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Snowfall/cover and snowmelt are essential determinants of winter soil processes/events that may force the biogeochemical dynamics of soils in temperate regions. Increasing variability of the European hydroclimate is expected to lead to more frequent intermittent warm periods, which cause snowmelt during winter and rapidly mobilize large amounts of dissolved organic matter (DOM). This study, conducted at the Hainich Critical Zone Exploratory in Germany, seeks to address a significant gap in understanding the molecular impacts of snowmelt-induced DOM flows and their ability to alter soil ecosystems rapidly. During two snowmelt events between January and March 2021, we observed that DOM concentration and composition varied more in forest soil seepage than in grassland soil seepage. Forest seepage showed a pronounced DOM flux peak and synchronous increases in the relative abundances of aromatic DOM components, indicating surface-derived transport of plant litter carbon. In the grassland, however, peak DOM fluxes were characterized by a marked increase in nitrogen-containing (N-containing) DOM components, indicating a predominance of microbial carbon. Notably, the unique DOM components specific to each ecosystem increased during peak fluxes in the forest but decreased in the grassland. We suggest that an overall higher molecular richness and the broader functional metabolic potentials in grassland may account for its relatively greater DOM stability compared to the forest during peak snowmelt events. [ABSTRACT FROM AUTHOR]

Snowfall/cover and snowmelt are essential determinants of winter soil processes/events that may force the biogeochemical dynamics of soils in temperate regions. Increasing variability of the European hydroclimate is expected to lead to more frequent intermittent warm periods, which cause snowmelt during winter and rapidly mobilize large amounts of dissolved organic matter (DOM). This study, conducted at the Hainich Critical Zone Exploratory in Germany, seeks to address a significant gap in understanding the molecular impacts of snowmelt-induced DOM flows and their ability to alter soil ecosystems rapidly. During two snowmelt events between January and March 2021, we observed that DOM concentration and composition varied more in forest soil seepage than in grassland soil seepage. Forest seepage showed a pronounced DOM flux peak and synchronous increases in the relative abundances of aromatic DOM components, indicating surface-derived transport of plant litter carbon. In the grassland, however, peak DOM fluxes were characterized by a marked increase in nitrogen-containing (N-containing) DOM components, indicating a predominance of microbial carbon. Notably, the unique DOM components specific to each ecosystem increased during peak fluxes in the forest but decreased in the grassland. We suggest that an overall higher molecular richness and the broader functional metabolic potentials in grassland may account for its relatively greater DOM stability compared to the forest during peak snowmelt events.

FT-ICR MS (Fourier-transform ion cyclotron resonance mass spectrometry) analysis has shown great potential to aid in the understanding of the extremely high molecular diversity of cloud water samples. The main goal of this work was to determine the differences in terms of formula assignment for analytical (i.e., measurement replicates) and experimental replicates of a given cloud water sample. The experimental replicates, obtained by solid phase extraction, were also compared to the results obtained for freeze-dried samples to evaluate whether the presence of salts interferes with the analysis. Two S/N ratios, generally adopted for atmospheric samples, were evaluated, and three different algorithms were used for assignment: DataAnalysis 5.3 (Bruker), Composer (Sierra Analytics), and MFAssignR (Chemical Advanced Resolution Methods Lab). In contrast to other works, we wanted to treat this comparison from the point of view of users, who usually must deal with a simple list of m/z ratios and intensity with limited access to the mass spectrum characteristics. The aim of this study was to establish a methodology for the treatment of atmospheric aqueous samples in light of the comparison of three different software programs, to enhance the possibility of data comparison within samples. [ABSTRACT FROM AUTHOR]

FT-ICR MS (Fourier-transform ion cyclotron resonance mass spectrometry) analysis has shown great potential to aid in the understanding of the extremely high molecular diversity of cloud water samples. The main goal of this work was to determine the differences in terms of formula assignment for analytical (i.e., measurement replicates) and experimental replicates of a given cloud water sample. The experimental replicates, obtained by solid phase extraction, were also compared to the results obtained for freeze-dried samples to evaluate whether the presence of salts interferes with the analysis. Two S/N ratios, generally adopted for atmospheric samples, were evaluated, and three different algorithms were used for assignment: DataAnalysis 5.3 (Bruker), Composer (Sierra Analytics), and MFAssignR (Chemical Advanced Resolution Methods Lab). In contrast to other works, we wanted to treat this comparison from the point of view of users, who usually must deal with a simple list of m/z ratios and intensity with limited access to the mass spectrum characteristics. The aim of this study was to establish a methodology for the treatment of atmospheric aqueous samples in light of the comparison of three different software programs, to enhance the possibility of data comparison within samples.

In recent years, ultrahigh performance liquid chromatography Fourier transform mass spectrometry (LC/FT-MS) based non-targeted screening (NTS) methods have become increasingly popular for comprehensive analysis of complex organic mixtures. However, applying these methods for environmental complex mixture analysis is challenging due to the extreme complexity of natural samples and a lack of standard samples or surrogates for environmental complex mixtures. Furthermore, limited molecular markers in the databases and insufficient data processing software workflows make the application of these methods more challenging for environmental complex mixtures. In this work, we implement a new NTS data processing workflow to process data collected from ultrahigh performance liquid chromatography and Fourier transform Orbitrap Elite Mass Spectrometry (LC/FT-MS) by combining MZmine2 and MFAssignR, two opensource data processing tools and commercial Mesquite liquid smoke as a surrogate for biomass burning organic aerosol. MZmine2.53 data extraction followed MFAssignR molecular formula assignment offered noise free and highly accurate 1733 individual molecular formulas presented in liquid smoke with 4906 molecular species, including isomers. The results of this new approach were consistent with the results of direct infusion FT-MS analysis confirming its reliability. Over 90% of the molecular formulas presented in mesquite liquid smoke were matched with the molecular formulas of ambient biomass burning organic aerosol. This suggests the potential use of commercial liquid smoke is an acceptable surrogate for biomass burning organic aerosol research. The presented method significantly improves the identification of the molecular composition of biomass burning organic aerosol by successfully addressing some of the limitations related to the data analysis and giving a semi quantitative insight into the analysis. [Display omitted] • A new non-targeted data processing strategy suitable for environmental complex mixtures is presented. • Results of the LC/FT-MS approach are consistent with direct infusion FT-MS results. • Liquid smoke is a potential surrogate for biomass burning organic aerosol. [ABSTRACT FROM AUTHOR]

High-resolution mass spectrometry (HRMS) provides molecular compositional information of dissolved organic matter (DOM) through isotopic assignment from the molecular mass. However, due to the inevitable deviation of molecular mass measurement and the limitation of resolving power, multiple possible solutions frequently occur for a given molecular mass. Lowering the mass deviation threshold and adding assignment restriction rules are often applied to exclude the incorrect solutions, which generally involves time-consuming manual post-processing of mass data. To improve the result accuracy in an automated manner, we developed a molecular formula assignment algorithm based on machine-learning technology. The method integrated a logistic regression model using manually corrected isotopic composition and the peak features of HRMS data (m/z, signal-to-noise ratio, isotope type, and number, etc.) as training data. The developed model can evaluate the correctness of a candidate formula for the given mass peak based on the peak features. The method was verified by various DOM samples FT-ICR MS data (direct infusion negative mode electrospray), achieving a ∼90% accuracy (compared to the traditional approach) for formula assignment. The method was applied to a series of NOM samples and showed a significant improvement in formula assignment compared with the mass matching method., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Ultrahigh resolution mass spectrometry is widely used for nontargeted analysis of complex environmental and biological mixtures, such as dissolved organic matter, due to its unparalleled ability to provide accurate mass measurements. Accurate and efficient characterization of these mixtures is critical to being better able to evaluate their effect on human health and climate. This characterization requires accurate mass signals free from isobaric interferences, instrument noise, and mass measurement biases, allowing for molecular formula identification. To address this need, an open source post-processing pipeline for ultrahigh resolution mass spectra of environmental complex mixtures software was developed. MFAssignR contains functions that perform noise estimation, 13C and 34S polyisotopic mass filtering, mass measurement recalibration, and molecular formula assignment as part of a consistent data processing environment. Novel applications of mass defect analysis were used in the functions for noise estimation and isotope pair identification. Using formula extensions, exact mass measurements are converted to unambiguous molecular formulas via data dependent pathways, reducing a priori decisions. Optional molecular formula ambiguity and multiple non-oxygen heteroatoms are provided for custom user applications, including isotopically labeled reactive species, halogen-containing species, or tandem ultrahigh resolution mass spectrometry. This represents uncommon flexibility for an open-source software package. To evaluate the performance of MFAssignR, it was used to characterize a sample of biomass burning influenced organic aerosol and the results were compared to those from other available methods of molecular formula assignment and noise estimation. The differences between the methods are described here. Overall, the inclusion of a full pipeline of data preparation functions and the data-dependent ambiguity reductions in MFAssignR render excellent results and make MFAssignR well-suited for the consistent and efficient analysis of environmental complex mixtures. MFAssignR is publicly available via GitHub. [ABSTRACT FROM AUTHOR]