5种吸附剂对水中离子液体的吸附性能

实验研究了蒙脱土、活性白土、人造沸石、活性炭和水滑石5种吸附材料在25℃和50℃下对[Bmim]Cl的吸附过程,结果表明,蒙脱土对离子液体的去除效果最好,温度对活性炭的吸附有一定的影响,而对其他吸附剂的影响不大,说明这些无机矿物发生了离子交换型吸附过程。实验进一步研究了蒙脱土在25℃下对[Bmim]Cl、[Bmim]BF4、[Bmim]PF6、[Bmim]DBP、[Hmim]Cl和[Omim]Cl 6种离子液体的吸附行为,结果表明,对于阳离子相同的离子液体,阴离子对吸附影响不大;对于阴离子相同的离子液体,其饱和吸附量随着阳离子链长的增加而减小。     

面向矿山废弃地复垦的炉渣污泥人工土壤的理化特性

通过添加不同比例的燃煤电厂粉煤灰和炉渣对城市污泥进行钝化,配制成不同灰渣比例的人工土壤,对人工土壤的pH、电导率、养分含量、重金属含量以及持水、渗水性能进行了研究。结果表明,污泥人工土壤pH介于7.5～8.0之间,属于弱碱性土壤;电导率变化范围为2.93～3.71 mS/cm,表明盐分含量较高。污泥人工土壤中有效态养分含量随炉渣比例提高而增加,所有配比人工土壤有机质和有效磷、速效钾的含量达到高肥力水平。人工土壤中Cd、Ni、Pb、Cu、Zn含量均明显低于国家农用标准(GB 4284-84、GB 8173-87)。粉煤灰渣对污泥中有效态Cd、Ni、Pb和Cu有钝化作用,使其向无效态转化,对Zn则有活化作用。炉渣对Cd、Ni的钝化作用要优于粉煤灰,而粉煤灰对Pb、Cu的钝化作用要优于炉渣。添加炉渣提高了污泥人工土壤水分的蒸发量,减弱了人工土壤的持水能力,但加入炉渣使人工土壤的渗透系数显著增加,提高了其渗水性能。     

长江上游月降水人工神经网络预测模型

长江上游月降水量预测对于三峡库区及整个长江流域水资源管理具有重要意义。根据长江上游不同气候区降水差异,选取玉树、九龙和宜宾3个代表性气象站点近60 a的月降水量数据,运用反向传播神经网络、径向基函数神经网络、广义回归神经网络和多元线性回归法,确定降水时滞和降水月份,建立月降水预测模型,来预测未来一个月的降水量,并采用均方误差和判定系数来验证和对比各种模型的模拟效果。结果显示：人工神经网络模型总体上优于多元线性回归,特别是反向传播神经网络的模拟结果各站表现较好,在确定合理的输入变量和网络结构后,可以尝试作为长江上游各站月降水预测模型。〖     

不同菌剂处理下猪粪和牛粪堆肥前期性质变化

以猪粪和牛粪为堆肥原料,设计了人工接种MixF-3菌剂堆肥、添加灭活菌剂堆肥和自然堆肥3个处理进行堆肥前期实验,探讨堆肥9d过程中温度、水分、有机碳、全氮和微生物量碳等参数的变化。结果表明,按1L/t接种MixF-3混合菌剂堆肥温度升温快,猪粪堆肥第48h和第84h时温度分别达到66℃和71.5℃,牛粪堆肥第48h时温度达69℃,明显高于灭活菌剂处理组和CK对照组。随着堆肥的进行,各处理组水分含量缓慢下降,牛粪比猪粪含水量略高。人工接种菌剂的猪粪和牛粪堆肥处理组中pH变化幅度小,有机碳碳含量的下降幅度比灭活组和自然堆肥组大,分别下降15.64%和11.25%,全氮含量缓慢升高但变化不大,人工接种牛粪比猪粪处理组中堆肥全氮含量升幅高;在猪粪和牛粪堆肥中人工接种菌剂微生物量碳降幅最小,分别为9537μg/g和3212μg/g,表明在猪粪和牛粪堆肥原料中添加MixF-3复合菌剂有利于微生物繁殖生长,同时拓宽了菌剂使用的单一性,更具有普遍性意义。     

随机森林理论及其在水文地质领域的研究进展

随机森林理论是近年来快速发展的一种人工智能集成学习算法,由于其对数据系列中异常值的容忍度较高,且预测结果准确度显著高于其他常用算法,在以水文地质领域为代表的自然科学研究中的应用越来越广泛.本文在介绍随机森林算法理论和应用方法的基础上,结合国外内已有研究成果,分析其在地下水潜力评估、地表水-地下水转化、地下水水质评价和地下水污染预测等水文地质领域的应用效果,对随机森林理论在水文地质领域的应用前景和进一步发展的方向进行了讨论.结果表明,随机森林理论可以有效解决水文地质领域研究中的参数和过程不确定性问题,在水文地质结构精确刻画、水文地质参数准确反演、水文地质过程的描述均具有广阔的应用前景.     

面向国土空间规划实施监督的监测评估预警模型体系研究

国土空间规划监测评估预警是监督规划实施管理、保障规划实施成效的重要手段,也是实现“可感知、能学习、善治理、自适应”智慧规划转型的关键支撑。针对当前国土空间规划监测评估预警研究重机制、轻理论,以及缺乏一套科学可用的监测评估预警技术方法支撑的不足,在系统分析国土空间规划监测评估预警目标与内涵的基础上,构建以人工智能（Artificial Intelligence）、大数据（Big Data）、云计算（Cloud Computing）等新型信息技术(“ABC”技术）为支撑,覆盖国土空间规划“监测—评估—预警”业务全生命周期的应用模型体系,并结合信息系统建设案例,介绍了基于“ABC”技术的国土空间规划监测评估预警模型体系对实现各地国土空间规划“动态监测、精准评估、及时预警”的重要支撑作用。     

岷江上游退耕还林与生态恢复的问题和对策

退耕还林因地域的差异存在不同的问题。长江上游山区本质属于受干扰山地系统,生态恢复需从多尺度和多角度入手。作为一个契机,退耕还林对生态恢复的作用在持续性和生态性上存在着矛盾和统一,同时也存在不同利益主体之间的博弈。针对岷江上游区域,在分析了社会、生态复杂性的基础上,通过对不同区域的对比,讨论了人工林种植后的生态效应,研究发现抚育较好的人工林生物多样性和土壤质量指数增加,人工林需要加强人为的干预,并根据在该区域的社会经济调查,指出了退耕还林存在的问题并提出了相应的对策。     

长江流域湿地特点及其开发利用

湿地系一种具有多功能的土地资源类型,不仅对农业,而且对水产业均具有开发利用价值,同时还是珍禽栖息地和鱼类繁殖场所。湿地可分为自然湿地和人工湿地两大类型。长江流域湿地分布面积较大,类型较多,具有重要的开发利用价值,尤其人工湿地系我国粮、棉、油生产基地,生产水平高,但仍然具有较大的增产潜力。湿地开发利用应从多目标、多途径进行,出产丰富产品,供应城乡需求,同时为美化环境、保护区域生态环境服务。     

Estimates of Natural Selection in a Salmon Population in Captive and Natural Environments

Abstract:  Captive breeding is a commonly used strategy for species conservation. One risk of captive breeding is domestication selection—selection for traits that are advantageous in captivity but deleterious in the wild. Domestication selection is of particular concern for species that are bred in captivity for many generations and that have a high potential to interbreed with wild populations. Domestication is understood conceptually at a broad level, but relatively little is known about how natural selection differs empirically between wild and captive environments. We used genetic parentage analysis to measure natural selection on time of migration, weight, and morphology for a coho salmon ( Oncorhynchus kisutch ) population that was subdivided into captive and natural components. Our goal was to determine whether natural selection acting on the traits we measured differed significantly between the captive and natural environments. For males, larger individuals were favored in both the captive and natural environments in all years of the study, indicating that selection on these traits in captivity was similar to that in the wild. For females, selection on weight was significantly stronger in the natural environment than in the captive environment in 1 year and similar in the 2 environments in 2 other years. In both environments, there was evidence of selection for later time of return for both males and females. Selection on measured traits other than weight and run timing was relatively weak. Our results are a concrete example of how estimates of natural selection during captivity can be used to evaluate this common risk of captive breeding programs.     

Field‐Scale Application of Three Types of Neural Networks to Predict Ground‐Water Levels1

Abstract: In this paper, a field‐scale applicability of three forms of artificial neural network algorithms in forecasting short‐term ground‐water levels at specific control points is presented. These algorithms are the feed‐forward back propagation (FFBP), radial basis networks (RBN), and generalized regression networks (GRN). Ground‐water level predictions from these algorithms are in turn to be used in an Optimized Regional Operations Plan that prescribes scheduled wellfield production for the coming four weeks. These models are up against each other for their accuracy of ground‐water level predictions on lead times ranging from a week to four weeks, ease of implementation, and execution times (mainly training time). In total, 208 networks of each of the three algorithms were developed for the study. It is shown that although learning algorithms have emerged as a viable solution at field scale much larger than previously studied, no single algorithm performs consistently better than others on all the criteria. On average, FFBP networks are 20 and 26%, respectively, more accurate than RBN and GRN in forecasting one week ahead water levels and this advantage drops to 5 and 9% accuracy in forecasting four weeks ahead water levels, whereas GRN posted a training time that is only 5% of the training time taken by that of FFBP networks. This may suggest that in field‐scale applications one may have to trade between the type of algorithm to be used and the degree to which a given objective is honored.