Presentation Abstract

NewFields supports the Washington State Department of Ecology (Ecology) with sediment issues related to cleanup in various ports and harbors in Puget Sound, Washington. To be compliant with Ecology’s regulations, sites must attain sediment cleanup levels (SCLs) for hazardous substances within a reasonable timeframe through a combination of active remediation and natural recovery. To derive optimal site cleanup scenarios compliant with regulations a methodology was needed to estimate the spatial and temporal extent of remediation. A GIS-based sediment remediation/recovery model was designed using ESRI ArcGIS Model Builder. The model incorporates the SEDCAM sediment attenuation model and analytical results derived from field samples producing various cleanup scenarios that are further evaluated as remedial alternatives. On a chemical-by-chemical basis, the model determines active remediation footprints required to meet SCLs at the end of a defined natural recovery period. Post-remediation natural recovery is 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.

Session Title

Water Quality and Hydrodynamics

Keywords

Sediment, GIS modeling, Remediation

Conference Track

SSE1: Habitat Restoration and Protection

Conference Name

Salish Sea Ecosystem Conference (Seattle, WA : 2018)

Document Type

Event

SSEC Identifier

SSE1-462

Start Date

4-4-2018 2:30 PM

End Date

4-4-2018 2:45 PM

Type of Presentation

Oral

Contributing Repository

Digital content made available by University Archives, Heritage Resources, Western Libraries, Western Washington University.

Geographic Coverage

Salish Sea (B.C. and Wash.)

Rights

This resource is displayed for educational purposes only and may be subject to U.S. and international copyright laws. For more information about rights or obtaining copies of this resource, please contact University Archives, Heritage Resources, Western Libraries, Western Washington University, Bellingham, WA 98225-9103, USA (360-650-7534; heritage.resources@wwu.edu) and refer to the collection name and identifier. Any materials cited must be attributed to the Salish Sea Ecosystem Conference Records, University Archives, Heritage Resources, Western Libraries, Western Washington University.

Type

text

Language

English

Format

application/pdf

Share

COinS
 
Apr 4th, 2:30 PM Apr 4th, 2:45 PM

A GIS solution to evaluating remedial alternatives in sediment remediation and recovery

NewFields supports the Washington State Department of Ecology (Ecology) with sediment issues related to cleanup in various ports and harbors in Puget Sound, Washington. To be compliant with Ecology’s regulations, sites must attain sediment cleanup levels (SCLs) for hazardous substances within a reasonable timeframe through a combination of active remediation and natural recovery. To derive optimal site cleanup scenarios compliant with regulations a methodology was needed to estimate the spatial and temporal extent of remediation. A GIS-based sediment remediation/recovery model was designed using ESRI ArcGIS Model Builder. The model incorporates the SEDCAM sediment attenuation model and analytical results derived from field samples producing various cleanup scenarios that are further evaluated as remedial alternatives. On a chemical-by-chemical basis, the model determines active remediation footprints required to meet SCLs at the end of a defined natural recovery period. Post-remediation natural recovery is 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.