Smart triggered sampling: low-cost devices and intelligent retrofits for capturing the moments that matter

Overview

Many water quality issues are event-driven. The most informative signals often appear during short windows associated with storms, illicit discharges, first flush, or operational upsets. Capturing these windows is genuinely hard. Manual sampling is often too slow, especially overnight or during fast-changing events. Conventional autosamplers help, but they are large, power-hungry, and typically deployed only at major assets, leaving smaller drains, tributaries, pump stations, and pollution hotspots without coverage. Even when an event is captured, fixed-interval sampling fills bottles after conditions have returned to background, generating unnecessary laboratory cost.

This PhD will develop two complementary capabilities. The first is a low-cost, low-power smart sampler that opens up sampling in places conventional autosamplers cannot reach, with embedded sensing for nutrient and biological hazard screening including chlorophyll-a and phycocyanin for algae and cyanobacteria. The second is a retrofit screening chamber that adds intelligence to the autosamplers partners already own, measuring water immediately before bottles are committed.

Research activities

Working as part of the IoT for Water Hub (an ARC Industrial Transformation Research Hub), you will:

• work with partners to define use cases for both pathways, including illicit discharge investigations, wet-weather event capture, and algal bloom screening
• design and build a compact battery-powered smart sampler with embedded surrogate sensing
• develop and prototype the retrofit screening chamber for use with partner-owned autosamplers
• develop adaptive triggering logic that operates across both pathways and learns from laboratory feedback over time
• run field pilots across illicit discharge, event capture, and bloom monitoring scenarios
• translate findings into Standard Operating Procedures, deployment guidance, and Theme 2 metadata structures.

You will be supported by a senior Research Fellow and a Research Assistant providing technical leadership on retrofit design and field deployment.

Outcomes

By the end of the PhD you will have delivered a complete event-sampling toolkit comprising a new low-cost smart sampler and an autosampler retrofit chamber, with field evidence across multiple use cases and partner-ready deployment guidance. You will graduate with strong skills in embedded electronics, fluidic system design, low-cost sensor integration, and field engineering, with career paths into water utilities, environmental services, sensor manufacturing, and instrumentation.

Skills and experience

We are looking for a student with a Bachelor (Honours) or Masters degree in mechatronic engineering, electrical engineering, environmental engineering, or a related discipline. Experience or interest in embedded systems, microcontrollers, fluidic design, or sensor electronics is highly desirable. The student should be comfortable with both workshop-based hardware development and fieldwork in stormwater and wastewater settings.

Scholarships

You may be eligible to apply for a research scholarship.

Explore our research scholarships

Keywords

• sampling
• IoT
• cyanobacteria
• water quality
• monitoring

Contact

Contact the supervisor for more information.