Development of an Internal Calibration Algorithm for Ultrahigh-Resolution Mass Spectra of Dissolved Organic Matter

Detection and Exclusion of False-Positive Molecular Formula Assignments via Mass Error Distributions in UHR Mass Spectra of Natural Organic Matter

Ultrahigh resolution mass spectrometry (UHRMS) routinely detects and identifies thousands of mass peaks in complex mixtures, such as natural organic matter (NOM) and petroleum. The assignment of several chemically plausible molecular formulas (MFs) for a single accurate mass still poses a major problem for the reliable interpretation of NOM composition in a biogeochemical context. Applying sensible chemical rules for MF validation is often insufficient to eliminate multiple assignments (MultiAs)─especially for mass peaks with low abundance or if ample heteroatoms or isotopes are included - and requires manual inspection or expert judgment. Here, we present a new approach based on mass error distributions for the identification of true and false assignments among MultiAs. To this end, we used the mass error in millidalton (mDa), which was superior to the commonly used relative mass error in ppm. We developed an automatic workflow to group MultiAs based on their shared formula units and Kendrick mass defect values and to evaluate the mass error distribution. In this way, the number of valid assignments of chlorinated disinfection byproducts was increased by 8-fold as compared to only applying 37Cl/35Cl isotope ratio filters. Likewise, phosphorus-containing MFs can be differentiated against chlorine-containing MFs with high confidence. Further, false assignments of highly aromatic sulfur-containing MFs ("black sulfur") to sodium adducts in negative ionization mode can be excluded by applying our approach. Overall, MFs for mass peaks that are close to the detection limit or where naturally occurring isotopes are rare (e.g., 15N) or absent (e.g., P and F) can now be validated, substantially increasing the reliability of MF assignments and broadening the applicability of UHRMS analysis to even more complex samples and processes.

Noise Filtering Algorithm Using Gaussian Mixture Models for High-Resolution Mass Spectra of Natural Organic Matter

High-resolution mass spectra of natural organic matter (NOM) contain a large number of noise signals. These signals interfere with the correct molecular composition estimation during nontargeted analysis because formula-assignment programs find empirical formulas for such peaks as well. Previously proposed noise filtering methods that utilize the profile of the intensity distribution of mass spectrum peaks rely on a histogram to calculate the intensity threshold value. However, the histogram profile can vary depending on the user settings. In addition, these algorithms are not automated, so they are handled manually. To overcome the mentioned drawbacks, we propose a new algorithm for noise filtering in mass spectra. This filter is based on Gaussian Mixture Models (GMMs), a machine learning method to find the intensity threshold value. The algorithm is completely data-driven and eliminates the need to work with a histogram. It has no customizable parameters and automatically determines the noise level for each individual mass spectrum. The algorithm performance was tested on mass spectra of natural organic matter obtained by averaging a different number of microscans (transients), and the results were compared with other noise filters proposed in the literature. Finally, the effect of this noise filtering approach on the fraction of peaks with assigned formulas was investigated. It was shown that there is always an increase in the identification rate, but the magnitude of the effect changes with the number of microscans averaged. The increase can be as high as 15%.

Graph-Based Method for Calibration of High-Resolution Mass Spectra of Natural Organic Matter

Inaccuracies in ion detection and signal processing can undermine confidence in the molecular formula assignment of high-resolution mass spectrometry, which relies on precise matching of the mass-to-charge ratio (m/z). This study proposes a novel graph-based spectra calibration method, MSCMcalib, which implements coordinate transformation and pattern detection. MSCMcalib maps uncalibrated m/z data onto a modified 2D mass defect plot, facilitating the automatic calibration of detected lines, i.e., the calibration of uncalibrated peaks aligned with these lines. The "propagation" method is subsequently employed to accurately and automatically calibrate 605 m/z values across multiple lines, encompassing 98% of the m/z range. The calibrated m/z values divide the m/z range of the spectrum into multiple subintervals, with each subinterval undergoing a process of "scaling" calibration. The utilization of narrower partitions effectively mitigates divergence issues at both ends that arise from the polynomial fitting of errors against m/z. The effectiveness of MSCMcalib is validated through the calibration of SRFA data with m/z error ranges spanning from -10 to -6 ppm, resulting in an additional assignment of 11%-30% more molecular formulas compared to the quadratic fitting calibration.

Terrestrial dissolved organic matter inputs accompanied by dissolved oxygen depletion and declining pH exacerbate CO2 emissions from a major Chinese reservoir

Terrestrial inputs and subsequent degradation of dissolved organic matter (DOM) in lake ecosystems can result in rapid depletion of dissolved oxygen (DO). Inputs of terrestrial DOM including organic acids can also lead to decreases in pH. However, to date, few studies have investigated the linkages between terrestrial DOM inputs, DO and pH levels in the water column, and carbon dioxide (CO2) emissions from lake ecosystems. Based on monthly field sampling campaigns across 100 sites in Lake Qiandao, a major man-made drinking water reservoir in China, from May 2020 to April 2021, we estimated an annual CO2 efflux (FCO2) of 37.2 ± 29.0 gC m-2 yr-1, corresponding to 0.02 ± 0.02 TgC yr-1 from this lake. FCO2 increased significantly with decreasing DO, chlorophyll-a (Chl-a) and δ2H-H2O, while FCO2 increased with increasing specific UV absorbance (SUVA254) and a terrestrial humic-like component (C2). We found that DO concentration and pH declined with increasing terrestrial DOM inputs, i.e. increased SUVA254 and terrestrial humic-like C2 levels. Vertical profile sampling revealed that the partial pressure of CO2 (pCO2) increased with increasing terrestrial DOM fluorescence (FDOM), while DO, pH, and δ13C-CO2 declined with increasing terrestrial FDOM. These results highlight the importance of terrestrial DOM inputs in altering physico-chemical environments and fueling CO2 emissions from this lake and potentially other aquatic ecosystems.

FT-ICR MS Spectral Improvement of Dissolved Organic Matter by the Absorption Mode: A Comparison of the Electrospray Ionization in Positive-Ion and Negative-Ion Modes

Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) in the absorption mode has a superior performance over the conventional magnitude mode. However, this improved performance for the analysis of dissolved organic matter (DOM) in negative-ion and positive-ion modes of electrospray ionization [ESI(-) and ESI(+), respectively] remains unknown. This study systemically compared the improved performance by the absorption mode for DOM FT-ICR MS spectra acquired with the low-field and high-field magnet instruments between two charge modes. The absorption mode enhanced the resolution and signal-to-noise ratio values of DOM peaks with factors of 1.88-1.94 and 1.60-1.72, respectively. The significantly higher improvement of mass resolution for the ESI(+) mode than that for the ESI(-) mode could resolve the extensive occurrence of mass doublets in the ESI(+) mode, yielding some formulas exclusively identified in the ESI(+) mode. The findings of this study have systemically demonstrated the superiority of the absorption mode in improving the spectra quality during the routine FT-ICR MS postdata analysis and highlighted its great potential in characterizing the molecular composition of DOM using the FT-ICR MS technique in both ESI(-) and ESI(+) modes.

TEnvR: MATLAB-based toolbox for environmental research

With the advancements in science and technology, datasets become larger and more multivariate, which warrants the need for programming tools for fast data processing and multivariate statistical analysis. Here, the MATLAB-based Toolbox for Environmental Research "TEnvR" (pronounced "ten-ver") is introduced. This novel toolbox includes 44 open-source codes for automated data analysis from a multitude of techniques, such as ultraviolet-visible, fluorescence, and nuclear magnetic resonance spectroscopies, as well as from ultrahigh resolution mass spectrometry. Provided are codes for processing data (e.g., spectral corrections, formula assignment), visualization of figures, calculation of metrics, multivariate statistics, and automated work-up of large datasets. TEnvR allows for efficient data analysis with minimal "by-hand" manual work by the user, which allows scientists to do research more efficiently. This manuscript is supplemented with a detailed tutorial, example data, and screenshots, which collectively provide instructions on how to use all codes. TEnvR is novice-friendly and experience in programming with MATLAB is not required. TEnvR fulfills the need for a concise MATLAB-based toolbox for working with environmental data and will be updated annually to keep pace with the latest advances and needs for computational work in the environmental sciences.

Complementary Elucidation of the Molecular Characteristics of Groundwater Dissolved Organic Matter Using Ultrahigh-Resolution Mass Spectrometry Coupled with Negative- and Positive-Ion Electrospray Ionization

The formula assignment of the Fourier transform ion cyclotron resonance mass spectrometry coupled with positive-ion electrospray ionization [ESI(+)-FT-ICR MS] is challenging because of the extensive occurrence of adducts. However, there is a paucity of automated formula assignment methods for ESI(+)-FT-ICR MS spectra. The novel automated formula assignment algorithm for ESI(+)-FT-ICR MS spectra developed herein has been applied to elucidate the composition of dissolved organic matter (DOM) in groundwater during air-induced ferrous [Fe(II)] oxidation. The ESI(+)-FT-ICR MS spectra of groundwater DOM were profoundly impacted by [M + Na]⁺ adducts and, to a lesser extent, [M + K]⁺ adducts. Oxygen-poor and N-containing compounds were frequently detected when the FT-ICR MS was operated in the ESI(+) mode, while the components with higher carbon oxidation states were preferentially ionized in the negative-ion electrospray ionization [ESI(-)] mode. Values for the difference between double-bond equivalents and the number of oxygen atoms from -13 to 13 are proposed for the formula assignment of the ESI(+)-FT-ICR MS spectra of aquatic DOM. Furthermore, for the first time, the Fe(II)-mediated formation of highly toxic organic iodine species was reported in groundwater rich in Fe(II), iodide, and DOM. The results of this study not only shed light on the further algorithm development for comprehensive characterization of DOM by ESI(-)-FT-ICR MS and ESI(+)-FT-ICR MS but also highlight the importance of appropriate treatment of specific groundwater prior to use.

Development of a Gaussian-Based Alignment Algorithm for the Ultrahigh-Resolution Mass Spectra of Dissolved Organic Matter

The alignment of ultrahigh-resolution mass spectra (UHR-MS) is critical to inspect the presence of unique and common peaks across multiple UHR-MS spectra. However, few attempts have been conducted to develop an automated alignment method. In this study, a novel automated alignment algorithm, namely, FTMSCombine, that follows a Gaussian distribution of mass errors was developed and then integrated with existing FTMSCalibrate and TRFu algorithms to establish an open-source analysis platform, namely, FTMSAnalysis, for the UHR-MS analysis of the dissolved organic matter. The developed FTMSCombine was capable of automatically aligning peaks across different UHR-MS spectra by averaging the m/z values of each peak cluster, although the alignment should be restricted to Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) spectra collected by instruments under similar conditions. The FTMSCombine exhibited an insignificant difference in the reproducibility of chemical formulae but significantly higher mass accuracy than the ICBM-OCEAN. In addition to improving the overall mass accuracy of the whole UHR-MS dataset, the FTMSCombine could effectively exclude scatters or noise peaks using an optional rule that restricts peaks (continuously) detected in at least a certain number of spectra in the UHR-MS spectra dataset. The successfully established FTMSAnalysis (freely available in the Supporting Information of this study) is of great potential in automatically analyzing UHR-MS spectra for dissolved organic matter (DOM) and will largely facilitate the elucidation of DOM chemodivesity by UHR-MS techniques including FTICR-MS.

Sources, Migration, Transformation, and Environmental Effects of Organic Carbon in Eutrophic Lakes: A Critical Review

Organic carbon (OC) plays a leading role in the carbon cycle of lakes and is crucial to carbon balances at regional and even global scales. In eutrophic lakes, in addition to external river inputs, the decomposition of endogenous grass and algae is a major source of organic carbon. Outbreaks of algal blooms (algal eutrophication) and the rapid growth of aquatic grasses (grass eutrophication) can lead to the accumulation and decay of large amounts of algae and aquatic grass debris, which increases the intensity of the carbon cycle of lakes and greatly impacts aquatic environments and ecosystems. The structures, decomposition processes, and distribution characteristics of algae and higher aquatic plant debris in eutrophic lakes are different from mesotrophic and oligotrophic lakes. Studying their accumulation dynamics and driving mechanisms is key to further understanding lake carbon cycles and their many interdependent pathways. This paper focuses on the carbon sources, tracing technologies, migration and transformation processes, and environmental effects of OC in eutrophic lakes. Based on the existing knowledge, we further combed the literature to identify the most important knowledge gaps preventing an in-depth understanding of the processes and driving mechanisms of the organic carbon cycle in eutrophic lakes.