二次灰色关联分析模型在环境质量评价中的应用研究——以山东省淄博市为例

简单分析了指数评价法、模糊综合评价法和灰色关联分析法的优点和不足之处．在此基础上，本文提出了二次灰色关联分析模型，该模型即能对某环境监测点的环境质量进行综合评价，又能对所有环境监测点的某一环境污染物的污染状况进行综合评价，使评价结果更加完善．同时，该模型也能避免指数评价法、模糊综合评价法和灰色关联分析法的不足之处。     

环境质量评价中的复合指数模型

本文提出了一种新的复合指数模型，该模型具有结构简单，计算方便，分辨率较高，所得结果可靠等特点，经实例分析表明，其评价结果与实际环境质量状况较符合。     

基于B—P网络的大气环境质量评价

选取大气环境质量标准作为运算样本，以大气污染物各级标准值作为样本输入信息，建立了大气环境质量的Ｂ－Ｐ网络评价模型，该模型用于武汉市大气环境质量评价，并与用模糊数学评价结果比较，表明Ｂ－Ｐ人工神经网络用于大气环境质量评价具有通用性、合理性和实用性。     

大气质量评价模糊加权综合修正模型

本文对大气环境质量模糊综合评价数学模型从综合评价的角度给出了明确的物理意义，并由此出发探讨了该模型的不足，进而提出了新的模型──模糊加权综合修正评价模型。并应用于具体实例获得满意效果。     

集对分析与企业环境经济效益综合评价

文中以集对分析理论为基础，建立了企业环境经济效益综合评价同一度模型．该模型综合考虑了影响企业环境经济效益的各种因素，经合理赋权最终得出各企业综合效益的优劣排序．实例分析表明，该方法具有概念明确，计算简便等特点，所得评价结果较符合实际状况。     

城市环境质量的灰色聚类分析

将灰色系统理论应用到城市环境质量评价中，建立了相应的隶属函数模型，经实例分析表明，该方法具有计算简便，分辨率高和已知信息利用率高等特点，所得评价结果更为客观，可靠。     

小秦岭金矿区矿山地质环境研究

为了了解小秦岭金矿区的地质环境，选用目前应用广泛的物元评价法、模糊数学评价法、灰色聚类评价法建立了地质环境评价模型，对小秦岭金矿区的地质环境进行了综合评价，结果表明，该地区的地质环境整体较好，局部地区已经严重恶化，同时，结合该地区的实际情况，提出了改善该地区地质环境质量的具体意见。     

关于区域环境质量的复合模糊距离评价方法及应用探讨

从分析环境质量结构和模糊理论对环境质量构成特征表达的客观性入手，通过用主要因子单要素对模糊距离的复合修订，建立了符合环境质量特征和环境质量评价本质要求的复合模糊距离的评价方法和模型，使评价结果突破以往的定性描述而达到准确定量判定。应用实例表明，该方法具有合理性和适用性，实用价值较高     

湖泊富营养化评价的Euclid贴近度评价法

针对湖泊富营养状态贴近度评价模型存在的“阈值漂移”问题，重新构造了评价参数隶属函数，提出了一种湖泊营养状态Ｅｕｃｌｉｄ贴近度评价模型，经过对全国２３个湖泊富营养状态的实例分析，证明这种评价模型结构合理，评价过程简单，模型通用性好。     

城市大气质量的模糊评价

应用模糊数学的方法给出了环境质量功能评价的模型及ＦＯＲＴＲＡＮ计算机程序，并对太原市大气质量进行评价，其结果与实际相吻合。     

Groundwater quality assessment based on rough sets attribute reduction and TOPSIS method in a semi-arid area, China

In order to enrich and improve the groundwater quality assessment system, a new coupled assessment model based on rough set attribute reduction and the technique for order preference by similarity to ideal solution (TOPSIS) was proposed. The proposed model was applied in the groundwater quality assessment of a semi-arid area, northwest China. The results show that most chemical indices except NH (4) (+) , F(-), and Mn meet the Standards for Drinking Water of China and the groundwater quality overall is good. All assessed water samples are found to be fit for human consumption according to the comprehensive assessment results. Rough set attribute reduction for groundwater quality assessment is practical. The assessment results after attribute reduction show a good consistency with those before attribute reduction. Rough set attribute reduction and TOPSIS evaluation coupled model is clear in ideas and simple in calculation, and evaluation results are reasonable as well. The coupled model can be applied to solve many multiple criteria decision making problems such as groundwater quality assessment.     

Approaches for performing uncertainty analysis in large-scale energy/economic policy models

A number of key policy insights have emerged from the application of large-scale economic/energy models, such as integrated assessment models for climate change. These insights have been particularly powerful in those instances when they are shared by all or most of the existing models. On the other hand, some results and policy recommendations obtained from integrated assessment models vary widely from model to model. This can limit their usability for policy analysis. The differences between model results are mostly due to different underlying assumptions about exogenous processes, about endogenous processes and the dynamics among them, differences in value judgments, and different approaches for simplifying model structure for computational purposes. Uncertainty analyses should be performed for the dual purpose of clarifying the uncertainties inherent in model results and improving decision making under uncertainty. This paper develops a unifying framework for comparing the different types of uncertainty analyses through their objective functions, categorizes types of uncertainty analyses that can be performed on large models, and compares different approaches to uncertainty analysis by explaining underlying assumptions, suitability for different model types, and advantages and disadvantages. The appendix presents a summary of integrated assessment models for climate change that explicitly account for uncertainty.     

基于线性回归的环境质量评价普适指数公式

阐述了利用遗传算法优化污染损害指数评价模型中参数时的优点和不足,提出采用普适质数公式对污染损害指数评价模型中参数进行线性优化时,可以大幅简化优化过程,便于实际应用.将适用于大气质量评价的普适指数公式与通过GA优化的评价结果进行比对,证实了该公式在线性优化中的可行性.     

Assessing the wildlife habitat value of New England salt marshes: I. Model and application

We developed an assessment model to quantify the wildlife habitat value of New England salt marshes based on marsh characteristics and the presence of habitat types that influence habitat use by terrestrial wildlife. Applying the model to 12 salt marshes located in Narragansett Bay, RI resulted in assessment scores that ranged over a factor of 1.5 from lowest to highest. Pre-classifying the results based on marsh size and morphology helped to compare assessment scores between marshes, and demonstrated that even the lower ranking marshes had substantial habitat value. Stepwise multiple regression analysis of assessment scores and model components demonstrated that salt marsh morphology, the degree of anthropogenic modification, and salt marsh vegetative heterogeneity were significant variables and accounted for 91.3% of the variability in component scores. Our results suggest that targeting these components for restoration may lead to improved assessment scores for our study marshes. We also examined the use of lower resolution remote sensing data in the assessment in order to minimize the time and effort required to complete the model. Scores obtained using smaller-scale, lower resolution data were significantly lower than those obtained using larger-scale, higher resolution data (df = 11; t = 2.2; p < 0.001). The difference was significantly positively correlated with the portion of the assessment score that could be attributed to trees, pools, and pannes and marsh size (r (2) =0.50, F = 4.6, p = 0.04), and could indicate a bias against smaller, more heterogeneous marshes. We conclude that potential differences need to be weighed against the time benefit of using this type of data, bearing in mind the marsh size and the goals of the assessment. Overall, our assessment can provide information to aid in prioritizing marshes for protection and restoration, identify marshes that may harbor significant biodiversity, or help monitor changes in habitat value over time.     

人工神经网络在水环境质量评价中的应用

为了将人工神经网络应用于水环境质量评价，应用了人工神经网络Ｂ—Ｐ算法，构造了水环境质量评价模型，该模型应用于实例评价结果表明，人工神经网络用于环境质量评价具有客观性，通用性和实用性。     

Assessing the wildlife habitat value of New England salt marshes: II. Model testing and validation

We tested a previously described model to assess the wildlife habitat value of New England salt marshes by comparing modeled habitat values and scores with bird abundance and species richness at sixteen salt marshes in Narragansett Bay, Rhode Island USA. As a group, wildlife habitat value assessment scores for the marshes ranged from 307-509, or 31-67% of the maximum attainable score. We recorded 6 species of wading birds (Ardeidae; herons, egrets, and bitterns) at the sites during biweekly survey. Species richness (r (2)=0.24, F=4.53, p=0.05) and abundance (r (2)=0.26, F=5.00, p=0.04) of wading birds significantly increased with increasing assessment score. We optimized our assessment model for wading birds by using Akaike information criteria (AIC) to compare a series of models comprised of specific components and categories of our model that best reflect their habitat use. The model incorporating pre-classification, wading bird habitat categories, and natural land surrounding the sites was substantially supported by AIC analysis as the best model. The abundance of wading birds significantly increased with increasing assessment scores generated with the optimized model (r (2)=0.48, F=12.5, p=0.003), demonstrating that optimizing models can be helpful in improving the accuracy of the assessment for a given species or species assemblage. In addition to validating the assessment model, our results show that in spite of their urban setting our study marshes provide substantial wildlife habitat value. This suggests that even small wetlands in highly urbanized coastal settings can provide important wildlife habitat value if key habitat attributes (e.g., natural buffers, habitat heterogeneity) are present.     

A new assessment method for urbanization environmental impact: urban environment entropy model and its application

The thermodynamic law is one of the most widely used scientific principles. The comparability between the environmental impact of urbanization and the thermodynamic entropy was systematically analyzed. Consequently, the concept "Urban Environment Entropy" was brought forward and the "Urban Environment Entropy" model was established for urbanization environmental impact assessment in this study. The model was then utilized in a case study for the assessment of river water quality in the Pearl River Delta Economic Zone. The results indicated that the assessing results of the model are consistent to that of the equalized synthetic pollution index method. Therefore, it can be concluded that the Urban Environment Entropy model has high reliability and can be applied widely in urbanization environmental assessment research using many different environmental parameters.     

Sensitivity analysis of TOPSIS method in water quality assessment: I. Sensitivity to the parameter weights

Sensitivity analysis is becoming increasingly widespread in many fields of engineering and sciences and has become a necessary step to verify the feasibility and reliability of a model or a method. The sensitivity of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method in water quality assessment mainly includes sensitivity to the parameter weights and sensitivity to the index input data. In the present study, the sensitivity of TOPSIS to the parameter weights was discussed in detail. The present study assumed the original parameter weights to be equal to each other, and then each weight was changed separately to see how the assessment results would be affected. Fourteen schemes were designed to investigate the sensitivity to the variation of each weight. The variation ranges that keep the assessment results unchangeable were also derived theoretically. The results show that the final assessment results will change when the weights increase or decrease by ±20 to ±50 %. The feedback of different samples to the variation of a given weight is different, and the feedback of a given sample to the variation of different weights is also different. The final assessment results can keep relatively stable when a given weight is disturbed as long as the initial variation ratios meet one of the eight derived requirements.     

Modeling Waste Incineration for Life-Cycle Inventory Analysis in Switzerland

This paper proposes a mathematical model for life-cycle inventory analysis (LCI) of waste incineration in Switzerland. In order to model conventional and new incineration technologies adequately, fundamental aspects of the different technologies relevant for the LCI are discussed. The environmental impact of these technologies strongly depends on the assessment of the long-term emissions of the solid incineration residues and is therefore related to value based decisions about the time horizon considered. The article illustrates that the choice of the landfill model has a significant influence on the results of life-cycle assessment of waste incineration.     

太湖流域景观生态风险评估

在GIS和RS技术支持下,以遥感数据和土地利用数据为基础,以行政区划为评价单元,构建景观生态风险评价模型,定量评价了研究区内景观生态风险的时空动态变化特征。结果表明,较低风险区主要分布于以苕溪为主的山地丘陵区,中等风险区主要分布于农业发达的平原区,较高风险区及高风险区主要集中于沿湖经济发达区。