Development of a Local-Scale Spatially Refined Multimedia Fate Model (LSRMFM) for Urban-Scale Risk Assessment: Model Formulation, GIS-Based Preprocessing, and Case Study

作者：Kim Jong Ho; Kwak Byoung Kyu; Shin Chee Burm; Jeon Won Jin; Park Hyeon Soo; Lee Sun Woo; Choi Kyunghee; Lee Woon Gi; Lee Jun Hee; Baek Sun Ho; Yi Jongheop^*

来源：Environmental Modeling & Assessment, 2011, 16(3): 265-281.

DOI：10.1007/s10666-011-9250-x

• Exposure
• Fugacity
• GIS
• Dispersion
• Multi-compartment
• Spatial resolution

A local-scale spatially refined multimedia fate model (LSRMFM) was developed to evaluate in detail the multimedia transport of organic compounds at a spatial level. The model was derived using a combination of an advection-dispersion-reaction partial differential equation, a steady-state multimedia fugacity model, and a geographical information system. The model was applied to predicting four major volatile organic compounds that are produced as emissions (benzene, toluene, xylene, and styrene) in an urban and industrial area (the 50 x 50-km area was divided into 0.5 x 0.5-km segments) in Korea. To test the accuracy of the model, the LSRMFM was used to predict the extent of dispersion and the data compared with actual measured concentrations and the results of a generic multimedia fate model (GMFM). The results indicated that the method developed herein is appropriate for predicting long-term multimedia pollution. However, the comparison study also illustrated that the developed model has some limitations (e.g., steady-state assumption) in terms of explaining all the observed concentrations, and additional verification and study (e.g., validation using a large observed data set, integration with a more accurate runoff model) would be desirable. In comparing LSRMFM and GMFM, discrepancies between the LSRMFM and GMFM outputs were found, as the result of geographical effects, even though the environmental parameters were identical. The geographical variation for LSRMFM output indicated the existence of considerable local human and ecological risks, whereas the GMFM output indicated less average risk. These results demonstrate that the model has the potential for improving the management of pollutant levels under these refined spatial conditions.

• 出版日期2011-6