In situ real-time Monitoring Monitoring for Early Warning Systems

In situ sensors can be a powerful alternative to conventional groundwater sampling and laboratory analysis; particularly under the new paradigm of monitoring focused on master variables. Although in situ sensors will not eliminate groundwater sampling as regulations in RCRA and CERCLA specify a minimum number of wells, this strategy can be used to reduce the number of wells and frequency of sampling. In addition, those controlling and master variables are often leading indicators of changes prior to plume movement–meaning in situ sensor deployment could be considered an early-warning system. The ALTEMIS team aims to establish an in situ monitoring system (automated data transmission and visualization technologies, and cloud platforms for real-time data processing and visualization). We will simultaneously develop the ML method for real-time contaminant monitoring coupled with in situ datasets by extending the approach developed by Schmidt et al. (2013). The team will expand the real-time data processing algorithm, as well as thresholding for automated anomaly detection.