Jake Mickelson | Aquatic Informatics

| IMAGE 1: Jefferson County Leeds Water Reclamation Facility (Images courtesy of Aquatic Informatics)

Every water and wastewater treatment facility generates a continuous stream of data including flow readings, chemical dosages, laboratory results, equipment runtime and supervisory control and data acquisition (SCADA) signals. For decades, most of that information ended up on clipboards, bench sheets and spreadsheets managed separately at each facility. The information was difficult to access quickly, making it time consuming for regulatory reporting and fine-tuning operational efficiency while also being vulnerable to human error entry and loss.

Utilities increasingly recognize that their data, properly organized and integrated, is a functional asset with direct bearing on chemical costs, energy consumption, compliance outcomes and the transfer of institutional knowledge. The shift from data storage to data use is not a technology problem so much as an operational one.

The Cost of Fragmentation

Staff shortages, the aging workforce and evolving regulatory requirements have made fragmented data management expensive. When a veteran operator leaves, they carry with them an understanding of how the plant behaves. For example, which processes drift during heavy weather events, how chemical feed responds to temperature changes, which SCADA readings tend to flag false anomalies, etc. Without a record of that knowledge, the next operator starts from the beginning.

The fragmentation also creates compliance exposure. Manual data transfer from field instruments to bench sheets to monthly operating reports introduces transcription errors at each step. Errors found late in the reporting cycle are difficult to trace and even harder to correct. In a regulatory context, where the defensibility of data matters as much as the data itself, these mistakes carry real consequences.

| IMAGE 2: A WIMS dashboard offers operators a quick snapshot of the facility—in this case, aeration savings, power savings, airflow and ammonia.

What Integration Looks Like in Practice

Modern data management platforms address fragmentation by pulling inputs from multiple sources, such as SCADA systems, laboratory information management systems (LIMS) and field operator entries into a single water information management system (WIMS) with consistent structure. Reports required for regulatory submission can be generated automatically from that database, in formats already accepted by state agencies, rather than assembled manually from disparate sources.

When process data, lab results and historical trends are available in a single interface, operators can see relationships that would not be apparent from any one data stream alone. For example, a pump running at its lowest setting for an extended period may not trigger concern during a routine shift walkthrough. However, plotted against effluent quality and influent flow over time, the same pump may reveal chronic overdosing. The data exists in both scenarios, but the difference is whether it is organized in a way that makes the pattern visible.

The same principle applies to energy use. Aeration is among the largest energy costs in biological treatment. Over-aeration wastes energy and can destabilize biological processes, but without trend data, it is difficult to know whether a given blower schedule is appropriate or excessive. Continuous monitoring integrated with process data provides the basis for that judgment.

Data Defensibility as an Operational Standard

Electronic systems with defined chain-of-custody, automated validation rules and role-based access controls produce records that are auditable in ways paper and spreadsheets are not.

When an unusual reading appears in a manually maintained spreadsheet, determining its origin can take days. The same reading in an integrated platform carries metadata including the source, the instrument, the operator, the timestamp and the method of collection. If the anomaly originated in a SCADA glitch, that is visible. If it reflects an actual process event, the surrounding data provides context. The investigation that once consumed days is reduced to minutes.

This traceability also affects how operators engage with their own data. Confidence in the record changes decision-making. When operators know that a number is verified, they use it differently than when they have reason to question its origin.

In the Field With Jefferson County

Jefferson County, Alabama, operates nine wastewater treatment facilities serving more than 700,000 residents. Combined permitted capacity is 259 million gallons per day (mgd), with peak flows exceeding 400 mgd. Prior to adopting an integrated data management system, the county managed compliance reporting through a combination of Excel spreadsheets, paper bench sheets, floppy disks and fax transmissions between plant operators and the central laboratory.

After implementing a centralized platform, the county integrated SCADA outputs, laboratory results and operator entries into a unified WIMS database. Monthly operating reports and discharge monitoring reports are now generated automatically. What previously required around 20 hours of data collection and reconciliation now takes roughly 25 minutes.

The operational gains extended beyond reporting. Chlorine feed rates at one facility had historically ranged between 300 and 500 pounds per day, yet disinfection residuals remained consistent across that range. Trend analysis revealed that chemical feed was not the controlling variable and that dosing was being driven upward by convention rather than process need. Standardizing feed to approximately 150 pounds per day reduced chemical costs without affecting compliance outcomes.

Energy consumption followed a similar pattern. Dashboard visibility into aeration data allowed operators to identify periods of excess aeration and adjust blower schedules accordingly. Alabama’s emerging nutrient removal regulations, which include tighter phosphorus limits, have prompted the county to configure multiyear trend views for phosphorus monitoring, providing enough historical context to distinguish genuine exceedance risk from normal process variation.

Maintenance prioritization has also improved. The audit trails in the current system allow the team to identify whether an anomalous reading originates from SCADA instrumentation, laboratory analysis or operator entry. Repeated anomalies flagged in the data move up the priority list for proactive maintenance rather than reactive response.

Institutional Knowledge as Infrastructure

One of the least quantified benefits of integrated data management is its function as an institutional memory system. A database that captures not only what happened, but when, why and how specific decisions were made becomes a training resource for incoming operators. The procedural knowledge that experienced staff accumulate over their careers can be preserved in annotated historical records, and in a way, this institutional knowledge becomes part of the facility’s infrastructure.

Jake Mickelson is an expert in water data management at Aquatic Informatics. For more information, visit aquaticinformatics.com.

In This Issue

Cover Series Wastewater