Managing Environmental Data for Conceptual Site Models Dr. David W. Rich Indianapolis, IN February 26, :45 – 3:30

Summary of Presentation Investigation and remediation projects have complex requirements Automated tools can help the process Previously the main tool for characterization was lab analyses Now higher resolution field data is becoming more important Use of field data is evolving from screening only, to directly impacting decision making Data management and displays must adjust to this So must site interpretation models The result should be better site understanding and project decisions 2/44

Overview of the Process Site issues Identify concerns Prepare a preliminary model with available data Identify gaps in the data Data management issues Gather data for detailed site characterization Discrete data Continuous data Import, QC and manage data Generate useful output Modeling issues Scale of model vs. scale of data Integration of geology and chemistry Making and implementing decisions 3/44

Site Issues Identify concerns What are the matrices - soil, water, air, etc.? Determine constituents of concern Identify impacts - type, severity Prepare a preliminary model with available data Gather and organize existing data - geology, chemistry, GIS data Present data so it can be analyzed Identify gaps in the data Where is the site well characterized, and where not? What new information is needed where? Fill the gaps and revise the model 4/44

Preliminary Model - Geology 5/44

Managing and Displaying Site Geology You might want to assign geology and lithology to each physical sample This makes it easy to tie the geology to field and lab data Or store formation “tops” by location independent of the samples This probably better represents the actual site geology 6/44

Introduction

Crosstab Callouts From the Database

Soil Borings with Values from the Database Benzene (ppm) Benzene (mg/kg) Lithology

Voxler Example 10/44

Data Management Issues High level view of the process Modern systems can manage more of the process Planning for sample events Gathering field data and taking samples Obtaining lab and field data Importing and storing data Discrete data Continuous data Addressing data issues - duplicates, non-detects, flagged data, dilutions, etc. Locations of software and data Data selection and formatting of results Output content and formats 11/44

High Level View of the Data Management Process Plan your sample events Manage field and lab activities Manage data and quality Analyze and display data Store in a robust repository It’s all in one location

Planning Your Sample Events Entering stations 13/44

Container Labels 14/44

Planning the sample event

Gathering Field Results 16/44

Gathering Soil Samples 17/44

Gathering Boring Log Data Images courtesy of LogItEasy.com Cloud-based log data entry 18/44

Gathering Continuous Data Images courtesy of Gathering continuous downhole data 19/44

Environmental Data and the “Cloud” 20/44

Environmental Data and the “Cloud” 21/44

Import Wizard Specifying Import Options 22/44

Quality Control - Consistency Checking 23/44

Quality Control - Validation 24/44

Quality Control - Validation Summary 25/44

Bulk Data 26/44

Selection and Display 27/44

Display Options Determine How Your Results Are Displayed Example options: Regulatory limits Values and flags Unit conversion Date display Calculated parameters Non-detects Significant figures Graph display options Custom queries 28/44

Generating Output

Typical Data Presentation 30/44

Modeling Issues Be sure to clearly state the problem to be solved Gather and organize all the different data components Maps and other GIS data Geology, hydrogeology Discrete data such as lab data Continuous data, such as direct push data We want to characterize three main things: rock properties, fluid properties, and concentrations Have a good understanding of your tools Use an appropriate process for the specific problem Present results clearly and succinctly 31/44

Modeling Issues Source: Mapping Research at the USGS Toxic Substances Hydrology Program Research Site to investigate Recalcitrant Contamination in Fractured Bedrock, by Pierre Lacomb and Rachel Dearden

Modeling Issues 33/44

Modeling Issues

Modeling Issues

Modeling Issues

Modeling Issues Source: Environmental Visualization: Applications to Site Characterization, Remedial Programs, and Litigation Support, Meng Ling and Jian Chen, Workshop on Visualisation in Environmental Sciences (EnvirVis) (2013) Source: Techniques for 3D Geological and Hydrogeological Modeling. A Case Study of Conawapa Generating Station, Sharif, S., Mann, J.D., & Smith, J.B., KGS Acres, Winnipeg, Manitoba, Canada, and Cook, G.N., Manitoba Hydro, Winnipeg, Manitoba, Canada. 37/44

Characterizing a Site Traditional conceptual site model 38/44

Characterizing a Site Gather contaminant data Grid and model Traditional result 39/44

Characterizing a Site Build a hi-res geological model Grid and model each unit Here’s a different way 40/44

Characterizing a Site Stack the models Traditional result The results can be very different 41/44

Making and Implementing Decisions Gather as much data as practical Use the right tools to manage and present the data Present it clearly and succinctly Tailor the presentation to the problem to be solved Determine alternative solutions Enumerate advantages and disadvantages Select from among these and implement Review results regularly

Conclusions Investigation and remediation projects have complex requirements Automated tools can help the process The industry is seeing more use of field data relative to lab data Data management and displays must adjust to this So must site interpretation models Use of high-resolution geology and concentration can lead to better site models The result should be better site understanding and project decisions 43/44

Managing Environmental Data for Conceptual Site Models Dr. David W. Rich Indianapolis, IN February 26, :45 – 3:30