Combining GIS with fuzzy multicriteria decision-making for landfill siting in a fast-growing urban region

Landfill siting is a difficult, complex, tedious, and protracted process requiring evaluation of many different criteria. This paper presents a fuzzy multicriteria decision analysis alongside with a geospatial analysis for the selection of landfill sites. It employs a two-stage analysis synergistically to form a spatial decision support system (SDSS) for waste management in a fast-growing urban region, south Texas. The first-stage analysis makes use of the thematic maps in Geographical information system (GIS) in conjunction with environmental, biophysical, ecological, and socioeconomic variables leading to support the second-stage analysis using the fuzzy multicriteria decision-making (FMCDM) as a tool. It differs from the conventional methods of integrating GIS with MCDM for landfill selection because the approach follows two sequential steps rather than a full-integrated scheme. The case study was made for the city of Harlingen in south Texas, which is rapidly evolving into a large urban area due to its vantage position near the US-Mexico borderlands. The purpose of GIS was to perform an initial screening process to eliminate unsuitable land followed by utilization of FMCDM method to identify the most suitable site using the information provided by the regional experts with reference to five chosen criteria. Research findings show that the proposed SDSS may aid in recognizing the pros and cons of potential areas for the localization of landfill sites in any study region. Based on initial GIS screening and final FMCDM assessment, "site 1" was selected as the most suitable site for the new landfill in the suburban area of the City of Harlingen. Sensitivity analysis was performed using Monte Carlo simulation where the decision weights associated with all criteria were varied to investigate their relative impacts on the rank ordering of the potential sites in the second stage. Despite variations of the decision weights within a range of 20%, it shows that "site 1" remains its comparative advantage in the final site selection process.     

Evolution,variability and sustainable development

SUMMARY

Sustainable development is a global challenge to science and society. In politics and economics the concepts for establishing a sustainable society are numerous, nonetheless necessary interdisciplinary approaches are still marginalised. Trying to foster a deeper communication between biology and social science, an evolutionary perspective on sustainable development is offered. Subsequently, the role of variability and diversity in evolution is highlighted. Hereby, special attention is devoted, among other things, to the links between variability and stability in the long term. Next, selection and variability are emphasised in the cultural context. This is followed by general implications for sustainability. In contrast to strategies aiming at short-term optimisation and economic efficiency, we argue that the conditions and circumstances maintaining variability and momentary sub-optimal alternatives are highly relevant for a socio-economic system that is heading for sustainable development.     

Mathematical Methods for Spatially Cohesive Reserve Design

The problem of designing spatially cohesive nature reserve systems that meet biodiversity objectives is formulated as a nonlinear integer programming problem. The multiobjective function minimises a combination of boundary length, area and failed representation of the biological attributes we are trying to conserve. The task is to reserve a subset of sites that best meet this objective. We use data on the distribution of habitats in the Northern Territory, Australia, to show how simulated annealing and a greedy heuristic algorithm can be used to generate good solutions to such large reserve design problems, and to compare the effectiveness of these methods.     

Optimal Harvesting of the Norwegian Spring-Spawning Herring: Variable Versus Fixed Harvesting Strategies

In this paper we study optimal harvesting of the Norwegian spring-spawning herring stock. The biological model is described by a discrete time age-structured model. The optimal harvesting patterns are studied numerically and the results show that when using a linear cost function and constant price in the optimisation model, the optimal harvesting pattern is pulse fishing. However, optimal constant fishing effort gives only slightly lower profit. Moreover, when price is made responsive to harvest the optimal harvesting strategy is substantially smoothed. This revised version was published online in July 2006 with corrections to the Cover Date.     

An integrated optimization and simulation approach for air pollution control under uncertainty in open-pit metal mine

Air Pollution Control model is developed for open-pit metal mines. Model will aid decision makers to select a cost-effective solution. Open-pit metal mines contribute toward air pollution and without effective control techniques manifests the risk of violation of environmental guidelines. This paper establishes a stochastic approach to conceptualize the air pollution control model to attain a sustainable solution. The model is formulated for decision makers to select the least costly treatment method using linear programming with a defined objective function and multi-constraints. Furthermore, an integrated fuzzy based risk assessment approach is applied to examine uncertainties and evaluate an ambient air quality systematically. The applicability of the optimized model is explored through an open-pit metal mine case study, in North America. This method also incorporates the meteorological data as input to accommodate the local conditions. The uncertainties in the inputs, and predicted concentration are accomplished by probabilistic analysis using Monte Carlo simulation method. The output results are obtained to select the cost-effective pollution control technologies for PM_2.5, PM₁₀, NO_x, SO₂ and greenhouse gases. The risk level is divided into three types (loose, medium and strict) using a triangular fuzzy membership approach based on different environmental guidelines. Fuzzy logic is then used to identify environmental risk through stochastic simulated cumulative distribution functions of pollutant concentration. Thus, an integrated modeling approach can be used as a decision tool for decision makers to select the cost-effective technology to control air pollution.     

Obtaining Environmental Favourability Functions from Logistic Regression

Logistic regression is a statistical tool widely used for predicting species’ potential distributions starting from presence/absence data and a set of independent variables. However, logistic regression equations compute probability values based not only on the values of the predictor variables but also on the relative proportion of presences and absences in the dataset, which does not adequately describe the environmental favourability for or against species presence. A few strategies have been used to circumvent this, but they usually imply an alteration of the original data or the discarding of potentially valuable information. We propose a way to obtain from logistic regression an environmental favourability function whose results are not affected by an uneven proportion of presences and absences. We tested the method on the distribution of virtual species in an imaginary territory. The favourability models yielded similar values regardless of the variation in the presence/absence ratio. We also illustrate with the example of the Pyrenean desman’s (Galemys pyrenaicus) distribution in Spain. The favourability model yielded more realistic potential distribution maps than the logistic regression model. Favourability values can be regarded as the degree of membership of the fuzzy set of sites whose environmental conditions are favourable to the species, which enables applying the rules of fuzzy logic to distribution modelling. They also allow for direct comparisons between models for species with different presence/absence ratios in the study area. This makes them more useful to estimate the conservation value of areas, to design ecological corridors, or to select appropriate areas for species reintroductions. Received: June 2005 / Revised: July 2005     

Emergy-based fuzzy optimization approach for water reuse in an eco-industrial park

The establishment of an eco-industrial park (EIP) provides opportunity for individual plants to cooperate with each other in order to utilize resources efficiently and thus reduce waste. The goal of an EIP is to “close the loop” through recycling and reuse of material and energy streams. Studies show with current freshwater consumption trends there would be water stress aggravated by global warming in the near future. This paper presents a model to design an EIP water reuse network that considers overall system sustainability as measured with emergy, as well as cost saving desired by individual plants. Case studies from literature are then solved to illustrate the advantage of this method in decision making. The illustrative examples show how the model achieves a compromise among the potentially conflicting fuzzy goals of the various EIP stakeholders.     

模糊综合评判在达州市环境空气质量评价中的应用

运用模糊综合评判的方法,选取二氧化硫、二氧化氮和可吸入颗粒物作为评价因子,参照我国环境空气质量标准,通过建立各污染物的隶属函数和权重集,计算出各污染物的隶属度和权重分配系数,进而对达州市空气质量进行模糊综合评判。并将模糊综合评判法与空气污染指数分析所得结果进行比较。     

Application of a trapezoidal fuzzy AHP method for work safety evaluation and early warning rating of hot and humid environments

Hot and humid environments are prevalent in many industries. People working in hot and humid environments are at great risk of specific heat-related disorders, the productivity decrease and safety problems. In order to guarantee workers’ health and safety, safety evaluation and early warning rating of the hot and humid environments are studied in this paper. The fuzzy analytic hierarchy process (AHP) method is proposed to evaluate the work safety in hot and humid environments. Trapezoidal fuzzy numbers are adopted to handle inherent uncertainty and imprecision of the data involved in decision process. Within the proposed methodology, a decision group is firstly established. A safety evaluation framework containing three factors (work, environment, and workers) and ten sub-factors are established. The fuzzy weights of the factors and sub-factors are calculated based on the pair-wise comparisons. Then the fuzzy evaluating vectors of the sub-factors and factors can be calculated according to the initial evaluation data. Therefore, the comprehensive safety index, safety grade and early warning grade can be determined. An example is given to demonstrate the proposed method. The results demonstrate the engineering practicability and effectiveness of this method in extreme environment evaluation.     

Predator–prey fuzzy model

In this work we have used fuzzy rule-based systems to elaborate a predator–prey type of model to study the interaction between aphids (preys) and ladybugs (predators) in citriculture, where the aphids are considered as transmitter agents of the Citrus Sudden Death (CSD). Simulations were performed and a graph was drawn to show the prey population, the potentiality of the predators, and a phase-plane. From this phase-plane, a classic model of the Holling–Tanner type is fitted and its parameters were found. Finally, we have studied the stability of the critical points of the Holling–Tanner model.