'New method to track groundwater contamination in real-time'

Scientists have developed a low-cost method that can monitor groundwater pollutants in real-time, and help reduce potential health risks.

Groundwater contamination is increasingly recognized as a widespread environmental problem, said researchers at the Lawrence Berkeley National Laboratory in the US.

The most important course of action often involves long-term monitoring.

"Conventional methods of monitoring involve taking water samples every year or every quarter and analyzing them in the lab," said Haruko Wainwright, a Berkeley Lab researcher.

"If there are anomalies or an extreme event, you could miss the changes that might increase contaminant concentrations or potential health risk," said Wainwright, who led the study published in the journal Environmental Science & Technology.

"Our methodology allows continuous monitoring in situ using proxy measurements, so we can track plume movement in real time," she said.

The researchers said that analysis of the autonomous in situ data can be rapidly analyzed remotely using machine learning methods.

It can act as an early warning system - we can detect sudden changes in contaminant levels, they said.

"These changes may indicate a need for more or less intervention in terms of the remediation strategy, ideally leading to improved as well as more cost-effective cleanup," Wainwright said.

The new approach starts with sensors to track water quality variables that have been determined to be reliable indicators of contaminant levels.

The researchers tracked levels of tritium and uranium-238 in the groundwater at the Savannah River Site, a former nuclear weapons production site in South Carolina in the US.

They measured the acidity (or pH) levels and specific conductance (a measure of electrical conductance).

These variables were determined to be reliable indicators for tritium and uranium-238 concentrations.

The data from the multiple sensors were then fed into a Kalman filter to estimate contaminant concentrations.

A Kalman filter is not a physical filter but rather a mathematical algorithm that can integrate mixed time-series data to make estimates.

It is commonly used in various fields, such as traffic prediction and remote sensing.

Using historical data from the Savannah River Site, the researchers found that their technique provided reliable information about plume behavior over the last 20 years.

This indicates that the new approach holds significant promise as a long-term monitoring strategy for rapidly assessing a contaminant's plume stability, researchers said.

Another advantage over conventional approaches is that it can reduce the frequency of manual groundwater sampling and lab analysis, and thus reduce the monitoring cost, they said.

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