New Study Explores Online 2D-LC Method for Micropollutant Profiling in Wastewater

A new study in the Journal of Chromatography A by Soraya Chapel, Marie Pardon, and Deirdre Cabooter of the Laboratory for Pharmaceutical Analyis at KU Leuven explored a new method using two-dimensional liquid chromatography (2D-LC) to profile organic micropollutants (OMPs) in wastewater. Their findings were published in the Journal of Chromatography A (1).

Modern urban wastewater treatment plant. | Image Credit: © M-Production - stock.adobe.com

Two-dimensional liquid chromatography (2D-LC) is a technique where two liquid-phase separation systems are used to analyze a sample, either through transferring a portion of the eluate from the first column to the second, or by sequentially transferring all of the eluent to the second dimension (2). Advanced 2D-LC can enable online sample analysis without required in-between manual intervention, which subsequently reduces offline workflow drawbacks, such as buffer exchange, sample loss, and contamination (3). This technique, which initially began as being used in in academic research laboratories, is now being used in industrial laboratories, with the number of peer-reviewed papers in the 2D-LC space with industry coauthors increasing (4).

The main method for monitoring wastewater treatment plant (WWTP) effluents is chemical analysis. One-dimensional liquid chromatography (1D-LC) often results in overlapping peaks for environmental samples, which are highly complex. Two-dimensional LC (2D-LC) offers high resolving power by combining two complementary LC systems. In 2D-LC, part of or the entire effluent of a first separation dimension (¹D) column, typically containing unresolved peaks, is transferred and further separated on a second-dimension (²D) column (1).

In this study, a systematic approach was developed for evaluating the orthogonality of separation modes for OMP profiling in wastewater. Water resources and ecosystems are increasingly threatened due to chemical contamination. Industrialization, advanced agriculture, and technological expansion have increased the levels of organic and inorganic contaminants in the environment. Typical municipal wastewater treatment technologies improve surface water quality, but they often fail to fully remove organic micropollutants (OMPs), such as pharmaceuticals, pesticides, and industrial chemicals, according to the researchers. OMPs can persist in treated effluents discharged into surface waters, contributing to water pollution and significantly impacting water quality.

Another goal of this method was to reduce the biases associated with sample characteristics and user interpretation. First, the research team developed an orthogonality score by incorporating multiple metrics commonly employed in 2D-LC studies. To automate this calculation, the mathematical algorithms of each metric, alongside other calculations, were incorporated in a Python-based tool. The method development involves broad screenings of various LC conditions for a sample containing representative OMP standards, to determine optimal combinations for separation using separation orthogonality and peak capacity as the key performance criteria. To minimize bias related to choices of specific orthogonality metrics, overall orthogonality scores are calculated by averaging nine different metrics. Optimized LC×LC conditions are then applied to the analysis of WWTP effluent samples.

The proposed orthogonality score proved effective for fairly comparing different LC×LC systems, providing a valuable tool for future method development. In making the Python-based tool an open-source resource, the scientists hoped to simplify LC×LC method development, offering a user-friendly approach to selecting appropriate conditions while being applicable across different applications. However, some prelimnary steps are required, including the careful selection of representative compounds, preliminary screening of multiple conditions, and systematic collection of retention times for a large number of compounds (176 in their case) across various sets of conditions (38 in their case). While this process can be time-consuming and labor-intensive, it is necessary, as it can ensure thorough and well-validated evaluations, contributing to the results’ reliability.

Future efforts will refine the orthogonality evaluation through different approaches, including incorporating additional metrics, testing the method's sensitivity for detecting trace level OMPs, and extending its application to other complex matrices. The potential for coupling this method with advanced MS/MS detection will also be explored to enable targeted OMP analyses, thus furthering their utility for environmental applications.

References

(1) Chapel, S.; Pardon, M.; Cabooter, D. Systematic Approach to Online Comprehensive 2D-LC Method Development for Organic Micropollutant Profiling in Wastewater. J. Chromatogr. A 2025, 1749, 465861. DOI: 10.1016/j.chroma.2025.465861

(2) Pirok, B. W. J.; Gargano, A. F. G.; Schoenmakers, P. J. Optimizing Separations in Online Comprehensive Two-Dimensional Liquid Chromatography. J. Sep. Sci. 2017, 41 (1), 68–98. DOI: 10.1002/jssc.201700863

(3) Rathor, A. S.; Auclauir, J.; Bhattacharya, S.; Sarin, D. Two-Dimensional Liquid Chromatography (2D-LC): Analysis of Size-Based Heterogeneities in Monoclonal Antibody–Based Biotherapeutic Products. LCGC N. Am. 2022, 40 (1), 27–31. DOI: 10.56530/lcgc.na.cz9881a2

(4) Stoll, D. R. Eyes on the Prize: Overcoming Uncertainty to Realize the Power of 2D-LC Separations. LCGC Int. 2024, 1 (6), 6–9. DOI: 10.56530/lcgc.int.ku8278q2