Systematic approach to online comprehensive 2D-LC method development for organic micropollutant profiling in...

The increasing global contamination of freshwater systems with organic micropollutants (OMPs) is an important environmental problem. OMPs are frequently detected in the effluents of wastewater treatment plants (WWTPs), due to the inability of WWTPs to effectively remove them, and are subsequently discharged into surface waters, severely reducing water quality. The characterization of these complex wastewater samples is challenging, due to the large variety in physicochemical properties of OMPs and the presence of various matrix compounds, such as inorganic salts, humic acids and microorganisms. An emerging and promising technology to tackle this challenge is comprehensive two-dimensional liquid chromatography (LC x LC), combining two orthogonal separation modes to drastically enhance the separation power. However, the method development of LC x LC is complicated, currently confining its application mainly to academic research. It is difficult to predict which combinations will result in an increased peak capacity for a specific sample, and there is no consensus on how to best describe orthogonality. Furthermore, no single metric can fully assess all aspects of the quality of a 2D-LC separation. This study presents a systematic approach to evaluating the orthogonality of different separation modes for a given sample, more specifically for OMP profiling in wastewater, with less bias related to the sample and the user. To achieve this, an orthogonality score is defined, based on several orthogonality metrics commonly applied in 2D-LC studies. To automate the calculation of the orthogonality score, the mathematical algorithms of each metric as well as all other calculations are incorporated in a Python-based tool. Based on their orthogonality score and predicted peak capacity, LC x LC conditions are selected and then further optimized and applied to the analysis of real WWTP effluent samples. It is demonstrated that the optimized sub-hour RPLC x RPLCHRMS method achieves a peak capacity of 1887, emphasizing its potential for practical applications in environmental analysis.