Z系列多功能药剂用于处理油田废水

采用Z系列多功能药剂处理油田废水,使絮凝、阻垢、防腐和 杀菌过程一步完成。处理后水中总铁质量浓度≤0.5mg/L,Ca^2 、Mg^2 的质量浓度由1875mg/L降低到350mg/L,其他指标匀达到部颁标准。处理 后水的水质更接近地层水,具有良好的稳定性和配伍性。     

Using diatoms to assess the impacts of prehistoric, pre-industrial and modern land-use on Danish lakes

The impact of contemporary agriculture on Danish lakes is acknowledged to be extreme. In particular, high loading of nutrients from agricultural soils contributes to the eutrophic conditions found in many of Denmark’s lakes. Palaeolimnological studies have shown that human disturbance of the Danish landscape since the introduction of agriculture around 6,000 years ago has had a major impact on lake ecosystems. The European Union’s Water Framework Directive requires an evaluation of reference conditions for lakes, the conditions expected with only minimal human impact. Monitoring data and palaeolimnological studies of Danish lakes demonstrate that many of the most detrimental effects of eutrophication have been experienced in recent decades. A new study has suggested that the reference status for Danish lakes may be set to the status in ad 1850–1900, probably providing attainable, realistic restoration targets for many sites. The aims of this study were to explore the impacts of past and contemporary land-use on Danish lakes, and to consider how appropriate the use of 1850 as a date to define reference status is for these sites. Catchment land-cover data for ad 1800, taken from historical maps, and sedimentary diatom assemblages of the same age, from dated sediment cores, were used to assess the impact of pre-industrial land-use on 20 Danish lakes. Analysis of contemporary land-cover data and surface-sediment diatom assemblages for the 20 sites was also made. In-lake total phosphorus (TP) concentrations were estimated using the sedimentary diatom assemblages and an existing calibration dataset for Danish lakes. The percentage of the lake catchment that was agricultural land in ad 1800 explained 8.8% of the total variation in the diatom data. The land-cover variables ‘built-up areas’ and ‘plantations’, together explained 16.9% of the variation in the diatom data for the modern samples. Diatom-inferred TP concentrations were high for both ad 1800 (mean 112 μg TP L⁻¹) and the present (mean 122 μg TP L⁻¹), the latter estimates reflecting efforts in recent decades to reduce nutrient loading to Danish lakes following very high levels of nutrient enrichment post-1950. The data presented highlight the impact that human activities 200 years ago, particularly agriculture, had on Danish lake systems. The long cultural history and major anthropogenic disturbance of the Danish landscape mean that true reference conditions for lakes (or ‘baseline’ conditions, those found prior to human impacts) can be found only by considering century to millennial timescales.     

The application of artificial neural networks to flow and phosphorus dynamics in small streams on the Boreal Plain,with emphasis on the role of wetlands

Historically, management strategies in Canada's boreal forest have focused on forest polygons and terrestrial biodiversity to address ecological considerations in forest management. The Forest Watershed and Riparian Disturbance (FORWARD) project examines the problem from a watershed perspective rather than a forest polygon viewpoint. The main objective of this study was to devise an artificial neural network (ANN) modeling tool that can predict flow and total phosphorus (TP) concentration for ungauged watersheds (where daily flow is not monitored). This dictates that all inputs should be easily accessed via a public domain database, like the Environment Canada weather database, without the need to install flow gauges in each modeled watershed. Daily flow and TP concentration for two of the project watersheds were modeled using ANNs. The two watersheds (1A Creek, 5.1 km² and Willow Creek, 15.6 km²) were chosen to reflect variations in wetland area and composition in the study area. Flow was modeled with a feed-forward multilayer perceptron ANN trained with the error back-propagation algorithm. Simulated values for flow were then used, as inputs, to model TP concentration using the same neural networks algorithm. One hidden layer with three slabs; each operating with a different activation function was utilized to simulate the conceptual differences between base flow, snowmelt, and storm events. Time domain analysis was conducted to identify possible model time-lagged inputs reflecting the time dependency of the modeled variables. Spectral analysis was used to address data hystereses. Our results highlight the capabilities of ANN in modeling complex ecosystems and highly correlated variables. Results also indicated that more research towards the phosphorus dynamics in wetlands is required to better represent the impact of wetland area and composition on the water-phase phosphorus in ANN modeling.     

TIME SERIES ANALYSIS IN PERSPECTIVE1

ABSTRACT: By employing a set of criteria for classifying the capabilities of time series models, recent developments in time series analysis are assessed and put into proper perspective. In particular, the inherent attributes of a wide variety of time series models and modeling procedures presented by the authors of the 18 papers contained in this volume are clearly pointed out. Additionally, it is explained how these models can address many of the time series problems encountered when modeling hydrologic, water quality and other kinds of time series. For instance, families of time series models are now available for modeling series which may contain nonlinearities or may follow nonGaussian distributions. Based upon a sound physical understanding of a problem and results from exploratory data analyses, the most appropriate model to fit to a data set can be found during confirmatory data analyses by following the identification, estimation and diagnostic check stages of model construction. Promising future research projects for developing flexible classes of time series models for use in water resources applications are suggested.     

REGIONALIZATION AND PREDICTION OF FLOODS IN THE FRASER RIVER CATCHMENT,B.C.1

Probability distributions that model the return periods of flood characteristics derived from partial duration series are proposed and tested in the Fraser River catchment of British Columbia. Theoretical distributions describing the magnitude, duration, frequency and timing of floods are found to provide a goof fit to the observed data. The five estimated parameters summarizing the flood characteristics of each basin are entered into a discriminant analysis procedure to establish flood regions. Three regions were identified, each displaying flood behavior closely related to the physical conditions of the catchment. Within each region, regression equations are obtained between parameter values and basin climatic and physiographic variables. These equations provide a satisfactory prediction of flood parameters and this allows the estimation of a comprehensive set of flood characteristics for areas with sparse hydrologic information.     

WATER CONSERVATION KITS: A TIME SERIES ANALYSIS OF A CONSERVATION POLICY1

ABSTRACT: The effect on water usage of installing water conservation kits in the city of Oxnard, California, is statistically evaluated. Using binary multiple regression analysis on a large sample of residential units, water consumption was compared before and after receipt of the kit. For this sample, installers reduced water consumption by 4.2 percent each billing period. Income (wealth) and household size elasticities are reported along with the Characteristics of the installing households.     

A MONTHLY TIME SERIES MODEL OF MUNICIPAL WATER DEMAND1

A multivariate time series model is formulated to study monthly variations in municipal water demand. The left hand side variable in the multivariate regression model is municipal water demand (gallons per connection per day) and the right hand side contains (explanatory) variables which include price (constant dollars), average temperature, total precipitation, and percentage of daylight hours. The application of the regression model to Salt Lake City Water Department data produced a high multiple correlation coefficient and F-statistic. The regression coefficients for the right hand side variables all have the appropriate sign. In an ex post forecast, the model accurately predicts monthly variations in municipal water demand. The proposed monthly multivariate model is not only found useful for forecasting water demand, but also useful for predicting and studying the impact of nonstructural management decisions such as the effect of price changes, peak load pricing methods, and other water conservation programs.     

高山区冰川突发洪水混沌机制研究

首先,介绍了重构相空间技术,并揭示了混沌特性的主要定量指标,即关联分维数Ｄ２,李雅普诺夫（Ｌｙａｐｕｎｏｖ）指数和柯尔莫奇诺夫（Ｋｏｌｏｍｏｇｏｒｏｖ）熵。然后,对叶尔河冰川突发洪水这一典型自然系统的无序现象,从洪水时序列出发,通过对上述指标的分析,讨论了高山区冰川突发洪水过程和形成的混沌机制,表明该类洪水时间序列具有混沌动力系统的一些特征。     

淮河流域洪涝变化可预报时间研究

在先前的淮河流域洪涝变化基本特征、混沌特征、耗散性等基础上,采用Ｋｏｌｍｏｇｏｒｏｖ熵和Ｌｙａｐｕｎｏｖ指数谱分析了淮河流域洪涝变化预报时间。研究表明,淮河流域洪涝变化长期不可预报,短期是可以预报的,其可预报时间为４￣６年,以４年为较合适。     

蒽醌染料废水处理技术

对蒽醌染料废水分别用混凝沉淀法,微电池法,Ｏ３法进行了预处理试验,用生物膜ＳＢＲ法和活性污泥ＳＢＲ法进行了生化处理试验。试验数据表明,废水经ＰＡＭ混凝沉淀－铁炭微电池预处理和生物膜ＳＢＲ处理后,可达标排放。