Acquisition of Representative Ground Water Quality Samples for Metals
- 1 August 1992
- journal article
- Published by Wiley in Ground Water Monitoring & Remediation
- Vol. 12 (3) , 167-176
- https://doi.org/10.1111/j.1745-6592.1992.tb00057.x
Abstract
R.S. Kerr Environmental Research Laboratory (RSKERL) personnel have evaluated sampling procedures for the collection of representative, accurate, and reproducible ground water quality samples for metals for the past four years. Intensive sampling research at three different field sites has shown that the method by which samples are collected has a greater impact on sample quality, accuracy, and reproducibility than whether the samples are filtered or not. In particular, sample collection practices that induce artificially high levels of turbidity have been shown to have the greatest negative impacts on sample quality. Results indicated the ineffectiveness of bailers for collection of representative metal samples. Inconsistent operator usage together with excessive purging generally resulted in excessive turbidity (>100 NTUs) and large differences in filtered and unfiltered metal samples. The use of low flow rate purging and sampling consistently produced filtered and unfiltered samples that showed no significant differences in concentrations. Turbidity levels were generally less than 5 NTUs, even in fine‐textured glacial till. We recommend the use of low flow rates, during both purging and sampling, placement of the sampling intake at the desired sampling point, minimal disturbance of the stagnant water column above the screened interval, monitoring of water quality indicators during purging, minimization of atmospheric contact with samples, and collection of unfiltered samples for metal analyses to estimate total contaminant loading in the system. While additional time is spent due to use of low flow rates, this is compensated for by eliminating the need for filtration, decreased volume of contaminated purge water, and less resampling to address inconsistent data results.Keywords
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