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By Christy Anyanwu
As industries intensify efforts to improve food safety, protect water resources and prevent environmental contamination, the ability to detect risks early and trace their sources is becoming increasingly important. For environmental scientist and chemical engineer, Temitope Isaiah Asefon, data-driven monitoring provides a critical bridge between scientific research and practical contamination prevention.
Asefon, whose professional experience cuts across chemical engineering, industrial production, environmental health and safety, municipal environmental work, academic research and food-processing quality assurance, believes environmental data should do more than document problems after they occur. Properly collected and interpreted, he argues, such data can help identify emerging risks, trace contamination to their sources and guide timely corrective action.
With an M.S. in Environmental Science from Youngstown State University and a background in Chemical Engineering from Obafemi Awolowo University, Asefon has researched the use of chloride and bromide levels to understand water-quality conditions in the Mahoning River watershed. He has also presented his research at the Water Management Association of Ohio Conference and published work on data analytics in water treatment and approaches to water-pollution mitigation.
Now working in quality assurance in poultry processing, Asefon applies many of the same principles to food safety, monitoring process water, chemical concentrations, sanitation conditions and microbiological indicators.
In this interview, he explains how environmental monitoring, statistical analysis, GIS, industrial process control and quality assurance can be integrated to strengthen contamination prevention and protect public health.
You began in chemical engineering and industrial production. What moved you towards environmental science?
My name is Temitope Isaiah Asefon. I am an environmental scientist and chemical engineer whose work spans environmental health and safety, quality assurance, water-quality monitoring, pollution-source identification, contamination control and risk management. Ihold a Master of Science in Environmental Science from Youngstown State University, as well as graduate training in Environmental Control and Management and a bachelor’s degree in Chemical Engineering from Obafemi Awolowo University.
My career has moved through industrial production, environmental health and safety, academic research, municipal environmental work and, currently, food-processing quality assurance. The common thread is prevention. I am interested in how chemical, microbiological, environmental, statistical and process data can be combined to identify a risk early, understand where it is coming from and support corrective action before it becomes a larger environmental or public-health problem. My move into environmental science was therefore less a departure from chemical engineering than an expansion of it. Chemical engineering taught me to think in terms of processes, material flows, reactions, operating conditions and control.
Working in production and environmental health and safety made the consequences of those processes more concrete. A deviation is not only an efficiency problem; depending on the process, it can become a worker-safety issue, an environmental release, a water-quality problem or a product-quality concern. Environmental science gave me the tools to follow those effects beyond the process boundary. I became particularly interested in water because it connects industrial activity, ecosystems, communities and public health. That eventually led to my graduate research on pollution-source identification in the Mahoning River watershed.
Your master’s research examined chloride and bromide in the Mahoning River watershed. What were you trying to understand?
I was trying to answer a practical question: when water chemistry changes, can we use the pattern in the data to learn something about where the pollution may be coming from?
Chloride is useful for water-quality monitoring, but it can enter a watershed from several sources. Looking at chloride alone may therefore tell you that conditions have changed without clearly telling you why. My thesis, titled “Utilizing Chloride and Bromide Levels as an Indicator of Water Quality in the Mahoning River Watershed,” examined chloride and bromide together and used their relationship as part of a source-tracing approach. I also incorporated statistical analysis and GIS-based watershed information. The objective was not simply to report concentrations, but to develop a more informative way of interpreting water-quality measurements in the context of potential pollution sources.
Why does pollution-source identification matter?
Because detecting contamination and understanding its likely source are different problems. Detection tells you where to look more closely. Source identification helps determine what intervention may actually work. If the same water-quality signal can be associated with several human activities, responding without additional evidence can waste time and resources. I am interested in combining chemical indicators with statistical patterns, spatial information, land use and process knowledge so that monitoring becomes more useful for prevention and corrective action. The closer we can get to the source and pathway, the better positioned we are to control the problem rather than repeatedly responding to its symptoms.
You presented this research at the 2024 Water Management Association of Ohio Conference. What did that experience add to your work?
Presenting the research at the 53rd Annual Water Management Association of Ohio Conference in Columbus allowed me to discuss the work with people involved directly in water management, environmental monitoring and related technical fields. A poster presentation is useful because people can challenge the assumptions, ask how a method might work in another setting and point out practical considerations that are easy to miss when you are focused on your own dataset. It also reinforced something that now shapes my work: a monitoring method becomes more valuable when it can be communicated, tested, adapted and used outside the original study. I want my research to contribute to practical environmental decision-making, not remain only an academic exercise.
Your publications also address data analytics in water treatment and synergistic approaches to water-pollution mitigation. How do these studies fit together?
They reflect the same broader question from different angles: how can we use better evidence to prevent or reduce environmental harm? My work on data-driven techniques in water-treatment facilities looks at how data analytics can support safety protocols and optimisation, while my work on synergistic chemical and ecological approaches considers pollution mitigation more broadly. The connection is that environmental problems rarely respond to one measurement or one intervention. Chemical monitoring, ecological understanding, process controls, statistics and operational data can complement one another. I am interested in building frameworks that connect those pieces so that environmental and industrial systems can be managed more proactively.
You now work in quality assurance at Case Farms. What does your role involve?
My work involves routine quality-assurance and food-safety inspections in poultry-processing operations. I monitor process and sanitation conditions, verify peracetic acid antimicrobial concentrations at critical control points, perform chemical concentration testing and titration, monitor process-water parameters such as pH, support microbiological sampling, and document results and corrective actions. The role requires attention to HACCP, SSOPs, GMPs, USDA-FSIS requirements, product specifications and process-control limits. When a result is outside an established range, the important question is not only whether it is out of specification, but what caused the deviation, what risk it creates and what action is needed to restore control.
How does your current quality-assurance work connect to your earlier water-quality research?
At first, they may look like different settings, but the analytical logic is very similar. In the watershed, I measured environmental indicators and used patterns in the data to investigate possible pollution sources. In a processing facility, I monitor chemical, microbiological, water, sanitation and process indicators to identify deviations and contamination risks. The difference is the time scale and the ability to intervene. In an industrial setting, monitoring can lead directly to corrective action. That makes it an important applied stage of my broader work. I am learning how environmental and contamination-control principles operate under real production constraints, where measurements have to be timely, repeatable, documented and connected to a decision.
Your proposed work emphasises integrating environmental monitoring with industrial process control. What would that look like in practice?
I envision an integrated framework in which water-quality measurements, chemical-control data, microbiological results, process conditions, statistical analysis and, where appropriate, geospatial information are evaluated together rather than in separate silos. For example, a recurring deviation in a water-intensive operation should not be treated only as a single failed measurement. Historical trends, process conditions, sanitation data, chemical concentrations and microbiological results may reveal patterns that help identify the underlying source. Over time, that can support earlier warning, stronger root-cause analysis, better corrective and preventive actions, and more efficient use of water and treatment chemicals.
Why are water-intensive food-processing operations an important setting for this work?
Water is involved throughout food processing—in washing, chilling, sanitation, antimicrobial applications, equipment cleaning and other process steps. That means water quality and process-water control sit at the intersection of product safety, worker practices, chemical management, environmental performance and public health. The challenge is that these systems generate a large amount of operational information, but information only becomes useful when it is interpreted in a way that supports action.I am interested in helping turn routine monitoring into a stronger contamination-prevention system rather than treating each measurement as an isolated compliance record.
What role do microbiological monitoring and chemical controls play in contamination prevention?
They provide different but complementary information. Chemical controls, such as verifying antimicrobial concentration and pH, tell us whether an intervention is operating within its intended range. Microbiological sampling provides evidence about contamination and the effectiveness of control measures. Sanitation and process observations add another layer. The strongest approach is to interpret these together. A microbiological result may prompt us to examine chemical concentration, process conditions, sanitation or other potential contributors. Likewise, a chemical deviation may indicate a need for additional verification. The goal is to build a chain of evidence that supports timely and defensible corrective action.
You have worked in municipal environmental health, industry, academia and food processing. How has that range shaped your perspective?
It has shown me that the same environmental problem looks different depending on where you stand. A regulator or municipal environmental professional may focus on exposure and compliance. A laboratory focuses on measurement quality. An industrial operation focuses on keeping a process under control. A researcher asks whether the pattern can be explained and generalised.I try to connect those perspectives. My experience in environmental sampling, laboratory work, EHS, production, teaching and quality assurance helps me think about whether a method is scientifically sound, operationally realistic and useful to the people who have to make a decision from the result.
What have you learned from environmental field sampling and laboratory work?
I have learned that data quality begins long before statistical analysis. Sample collection, preservation, instrument condition, calibration, documentation and consistent procedures all affect whether a result can be trusted. My work has involved water and soil sampling and instruments such as pH meters, spectrophotometers, dissolved-oxygen probes and filtration units. That experience has made me careful about data-driven claims. A sophisticated model cannot rescue unreliable measurements. Before asking what a dataset means, I want to know how the data were produced and whether the process was controlled well enough to support the interpretation.
You are a Project Management Professional and a Lean Six Sigma Black Belt. How do those approaches influence your environmental work?
They make me think about implementation. Environmental science can identify a problem, but improvement requires a structured way to define the problem, measure it, analyse causes, implement changes and verify whether those changes worked. Lean Six Sigma is particularly compatible with the way I think about monitoring because it emphasises variation, root causes, measurable improvement and control. Project management adds planning, stakeholder coordination, scheduling, risk management and accountability. Those skills are useful when an environmental or contamination-control idea has to move from a technical recommendation into an operational system.
You have begun communicating with researchers whose work covers wastewater treatment, hydrogeology, pollutant transport and environmental health. Why is collaboration important?
Because contamination problems do not respect disciplinary boundaries. Chemistry may identify a signal, hydrogeology can explain movement, environmental engineering can address treatment, GIS can add spatial context, statistics can identify patterns, and public-health analysis can explain why the exposure matters. I have been interested in collaborations that bring those perspectives together. A method developed in one watershed or industrial setting should be tested carefully before it is assumed to work elsewhere. Collaboration makes that possible because researchers with different expertise can examine the same problem from different directions and identify limitations that one person might miss.
What role do you see data analytics and technology playing in environmental monitoring over the next five years?
I expect monitoring to become more integrated and more predictive, but I think the important issue is not simply collecting more data. Facilities and environmental programmes already generate large amounts of information. The challenge is connecting the right measurements and turning them into decisions. Statistical trend analysis, GIS, automated data logging, dashboards and, eventually, more advanced predictive methods can help identify patterns earlier. But technology should strengthen measurement and judgement, not replace them. A dashboard is useful only if the underlying data are reliable and the person using it understands what action a change in the signal should trigger.
What informed your choice of study and professional direction?
My path has been shaped by the connection between engineering systems and their environmental consequences. Chemical engineering gave me the process foundation. Environmental Control and Management broadened that towards environmental protection, and my M.S. in Environmental Science at Youngstown State University allowed me to focus directly on watershed monitoring, source tracing, GIS and environmental data analysis. My professional experience then added the implementation side: production, EHS, regulatory compliance, municipal environmental work, laboratory practice and quality assurance. I chose this direction because I want to work on environmental problems at the point where scientific evidence can still change an operational decision.
How do you define success in your work?
Success is when monitoring leads to prevention. A technically accurate report matters, but I want the information to change what happens next—an earlier investigation, a corrected process condition, a prevented exposure, a reduced contamination risk or a better-designed control. In the longer term, I would like to develop and validate monitoring and contamination-control approaches that can be adapted beyond one facility or one watershed. If a method helps organisations identify problems earlier, use water and treatment resources more intelligently, strengthen environmental compliance and protect public health, then the work has moved from measurement to impact.
