SBIR Phase I: Envimetric - Soil and water contamination predictive modeling tools

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.