氯胺消毒管网“三氮”与总氯变化的预测模型

为探讨氯胺消毒供水管网的水质问题,建立了一种试验规模的氯胺消毒供水管网水质模拟系统。基于质量守恒原理和双Monod方程建立了该系统的硝化与氯胺衰减动力学耦合模型,并通过试验对该模型进行了验证。模型中的部分参数采用非线性多项式回归方法进行估计。应用该模型确定了活性氨氧化菌(AOB)和亚硝酸盐氧化菌(NOB)的数量。该模型对管网模拟系统内氨氮、硝酸盐氮与总氯的预测值与实测值基本一致,但对亚硝酸盐氮的预测值与实测值存在较大偏差。     

驾驶行为模型的研究进展

驾驶员行为模型的研究对于预测和干预驾驶员的风险行为、设计相关的道路安全设施与车内设备,以及制定交通法律法规等具有重要的意义。为了解和掌握学术界关于驾驶行为模型的研究进展,搜集、筛选和归纳了1960—2010年被SCI数据库索引的相关文章,将驾驶行为模型分类为描述性模型、信息处理模型、动机模型、计划行为理论(TPB)和躯体标识假设,并对每种模型进行评述和总结,理清这些模型间的内在联系。研究发现,现有各模型只是从某个角度研究驾驶员行为的部分特征,而不能解释驾驶员的全部行为。今后应不断完善和整合各类模型,并借鉴心理学、生理学和行为科学等相关领域的理论、知识,使驾驶行为模型变得更为实用、有效。     

火灾烟气毒性的定量评价方法评述

火灾烟气毒性定量评价是毒理学研究的一个新兴领域.烟气毒性与建筑材料、燃烧条件、烟气毒物在建筑物内的传播规律及暴露时间有关,这些都是传统毒理学评价方法难以解决的问题.烟气毒性定量评价的目的是通过建立数学模型,以最少的动物试验,从宏观上更加定量化、系统化地评价和预测火灾烟气毒性.本文介绍了国外火灾烟气毒性定量评价的标准、烟气定量评价的数学模型,评述了这些模型的适用范围和优缺点,分析了火灾烟气毒性定量评价的发展方向.     

Numerical and field investigation of enhanced in situ denitrification in a shallow-zone well-to-well recirculation system

This study investigated efficiency of in situ enhanced biological denitrification of nitrate-contaminated groundwater which employs a well-to-well circulation in a shallow zone where oxygen might give an adverse affect on the denitrification processes. The numerical model developed for the efficiency test included sequential aerobic and nitrate-based respiration, multi-Monod kinetics of reactive components, growth and decay of biomass, and denitrification inhibition associated with the presence of oxygen. Moreover, reaction kinetics for production of toxic intermediates such as nitrite and nitrous oxide were also included in the model. The developed model was applied to the analysis of enhanced in situ denitrification using an injection/extraction well pair. To evaluate the relative remediation effectiveness, comparisons were made between a continuous fumarate injection test (CFIT) system and a pulsed fumarate injection test (PFIT) system, where both systems had the same total fumarate mass injected into the aquifer. The PFIT system was preferable to the CFIT system because of the high possibility of occurrence of clogging in the latter case at the injection well, with no other significant advantages found in either the CFIT or the PFIT system. Accordingly, this developed numerical model is useful to predict and evaluate an in situ bioremediation by denitrification in aquifers.     

Instream and offstream environmental conditions and stream biotic integrity: Importance of scale and site similarities for learning and prediction

Two different methods to predict biotic integrity were tested and compared in the present paper. The first one tries to predict the fish indices of biotic integrity (IBI) at the state or regional scale based on the most similar observations to a specific target site of interest using the simple to implement k-nearest neighbors (or kNN) method. Two different distance functions were considered to find the k-nearest neighbors: the Euclidean and the Mahalanobis. The second method was applied on the same datasets and consisted of a step-wise multiple regression. The two modeling approaches yielded similar results but kNN proved to be more time-efficient and very fast computationally for the given dataset sizes, which allowed applying a leave-one-out cross validation.In an attempt to reveal the importance of scale in the prediction of IBI, regression models were constructed at the state (or regional) scale and at the more refined cluster of sampling sites scale. Clusters of sites were extracted using Kohonen's self-organizing maps (SOM) followed by k-means clustering of the SOM neurons. Cluster-level regression models, constructed after site patterning, performed better in IBI prediction than global regression models constructed without any previous site patterning. The importance of identifying groups of sites with similar environmental characteristics affecting the IBI was revealed. The combined use of site patterning and regression modeling for IBI prediction also helped identifying important variables acting at the local scale which remain latent at the global scale.     

Modeling changes in the coastal ecosystem of the Pearl River Estuary from 1981 to 1998

The coastal ecosystem of the Pearl River Estuary (PRE) has been overfished and received a high level of combined pollution since the 1980s. Ecopath with Ecosim was used to construct two ecosystem models (for 1981 and 1998) to characterize the food web structure and functioning of the ecosystem. Pedigree work and simple sensitivity analysis were carried out to evaluate the quality of data and the uncertainty of the models. The two models seem reliable with regards to input data of good quality. Comparing the variations of outputs of these two models aimed to facilitate assessment of changes of the ecosystem during the past two decades.The trophic structure of the ecosystem has changed with an increase in the biomass proportion of lower trophic level (TL) organisms and a decrease in top predator biomass proportion. All the indices of ecosystem maturity examined show that the system was in a more mature condition in 1981 than in 1998, although the system has been in a condition of stress due to anthropogenic disturbances, such as environmental pollution and habitat destruction since 1981. The ecosystem was aggregated into six and seven integral TLs in 1981 and 1998, respectively, using the trophic aggregation routine of Ecopath. Most of the total system biomass and catch took place at TL II and III in both years. But the distribution of the total system biomass and catch at different TLs changed with decreasing proportions in higher TLs in 1998. The mean transfer efficiency was 9.1% and 10.2% in 1981 and 1998, respectively.Comparative network analysis allowed quantification of the importance of direct and indirect trophic interactions among functional groups. Moreover, a method derived from the mixed trophic impact (MTI) analysis allowed estimating importance of groups in terms of “keystoneness” and identifying the keystone species in the two models over the past two decades. The results indicate that there were no clear keystone species in 1998 but two keystone species at medium trophic levels were identified in 1981. Moreover, organisms located at low trophic levels such as phytoplankton, zooplankton and benthic invertebrates were identified to have relatively high keystoneness in the ecosystem.     

Are closure terms appropriate or necessary descriptors of zooplankton loss in nutrient–phytoplankton–zooplankton type models?

Our current knowledge of plankton ecology ascribes a large proportion of zooplankton losses to zooplankton cannibalism and carnivory, rather than via the activity of higher trophic levels beyond the plankton. However, planktonic ecosystem models, such as the widely used nutrient–phytoplankton–zooplankton (NPZ) type models, typically represent all zooplankton losses by mathematically (rather than biologically) justified closure functions. Even where it is assumed that these closure functions include zooplanktonic cannibalism and carnivory, these processes are not explicitly implemented within the grazing function of the zooplankton. Here it is argued that this representation of zooplankton losses through “closure” terms within planktonic food web models is neither appropriate nor necessary. The general consequences of implementing a simple function incorporating zooplankton cannibalism and carnivory (intra-guild predation) within a planktonic food web model are compared against models implementing different types of traditional closure functions. While the modelled biomass outputs may appear similar, the fate of annual primary production and f-ratios vary widely. There appears no justification for the continued use of traditional closure term to depict zooplankton loss processes on biological or modelling arguments. To do so can seriously misrepresent the fate of primary production and thence trophic dynamics.     

Social network analysis and valid Markov chain Monte Carlo tests of null models

Analyses of animal social networks derived from group-based associations often rely on randomisation methods developed in ecology (Manly, Ecology 76:1109–1115, 1995) and made available to the animal behaviour community through implementation of a pair-wise swapping algorithm by Bejder et al. (Anim Behav 56:719–725, 1998). We report a correctable flaw in this method and point the reader to a wider literature on the subject of null models in the ecology literature. We illustrate the importance of correcting the method using a toy network and use it to make a preliminary analysis of a network of associations among eagle rays.

区域及全球尺度的NPP过程模型和NPP对全球变化的响应

植被净第一性生产力（NPP）不仅是表征植被活动和生态过程的关键参数,而且是判定生态系统碳汇和反映生态系统对全球变化响应的主要因子。当前,模型模拟成为大尺度NPP研究的主要手段,而在众多NPP估算模型中,过程模型逐渐趋于主导地位。虽然目前有关NPP的研究有很多,但还没有关注于大尺度上应用的过程模型及其模拟的NPP对全球变化的响应。因此本文主要侧重于 NPP 过程模型在区域及全球尺度上的应用,具体包含以下内容,①进一步将区域及全球尺度的NPP过程模型分为静态植被模型和动态植被模型。②阐明这些模型间存在的区别与联系。③归纳出NPP过程模型在区域及全球尺度上应用的3大挑战：时空尺度转换、多源数据的获取与融合以及模型模拟结果的验证与评价,并根据其解决方案总结出通用的模型应用框架。④从气候变化、大气成分变化和土地利用/土地覆盖变化3个方面探讨NPP对全球变化的响应机制,以期找到NPP变化的规律与模式。最后根据NPP模型的发展对未来区域及全球尺度的NPP过程模型进行展望,认为未来模型的综合性将更高,机理性也将更强,同时与全球变化研究结合得更加紧密,且基于多个已有模型的混合模型也是未来NPP模型发展的一个重要方向。此外,本文认为对NPP模拟结果的尺度效应研究也是未来NPP研究的热点之一。