Predicting quantiles of water quality from catchment characteristics

Water quality is often highly variable both in space and time, which poses challenges for modelling the more extreme concentrations. This study developed an alternative approach to predicting water quality quantiles at individual locations. We focused on river water quality data that were collected over 25 years, at 102 catchments across the State of Victoria, Australia. We analysed and modelled spatial patterns of the 10th, 25th, 50th, 75th and 90th percentiles of the concentrations of sediments, nutrients and salt, with six common constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate-nitrite (NO_x), and electrical conductivity (EC). To predict the spatial variation of each quantile for each constituent, we developed statistical regression models and exhaustively searched through 50 catchment characteristics to identify the best set of predictors for that quantile. The models predict the spatial variation in individual quantiles of TSS, TKN and EC well (66%–96% spatial variation explained), while those for TP, FRP and NO_x have lower performance (37%–73% spatial variation explained). The most common factors that influence the spatial variations of the different constituents and quantiles are: annual temperature, percentage of cropping land area in catchment and channel slope. The statistical models developed can be used to predict how low- and high-concentration quantiles change with landscape characteristics, and thus provide a useful tool for catchment managers to inform planning and policy making with changing climate and land use conditions.     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

A modified numerical model for moisture-salt transport in unsaturated sandy soil under evaporation

Soil salinization, caused by salt migration and accumulation underneath the soil surface, will corrode structures. To analyze the moisture-salt migration and salt precipitation in soil under evaporation conditions, a mathematical model consisting of a series of theoretical equations is briefly presented. The filling effect of precipitated salts on tortuosity factor and evaporation rate are taken into account in relevant equations. Besides, a transition equation to link the solute transport equation before and after salt precipitation is proposed. Meanwhile, a new relative humidity equation deduced from Pitzer ions model is used to modify the vapor transport flux equation. The results show that the calculated values are in good agreement with the published experimental data, especially for the simulation of volume water content and evaporation rate of Toyoura sand, which confirm the reliability and applicability of the proposed model.     

阿吾拉勒山西段二叠纪火山岩组合与构造环境分析

阿吾拉勒山体西段发育了厚度巨大的下二叠统火山岩,这些火山岩均属碱性玄武岩系列,以钾质类型为主,钠质类型次之,岩浆演化呈现出跨越B型趋势。这种火山活动是在大陆地壳内部产生的,代表了陆内裂谷演化的早期阶段。在早二叠世末期,由于受到新源运动的影响,这种火山活动被终止了。     

广西老厂铅锌矿床地质特征及其成因探讨

本文是在专题研究基础上,探讨广西老厂铅锌矿床地质特征和矿床成因。该矿床位于海洋山复式背斜南段的老厂穹窿构造核部,铅锌矿化受构造、地层和岩性控制。矿体呈脉状,产于NE向组张扭性断裂构造中;围岩为寒武系白云岩,板岩、砂岩;矿石类型以锌为主,矿物成分简单,以闪锌矿、方铅矿与石英、白云石为主,具充填、交代矿石结构、构造,矿石平均品位含Zn9.94%、Pb1.50%;围岩蚀变主要为硅化、绢云母化和碳酸盐化。据地层岩石元素丰度值、稀土元素配分和稳定同位素数据、资料,探讨了成矿物质来源于围岩,热液主要来自大气降水。因此,认为本矿床实属产于寒武系轻变质岩中的层控矿型大气降水热液铅锌矿床。     

NaCl胁迫下杂交稻农艺性状配合力及遗传力分析

采用4×4不完全双列杂交设计,在NaCl胁迫下对杂交水稻主要农艺性状进行了配合力和遗传力分析.结果表明,在调查的10个性状中有4个性状受NaCl胁迫影响差异较大,母本的千粒重及父本的粒厚一般配力差异显著,杂交组合的株高、粒厚、结实率及千粒重特殊配合力差异极显著,其中杂交组合N28S×中花14具有较高的配合力,受NaCl胁迫影响较低。株高及千粒重的广义遗传力和狭义遗传力均较高,可作为杂交水稻耐盐性的选育依据。     

胶东“玲珑-焦家式”金矿资源潜力与找矿

“玲珑焦家式”金矿的成岩成矿,从新太古代至中生代存在着继承性的成因关系,成矿具有长期性、多源性特点,但中生代燕山早期岩浆活动是主导成矿因素。区域化探成果显示胶东金矿直接矿源岩———郭家岭花岗岩金的丰度比原始矿源岩———早前寒武纪变质沉积岩系及侵入岩系金的丰度低1/2。郭家岭花岗岩面积在2 km2以上岩体定量估计金亏损总量与胶东金矿总量预测的资源量相当,从一个侧面展现了胶东金矿资源的潜力。望儿山金矿的找矿实践展示,焦家式金矿在1 000 m以下尚有巨大潜力,整个胶东金矿产区在3 000 m以上的资源潜力应在5000 t以上。     

物探方法在江西省茅排金矿的应用效果

茅排金矿床属中－低温硫化物金矿床，矿体的分布严格受断裂构造控制．通过应用不同的物探方法来寻找控矿断裂，从而达到间接地寻找矿体分布的目的，取得了较好的效果。     

四川石棉独立碲矿床蚀变岩的地质地球化学研究

四川石棉大水沟独立碲矿床堪称世界首例，其矿体两侧的围岩蚀变较窄，且蚀变带与矿脉及未蚀变岩呈清楚而截然的接触关系。主要围岩蚀变为白云石化、白（绢）云母化、云英岩化及电气石化等。这些蚀变作用分别发生于１７７．７～１６５．１Ｍａ和９１．７１～８０．１９Ｍａ。蚀变作用过程中ＳｉＯ２大量析出，Ａｌ２Ｏ３、ＴｉＯ２、ＣａＯ、ＭｇＯ、Ｋ２Ｏ、Ｈ２Ｏ、ＣＯ２、Ｆｅ２Ｏ３、ＭｎＯ则有不同程度地带入。与此同时，ＲＥＥ在此过程中包集体迁出，Ｔｅ、Ｂｉ、ＡＳ等元素的含量则决定于蚀变作用及其强度，而与原岩无关。