Determination of First‐Order Degradation Rate Constants from Monitoring Networks

In this article, different strategies for estimating first-order degradation rate constants from measured field data are compared by application to multiple, synthetic, contaminant plumes. The plumes were generated by numerical simulation of contaminant transport and degradation in virtual heterogeneous aquifers. These sites were then individually and independently investigated on the computer by installation of extensive networks of observation wells. From the data measured at the wells, that is, contaminant concentrations, hydraulic conductivities, and heads, first-order degradation rates were estimated by three 1D centerline methods, which use only measurements located on the plume axis, and a two-dimensional method, which uses all concentration measurements available downgradient from the contaminant source. Results for both strategies show that the true rate constant used for the numerical simulation of the plumes in general tends to be overestimated. Overestimation is stronger for narrow plumes from small source zones, with an average overestimation factor of about 5 and single values ranging from 0.5 to 20, decreasing for wider plumes, with an average overestimation factor of about 2 and similar spread. Reasons for this overestimation are identified in the velocity calculation, the dispersivity parameterization, and off-centerline measurements. For narrow plumes, the one- and the two-dimensional strategies show approximately the same amount of overestimation. For wider plumes, however, incorporation of all measurements in the two-dimensional approach reduces the estimation error. No significant relation between the number of observation wells in the monitoring network and the quality of the estimated rate constant is found for the two-dimensional approach.

[1]  E. Eric Adams,et al.  Field study of dispersion in a heterogeneous aquifer , 1992 .

[2]  J. Bear Dynamics of Fluids in Porous Media , 1975 .

[3]  G. Dagan Solute transport in heterogeneous porous formations , 1984, Journal of Fluid Mechanics.

[4]  A. Bellin,et al.  On the use of block‐effective macrodispersion for numerical simulations of transport in heterogeneous formations , 2003 .

[5]  Sebastian Bauer,et al.  A process-oriented approach to computing multi-field problems in porous media , 2004 .

[6]  Charles J. Newell,et al.  Natural Attenuation of Fuels and Chlorinated Solvents in the Subsurface , 1999 .

[7]  Todd H. Wiedemeier,et al.  Approximation of Biodegradation Rate Constants for Monoaromatic Hydrocarbons (BTEX) in Ground Water , 1996 .

[8]  Impact of transverse and longitudinal dispersion on first-order degradation rate constant estimation. , 2004, Journal of contaminant hydrology.

[9]  J. Vanbriesen,et al.  Microbiological processes in reactive modeling , 1996 .

[10]  Clifton C. Casey,et al.  Methodology for estimating times of remediation associated with monitored natural attenuation , 2003 .

[11]  Jianfeng Wu,et al.  A comparative study of Monte Carlo simple genetic algorithm and noisy genetic algorithm for cost-effective sampling network design under uncertainty , 2006 .

[12]  D. Lovley,et al.  Measuring Rates of Biodegradation in a Contaminated Aquifer Using Field and Laboratory Methods , 1996 .

[13]  Olaf Kolditz,et al.  Assessing measurement uncertainty of first‐order degradation rates in heterogeneous aquifers , 2006 .

[14]  Albert J. Valocchi,et al.  A method for the optimal location of monitoring wells for detection of groundwater contamination in three‐dimensional heterogenous aquifers , 1997 .

[15]  Jeanne M. VanBriesen,et al.  Chapter 7. MICROBIOLOGICAL PROCESSES IN REACTIVE MODELING , 1996 .

[16]  Olaf Kolditz,et al.  Uncertainty assessment of contaminant plume length estimates in heterogeneous aquifers. , 2006, Journal of contaminant hydrology.

[17]  T. Buscheck,et al.  Regression techniques and analytical solutions to demonstrate intrinsic bioremediation , 1995 .

[18]  George F. Pinder,et al.  Search strategy for groundwater contaminant plume delineation , 2003 .

[19]  Carl I. Steefel,et al.  Reactive transport in porous media , 1996 .

[20]  E. Sudicky A natural gradient experiment on solute transport in a sand aquifer: Spatial variability of hydraulic conductivity and its role in the dispersion process , 1986 .

[21]  You‐Kuan Zhang,et al.  An Improved Method for Estimation of Biodegradation Rate with Field Data , 2003 .

[22]  Philip H. Howard,et al.  Anaerobic Biodegradation of Organic Chemicals in Groundwater: A Summary of Field and Laboratory Studies , 1997 .

[23]  Walt W. McNab,et al.  A Critique of a Steady‐State Analytical Method for Estimating Contaminant Degradation Rates , 1998 .

[24]  Bruce E. Rittmann,et al.  Definition, Objectives, and Evaluation of Natural Attenuation , 2004, Biodegradation.

[25]  J. Skopp,et al.  Physical and Chemical Hydrogeology, 2nd edition , 1999 .

[26]  S. Thornton,et al.  Challenges in Monitoring the Natural Attenuation of Spatially Variable Plumes , 2004, Biodegradation.

[27]  Kuo‐Chin Hsu The influence of the log-conductivity autocovariance structure on macrodispersion coefficients. , 2003, Journal of contaminant hydrology.

[28]  Michael O'Sullivan,et al.  Modeling Biogeochemical Processes in Leachate-Contaminated Soils: A Review , 2001 .