Real-time PFAS detection methods at parts per trillion: Mission impossible?

Per- and polyfluoroalkyl substances (PFAS), the ‘forever chemicals’, are now a global regulatory priority, with emerging safety limits requiring detection at exceptionally low, parts-per-trillion concentrations. While these PFAS compounds are nearly ubiquitous, the industry’s reliance on slow, lab-based testing creates gaps between strict legal compliance and the ability to monitor contamination in real time. Based on our research into sensor technologies, it appears bridging this technical divide requires more than just the race to develop a perfect sensor; it demands a pragmatic, systems-based approach to sensing and data processing to better understand your risk profile.

Confronting the PFAS detection challenge

PFAS compounds resist natural breakdown, accumulating in groundwater and soil across nearly every geography and industry. In response, global regulators are enforcing unprecedented standards – often in the low parts-per-trillion – that push current PFAS detection methods to their limits. For businesses and utilities, this creates an urgent technical hurdle: how do you reliably monitor a substance that is regulated at levels nearly indistinguishable from zero?

This challenge is further complicated by a fragmented global regulatory landscape, with evolving bans and limits across the US, Europe, and beyond. While the patchwork of rules adds complexity, the trajectory is clear – the burden of proof now rests on industry. Meeting this high safety bar requires more than long-term research; it demands immediate, pragmatic innovation. To manage risk effectively, we must bridge the gap between these stringent mandates and our current technological ability to see the invisible.

Lab-bound and slow: The limits of current PFAS detection methods

Despite the high-tech image that terms like 'parts per trillion' evoke, most PFAS monitoring today relies on traditional lab-bound processes. Typically, water or soil samples must be physically collected and shipped to specialised labs for analysis. The gold-standard method of liquid chromatography coupled with mass spectrometry (LC-MS/MS) is highly sensitive, but slow and expensive. Turnaround times of weeks are common, during which a contamination plume might have migrated, a community could unknowingly be exposed, or a project could be stalled waiting on data.

This laboratory bottleneck doesn’t scale well, which makes real-time decision-making impossible. And the long wait for lab data severely limits the ability to optimise PFAS treatment processes or quickly respond to emerging hot spots. Clearly, the status quo of batch sampling and remote lab analysis is too slow and too siloed to meet the new demands. However, a faster solution has so far proven elusive.

The unmet need: Real-time PFAS sensing technology

Given the shortcomings of lab testing, you’d think by now we’d have a simple sensor device. After all, we have real-time sensors for many other contaminants such as nitrates, ammonia and chlorine. Yet viable, real-time PFAS sensing technology is still missing. This absence is not for lack of trying. The challenge is rooted in the very nature of PFAS and the task at hand: detecting vanishingly small quantities of a family of chemicals that are both chemically inert and diverse in structure.

Consider the technical hurdles that sensor developers face. PFAS molecules are often present at much lower concentrations than a slew of other contaminants in the same water, so a sensor must pick out a faint 'signal' from background noise. Moreover, 'PFAS' is a catch-all term for thousands of related compounds, each with slight molecular differences. An ideal sensor would need to distinguish between, say, Perfluorooctanesulfonic acid (PFOS) and Perfluorobutanesulfonic acid (PFBS), and countless others. Building a single device that is sensitive enough and selective enough is a formidable scientific problem.

Despite these challenges, innovators around the world are racing to crack the code. Promising prototypes are emerging in research labs, for example novel chip-based electrochemical sensor for PFOS – portable biosensors using engineered proteins that change shape with PFAS. These breakthroughs are exciting. However, translating such prototypes into robust products, and then scaling up remains the key gap. Sensors that work in carefully controlled conditions might struggle with real-world variables. What do we do in the meantime?

Pragmatic innovation beyond the sensor

The answer lies in being innovative about the overall approach. In practice, this means adopting strategies that work around current technological limits – making the most of what is feasible now, while positioning yourself to leverage better tools when they arrive. For example, instead of insisting on measuring 1 part per trillion (one drop in 20 Olympic sized swimming pools) directly in the field, we can ask: are there ways to make that 1 ppt easier to detect? Or, do we always need to detect 1 ppt in real time? Embracing this kind of pragmatic mindset opens up several creative tactics. A few approaches gaining traction include:

1. Pre-concentrate and pre-condition samples: Rather than trying to detect a ’needle in a haystack’, shrink the haystack. Field-deployable pre-treatment units can filter or adsorb large volumes of water, concentrating PFAS into a smaller sample volume before analysis. This effectively amplifies the PFAS signal so that even a less sensitive detector can get a meaningful reading.
2. Use partial solutions for rapid screening in high-risk environments: Moderately sensitive detectors can still provide significant value in situations where contamination is already suspected or likely. For example, this kind of sensor could be deployed in semiconductor wastewater streams, firefighting training grounds, or other known hotspots. This approach enables operators to quickly identify elevated levels and take action or send samples for lab confirmation without waiting days for results.
3. Leverage surrogate metrics and indirect indicators: Sometimes innovation means measuring something related when the target is hard to measure. In the PFAS context, one idea is to monitor total organic fluorine as a proxy for total PFAS. Fluorine detection can be easier than differentiating individual PFAS compounds. While it doesn’t tell you exactly which PFAS are present, a sudden rise in total fluorine could indicate a PFAS leak or hotspot. Additionally, advanced data analytics can correlate such indirect measurements with known PFAS patterns, or even predict likely problem areas, guiding more efficient sampling.

None of these tactics alone is a panacea – but together, they can form a pragmatic stopgap solution. By combining smarter sampling, tiered monitoring, and surrogate measures, organisations can greatly improve their situational awareness on PFAS. In doing so, they often find that the real innovation lies not just in a new widget, but in rethinking the process. For example, an industrial site might install a prototype concentrator + sensor system as a pilot, knowing it doesn’t meet regulatory detection limits yet, but using it to get trends and early warnings. Understanding potential impacts of PFAS across the supply chain is still a massively valuable undertaking.

Leading the way in PFAS monitoring solutions

PFAS may be dubbed forever chemicals, but the current limitations in monitoring them will not last forever. The momentum in research and the increasing focus on pragmatic solutions indicate that a new ecosystem of PFAS monitoring is on the rise – one that blends advanced technology with clever strategy. By understanding the risks, acknowledging the gaps, and actively pursuing innovative approaches, organisations can transform how they deal with PFAS from a reactive liability into a managed risk. Solving the PFAS monitoring puzzle isn’t just about inventing a better sensor; it’s about viewing the problem holistically and being willing to adapt and innovate at each step. With regulators pushing hard and public awareness growing, the time to act is now.