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A GIS SOLUTION TO EVALUATING REMEDIAL ALTERNATIVES IN SEDIMENT REMEDIATION AND RECOVERY
Delwiche, L.M. | 2018 Salish Sea Ecosystem Conference, 4-6 April, Seattle, Washington, 16 slides, 2018
Filed Under: Research
Filed Under: Research
A GIS-based sediment remediation/recovery model was designed using ESRI ArcGIS Model Builder that incorporates the SEDCAM sediment attenuation model and analytical results derived from field samples to produce various cleanup scenarios. These scenarios were then further evaluated as remedial alternatives. On a chemical-by-chemical basis, the model determined active remediation footprints required to meet sediment cleanup levels at the end of a defined natural recovery period. Post-remediation natural recovery was incorporated through site-specific parameters such as sedimentation rate, watershed loading chemical concentrations, and the depth of the biologically active zone. The model can also be used to test the site-specific sensitivity to model input parameters. Such information can potentially identify data gaps required for the accurate prediction of future sediment conditions. https://cedar.wwu.edu/cgi/viewcontent.cgi?article=2480&context=ssec
Filed Under: Research
Filed Under: Research
A GIS-based sediment remediation/recovery model was designed using ESRI ArcGIS Model Builder that incorporates the SEDCAM sediment attenuation model and analytical results derived from field samples to produce various cleanup scenarios. These scenarios were then further evaluated as remedial alternatives. On a chemical-by-chemical basis, the model determined active remediation footprints required to meet sediment cleanup levels at the end of a defined natural recovery period. Post-remediation natural recovery was incorporated through site-specific parameters such as sedimentation rate, watershed loading chemical concentrations, and the depth of the biologically active zone. The model can also be used to test the site-specific sensitivity to model input parameters. Such information can potentially identify data gaps required for the accurate prediction of future sediment conditions. https://cedar.wwu.edu/cgi/viewcontent.cgi?article=2480&context=ssec
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