Assessment of Multiorigin Humin Components Evolution and

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Agricultural and Environmental Chemistry

Assessment of Multiorigin Humin Components Evolution and Influencing Factors Xinyu Xie, Xintong Gao, Chaonan Pan, Zimin Wei, Yue Zhao, Xu Zhang, Sheng Luo, and Jinxiang Cao J. Agric. Food Chem., Just Accepted Manuscript • Publication Date (Web): 25 Mar 2019 Downloaded from http://pubs.acs.org on March 26, 2019

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Journal of Agricultural and Food Chemistry

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Assessment of Multiorigin Humin Components Evolution and Influencing Factors

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Xinyu Xiea†, Xintong Gaoa†, Chaonan Pana, Zimin Weia*, Yue Zhaoa*, Xu Zhanga,

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Sheng Luob, Jinxiang Caob

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a College of Life Science, Northeast Agricultural University, Harbin 150030, China

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b Yi’ an County Agricultural Technology Promotion Center, Heilongjiang 161500,

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China

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† These authors contributed equally to this work.

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*Corresponding author Address: Northeast Agricultural University, Harbin 150030,

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China. Tel./Fax: +86 451 55190413. E-mail address: [email protected] or

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[email protected]

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ACS Paragon Plus Environment


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Abstract:

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Humin (HM) is a complex mixture of molecules produced in the different biological

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processes, and the structural evolution of HM in the agricultural wastes composting are

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not well-known. Elucidating and comparing the structural evolution during livestock

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manure (LMC) and straw wastes (SWC) composting can help one to better understand

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the fates, features, and environmental impacts of HM. This study exploites excitation

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emission matrix-parallel factor (EEM-PARAFAC), two-dimensional correlation

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spectroscopy (2D-CoS), hetero-2DCoS and structural equation model (SEM) to

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compare the fate of the HM. We fit a three-component EEM-PARAFAC model to

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characterize HM extracted from LMC and SWC. The results show that the HM

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evolution have signification difference between LMC and SWC. As a result, the

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opposite change tendency and different change order of HM fluorescent components

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determine the different synthesize formation and evolution mechanism. Diverse the

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organic matter composition and dominant microbes might be the reason for the different

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evolution mechanism. Based on these results, a comprehensive view of the component

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changes of HM in the composting process is obtained. Furthermore, the superior

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potential of such an integrated approach during investigating the complex evolution in

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the environment was also demonstrated.

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Keywords: humin, livestock manures composting, straw wastes composting, EEM-

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PARAFAC, 2D-COS, SEM

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1. Introduction Humin (HM) is a major humic substances, it is discovered in soil, composting, water

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etc.1. It plays an important role in the carbon budget, electron transfer, anaerobic

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biodegradation and strongly affects the sorption behavior2. HM, humic acid (HA) and

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fulvic acid (FA) are the three major types of humic substances in the composting.

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Currently, more reports on the HA and FA and few reports on the HM in compost 3, 4.

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However, considering the applications and potential contribution of HM during

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composting, the fate of the HM inner components cannot be ignored or underestimated.

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Approaches studying the substance characteristics in the compost are quite diverse,

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including nuclear magnetic resonance, high performance liquid chromatography-mass

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spectrum (HPLC-MS), gas chromatograph-mass spectrum (GC-MS), fourier transform

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infrared spectroscopy (FTIR), and EEM-PARAFAC analysis5. EEM-PARAFAC have

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been extensively used to explore the chemical structure of humic matter due to its

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characteristic to the abundant light absorbing and radiating groups in fluorescent

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substance 6, 7. Researchers use Fmax of each component to characterize the change of the

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fluorescence component. However, there are always two or more peaks in one

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PARAFAC component 8. Only one Fmax value cannot explain the detailed inner peaks

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changes of one PARAFAC component, it can only express the overall change of a

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PARAFAC component. In addition, previous studies showed that the HM had high

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proportion of conjugated structure, and had fluorescence activity which could be applied

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to PARAFAC analysis 9, 10. Therefore, further investigation on the evolution of

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fluorescence peaks in HM-PARAFAC components is of interest to understand the HM

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biological process during composting.

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Two-dimensional correlation spectroscopy (2DCoS) is capable of distinguishing

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overlapped peaks by extending spectra along the second dimension as well as providing

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information about the relative directions and sequential orders of structural variations 11,

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12.

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variables. If the correlation is strong, it indicates that the associated bands might from

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the same origin 13. Thus, it has been applied to probe the interaction and evolution

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mechanisms of some substances 14, 15. The UV, FTIR, fluorescence spectroscopy etc.

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techniques are the major probes used in 2DCoS analysis, it gives the particular

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molecular information about the binding process 14, 16. Moreover, 2D-heterospectral

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correlation (hetero-2DCoS) analysis compares two spectral data for a system obtained

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by using spectral probes under the same external perturbation 17. The perturbation

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conditions can be continuous variables, such as sampling time, temperature, pH values

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etc. during composting 18. For these appealing features, 2DCoS and hetero-2DCoS are

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among the most effective approaches of substances evolution research.

In essence, 2DCoS is to estimate the correlation coefficients between two spectral

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In this study, according to the organic matter content, six raw materials were divided

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into two categories: livestock manure (LMC) and straw wastes (SWC) composting. We

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aimed to confirm the HM evolution mechanisms and factors between LMC and SWC.

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EEM-PARAFAC, 2DCoS, hetero-2DCoS and SEM analysis were used to evaluate the

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evolution mechanism, the sequential order and influencing factors of HM components.

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This might, to best of our knowledge, be the first time to use these methods to study the

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HM evolution during composting. The insights gained from this work should aid in

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understanding the potential function and molecular-level evolution mechanisms of HM

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during composting in further study.

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2. Methods

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2.1. Composting Procedure

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Six compost raw materials used in this study were chicken manure, cattle manure, pig

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manure, wheat straw, maize straw and paddy straw (obtained from the village in Harbin,

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China). Compost samples were all obtained from the laboratory. All the composts were

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carried out in the same reactors with the same composting conditions. The Compost

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processes were completed when the piles temperature dropped to ambient temperature

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(20 ± 2 °C). Compost samples were collected on days 0, 1, 7, 10, 14, 21, 28, 37, 40, 45,

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48 and 60 from each pile. According to the evolution of microbial communities and

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temperature during composting, the sampling time was divide into Phase 1 (0 - 10 d),

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Phase 2 (14 - 37 d) and Phase 3 (40 - 60 d) 19, 20.

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2.2. Extraction of the HM HM was extracted and pacificated following the method described by Preston 21 with

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some modifications. Briefly, the compost samples were wetted with distilled water

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(solid to liquid ratio of 1: 20, w/v) for 12h in a shaker at 25 °C. The mixture was

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centrifuged at 1500 rpm for 10 min to remove light fraction and supernatant. The

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compost precipitate was mixed with 0.1 M NaOH and 0.1 M Na4P2O7 (solid to liquid of

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1: 20, w/v) for 12h at 25 °C to remove HA and FA, this procedure was repeated eight

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times until the supernatant was faint yellow. Then the same treatments were performed

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with 0.5 M H2SO4. After above steps, the solid residual was treated with HCl - HF

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solution for 12h in different proportion (HCl: HF = 1:9, 3:7 and 5:5, v/v) to remove the

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inorganic minerals. After centrifugation, the residual was dissolved in 0.2 M NaOH, and

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then adjusted solution pH to 7.0 with 1 M HCl. The precipitate was obtained after

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standing 12h. Afterwards, the precipitate was washed and pacificated with HCl and

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distilled water until no chloride anions existed in supernatant as determined by AgNO3.

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The final precipitate was the HM. Eventually, the HM was dissolved in 0.1 M NaOH

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for further use.

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2.3. EEMs Fluorescence Spectroscopy and PARAFAC Modeling

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EEMs fluorescence spectra were recorded using a Hitachi F-7000 fluorescence

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spectrophotometer (Hitachi High Technologies, Japan). The operation temperature was

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at 20 ± 3 °C. The raw materials of LMC covered with chicken manure, cattle manure

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and pig manure. The raw materials of SWC materials covered with wheat straw, maize

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straw and paddy straw. During the PARAFAC analysis, LMC were the first group, and

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SWC were the second group. More than two hundred samples are assessed for each set

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of data. The HM concentration of all samples was ad justed to 10 mg l-1 with distilled

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water. To ensure the veracity of the results and increase the machinery life, we carried

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out repeatedly pre-experiments to determine the spectral scanning range. Both in the

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LMC and SWC, we found that the fluorescence of HM was only detected in the 250-

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600 nm of emission wavelengths range using 2-nm increments. Excitation wavelengths

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range are also determined in the same way. Hence, the emission wavelengths range

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from 250 to 600 using 2-nm increments, while the excitation wavelength range was set

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between 200 and 550 nm and determined at 1-nm intervals. Moreover, the scan speed

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was locked at 2400 nm min-1. Scanning time for EEM of each sample was about 20

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min. The effects of Rayleigh and Raman scattering were removed by subtracting a

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Milli-Q EEM 22.

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PARAFAC modeling and analysis were carried out in MATLAB 2015a by using the

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DOMFlour Toolbox 23. Nonnegative constraints were applied in PARAFAC model.

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Several steps were used to minimize the scatter lines and determine the beat-fit

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modeling, including split-half validation 24 and the sum of squared error analysis 23, 25.

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Finally, the excitation and emission spectra of the components can be separated by

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PARAFAC analysis. The concentration of the PARAFAC components was described

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by the maximum fluorescence intensity (Fmax, Raman Units) 26.

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2.4. Two-dimensional Correlation Spectra Analysis

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The series of the excitation loadings data fluorescence component spectrums obtained

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from the HM-FARAFAC upon varying composting periods were transformed into a

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new spectral matrix suitable for two-dimensional Correlation Spectra Analysis

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(2DCOS) 18. 2DCoS was performed referring to the method established by Noda 27. 2D

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Shige software (version 1.3, Kwansei-Gakuin University, Japan) was applied to spread

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the one-dimensional spectra into the second spectral dimension with the same

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wavenumbers in both dimensions. 2DCoS analysis provided both synchronous and

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asynchronous correlation maps. The analytical rules of two generated spectra and the

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interpretation concerning the signs were referred to in previous literature 12.

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2.5. Statistical Analysis

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Structural equation model (SEM) was a powerful statistical method to study interactive

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relationships among observed and potential variables. SEM was widely applied to

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explain and predict correlations in multivariate datasets5. SEM Statistical analyses on

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replicates of each sample treatment were conducted with SPSS 21.0. Statistical analyses

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on replicates of each sample treatment were conducted with SPSS 21.0. Six products in

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the initial model, including the physical and chemical parameters (C/N, organic matter

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and humification index, data from the laboratory), microbial community index (data

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from NMDS Axis) and the change of the HM components (data from PARAFAC). We

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use the chi-square test to verify the quality of the fit. If the P value is greater than 0.05,

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then this SEM is true. Correlation analyses were used to examine the relationships

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among variables. Significance levels were reported as significant (*, 0.05> p > 0.01), or

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highly significant (**, p< 0.01). Finally, we repeatedly deleted all non-significant

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missing paths and each time re-tested the model's veracity. Moreover, we use

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standardized total effects from SEM to identify direct and indirect relationships in the

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model.

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3. Results and Discussion

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3.1. Change of the HM during composting

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The HM content was detected among different raw materials at Phase 1, 2 and 3 (Fig.

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S1). Fig. 1 summarized the overall HM content at different phases during LMC and

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SWC. Both in LMC and SWC, the HM content increased gradually, and it had the

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marked increasing at the Phase 2. Suggesting that the HM was mainly synthetized at

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cooling phase during composting. On the one hand, since the easily degradable

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macromolecular organic matter (e.g., protein, carbohydrate and lipids) has been

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decomposed into micromolecular substances, and the micromolecular substances might

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begin to synthesize other complex organic matter (i.e., HA, FA and HM) at this phase

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28.

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In the degradation process of lignocellulose biomass, some carbohydrates molecules

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might involve acid catalyzed hydrolysis or dehydration reactions which lead copious

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production of HM 29, 30. However, the HM content had significant difference in the two

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kinds of raw materials composting. The HM growth in LMC was lower than that in

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SWC. Final HM average content was 8.41 mg/g in SWC which was approximately 2-

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fold of that in LMC.

On the other hand, the lignocellulose biomass degraded actively in the cooling phase.

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Fig. 1 goes here.

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3.2. HM characterization using PARAFAC analysis This study used PARAFAC analysis to determine the evolution of the HM components in the different phase of the LMC and SWC. PARAFAC analysis can be

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used for quantitative analysis of the compost samples specific components 31. As shown

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in Fig. 2, the peak features of EEM fluorescence were more obvious at each phase after

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removing the Raman and Rayleigh scatters. The split-half, residuals analysis and visual

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inspection were used to determine the proper components32 (Fig. S2, S3). It was more

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than three components were appropriate for the datasets of different composting

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samlpes. Combining with the split-half analysis, the validation results showed that three

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components were appropriate (Fig. S2, S3 and 2). However, the four and five

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components model could not satisfy the validation32. Hence, the fluorescence EEMs of

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different composting samples could be successfully decomposed into a three-component

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model by PARAFAC33. Therefore, total three components were identified in each

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sample (livestock manures and straw wastes) by EEMs spectra combining with

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PARAFAC analysis, named C1, C2 and C3 (Fig. S2). Although three individual

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components in LMC and SWC were determined for the dataset using the PARAFAC

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model, our results did not demonstrate that only three components were presented in all

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samples. Our fluorescent groups are certain existence, but their influences are so weak

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that they cannot be distinguished from the noise. Therefore, three HM fluorescence

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components could explain most of the variation. All the PARAFAC components had

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two maximum excitation peaks and a single maximum emission peak at different

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sampling time in the composting (Fig. S3). Peak positions of PARAFAC components

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C1, C2 and C3 existed no significant change during LMC and SWC. Spectral

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characteristics of excitation and emission maxima of the C1, C2 and C3 were shown in

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Table S1. Both in the LMC and SWC, the emission peak in C1 was at 350nm, and the

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excitation peaks were at 280nm and 222nm. C2, with the highest peak located 240

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(320)/420 nm (Ex/Em), and the peaks of C3 were located at 260 (360)/475 nm (Ex/Em).

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However, since there had no clearly identified components about HM in the previous

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study, hence the peak in the C1, C2 and C3 were defined as the HM substance with the

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low, secondary and high degree of aromatization.

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Fig. 2 goes here.

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After validation of the three component models, the fate of the components across the

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LMC and SWC were tracked using the maximum fluorescence intensities (Fmax). Fmax

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could provide the quantitative information on the contribution of each component in

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studied samples 34. The relative contributions of the components in different raw

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compost materials at each composting time are shown in Fig. S4. There is a significant

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difference for the same component derived from LMC and SWC at the same sampling

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time. Figure 3 showed the average relative contribution of the components in LMC and

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SWC. In LMC, the average relative contribution of C1 decreased from 52.9±0.15 % to

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42.0±0.03 % at the whole composting process. Especially, the average relative

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contribution of C1 decreased significantly at cooling phase. However, the average

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relative contribution of C2 and C3 increased in the composting. By contrast, different

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distribution and evolution tendency existed in SWC. The average relative contribution

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of C1 increased from 21.0±0.07 % to 42.5±0.15 %. The relative contribution of C2

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decreased slightly in the whole composting process. Also, C3 decreased from

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44.9±0.15 % to 25.6±0.15 %. Interestingly, the change of C2 focus on the cooling

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phase, and there were fewer changes in C2 relative to C1 and C3 during composting in

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LMC and SWC. In addition, the relative contribution change trend of C1 was opposite

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to C2 and C3 both in LMC and SWC, and the change trend of C1, C2 and C3 in LMC

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were also opposite to that in SWC. In general, straw is one of the plant residues, and the

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basis of straw wastes are lignocellulose substances. Lignocellulose substances have a

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high aromatization degree35. That might also be the reason for the large relative

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contribution of a higher degree of aromatization substances (C2 and C3) at beginning of

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the SWC. Previous reports showed that the HM originated from the plants and

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microorganisms and their residues21, 36 or the acid-catalyzed dehydration of

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carbohydrates 37. Lignocellulose substances could carry out acid catalyzed hydrolysis

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reaction to form the carbohydrates such as furan and fructose, etc35, 38. Such

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carbohydrates could aggregate into HM39-42. Moreover, C2 and C3 might sequentially

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decompose or hydrolyze for forming the lower degree of aromatization substances in

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SWC, leading to the relative contribution of C1 increased, C2 and C3 decreased.

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Furthermore, due to the low content of lignocellulose substances in LMC, HM could not

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be formed from the macromolecular carbohydrates hydrolysis. Previous reports showed

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the amino acid could transform to carbohydrates through the amino sugar

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biosynthesis43. And Zandvoort et al. 37 found that HM might also originate from the

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micromolecular carbohydrates (such as glucose and fructose) hydrolysis. Thus, we

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inferred a portion of HM might originate from the protein-derived carbohydrates in

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LMC. That might be the reason for why the average relative contribution of C1

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decreased, nevertheless C2 and C3 increased in LMC. Therefore, on the one hand, it can

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be reasonably concluded that different raw material compositions might cause the

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district succession of HM components in LMC and SWC. On the other hand, the

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carbohydrates might play vital roles in the HM evolution.

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Fig. 3 goes here.

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3.3. Factors influencing the evolution of HM components in composting The various HM components and their evolution tendency among the different

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composting phase prompted us to identify the main factors shaping HM in LMC and

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SWC. The structural equation model (SEM) is an advanced statistical method that

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allows for hypotheses testing of complex relationships networks 44, 45. The indicators of

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the latent variable for C1, C2 and C3 were entirely different in the LMC and SWC. As

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for C1 in LMC (Fig. 4), the microbial community 2 (MC2) had significantly positive

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influences on it (λ = 0.73, p < 0.001). Also, C2 and C3 were directly influenced by MC2

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and humification index (HI). In addition, microbial community 3 (MC3) were an

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important factor (λ = 0.31, p < 0.01) in affecting the C3. It might suggest that the

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microbes are the main factor affecting the evolution of HM components in LMC.

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However, the SEMs demonstrated that the influence of the SWC on C1, C2 and C3

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were mediated by the Bacilli, C/N and HI. Moreover, C1 and C3 was directly

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influenced by MC3 (λ = -0.61, P < 0.001) and OM (λ = 054, P < 0.001). These results

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suggested that besides the microbial effect, the compositions of raw materials were also

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an important factor affecting the evolution of HM during SWC. Meanwhile, combining

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with the average relative contribution of components results, different fluorescence

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components were reflected in different carbohydrate contents mainly. Hence, different

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HM evolution mechanism was resulted from multiple factors in the LMC and SWC.

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Fig. 4 goes here.

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3.4. Two-Dimensional Correlation Spectroscopy (2DCoS) Analysis

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2D-CoS analysis can allow identifying the sequential order of any subtle spectral

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change in response to external perturbations, as a result, interpreting the fate of the HM

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during composting 46. As shown in Fig. 5, the synchronous and asynchronous 2D-CoS

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of PARAFAC component internal peaks (2D-PCIP-CoS) was obtained, which was

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constructed from the time-dependent excitation spectrum of fluorescent components.

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The change of the peak direction and order was different during LMC and SWC (Tab.

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S1). In the LMC, two auto-peaks were detected at the wavelength pairs of 222 (peak

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A1) /280 (peak A2) nm, and were negatively correlated with each other 47 in the

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synchronous map of C1. That suggested the peak A1 and A2 (HM substances with a

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low degree of aromatization) varied in the opposite tendency during composting 48. In

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addition, combining with the asynchronous map of C1, a negative peak was observed at

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the wavelength pairs of 222/280 nm and a positive peak detected at 222/280 nm.

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According to Noda’s rule 47, the sequence of the band variation follows the order ( “→”

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means prior to or earlier than) peak A1(C1a) → peak A2(C1a) during LMC. In other

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words, the substances represent by peak A1 changed prior to peak A2 under the same

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conditions. Likewise, in the LMC, the main auto-peaks 240 (peak B1) and 320 (peak

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B2) are changed in the different direction in the synchronous map of C2. The sequence

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of the band change followed the order peak B1(C2a) → peak B2(C2a). The fate of

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fluorescence component C3 during LMC was also investigated, and the

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synchronous/asynchronous spectra are shown in Fig. 5, respectively. Two main auto-

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peaks 260 (peak D1) and 360 (peak D2) nm varied in the same direction, and the order

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of the changes are as following: peak D2(C3a) → peak D1(C3a).

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On the other hand, the evolution of the components inner were detected based on the

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synchronous and asynchronous in SWC (Fig. 5 and Table S2). The peak A1(C1s)

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(220nm) and peak A2(C1s) (280nm) varied in the same direction. Moreover, the peak

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A1(C1s) and A2(C1s) had a positive correlation in the SWC. And the order of the

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change as follows: peak A1(C1s) → peak A2(C1s). The condition of the C2 was similar

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between SWC and SWC. Finally, the peak D1 (C3s) and peak D2 (C3s) changed in

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different direction, the change order was peak D1 (C3s) → peak D2 (C3s). It is worth

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noting that both in LMC and SWC, the change order of peaks in C1 and C2 were all

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from long-wavelength to short-wavelength, however, the change order of peaks in C3

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was different. The long-wavelength (peak D2) changed firstly in C3 during LMC, and

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short-wavelength was the first to change in SWC.

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Fig. 5 goes here.

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The hetero-2DCOS was performed to confirm the fate of the inner fluorescence peaks

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among components during composting between the excitation loadings of each

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component 49. Figure 6 and Tab. S1show the synchronous and asynchronous maps of

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the hetero-2DCOS, where the different fluorescence components shift are plotted on the

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x-axis and y-axis, respectively. In the synchronous hetero-2DCOS, the positive cross-

327

peaks represent the two bands were from the same origin, on the contrary, the negative

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cross peaks indicate that two bands were from the different origins 47. In the

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synchronous maps of the hetero-2DCOS analyzed by excitation loadings of component

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C1 and C2, C1 and C3, C2 and C3 in LMC and SWC, respectively. Remarkably, five

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major positive cross-peaks at Ψ(220 and 240 nm), Ψ(280 and 320 nm), Ψ(240 and 360

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nm), Ψ(240 and 260 nm), Ψ(220 and 260 nm) and Ψ(220 and 360 nm) were detected in

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LMC. These results suggest that the peak A1(C1a) and B1(C2a), peak A1(C1a) and

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D1(C3a), peak A1(C1a) and D2(C3a), peak A2(C1a) and B2(C2a), peak B1(C2a) and

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D2(C3a), peak B1(C2a) and D1(C3a) either increased or decreased together and showed

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the same origins13, 50. In the asynchronous maps of LMC, the red and blue were used to

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determine the order of succession based on the results from the synchronous maps 12.

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Using the sequential order rules, it could be concluded that the change sequence of

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substances in LMC following the order: peak B1(C2a) →D2(C3a) →B2(C2a)

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→A1(C1a) →A2(C1a) →D1(C3a). It means that peak B1 could be affected easier than

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other peaks in the LMC. Moreover, the peak D1 was relatively stable in the composting.

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Therefore, the change order of HM components in LMC was C2a→C3a→C1a. The

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origin and evolution of the substances in SWC could be determined in the same way.

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Similar to the LMC, the peak A2(C1s) and B2(C2s), peak A1(C1s) and D2(C3s), peak

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B1(C2s) and D1(C3s) might also from the same origins in SWC. In addition, the peak

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A1(C1s) and B2(C2s), peak A2(C1s) and D2(C3s), peak B2(C2s) and D2(C3s) changed

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in the same direction in SWC. Furthermore, the sequence order for the substances

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evolution in the SWC can be deduced: peak D1(C3s) →D2(C3s) →B1(C2s) →B2(C2s)

349

→A1(C1s) →A2(C1s). The change order in SWC was in the following sequence:

350

C3s→C2s→C1s.

351 352

Fig. 6 goes here.

353 354

As we all know, the composting is a biochemical process 51, 52. Some substances were

355

decomposed, and some substances were aggregated into other substances, leading to the

356

normal operation of humification process 53. These peaks from the same origin might

357

transform from each other or synthesize from the same substances, resulting in the

- 17 -



358

change of the average relative contribution and HM evolution. Moreover, the change

359

orders of the components showed that the substances with a higher degree of

360

aromatization changed preferentially both in the LMC and SWC, illustrating these

361

substances were affected by composting environment or microbes easily. Furthermore,

362

different change order, direction and origins of the PARAFAC fluorescent components

363

peak proved the different synthesis and evolution mechanism in LMC and SWC. Hence,

364

the in-depth elucidation of such an evolution variance will enable a better understanding

365

of the fate and transformation of HM among various materials composting and even

366

promote the design of more environmentally friendly HM products. Although it noted

367

that fluorescent HM may not be representative of the total HM. Our findings establish a

368

basis for continued investigation into the fate of livestock manures and straw wastes

369

fluorescence components during composting. Additional studies should be performed to

370

understand the fate of HM in organic waste composting fully. Furthermore, such an

371

integrated approach could also be applied to probe other substrates evolution processes

372

in natural and engineered environments.

373

4. Conclusions

374

Combining various correlation with EEM-PARAFAC, 2D-CoS and SEMs results, this

375

study provides the first comparison of changes to fluorescence HM components in LMC

376

and SWC. EEM-PARAFAC analysis determined the change of the HM components,

377

and showed the different HM compositions difference between LMC and SWC.

378

Moreover, 2D-PCIP-CoS and hetero-2DCoS offer a unique insight into understanding

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Page 18 of 35

Page 19 of 35


379

the evolution of HM in the different composting systems and clarifies the change order

380

between the fluorescent components in HM. SEMs analysis suggest that the microbes

381

and raw material compositions were the main factors, which lead the HM evolution

382

variances in LMC and SWC.

383

SUPPORTING INFORMATION AVAILABLE

384

The results of HM content are presented in Fig. S1. Sum of squared error of the

385

different numbers of PARAFAC components are presented in Fig. S2. Excitation and

386

emission spectra of EEM components are presented in Fig. S3. Relative contributions

387

(Fmax %) of the PARAFAC-modeled components in each compost pile are presented in

388

Fig. S4. Spectral characteristics of excitation and emission maxima of the C1, C2 and

389

C3 identified by PARAFAC modeling for the EEMs data set compared to previously

390

identified sources are presented in Table S1. Change sequences of different excitation

391

spectra of PARAFAC components are presented in Table S2.

392

ACKNOWLEDGEMENTS

393

This work was financially supported by the National Natural Science Foundation of

394

China (No. 51878132, No. 51178090, No. 51778116 and No. 51378097).

395

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Figure Captions

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Fig. 1. Averages of the HM content in the LMC and SWC at different phases.

561

Fig. 2. EEM-PARAFAC components in LMC and SWC at different phase. (a)-(i) is

562

AMC components; (j)-(r) is SWC components.

563

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564

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(MC 1-3) on HM components (C1, C2 and C3). Arrows depict casual relationships: red

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570

proportional to r values. Paths with coefficients non-significant different from 0 (p >

571

0.05) are presented with dotted gray lines. *p

604

0.05) are presented with dotted gray lines. *p

Assessment of Multiorigin Humin Components Evolution and

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