Soil contamination often occurs over vast areas. The selection and cost of remediation alternatives requires a good understanding of the distribution and levels of contamination across the site. Current characterization practices require expensive and time-consuming measurements that often include large numbers of samples that are below detection, thereby providing minimal information. Autonomous vehicles (robots) equipped with appropriate on-board detectors and appropriate ML/AI algorithms to direct the robot to high value sampling locations can potentially provide rapid, cost-effective characterization of soil contamination. However, there are significant challenges with respect to mobility of the vehicles, sample collection, avoiding contamination between sampling events, and waste valorization that must be overcome to make these approaches robust. Similarly, there are challenges developing robust and accurate on-board detection methods capable of operating autonomously. Initial investigations have identified mobility platforms and detection systems capable of autonomously sampling for important soil contaminants including petroleum hydrocarbons and salt. These investigations have developed potential AI/ML algorithms to rapidly locate and characterize contamination hotspots. These findings are presented along with a forward-look at other opportunities for automation in environmental remediation.

Robots and AI for rapidly characterizing and remediating contaminated soils

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.