Non-targeted analysis with high-resolution mass spectrometry for investigation of riverbank filtration processes

Assessment of dynamics and variability of organic substances in river bank filtration for prioritisation in analytical workflows

Bank filtration supports the growing global demand for drinking water amidst concerns over organic micropollutants (OMPs). Efforts to investigate, regulate and manage OMPs have intensified due to their documented impacts on ecosystems and human health. Non-targeted analysis (NTA) is critical for addressing the challenge of numerous OMPs. While identification in NTA typically prioritises compounds based on properties like toxicity, considering substance quantity, occurrence frequency and exposure duration is essential for comprehensive risk management. A prioritisation scheme, drawing from intensive sampling and NTA of bank filtrate, is presented and reveals significant variability in OMP occurrence. Quasi-omnipresent substances, though only 7% of compounds, accounted for 44% of cumulative detections. Moderately common substances, constituting 31% of compounds, accounted for 50% of cumulative detections. Rare compounds, comprising 61%, contributed only 6% to cumulative detections. The application of suspect screening for 31 substances to the dataset yielded results akin to NTA, underscoring NTA's value. Correlation between both methods demonstrates the efficacy of high-resolution mass spectrometry-based NTA in assessing temporal and quantitative OMP dynamics.

Reproducibility in nontarget screening (NTS) of environmental emerging contaminants: Assessing different HLB SPE cartridges and instruments

Non-targeted screening (NTS) methods are integral in environmental research for detecting emerging contaminants. However, their efficacy can be influenced by variations in hydrophilic-lipophilic balance (HLB) solid phase extraction (SPE) cartridges and high-resolution mass spectrometry (HRMS) instruments across different laboratories. In this study, we scrutinized the influence of five HLB SPE cartridges (Nano, Weiqi, CNW, Waters, and J&K) and four LC-HRMS platforms (Agilent, Waters, Thermo, and AB SCIEX) on the identification of emerging environmental contaminants. Our results demonstrate that 87.6 % of the target compounds and over 59.6 % of the non-target features were consistently detected across all tested HLB cartridges, with an overall 71.2 % universally identified across the four LC-HRMS systems. Discrepancies in detection rates were primarily attributable to variations in retention time stability, mass stability of precursors and fragments, system cleanliness affecting fold change and p-values, and fragment response. These findings confirm the necessity of refining parameter criteria for NTS. Moreover, our study confirms the efficacy of the PyHRMS tool in analyzing and processing data from multiple instrumental platforms, reinforcing its utility for multi-platform NTS. Overall, our findings underscore the reliability and robustness of NTS methods in identifying potential water contaminants, while also highlighting factors that may influence these outcomes.

Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis

Mass-Suite (MSS) is a Python-based, open-source software package designed to analyze high-resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) data, particularly for water quality assessment and other environmental applications. MSS provides flexible, user-defined workflows for HRMS data processing and analysis, including both basic functions (e.g., feature extraction, data reduction, feature annotation, data visualization, and statistical analyses) and advanced exploratory data mining and predictive modeling capabilities that are not provided by currently available open-source software (e.g., unsupervised clustering analyses, a machine learning-based source tracking and apportionment tool). As a key advance, most core MSS functions are supported by machine learning algorithms (e.g., clustering algorithms and predictive modeling algorithms) to facilitate function accuracy and/or efficiency. MSS reliability was validated with mixed chemical standards of known composition, with 99.5% feature extraction accuracy and ~ 52% overlap of extracted features relative to other open-source software tools. Example user cases of laboratory data evaluation are provided to illustrate MSS functionalities and demonstrate reliability. MSS expands available HRMS data analysis workflows for water quality evaluation and environmental forensics, and is readily integrated with existing capabilities. As an open-source package, we anticipate further development of improved data analysis capabilities in collaboration with interested users.

Importance of hydraulic travel time for the evaluation of organic compounds removal in bank filtration

The growing global demand for drinking water is driving both the diversification of water supply sources and their sustainability. River bank filtration (RBF) is an excellent option since it strongly reduces the extent of treatment steps compared to direct usage of surface water. Organic micropollutants (e.g. pharmaceuticals) are widely recognized as a hazard in drinking water production from surface water. Due to their potentially high mobility, stability, bioaccumulation and persistency, these substances can pass through RBF-systems. Scientific studies on compound removal and attenuation efficiency of RBF rely on the knowledge of travel time to compare concentrations in the river to the ones in the bank filtrate since water quality in rivers can change rapidly. However, bank filtrate samples represent a mixture of water with different travel times as the flow paths vary. This has not yet been considered in studies of bank filtration removal efficiency for organic micro pollutants. Here we present a method that considers the residence-time distribution of the bank filtrate sample obtained by groundwater modelling to evaluate the removal efficiency of RBF for organic micropollutants. The method was tested in a comprehensive study with 50 samples taken over a one-year-period at a river bank filtration site in Vienna (Austria). Our findings revealed that better coverage of varying river water quality (higher sampling frequency during the period of infiltration) resulted not only in a higher number of compounds considered as removed but also significantly reduced the number of compounds considered to have formed during the RBF process. The application of the presented method indicated that RBF is very effective in removing organic micropollutants. Considering different travel times will provide better models and a better understanding of the potential of RBF for pollutant removal and thus supports its safe application as a solution to the growing demand for drinking water.