D.11 Monitoring and Remediation Optimization System Software (MAROS)

D.11 MONITORING AND REMEDIATION OPTIMIZATION SYSTEM SOFTWARE (MAROS)

Approximate Cost: Free

Source: GSI website (http://www.gsi-net.com/en/software/free-software/maros-30.html)

Current Version: version 3.0

Operating System Needs: Microsoft Office Access 2007 or 2010; Windows 7 (older versions of software are available compatible with Windows XP and earlier versions of MS Office)

Input Structure: Microsoft Excel spreadsheet or Access database

Overview

The Monitoring and Remediation Optimization System (MAROS) software was developed by GSI Environmental Inc. (GSI) on behalf of Air Force Civil Engineer Center (AFCEC), known previously as Air Force Center for Engineering and the Environment (AFCEE), in 1998. It is a public-domain, data management and evaluation tool specifically designed to improve long-term groundwater monitoring (LTM) programs using both qualitative and quantitative methods. MAROS provides both:

Optimization routines, to help determine the appropriate number of sample locations, sampling frequency, and laboratory analytes for the specified monitoring objective
Statistical analysis tools to evaluate the plume stability condition and the effectiveness of natural attenuation and active remediation efforts

Statistical Functions

Statistical Method	Capability As Is	Capability with Scripts/Add-Ins
Handling of NDs
Simple Substitution	●	N/A
Kaplan-Meier	●	N/A
ROS		N/A
Cohen/MLE		N/A
Exploratory/Diagnostic Tools
Summary Statistics	●	N/A
Distributional tests	●	N/A
Outlier tests	●	N/A
Data transformations	●	N/A
Statistical Design
Statistical Power	●	N/A
SWFPR		N/A
Contaminant ranking	●	N/A
Monitoring network optimization	●	N/A
Statistical Limits
Confidence Limits	●	N/A
Tolerance Limits		N/A
Prediction Limits		N/A
Testing Compliance Limits	●	N/A
Graphics
Plots/Charts	●	N/A
Batch plots		N/A
Tweaking of graphics		N/A
Statistical Comparisons
t-tests	●	N/A
ANOVA		N/A
Spatial Analysis
Geostatistics/Mapping		N/A
Kriging/Interpolation		N/A
Spatial smoothing		N/A
Regression/Time Series
Trend Tests	●	N/A
Mann-Kendall	●	N/A
Linear regression	●	N/A
Nonlinear regression		N/A
Theil-Sen line		N/A
Time Series analysis		N/A
Multivariate Analysis
Multiple regression		N/A
Factor/Discriminant analysis		N/A
Bootstrapping		N/A

Capability Ratings:

N/A = Not applicable or not available

● = Full capability

◒ = Some capability

(blank cell) = No capability

Add-Ins Available

None

Ease of Use and Data Import

The MAROSMonitoring and Remediation Optimization Software tool analyzes groundwater data collected from a network of permanent or semi-permanent groundwater monitoring locations. Modules within the MAROS tool evaluate affected groundwater sites where substantial site characterization has already occurred. MAROS is not specifically designed for detection monitoring programs. The intended use of this software is to review data and evaluate the efficacy of the network to address site monitoring and remediation objectives.

The MAROSMonitoring and Remediation Optimization Software software uses site-specific data from the full historical record for statistical analysis and review. Groundwater analytical data can be imported into the software from AFCEC'sAir Force Civil Engineer Center's Environmental Resource Program Information Management System (ERPIMS) database or Microsoft Excel or Access files. Minimum data requirements for the MAROS tool include data from at least six monitoring locations with detected concentrations of contaminants and at least four sampling events. The software can evaluate data from as many as 200 wells (speed of calculations may be slow with more than 200 wells). Input files include data such as well name, X, Y coordinates, sample date, contaminant name, analytical result, detection limit, and data flags.

The software can currently import data for over 2,000 different chemicals that may be dissolved in groundwater. Internal MAROSMonitoring and Remediation Optimization Software databases contain regulatory screening levels from various programs and physical parameters for these chemicals. Data from these databases are used to prioritize contaminants for long term monitoring optimization (LTMO) analyses.

Types of Distributions

The software contains modules that prioritize contaminants, calculate summary statistics, find outliers, identify data distributions, determine temporal trends at individual wells (using both Mann-Kendall and linear regression techniques), and calculate plume stability metrics and their trends over time (such as total dissolved mass, center of mass, and spread of mass).

Visualization

MAROSMonitoring and Remediation Optimization Software has several data visualization options that allow you to review concentration versus time graphs and tables prior to more in-depth analyses. You can export results of analyses either as formatted reports, spreadsheet output, or database output at various locations in the software.

Primary Uses for Groundwater Data Analysis

The program uses individual well statistics to prioritize wells in the network and identify locations that have attained cleanup goals. Optimization analyses identify redundant locations using qualitative decision logic and a nearest neighbor spatial geometry approach, estimate plume concentration uncertainty to recommend new well locations, and use a sampling frequency module to recommend optimal sampling intervals.

Benefits

Uses simple statistics and decision frameworks to prioritize data collection efforts and link data to defensible site management decisions
Can use results from this software to develop lines of evidence, which combined with professional judgment, can be used to inform site management decisions for safe and economical long-term monitoring of groundwater plumes.
Can use MAROSMonitoring and Remediation Optimization Software to help design and calculate remediation performance metrics and as a tool to evaluate progress toward site remedial goals

Limitations

MAROSMonitoring and Remediation Optimization Software provides a two-dimensional analysis of the plume, so analyze nested wells separately (wells with the same X, Y coordinates but different depths).
Evaluate multiple saturated units separately. Some modules assume a single source location.
MAROS does not evaluate the plume outside of the imported well locations.
Some statistical modules have limitations in the number of sample events that can be analyzed. For example, in the current version, the Mann-Kendall trend tool has an upper limit of 40 samples while the outlier analysis (Dixon’s method) is limited to 25 samples.

References

AFCEC (Air Force Civil Engineer Center). 2012. "Monitoring and Remediation Optimization System (MAROS) Software, User's Guide and Technical Manual." In: Air Force Center for Environmental Excellence.

AFCEE (Air Force Center for Engineering and the Environment).1997. AFCEE Long-Term Monitoring Optimization Guide Version 1.1. Brooks AFB, Brooks City, TX, Air Force Center for Environmental Excellence.

AFCEE. 2004. Monitoring and Remediation Optimization Software User's Guide. Air Force Center for Environmental Excellence. http://www.gsi-net.com/software/MAROS_V2_1Manual.pdf.

Aziz, J. A., C. J. Newell, M. Ling, H. S. Rifai and J. R. Gonzales. 2003. "MAROS: A Decision Support System for Optimizing Monitoring Plans." Ground Water 41(3): 355-367.

Ling, M., H.S. Rifai, C.J. Newell, J.J. Aziz, and J.R Gonzales. 2003. “Groundwater Monitoring Plans at Small-Scale Sites: An Innovative Spatial and Temporal Methodology”, Journal of Environmental Monitoring, 5: 126-134.

Ling, M., H. S. Rifai, J. J. Aziz, C. J. Newell, J. R. Gonzales, and J. M. Santillan. 2004a. “Strategies and Decision-Support Tools for optimizing Long-Term Groundwater Monitoring Plans-MAROS 2.0”, Bioremediation Journal, 8 (3-4): 109-128.

Ling, M., H. S. Rifai, and C. J. Newell. 2004b. "Optimizing Long-Term Monitoring Networks Using Delaunay Triangulation Spatial Analysis Techniques", Environmentrics, Accepted, August 2004.

Vanderford, M. 2010. "A Comprehensive Approach to Plume Stability." Remediation Winter 2010: 21-37.

Publication Date: December 2013

Permission is granted to refer to or quote from this publication with the customary acknowledgment of the source (see suggested citation and disclaimer).