Automating sampling and sensing of contaminants in the shallow regolith

The Earth’s shallow regolith (top 10 to 20 m below ground surface) is a zone heavily impacted by various contaminants largely resulting from anthropogenic activities such as mining, agriculture, industry, and conflict. The zone itself plays an important role in terms of ecosystems and biodiversity, but also it provides some protection to groundwater supplies. The management and remediation of soil and groundwater from contaminants is estimated to cost in the order of >$2B per annum.

The Problem

The effective management and remediation of contaminants in shallow regolith requires ongoing monitoring to allow timely decision making. Manual sampling and data collection are labour intensive that takes up days and even months of data collection. Currently there is a lack of field deployable sensors that can replace the manual monitoring process. Furthermore, for field deployed sensors to be efficiently operational and be robust, these sensors might be exposed to challenging circumstances, such as thermal fluctuations and mixed contaminants. Thus, understanding the interactions and processes of contaminants is also critical for the managements of contaminants in our environment.

Our solution

This project aims to combine the knowledge of nano-sensors for detection of contaminants in aqueous phase with the usage of suction lysimeter for porewater sampling to develop a new field deployable autonomous sensor. This sensor will achieve accurate, fast, and robust contaminants monitoring. Furthermore, the correlation between contaminants in porewater samples and regolith will also be built through various interaction studies which will help improve the detection accuracy.

Collaborators

The project is led by John Rayner (Enviro) in collaboration with Adrian Trinchi (Manu) and Tim van der Lann (Manu) and is supported by Postdoctoral Fellows Bin Qian (Enviro) and Anand Kumar (Enviro).