Meet the turbidity MacGyvers of UQ

Posted by Stephane Malhomme on Jul 9, 2019, 12:47:00 PM

 

IoT-based environmental monitoring (EIoT) systems usually bring most value to medium/longer term projects. 

The upfront capital cost of sensors, gateways and system design and install have prevented the use of remote environmental monitoring systems in the past.

Staff at the University of Queensland have deployed a low cost turbidity sensor commonly used in washing machines to accurately measure sediment loads in recent events.

What monitoring system would you choose to measure sedimentation?

  • One made of a few high-accuracy, expensive sampling stations?
  • One made of a network of dozens of less accurate, affordable sensors?

That question became salient for the University of Queensland, when they discovered a dramatic increase in mud distribution across a large sub-tropical embayment, Moreton Bay, Australia.

figure 2-3The team at UQ were seeking the source of fluvial mud deposited over a 460km2 area in Moreton bay thatcould not be accounted for using historical river turbidity data. Data that was historically sparse because:

  1. Fluvial TSS/turbidity is notoriously challenging to measure
  2. Fluvial turbidity is a very short, intense, rare event.

A series of floods hit Brisbane starting Nov. 2010, culminating in cyclone Yasi. The floods forced the evacuation of thousands of people across 90 towns, affecting 200,000. Damage: $2.38 billion. Reduction in Australia’s GDP: A$30Bn.

On 30 January 2011, Category 5 Cyclone Yasi made landfall in northern Queensland. Extremely low-pressure, it hovered 22 hours over a gigantic area. Mass rainfall (140mm per day to some locations, over a week). Wind gusts over 285km/h. $3.5Bn of damage. Environmental monitoring hardware and river sampling stations were destroyed. Carrying decades of sedimentation equivalent in days.

The soil erosion, sediment transport and discharge had a significant impact on critical infrastructure and environment.

  • Mud is now the main soil component of Moreton Bay.
  • The Port of Brisbane was nearly blocked with mud.

Ex-cyclone Oswald in 2013 also nearly cut out Brisbane’s supply of drinking water, shutting down Mount Crosby water treatment plant after the high levels of TSS in the Brisbane river caused record turbidity levels.

 

Business model for turbidity sensing

figure 3

 

The business case for deploying expensive automatic sampling stations, over a catchment area of 20,000km2, is a tough one.

Especially that each can cost up to about $75,000 all fitted, the event may occur over 3-4 days per decade, such stations are very exposed to physical damage.

And flow profiles can change in seconds while measurements are taken every 15-30 minutes

 
 

figure 4

 

So why not use turbidity sensors for automated continuous monitoring?.

At face value, turbidity sensors are a much better solution, presenting a much finer, and much more thorough picture of the peak flow event.

figure 5Problem is, for a dedicated sensor measuring only turbidity (as opposed to sampling stations) turbidity sensors remain very expensive.

And an environmental engineer installing turbidity sensors worth $10,000 each will be extremely choosy about where, and how many to deploy, which can also limit results. Having more sensor points is also better for system resilience.

 

Ad-hoc networks using $9.90 dishwasher sensors

figure 6

 

No sense engineering a solution, however cost-effective, if it is washed away during peak flow.

As it turns out, in real life, it is preferable to have seven $9.90 turbidity sensors zip-tied to trees at branch height, than one large monitoring station in the area. Especially when said station was washed away by the last event.

UQ engineers designed a turbidity monitoring node consisting of a dishwasher turbidity sensor, a total suspended sediment (TSS) logger, a single stage sampler, and a time-lapse camera for a total cost of less than US$420.

 

Pro’s of ad-hoc, IoT fluvial TSS monitoring:

New insights: Seven sensors were deployed across an elevation gradient on the stream bank of Laidley Creek, Queensland, Australia, and two of these nodes successfully characterised the near-bed suspended sediment dynamics across a major flood event. Near-bed TSS concentrations were closely related to stream flow, with the contribution of suspended bed material dominating the total suspended load during peak flows. Sedimentation data measured through these cheap sensors, was multiple times more accurate (and larger) than historical data from expensive sampling stations.

You can ask different questions,” explains Alistair Grinham researcher at University of Queensland and mentor at UQ’s Turbidity challenge. “What is the concentration profile during X event for example?"

Noise reduction: This approach is a useful way of separating different sediment type loading events. You can get a much better signal to noise ratio at higher concentrations.

Resilience: Installing more sensors immediately bolsters system resilience. UQ installed 7, most got washed away, but with the 2 remaining ones they had strapped to trees, they uncovered colossal insights.

 

Con's of ad-hoc, IoT fluvial TSS monitoring:

figure 7That reactive type approach requires an environmental team to predict the event, to go on-site, zip-line homemade turbidity sensor/loggers to trees. Limiting operationally, though usually feasible, given modern weather forecasting.

These small sensors are not very accurate below 20 NTU. They do not measure background concentrations very well, or in the ocean, lower than 20 NTU. Which is not really a limitation as these thresholds indicate extremely clear water, not a meaningful data set for the purposes of floodwater fluvial sedimentation monitoring.

These small sensors were not designed for field deployment/immersion. They will rust and foul if immersed in water for a few days.

Before and after the flood: Guerrilla turbidity EIoT monitoring

Unprecedented pressure (demography, climate change) force growing numbers into adopting absolute best practices in water monitoring. Turbidity is central to water monitoring.

Water monitoring systems will likely rest on a combination of methods (sampling stations, laboratory analysis, EIoT water sensors) for the foreseeable future. By the same token, EIoT adoption, when possible, brings staggering benefits and can yet be adopted in many more cases.

Using EIoT type turbidity monitoring invariably translates to much higher performance, automated alerting for remediation, lower operational costs, higher predictability and responsiveness.

Even in edge use cases, such as stormwater TSS monitoring, ingenious home-made solutions can bring these benefits. The low-cost aspect of the system is vital, not anecdotical to such projects. Much cheaper system engineering = many more data sources = much deeper data sets.

As writes Ron Johnstone from SEMAT, who specialise in affordable aquatic monitoring on their website:

The low cost associated with the system makes it pervasive. For example, 100 lower-cost (potentially less precise; ≤ 5% error) monitoring units can cover a greater spatial area and provide more meaningful data than 10 expensive, highly precise competing units. These could be compared in real-time to few, high accuracy, calibrated devices for data assurance.”

There are still vast amounts of critical water assets out there across the globe, entirely unmonitored. Environmental protection and environmental research will invariably benefit from the democratisation of EIoT based turbidity monitoring. The cunning handiwork of the civil engineers at UQ can be seen here in detail. Complete with walk-through instructions, on how to drill, solder and connect yourself into the DIY Valhala of low-cost EIoT turbidity monitoring.

Topics: IoT water quality system engineering, Remote Water Quality Monitoring, Environmental data mining, Hydrology & Hydrography, Meteorology, EIoT

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