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2015, Volume 9, Issue 3

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Frontiers of Environmental Science & Engineering >> 2015, Volume 9, Issue 3 doi: 10.1007/s11783-013-0609-x

An enhanced environmental multimedia modeling system based on fuzzy-set approach: I. Theoretical framework and model development

1. Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.2. Department of Building and Transportation Engineering, Beijing Urban Construction School, Beijing 100026, China.3. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China.4. Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China.5. School of Marine Sciences, China University of Geosciences, Beijing 100083, China

Accepted: 2013-12-03 Available online: 2015-04-30
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Abstract

Multimedia environmental modeling is extremely complex due to the intricacy of the systems with the consideration of many related factors. Traditional environmental multimedia models (EMMs) are usually based on one-dimensional and first-order assumptions, which may cause numerical errors in the simulation results. In this study, a new user-friendly fuzzy-set enhanced environmental multimedia modeling system (FEEMMS) is developed, and includes four key modules: an air dispersion module, a polluting source module, an unsaturated zone module, and a groundwater module. Many improvements over previous EMMs have been achieved through dynamically quantifying the intermedia mass flux; incorporating fuzzy-set approach into environmental multimedia modeling system (EMMS); and designing a user-friendly graphic user interface (GUI). The developed FEEMMS can be a useful tool in estimating the time-varying and spatial-varying chemical concentrations in air, soil, and groundwater; characterizing the potential risk to human health presented by contaminants released from a contaminated site; and quantifying the uncertainties associated with modeling systems and subsequently providing robustness and flexibility for the remediation-related decision making.

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