Theme: 10. Characterization, Fate and Transport

Topic Area: 10b. Advanced Sampling and Analysis Tools and Techniques

Formatted Title:

A New Automated Approach to Grain Size Analysis for Environmental Practitioners

Background/Objectives:

An automated procedure was developed for the Excel-based program, HydrogeoSeiveXL™, to streamline the calculation of hydraulic conductivity (K) estimates in support of two-dimensional (2D) stratigraphic flux evaluations for multiple U.S. Air Force PFAS remedial investigations (RIs). The value of the conventional spreadsheet method for estimation of K from grain size distribution curves is well established with environmental practitioners as a beneficial starting point for hydrogeological investigations. This new tool extends analysis to include 16 methods to provide better indication of the statistically supported range of K that might apply. The application of stratigraphic flux involves the collection of high-resolution soil and groundwater sample pairs to map the vertical and horizontal distribution of both aquifer permeability and contaminant concentrations in groundwater. The primary objectives of the new automation are to refine data ingestion, significantly reduce the time required for data processing and evaluation, and eliminate the substantial labor costs associated with the conventional method for analyzing large quantities of grain size data in support of stratigraphic flux and other evaluations.

Approach/Activities:

A digital workflow was developed to integrate laboratory electronic data deliverables (EDDs) of grain size analysis results with automated data quality review, formatting, analyses and reporting. The automated grain size analysis tool integrates the same logic built into the conventional HydrogeoSeiveXL™ tool through scripts created in R software. The new tool initially calculates the effective grain size diameter used in the estimation of K using specialized formulas associated with the selected fundamental method. The tool then provides a qualitative description of the sample (soil classification) in terms of its uniformity, textural class and the presence or absence of fine material. The weight percentages of the sample (in standard sieve and hydrometer ranges) are then calculated to assess the percent passing of the various size fractions in the sample. Estimates of K are subsequently calculated using each of the sixteen established methods and a determination made regarding whether criteria are met for each equation. An innovative extrapolation to the zero (fine-grained) fraction using monotone piecewise cubic interpolation is further computed with a decision matrix for when the extrapolation is applied. The resulting K estimates are reported for each method (in preferred units) and an arithmetic and geometric mean are calculated using the selected methods, where an automated recommendation is generated for selecting the representative mean as an expression of the central tendency. As a final step, the automated tool generates a one-page summary of the grain size evaluation for each individual sample and a master summary table of all outputs as a batch export.

Results/Lessons Learned:

The new automated tool expedites the data analysis and visualization process for an established industry standard approach to grain size analysis. The tool eliminates traditional manual data entry, has the capability to analyze an unlimited number of grain size results as batch exports, incorporates strengthened statistical review, generates report-ready exhibits, and greatly reduces the data processing time required with the conventional spreadsheet process from hours to minutes, resulting in significant time and cost savings.

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Caitlin Cisco