Monday, October 11, 2004
6:00 - 7:30 pm
Pellissippi State Technical Community College
10915 Hardin Valley Road, Knoxville
Lamar Alexander Building
Optimization of a Groundwater Monitoring Network
Jamie M. Bartel,
Shaw Environmental & Infrastructure, Inc.
In the current economic climate, organizations demand that services be provided better, faster, and cheaper than ever before. In the environmental service industry, the ultimate goal is to clean up site contamination, receive a clean bill of health from regulatory agencies, and be done with it. However, this is idealistic because the regulatory audience typically is reluctant to close a site with residual contamination and typically requires a long-term monitoring (LTM) program to track site contamination that does not meet regulatory standards for &ldquoclean&rdquo. Many sites will never meet regulatory cleanup levels, have ongoing LTM, and have the potential for reducing LTM while maintaining project objectives and satisfying regulatory requirements. Through the skillful application of Monitoring Network Optimization (MNO), we can deliver a better product to our clients at reduced cost while achieving project objectives and regulatory requirements.
The MNO protocol was developed by the Air Force Center for Environmental Excellence. The guidance was developed in an effort to program wisely and manage LTM resources in a more cost-effective manner. The MNO protocol identifies and applies the appropriate optimization tools and strategies to achieve the following objectives.
1) Evaluate long-term temporal trends in concentrations (time)
2) Evaluate extent of contaminant plume migration or reduction (spatial)
3) Eliminate redundancy within the LTM program
The MNO goals seek to ensure that sufficient and necessary data are captured and analyzed to support crucial decisions. This is done through evaluation of well location and sampling frequency, with consideration to climactic conditions, seasonal changes, professional judgment and site knowledge.
Optimization tools applied by AFCEE have included geostatistical algorithms (kriging), software specifically developed to provide decision support based on statistics applied to site-specific data, and other statistical tools. The case study that will be presented used a simplified version of the AFCEE MNO process and applications that were cost effective for the study site, and there was no need for a statistician!
Page updated September 21, 2004