Novel method to track contamination of water

Scientists from Berkeley Lab, USA have developed a new method to track the contamination levels of ground water in a continuous manner at a low cost. As per the study published in the journal, Environmental Science & Technology, the team has developed a method for real-time monitoring of pollutants using commonly available sensors.This method could be especially useful during an unexpected event such as a storm and related flooding.

"Analysis of the autonomous in situ data can be rapidly analyzed remotely using machine learning methods," said Haruko Wainwright, a Berkeley Lab researcher who led the study. "It can act as an early warning system - we can detect sudden changes in contaminant levels. These changes may indicate a need for more or less intervention in terms of the remediation strategy, ideally leading to an improved as well as more cost-effective cleanup."

The new approach employs sensors to track water quality parameters such as levels of tritium and uranium-238 in the groundwater, the acidity (or pH) levels and specific conductance (a measure of electrical conductance). 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.)

This methodology can be used for both surface and underground water and can potentially be used to track other metals, radionuclides, and organic compounds commonly found in groundwater, such as arsenic, chromium, and fuels.