This paper presents an integrated multi-attribute decision-making (MADM) approach to aid selection of commercially available materials in the context of sustainable design. The MADM couples grey relational analysis (GRA) with an analytic hierarchy process (AHP) to rank alternative materials in terms of their economic, environmental, and social performance. AHP is used to determine the corresponding weighting values for the selected indicators. In addition, a case example is used to verify the proposed MADM method and demonstrate its practical application. Three alternative polymer materials, i.e., poly(vinyl chloride) (PVC), polypropylene (PP), and polyethylene (PE), are examined to determine their sustainability for plastic pipe design. The associated MADM result and the limitations of the approach are discussed to lay the foundation for further improvement.

Numerical modeling is an efficient and useful method for understanding the hydrodynamics and water quality responses to nutrient loading changes and other management in estuarine and coastal systems. In this study, the Environmental Fluid Dynamic Code (EFDC) was applied in the Famosa Slough, a small tidal marsh system in urban San Diego County, California, to analyze multiple management scenarios focusing on different aspects of controlling processes: watershed load reduction, macroalgae harvesting, dredging, and the combination of different options. In order to evaluate these management scenarios, a previous EFDC model was enhanced through modifying the model code to allow simulations of both benthic and floating macroalgae as separate state variables, and configuring a sediment diagenesis model to predictively represent the dynamic interactions between the watershed load and benthic nutrient flux. The model was calibrated and verified by comparing model predictions with the observed data of hydrodynamic and water quality parameters throughout 2008. The calibrated model was then applied to simulate the water quality response to various management scenarios. The simulated results showed that combining watershed nutrient load reductions and harvesting floating macroalgae can produce significant water quality benefits. The results provide useful information for hydrological ecosystem protection and can be used for determining cost-effective implementation actions in the future.

A multi-objective chance-constrained programming integrated with Genetic Algorithm and robustness evaluation methods was proposed to weigh the conflict between system investment against risk for watershed load reduction, which was firstly applied to nutrient load reduction in the Lake Qilu watershed of the Yunnan Plateau, China. Eight sets of Pareto solutions were acceptable for both system investment and probability of constraint satisfaction, which were selected from 23 sets of Pareto solutions out of 120 solution sets. Decision-makers can select optimal decisions from the solutions above in accordance with the actual conditions of different sub-watersheds under various engineering measures. The relationship between system investment and risk demonstrated that system investment increased rapidly when the probability level of constraint satisfaction was higher than 0.9, but it reduced significantly if appropriate risk was permitted. Evaluation of robustness of the optimal scheme indicated that the Pareto solution obtained from the model provided the ideal option, since the solutions were always on the Pareto frontier under various distributions and mean values of the random parameters. The application of the multi-objective chance-constrained programming to optimize the reduction of watershed nutrient loads in Lake Qilu indicated that it is also applicable to other environmental problems or study areas that contain uncertainties.