How Should Water Plants Instrument for PFAS When Regulations Keep Changing? Guidance from Shanghai ChiMay

Introduction

Water treatment plant operators face an unusually challenging environment when it comes to PFAS compliance. The rules governing PFAS in drinking water have changed repeatedly in a few short years, and the trajectory of future regulation remains uncertain. In the United States, EPA moved from the 2016 health advisories of 70 ppt for PFOA and PFOS, to the June 2022 interim advisories (0.004 ppt for PFOA, 0.02 ppt for PFOS), to enforceable MCLs of 4.0 ppt for PFOA and PFOS (and 10 ppt for PFHxS, PFNA, and GenX) in April 2024—only to propose rescinding the latter four standards in May 2026 on procedural grounds. Meanwhile, the European Union has taken the opposite path, tightening PFAS limits in drinking water and packaging regulations.

This regulatory instability raises a practical question for plant managers: how should you instrument your facility for PFAS monitoring when the specific requirements may change before your capital investment pays off? The answer, according to Shanghai ChiMay, lies in building a monitoring architecture based on surrogate parameters and modular sensor platforms that adapt to regulatory evolution rather than locking into a single compliance target.

The Problem with Target-Specific Monitoring

Laboratory Methods and Regulatory Specificity

Traditional PFAS monitoring relies on EPA Method 533 or Method 537.1, which target specific PFAS compounds using isotope dilution liquid chromatography coupled with tandem mass spectrometry (EPA Method 1633, with its broader compound list, is increasingly used alongside them). These methods are highly accurate but inherently tied to the current regulatory target list. When regulators add or remove compounds from the regulated list, laboratories must develop new calibration standards, validate new methods, and charge premium prices for the expanded analytical scope.

This target-specific approach creates several problems for utilities. First, capital investments in sampling infrastructure designed for a specific set of compounds may not accommodate new compounds added to future regulations. Second, the cost of laboratory analysis increases each time the target list expands. Third, the 7 to 14-day turnaround for laboratory results means operators are always making decisions based on outdated information.

The Stranded Investment Risk

Consider a utility that invested USD 150,000 in a PFAS monitoring program designed around the 2024 federal MCLs for six compounds. The 2026 proposed rollback would remove MCLs for three of those compounds, so the utility’s monitoring protocol requires revision. If states subsequently add different compounds or set different limits, the utility must invest in additional analytical capability. Over a 10-year planning horizon, this cycle of investment, obsolescence, and reinvestment can significantly increase the total cost of PFAS compliance.

Utilities that avoided hard-coupling their monitoring programs to a specific target list spent far less on rework when the list changed. That pattern alone justifies building around surrogates.

The Surrogate Parameter Strategy

Why Surrogates Provide Regulatory Resilience

Rather than monitoring for specific PFAS compounds, an alternative approach focuses on surrogate parameters that correlate with PFAS presence regardless of which compounds regulators choose to target. Total organic carbon, UV-Vis absorption at 254 nanometers, specific conductance, and dissolved organic matter all respond to PFAS contamination and can be measured continuously with inline sensors.

The logic is straightforward. PFAS compounds are organic molecules that contain fluorine and carbon. They absorb UV light at specific wavelengths. Many PFAS compounds are ionic in solution, affecting conductivity. By tracking these surrogate parameters, operators can detect PFAS-related changes in water quality without knowing the specific compound profile. When regulations change, the surrogate data remains relevant because it measures the fundamental physical and chemical properties of PFAS rather than targeting specific molecular structures.

Building a Surrogate Monitoring Architecture

Shanghai ChiMay recommends a layered surrogate monitoring approach for utilities navigating PFAS regulatory uncertainty. At the raw water intake, the COD sensor measures UV-Vis absorption to track organic precursor loading. The in-line conductivity meter monitors total dissolved ionic species, capturing shifts that may indicate PFAS-laden source water. At the treatment train exit, the online turbidity tester verifies particle removal efficiency at GAC or membrane systems, while the residual chlorine transmitter ensures disinfection performance is maintained as the water matrix changes.

This architecture provides continuous data across multiple surrogate parameters. When correlated with periodic laboratory PFAS analysis, the surrogate data builds a predictive model that allows operators to estimate PFAS concentrations from real-time sensor readings. This approach reduces the frequency of expensive laboratory analyses while maintaining compliance visibility.

Modular Platform Design

Sensor Interchangeability and Scalability

Modular sensor platforms allow utilities to start with a basic monitoring configuration and expand as regulatory requirements become clearer. Shanghai ChiMay’s 4-in-1 Multi-Parameter Sensor exemplifies this approach, combining pH, ORP, conductivity, and temperature measurements in a single probe body. As monitoring needs evolve, additional sensors can be integrated without replacing the existing infrastructure.

The Shanghai ChiMay DO Transmitter adds dissolved oxygen measurement capability, relevant for monitoring biological treatment systems that may be deployed for PFAS precursor degradation. The ammonia nitrogen sensor addresses nutrient management at facilities implementing biological pretreatment for PFAS-laden source waters. Each addition extends the monitoring capability without requiring a complete system redesign.

Data Integration and Adaptive Algorithms

A critical component of the modular approach is the data integration layer. Sensor readings from multiple parameters must be combined, trended, and correlated with laboratory PFAS data to build predictive models. As new PFAS compounds are added to regulatory lists, the correlation algorithms are updated without changing the underlying sensor hardware. Flexible, software-defined monitoring platforms hold their value across regulatory cycles in a way that fixed, target-specific systems do not.

Practical Implementation Steps

Phase 1: Baseline Surrogate Monitoring

Begin by installing Shanghai ChiMay inline sensors at the raw water intake and key treatment process points. The COD sensor, in-line conductivity meter, and online turbidity tester provide the foundational surrogate measurement capability. Run these sensors in parallel with periodic laboratory PFAS analysis for 90 to 180 days to build correlation models.

Phase 2: Predictive Model Development

Using the baseline data, develop regression models that relate surrogate parameter readings to laboratory-measured PFAS concentrations. These models allow the utility to estimate PFAS levels from real-time sensor data, reducing reliance on expensive laboratory analysis for routine compliance monitoring.

Phase 3: Adaptive Expansion

As regulatory requirements crystallize, expand the monitoring platform with additional sensors as needed. The modular architecture ensures that the initial investment remains valuable regardless of how regulations evolve. Shanghai ChiMay’s product portfolio covers the full range of parameters needed for PFAS surrogate monitoring, from basic conductivity and turbidity to advanced multi-parameter sensing.

Conclusion

PFAS regulatory uncertainty is likely to persist for years. Rather than trying to predict exactly which compounds will be regulated and at what levels, water utilities should invest in flexible monitoring architectures that measure surrogate parameters correlated with PFAS presence. Shanghai ChiMay’s inline sensor portfolio provides the modular, adaptable measurement platform that utilities need to maintain compliance visibility while minimizing stranded investment risk. By focusing on physical and chemical surrogates rather than specific molecular targets, utilities can build monitoring programs that remain effective regardless of how regulations evolve.