中亚热带次生林成林规律及经营技术研究——Ⅱ.次生林的演替规律

本文研究了湖南省中亚热带次生林的种群动态,建立了静态生命表、存活曲线、结构模型以及材积或生物量动态模型,提出了中亚热带次生林的演替基本模式。     

基于PBPK模型评价三氯乙烯的职业暴露健康风险

三氯乙烯(TCE)作为脱脂和清洗剂被广泛应用于五金、电镀和电子等行业。TCE的职业暴露会产生一系列健康风险,包括过敏症和致癌等。2012年TCE被美国环保局(US EPA)和国际癌症研究机构列为1类致癌物。采用吸附管采样-热脱附/气相色谱-质谱法分析了大连市某企业车间生产工况下空气中TCE浓度。基于生理学的药代动力学(PBPK)模型预测了呼吸暴露途径下TCE在职业工人体内组织中的动态分布、代谢产物生成情况和致癌风险。TCE在不同组织中预测的最大浓度呈现出脂肪肠充分灌注室支气管非充分灌注室肝脏静脉血动脉血的趋势。预测的与致癌有关的代谢产物最大浓度表现为三氯乙酸二氯乙酸三氯乙醛S-二氯乙烯基-L-半胱氨酸。在监测的TCE水平(39.2±24.4)μg·m-3下,暴露8 h·d-1,连续暴露20年,基于外暴露评价的职业工人致癌风险均值为1.31×10-5,该暴露水平下,基于PBPK模型预测的TCE内暴露与外暴露计算的致癌风险水平相近,但基于具有致癌性主要代谢产物的内暴露致癌风险值是外暴露风险值的1.17～1.73倍。TCE的暴露水平越高,基于内暴露方法和外暴露方法的致癌风险评价结果差异越大。敏感性分析表明,心输出血流量和充分灌注室血流量对PBPK模型输出结果具有重要影响。不确定性分析表明,模型参数变化会显著地影响PBPK模型输出结果,但变异在可接受水平。本研究结果说明,评价TCE暴露对人的致癌风险需要考虑其在体内的分布和代谢过程。     

城市大气质量模式研究进展

本文通过对大气质量模式发展历史的回顾,及其发展趋势的讨论,认为城市大气质量预报模式的建立与发展主要受以下三个动态因素的支配：（１）政府环境管理目标;（２）科学进步;（３）计算机及信息处理和通讯技术。结合三要素就建立我国城市空气质量预报系统框架进行了讨论。     

粘性泥石流运动模型的实验研究

粘性泥石流运动模型的研究因受实验条件的限制,还很不完善。国内外学者应用较多的５种模型都有一定的适用性,但也都存在不同的缺陷。本文应用粘性泥石流土样中的细颗粒和粗颗粒进行流变实验,不仅得到了粗颗粒在浆体中的沉降特性和泥石流体的流变特性,还研究了粘性泥石流运动模型及其参数。所提模型能很好地反映泥石流体的流变特性,较现有的５种模型能更好地描述粘性泥石流。     

水稻生产气象灾害危险性分析：—以湖南省常德市水稻生产为例

本文通过对研究区水稻气象灾害规律的研究和危险性评价,计算了不同等级的水稻综合风险率,为保险部门制定水稻保险费率提供了科学依据.     

城市给水管网优化设计研究

研究了几种典型的城市给水管网优化设计数学模型,并给出了求解方法,指出在给水管网系统的优化设计中进一步完善广义简约(GRG)算法的必要性.     

Ecological scenarios analyzed and evaluated by a shallow lake model

We applied the complex ecosystem model EMMO, which was adopted to the shallow lake Müggelsee (Germany), in order to evaluate a large set of ecological scenarios. By means of EMMO, 33 scenarios and 17 indicators were defined to characterize their effects on the lake ecosystem. The indicators were based on model outputs of EMMO and can be separated into biological indicators, such as chlorophyll-a and cyanobacteria, and hydro-chemical indicators, such as phosphorus. The question to be solved was, what is the ranking of the scenarios based on their characterization by these 17 indicators? And how can we handle high quantities of complex data within evaluation procedures? The scenario evaluation was performed by partial order theory which, however, did not provide a clear result. By subsequently applying the hierarchical cluster analysis (complete linkage) it was possible to reduce the data matrix to indicator and scenario representatives. Even though this step implies losses of information, it simplifies the application of partial order theory and the post processing by METEOR. METEOR is derived from partial order theory and allows the stepwise aggregation of indicators, which subsequently leads to a distinct and clear decision. In the final evaluation result the best scenario was the one which defines a minimum nutrient input and no phosphorus release from the sediment while the worst scenario is characterized by a maximum nutrient input and extensive phosphorus release from the sediment. The reasonable and comprehensive results show that the combination of partial order, cluster analysis and METEOR can handle big amounts of data in a very clear and transparent way, and therefore is ideal in the context of complex ecosystem models, like that we applied.     

GIS支持下乡镇域土壤肥力评价与分析

在地理信息系统支持下,以鄂南典型红壤丘陵区为例,研究了乡镇域土壤肥力评价的方法和过程,分析了土壤肥力变化原因。结果表明,应用GIS对乡镇域土壤肥力进行评价能克服速度慢、数据更新不方便的缺点,避免乡镇农业决策者主观判断土壤肥力等级的弊病,为生产布局和用地决策提供准确的科学依据。试区土壤肥力与土壤养分、土地利用和区域经济有关,肥力等级有向中等级变化的趋势。     

基于数字流域的水文过程模拟研究

考虑流域下垫面空间变异性,基于数字高程模型构建了数字流域,并在此基础上对描述流域水文物理过程的数学方法进行了探讨,文章认为,数字水文模型是一种有物理基础的包含大容量信息的现代模拟灌河实例研究表明,数字水文模型可以十分方便地输出水文要素和状态变量的空间分布与时间序列,这对充分利用现有观测信息进行水文信息的深层挖掘创新了条件。     

Semi-parametric statistical approaches for space-time process prediction

The problem of estimation and prediction of a spatial-temporal stochastic process, observed at regular times and irregularly in space, is considered. A mixed formulation involving a non- parametric component, accounting for a deterministic trend and the effect of exogenous variables, and a parametric component representing the purely spatio-temporal random variation is proposed. Correspondingly, a two-step procedure, first addressing the estimation of the non- parametric component, and then the estimation of the parametric component is developed from the residual series obtained, with spatial-temporal prediction being performed in terms of suitable spatial interpolation of the temporal variation structure. The proposed model formula-tion, together with the estimation and prediction procedure, are applied using a Gaussian ARMA structure for temporal modelling to space-time forecasting from real data of air pollution concentration levels in the region surrounding a power station in northwest Spain.