This page contains downloadable presentations, white papers, and other resources for our clients and friends. By clicking on the thumbnail these can be viewed or saved. Some are also available in multiple formats where there is a link in the article. 

Free Environmental Computing Glossary

Geotech is pleased to provide our clients with this useful reference of environmental computing terms. It contains 38 pages of common terms used in the environmental and computing industries. We encourage your feedback, especially if you would like to suggest any corrections.

Water Suppply and Wastewater Applications in Enviro Data

Enviro Data is a program for storing and displaying environmental quality data. It has applications in a variety of areas including facility operations, site investigation, remediation, potable water supply, and wastewater monitoring. This document discusses the application of Enviro Data for potable water supply and wastewater to save time, improve work quality, and minimize drudgery.

As the water supply and wastewater fields continue to diversify, and as sources and usage of water become more complex, it is critical to have an efficient tool to manage constituent concentration and other information about the water for effective decision making and potential regulatory compliance.

Data Management and Validation Workflow

The amount of data being gathered on DOD investigation and monitoring projects is growing at ever increasing rates. Action levels are becoming more varied and stringent, leading to more exceedences, and expectations for using the data are growing rapidly. The importance of maintaining data quality through the data management process, and especially detailed and efficient data validation, remains of utmost importance. The need for efficient tools for managing lab and field data for water and other matrices is clear. Tools such as affordable GPS receivers, field data entry devices, target levels (MCLs) available in digital form, and readily available base map data, are making it easier to manage most or all project data without resorting to paper. This presentation follows the data through the data management process from the field to the final uses of the data, with a particular focus on data validation. It also discusses problem areas and pitfalls in running a data management project, and how to overcome them. Cost savings of 50% or more along with significant improvements to quality can be documented, resulting from more efficient data management and display. Download the presentation in PowerPoint.

Managing Environmental Data from the Field to the Map

The amount of data being gathered on water, groundwater, and soil investigation and monitoring projects is growing at ever increasing rates. Action levels are becoming more varied and stringent, leading to more exceedences, and the expectations for using the data are also growing rapidly. Most people recognize the need for efficient tools for managing laboratory and field data for water and other matrices. Tools such as affordable GPS receivers, field data entry devices, target levels (MCLs) available in digital form, and readily available base map data in a variety of formats, are making it easier to manage most or all project data without resorting to paper.

This presentation follows the data through the data management process from the field to the final uses of the data. It covers the various steps in the process, from preparing for a field event, gathering field data, interaction with laboratories, data import, checking and validation, data selection, reporting, and GIS mapping, using both local and cloud systems. It also discusses problem areas and pitfalls in running a data management project, and addresses how to overcome them. We will pay particular attention to the specific problems of managing laboratory data, as well as to issues related to mapping groundwater and related data. Cost savings of 50% or more can be documented resulting from more efficient data management and display, and these savings can result in a high return on investment for software purchases, staff training, and data conversion. Download the PDF, in PowerPoint or, watch the live presentation recorded Oct. 2018.

Better Data for Better Site Conceptual Models

Some site remediation projects have been very successful, while others have not. One of the biggest factors in remediation project success is effective use of site conceptual models. Better use of these models can significantly increase the chance of a positive outcome. For many projects, the process is very linear: investigation, feasibility study, remedy selection, and implementation. Then, if the remedy doesn’t work, try another approach until something finally works. Often the investigation phase includes a site conceptual model. Usually the model is based on regional geology with some input from surface geology at the site, perhaps supplemented by well data. Many times, once the model has been created, it is used throughout the life of the project as it was originally defined. Two changes can greatly improve the use of the site conceptual model, leading to a more positive outcome.

The first is to more thoroughly involve input from geologists in creating the original model. The flow of groundwater, contaminants, and reactants is very complex, involving interactions between one or more liquid phases, various parts of the solid matrix, other constituents like organic matter and manmade materials, along with contaminants and reagents. Practitioners should gather enough geologic information to adequately characterize all of these materials, recognizing that many interactions operate at a micro, rather than a macro scale.

Secondly, the site conceptual model should be a dynamic, rather than a static document. Throughout the project additional information is often gathered about subsurface conditions. This additional information should be integrated into the model on an ongoing basis so that the best decisions can be made at all stages of the project.

By integrating these two changes into accepted best industry practices and government guidance documents, and managing projects this way, we can greatly increase the number of successfully completed projects.

Gathering, Managing, and Displaying Site Environmental Data Using Automated Tools

The requirements for managing environmental data for environmental investigation and remediation projects remains a challenge, but automated tools can help. While in the past the main tool for analyzing was laboratory analysis, and that remains important, field observations of various types are increasing in importance. Field techniques were used in the past primarily for screening; however their low cost relative to lab analyses has caused their role in environmental studies to continue to increase.

Automated tools for data management and mapping can be a significant contributor to investigation and remediation project success. This starts with planning your sample events, whether for field sensing or laboratory analyses. Rapid automated analysis can allow flexibility in the field so that the maximum information can be obtained in the minimum amount of time. Commercial data management tools have had to evolve to better handle the increasing importance of field sensing data over depth or time. For lab data, software tools can automate preparation of field materials (container labels, chains of custody, and files for field data entry). Laboratories are now good at providing useful electronic data deliverables, but these files must be checked for accuracy and consistency as part of the data import process, or as a separate data validation step. Environmental data has a lot of issues, such as duplicates at the sample and analysis levels, non-detected and other flagged data, extracted data, analytes measured by different, incompatible methods, and so on that must be handled correctly from import to output in order to generate useful results, so that questions can be answered and reliable decisions can be made based on the data.

Geology Rising - Making Better Site Conceptual Models

Some site remediation projects have been very successful, while others have not. One of the biggest factors in remediation project success is effective use of site conceptual models. Better use of these models can significantly increase the chance of a positive outcome. For many projects, the process is very linear: investigation, feasibility study, remedy selection, and implementation. Then, if the remedy doesn’t work, try another approach until something finally works. Often the investigation phase includes a site conceptual model. Usually the model is based on regional geology with some input from surface geology at the site, perhaps supplemented by well data. Many times, once the model has been created, it is used throughout the life of the project as it was originally defined.

Two changes can greatly improve the use of the site conceptual model, leading to a more positive outcome. The first is to more thoroughly involve input from geologists in creating the original model. The flow of groundwater, contaminants, and reactants is very complex, involving interactions between one or more liquid phases, various parts of the solid matrix, other constituents like organic matter and manmade materials, along with contaminants and reagents. Practitioners should gather enough geologic information to adequately characterize all of these materials, recognizing that many interactions operate at a micro, rather than a macro scale.

Secondly, the site conceptual model should be a dynamic, rather than a static document. Throughout the project additional information is often gathered about subsurface conditions. This additional information should be integrated into the model on an ongoing basis so that the best decisions can be made at all stages of the project.
By integrating these two changes into accepted best industry practices and government guidance documents, and managing projects this way, we can greatly increase the number of successfully completed projects.

Data Management for the New and Expected Petroleum Baseline Sampling Rules

In February, 2013, the Colorado Oil and Gas Conservation Commission (COGCC) published Rules 609 and 318A. The rules made Colorado the first state in the country to require pre- and post-drilling sampling of water sources near new oil and gas wells permitted after May 1, 2013. A number of oil and gas operators predict that requirements similar to these will be implemented by other states in the near future. This talk will discuss the new Colorado rules and the data management requirements for the sampling. For example, all laboratory results must be uploaded to the COGCC website in one of their specified formats, to be made available to the public. Integrating these requirements into a comprehensive data management process lets project staff perform in-house quality control, reporting, and mapping, and then upload the data to COGCC, properly handling data details like Facility IDs and Sample IDs generated by the state, and allowing easy comparison of pre- and post-drilling samples. As with any other petroleum-related data, the process needs to take into consideration issues like QC samples, reanalyses, and non-detected results, comparison to regulatory limits, and so on, but effective automation of the process can streamline project work and minimize errors.

Managing Environmental Data for Conceptual Site Models

The requirements for managing environmental data for soil investigation and remediation projects remains a challenge, but automated tools can help. While in the past the main tool for analyzing this data was laboratory analysis, and that remains important, field observations of various types are increasing in importance. Field techniques were used in the past primarily for screening; however their low cost relative to lab analyses has caused their role in soil studies to continue to increase.

Automated tools for data management and mapping can be a significant contributor to site investigation and remediation project success. This starts with planning your sample events, whether for field sensing or laboratory analyses. Rapid automated analysis can allow flexibility in the field so that the maximum information can be obtained in the minimum amount of time. Commercial data management tools have had to evolve to better handle the increasing importance of field sensing data over depth or time. For lab data, software tools can automate preparation of field materials (container labels, chains of custody, and files for field data entry). Laboratories are now good at providing useful electronic data deliverables, but these files must be checked for accuracy and consistency as part of the data import process, or as a separate data validation step. Soil and water data has a lot of issues, such as duplicates at the sample and analysis levels, non-detected and other flagged data, extracted data, analytes measured by different, incompatible methods, and so on, that must be handled correctly from import to output in order to generate useful results, so that questions can be answered and reliable decisions can be made based on the data.

Another important issue is proper data modeling and presentation. Traditional conceptual site models may not provide adequate geologic and hydrogeologic guidance for effective high resolution modeling that accurately characterizes the rock and fluid properties, and contaminate distribution. High resolution field methods such as direct push downhole sensing of physical and chemical properties, and other tools, can be a big contributor to better understanding and addressing site conditions. A good data management and display system will enable you to efficiently integrate high resolution data with traditional lab and field data to maximize the return on your investigation and remediation budgets.

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