Long-Term Monitoring Optimization Methods and Software
Julia J. Aziz, Groundwater Services, Inc.
Kirk Cameron, Ph.D., MacStat Consulting, Ltd
Barbara S. Minsker, Ph.D., Moire, Inc.
Carolyn Nobel, Ph.D., Parsons Inc.
Kathleen Yager, US EPA, Office of Superfund Remediation and Technology Innovation (OSRTI)
Abstract:
This workshop provides an overview of the following field-tested methods/software for optimizing existing site-specific long-term groundwater monitoring programs that are tracking contaminant migration.
Monitoring and Remediation Optimization System (MAROS 2.0) software is a decision support tool that accounts for relevant current and historical site data as well as hydrogeologic factors and the location of potential receptors. Based on this site-specific information, the software uses both temporal methods (Mann-Kendall Analysis, Linear Regression Analysis, or Cost Effective Sampling) and spatial methods (Delaunay Triangulation or Moment Analysis) to determine the minimum number of wells and the minimum sampling frequency required for future compliance monitoring at the site. Graphical and spatial visualization tools within the software assist the user in assessing the trend results at each monitoring point. The MAROS software is available for free download at www.gsi-net.com.
Parsons’ three-tiered method for long term monitoring optimization consists of a qualitative evaluation, an evaluation of temporal trends in contaminant concentrations, and a geostatistical spatial analysis. The results of the three evaluations are combined to assess the degree to which the monitoring network addresses the primary objectives of monitoring. A decision algorithm is applied to assess the optimal frequency of monitoring and the spatial distribution of the components of the monitoring network, and to develop recommendations for monitoring program optimization.
The Geostatistical Temporal/Spatial (GTS) algorithm is a decision-logic-based strategy for optimizing long-term ground-water monitoring networks developed for the Air Force Center for Environmental Excellence (AFCEE). GTS allows one to optimize both sampling frequencies and the number of wells used in a particular network. GTS has been employed at a number of Air Force sites with cost savings typically on the order of 30% or more of total LTM budget. In this workshop, the geostatistical methodology undergirding GTS will be explained in the context of a recent case study application. We will also discuss the current effort to convert GTS into free-standing software.
Multi-objective LTM Optimizer (M-LTMO) was developed at the University of Illinois and Moire, Inc., for identifying spatial and temporal redundancies in monitoring networks. The software combines a suite of interpolation modeling approaches (in both space and time) with user-friendly automated optimization approaches. It employs state-of-the-art multi-objective genetic algorithms that allow users to identify tradeoffs among multiple monitoring objectives and to explicitly consider data uncertainty in developing optimal monitoring designs. M-LTMO accesses a library of analytical tools for visualizing and analyzing data, developing interpolation models, entering any type of monitoring objectives and constraints in a user-friendly interface, automatically setting appropriate optimization parameters, and visualizing multi-objective optimization results. The software is being tested at two BP field sites.