The interactive PFAS model for Colorado offers a compelling snapshot of how PFAS compounds are distributed across the state—particularly through water providers and purveyors. What you’ll see in the model are visualizations—often in the form of color-coded pie charts—representing relative PFAS concentrations at various sampling locations. But it’s important to understand what that really means.

Larger pie charts don’t automatically equate to a severe health hazard or emergency situation. Instead, they show where PFAS concentrations are higher relative to other sampled locations in the dataset. It’s all about comparison, not absolutes. For instance, a smaller pie chart at one site might represent a type of PFAS associated with a major airport’s runoff, while a larger chart elsewhere could come from an entirely different source—perhaps industrial discharge or consumer waste. The scale and significance of each pie chart should be interpreted with care.

That said, the model does offer early clues about how PFAS may be spreading across landscapes. If you notice the coloring in the visualization and look at certain areas where the state did low-level surveys of manufacturers that were potentially putting Teflon coatings on pans, you can see hints of those colors coming down some of Colorado’s drainages. However, as Zach emphasizes, these observations are preliminary. They’re not conclusions—they’re conversation starters.

“These are demonstration models,” Zach notes. “They haven’t gone through peer review, and they’re based on raw, publicly available data processed in a way that I thought might be helpful. Are they valuable? I believe they are—but they’re meant to be explored, refined, and interpreted with context.”

Ultimately, the goal of the Colorado PFAS model is not to sound alarms—it’s to encourage exploration, provide accessible insight, and empower people to start asking the right questions about where PFAS is showing up, how it may be spreading, and where to prioritize future research or remediation.

To provide helpful insight, creating the Colorado PFAS model required the right combination of data processing tools and visualization software, and that’s where both Surfer and Microsoft 365’s Power Query and BI came into play.

To start, Zach used Microsoft 365 tools in the initial data reduction phase. Power Query equipped Zach to efficiently clean and organize raw PFAS datasets—many of which were pulled from publicly available sources provided by state agencies. These datasets can be massive, so the ability to reduce the data to what matters most was essential. Once processed, Surfer brought the data to life spatially—and served as an essential validation tool.

“Surfer was incredibly helpful in the geospatial analysis,” Zach shared. “Using the BNA files, I was able to very quickly put together the state boundaries, the counties, and the cities and get all those things onto the map. Then, I created my pie charts and put them relatively where I needed them.”

Because many of the Colorado datasets lacked geo-referenced coordinates, Zach manually hand-referenced sites based on descriptions and his own familiarity with the state. Using Surfer’s support for BNA files and built-in state, county, and city boundaries, he was able to assemble a layered, visually rich map in a short amount of time. The class posting and pie mapping tools in Surfer made it easy to visualize the relative importance of each PFAS compound at different sites—helping to show both overall concentration and chemical composition.

“The pie charts gave me the big picture—what are the important PFAS compounds and where are they concentrated?” Zach explained. “Then, I could zoom in to identify which site is which and rank them by impact across the state.”

Importantly, Surfer also helped verify the data’s integrity. If the data wasn’t formatted properly—if there were missing fields or invalid records—Surfer would raise red flags, allowing Zach to catch issues he might not have seen in Microsoft tools. This made Surfer a critical step in his QA/QC process.

“Surfer allowed me to verify that my data was correct,” Zach said. “Surfer will fail if my data is not in the proper order. Microsoft will fail, too—but it’ll fail in a different way. Surfer, however, will tell me certain things that I might not otherwise see, so it’s part of my data-checking process.

The result? A beautiful, high-quality, and accurate map that was easy to explore and easy to trust. 

“Surfer presents data in a way no other program could. It accepted my cleaned-up data and generated all those pie charts and map layers nearly instantaneously,” Zach said. 

From early-stage verification to polished visual outputs, Surfer became an essential tool for beautifully visualizing accurate data. All Zach had to do from there was take everything he had created to build a dynamic, shareable model using Power BI. This tool helped him take everything and turn it into an online interactive dashboard where users can explore data by region, filter by specific PFAS compounds, and view state-wide trends.