Long-term monitoring (LTM) costs make up a substantial percentage of environmental restoration budgets for both government agencies and the private sector. A major portion of LTM costs are associated with groundwater sampling and analysis. One promising cost-saving strategy is the replacement of traditional groundwater sampling approaches by a combination of passive sampling devices and in situ sensors. However, regulatory and operational acceptance of changes in groundwater sampling methodologies is conditional on both laboratory and field validation of the methods and an in-depth understanding of the way such methods represent groundwater chemistry.

The objective of this project was to develop a fuller understanding and description of how contaminant concentrations measured in a well—using either passive sampling devices or in situ sensors—relate to contaminant concentrations in the surrounding formation. The research has elucidated several key factors in the relationship between solute concentrations in wells and in the surrounding formation, thus providing additional scientific basis for cost-effective application of alternative sampling and monitoring strategies. Solid scientific basis for non-purge groundwater monitoring is imperative for gaining user and regulatory acceptance of the alternate techniques. Defining the measurable attributes of aquifer/well conditions that either suggest likelihood or imply limits to well mixing phenomena were primary objectives of the work.

Technical Approach

Physical and numerical modeling efforts were completed to illustrate the relationship between solutes in groundwater and those found in wells. Physical mixing processes that occur during ambient flow in aquifers and during active pumping activities were explored to show how potentially stratified contaminants may manifest in open well screens. Complex flow patterns driven by flow velocity changes, very small density contrasts, and temperature contrasts were identified as possible sources of well convection and mixing. Specifically, the modeling explored:

  • 2D and 3D Physical Tank Models. Dye tracer testing was conducted to illustrate flow in and around model wells.
  • Analytical Models. Models of well flow patterns were used to resolve well flushing during purging.
  • Axisymmetric Numerical Models. Pumping models were constructed to simulate pumping flow to a well, allowing variations in adjacent hydraulic properties and contaminant distribution to be easily tested.
  • Numerical Bore Model. In-well density convection phenomena were explored using a closed bore well model.
  • Numerical Section Model. 2D mass and energy transport models were constructed to test inflow phenomena, density-driven convection, and the effects of in-well flow limiting devices.

Physical field testing was completed to test and illustrate contaminant fluctuations and differentials in field conditions. As such, these tests were less well-controlled and near-well flow conditions were only inferentially known until experimental data was generated. This approach is much more similar to practitioner conditions where collected data drives interpretation.

  • Multilevel Passive Sampling. Multilevel sampling was conducted using two approaches:  (1) open well sampling and (2) isolated zone sampling.
  • Well Installation. New wells were installed with special design configurations to explore the relationship between contaminant concentrations inside and outside the well during ambient flow and during pumping.
  • Purge Dynamics Testing. Special purging protocols tested how pumping rate, consistency of flow rate, changes in flow rate prior to sampling, and duration of pumping affects pump discharge concentrations, relative to concentrations measured in the well.
  • Bottle Filling Test. An outdoor field test of methods that are commonly used to fill volatile organic analysis (VOA) vials was conducted. This test was conducted to assess the degree to which field sample handling affects analytical results.


Passive sampling (or remote passive sensing) in unpumped wells. Wells tend to redistribute adjacent aquifer contaminant heterogeneity. Homogenization often occurs, but the degree of mixing varies from well to well. Redistributive effects are highly sensitive to very small density contrasts in the inflowing fluid. Because of these density-driven effects, when aquifer contaminant chemistry is stratified, solute distribution in a well may not match that in the adjacent formation. Often times, the passive sample closely matches the flow-weighted-average of inflowing water. This condition is not perfect, however, and concentration distributions vary from well to well. There are many factors affecting contaminant distribution in an unpumped well that are not readily known without exhaustive testing. Pumping dynamics are also complex, meaning that differences from pumped comparators do not always clarify which method is “correct” in such circumstances. Contaminant position in the aquifer relative to the passive sampling device position in the well is usually not known, and understanding of that aquifer-contaminant geometry may not be improved by multilevel passive sampling in an open bore. Isolated zone sampling can improve determination of aquifer contaminant stratification, but the degree of improvement is variable and well-dependent. As a general replacement for purge sampling, passive sampling or passive sensing do yield similar results in most cases. Where results are different, and information is desired, causes can often be found through more thorough multiple-sample-testing during purging, multilevel passive sampling within the well, or a combination of the two.

Pumping dynamics. Aquifer contaminant position relative to the pump intake position is rarely known, but knowledge may be improved by collecting multiple samples during purging. Contaminant position in the aquifer relative to the pump position in the well drives concentration stability during pumping. Volume of water removed from the well is a more reliable predictor of contaminant concentration stability than measurement of traditional “purge parameters” such as temperature, pH, electrical conductivity, oxidation reduction potential, and dissolved oxygen; yet concurrently, large volumes removed do not always assure contaminant concentration stability. Some contaminants of interest stabilize faster than others, depending on the specific well, suggesting that unique contaminant distribution heterogeneity and chemical-specific biological activity can influence the stability of contaminant concentrations during purging.

Physical, numerical, and analytic models. Good matches between simulation results, experimental data, and theoretical analysis of flow and transport support the project’s hypothesis that redistributive effects by vertical mixing is common. Physical model experiments show that small heterogeneities exert strong influence on flow in the open bore of a model well. Horizontal laminar flow across the model bore could not be reproduced except under conditions in which density variations are much less than would be expected in the field. Density contrasts equivalent to as little as 10 parts per billion of dissolved solids are enough to cause near complete vertical redistribution in simulated wells. Thermal convective behavior induced by shallow seasonal thermal changes and deeper geothermal gradients may also either promote or inhibit well mixing effects.


Contaminant redistributive effects in wells are nearly always present. Complete mixing appears to be very common; however, it is not universal. There is a continual balance between inflowing contaminant stratification (where present) and factors driving in-well mixing. The project’s findings imply common and very small drivers are responsible for slow but vigorous mixing relative to the residence time of water flowing through a typical well screen. Therefore, a tendency toward homogenization is anticipated to be common in field conditions. Most wells should experience strong redistribution effects, but some wells may maintain stratification or perhaps re-stratify differently from the surrounding formation. Ongoing technical transfer of these findings will promote better understanding in the environmental community that wells often represent a mixed flow-weighted average of the adjacent formation chemistry. This better understanding will yield cost savings in both short-term and long-term timeframes by accelerating the approval process for non-purge alternative sampling strategies, including passive sampling and in situ sensor technologies.