Using a Weibull Model to Describe the Binodal Curve on a Cosolvent-NAPL-Water Ternary Phase Diagram
Kenneth Y. Lee, University of Massachusetts Lowell, Lowell, MA

A Parametric Model for Estimating Costs for Remediating Contaminated Sediment Sites Using a Dredging Method
John Rosengard, Environmental Risk Communications Inc., San Francisco, CA
Jeff Wallace, Environmental Risk Communications Inc., San  Francisco, CA
Mark Otten, Parsons Corporation, Louisville, KY

Using a Weibull Model to Describe the Binodal Curve on a Cosolvent-NAPL-Water Ternary Phase Diagram
Kenneth Y. Lee, Civil and Environmental Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA, Tel: 978-934-2255, Fax: 978-934-3052, Email: kenneth_lee@uml.edu

In this study, a regression curve based on a Weibull distribution model is generated to describe the binodal curve on a three component cosolvent-NAPL-water ternary phase diagram.  The methodology involves transforming the experimental phase partitioning data points of each mixture from the ternary phase diagram to equivalent Cartesian coordinates.  A regression curve is then generated from the Cartesian coordinates, and the regression curve becomes the Weibull-derived binodal curve by superimposing the regression curve onto the original ternary phase diagram.  A collection of Weibull-derived binodal curves for common cosolvent-NAPL-water systems are presented.  From regression analysis, the resulting R2 value for most mixtures is very close to one, indicating that there is strong correlation between the regression curve and the transformed experimental data points.  The method developed in this study enables researchers to empirically describe a binodal curve, which is useful in analyzing and predicting the various phase partitioning scenarios of a cosolvent-NAPL-water system. 

A Parametric Model for Estimating Costs for Remediating Contaminated Sediment Sites Using a Dredging Method
John Rosengard, Environmental Risk Communications Inc., 475 Sansome Street, Suite 1710, San Francisco, CA 94111, USA, Tel: 415-982-3100, Email: John@erci.com
Jeff Wallace, Environmental Risk Communications Inc., 475 Sansome Street, Suite 1710, San Francisco, CA 94111, USA, Tel: 415-982-3100 Email: Jeff@erci.com
Mark Otten, Parsons Corporation, 4156 Westport Road, Suite 205 , Louisville , KY 40223 , USA , Tel: 502-721-0292 Email: Mark.Otten@parsons.com

Contaminated sediments, whether in freshwater or marine systems, pose a significant environmental challenge both within the United States and across the globe. When it comes to cost estimating for sediment-related cleanup projects, headline after headline seems to read something like “Cost Estimates Increased for XYZ Project” or “Cost Estimate Rises to $(fill in your own astronomical number way above original estimates)”. Why do these calculations remain such a persistent challenge to financial professionals and planners charged with estimating such cleanup efforts? One predominant reason is that estimating the true costs of such projects is tremendously difficult and riddled with high degrees of uncertainty. Simply put, what professionals need is a “better mousetrap.”

To develop a better “mousetrap”, we assess the current practices employed in developing such estimates. According to the U.S. Department of Defense and U.S. Department of the Army, there are three basic types of cost estimation techniques that are used either individually or in combination - Analogy, Build Up, and Parametric Modeling. Each approach has been used throughout industry with varying degrees of success. However, according to the DoD/DoA, there are currently no real-world examples of parametric models for estimation of sediment treatment project costs.

We’ve created a viable Parametric Model for assisting financial professionals and planners in developing appropriate cost estimates for the processing and disposal of dredged materials which can be used for planning and budgetary purposes, communicating with appropriate stakeholders, and providing guidance to senior management.  This multi-variable financial model enables cost estimates for either a single site or a portfolio of sites [while still allowing for individual site specifications] by providing cumulative costs over the collective remediation period. It allows for “what if” scenarios and provides both numerical and graphical depictions of these aforementioned cost estimates.