Analyzing postprandial metabolomics data using multiway models: a simulation study.

BACKGROUND: Analysis of time-resolved postprandial metabolomics data can improve the understanding of metabolic mechanisms, potentially revealing biomarkers for early diagnosis of metabolic diseases and advancing precision nutrition and medicine. Postprandial metabolomics measurements at several time points from multiple subjects can be arranged as a subjects by metabolites by time points array. Traditional analysis methods are limited in terms of revealing subject groups, related metabolites, and temporal patterns simultaneously from such three-way data. RESULTS: We introduce an unsupervised multiway analysis approach based on the CANDECOMP/PARAFAC (CP) model for improved analysis of postprandial metabolomics data guided by a simulation study. Because of the lack of ground truth in real data, we generate simulated data using a comprehensive human metabolic model. This allows us to assess the performance of CP models in terms of revealing subject groups and underlying metabolic processes. We study three analysis approaches: analysis of fasting-state data using principal component analysis, T0-corrected data (i.e., data corrected by subtracting fasting-state data) using a CP model and full-dynamic (i.e., full postprandial) data using CP. Through extensive simulations, we demonstrate that CP models capture meaningful and stable patterns from simulated meal challenge data, revealing underlying mechanisms and differences between diseased versus healthy groups. CONCLUSIONS: Our experiments show that it is crucial to analyze both fasting-state and T0-corrected data for understanding metabolic differences among subject groups. Depending on the nature of the subject group structure, the best group separation may be achieved by CP models of T0-corrected or full-dynamic data. This study introduces an improved analysis approach for postprandial metabolomics data while also shedding light on the debate about correcting baseline values in longitudinal data analysis.

Tensor Decomposition-based Feature Extraction and Classification to Detect Natural Selection from Genomic Data.

Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under nonconvex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data although preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx, which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.

Characterizing human postprandial metabolic response using multiway data analysis.

INTRODUCTION: Analysis of time-resolved postprandial metabolomics data can improve our understanding of the human metabolism by revealing similarities and differences in postprandial responses of individuals. Traditional data analysis methods often rely on data summaries or univariate approaches focusing on one metabolite at a time. OBJECTIVES: Our goal is to provide a comprehensive picture in terms of the changes in the human metabolism in response to a meal challenge test, by revealing static and dynamic markers of phenotypes, i.e., subject stratifications, related clusters of metabolites, and their temporal profiles. METHODS: We analyze Nuclear Magnetic Resonance (NMR) spectroscopy measurements of plasma samples collected during a meal challenge test from 299 individuals from the COPSAC2000 cohort using a Nightingale NMR panel at the fasting and postprandial states (15, 30, 60, 90, 120, 150, 240 min). We investigate the postprandial dynamics of the metabolism as reflected in the dynamic behaviour of the measured metabolites. The data is arranged as a three-way array: subjects by metabolites by time. We analyze the fasting state data to reveal static patterns of subject group differences using principal component analysis (PCA), and fasting state-corrected postprandial data using the CANDECOMP/PARAFAC (CP) tensor factorization to reveal dynamic markers of group differences. RESULTS: Our analysis reveals dynamic markers consisting of certain metabolite groups and their temporal profiles showing differences among males according to their body mass index (BMI) in response to the meal challenge. We also show that certain lipoproteins relate to the group difference differently in the fasting vs. dynamic state. Furthermore, while similar dynamic patterns are observed in males and females, the BMI-related group difference is observed only in males in the dynamic state. CONCLUSION: The CP model is an effective approach to analyze time-resolved postprandial metabolomics data, and provides a compact but a comprehensive summary of the postprandial data revealing replicable and interpretable dynamic markers crucial to advance our understanding of changes in the metabolism in response to a meal challenge.

Adulteration Detection and Quantification in Olive Oil Using Excitation-Emission Matrix Fluorescence Spectroscopy and Chemometrics.

This research investigates the use of excitation-emission matrix fluorescence (EEMF) in conjunction with chemometric models to rapidly identify and quantify adulteration in olive oil, a critical concern where sample availability is limited. Adulteration is simulated by blending soybean, peanut, and linseed oils into olive oil, creating diverse adulterated samples. Principal component analysis (PCA) was applied to the EEMF spectral data as an initial exploratory measure to cluster and differentiate adulterated samples. Spatial clustering enabled vivid visualization of the variations and trends in the spectra. The novel application of parallel factor analysis (PARAFAC) for data decomposition in this paper focuses on unraveling correlations between the decomposed components and the actual adulterated components, which offers a novel perspective for accurately quantifying adulteration levels. Additionally, a comparative analysis was conducted between the PCA and PARAFAC methodologies. Our study not only unveils a new avenue for the quantitative analysis of adulterants in olive oil through spectral detection but also highlights the potential for applying these insights in practical, real-world scenarios, thereby enhancing detection capabilities for various edible oil samples. This promises to improve the detection of adulteration across a range of edible oil samples, offering significant contributions to food safety and quality assurance.

Unravelling structure evolution of dissolved organic matter during oxidation by persulfate: Insights from aromaticity and fluorescence analysis.

Persulfate advanced oxidation technology is widely utilized for remediating organic-contaminated groundwater. Post-remediation by persulfate oxidation, the aromaticity of dissolved organic matter (DOM) in groundwater is significantly reduced. Nevertheless, the evolution trends of aromaticity and related structural changes in DOM remained unclear. Here, we selected eight types of DOM to analyze the variation in aromaticity, molecular weight, and fluorescence characteristics during oxidation by persulfate using optical spectroscopy and parallel faction analysis combined with two-dimensional correlation spectroscopy analysis (2D PARAFAC COS). The results showed diverse trends in the changes of aromaticity and maximum fluorescence intensity (Fmax) among different types of DOM as the reaction time increases. Four types of DOM (humic acid 1S104H, fulvic acid, and natural organic matters) exhibited an initially noteworthy increase in aromaticity followed by a decrease, while others demonstrated a continuous decreasing trend (14.3%-69.4%). The overall decreasing magnitude of DOM aromaticity follows the order of natural organic matters ≈ commercial humic acid > fulvic acid > extracted humic acid. The Fmax of humic acid increased, exception of commercial humic acid. The Fmax of fulvic acid initially decreased and then increased, while that of natural organic matters exhibited a decreasing trend (86.4%). The fulvic acid-like substance is the main controlling factor for the aromaticity and molecular weight of DOM during persulfate oxidation process. The oxidation sequence of fluorophores in DOM is as follows: fulvic-like substance, microbial-derived humic-like substance, humic-like substance, and aquatic humic-like substance. The fulvic-like and microbial-derived humic-like substances at longer excitation wavelengths were more sensitive to the response of persulfate oxidation than that of shorter excitation wavelengths. This result reveals the structure evolution of DOM during persulfate oxidation process and provides further support for predicting its environmental behavior.

Effects of photodegradation on the composition characteristics and metal binding behavior of sediment-derived dissolved organic matter (SDOM) in nansi lake, China.

Sediment-derived dissolved organic matter (SDOM) is instrumental in the cycling of nutrients and heavy metals within lakes, influencing ecological balance and contaminant distribution. Given the influence of photodegradation on the alteration and breakdown of SDOM, further understanding of this process is essential. In this research, the properties of the SDOM photodegradation process and its metal-binding reactions in Nansi Lake were analyzed using the EEM-PARAFAC and 2D-SF/FTIR-COS techniques. Our study identified three sorts of humic-like components and one protein-like component in SDOM, with the humic-like material accounting for 71.3 ± 5.19% of the fluorescence intensity (Fmax). Photodegradation altered the abundance and structure of SDOM, with a 41.6 ± 5.82% decrease in a280 and a 29.1 ± 9.31% reduction in Fmax after 7 days, notably reducing the protein-like component C4 by 54.0 ± 5.17% and the humic-like component C2 by 48.5 ± 2.54%, which led to SDOM being formed with lower molecular weight and aromaticity. After photodegradation, the LogKCu values for humic-like and protein-like substances decreased (humic-like C2: LogKCu: 1.35 ± 0.10-1.11 ± 0.15, protein-like C4: 1.49 ± 0.14-1.29 ± 0.34), yet the preferential binding sequence of protein-like materials and specific functional groups with Cu2+ such as aliphatic C-OH, amide (I) C=O and polysaccharide C-O groups remained unaltered. Our results enhance the knowledge of light-induced SDOM alterations and offer insights into SDOM-metal interactions in aquatic ecosystems.

Insight into the dynamics of dissolved organic matter components under latitude change perturbation.

Dissolved organic matter (DOM) which can help the transportation of nutrients and pollutants plays essential role in the aquatic ecosystems. However, the dynamics of individual DOM component under the change of latitude have not been elucidated to date. The composition and dynamics of DOM were assessed in this study. Two individual parallel factor analysis (PARAFAC) components were found in each sampling site in Heilongjiang. To further characterize the inner change of the identified PARAFAC components, two-latitude correlation spectroscopy (2DCOS) technique was applied to the excitation loadings data. Interestingly, not all the fluorophore in a PARAFAC component change in the same direction as the overall change of a component. From upstream to downstream, the peak A1 in PARAFAC component C1 showed a downward trend, but peak A2 presented an upward trend. In PARAFAC component C2, the peak T2 and peak T3 showed an inverse changing trend under latitude perturbation. Furthermore, basic nutrients parameters in Heilongjiang were also characterized in each sampling sites. The relationships between DOM and nutrients showed that component C1 made a significant contribution to chemical oxygen demand (COD) and biochemical oxygen demand (BOD5). The evolutions of DOM peak A1 and peak A2 were accompanied by the changing of Total phosphorus (TP). The findings in this study could make a contribution to explore the fate of DOM in high humic-like substance containing river.

Chemiluminescence data modeling using parallel factor analysis for the simple and sensitive determination of diphenoxylate in human plasma and tablets.

In this study, a chemiluminescence (CL) method was developed to determine diphenoxylate in tablets and human plasma. This is the first CL method proposed to determine diphenoxylate. Creating three-dimensional data caused the parallel factor analysis algorithm (PARAFAC) to be used for the first time in CL methods. The method is based on the fact that diphenoxylate enhances the weak CL produced in the reaction of Ru(phen)3 2+ and acidic Ce(IV), and the concentration of Ce(IV) solution has a different effect on the CL response of diphenoxylate and the blank plasma. The calibration curve was linear from 4.0 × 10-8 to 1.6 × 10-6 mol L-1 (R2 = 0.9954), and the detection limit was 1.3 × 10-8 mol L-1 (S/N = 3). The sampling rate was about 30 samples per hour, and the % RSD for 10 repeated measurements of 4 × 10-7 mol L-1 diphenoxylate was 5.4%. The interference effects of some ions, amino acids, and common additives were also investigated. The CL method was successfully used to determine diphenoxylate in tablets, and the results were statistically confirmed by the reference method. The proposed CL method and the PARAFAC algorithm were successfully used to determine the concentration of diphenoxylate in human blood plasma samples.

Persistence of dissolved organic matter in sediments influenced by environmental factors：Implication for nutrition and carbon cycle.

The persistence of dissolved organic matter (DOM) plays a crucial role in the cycling and distribution of carbon and nutrients. Nonetheless, our understanding of how environmental alterations affect the persistence of sedimentary DOM remains incomplete. Excitation Emission Fluorescence Matrix-Parallel Factor Analysis (EEM-PARAFAC) was used to examine the fluorescence and compositional characteristics of hydrophilic and hydrophobic DOM (separated using XAD-8 resin) within sediments from twelve lakes and reservoirs. Fluorescence analysis indicated that DOM persistence is dependent on the proportions of the three components derived from PARAFAC. The Mantel test showed that climatic factors had the most significant impact on DOM persistence (Mantel's r = 0.46-0.54, Mantel's p = 0.001-0.007), while anthropogenic (Mantel's r = 0.24-0.32, Mantel's p = 0.03-0.05) and hydrological factors (Mantel's r = 0.03-0.22, Mantel's p = 0.06-0.40) had a somewhat lesser influence. Environmental changes resulted in a consistent decline in DOM persistence from Northeast to Southwest China, accompanied by an increase in gross primary productivity (GPP). Reduced DOM persistence due to climate, hydrological, and anthropogenic factors may lead to elevated concentrations of total phosphorus (TP), contributing to deteriorating water quality and events such as algal blooms. The decline in water quality due to reduced DOM persistence in lakes with high GPP can exacerbate the transition from carbon sinks to carbon sources. Consequently, the persistence of sedimentary DOM significantly influences nutrient and carbon cycling in lakes. Investigating DOM persistence in lakes across diverse geographic locations offers a new perspective on lake eutrophication and carbon emissions. Furthermore, it is crucial to develop targeted recommendations for lake restoration and management.

Feasibility of excitation-emission fluorescence spectroscopy in tandem with chemometrics for quantitation of trans-resveratrol in vine-shoot ethanolic extracts.

BACKGROUND: trans-Resveratrol (TR) is a well-known phytochemical compound with important biological properties. It can be recovered from agri-food by-products or wastes, such as vine shoots. Once recovered, its concentration should be measured, possibly in a green, non-destructive, and efficient manner. With these premises, this work aimed to explore the feasibility of excitation-emission fluorescence spectroscopy combined with chemometrics for the analysis of TR in raw extracts obtained from vine shoots. A total of 75 extracts were produced and analyzed by ultra-performance liquid chromatography method with diode array detection (UPLC-DAD) and spectrofluorimetry. Then, the feasibility of two calibration strategies for TR quantitation was assessed - a parallel factor analysis (PARAFAC)-based calibration and the N-way partial least squares (NPLS) regression. RESULTS: The extracts showed variable TR content, the excitation/emission maxima of which were at around 305/390 nm, respectively. The best PARAFAC-based calibration allowed a root mean square error of prediction (RMSEP) of 22.57 mg L-1 , and a relative prediction deviation (RPD) of 2.91 to be obtained but a large number of PARAFAC components should be considered to improve the predictions. The results of the NPLS regression were slightly better, with a RMSEP of 19.47 mg L-1 , and an RPD of 3.33 in the best case. CONCLUSION: Fluorescence could be an alternative analytical technique to measure TR in complex samples. Chemometric tools allowed the identification of the TR signal in the fluorescence landscapes, which could be further used for its non-destructive quantitation. The need for a more accurate criterion for optimal PARAFAC complexity emerged. © 2024 Society of Chemical Industry.

Identification of oil contamination in process water using fluorescence excitation emission matrix (FEEM) and parallel factor analysis (PARAFAC).

Fuel oil is widely used within Eskom, a power generation company in South Africa. Eskom's coal-fired power stations use up to 30,000 L of fuel oil per hour during a cold start-up, a consequence of which results in oil leaks to the dams. Oil contamination in water treatment plants causes irreversible membrane fouling, requiring costly replacement. This research work focused on the development of a rapid method for the identification of low concentrations of the water-soluble oil component fraction of crude fuel oil. For the developed method, known volumes of the water-soluble fraction of crude oil were spiked into various matrices of process water. FEEMs were collected using the patented HORIBA Aqualog spectrometer and data were modelled with PARAFAC. The results were well described with a four-component model, which included an oil component and three natural organic matter components, with a split-half validation match of 90%. The oil component was verified using linear regression of the PARAFAC component scores yielding an R2 value of 0.98. From the scores, a qualitative pass/fail test was developed such that process water can be analysed and subjected to the model to indicate the presence of oil contamination beyond a damaging threshold.

Probabilistic PARAFAC2.

The Parallel Factor Analysis 2 (PARAFAC2) is a multimodal factor analysis model suitable for analyzing multi-way data when one of the modes has incomparable observation units, for example, because of differences in signal sampling or batch sizes. A fully probabilistic treatment of the PARAFAC2 is desirable to improve robustness to noise and provide a principled approach for determining the number of factors, but challenging because direct model fitting requires that factor loadings be decomposed into a shared matrix specifying how the components are consistently co-expressed across samples and sample-specific orthogonality-constrained component profiles. We develop two probabilistic formulations of the PARAFAC2 model along with variational Bayesian procedures for inference: In the first approach, the mean values of the factor loadings are orthogonal leading to closed form variational updates, and in the second, the factor loadings themselves are orthogonal using a matrix Von Mises-Fisher distribution. We contrast our probabilistic formulations to the conventional direct fitting algorithm based on maximum likelihood on synthetic data and real fluorescence spectroscopy and gas chromatography-mass spectrometry data showing that the probabilistic formulations are more robust to noise and model order misspecification. The probabilistic PARAFAC2, thus, forms a promising framework for modeling multi-way data accounting for uncertainty.

Water-soluble brown carbon in atmospheric aerosols from the resource-dependent cities: Optical properties, chemical compositions and sources.

As a vital type of light-absorbing aerosol, brown carbon (BrC) presents inherent associations with atmospheric photochemistry and climate change. However, the understanding of the chemical and optical properties of BrC is limited, especially in some resource-dependent cities with long heating periods in northwest China. This study showed that the annual average abundances of Water-soluble BrC (WS-BrC) were 9.33±7.42 and 8.69±6.29 µg/m3 in Baotou and Wuhai and the concentrations, absorption coefficient (Abs365), and mass absorption efficiency (MAE365) of WS-BrC presented significant seasonal patterns, with high values in the heating season and low values in the non-heating season; while showing opposite seasonal trends for the Absorption Ångström exponent (AAE300-400). Comparatively, the levels of WS-BrC in developing regions (such as cities in Asia) were higher than those in developed regions (such as cities in Europe and Australia), indicating the significant differences in energy consumption in these regions. By combining fluorescence excitation-emission matrix (EEM) spectra with the parallel factor (PARAFAC) model, humic-like (C1 and C2) and protein-like (C3) substances were identified, and accounted for 61.40%±4.66% and 38.6%±3.78% at Baotou, and 60.33%±6.29% and 39.67%±4.17% at Wuhai, respectively. The results of source apportionment suggested that the potential source regions of WS-BrC varied in heating vs. non-heating seasons and that the properties of WS-BrC significantly depended on primary emissions (e.g., combustion emissions) and secondary formation.

Effects of Microplastics on Soil Carbon Mineralization: The Crucial Role of Oxygen Dynamics and Electron Transfer.

Although our understanding of the effects of microplastics on the dynamics of soil organic matter (SOM) has considerably advanced in recent years, the fundamental mechanisms remain unclear. In this study, we examine the effects of polyethylene and poly(lactic acid) microplastics on SOM processes via mineralization incubation. Accordingly, we evaluated the changes in carbon dioxide (CO2) and methane (CH4) production. An O2 planar optical sensor was used to detect the temporal behavior of dissolved O2 during incubation to determine the microscale oxygen heterogeneity caused by microplastics. Additionally, the changes in soil dissolved organic matter (DOM) were evaluated using a combination of spectroscopic approaches and ultrahigh-resolution mass spectrometry. Microplastics increased cumulative CO2 emissions by 160-613%, whereas CH4 emissions dropped by 45-503%, which may be attributed to the oxygenated porous habitats surrounding microplastics. Conventional and biodegradable microplastics changed the quantities of soil dissolved organic carbon. In the microplastic treatments, DOM with more polar groups was detected, suggesting a higher level of electron transport. In addition, there was a positive correlation between the carbon concentration, electron-donating ability, and CO2 emission. These findings suggest that microplastics may facilitate the mineralization of SOM by modifying O2 microenvironments, DOM concentration, and DOM electron transport capability. Accordingly, this study provides new insights into the impact of microplastics on soil carbon dynamics.

The priming effects of plant leachates on dissolved organic matter degradation in water depend on leachate type and water stability.

The modification of dissolved organic matter (DOM) degradation by plant carbon inputs represents a critical biogeochemical process that controls carbon dynamics. However, the priming effects (PEs) different plant tissues induce on the degradation of DOM pools with different stabilities remain unknown. In this study, PEs, induced by different tissue leachates of Phragmites australis, were evaluated via changes in DOM components and properties of both fresh and tidal water (with different stabilities). The results showed that DOM derived from different plant tissue leachates differed in composition and bioavailability. Inputs of tissue leachates induced PEs with different intensities and directions (negative or positive) on DOM degradation of fresh and tidal water. In fresh water, the PEs of leaf and root leachates were significantly higher than those of stem and rhizome leachates. The PE direction changed for DOM degradation between fresh and tidal water. The addition of leaf and root leachates tended to induce positive PEs on DOM degradation of fresh water, while resulting in negative PEs on DOM degradation of tidal water. Negative PEs for tidal water DOM may be due to preferential utilization of microbes, high salinity, and/or the promotion of exogenous DOM production from plant tissues. The results indicate that intensity and direction of PEs induced by plant leachates depend on both leachate type and water stability. The findings highlight the necessity to examine the nature of exogenous and native DOM when interpreting the interactive processes that regulate DOM degradation.

Sources, characteristics, and in situ degradation of dissolved organic matters: A case study of a drinking water reservoir located in a cold-temperate forest.

Dissolved organic matter (DOM) plays a pivotal role in the biogeochemical cycles of elements and the regulation of forest ecosystem functions. However, studies on the regional and seasonal characteristics of DOM in cold-temperate montane forests are still not comprehensive. In this study, samples of water, soil, and sediment from different sites in the forest drainage basin were collected, and their DOM was characterized by an excitation-emission matrix and parallel factor analysis (EEM-PARAFAC). The results showed that terrestrial-sourced humic-like substances were the dominant DOM in the studied reservoir and inflowing rivers. The quality and quantity of DOM exhibited spatiotemporal variations with the influence of terrain and monsoonal precipitation. The average concentration of dissolved organic carbon (DOC) in the wet season was 11.62 mg/L, which was higher than that in the dry season (8.18 mg/L). Higher humification index (HIX) values were observed in the wet season and upstream water than in the dry season and reservoir water. Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) was used to further develop a molecular-level understanding of the in situ degradation process of DOM. The results indicated that photodegradation rather than biodegradation may play a dominant role in the in situ degradation of terrestrial-sourced humic-like substances under natural conditions. The biodegradability of DOM was enhanced after the in situ degradation process. Additionally, a significant decrease in the precursors of disinfectant byproducts in DOM was observed after in situ degradation. To our knowledge, this is the first study of the sources, characteristics, and in situ degradation of DOM in a reservoir in a cold-temperate forest. These findings help better understand the quality, quantity, and biogeochemical process of DOM in the studied reservoir and may contribute to the selection of drinking water treatment technologies for water supply.

Source and quality of dissolved organic matter in streams are reflective to land use/land cover, climate seasonality and pCO2.

Sources and quality of dissolved organic matter (DOM) in streams may be largely controlled by the landscape and season. In this study, we attempted to answer three critical questions: 1) Do land use/land cover (LULC) types affect DOM characteristics? 2) Is there a seasonal fluctuation in DOM components? 3) How do DOM quality and LULC types influence aqueous carbon dioxide partial pressure (pCO2). To achieve this, we investigated the fluorescence characteristics of DOM and its implication for pCO2 in three streams draining land with different urban intensities under distinctive dry and wet seasons. Four fluorescence components were identified, including two terrestrial humic-like components, one protein-like component and one microbial humic-like component. We found a significant positive relationship of the maximum fluorescence intensity (Fmax) of the four components and fluorescence index (FI370) with urbanization intensity in both the dry and wet seasons. The mean Fmax, biological index (BIX) and FI370 all exhibited an increasing trend from upstream to downstream in the stream with highest proportions of urban and cropland. The fluorescence characteristics were negatively related to proportion of forested land in the both seasons. The terrestrial humic-like DOM was dominating in the studied streams. Moreover, the seasonality altered the DOM composition, with protein-like component emerging only in stream waters during the dry season, while microbial humic-like component exclusively occurred during the wet season. pCO2 values were positively related to terrestrial humic-like and biological protein-like components, and urban land. The dry season had much higher pCO2 than the wet season. Results from the Partial Least Squares Path (PLS-PM) models further indicated that LULC types were important in mediating fluorescence DOM whilst pCO2 was more sensitive to the direct effect from FDOM dynamics. We conclude that DOM source and quality in streams are reflective to LULC and climate seasonality, and are good indicators of pCO2 via source tracer and quality of fluorescence components.

Insight into humification of mushroom residues under addition of Rich-N sources: Comparing key molecular evolution processes using EEM-PARAFAC and 2D-FTIR-COS analysis.

Accelerating the humification of organic solid waste is one of the most important issues in composting. This present study aims to study and compare the humification process of different rich-N sources (chicken manure, cattle manure, and urea) addition during the composting of mushroom residues, from macro physicochemical properties to micro humic molecular structure evolution process. The physicochemical elements and humic components were determined for evaluating the compost quality and humification degree as composting proceed. The coupled analysis of excitation-emission matrix with parallel factor analysis (EEM-PARAFAC) and two-dimensional correlation with Fourier transform infrared spectrum (2D-FTIR-COS) were used to characterize the functional molecular structure evolution of dissolved organic matter during humification process. The results indicated that the rank order for humification level were the treatments of chicken manure (HM), urea (UM), cattle manure (CM), and single mushroom residue treatment (CK), with their humification index of 22.18%, 22.05%, 18.47%, and 16.52%, respectively. Humic substance, humic acid, and fulvic acid were obtained the highest in HM treatment with contents of 35.41 ± 0.86%, 23.32 ± 1.57%, and 10.97 ± 0.52%, respectively. The rich-N source addition enhanced the degradation of protein-like and polysaccharides-like substances in dissolved organic matter, thus accelerating the humification process of mushroom residues. The key structure evolution of dissolved organic matter in the HM treatment, in which the CO and CC stretching of quinone, amide, or ketone, and the C-O stretching of polysaccharides may be responsible for the faster formation of humus compared to the other nitrogen treatments. In this study, redundancy analysis indicated that the total nitrogen (TN) and nitrate nitrogen (NO3--N) may be the potential indicators for determining the humification level as composting proceed. The result provides significant insight into the humification mechanism of mushroom residue under different types of nitrogen sources at the molecular level, and will be reference for improving the composting technique in practical field.

Characterization of copper binding to different molecular weight fractions of dissolved organic matter in surface water.

Dissolved organic matter (DOM) is a kind of substance with complex compositions and wide molecular weight distribution, which can strongly combine with various pollutants. Therefore, the binding characteristics of DOM and heavy metal pollutants can be studied specifically according to the binding characteristics of DOM and pollutants. In this study, DOM in surface water bodies was divided into three levels (MW < 1 kDa, 1 kDa < MW < 5 kDa, MW > 5 kDa) according to different molecular weights (MW). The binding properties were investigated by fluorescence spectrum analysis and complex model. Four components (C1-C4) were identified by PARAFAC. Among them, the contribution rate of protein-like components C1, C2 and C4 to the total fluorescence intensity reached more than 78%, and the log Ka values of low molecular weight components were the highest, which were 3.28, 3.14 and 3.47, respectively, indicating higher binding ability with Cu2+.With the decrease of molecular weight, the log Kb value increases, indicating that the complexation is more stable. The humic component C3 in high molecular weight has stronger binding stability with Cu2+, but the number of binding sites for C3 is 0.36, while that for C2 is 1.51, indicating that its binding sites and binding ability are relatively low. The results showed that the DOM ligand of Cu2+ in surface water showed a certain molecular weight dependence. In addition, different MW DOM lead to different pollution forms. Different properties of DOM ligand combined with Cu2+ were studied in order to control the migration, transformation, bioavailability, morphology and stability of heavy metal pollutants, and to provide theoretical support for the practical application management of surface water pollution control.

Insight into variation and controlling factors of dissolved organic matter between urban rivers undergoing different anthropogenic influences.

Dissolved organic matter (DOM), known as a key to the aquatic carbon cycle, is influenced by abiotic and biotic factors. However, the compositional variation and these factors' effects on fluorescence DOM (FDOM) in urban rivers undergoing different anthropogenic pressure are poorly investigated. Herein, using fluorescence excitation-emission matrix and parallel factor analysis (EEM-PARAFAC), four FDOM components (C1, C2, C3, and C4) were identified in a less urbanized north river (NR) and a more urbanized west river (WR) of Jiulong River Watershed in Fujian province, China. C1, C2, and C4 were related to humic-like substances (HLS) and C3 to protein-like substances (PLS). HLS (63.9% in WR and 36.4% in NR) and PLS (62.7% in WR and 37.3% in NR) exhibited higher fluorescence in the more urbanized river. We also found higher PLS in winter, but higher HLS in summer for both rivers. Although the coefficient of variation indicated a difference in FDOM components stability to some extent between the two rivers, the typhoon event that occurred in summer had a stronger disruptive impact on the CDOM and FDOM of a more urbanized river than that of a less urbanized river. We explore abiotic and biotic factors' effects on FDOM using the partial least squares path model (PLS-PM). PLS-PM results revealed higher significant influences of biotic factors on FDOM in the more urbanized river. This study enhances our understanding of FDOM dynamics of rivers with different anthropogenic pressure together with the abiotic and biotic factors driving them.