Abstract. A geostatistical approach to contaminant source estimation is presented. The problem is to estimate the release history of a conservative solute given point concentration measurements at some time after the release. A Bayesian framework is followed to derive the best estimate and to quantify the estimation error. The relation between this approach and common regularization and interpolation schemes is discussed. The performance of the method is demonstrated for transport in a simple onedimensional homogeneous medium, although the approach is directly applicable to transport in two-or three-dimensional domains. The methodology produces a best estimate of the release history and a confidence interval. Conditional realizations of the release history are generated that are useful in visualization and risk assessment. The performance of the method with sparse data and large measurement error is examined. Emphasis is placed on formulating the estimation method in a computationally efficient manner. The method does not require the inversion of matrices whose size depends on the grid size used to resolve the solute release history. The issue of model validation is addressed.

A method to evaluate first‐order and zero‐order in situ reaction rates from a push‐pull test is presented. A single‐well push‐pull test starts with the rapid injection of a well‐mixed slug containing a known quantity of a conservative tracer and a reactive solute into the saturated zone. The slug is then periodically extracted and sampled from the same well. For zero‐ or first‐order reactions, in the absence of sorption and assuming negligible background concentrations, these measurements can be used to evaluate reaction rate coefficients directly. The method does not involve computer‐based solute transport models and requires no knowledge of regional ground water flow or hydraulic parameters. The method performs well when the dominate processes are advection, dispersion, and zero‐ or first‐order irreversible reactions. Regional flow velocities must be sufficiently low such that the slug stays within the area of the well during the sampling phase. In the case of zero‐order reactions, results using the method proposed here are compared with those obtained through the traditional method of calibrating a computer‐based transport model. The two methods give similar estimates of the reaction rate coefficient. The method is general enough to work with a broad range of push‐pull experiment designs and sampling techniques.

This paper presents a geostatistical approach to multi-directional aquifer stimulation in order to better identify the transmissivity ®eld. Hydraulic head measurements, taken at a few locations but under a number of different steady-state¯ow conditions, are used to estimate the transmissivity. Well installation is generally the most costly aspect of obtaining hydraulic head measurements. Therefore, it is advantageous to obtain as many informative measurements from each sampling location as possible. This can be achieved by hydraulically stimulating the aquifer through pumping, in order to set-up a variety of¯ow conditions. We illustrate the method by applying it to a synthetic aquifer. The simulations provide evidence that a few sampling locations may provide enough information to estimate the transmissivity ®eld. Furthermore, the innovation of, or new information provided by, each measurement can be examined by looking at the corresponding spline and sensitivity matrix. Estimates from multi-directional stimulation are found to be clearly superior to estimates using data taken under one¯ow condition. We describe the geostatistical methodology for using data from multi-directional simulations and address computational issues.

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