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。     

短程硝化—厌氧氨氧化组合工艺在脱氮领域的研究进展

基于全程硝化反硝化的传统生物脱氮工艺在硝化过程中需要大量氧气供应,反硝化过程需要有机物作为碳源,存在能耗与药耗过大的问题.为了降低废水脱氮的成本,短程硝化(PN)—厌氧氨氧化(ANAMMOX)组合工艺(PNA工艺)得到了高速发展.综述了PNA工艺的影响因素,重点介绍了4种基于PN与ANAMMOX原理开发的衍生PNA工艺...     

负载型纳米TiO2光催化降解水中微量溶解性间二甲苯

以高压汞灯为光源 ,采用浸涂 -烧结法制备的负载型纳米TiO2 作为光催化剂 ,通过对水中微量溶解性间二甲苯的光催化氧化过程的研究表明 ,初始浓度在 6 .6 8— 17.36mg/L的范围内 ,间二甲苯的光催化反应遵循表观一级反应动力学规律 ,反应的表观速率常数随溶液初始浓度的增大而减小 ,半衰期则随初始浓度的增大而增加 ,经 1.5h反应后 ,溶液中间二甲苯的去除率从 17.36mg/L的 5 4 .4 4 %增加到 6 .6 8mg/L的 75 .90 %。     

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。     

二段生物接触氧化工艺在中水回用中的特性研究

以规模小区生活污水为水源,对采用聚丙烯网状填料和强化炉渣组合填料的二段生物接触氧化工艺（以下简称二段法）进行了试验研究,试验针对本工艺的各项技术指标、影响因素以及各相关参数进行了较系统的试验测试。结果表明,反应器在第一接触氧池HRT为20 min、气水比为6∶1和第二接触氧池HRT为25 min、气水比为5∶1条件下,接触氧化池达到最佳降碳条件;反应器在第一接触沉池上升流速5.5 m/h和第二接触沉池上升流速4.5 m/h条件下,接触沉淀池达到最佳处理条件。     

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。     

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.     

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.