Mathematical chromatography deciphers the molecular fingerprints of dissolved organic matter.

High-resolution mass spectrometry (HRMS) elucidates the molecular composition of dissolved organic matter (DOM) through the unequivocal assignment of molecular formulas. When HRMS is used as a detector coupled to high performance liquid chromatography (HPLC), the molecular fingerprints of DOM are further augmented. However, the identification of eluting compounds remains impossible when DOM chromatograms consist of unresolved humps. Here, we utilized the concept of mathematical chromatography to achieve information reduction and feature extraction. Parallel Factor Analysis (PARAFAC) was applied to a dataset describing the reverse-phase separation of DOM in headwater streams located in southeast Sweden. A dataset consisting of 1355 molecular formulas and 7178 mass spectra was reduced to five components that described 96.89% of the data. Each component summarized the distinct chromatographic elution of molecular formulas with different polarity. Component scores represented the abundance of the identified HPLC features in each sample. Using this chemometric approach allowed the identification of common patterns in HPLC-HRMS datasets by reducing thousands of mass spectra to only a few statistical components. Unlike in principal component analysis (PCA), components closely followed the analytical principles of HPLC-HRMS and therefore represented more realistic pools of DOM. This approach provides a wealth of new opportunities for unravelling the composition of complex mixtures in natural and engineered systems.

The Molecular Fingerprint of Fluorescent Natural Organic Matter Offers Insight into Biogeochemical Sources and Diagenetic State.

Investigating the biogeochemistry of dissolved organic matter (DOM) requires the synthesis of data from several complementary analytical techniques. The traditional approach to data synthesis is to search for correlations between measurements made on the same sample using different instruments. In contrast, data fusion simultaneously decomposes data from multiple instruments into the underlying shared and unshared components. Here, Advanced Coupled Matrix and Tensor Factorization (ACMTF) was used to identify the molecular fingerprint of DOM fluorescence fractions in Arctic fjords. ACMTF explained 99.84% of the variability with six fully shared components. Individual molecular formulas were linked to multiple fluorescence components and vice versa. Molecular fingerprints differed in diversity and oceanographic patterns, suggesting a link to the biogeochemical sources and diagenetic state of DOM. The fingerprints obtained through ACMTF were more specific compared to traditional correlation analysis and yielded greater compositional insight. Multivariate data fusion aligns extremely complex, heterogeneous DOM data sets and thus facilitates a more holistic understanding of DOM biogeochemistry.

Investigating Fluorescent Organic-Matter Composition as a Key Predictor for Arsenic Mobility in Groundwater Aquifers.

Dissolved organic matter (DOM) is linked to the heterogeneous distribution of elevated arsenic (As) in groundwater used for drinking and irrigation purposes, but the relationship between DOM characteristics and arsenic mobility has yet to be fully understood. Here, DOM from groundwater sampled in the Bengal Basin region was characterized using both conventional bulk emission-excitation (EEM) spectroscopy and high-performance size-exclusion chromatography coupled to spectroscopy (HPSEC-EEM). Notably, application of the novel HPSEC-EEM approach permitted the total fluorescence of individual samples to be independently resolved into its underlying components. This allowed the external validation of the bulk-sample fluorescence decomposition and offered insight into the molecular size distribution of fluorescent DOM. Molecular size distributions were similar for the UVA fluorescent (C310 and C340) as well as the three visible fluorescent (C390, C440, and C500) components. There was a greater visible fluorescence in shallow aquifer samples (10-33 m) with high As (SH, up to 418 µg/L) than in samples from the same depth with lower As (up to 40 µg/L). This indicated a link between DOM quality and As mobility within the shallow aquifer. The deep aquifer samples (170-200 m) revealed DOM characteristics similar to SH samples but had low As concentrations (<4 µg/L), signifying that the deep aquifer is potentially vulnerable to As contamination. These findings pave the way for a more comprehensive assessment of the susceptibility of drinking water aquifers, thereby supporting the management of groundwater resources.

The One-Sample PARAFAC Approach Reveals Molecular Size Distributions of Fluorescent Components in Dissolved Organic Matter.

Molecular size plays an important role in dissolved organic matter (DOM) biogeochemistry, but its relationship with the fluorescent fraction of DOM (FDOM) remains poorly resolved. Here high-performance size exclusion chromatography (HPSEC) was coupled to fluorescence emission-excitation (EEM) spectroscopy in full spectral (60 emission and 34 excitation wavelengths) and chromatographic resolution (<1 Hz), to enable the mathematical decomposition of fluorescence on an individual sample basis by parallel factor analysis (PARAFAC). The approach allowed cross-system comparisons of molecular size distributions for individual fluorescence components obtained from independent data sets. Spectra extracted from allochthonous DOM were highly similar. Allochthonous and autochthonous DOM shared some spectra, but included unique components. In agreement with the supramolecular assembly hypothesis, molecular size distributions of the fluorescence fractions were broad and chromatographically unresolved, possibly representing reoccurring fluorophores forming noncovalently bound assemblies of varying molecular size. Samples shared underlying fluorescence components that differed in their size distributions but not their spectral properties. Thus, in contrast to absorption measurements, bulk fluorescence is unlikely to reliably indicate the average molecular size of DOM. The one-sample approach enables robust and independent cross-site comparisons without large-scale sampling efforts and introduces new analytical opportunities for elucidating the origins and biogeochemical properties of FDOM.

Electrochemical and microbiological response of exoelectrogenic biofilm to polyethylene microplastics in water.

Exoelectrogenic biofilm and the associated microbial electrochemical processes have recently been intensively studied for water treatment, but their response to and interaction with polyethylene (PE) microplastics which are widespread in various aquatic environments has never been reported. Here, we investigated how and to what extent PE microplastics would affect the electrochemistry and microbiology of exoelectrogenic biofilm in both microbial fuel cells (MFCs) and microbial electrolysis cells (MECs). When the PE microplastics concentration was increased from 0 to 75 mg/L in the MECs, an apparent decline in the maximum current density (from 1.99 to 0.74 A/m2) and abundance of electroactive bacteria (EAB) in the exoelectrogenic biofilm was noticed. While in the MFCs, the current output was not significantly influenced and the abundance of EAB lightly increased at 25 mg/L microplastics. In addition, PE microplastics restrained the viability of the exoelectrogenic biofilms in both systems, leading to a higher system electrode resistance. Moreover, the microbial community richness and the microplastics-related operational taxonomic units decreased with PE microplastics. Furthermore, the electron transfer-related genes (e.g., pilA and mtrC) and cytochrome c concentration decreased after adding microplastics. This study provides the first glimpse into the influence of PE microplastics on the exoelectrogenic biofilm with the potential mechanisms revealed at the gene level, laying a methodological foundation for the future development of efficient water treatment technologies.

When microbial electrochemistry meets UV: The applicability to high-strength real pharmaceutical industry wastewater.

Wastewater from pharmaceutical and related industries contains many residual pharmaceutical components rich in color and high COD contents, which cannot be removed through the traditional wastewater treatment processes. Recently, microbial electrolysis ultraviolet cell (MEUC) process has shown its promising potential to remove recalcitrant organics because of its merits of wide pH range, iron-free, and without complications of iron sludge production. However, its application to the real pharmaceutical-rich industrial wastewater is still unknown. In this study, the MEUC process was validated with real ciprofloxacin-rich (6863.79 ± 2.21 µg L-1) industrial wastewater (6840 ± 110 mg L-1 of COD). The MEUC process achieved 100% removal of ciprofloxacin, 100% decolorization, and 99.1% removal of COD within 12, 60 and 30 h, respectively, when it was operated at pH-controlled at 7.8, applied voltage of 0.6 V, UV intensity of 10 mW cm-2, and cathodic aeration velocity of 0.005 mL min-1 mL-1. Moreover, fluorescence analysis showed that protein- and humic-like substances in such wastewater were effectively removed, providing further evidence of its high treatment efficiency. Furthermore, eco-toxicity testing with luminescent bacteria Vibro Feschri confirmed that the treated effluent was utterly non-toxic. The results demonstrated the broad application potential of MEUC technology for treating industrial wastewater.

Unified understanding of intrinsic and extrinsic controls of dissolved organic carbon reactivity in aquatic ecosystems.

Despite our growing understanding of the global carbon cycle, scientific consensus on the drivers and mechanisms that control dissolved organic carbon (DOC) turnover in aquatic systems is lacking, hampered by the mismatch between research that approaches DOC reactivity from either intrinsic (inherent chemical properties) or extrinsic (environmental context) perspectives. Here we propose a conceptual view of DOC reactivity in which the combination of intrinsic and extrinsic factors controls turnover rates and determines which reactions will occur. We review three major types of reactions (biological, photochemical, and flocculation) from an intrinsic chemical perspective and further define the environmental features that modulate the expression of chemically inherent reactivity potential. Finally, we propose hypotheses of how extrinsic and intrinsic factors together shape patterns in DOC turnover across the land-to-ocean continuum, underscoring that there is no intrinsic DOC reactivity without environmental context. By acknowledging the intrinsic-extrinsic control duality, our framework intends to foster improved modeling of DOC reactivity and its impact on ecosystem services.

A simple method to isolate fluorescence spectra from small dissolved organic matter datasets.

Dissolved organic matter (DOM) is a complex pool of compounds with a key role in the global carbon cycle. To understand its role in natural and engineered systems, efficient approaches are necessary for tracking DOM quality and quantity. Fluorescence spectroscopy combined with parallel factor analysis (PARAFAC) is very widely used to identify and quantify different fractions of DOM as proxies of DOM source, concentration and biogeochemical processing. A major limitation of the PARAFAC approach is the requirement for a large data set containing many variable samples in which the fractions vary independently. This severely curtails the possibilities to study fluorescence composition and behavior in small or unique datasets. Herein, we present a simple and inexpensive experimental procedure that makes it possible to mathematically decompose a small dataset containing only highly-correlated fluorescent fractions. The approach, which uses widely-available commercial extraction sorbents and previously established protocols to expand the original dataset and inject the missing chemical variability, can be widely implemented at low cost. A demonstration of the procedure shows how a robust six-component PARAFAC model can be extracted from even a river-water dataset with only five bulk samples. Widespread adoption of the procedure for analyzing small fluorescence datasets is needed to confirm the suspected ubiquity of certain DOM fluorescence fractions and to create a shared inventory of ubiquitous components. Such an inventory could greatly simplify and improve the use of fluorescence as a tool to investigate biogeochemical processing of DOM in diverse water sources.