed to highly synchronized behavior among the agents, which caused occasional large-scale ecological crises between periods of stable and high utility returns. A constructed network involving a small set of experimenting agents was capable of combining high utility returns with high resilience, conforming to theories underlying the concept of adaptive comanagement. We conclude that (1) the ability to manage for resilience (i.e., to stay clear of the threshold leading to the undesired state as well as the ability to re-enter the desired state following a collapse) resides in the network structure and (2) in a coupled social–ecological system, the systemwide state transition occurs not because the ecological system flips into the undesired state, but because managers lose their capacity to reorganize back to the desired state. An erratum to this article can be found at .     

A multi-objective optimisation approach to water management

The management of river basins is complex especially when decisions about environmental flows are considered in addition to those concerning urban and agricultural water demand. The solution to these complex decision problems requires the use of mathematical techniques that are formulated to take into account conflicting objectives. Many optimization models exist for water management systems but there is a knowledge gap in linking bio-economic objectives with the optimum use of all water resources under conflicting demands. The efficient operation and management of a network of nodes comprising storages, canals, river reaches and irrigation districts under environmental flow constraints is challenging. Minimization of risks associated with agricultural production requires accounting for uncertainty involved with climate, environmental policy and markets. Markets and economic criteria determine what crops farmers would like to grow with subsequent effect on water resources and the environment. Due to conflicts between multiple goal requirements and the competing water demands of different sectors, a multi-criteria decision-making (MCDM) framework was developed to analyze production targets under physical, biological, economic and environmental constraints. This approach is described by analyzing the conflicts that may arise between profitability, variable costs of production and pumping of groundwater for a hypothetical irrigation area.     

BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources.     

混合接种PN与PN/A颗粒污泥快速启动连续流自养生物脱氮反应器

在连续流条件下,快速培养亚硝化-厌氧氨氧化（PN/A）颗粒污泥是实现污水高效生物脱氮处理的关键技术. 与PN/A污泥相比,亚硝化（PN）颗粒污泥具有生长周期短、易于批量化培养的优点,并可作为富集厌氧氨氧化菌（AMX）的载体. 在3个完全混合流反应器（R1~R3）中,分别按照质量比3∶1、1∶1和1∶3混合接种PN/A和PN颗粒污泥,并通过设置高氨氮负荷、短水力停留时间和强水力剪切条件,成功启动了连续流自养生物脱氮工艺. 结果表明,尽管R3的启动时长较R1和R2更长,但污泥接种比并未显著影响连续流反应器在稳定状态下的脱氮性能,总氮去除负荷均可达到2.6 kg·（m³·d）^-1以上. 接种的PN颗粒污泥通过提供好氧氨氧化菌种（AOB）,为AMX生长供给了充足的亚硝态氮基质,充分发挥了培养PN/A颗粒污泥的前驱体作用. 由高通量测序结果可知,R1~R3中成熟颗粒的微生物丰度和多样性指数均明显高于接种污泥. AOB（Nitrosomonas属）和AMX（Candidatus Kuenenia和Brocadia属）与Chloroflexi、Bacteroidetes和Chlorobi等异养菌门是驱动自养生物脱氮和维持颗粒结构稳定的关键菌群. 总之,PN与PN/A颗粒污泥的混合接种是快速启动连续流自养脱氮工艺的可行策略,对工程应用具有指导意义.     

加油站地下储油罐压力管理系统的相关研究

在当前普遍采用的加油站真空辅助式第二阶段油气回收系统中,为了避免回收到地下储油罐中的油气再次通过P/V阀排空,必须对其中气相空间的压力实施有效控制。从国外的发展情况来看,地下储油罐的压力管理系统主要分为基于膜分离技术、基于弹性气囊和基于活性炭吸附三大类,其中膜法油气回收装置在国内已经得到了较多关注。首次系统介绍了气囊式压力管理系统的工作原理及其主要结构设计,具体包括管线布局、钢制压力容器壳体、位于容器顶部封头上的接口组件和全部位于容器内的气囊等;同时对另外一种可行方案进行了分析。     

Property-performance relationship of core-shell structured black TiO2 photocatalyst for environmental remediation

● Properties and performance relationship of CSBT photocatalyst were investigated. ● Properties of CSBT were controlled by simply manipulating glycerol content. ● Performance was linked to semiconducting and physicochemical properties. ● CSBT (W:G ratio 9:1) had better performance with lower energy consumption. ● Phenols were reduced by 48.30% at a cost of $2.4127 per unit volume of effluent. Understanding the relationship between the properties and performance of black titanium dioxide with core-shell structure (CSBT) for environmental remediation is crucial for improving its prospects in practical applications. In this study, CSBT was synthesized using a glycerol-assisted sol-gel approach. The effect of different water-to-glycerol ratios (W:G = 1:0, 9:1, 2:1, and 1:1) on the semiconducting and physicochemical properties of CSBT was investigated. The effectiveness of CSBT in removing phenolic compounds (PHCs) from real agro-industrial wastewater was studied. The CSBT synthesized with a W:G ratio of 9:1 has optimized properties for enhanced removal of PHCs. It has a distinct core-shell structure and an appropriate amount of Ti³⁺ cations (11.18%), which play a crucial role in enhancing the performance of CSBT. When exposed to visible light, the CSBT performed better: 48.30% of PHCs were removed after 180 min, compared to only 21.95% for TiO₂ without core-shell structure. The CSBT consumed only 45.5235 kWh/m³ of electrical energy per order of magnitude and cost $2.4127 per unit volume of treated agro-industrial wastewater. Under the conditions tested, the CSBT demonstrated exceptional stability and reusability. The CSBT showed promising results in the treatment of phenols-containing agro-industrial wastewater.     

土壤重金属的植物污染化学研究进展

针对中国土壤重金属污染加剧的趋势,为改善土壤环境质量和保障农产品安全,提出了土壤重金属的植物污染化学研究领域.结合多年的研究工作,从土壤重金属的植物根际化学行为、土壤重金属的植物吸收与解毒机制和重金属污染土壤的植物-微生物交互作用等方面简要阐述了土壤-植物系统中重金属的分布、存在形态、迁移转化、累积及生物学效应和控制规律的研究进展,并对将来的植物污染化学理论研究提出了展望.     

环境管理、规划管理与项目管理

文章从深入分析环境管理的内涵出发,进而提出树立项目管理意识,强化项目管理的概念,说明项目管理在环境管理中的地位和作用,从这个意义上讨论环境管理与项目管理的关系,简述环境保护项目管理的主要环节,达到提高环境管理效能的途径的目的。     

聚苯乙烯基硫代磺酸钾树脂的合成及其与卤代烃的反应

由聚苯乙烯基磺酰氯树脂与硫化氢钾反应制备聚苯乙烯基硫代磺酸钾树脂（Ｉ）,树脂Ｉ与亲电的卤代烃反应,生成聚苯乙烯基硫代磺酸酯树脂（Ⅱ）,树脂Ⅱ经过乙硫醇处理,使卤代烃结构部分以不对称二硫醚的形式游离下来,用ＧＣ／ＭＳ对其结构进行鉴定。从不对称二硫醚的结构可推导出原卤代烃的结构,所制备的聚苯乙烯基硫代磺酸钾树脂的转化效率为１．１１ｍｍｏｌ·ｇ＾－１。     

活性碳纤维改性技术研究进展

介绍了活性碳纤维(ACF)的孔结构及表面官能团,综述了国内外ACF改性技术的研究现状.ACF的改性主要包括孔结构调控和表面化学改性,表面化学改性包括氧化还原法、表面负载法、浸渍法、热处理法、远程等离子体处理法、微波辐照法等,也可将多种方法结合来对ACF进行改性,以改善和提高ACF的吸附性能.在总结现有研究的基础上,对ACF改性技术发展趋势进行了展望.