旱区生态环境质量的综合定量评价模型

生态环境质量是社会经济可持续发展的基础。因此 ,建立旱区生态环境质量的定量评价是区域可持续发展的主要依据。现有的评价方法大多是通过根据评价区评价指标量化值与评价等级标准来建立评价模型 ,评价区不同 ,评价模型也不相同 ,计算工作量较大。根据给定的生态环境质量评价等级标准 ,采用随机技术模拟生成足够数量的评价指标序列 ,应用人工神经网络模型 (artificial neural network,ANN) ,以评价指标生成序列与其所属的评价等级值进行网络训练。网络训练后 ,以评价区的评价指标为网络的输入 ,通过计算 ,可获得相应的生态环境质量评价等级值。并以甘肃省石羊河流域生态环境脆弱的民勤县为研究对象 ,应用 1975～ 2 0 0 0年资料进行了实例研究。结果表明 ,民勤县 1975～ 2 0 0 0年生态环境质量效应评价值分别为 2 .95 0 1,4 .0 0 90 ,4 .1342 ,4 .16 37,4 .9736 ,5 .0 12 8,说明该地区的生态环境质量是持续下降的 ,与以往采用的模糊综合评价等级值 3,4 ,4 ,4 ,5 ,5一致。文中 ANN模型建立后 ,对于不同评价区 ,只要给定相应的评价指标值 ,通过 ANN模型计算 ,可直接得出生态环境质量评价等级值。因此 ,模型具有实用、可操作性强的特点 ,大大减少了评价区的计算工作量 ,可以用于生态环境质量效应评价     

基于BP人工神经网络的小城镇生态环境质量评价模型

针对中国小城镇生态环境质量综合评价存在的问题,以生态环境质量指标体系作为神经网络的输入、以生态环境等级评分作为输出,基于BP人工神经网络,建立了具有20个隐含层节点、3层网络的小城镇生态环境质量评价模型;以生态环境指标的各级评价标准作为模型的训练样本,以训练样本数量的10%以及各指标各等级的临界值、中间值作为检验样本,以研究区生态环境质量的实际监测值作为预测样本,利用MATLAB软件对BP人工神经网络进行训练,并对鄂州市杜山镇生态环境质量等级进行了模式识别.结果表明：利用BP人工神经网络方法对小城镇生态环境质量进行预测是可行的、可靠的,它不仅能很好地评价区域生态环境质量,而且能够与区域生态环境的实际特征相结合.     

可鲁克湖流域生态环境质量诊断

为查清可鲁克湖流域生态环境质量及其演化特征,依据环境生态学相关理论,运用综合调查、遥感解译与反演、统计分析等技术手段,选取自然地理、气象、土地利用/覆盖和社会经济4个方面的15个因子,采用因子分析和熵值法计算指标权重,建立流域土壤质量模型和生态环境质量诊断模型,分析2000、2005、2010和2015年可鲁克湖流域土壤与生态环境质量的变化特征。结果表明: 2000、2005、2010和2015年,可鲁克湖流域生态环境质量均值依次为21、47、54和72,呈稳定上升趋势,生态环境质量等级由较差转为良好,土壤质量整体处于中等水平;空间上,北部山区和流域下游湿地及河流周边区域的生态环境质量明显转好。流域生态环境质量变化是人类活动与自然因素共同作用的结果,土壤质量与湖泊面积是指示流域生态环境的关键因子,可鲁克湖最小生态需水量是维持流域生态环境良性发展的基本保障。     

氨基酸序列集熵值计算工具实现及应用

氨基酸序列保守区和可变区分析是蛋白质结构和功能分析预测的关键环节。本研究根据该需求,编写了Entropy软件,实现了氨基酸序列集熵值计算、统计分析和优势序列模型自动生成等功能,并利用其对A型流感病毒血凝素氨基酸序列的特征进行了分析。该软件为氨基酸序列集保守性分析提供了可靠工具。     

基于RS和GIS的石羊河流域生态环境质量空间分布特征及其影响因素

基于RS和GIS技术方法,选取自然资本、社会压力和经济支撑3方面的11个指标,利用空间主成分分析法、变异系数法和层次分析法,构建自然资本指数(NCI)、社会压力指数(SPI)、经济支撑指数(ESI)以及生态环境质量评价指数(EQEI),对石羊河流域生态环境质量空间分布特征及影响因素进行分析.结果表明: 石羊河流域生态环境质量整体处于较差水平,生态环境质量等级为优、良的区域主要集中在流域上游的祁连山地区,等级为较差及其以下的区域主要集中在中、下游的低山丘陵区和荒漠区.石羊河流域EQEI值在西南-东北方向上具有两极分化现象,随着距离变化,该值变化幅度较大,存在明显的空间异质性.石羊河流域生态环境质量以高-低值两种聚集模式共存,主要呈现“断裂式”分布,并具备热点高度聚集、冷点高度聚集的特征.在石羊河流域生态环境质量的影响因素中,自然资本因素占据主导地位,社会压力因素次之,经济支撑因素的影响最小.     

BP神经网络在沙棘生态功能评价中的应用

BP人工神经网络技术在环境评价领域中已经得到越来越广泛的运用,将该法引入到陕蒙砒砂岩区沙棘生态功能综合评价的研究中,以沙棘生态功能评价指标标准值作为样本输入,综合评价级别作为网络输出,建立了一个含有4个输入神经元节点、6个隐含神经元节点和1个输出神经元节点的BP人工神经网络等级模型。将目标年(2008年)各评价指标实际数据作为输入,得到输出值是0.44,大于Ⅱ级标准,研究结果表明:砒砂岩区种植十年沙棘后,其生态效益很好,对砒砂岩地区的生态环境改善作用显著。BP神经网络的评价结果与较成熟的AHP-模糊综合评价结果一致,证明将BP人工神经网络模型用于沙棘生态功能评价是可行的,且评价结论客观。     

基于生态需水保障的农业生态补偿标准

面向流域农业需水和生态需水间的矛盾问题和协调发展的要求,提出了基于生态需水保障的农业生态补偿标准计算方法。其中考虑农业用水定额计算基于生态需水保障的农业用水短缺,引入水分生产函数模型建立保障生态需水量产生的农业用水短缺与产量损失间的关系,根据不同季节作物产量响应系数的变化,定量确定具有时间和等级差异性的农业生态补偿标准。以保障黄河口生态需水引起的山东引黄灌区农业损失补偿标准分析为实例,计算了冬小麦和夏玉米种植户不同等级的生态补偿标准。结论认为,农业生态补偿标准需根据不同的来水过程及生态需水等级确定,面积稳定和保障功能显著的粮食作物应作为补偿标准计算的依据。     

基于GIS长春市郊农地土壤肥力综合评价

以长春市郊区农地为例,应用ArcInfo进行空间数据采集、处理与分析,以土地利用类型作为评价单元,采用层次分析法确定评价因素的权重,引入模糊数学方法,建立各评价因素对肥力的隶属函数,计算各个肥力评价因素的评分,最后用指数和法求出各评价单元的综合评价值,根据综合评价值的大小划分土壤肥力等级,并分级统计其面积.应用ArcView GIS绘制了评价结果图.     

投影寻踪模型在区域生态环境质量评价中的应用

大量相关因子的简化和降维处理是生态环境质量评价的关键问题之一。而投影寻踪模型可将多个指标投影到线性空间,实现指标的降维处理。本文以巢湖流域生态环境质量综合评价进行了实证分析,巢湖流域生态环境质量为3级,其中合肥市和六安市所属区域生态环境质量为3级,巢湖市为4级。研究表明,应用投影寻踪模型进行区域生态环境质量评价人为干扰少,操作简便,便于在生产实践中应用,为区域生态环境质量评价提供了一条新途径。     

基于属性识别模型的巢湖流域生态安全评价

为建立有效的流域生态安全评价模型,采用AHP赋权方法与属性识别模型相耦合的途径,以巢湖流域生态安全为研究对象,对基于属性识别模型的巢湖流域及其次级评价单元的生态安全评价问题进行了研究.结果表明:巢湖流域9区(县)及流域整体生态安全状况从优到劣的排序结果依次为合肥、流域整体、居巢、无为、和县、含山、舒城、庐江、肥东和肥西;合肥和肥西的生态安全状况分别为Ⅳ级和Ⅰ级,其它评价单元均处于Ⅱ级.基于属性识别模型的流域生态安全评价方法涵义明确、计算过程简单,能同时实现生态安全等级的分类和排序,在其它生态系统综合评价中具有一定的应用价值.     

额济纳天然绿洲景观健康评

在景观尺度上开展健康评价研究,不但可提供一个将自然生态过程与社会经济价值进行综合研究的基础,而且景观健康概念本身蕴含着在针对资源与环境问题开展研究时更为合适的时间和空间尺度.额济纳天然绿洲位于我国西北干旱内陆河黑河流域下游.近年来,随着黑河中、上游地区下泄地表径流量的减少,绿洲景观退化严重.基于研究区近十几年来的景观演变特征,分别从景观变化指标、生物物理指标、生态环境指标以及社会经济指标4个方面发展并构建了景观健康评价指标体系,制定了评价标准,并分别赋予了各指标相应的权重值,运用综合评判方法,得到了具有动态性、定量性的绿洲景观健康评价结果.结果表明, 研究区景观近10多年来总体的景观健康指数为0.3042,健康等级紧邻“濒危的”级别.该结果较为客观、真实地反映了当前额济纳天然绿洲景观的健康状况,说明近10多年来研究区的景观健康状况与较早时期相比趋于恶化, 发展态势表现出严重的不可持续性.     

Development of indicators of ecosystem functioning in a temperate shelf sea: a combined fieldwork and modelling approach

A conceptual model of the main carbon and nitrogen flows through pelagic and benthic food webs was used to identify the key biogeochemical processes representing ecosystem functioning, and to select indicators of each of these processes. A combined fieldwork and modelling approach was used to provide the data required to evaluate the indicators in terms of their suitability for assessing and managing the impacts of climate change and demersal trawling. Four of our 16 proposed indicators (phytoplankton production and productivity, near-bed oxygen concentrations and oxygen penetration of the seabed) met the majority of criteria we used for evaluating indicators. Five indicators (depth of anoxic sediment, zoobenthos biomass, production, productivity and bioturbation potential) did not comply with sufficient criteria to be considered as good indicators. Six of our proposed indicators (zooplankton biomass, size structure, production and productivity; ecosystem productivity; ecosystem balance) could not be assessed for sensitivity and specificity using our models, and therefore need to be addressed in future work aimed at improving both the models and the fieldwork. Our results indicate that evaluation of indicators is difficult, because of the number and variety of human pressures which need to be considered in reality, and the interactions between these pressures and the ecosystem components which they affect. The challenge will be to establish if there are indeed any indicators which are able to meet the majority of criteria for good indicators in holistic ecosystem-based assessments.     

Co-construction of a method for evaluating the intrinsic quality of bovine milk in relation to its fate

There are time-tested assessments for the environmental and economic aspects of sustainability. Its societal aspect has mainly been approached through the assessment of animal welfare. However, the intrinsic quality of milk is seldom taken into account. We developed a participatory construction method for the overall assessment of intrinsic milk quality in its different dimensions (sensory, technological, nutritional and health), according to the fate of the raw milk. Two assessment models were developed, for semi-skimmed standardized ultra-high temperature (UHT) milk and for pressed uncooked non-standardized raw milk cheese. They were constructed by a participatory approach involving experts in the dairy sector with the aim to obtain a diagnostic tool that could be used in the field to help farmers to manage the quality of their milk (by prioritizing improvements on major problems). They were shaped from prerequisite specifications (limited costs and time of application, desire to obtain a transparent tool with all the steps kept visible) and current technical and scientific knowledge. They were based on indicators obtained from raw bulk tank milk analyses (30 for UHT milk and 50 for cheese assessments), which were then aggregated into criteria, principles, dimensions and overall intrinsic quality at farm level. The assessment models had parts in common, for example, same four dimensions, common indicators for health and nutritional dimensions. They also had process-specific features: units chosen, criteria, indicators and weightings in relation to the final product specifications. For instance, sensory and technological dimensions are more complex and preponderant in the cheese assessment (three principles for cheese vs one for UHT milk in both dimensions). Another example is the lack of microbial pathogens (as potential health risk for consumer) in the UHT milk assessment because of pasteurization. The assessment models then underwent a sensitivity analysis and an application in 30 farms in indoor and grazing periods to finally obtain overall UHT milk and cheese quality scores at a 1-year level. The tool was found to be applicable at farm level. However, we observed low overall quality scores with a narrow dispersion, characteristic of a severe evaluation. Even so, the assessment models showed up seasonal differences of the UHT milk and cheese quality at both overall and dimensional levels. In the light of new scientific knowledge and future quality objectives, these are adaptable to other dairy products allowing for their specific features.     

Robustness of the biotic indicators used for classification of ecological status of lotic water bodies: A testing method when the data series are short

Successful implementation of the Water Framework Directive and achieving its objective of good ecological status of all water bodies depend on the power of the set of monitoring indicators to capture the change in the ecological status of aquatic systems. In this context, testing the robustness and sensitivity of ecological indicators currently used for assessing the status of lotic water bodies is instrumental for the adaptation and further development of assessment methods. This is also a prerequisite for an effective, context-based monitoring system and for improving the quality of the decision making for water bodies. This is particularly challenging in regions where the sets of indicators are under development, the data series are relatively short and data which addresses the individual error sources are lacking. Here we show that hierarchical clusters and ordination analysis provide appropriate tools with which the validity of the ecological status of water bodies set up based on biological multimetric monitoring indices in a small water basin could be tested. We hypothesize that robust and informative monitoring methods classify all water bodies belonging to a single ordination grouping in the same quality class (high, good, moderate, poor or bad). In our case study multimetric biological indicators failed to discriminate between the good and moderate ecological status. Community structure as well as water conductivity and nitrate load were primarily responsible for the observed difference between ordination groupings. Inconsistencies shown in our case study are likely to be induced by insufficient refinement of monitoring schemes and by the constraints existing in the data series and available metadata. We show that multiplication of indicators leads to discrepant interpretation and problematic application. Proposed ordination analysis proves to be a simple and useful tool to detect such discrepancies and support further progress in indicator development. Integrated and longer data and metadata series are needed to refine context-based monitoring methods.     

河流生境的评估方法及应用的研究进展

河流生境是水生生物赖以生存的物理、化学和生物环境的综合体, 是河流生态系统的重要组成部分。河流生境评价有助于掌握河流的生态健康状况, 识别河流退化的原因, 为河流的生态修复提供依据。文章梳理总结了20世纪80年代至2018年文献中报道的全球范围内河流生境评估的方法, 然后根据每种方法的侧重点和目标, 将它们分为预测模型法和多指标综合评估法两种类型, 并比较了它们各自的优势和不足。预测模型法适于长期的生境动态监测, 但该方法需要以自然无干扰的河流为参照, 并且需要大量历史数据, 统一评估不易实现; 多指标综合评估法评估相对快速方便, 但评估过程复杂, 且评价标准不一, 结果有一定的局限性。这些方法的适用范围从中小型的可涉水河流到较大的不可涉水河流, 但适用于大型不可涉水河流的生境评估方法和案例非常有限。通过分类整理, 发现我国河流生境评估方法多是参考国外几种常用的河流评估方法, 种类单一, 而且多是针对个别河流, 并且未对这些河流所在的流域的生境状况进行深入研究, 广适性较差。因此, 文章从以下几个方面对我国河流生境评估体系的发展提出几点建议: (1)确定科学和标准化的评分指标, 因地制宜, 以满足不同河流特征; (2)扩大评估范围, 从流域和景观尺度开展生境监测, 关注全流域的健康状况; (3)扩大时间尺度, 建立模型, 进行长期的动态评估; (4)鼓励政府宏观规划, 促进不同管理部门间的合作, 整合不同地区的河流生境数据并建立国家尺度的河流生境大数据库, 以实现我国河流生态健康的维护和流域的可持续发展。     

Improving real-time forecasting of water quality indicators with combination of process-based models and data assimilation technique

Water quality indicators can be used to characterize the status and quantify and qualify the change of aquatic ecosystems under different disturbance regimes. Although many studies have been done to develop and assess indicators and discuss interactions among them, few studies have focused on how to improve the predicted indicators and explore their variations in receiving water bodies. Accurate and effective predictions of ecological indictors are critical to better understand changes of water quality in aquatic ecosystems, especially for the real-time forecasting. Process-based water quality models can predict the spatiotemporal variations of the water quality indicators and provide useful information for policy-makers on sound management of water resources. Given their inherent constraints, however, such process models alone cannot actually guarantee perfect results since water quality models generally have a large number of parameters and involve many processes which are too complex to be efficiently calibrated. To overcome these limitations and explore a fast and efficient forecasting method for the change of water quality indictors, we proposed a new framework which combines the process-based models and data assimilation technique. Unlike most traditional approaches in which only the model parameters or initial conditions are updated or corrected and the models are run online, this framework allows the information extracted from observations and outputs of process models to be directly used in a data-driven local/modified local model. The results from the data-driven model are then assimilated into the original process model to further improve its forecasting ability. This approach can be efficiently run offline to directly correct and update the output of water quality models. We applied this framework in a real case study in Singapore. Two of the water quality indicators, namely salinity and oxygen were selected and tested against the observations, suggesting that a good performance of improving the model results and reducing computation time can be obtained. This approach is simple and efficient, especially suitable for real-time forecasting systems. Thus, it can enhance forecasting of water quality indictors and thereby facilitate the effective management of water resources.     

基于指标自动筛选的新疆开孔河流域生态健康评价

生态健康评价对了解区域生态健康状况和促进区域可持续发展具有重要意义,如何自动筛选出能反映生态系统特性的重要指标,是生态健康定量评估的关键问题。基于压力-状态-响应(PSR,Press-State-Response)框架和生态等级网络框架(EHN,Ecological Hierarchy Network),通过文献调研和因果分析建立要素层与指标层之间的交叉联系,构建了生态健康评价"网状"指标体系;在保证指标体系完备性基础上,通过结合主成分分析和熵权法的候选指标权重的客观计算,基于目标优化理论构建了评价指标的自动筛选模型,并基于中选指标计算了新疆开孔河流域2001—2017年生态健康指数(EHCI,Ecological Health Comprehensive Indexes),分析其空间分异和时间变化特征。结果表明:利用所建立的评价指标自动筛选模型,开孔河流域生态健康评价指标由31个候选指标自动筛选出了17个中选指标,用54.8%的指标表达了85.98%的信息,中选的17个指标在干旱/半干旱区域有关文献中应用较多,使用频次比例都在20%以上,其中归一化植被指数(NDVI,Normalized Difference Vegetation Index)、年降水量和植被覆盖度(FVC,Fractional Vegetation Coverage)3个指标的使用频次百分比均超过了50%,说明指标自动筛选模型的合理性;开孔河流域空间分布差异显著,总体上西北高、东南低,东南部和中部绿洲区外围生态健康状况较差,西北部河谷地带和中部两大绿洲区生态健康状况较好;17年来,流域生态质量整体趋于改善,显著改善区域占10.26%,远高于显著退化的1.61%,显著改善区域以孔雀河绿洲最为明显。开孔河流域生态健康的总体好转趋势说明区域生态综合治理取得一定成效。     

Local alignment refinement using structural assessment

Homology modeling is the most commonly used technique to build a three-dimensional model for a protein sequence. It heavily relies on the quality of the sequence alignment between the protein to model and related proteins with a known three dimensional structure. Alignment quality can be assessed according to the physico-chemical properties of the three dimensional models it produces. In this work, we introduce fifteen predictors designed to evaluate the properties of the models obtained for various alignments. They consist of an energy value obtained from different force fields (CHARMM, ProsaII or ANOLEA) computed on residue selected around misaligned regions. These predictors were evaluated on ten challenging test cases. For each target, all possible ungapped alignments are generated and their corresponding models are computed and evaluated. The best predictor, retrieving the structural alignment for 9 out of 10 test cases, is based on the ANOLEA atomistic mean force potential and takes into account residues around misaligned secondary structure elements. The performance of the other predictors is significantly lower. This work shows that substantial improvement in local alignments can be obtained by careful assessment of the local structure of the resulting models.     

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

To facilitate decision support in freshwater ecosystem protection and restoration management, habitat suitability models can be very valuable. Data driven methods such as artificial neural networks (ANNs) are particularly useful in this context, seen their time-efficient development and relatively high reliability. However, specialized and technical literature on neural network modelling offers a variety of model development criteria to select model architecture, training procedure, etc. This may lead to confusion among ecosystem modellers and managers regarding the optimal training and validation methodology. This paper focuses on the analysis of ANN development and application for predicting macroinvertebrate communities, a species group commonly used in freshwater assessment worldwide. This review reflects on the different aspects regarding model development and application based on a selection of 26 papers reporting the use of ANN models for the prediction of macroinvertebrates. This analysis revealed that the applied model training and validation methodologies can often be improved and moreover crucial steps in the modelling process are often poorly documented. Therefore, suggestions to improve model development, assessment and application in ecological river management are presented. In particular, data pre-processing determines to a high extent the reliability of the induced models and their predictive relevance. This also counts for the validation criteria, that need to be better tuned to the practical simulation requirements. Moreover, the use of sensitivity methods can help to extract knowledge on the habitat preference of species and allow peer-review by ecological experts. The selection of relevant input variables remains a critical challenge as well. Model coupling is a missing crucial step to link human activities, hydrology, physical habitat conditions, water quality and ecosystem status. This last aspect is probably the most valuable aspect to enable decision support in water management based on ANN models.