The carbon versus mass diagram to visualize and exploit FTICR-MS data of natural organic matter

Variability in molecular composition and optical absorption of atmospheric brown carbon aerosols in two contrasting urban areas of China

Atmospheric brown carbon (BrC) aerosols were investigated at two urban sites in southern (Hefei) and northern (Shijiazhuang) China during summer and winter of 2019-2020 to explore regional variability in their compositional and optical properties. Organic matter in ambient PM2.5 samples were characterized at molecular level using ultrahigh performance liquid chromatography coupled with a diode array detector and an Orbitrap mass spectrometer. Although the molecular composition of organic aerosols varied substantially over different ambient environments, they were mainly composed by CHO and CHON species in positive ionization mode while CHO and CHOS species in negative mode. The mass absorption coefficients of BrC aerosols at wavelength range 250-450 nm were relatively higher for winter samples in both cities and for Shijiazhuang samples in both seasons, partly attributed to the higher concentration levels of anthropogenic air pollutants in these environments. The absorption Ångström exponents further revealed that BrC aerosols in winter seasons and in Shijiazhuang had a greater capacity of absorption at shorter wavelengths. A total of 26 BrC species with strong absorption were unambiguously identified from different environments, which mainly consisted of CHO, CHON, and CHN species and had higher degrees of unsaturation and lower degrees of oxidation. The presence and abundance of these BrC species varied dynamically across the seasons and cities, with a greater number of species presented in the winter of Shijiazhuang. The BrC species together contributed 12-26 % in the total absorbance of light-absorbing organic components at 250-450 nm. This study highlights the regional differences in BrC properties influenced by the sources and atmospheric processes, which should be taken into account to assess their climate impacts.

Mass Difference Matching Unfolds Hidden Molecular Structures of Dissolved Organic Matter

Ultrahigh-resolution Fourier transform mass spectrometry (FTMS) has revealed unprecedented details of natural complex mixtures such as dissolved organic matter (DOM) on a molecular formula level, but we lack approaches to access the underlying structural complexity. We here explore the hypothesis that every DOM precursor ion is potentially linked with all emerging product ions in FTMS² experiments. The resulting mass difference (Δm) matrix is deconvoluted to isolate individual precursor ion Δm profiles and matched with structural information, which was derived from 42 Δm features from 14 in-house reference compounds and a global set of 11 477 Δm features with assigned structure specificities, using a dataset of ∼18 000 unique structures. We show that Δm matching is highly sensitive in predicting potential precursor ion identities in terms of molecular and structural composition. Additionally, the approach identified unresolved precursor ions and missing elements in molecular formula annotation (P, Cl, F). Our study provides first results on how Δm matching refines structural annotations in van Krevelen space but simultaneously demonstrates the wide overlap between potential structural classes. We show that this effect is likely driven by chemodiversity and offers an explanation for the observed ubiquitous presence of molecules in the center of the van Krevelen space. Our promising first results suggest that Δm matching can both unfold the structural information encrypted in DOM and assess the quality of FTMS-derived molecular formulas of complex mixtures in general.

Analysis of environmental organic matters by Ultrahigh-Resolution mass spectrometry-A review on the development of analytical methods

Owing to the increasing environmental and climate changes globally, there is an increasing interest in the molecular-level understanding of environmental organic compound mixtures, that is, the pursuit of complete and detailed knowledge of the chemical compositions and related chemical reactions. Environmental organic molecule mixtures, including those in air, soil, rivers, and oceans, have extremely complex and heterogeneous chemical compositions. For their analyses, ultrahigh-resolution and sub-ppb level mass accuracy, achievable using Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS), are important. FT-ICR MS has been successfully used to analyze complex environmental organic molecule mixtures such as natural, soil, particulate, and dissolved organic matter. Despite its success, many limitations still need to be overcome. Sample preparation, ionization, structural identification, chromatographic separation, and data interpretation are some key areas that have been the focus of numerous studies. This review describes key developments in analytical techniques in these areas to aid researchers seeking to start or continue investigations for the molecular-level understanding of environmental organic compound mixtures.

UltraMassExplorer: a browser-based application for the evaluation of high-resolution mass spectrometric data

RATIONALE

High-resolution mass spectrometry (HRMS) with high sample throughput has become an important analytical tool for the analysis of highly complex samples and data processing has become a major challenge for the user community. Evaluating direct-infusion HRMS data without automated tools for batch processing can be a time-consuming step in the analytical pipeline. Therefore, we developed a new browser-based software tool for processing HRMS data.

METHODS

The software, named UltraMassExplorer (UME), was written in the R programming language using the shiny library to build the graphical user interface. The performance of the integrated formula library search algorithm was tested using HRMS data derived from analyses of up to 50 extracts of marine dissolved organic matter.

RESULTS

The software supports the processing of lists of calibrated masses of neutral, protonated or deprotonated molecules, with masses of up to 700 Da and a mass accuracy <3 ppm. In the performance test, the number of assigned peaks per second increased with the number of submitted peaks and reached a maximum rate of 4745 assigned peaks per second.

CONCLUSIONS

UME offers a complete data evaluation pipeline comprising a fast molecular formula assignment algorithm allowing for the swift reanalysis of complete datasets, advanced filter functions and the export of data, metadata and publication-quality graphics. Unique to UME is a fast and interactive connection between data and their visual representation. UME provides a new platform enabling an increased transparency, customization, documentation and comparability of datasets.

Online HPLC-ESI-HRMS Method for the Analysis and Comparison of Different Dissolved Organic Matter Samples

Natural dissolved organic matter (DOM) is an ultracomplex mixture that is essential to global carbon cycling but is poorly understood because of its complexity. The most powerful tool for the DOM characterization is high-resolution mass spectrometry (HRMS) generally combined to direct infusion (DI) as sample introduction. Liquid chromatography (LC) represents a compelling alternative to DI; however, state-of-the-art techniques involve only offline LC-HRMS approaches, which have important logistical drawbacks that make DOM analysis more challenging. This study introduces a new method based on online coupling of liquid chromatography to high resolution mass spectrometry, able to overcome the disadvantages of usual approaches. It is characterized by high reproducibility (% Bray-Curtis dissimilarity among replicates ≈ 2.5%), and it reduces transient complexity and contaminant interferences, thus increasing the signal-to-noise ratio (S/N), leading to the identification of an overall larger number of formulas in the mixture. Moreover, the application of an in silico fractionation prior to the statistical analysis allows an easy, flexible, fast, and detailed comparison of DOM samples from a variety of sources with a single chromatographic run.

Nontargeted homologue series extraction from hyphenated high resolution mass spectrometry data

BACKGROUND

A large proportion of polar anthropogenic compounds routinely released into the environment comprises homologue series, i.e., sets of chemicals differing in a repeating chemical unit. Using analytical techniques such as liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS), these compounds are readily measurable as signal sets with characteristic differences in mass and typically retention time. However, and despite such distinct characteristics, no computational approach for the direct, simultaneous and untargeted detection of all such signal sets comprising both LC and HRMS information has to date been presented.

RESULTS

A fast two-staged approach has been developed to extract LC-HRMS signal patterns which can be indicative of homologous analytes. In a first stage, a k-d tree representation of picked LC-HRMS peaks is used to extract all feasible 3-tuples of peaks with restrictions in, e.g., mass defect differences. A second stage then recombines these 3-tuples to larger series tuples while ensuring smooth changes in their retention time characteristics. This unsupervised approach was evaluated for ten effluent samples from Swiss sewage treatment plants (STPs), in both positive and negative electrospray-ionization.

CONCLUSIONS

Beside recovering all continuous series of previously identified homologues, substantial fractions of nontargeted peaks could subsequently be assigned into very diverse peak series, although assignments were often not unique. The latter ambiguities were resolved by a self-organizing map technique and revealed both distinctive series meshing and rivaling combinatorial solutions in the presence of isobaric or gapped series peaks. When comparing STPs, several ubiquitous yet partially low-frequent series mass differences emerged and may prioritize future identification efforts. The presented algorithm is freely available as part of the R package nontarget and as a user-friendly web-interface at www.envihomolog.eawag.ch.Graphical AbstractSearch for systematic series indicative of homologous compounds is based on a partitioned representation of LC-HRMS signal characteristics. This nontargeted search first extracts series triplets in a nearest-neighbour walk and then recombines them to larger ones. For illustration, the two dimensions involving mass defect characteristics are represented by one only.

Chemical Composition and Potential Environmental Impacts of Water-Soluble Polar Crude Oil Components Inferred from ESI FT-ICR MS

Polar petroleum components enter marine environments through oil spills and natural seepages each year. Lately, they are receiving increased attention due to their potential toxicity to marine organisms and persistence in the environment. We conducted a laboratory experiment and employed state-of-the-art Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) to characterize the polar petroleum components within two operationally-defined seawater fractions: the water-soluble fraction (WSF), which includes only water-soluble molecules, and the water-accommodated fraction (WAF), which includes WSF and microscopic oil droplets. Our results show that compounds with higher heteroatom (N, S, O) to carbon ratios (NSO:C) than the parent oil were selectively partitioned into seawater in both fractions, reflecting the influence of polarity on aqueous solubility. WAF and WSF were compositionally distinct, with unique distributions of compounds across a range of hydrophobicity. These compositional differences will likely result in disparate impacts on environmental health and organismal toxicity, and thus highlight the need to distinguish between these often-interchangeable terminologies in toxicology studies. We use an empirical model to estimate hydrophobicity character for individual molecules within these complex mixtures and provide an estimate of the potential environmental impacts of different crude oil components.