How Confident Are You in Your CCR Groundwater Monitoring Data?
Many utility companies regulated under the US EPA Coal Combustion Residuals (CCR) Rule are nearing, or have completed, their second year of groundwater monitoring. As these CCR monitoring programs approach the deadline for 2nd annual reports, it may seem that aspects of programs are “set.” However, the introduction of a quality assurance program focused on chemistry and geoscience can be beneficial to mature environmental monitoring programs. As these programs face the possible transition from detection monitoring to assessment monitoring, now is the time to thoroughly evaluate the data that have been generated and make any necessary adjustments to well networks.
Often, it seems that incorporating existing groundwater monitoring wells into the CCR Rule certified monitoring network is efficient and economical. In some cases, this may be true, but for others, it could create problems down the road. For example, the correlation between elevated turbidity and elevated inorganic concentrations is indisputable, but it is important to determine the specific cause of the elevated turbidity. It is possible that the geologic formation being monitored naturally produces highly turbid groundwater, or the monitoring well itself may be poorly developed, therefore, biasing the data. Evaluation of well-construction details to determine how wells were completed, down-hole camera inspections to identify damaged wells, and routine maintenance programs are necessary to paint the whole picture and determine if groundwater quality issues are “real” or are the result of a problematic monitoring well.
Data used for modeling and statistics need to be reliable and consistent. Even relatively small biases or inconsistencies in analytical data could lead to unnecessary assessment monitoring or other CCR Rule actions. In many cases, decisions are made based on trace-level metals results; these low-level results are highly susceptible to bias from a number of analytical issues. Critical data validation may eliminate false positive results (such as those attributable to contamination or instrumental interference) that would otherwise be used to incorrectly initiate assessment monitoring or corrective action. A consistent data review process provides a high degree of confidence in the data set, such that utilities can be confident they are making correct decisions.
Managing data over long time periods can pose a unique set of challenges, particularly in the absence of a formalized Data Management Plan. Inconsistencies in sample or location nomenclature may cause difficulties in spatial visualization of results. Inconsistencies in analytical methods, parameter names, or fraction codes can cause problems when creating standardized tables. Formalized data management is an essential component of a comprehensive data review process that results in a usable and consistent data set.
Approaching CCR Rule groundwater monitoring as a dynamic, as opposed to a static, system allows for the constant evaluation of data, as to allow for programmatic improvements. When data quality is viewed from a multidisciplinary perspective, data users can have confidence in the data set used to make critical operational decisions.
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This article is brought to you by Jacob Gruzalski. For more information you can reach him at firstname.lastname@example.org