Constructing linguistic versions for the multicriteria decision support systems preference ranking organization method for enrichment evaluation I and II

Incorporating linguistic, probabilistic, and possibilistic information in a risk-based approach for ranking contaminated sites

Different types of uncertain information-linguistic, probabilistic, and possibilistic-exist in site characterization. Their representation and propagation significantly influence the management of contaminated sites. In the absence of a framework with which to properly represent and integrate these quantitative and qualitative inputs together, decision makers cannot fully take advantage of the available and necessary information to identify all the plausible alternatives. A systematic methodology was developed in the present work to incorporate linguistic, probabilistic, and possibilistic information into the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), a subgroup of Multi-Criteria Decision Analysis (MCDA) methods for ranking contaminated sites. The identification of criteria based on the paradigm of comparative risk assessment provides a rationale for risk-based prioritization. Uncertain linguistic, probabilistic, and possibilistic information identified in characterizing contaminated sites can be properly represented as numerical values, intervals, probability distributions, and fuzzy sets or possibility distributions, and linguistic variables according to their nature. These different kinds of representation are first transformed into a 2-tuple linguistic representation domain. The propagation of hybrid uncertainties is then carried out in the same domain. This methodology can use the original site information directly as much as possible. The case study shows that this systematic methodology provides more reasonable results.

Prioritizing groundwater remediation policies: a fuzzy compatibility analysis decision aid

The implementation of groundwater remediation strategies in contaminated areas includes not only a cost-benefit analysis and an environmental risk assessment but also another type of study called compatibility analysis. A compatibility analysis targets the interactions between remediation technologies and site characteristics, such as the types of active contaminants and their concentrations, soil composition and geological features, etc. The purpose of this analysis is to identify the most compatible remediation plan for the contaminated site. In this paper, we introduce a decision support system for the prioritization of remediation plans based on their estimated compatibility index. As this model receives data in terms of linguistic judgments and experts' opinions, we use fuzzy sets theory to deal with these uncertainties. First, we break down the concept of compatibility into the measurable factors. Then by using a multiple-attribute decision-making (MADM) outline, we compute a factorial, regional and overall compatibility indicator for each plan. Finally, by comparing these generated indicators, we rank the remediation policies.

Application of multicriteria decision making methods to air quality in the microenvironments of residential houses in Brisbane, Australia

This paper reports the first application of the multicriteria decision making methods, PROMETHEE and GAIA, to indoor and outdoor air quality data. Fourteen residential houses in a suburb of Brisbane, Australia were investigated for 21 air quality-influencing criteria, which included the characteristics of the houses as well as the concentrations of volatile organic compounds, fungi, bacteria, submicrometer, and supermicrometer particles in their indoor and outdoor air samples. Ranking information necessary to select one house in preference to all others and to assess the parameters influencing the differentiation of the houses was found with the aid of PROMETHEE and GAIA. There was no correlation between the rank order of each house and the health complaints of its occupants. Patterns in GAIA plots show that indoor air quality in these houses is strongly dependent on the characteristics of the houses (construction material, distance of the house from a major road, and the presence of an in-built garage). Marked similarities were observed in the patterns obtained when GAIA and factor analysis were applied to the data. This underscores the potential of PROMETHEE and GAIA to provide information that can assist source apportionment and elucidation of effective remedial measures for indoor air pollution.