Envimetric - Soil and water contamination predictive modeling tools
Award last edited on: 2/27/2019

Sponsored Program
Awarding Agency
Total Award Amount
Award Phase
Solicitation Topic Code
Principal Investigator
Jason R Dalton

Company Information

Azimuth1 LLC

1751 Pinnacle Drive Suite 600
Mclean, VA 22102
   (703) 618-8866
Location: Single
Congr. District: 11
County: Fairfax

Phase I

Contract Number: 1721607
Start Date: 7/1/2017    Completed: 6/30/2018
Phase I year
Phase I Amount
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to help environmental engineers identify and delineate the bounds and concentrations of soil and groundwater contaminants with greater speed and accuracy. Over 30,000 contaminant spills have been identified in the United States alone, with thousands yet to be investigated and returned to safe levels. The resources and effort being applied to these sites is insufficient to ensure the safety of the affected communities across America. Focusing the remediation resources available, in the right place increases the rate at which federal and state regulators can close site investigations. As a result, environmental professionals will perform remediation earlier, making the site safe and productive again for local communities. The innovations developed in this project will enhance understanding of contaminant migration and develop a technical capability to use these findings to prepare more accurate conceptual site models during a contaminant investigation. Faster site models resulting in successful remediation directly translates into cost savings for environmental clean-ups, reduction in damage to the environment, as well as increased throughput and efficiency for the environmental engineering industry. This SBIR Phase I project proposes to create unique summary models for the flow, extent, depth, and shape of contaminant plumes, with the goal of targeting resources to accelerate the remediation process for local communities. The project leverages algorithmically derived models of contaminant migration combined with public and private data from decades of environmental investigations across the country. Once the data are aggregated and analyzed, the project team will produce a collection of guideline statistics and software tools for use by engineers investigating future sites that are mathematically similar to those in the combined database. The project uses predictive algorithms to determine the likely extent of underground contaminations and applies statistical uncertainty measures to the conceptual site model. These mechanisms will enable environmental professionals to understand when and where additional data is required. In addition, by using a more accurate and sophisticated measure of uncertainty, the project?s models will provide definitive guidance to field engineers on where to collect new sample data and where they have sufficient certainty to remediate the site using excavation or other means. These innovations will lead to the goal of safer and cleaner communities in less time, with fewer costs, with reduced environmental damage.

Phase II

Contract Number: 1831137
Start Date: 9/15/2018    Completed: 8/31/2020
Phase II year
(last award dollars: 2021)
Phase II Amount

The broader impact/commercial potential of this Small Business Innovative Research (SBIR) Phase II project is a significant reduction in the cost and time to remove hazardous contaminants from the soils and groundwater impacting communities. Properties observed from thousands of contaminated sites serve as inputs to a computerized mathematical model of the site, forecasting the most likely shape and depth of a contaminant plume. This machine learning model gives remediation planners access to a fast delineation of volume to be remediated as well as the uncertainty of the modeled estimate. This saves time and money searching for these contaminants that are deep underground and in groundwater. This Phase II project will expand on the Phase I prototype, creating an operational product capable of reaching the needs of environmental engineers and scientists around the globe, providing the stimulus to cut remediation time and cost in half.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.