How AquaRealTime Helped an Ohio City Predict and Monitor HABs

Alliance, Ohio, has long managed a complex drinking water system built around two reservoirs — Walborn and Deer Creek — with a combined storage of nearly 2 billion gallons. For Amy Elliott, superintendent of the Alliance Water Treatment Plant, protecting that water means keeping pace with rising threats from algae-related taste and odor compounds like 2-methylisoborneol (MIB). This task has only become more difficult over time, as algae bloom seasons grow longer and water treatment costs climb.

The challenge

For years, the city noticed seasonal spikes in the taste and odor issues with its water. Initially isolated to the holidays, those episodes gradually extended well into the winter. What started as a three-week problem was now spanning months. The situation wasn’t just frustrating — it was also expensive. The plant’s cost per thousand gallons has more than doubled over the course of Amy’s 29-year career.

Alliance attempted to mitigate the problem with an ultraviolet advanced oxidation process (UV-AOP) designed to degrade MIB and other odor-causing compounds. While partially effective, even the UV process couldn’t fully address the issue. MIB levels remained difficult to predict, and with treatment costs rising, the need for accurate, real-time data became critical. Without better upstream visibility, operators were left reacting to taste and odor events rather than preparing for them.

At the time, Alliance relied on a single solar-powered buoy equipped with a multiparameter sonde. The buoy itself weighed 250 pounds and required a crew, a crane-equipped pontoon boat, and multiple hours to launch and maintain. Every calibration cycle meant lifting the unit out of the water, cleaning and inspecting the sensors, and redeploying it — sometimes at the cost of staff injuries and downtime.

The system had also become unreliable. Signal transmission failures and modem issues meant Amy often had no real-time data to act upon. Sampling and calibration routines consumed hours, and spatial coverage was limited to one fixed location.

Engagement

Familiar with the system’s limitations, the city’s consulting firm suggested trying AquaRealTime’s AlgaeTracker units. For Amy, the appeal was immediate. Each device weighed just 10 pounds, didn’t require external sensors or solar panels, and could be deployed by hand. Instead of committing to another expensive, single-point system, Alliance could get three AlgaeTrackers for less than the cost of replacing their old buoy and sonde.

The city deployed one AlgaeTracker in Walborn Reservoir near the dam — where algae tend to accumulate in late summer — and another at the Deer Creek inflow — where Walborn water enters the second reservoir. The third unit was placed directly at the intake. This spatial arrangement allowed the utility to track algae transport from source to treatment, delivering early warning data that made operational planning far more precise.

What stood out immediately was the simplicity of deployment. The team turned the devices on, confirmed the brush system was operating, tethered them in place, and began receiving real-time data almost instantly. There were no cables to route, no heavy lifting, no solar panels to test. This reduced launch time from several hours to minutes.

“The AlgaeTracker has been very reliable,” Amy said. “It’s lightweight. It’s all in one unit. You pretty much go out to the lake, drop it in, and you’re done. It’s just been very helpful, allowing me to track the algae blooms on my reservoir.”

The ease of repositioning added another layer of value. Amy used the third unit to explore different areas of the reservoirs to pinpoint bloom hot spots and refine algaecide application strategies.

“I really love the flexibility,” she added. “And they’re so light, you can move them. They are great.”

Results

Within the first few months of using AlgaeTrackers, Alliance realized several strategic and operational benefits:

• Improved spatial resolution: By monitoring three critical locations — Walborn’s dam, Deer Creek’s inflow, and the intake — operators could visually track the movement of algal biomass. This allowed the team to anticipate when bloom material would reach the treatment facility rather than waiting for the intake sonde to confirm its presence.

• Faster deployment and redeployment: Unlike the 250-pound legacy buoy, which required a crane-equipped pontoon boat to move, the AlgaeTrackers could be easily repositioned. Amy used the third unit to experiment with different reservoir locations, helping the team identify where algaecide would be most effective.

• Reduced maintenance overhead: The AlgaeTrackers required no midseason calibration or sensor replacement. While staff still visually inspected the units to check for fouling, they didn’t have to perform invasive maintenance or pull devices for cleaning. This freed up staff to focus on sampling, reporting, and optimization.

• More reliable data delivery: Real-time signal transmission was restored. Amy could check the status of each AlgaeTracker from her computer, validate their locations, and download data for trend analysis. With earlier tools, she sometimes discovered a unit had drifted halfway across the lake days after the fact. With AquaRealTime, positional awareness was built in with the included live GPS location and alerting features.

• Improved operational planning: The data allowed for better coordination of chemical treatment. Instead of guessing where algae concentrations might peak, the utility could target hot spots and fine-tune algaecide dosing based on current reservoir conditions, enabling less expensive in-plant treatment.

Perhaps most importantly, the real-time insights allowed the city to confirm its assumptions. Historically, Walborn was considered the primary source of algae that caused taste and odor events. Within a single season of data collection, Alliance confirmed that 98% of the problem originated from Walborn — not Deer Creek. This insight is now guiding a full reservoir study to evaluate whether dredging or other remediation strategies should be focused on Walborn alone. By narrowing the scope of future interventions, the city could avoid unnecessary spending on less-impacted areas and allocate resources more strategically.

Insights

Water treatment operations are under increasing pressure to manage complex source water issues without increasing labor or escalating costs. Legacy monitoring tools often fall short — not just due to cost or maintenance but also because they fail to deliver flexible, actionable insights across spatial and temporal scales.

Alliance’s experience illustrates how distributed, IoT-based sensors like AlgaeTracker can unlock a new level of water quality intelligence. By shifting from a static monitoring approach to a dynamic one, the city gained situational awareness that enabled better decisions — from where to sample and when to treat to which reservoir warranted capital improvement.

Utilities that rely on single-location sensors or high-maintenance buoy systems may be missing out on the kind of proactive control that AquaRealTime delivers. In Amy’s case, adopting the AlgaeTrackers wasn’t just about convenience. It reshaped how her team thinks about seasonal risk, resource allocation, and long-term planning.

To learn more about AquaRealTime’s AlgaeTracker and how it supports smarter water monitoring, visit AquaRealTime.com.