Novel Non-targeted Screening (NTS) Workflow to Assess New Materials for Wastewater Treatment

In your paper “Developing a cost and time efficient strategy for assessing novel materials for wastewater treatment with non-target screening analysis”,(1) you opted for a non-target screening (NTS) approach rather than traditional target-based methods. Could you elaborate on this choice, and how you see NTS enabling the identification of removal behaviors or compounds that might otherwise be missed?

The main reason we opted for non-target screening (NTS) is that wastewater is highly complex. Traditional laboratory methods of evaluating new adsorbents only look for a small list of 'known' chemicals. If we only used these methods, we would miss thousands of other pollutants. NTS enables us to analyze almost everything in the water simultaneously. This allows us to identify 'hidden' compounds or new chemicals that are removed by the filters but not included in standard lists. In our work, this approach revealed adsorbent properties like rice husk silica’s (RH-SiO2) attraction to large organic compounds, that would otherwise have been overlooked. In other cases, NTS has helped identify leaching from drinking water adsorbent resins that would otherwise have been overlooked.(2)

You combined liquid chromatography with high resolution mass spectrometry (LC–HRMS) with hierarchical cluster analysis to interpret the non-target features. How does this integration help reveal complex removal patterns, and what advantages does it offer over standard lab-scale evaluations?

LC–HRMS gives us a massive amount of data (thousands of features or chemicals). If we just looked at a spreadsheet with features and their intensities for each sample, we wouldn't see the big picture. The benefit of using hierarchical cluster analysis (HCA) is that we can group chemicals that behave the same way with each treatment. So instead of checking features one by one, we can see patterns. For example, we can see that one material is great at removing a whole group of similar chemicals, which helps us understand how the filter works much faster than standard tests.

RH-SiO₂ exhibited selective removal of macrolides, cationic compounds, and poly(propylene glycols) with their metabolites. Based on your NTS data, what can you infer about the mechanisms or interactions driving these selective behaviors?

Our NTS data revealed that RH-SiO₂ exhibits specific strengths. Its surface is negatively charged at pH levels above 2, meaning that at a wastewater pH, it will naturally attract cationic compounds. Secondly, its pores are larger (6.4 nm) than those of standard activated carbon filters, helping it to 'trap' very large molecules such as macrolides (an antibiotic such as clarithromycin) and polypropylene glycols (PPGs, industrial chemicals). Other types of interaction were elucidated, such as bonding with neutral compounds through hydrogen bonding to 'stick' them to the surface.

You identified 37 compounds with varying confidence levels. How do you suggest these confidence levels be interpreted when linking removal behaviors to physicochemical properties, particularly for prioritizing compounds or materials?

These identifications were categorized according to the levels established by (3) While Level 1 identifications represent absolute certainty using reference standards, other features were assigned lower levels based on confirmation with spectral 'fingerprints'. Because not every detected feature can be confirmed, these confidence levels act as a filter, enabling us to prioritize the most reliable annotations for further study.

In this case, we used the high-confidence compounds to establish an initial link between the physicochemical properties of the molecules and their removal behavior across different adsorbents. This initial correlation provided the foundation for our suspect screening. Using clustering techniques, we identified groups of features with lower identification confidence that shared distinct physicochemical signatures, such as specific charge, hydrophilicity levels (log D) and size. This approach suggests that these unknown features likely have similar removal mechanisms to our confirmed compounds.

One of the strengths highlighted in your paper is cost- and time-efficiency. Could you describe how this NTS-based framework achieves that compared to conventional target-based approaches, and why this is significant for evaluating emerging wastewater treatment technologies?

With traditional adsorbent testing, you need to purchase expensive standards for each chemical you want to track and run multiple methods for different compounds. With this NTS-based framework, however, you can obtain more information without necessarily having to purchase standards for all the compounds you are tracking. This approach provides many answers from a single test: we run the sample once and obtain data on thousands of features simultaneously.

In this way, we perform an early screening that allows us to determine if a new material, such as RH-SiO2, is promising before investing in costly, pilot-scale tests. This is especially important as wastewater treatment plants (WWTPs) now need to implement a fourth treatment step to remove micropollutants EU Directive 2024/3019, 2024 (4) With many new technologies emerging for this purpose, our approach makes it possible to identify the strengths and weaknesses of these treatments early in the R&D process.

Beyond wastewater treatment, do you see this NTS screening approach being applicable in other complex chemical matrices, and if so, in what contexts might it be particularly valuable?

Yes, this approach is not just for wastewater. It is very valuable for any process that involves transformations or changes to the same matrix between 'before' and 'after'. Whether you are upgrading sewage sludge or recycling plastics, you should check not only your targets but also look for unexpected compounds, by-products (5), or leaching (6). This method is perfect for any situation where the matrix contains unknown compounds and is being processed. Even if you think you are only targeting a few compounds, you are affecting many others in an untargeted way.

Looking ahead, what challenges do you anticipate when translating these lab-scale NTS observations to pilot- or full-scale applications, and how does your methodology help address or highlight these challenges?

Transitioning from small laboratory tests to a real-world treatment plant can be challenging for two main reasons. First, there is the issue of complexity because real-world water is constantly changing with the seasons or even during the day, which makes interpreting the data much more difficult than in a controlled setting. Second, there is the challenge of run times since a real filter needs to last for many months instead of just a few hours like in a lab experiment. During those months, the filter may clog, will need to be cleaned, or may develop a biofilm, all of which add complexity to the interactions between the adsorbent and the micropollutants.

We are currently working on how to integrate these variations into explaining the removal efficiencies of wastewater treatments. However, much work still needs to be done; more variables need to be studied, such as different matrix compositions and the impact of time. Additionally, the operational parameters of our lab-scale experiments need to be optimized—for example, by testing longer empty bed contact times (EBCTs). By understanding the effect of these factors, it will be much easier to predict how a pilot-scale system will behave before it is actually built. This work is now continuing through the DAWN project, which started in August 2025, where we use these roadmaps to help bridge the gap between our lab results and full-scale water treatment applications.

References

1. Rodriguez-Otero, A.; Tisler, S.; Reinhardt, L. M.; Jørgensen, M. B.; Pattison, D. I.; Bouyssiere, B.; Christensen, J. H. Developing a Cost and Time Efficient Strategy for Assessing Novel Materials for Wastewater Treatment with Non-Target Screening Analysis. J. Hazard. Mater. 2026, 503, 141115. https://doi.org/10.1016/j.jhazmat.2026.141115
2. Tisler, S.; Mrkajic, N. S.; Reinhardt, L. M.; Jensen, C. M.; Clausen, L.; Thomsen, A. H.; Albrechtsen, H.-J.; Christensen, J. H. A non-target evaluation of drinking water contaminants in pilot-scale activated carbon and anion exchange resin treatments. Water Res. 2025, 271, 122871. https://doi.org/10.1016/j.watres.2024.122871.
3. Schymanski, E. L.; Jeon, J.; Gulde, R.; Fenner, K.; Ruff, M.; Singer, H. P.; Hollender, J. Identifying small molecules via high-resolution mass spectrometry: Communicating confidence. Environ. Sci. Technol. 2014, 48 (4), 2097–2098. https://doi.org/10.1021/es5002105.
4. European Parliament and Council. Directive (EU) 2024/3019 of the European Parliament and of the Council of 27 November 2024 concerning urban wastewater treatment. Official Journal of the European Union, 2024.
5. Lübeck, J. S.; Stummann, M. Z.; Sjøholm, K. K.; Hansen, J. A.; Hansen, A. B.; Christensen, J. H. Molecular composition and hydrotreatment effects in sewage sludge pyrolysis biocrude revealed by supercritical fluid chromatography–mass spectrometry. Energy Fuels 2025, 9 (18). https://doi.org/10.1021/acs.energyfuels.5b00421.

6. Chibwe, L.; De Silva, A. O.; Spencer, C.; Teixeira, C. F.; Williamson, M.; Wang, X.; Muir, D. C. G. Target and nontarget screening of organic chemicals and metals in recycled plastic materials. Environ. Sci. Technol. 2023, 57 (8), 3271–3283. https://doi.org/10.1021/acs.est.2c07176.

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