Directed site exploration for permeable reactive barrier design

被引:10
作者
Lee, Jejung [1 ]
Graettinger, Andrew J. [2 ]
Moylan, John
Reeves, Howard W. [3 ]
机构
[1] Univ Missouri, Dept Geosci, Kansas City, MO 64110 USA
[2] Univ Alabama, Dept Civil & Environm Engn, Tuscaloosa, AL 35487 USA
[3] US Geol Survey, Michigan Water Sci Ctr, Lansing, MI 48911 USA
关键词
Permeable reactive barrier; Site exploration; Uncertainty analysis; SAMPLING DESIGN; GROUNDWATER; UNCERTAINTY; PERFORMANCE; FLOW;
D O I
10.1016/j.jhazmat.2008.05.026
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Permeable reactive barriers (PRBs) are being employed for in situ site rennediation of groundwater that is typically flowing under natural gradients. Site characterization is of critical importance to the success of a PRB. A design-specific site exploration approach called quantitatively directed exploration (QDE) is presented. The QDE approach employs three spatially related matrices: (1) covariance of input parameters, (2) sensitivity of model outputs. and (3) covariance of model outputs to identify the most important location to explore based on a specific design. Sampling at the location that most reduces overall site uncertainty produces a higher probability of success of a particular design. The QDE approach is demonstrated on the Kansas City Plant, Kansas City, MO, a case study where a PRB was installed and failed. It is shown that additional quantitatively directed site exploration during the design phase could have prevented the remedial failure that was caused by missing a geologic body having high hydraulic conductivity at the south end of the barrier. The most contributing input parameter approach using head uncertainty clearly indicated where the next sampling should be made toward the high hydraulic conductivity zone. This case study demonstrates the need to include the specific design as well as site characterization uncertainty when choosing the sampling locations. (C) 2008 Elsevier B.V. All rights reserved.
引用
收藏
页码:222 / 229
页数:8
相关论文
共 31 条
[1]   Adifor 2.0: Automatic differentiation of Fortran 77 programs [J].
Bischof, C ;
Khademi, P ;
Mauer, A ;
Carle, A .
IEEE COMPUTATIONAL SCIENCE & ENGINEERING, 1996, 3 (03) :18-32
[2]   Algorithmic funnel-and-gate system design optimization [J].
Buerger, Claudius M. ;
Bayer, Peter ;
Finkel, Michael .
WATER RESOURCES RESEARCH, 2007, 43 (08)
[3]   SAMPLING DESIGN FOR CLASSIFYING CONTAMINANT LEVEL USING ANNEALING SEARCH ALGORITHMS [J].
CHRISTAKOS, G ;
KILLAM, BR .
WATER RESOURCES RESEARCH, 1993, 29 (12) :4063-4076
[4]   Uncertainty and data worth analysis for the hydraulic design of funnel-and-gate systems in heterogeneous aquifers -: art. no. W11502 [J].
Cirpka, OA ;
Bürger, CM ;
Nowak, W ;
Finkel, M .
WATER RESOURCES RESEARCH, 2004, 40 (11) :W1150201-W1150212
[5]  
DETHAN D, 2003, THESIS U ALABAMA TUS
[6]  
*DOE, 2000, LOW NE AR CHAR IR WA
[7]   Effects of aquifer heterogeneity and reaction mechanism uncertainty on a reactive barrier [J].
Eykholt, GR ;
Elder, CR ;
Benson, CH .
JOURNAL OF HAZARDOUS MATERIALS, 1999, 68 (1-2) :73-96
[8]   Design and construction techniques for permeable reactive barriers [J].
Gavaskar, AR .
JOURNAL OF HAZARDOUS MATERIALS, 1999, 68 (1-2) :41-71
[9]  
GELMAN A, 1995, BAYESIAN DATA ANAL, P478
[10]   Directing exploration with 3D FEM sensitivity and data uncertainty [J].
Graettinger, AJ ;
Dowding, CH .
JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING, 1999, 125 (11) :959-967