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Use of Quantitative Models to Design Microbial Transport Experiments in a Sandy Aquifer
Author(s) -
Scheibe Timothy D.,
Chien YiJu,
Radtke Jonathan S.
Publication year - 2001
Publication title -
groundwater
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 94
eISSN - 1745-6584
pISSN - 0017-467X
DOI - 10.1111/j.1745-6584.2001.tb02302.x
Subject(s) - tracer , aquifer , hydraulic conductivity , soil science , environmental science , dispersion (optics) , hydrogeology , scale (ratio) , aquifer properties , geology , hydrology (agriculture) , geotechnical engineering , groundwater , soil water , physics , optics , quantum mechanics , groundwater recharge , nuclear physics
A suite of numerical models was applied to the problem of designing field tracer and bacterial injection experiments in a sandy surficial aquifer near Oyster, Virginia. The models were constructed based on the integration of diverse characterization data including hydrologic, geophysical, geological, geochemical, and biological information. A one‐dimensional particle‐tracking model was used to analyze laboratory transport experiments conducted using intact core samples to prescribe transport parameters describing solute dispersion and bacterial fate. A geostatistical model of three‐dimensional hydraulic conductivity variations was developed, conditioned on in situ measurements of hydraulic conductivity and interpretations of geophysical data, and used to generate alternative aquifer descriptions. A regional‐scale, two‐dimensional flow model was used to design pumping rates of a forced‐gradient hydraulic control system. Information from these various models was then combined into a high‐resolution, three‐dimensional flow and transport model for the prediction of field‐scale solute and bacterial transport. Model predictions were used in an iterative experimental design process to specify. (1) the locations of multilevel samplers for monitoring transport; (2) frequency and timing of sample collection during bromide tracer injection experiments; and (3) frequency and timing of sample collection during a bacterial injection experiment. At each stage of the design, information gained during the previous stage was used to refine the model and target subsequent experimentation.