FACTORS INVOLVED IN EVALUATING GROUND WATER IMPACTS OF DEEP COAL MINE DRAINAGE1

ABSTRACT: The determination of probable ground water impacts of proposed deep coal mining is required as part of permit applications. Impact prediction generally involves well production test analysis and modeling of ground water systems associated with coal seams. Well production tests are often complicated due to the relatively low permeabilities of sandstones and shales of ground water systems. The effects of the release of water stored within finite diameter production wells must be considered. Well storage capacity appreciably affects early well production test time drawdown or time recovery data. Low pumping rates, limited cones of depression, and length of required pumping periods ate important well production test design factors. Coal seam ground water system models are usually multilayered and leaky artesian. Mine drafts partially penetrate the ground water system. Simulation of coal mine drainage often involves the horizontal permeability and storage coefficient of the coal seam zone, vertical permeabilities of sandstones and shales (aquitard) above and below the coal seam zone, and the hydrologic properties of the source bed above the aqultard overlying the coal seam zone. Ground water level declines in both the coal seam zone and source bed near land surface are necessary factors in impact analysis. An example of evaluation studies in southwest Indiana will illustrate factors involved in deep coal mine drainage modeling efforts.     

A HYBRID COMPUTER PROGRAM FOR PREDICTING THE CHEMICAL QUALITY OF IRRIGATION RETURN FLOWS1

ABSTRACT. A hybrid computer program was developed to predict the water and salt outflow from a river basin in which irrigation is the major user of water. The model combines a chemical model which predicts the quality of water percolated through a soil profile with a general hydrologic model. The chemical model considers the reactions that occur in the soil, including the exchange of calcium, magnesium, and sodium cations on the soil complex, and the dissolution and precipitation of gypsum and lime. The chemical composition of the outflow is a function of these chemical processes within the soil, plus the blending of undiverted inflows, evaporation, transpiration, and the mixing of sub surface return flows with groundwater. The six common ions of western waters, namely calcium (Ca⁺⁺), magnesium (Mg⁺⁺), sodium (Na⁺), sulfate (SO₄⁼), chloride (Cl^?), and bicarbonate (HCO₃^?) were considered in the study. Total dissolved solids (TDS) outflow was obtained by adding the individual ions. The overall model operates on a monthly time unit. The model was tested on a portion of the Little Bear River basin in northern Utah. The model successfully simulated measured outflows of water and each of the six ions for a 24-month period. The usefulness of the model was demonstrated by a management study of the prototype system. For example, preliminary results indicated that the available water supply could be used to irrigate additional land without unduly increasing the salt outflow from the basin. With minor adjustments the model can be applied to other hydrologic areas.     

梁腹板开圆孔的钢框架抗震节点

介绍了梁腹板开圆孔的钢框架梁柱节点 ,笔者探讨的基本思路是希望通过对梁截面的削弱确保结构在强震作用时的塑性铰远离节点域而出现在梁上 ;同时 ,该节点形式应用于住宅体系时 ,使水暖管线穿梁和降低结构空间占用成为可能。因此 ,笔者对该节点形式的基本受力性能及设计中需要考虑的构造措施进行了较详细的探讨 ,并利用ANSYS对其塑性区分布和剪应力分布进行了分析。研究结果初步表明 ,梁腹板开圆孔的钢框架节点具有理论上的合理性和实用上的可行性 ,可达到塑性铰外移和增加使用功能的目的。     

复杂地基重力坝深层抗滑稳定非线性有限元分析

重力坝的深层抗滑稳定是保证大坝安全的一个重要条件。针对《混凝土重力坝设计规范》(DL5 10 8—1999)中关于复杂地基重力坝的稳定性需进行有限元分析的新规定,笔者结合某重力坝稳定问题实例,用非线性有限元方法进行了分析计算,并与刚体极限平衡法及结构可靠度分析方法的成果进行了对比。对有限元法的适用性,岩体侧面阻滑力的影响及抗滑稳定安全度的表达方法等问题进行了探讨     

深埋地铁岛式站点火灾模型实验研究(2)--列车火灾

列车停靠站台时一旦发生火灾,火灾烟气将向站台和区间隧道空间蔓延.尤其对于深埋地铁车站,如何控制车站列车火灾是地铁设计过程中必需解决的科学问题之一.这里,笔者利用深埋地铁车站模型实验台研究了列车停靠在站台时发生火灾情况下,火灾烟气蔓延规律,分析了火灾烟气有效控制方案,研究结果有利于火灾时深埋车站排烟模式的选择.     

草原旅游资源深度开发研究——以内蒙古自治区为例

旅游资源是构成旅游业发展的基础，草原旅游资源的合理利用与深度开发具有重要意义。着眼于内蒙古草原旅游现状，通过分析内蒙古草原旅游资源的特征和深度开发的必要性与可行性，提出开发理念、开发模式、区域合作和产品开发方面的设想，尤其基于资源的状况提出3种资源开发模式：依托市场模式、组合集聚模式和依托资源模式。     

深部岩体分区破裂化模拟试验模型几何尺寸的确定

为了揭示深部岩体在开挖时所发生的一系列变化情况,需要进行模拟试验研究。简要介绍了深部岩体在开挖洞室或巷道时发生的分区破裂化现象及其发展规律。根据分区破裂化力学现象、模拟试验背景和试验加载条件,运用弹性力学的基本理论,通过分析与计算,确定了采用b/a≥10作为模型几何参数的选取原则。在实验室条件下,可以采用石膏半径b=1 000 mm,钻孔半径a=100 mm的圆柱体模型来模拟深部岩体的分区破裂化现象及其规律。     

Googling Trends in Conservation Biology

Web‐crawling approaches, that is, automated programs data mining the internet to obtain information about a particular process, have recently been proposed for monitoring early signs of ecosystem degradation or for establishing crop calendars. However, lack of a clear conceptual and methodological framework has prevented the development of such approaches within the field of conservation biology. Our objective was to illustrate how Google Trends, a freely accessible web‐crawling engine, can be used to track changes in timing of biological processes, spatial distribution of invasive species, and level of public awareness about key conservation issues. Google Trends returns the number of internet searches that were made for a keyword in a given region of the world over a defined period. Using data retrieved online for 13 countries, we exemplify how Google Trends can be used to study the timing of biological processes, such as the seasonal recurrence of pollen release or mosquito outbreaks across a latitudinal gradient. We mapped the spatial extent of results from Google Trends for 5 invasive species in the United States and found geographic patterns in invasions that are consistent with their coarse‐grained distribution at state levels. From 2004 through 2012, Google Trends showed that the level of public interest and awareness about conservation issues related to ecosystem services, biodiversity, and climate change increased, decreased, and followed both trends, respectively. Finally, to further the development of research approaches at the interface of conservation biology, collective knowledge, and environmental management, we developed an algorithm that allows the rapid retrieval of Google Trends data.     

基于深度学习的华东地区PM2.5浓度遥感反演

PM_2.5作为大气污染的主要来源,对人类身体健康有着极大的影响.本文提出基于深度学习模型的多要素联合PM_2.5反演方法,以PM_2.5浓度作为真值数据,引入Himawari气溶胶光学厚度（AOD）日数据产品与温度、相对湿度和气压等10个要素作为反演要素.为验证方法的有效性,采用华东地区2016~2018年的数据分季节开展实验,并与传统反演方法进行对比.结果表明,PM_2.5浓度与AOD、降水、风速、高植被覆盖指数呈正相关关系,与矮植被覆盖指数呈负相关关系,与温度、湿度、气压以及DEM的相关性随季节的变化而改变;基于深层神经网络（DNN）反演的PM_2.5精度高于传统的线性和非线性模型,各个季节R²均在0.5以上并且误差较小,其中秋季的反演效果最好R²为0.86,夏季为0.75,冬季为0.613,春季为0.566;模型的可视化结果显示,DNN模型的反演结果更接近地面监测站点插值的PM_2.5浓度分布,分辨率更高且更精确.     

海底深部生物圈真菌的研究现状

海底深部生物圈是科学大洋钻探的最大发现之一,已证明以厌氧、高压、贫营养、温度跨幅大为特点的海底深部生物圈中存在大量微生物类群,其中研究较多的是细菌和古菌,而对真菌的研究则很少。本文重点综述目前有关海底深部生物圈真菌的研究进展,为我国从事海底深部生物圈真菌研究提供参考。