TerraIndex attended the PFAS Congress in Paris on 17 and 18 June. In soil investigation, the challenge is shifting from more measurements to smarter investigation, broader interpretation and decision-making under uncertainty. This makes a traceable method and a complete data chain from sample to conclusion more important than ever.
What Paris confirmed: PFAS are becoming an operational field
A different substance, a different scale
It is not the measurement itself that makes the difference, but the ability to put it in context, connect it to other data and defend it.
PFAS regulation in the Netherlands in 2026
From more measurements to smarter investigation and interpretation
The shift felt in Paris comes down to one movement: the emphasis is moving from more measurements to smarter investigation, broader interpretation and decision-making under uncertainty. Three shifts, one common thread.
With a growing range of sources, from firefighting foam to sludge, and diffuse spread, questions become more complex and smarter investigation is essential.
PFAS distribute across soil, groundwater and pore water. A result only becomes meaningful in the context of the entire project file, not as an isolated number.
Without a complete set of standards or a fully comprehensive analyte list, you make decisions that you must be able to justify later.

The real problem is fragmentation
- Where have we already sampled, and using which method?
- What results were obtained, and which samples belong to this zone?
- How have concentrations changed over time?
- Can we demonstrate the connection between the field measurement, the analytical result and the conclusion in the report?
The information usually exists. The problem is fragmentation: some is in a PDF report, some in a spreadsheet, some in a service provider’s archive and the rest in a project manager’s inbox.
The result: data are present but difficult to use. In practice, data you cannot find are unusable data.
Four questions that reveal the maturity of your data chain
Before discussing tools, it helps to establish where your data chain stands today. These four questions provide a quick guide.
|
Dimension |
The question to ask yourself |
|
Retrieve |
How long does it take to confirm whether a zone has already been sampled? A few seconds, or half a day? |
|
Connect |
Are your field and laboratory data in the same dataset, or must you reconstruct their relationship manually? |
|
Defend |
Can you demonstrate step by step how each result was obtained, from sampling to conclusion? |
|
Preserve |
If the project manager leaves tomorrow, does the project knowledge leave with them? |
The more your answers resemble “a few seconds”, “the same dataset” and “yes, step by step”, the more confidently you can face an inspection, audit or client question.
A complete data chain from sample to conclusion

- One source of truth.
Field teams, laboratories and office staff work with the same dataset, not copies that diverge with each handover. - Traceability within the process, not in the archive.
The connection between borehole, sample, analytical result and conclusion is established when the data are created, not reconstructed afterwards. - Fewer manual handovers.
Each retyping step between the field, laboratory portal and spreadsheet is a known source of transcription errors. Fewer handovers tackle the risk at its source.
This is not a revolution. It is a logical evolution in working methods, one towards which the challenging characteristics of PFAS naturally push consultancies.
Strengthen your data chain: 8 tips from our experts
Eight tips you can apply today to strengthen your data chain before considering a new tool
Retrieve – make every piece of data findable- Use one systematic naming convention for points and samples (zone, campaign, point, depth), applied consistently across the team. Consistent coding turns a half-day search into a query taking seconds, even in a spreadsheet.
- Record location at the source. Capture GPS coordinates when taking the sample, rather than from memory back at the office. Data tied to a point on a map are easier to trace, regardless of who was in the field.
- Maintain a master index for each site: one reference document listing every campaign, the sampled zones, the target analytes and where the report can be found. It is the contents page of your site history and the first reference when a file needs reopening months later.
- Link analytical results to the sample ID on receipt, rather than storing them in a separate folder. The sample–result link is the one that breaks most often and is most expensive to reconstruct.
- Record the reasoning behind analyte selection. Note why a particular PFAS list was chosen for a specific context. This makes a “negative” result defensible: you show that the analytical scope was reasoned, not simply accepted.
- Build the chain of evidence as you go. At each step (sampling, laboratory order, result, interpretation), connect new data to the previous step as they are created. Reconstructing traceability afterwards costs much more and remains vulnerable.
- Write a short site note for each project file: context, assumptions, key decisions and points requiring attention. One page that lets a colleague take over without starting from scratch.
- Standardise handover points. A short checklist at each handover prevents transcription errors more effectively than a single final check.
None of these practices depends on software: they work on paper, in spreadsheets or in separate tools. The challenge is sustaining them, because every action relies on the manual discipline of a team under pressure. This is where an integrated system becomes valuable: it does not replace the principles, but automates them. Naming conventions, geolocation, the sample–result link and traceability stop being separate actions and become the default way data flow.
Why traditional methods are reaching their limits
PFAS are not just an environmental challenge. They are an information challenge too.
For PFAS, the challenge is shifting from more measurements to smarter investigation, broader interpretation and decision-making under uncertainty. This makes a traceable method and a complete data chain from sample to conclusion essential. They make a final judgement defensible even if standards become stricter later.