From Ohio’s Mahoning River to food-processing operations, the environmental scientist and chemical engineer is developing a data-driven approach that connects water-quality signals to contamination sources, and ultimately to prevention.
When a water sample shows an unusual concentration of a contaminant, the result can answer an important question: Is something wrong?
For environmental scientist and chemical engineer Temitope Isaiah Asefon, that is only the beginning.
The harder question is where the contamination came from.
That distinction has shaped Asefon’s research in Ohio’s Mahoning River watershed and now follows him into a very different environment: industrial food processing, where water quality, chemical controls, microbiological monitoring and process conditions must be watched closely enough to detect problems before they become larger contamination or public-health risks.
“Detection tells you that a problem may exist,” Asefon said. “Source identification helps you decide what to do about it. The closer we can get to understanding the source and pathway, the better positioned we are to prevent the problem rather than repeatedly responding to its symptoms.”
It is an approach rooted in an unusual combination of disciplines. Asefon has a background in chemical engineering, graduate training in environmental control and management, and a Master of Science in Environmental Science from Youngstown State University. His professional experience has crossed industrial production, environmental health and safety, laboratory and field monitoring, municipal environmental work and, most recently, quality assurance in food processing.
The settings have changed. The underlying problem has not.
Reading a river for clues
Asefon’s graduate research at Youngstown State University focused on the Mahoning River watershed, a water system shaped by urbanization, transportation, industrial activity and other human influences.
His thesis, “Utilizing Chloride and Bromide Levels as an Indicator of Water Quality in the Mahoning River Watershed,” examined whether chloride and bromide measurements could provide more than a snapshot of water chemistry.
Chloride can enter freshwater systems through multiple sources. That makes an elevated concentration useful as a warning signal, but not necessarily sufficient for identifying what produced it.
Asefon examined chloride alongside bromide and considered their relationship as part of a broader pollution-source assessment. His work incorporated statistical and geospatial methods to investigate patterns in the watershed rather than treating each measurement as an isolated result.
“Environmental monitoring becomes more useful when laboratory measurements are connected to context,” Asefon said. “I wanted to move from saying, ‘This concentration is elevated,’ toward asking, ‘What does the pattern tell us about the possible source?’”
The research incorporated methods including statistical analysis and GIS-based assessment. Asefon presented related work, “Utilizing Chloride and Bromide Levels to Identify Pollution Sources in the Lower Mahoning River Watershed,” at the 53rd Annual Conference of the Water Management Association of Ohio in Columbus in 2024.
The experience reinforced a principle that now runs through his work: environmental measurements matter most when they can support a decision.
From measurement to prevention
Asefon’s broader research has followed that theme.
His published work has examined data-driven techniques and analytics in water-treatment facilities, approaches to water-pollution mitigation, environmental remediation, and other environmental and engineering problems.
One study, co-authored with Paul Quarshie, examined “Data-Driven Techniques and Data Analytics in Water Treatment Facilities: Innovative Safety Protocols and Optimization.” Another explored “Mitigating Water Pollution through Synergistic Chemical and Ecological Approaches.”
Together, the work reflects a shift occurring across environmental management: monitoring systems are increasingly expected not simply to document what has happened, but to help organizations recognize patterns early enough to intervene.
For Asefon, that means bringing different forms of evidence together.
“A chemical measurement gives you one part of the picture,” he said. “Statistics can show you patterns. GIS can provide spatial context. Process data can tell you what was happening operationally. Microbiological results can tell you something different again. The opportunity is in connecting those pieces.”
That idea has become increasingly practical as his career has moved from watershed research into industrial operations.
A laboratory with a production line
Asefon now works in quality assurance at Case Farms in Canton, Ohio, where his responsibilities place him much closer to the point at which monitoring becomes immediate operational action.
His work includes quality and food-safety inspections in poultry-processing operations, monitoring antimicrobial concentrations at critical control points, chemical concentration testing and titration, process-water monitoring, microbiological sampling, sanitation verification and documentation of corrective actions.
Peracetic acid, or PAA, is among the antimicrobial controls he monitors across processing stages. Process-water parameters such as pH must also remain within established operating ranges.
When they do not, the response cannot wait for a research paper.
“In an industrial setting, a measurement is connected directly to a decision,” Asefon said. “If something is outside its operating range, you have to identify the deviation, understand the risk, communicate it and support corrective action.”
The setting is different from a river watershed, but Asefon sees a common analytical structure.
In both cases, there is a signal. Then comes interpretation. Then source identification. And finally, intervention.
“In the watershed, I was looking at chemical indicators and patterns to understand possible pollution sources,” he said. “In a processing facility, you are looking at chemical, microbiological, water, sanitation and process indicators to identify deviations and contamination risks. The logic of using evidence to find the source of a problem is very similar.”
Where water quality meets food safety
Food processing provides a particularly demanding environment for that approach because water touches so many parts of production.
It can be involved in washing, chilling, sanitation, antimicrobial applications and equipment cleaning. The quality and management of that water therefore intersect with product safety, environmental performance, chemical use and regulatory compliance.
Asefon believes those intersections create an opportunity for environmental monitoring methods to become more integrated with industrial contamination-control systems.
His longer-term research direction is to develop and evaluate data-driven environmental monitoring and contamination-control methodologies that combine water-quality analysis, chemical and microbiological monitoring, statistical and geospatial analysis and process-control principles.
The aim is not to create more measurements for their own sake. It is to make existing measurements more informative.
“A facility can generate a lot of data,” Asefon said. “But having data and understanding what it is telling you are two different things. If we can connect historical trends, water parameters, chemical concentrations, microbiological results and process conditions, we may be able to recognize contamination risks earlier and improve root-cause analysis.”
The value of looking across disciplines
That ambition has also pushed Asefon toward researchers outside his immediate field.
His professional and research interests have brought him into discussions around wastewater treatment, sustainable water-resource management, hydrogeology, pollutant transport, environmental contamination and public health.
The reason, he says, is straightforward: pollution rarely belongs to a single discipline.
Chemistry can reveal a signal. Hydrogeology can help explain where contamination moves. Environmental engineering can address treatment. GIS can show spatial relationships. Statistical analysis can separate meaningful patterns from background variation. Public-health research can help explain why exposure matters.
“No one discipline gives you the entire contamination pathway,” Asefon said. “The strongest environmental work comes from putting those perspectives together and being willing to test whether a method developed in one setting actually works in another.”
The data still has to be trustworthy
For all the attention now given to analytics, automation and predictive models, Asefon is cautious about starting too far downstream.
Before a dataset can be analyzed, the measurements themselves must be dependable.
His laboratory and environmental work has involved water and soil sampling and the use of instruments including pH meters, spectrophotometers, dissolved-oxygen probes and filtration systems. His industrial work adds titration, chemical concentration monitoring, microbiological sampling and process documentation.
Those experiences have made him skeptical of treating sophisticated analytics as a substitute for basic measurement discipline.
“A sophisticated model cannot rescue unreliable measurements,” he said. “Before asking what the data means, you have to ask how it was collected, whether the instruments and procedures were controlled, and whether the result is reliable enough to support the decision you want to make.”
What changes next
The next stage of environmental monitoring, Asefon believes, will depend less on generating entirely new categories of information than on connecting information that organizations already collect.
Statistical trend analysis, automated data logging, GIS, process-control information and predictive methods could make it possible to recognize abnormal patterns earlier.
But the purpose, he argues, should remain practical.
A monitoring system succeeds when someone can act on it.
That philosophy also draws on his training as a Project Management Professional and Lean Six Sigma Black Belt, where measurement is tied to root-cause analysis, corrective action, measurable improvement and sustained process control.
“Environmental science can identify a problem,” Asefon said. “But improvement requires a structured way to understand the cause, implement a change and verify that the change actually worked.”
For Asefon, the long-term objective is to move environmental monitoring closer to that model—particularly in water-intensive industrial and food-processing operations where contamination prevention can simultaneously affect environmental performance, regulatory compliance, operational reliability and public health.
The work begins with a sample. But he does not want it to end there.
“Success is when monitoring leads to prevention,” Asefon said. “If the information leads to an earlier investigation, a corrected process condition, a reduced contamination risk or a prevented exposure, then the measurement has actually done something.”
Temitope Isaiah Asefon is an environmental scientist and chemical engineer with experience in water-quality research, environmental health and safety, laboratory and field monitoring, industrial quality assurance and contamination control. He earned an M.S. in Environmental Science from Youngstown State University, where his graduate research investigated chloride and bromide as indicators for identifying potential pollution sources in the Mahoning River watershed. His published work includes research on water-treatment data analytics, water-pollution mitigation and environmental engineering.
