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Agricultural and Environmental Chemistry

Bioavailability evaluation of dissolved organic matter derived from compost-amended soils Xu Zhang, Jingping Ge, Shuang Zhang, Yue Zhao, HongYang Cui, Zimin Wei, Sheng Luo, and Jinxiang Cao J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.9b01073 • Publication Date (Web): 09 May 2019 Downloaded from http://pubs.acs.org on May 10, 2019

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Journal of Agricultural and Food Chemistry

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Bioavailability evaluation of dissolved organic matter derived from

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compost-amended soils

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Xu Zhanga, Jingping Geb, Shuang Zhanga, Yue Zhaoa, Hongyang Cuia, Zimin Weia*,

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Sheng Luoc, Jinxiang Caoc

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a

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

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b

College of Life Science, Heilongjiang University, Harbin 150030, China

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c

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

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China

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*Corresponding author

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E-mail address: [email protected]

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Tel./Fax: +86 451 55190413

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ACS Paragon Plus Environment


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ABSTRACT

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In this study, Hetero two-dimensional correlation spectroscopy (Hetero-2DCOS)

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combined with PARAFAC was employed to reveal the inner changes in the Dissolved

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organic matter (DOM) components derived from soil amended with seven different

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composts. The dynamics of the four DOM components showed that the fluorescence

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peaks in each component varied in different directions during mineralization. Structural

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equation models (SEM) demonstrated that the compost amendments changed the

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correlations of the total organic carbon (TOC), total nitrogen (TN) and bacterial

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community composition with DOM components and strengthened the cooperative

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function related to DOM components’ transformation. The compost sources were

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further ranked as CW (cabbage waste) > CM (chicken manure), DCM (dairy cattle

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manure), TSW (tomato stem waste), P (peat) > MSW (municipal solid waste), SS

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(sewage sludge) by Projection Pursuit Regression analysis (PPR). It is helpful to

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improve the bioavailability of compost products in order to obtain composts with a

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particular function.

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Keywords: DOM dynamic; Compost amendment; Bioavailability; Humification;

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2DCOS Correlation Analysis

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Graphic Abstract

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INTRODUCTION

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Different types of organic wastes can be minimized and stabilized through

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composting which is considered as an environmentally friendly way.1-3. The organic

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matter level and soil water-holding capacity can be significantly increased following

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the addition of compost4-6. Meanwhile, the activity of soil microbial communities can

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also be stimulated due to compost amendments7-9.

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The transformation and stabilization of soil organic carbon (SOC) in agricultural

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soils of China are of vital importance10. Dissolved organic matter (DOM) have a crucial

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role in soil ecosystems as a major source of carbon and nutrients11. Therefore, the

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possible effects of compost on soil properties can be potentially characterized by DOM.

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Thus, DOM is regarded as a good nutrient medium for microbial which is relatively

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labile and good substrate growth12,13. Organic amendments such as compost can affect

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the DOM level in soil. After the addition of compost, the DOM level in soil generally

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increases first and then goes back to the background levels. DOM is a small fraction of

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the total soil carbon. DOM is composed of humic acids, fulvic acids, proteins,

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polysaccharides, and hydrophilic organic acids. It can indicate the microbial activity

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during compost decomposing in soil14. DOM consists of humic acid, fulvic acids,

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proteins, polysaccharides, and hydrophilic organic acids, and about 25%-50% of DOM

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is humic acid and fulvic acids15.

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In some studies, the nature of DOM from compost, municipal solid waste, and

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plant residues was explored through Excitation-emission matrixes (EEMs) combined

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with parallel factor analysis (PARAFAC) modeling16-18. Then, the environmental 4


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dynamic of DOM was successfully evaluated using this thechnique19,20. Fluorescence

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spectroscopy has been most commonly used to assess the properties of humic and fulvic

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acids and the quality of organic matter in soil21,22. Fluorescence provides a chance to

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explore the chemistry of a DOM fraction and its dynamic during the decomposition in

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soil23.

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Two-dimensional correlation spectroscopy (2DCOS) is widely regarded as well-

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established spectrum analytical technique that provides considerable utility and benefits

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in various spectroscopic studies24,25. Hetero-2DCOS is one of the powerful forms of

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2DCOS analysis26. In particular, two sets of samples spectral data with different

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variables but from the same perturbation conditions could be compared by this

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technique. Given the analytical ability of 2DCOS, in this study, the amended time was

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selected as the external perturbation to investigate the inner dynamics of DOM

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PARAFAC components derived from compost-amended soil. From the response

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patterns of the system, which were monitored by two different components under the

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same perturbation, one can detect the sequence of the spectral peak changes during the

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amendment process. Therefore, 2DCOS combined with EEM-PARAFAC can be

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applied to characterize the DOM during the compost-amended soil process.

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The soil quality and functioning may be potentially enhanced by the DOM form

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the composts. To establish judicious management of the resources, in-depth chemical

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properties of DOM origin from compost-amended soil should be illustrated. The

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objectives of this study were to show that 2DCOS combined with EEM-PARAFAC is

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a reliable method to characterize the fate of DOM during the compost-amended soil 5



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process and to assess the amended properties of the fluorescence peaks. It is helpful for

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precise fertilization to regulate the transformation of substances in composts to obtain

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composts that present a particular function. Therefore, there is far-reaching significance

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to revealing the inner changes in the DOM PARAFAC components derived from

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compost-amended soil.

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MATERIALS AND METHODS

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Properties of soil and composting sample. In this study, the black soil (CK) from

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Xiangfang Experimental Farm (Heilongjiang Province, China). The Shanghai

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Songjiang Composting Plant provided seven types of trapezoidal piles which are

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composed of CM, DCM, MSW, CW, TSW, P, and SS respectively. The properties of

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soil and composting samples were the same as our previous study and the detail

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information about them was provided in supplementary file (Text S2)27.

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Mineralization experiment. The soil was added to the compost samples with 0%,

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5%, 15%, and 30% volume ratio, and aerobically incubated at 25ºC for 50 days where

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was kept the highest moisture content at 60% with distilled water. The design of the

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mineralization experiment was the same as our previous study27 and the detail

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information about it was provided in supplementary file (Text S2)

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Extraction of DOM. DOM was extracted based on the procedure designed by

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Said-Pullicino et al. (2007)9. Generally, Milli-Q water (with 1:10 of solid to water ratio,

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w/v) was employed to extract the DOM in compost samples and the extraction was

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conducted in a horizontal shaker at 25 ˚C for 24h. Milli-Q water was acquired from a 6


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Milli-Q water purification system (Millipore Corporation, Hayward, CA, USA)

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operated at 18.2 MΩ·cm. The extractrs were centrifuged at 10,000 rpm for 10 min.

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After the centrifugation, the supernatants were filtered through a 0.45-μm membrane

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filter.

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Fluorescence spectroscopy analysis. Hitachi model F-7000 fluorescence

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spectrophotometer (Hitachi High Technologies, Japan) was employed to acquire the

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fluorescence spectrum of the DOM samples. All the samples were analyzed in a 1-cm

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clear quartz cuvette at room temperature (20±2°C). Before the measurement, Milli-Q

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water was used to dilute the samples to confirm the DOC contents below 20 mg L-1 to

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avoid inner filter effects for fluorescence analysis. The slit widths of both the excitation

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and emission monochromators were 5 nm, and the scan speed was 1200 nm min-1. In

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addition, the EEM spectra were obtained by subsequently scanning the emissions

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spectra from 250 to 600 nm by increasing the excitation wavelength from 200 to 550

140

nm. Roman Units (R.U.) was used to reported the fluorescence intensities. Besides, the

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fluorescence spectrum of Milli- Q water was subtracted from the spectra of the DOM

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samples to eliminate the influence of Rayleigh and Raman scattering28. The EEM

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spectrum of Milli-Q water was plotted in the supporting information (Fig. S10).

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Parallel factor analysis. 110 EEMs of DOM samples were input into the

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PARAFAC analysis, and the MATLAB 2013a (Mathworks, Natick, MA) was

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employed to carry out in the PARAFAC analysis using the DOMFluor toolbox

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(http://www.models. life.ku.dk/) based on the procedure described by Stedmon and

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Bro29. In the PARAFAC, the excitation wave-lengths of 200 to 220 nm were deleted 7



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from all the EEM spectrum due to the random data fluctuations19. PARAFAC

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statistically decomposes the three-way DOM data into individual fluorescence

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components with regard to their spectral shape or number30. The model was run with

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non-negativity constraints applied to each dimension29,31. An initial exploratory

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analysis was performed in which outliers were considered. Two to six components were

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tested for the EEMs. The determination of the number of components was primarily

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based on the explained variance and core consistency of the PARAFAC analysis29.

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Maximum fluorescence intensities (Fmax) (R.U.) was used to express the concentration

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scores of the PARAFAC components.

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2D correlation spectra analysis. The 2DCOS analysis was conducted on the 2D

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Shige version 1.3 software which developed according to the theory of 2DCOS analysis

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based on the detailed algorithm reported by Noda24. For the 2DCOS, a set of compost-

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amended soil time-dependent PARAFAC components’ excitation loadings data was

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obtained, and the 2DCOSs were produced based on the PARAFAC components

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excitation loadings data using the incubation time as the external perturbation (Text

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S1). For the hetero 2DCOS analysis, they were produced using the different PARAFAC

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components’ excitation loading data.

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DGGE and 16S rDNA sequences. The method for the DGGE and 16S rDNA

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sequences analysis was based on our previous study. Detail procedures were shown in

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supplementary file (Text S2).

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Projection pursuit model. PPR model was employed analyze the compost

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sources rank. Friedman and Stuetzle (1981) proposed PPR model which was based on 8


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a nonlinear multivariate regression procedure32. The aim of this model was to project

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high dimension data to a low-dimensional space, In the low-dimensional space,

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intrinsic structural information could be found through this model33.

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Structural equation models. SEM is an a priori approach. The casual

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relationships between variables could be easily visualized through fitting data to the

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model representing casual hypotheses. To confirm the overall goodness of fit for SEM,

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several parameters were tested (such as non-significant chi-square test, high goodness-

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of-fit index, and low root mean square errors of approximation). The P value in the non-

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significant chi-square test should be larger than 0.0534. The GFI value in the high

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goodness-of-fit index should be larger than 0.90. The RMSEA value should be less than

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0.05.

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Statistical analysis. The analytical data were subjected to one-way ANOVA using

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Statistic 8.0 software. The structural equation models (SEM) were constructed using

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the IBM SPSS AMOS 23.0 software. PARAFAC and PPR analysis was carried out

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using MARLAB 2013a.

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RESULTS

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DOM characterization using PARAFAC analysis. During the 50 days of

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incubation, the dynamics of the DOC concentrations derived from soil amended with

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seven composts are shown in Fig.S1. The DOC concentrations were decreased in all

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compost amendments throughout the entire incubation. The magnitudes of the DOC

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concentrations are in the order 30% > 15% > 5% > CK (P < 0.01). In this study, 9



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PARAFAC analysis was used to acquire more details about the DOM mineralization

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characterization in the compost-amended soil. The PARAFAC model was based on the

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explained variance and core consistency of PARAFAC analysis. The results of the

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explained variance and core consistency of PARAFAC analysis at each sampling time

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are presented in Table S2. Combined with the results of the explained variance and core

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consistency, the results showed that four components were appropriate (Fig. S2 and S3;

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Table S2). Thus, the EEMs of different sampling time could be successfully divided

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into a four-component model using PARAFAC analysis (Fig. S4). In the PARAFAC

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model, four components in each sampling time were detected for the dataset. However,

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our results did not suggest that only four components were present in all samples. This

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means that the existing components were present in the most of the compost-amended

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soil samples. Certainly, other fluorescent materials certainly exist, but their weak

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intensity made them difficulty to be distinguished from the noise. Thus, most of the

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variation could be explained by these four PARAFAC components. Besides, Table S3

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presented the EEM spectra of the identified PARAFAC components at each sampling

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time. The determined PARAFAC were also compared with the similar DOM that other

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researchers who modeled in various natural environments (Table S3).

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Component 1 (C1) with maximum excitation/emission (Ex/Em) wavelength pair

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centered at 230 (280)/345 nm was characterized by two fluorescence peaks, and its (Fig.

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S4 and Table S3). This component was characterized as having tryptophan-like

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component peak T1(C1) and peak T2(C1). The protein-like components are often used

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as indicators of DOM lability and bacterial production35. Component 2 (C2) was also 10


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contained two peaks with maximum excitations at 250 and 340 nm, maximum

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emissions were found in the EEMs at 414 nm (Fig. S4 and Table S3). However, the

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peak at Ex/Em = 250/415 was weak in the initial incubation. This component was

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characterized as a combination of the humic-like fluorescence components peak A1(C2)

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and peak A2(C2). Component 3 (C3) showed a fluorescence peak at the

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excitation/emission wavelength pair of 260/435 nm (Fig. S4 and Table S3), which was

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characterized as a humic acid-like component peak B1. Furthermore, C3 also showed a

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fluorescence shoulder peak (B2) at the excitation/emission wavelength pair of 295/435

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nm. Component 4 (C4) showed excitations at 280 and 370 nm, with maximum

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emissions at 470 nm (Fig. S4 and Table S3). This component was characterized as a

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combination of the terrestrial humic-like peak Z1(C4) and the ubiquitous humic-like

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peak Z2(C4) according to Coble36. The fluorescence components C3 and C4 were

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similar to the components produced from biogeochemical processing (eg. microbial

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oxidized components and humic-acid-like components). However, the DOM

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components in soil were also characterized using PARAFAC analysis. The DOM in

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CK was successfully divided into three components based on the explained variance

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and core consistency from the PARAFAC analysis (Fig. S5).

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To acquire more details characterization about the changes of DOM components

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in compost-amended soil, the PARAFAC analysis can quantitative information of

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DOM components in the studied samples37. The relative concentrations of the

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PARAFAC components at each sampling time are shown in Fig. 1 and S6. As the

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mineralization progresses, the relative concentration of each component is similar, 11



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regardless of whether there is 5%, 15%, or 30% compost in the compost-amended soil.

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The relative concentration of C1 and C3 presented decreasing trends, and those of C2

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and C4 presented increasing trends.

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The general 2DCOS analysis. To obtain further evolutionary information of

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DOM in compost-amended soil, the general 2DCOS analysis was employed to analyze

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the excitation loadings of the PARAFAC components with time as the external

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perturbation. Fig S7 shows the synchronous and asynchronous 2DCOS maps of the

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PARAFAC component fluorescent peaks, which were constructed based on the

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excitation loading spectra of the fluorescent components from incubation time-

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dependent samples. In addition, the rules for identifying peaks in 2DCOS maps are

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detailed in Text S1. The intensity changes in the fluorescence peaks in these PARAFAC

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components during incubation are shown in Table S4.

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In the synchronous 2DCOS of component C1, peak T1(C1) was negatively

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correlated with peak T2(C1) (Fig. S7 and Table 1). This revealed that the tryptophan-

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like component peak T1(C1) and peak T2(C1) varied in different directions during the

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incubation. In the asynchronous 2DCOS of component C1, a positive peak was found

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between peak T1(C1) and peak T2(C1). Based on the Noda’s rule, the band variation

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sequence with the compost’s amendment time follows the order peak T1(C1) → peak

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T2(C1). This result indicated that, as a biodegradable component, peak T1(C1) in

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component C1 was more easily biodegradable in compost-amended soil.

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In the synchronous maps, a negative peak was observed between peak A1(C2) and

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peak A2(C2), peak B1(C3) and peak B2(C3), and peak Z1(C4) and peak Z2(C4), 12


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suggesting the different directions of these two bands’ intensity changes (Fig. S7 and

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Table 1). The results of the general 2DCOS synchronous maps show that the two peaks

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in each component are in opposite directions, which is consistent with the results of

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Table 1 and S5. In the synchronous and asynchronous maps, the variation of the humic-

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like component peak A1(C2) was ahead of the humic-like component peak A2(C2), the

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sequence of the peaks in component C3 follows the order peak B2(C3) → peak B1(C3)

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and the sequence of the peaks in component C4 follows the order peak Z1(C4) → peak

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Z2(C4).

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Hetero-2DCOS Analysis. To investigate the evolution of the fluorescence peaks

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in the incubation, hetero-2DCOS (between the excitation loadings of each component)

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analysis was performed. Fig. 2 and Table 1 show the synchronous/asynchronous hetero-

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2DCOS maps of the components in compost-amended soils.

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The excitation loadings of C1 and C2 were employed to analyze the synchronous

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and asynchronous spectra of the hetero-2DCOS (Fig. 2; Table 1). Remarkably, in the

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synchronous hetero-2DCOS map, peak T1(C1) was negatively correlated with peak

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A1(C2) but positively correlated with peak A2(C2). Peak T2(C1) was negatively

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correlated with peak A2(C2) but was positively correlated with peak A1(C2). These

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results indicated that peak T1(C1) and peak A2(C2) have similar fates and that peak

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T2(C1) and peak A1(C2) have similar fates. In the asynchronous hetero-2DCOS map,

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the changing sequence of peaks in components C1 and C2 in compost-amended soil

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followed the order of peak A1(C2) → peak A2(C2) → peak T1(C1) → peakT2(C1).

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The fluorescence response of the tryptophan-like component C1 occurred after the 13



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spectral changes in the humic-like component C2.

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The excitation loadings of C1 and C3, C1 and C4, C2 and C3, C2 and C4, and C3

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and C4 were used to construct the synchronous and asynchronous maps (Fig. 2). In the

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synchronous hetero-2DCOS map, peak T1(C1) and peak B2(C3), peak T2(C1) and

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peak B1(C3), peak T1(C1) and peak Z2(C3), peak T2(C1) and peak Z1(C3), peak

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A1(C2) and peak B1(C3), peak A2(C2) and peak B2(C3), peak A1(C2) and peak

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Z1(C3), peak A2(C2) and peak Z2(C3), peak B1(C2) and peak Z1(C3), and peak B2(C2)

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and peak Z2(C3) have the same basic fluorescent units during the incubation (Table 1).

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In the asynchronous hetero-2DCOS map, the changing sequence of peaks in

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components C1 and C3 in compost-amended soil followed the order peak B2(C3) →

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peakT1(C1) → peak T2(C1) → peak B1(C3); in components C1 and C4, it followed

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peak T1(C1) → peak T2(C1) → peak Z1(C4) → peak Z2(C4); in components C2

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and C3, it followed peak B2(C3) → peak B1(C3) → peak A1(C2) → peak A2(C2);

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in components C2 and C4, it followed peak A1(C2) → peak A2(C2) → peak Z1(C4)

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→ peak Z2(C4); and components C3 and C4, it followed peak Z1(C4) → peak Z2(C4)

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→ peak B2(C3) → peak B1(C3).

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Bacterial community dynamics and diversity. The DGGE diagram showed the

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bacteria communities observed through the whole incubation process (Fig. S8).

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Twenty-two different dominant 16S rDNA gene fragments were isolated. These gene

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fragments were identified as belonging to two domains: Proteobacteria and

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Bacteroidetes (Table. S5). Most of the bands in the treatment were classified as

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Proteobacteria. Soil microorganisms are involved in the decomposition of organic soil 14


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matter and the formation of humus.

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The diversity of bacteria was investigated according to the H′, and each treatment

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had a significant difference compared with CK (Fig. S9). The dynamics of the bacteria

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community were high before the primary stage and then decreased until the end of the

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incubation. The maximum and minimum of the bacteria diversity index were found in

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the CM treatment and P treatment.

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DISCUSSION

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Some studies showed that the soil physical properties and soil nutrient availability

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can be improved through the application of mature composts to soil38,39. After being

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amended with composts, the content and composition of the DOM components in soil

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were significantly changed. From the results of the relative concentration of each DOM

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component, the overall change in each component can be shown during incubation. C2

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and C4 were accumulated, while components C1 and C3 were degraded during the 50

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days of DOM mineralization (Fig. 1 and S6). In the initial stage of incubation, the

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concentration of the protein-like component is significantly higher than any other

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component. As the mineralization proceeds, the protein-like component is decomposed

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by microbial organisms due to the formation and accumulation of the humic-like

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component. These results indicated that the DOM components evolved during

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incubation. However, the inner changes in the PARAFAC component cannot be given

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because there are always two or more peaks in one fluorescent component. Based on

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the analytical ability of 2DCOS, PARAFAC combined with 2DCOS was employed to 15



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reveal the detailed evolutionary information of the DOM PARAFAC components.

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The fate of DOM components can be concluded based on the synchronous and

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asynchronous 2DCOS spectra (Figs. 2 and S7; Table 1), which were analyzed using the

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excitation loadings of the fluorescence components. Summarizing the results of

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2DCOS and the relative changes in fluorescence components, the intensities of the

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fluorescence peaks T1(C1), A2(C2), B2(C3) and Z1(C4) decreased, but the intensities

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of peaks T2(C1), A1(C2), B1(C3) and Z2(C4) increased during incubation (Table S4).

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The fate of each component in the composts amended soil was plotted in Fig. 3. We

333

found that the increases in fluorescence peaks T2, A1 and B1 may be derived from Z1,

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and the increase in fluorescence peak Z2 may be transformed from T1, A2 and B2.

335

These results revealed the characterization of the inner dynamic of the DOM

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fluorescence components derived from the compost-amended soil. After the application

337

of composts to soil, the available organic compounds can be used by microorganisms

338

to synthesize enzymes, thereby accelerating soil native organic matter degradation40.

339

This study revealed that soil’s indigenous microbes can rapidly decompose complex

340

humic acids (Z1) with the formation of peaks T2, A1 and B1, and these peaks can also

341

exacerbate the microbial activity in the soil. Simultaneously, the decomposition product

342

of organic composts contains some precursors (such as polyphenols, nitrogen-

343

containing organic compounds (e.g., amino acids, peptides, etc.) and other mineralized

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intermediate products), which can form the humic acid molecules (Z2). After the

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compost is applied to soil, the mineralization and humification of soil DOM occur

346

simultaneously. 16


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SEM is an a priori approach allowing for an intuitive graphical representation of

348

the complex networks relationships between variables17. This study constructed SEMs

349

to further test the causal relationships among the basic chemical characteristics, bacteria

350

community information and the PARAFAC components (Fig. 4). During

351

mineralization the changes and distribution of bacteria could be directly affected by the

352

physical–chemical parameters. To identify which of the chemical parameters might

353

affect the variation occurring in the key bacteria community is of great important to

354

improve humification. In the early stage of incubation, the TOC, TN and bacterial

355

community affected four components. During the whole mineralization period, the

356

TOC concentration has a direct impact on the transformation of the four fluorescence

357

components. Bacteria have direct impacts on the conversion of the C2, C3 and C4

358

fluorescence components. TN directly or indirectly affects the components through

359

bacteria.

360

In addition, in the early stage of mineralization, protein-like substances (C1) are

361

more affected by TOC and TN. This result indicated that with the progress of

362

mineralization, the simply constructed, and easily degradable organic substances such

363

as soluble sugars, organic acids, and starches could preferentially be used by

364

microorganisms. However, in the later stages of mineralization, the formation of the

365

humic acid-like substance (C4) with a complex structure is mainly affected by TN and

366

microbial bacteria, indicating that the organic nitrogen compounds can contribute to the

367

formation of the humic acid-like substance (C4). These results indicated that N is an

368

important factor that can influence the dynamic of DOM components. The organic 17



369

compost can improve the nutrient retention and fertilizer supply of soil27. Therefore,

370

based on the N containing and DOM conversions, the composts could be differentiated

371

for different applications. In this study, the high component C1 contains compost that

372

can be regarded as soil amendments that improve the fertility of poor soil. The high

373

humic-like components (C4) contain compost that can be applied to long-term

374

insurance for nutrient retention to improve soil structural properties.

375

Fluorescence parameters alone are unable to express the structural changes of

376

DOM. However, the PPR analysis could be regarded as a useful method to reveal the

377

structural changes of DOM form composts41. The humification of DOM was ranked

378

based on PPR model in seven compost-amended soils based on the Fmax values of C1,

379

C2, C3, and C4. Generally, the projection value was of positive relevance with the the

380

degree of humification. In CW, the humification degree of DOM was the highest. The

381

lowest humification degree of DOM presented in MSW and SS. The intermediate

382

humification degree of DOM presented in P, CM, DCM and TSW (Fig. 5). It would be

383

difficult to degrade for the DOM from the CW compost treatment with high degree of

384

humification through biological means after soil amendment. Thus, the higher

385

humification, results in lower bioavailability42. Besides, high humification composts

386

can be considered as long-term insurance for nutrient retention. To maintain the fertility

387

of soil, these kinds of composts can be sustainably applied to soil27.

388

DOM from compost products plays key roles in the dynamics of carbon and

389

nutrient and microbial activity in soil ecosystems43. In soil, DOM is an active chemical

390

composition, which can be directly used by microbes during the mineralization phase. 18


Page 18 of 37

Page 19 of 37


391

The major source of microbial energy and nutrients comes from DOM. The microbial

392

activity in the application of organic compost was significantly higher than CK,

393

indicating that the compost directly supplements soil nutrients for microbial growth,

394

and complements a certain amount of microorganisms that are used in the soil. The

395

crucial process which leading the the bacterial communities changes could be expressed

396

by the clustered together samples collected in the same day. The bacterial communities

397

among different treatments presented significant differences in different stages,

398

indicating that the bacterial community composition presented significant differences

399

according to the different composts. During incubation, the bacterial community

400

composition of P, MSW and CW showed no significant difference, suggesting the

401

similar bacterial community composition was excited in P, MSW and CW, while the

402

bacterial communities of CM, DCM, SS and TSW were more dispersed among all

403

inoculated groups (Fig. S8 and S9). It could be regarded as an effective method to

404

control microbial communities through altering the N contents in composts and thereby

405

influence the DOM components in the whole incubation period.

406

In conclusion, the transformation among the four fluorescence components of

407

DOM can be regulated through their microhabitat, chemical factors and

408

microorganisms.

409

transformation of various organic components can be regulated to produce more stable

410

humic acid-like substances, improve soil fertility and reduce CO2 production.

411

Consequently, this regulating model can provide theoretical guidance for compost-

412

amended soil practical application management in the future.

Therefore,

during

the

compost

19


amendment

process,

the


413

In summary, the peaks in each DOM PARAFAC component varied in different

414

directions during the composts amendment process. The compost amendments altered

415

the correlation of the physical and chemical index (TOC, TN) bacterial community

416

composition with DOM components and improved the cooperative function related to

417

DOM component transformation during mineralization. PPR model ranked the compost

418

sources as CW > CM, DCM, TSW, P > MSW, SS based on their humification degree

419

in DOM. Thus, through regulating the transformation of substances in composts,

420

specific DOM component compositions that represent particular functional composts

421

can be obtained to help provide more precise fertilization.

422 423

SUPPORTING INFORMATION AVAILABLE

424

2DCOS Analysis in Text S1. Properties of soil and composting sample,

425

Mineralization experiment and DGGE and 16S rDNA sequences in Text S2.

426

Chemical compositions of the compost-amended soils (% dry wt.) in Table S1.

427

Explained variance and core consistency as a percentage vs the number of

428

components for PARAFAC models of the fluorescence data with 2-6 components in

429

Table S2. Table S3. Spectral characteristics of the maximum excitation and emission

430

of the four components identified by PARAFAC modeling for the EEMs data set

431

collected from different composting phytotoxicity grades compared to previously

432

identified sources in Table S3. The fluorescence intensity changes of the peaks in the

433

PARAFAC components during composting in Table S4. Sequence analysis of the

434

bands excised from the DGGE gel. in Table S5. Dynamics of the DOC concentration 20


Page 20 of 37

Page 21 of 37


435

from soil amended with seven types of typical organic waste composts during 50 days

436

of incubation Fig. S1. Spectral loadings of split-half validation results of the

437

components. in Fig. S2. Excitation (blue lines) and emission (green lines) spectra of

438

the EEM components identified by the PARAFAC model in Fig. S3. The different

439

fluorescent components found by the PARAFAC model in each sampling time in Fig.

440

S4. The fluorescent components found by the PARAFAC model in soil DOM in Fig.

441

S5. Changes in the relative distribution (Fmax %) of the PARAFAC-modeled

442

components from soil amended with 5% and 15% compost and unamended soil (CK)

443

during 50 days of incubation in Fig. S6. Synchronous and asynchronous maps

444

obtained by two-dimensional correlation analysis of the excitation spectra of

445

PARAFAC components during incubation in Fig. S7. Diagram of dynamic changes in

446

the bacterial community using DGGE fingerprint during the incubation in different

447

treatments in Fig. S8. Shannon-Wiener index for seven compost-amended soil

448

samples and CK treatments on days 7, 14, and 42 in Fig. S9. The EEM fluorescent

449

spectrum of Milli-Q water in Fig. S10.

450 451 452 453

ACKNOWLEDGMENTS This work was financially supported by the National Natural Science Foundation of China (No. 51708093, 51778116).

454 455

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24. Noda, I., Ozaki, Y. Two-Dimensional Correlation Spectroscopy - Applications in Vibrational and Optical Spectroscopy. John Wiley & Sons, Ltd. 2005. 25. Noda, I. Techniques useful in two-dimensional correlation and codistribution

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of Chemometrics, 2006, 20, 99-105.

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29. Stedmon, C. A.; Bro, R. Characterizing dissolved organic matter fluorescence

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with parallel factor analysis: a tutorial. Limnology and Oceanography: Methods,

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30. Stedmon, C. A.; Markager, S. Resolving the variability in dissolved organic

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matter fluorescence in a temperate estuary and its catchment using PARAFAC

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analysis. Limnology and Oceanography, 2005, 50, 686-697.

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31. Murphy, K. R.; Stedmon, C. A.; Graeber, D.; Bro, R. Fluorescence spectroscopy

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6566.

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32. Friedman J, WernerStuetzle. Projection Pursuit Regression. Publications of the American Statistical Association, 1981, 76, 817-823. 33. Ghasemi, J.B., Zolfonoun, E. Simultaneous spectrophotometric determination of

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Environmental Monitoring and Assessment, 2013, 185, 2297-2305.

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34. Wu, J., Zhao, Y., Qi, H., Zhao, X., Yang, T., Du, Y., Zhang, H., Wei, Z.

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Identifying the key factors that affect the formation of humic substance during

566

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36. Coble, P. G. Characterization of marine and terrestrial DOM in seawater using

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37. Wu, J.; Zhang, H.; Shao, L.-M.; He, P.-J. Fluorescent characteristics and metal

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binding properties of individual molecular weight fractions in municipal solid

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waste leachate. Environmental pollution, 2012, 162, 63-71.

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38. Peltre, C., Gregorich, E. G., Bruun, S., Jensen, L. S., Magid, J. Repeated

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application of organic waste affects soil organic matter composition: evidence

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from thermal analysis, ftir-pas, amino sugars and lignin biomarkers. Soil Biology

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39. Garcı́A-Gil, J. C., Plaza, C., Soler-Rovira, P., Polo, A. Long-term effects of

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municipal solid waste compost application on soil enzyme activities and

582

microbial biomass. Soil Biology and Biochemistry, 2000, 32, 1907-1913.

583

40. Blagodatskaya, E., Kuzyakov, Y. Mechanisms of real and apparent priming

584

effects and their dependence on soil microbial biomass and community structure:

585

critical review. Biology and Fertility of Soils, 2008, 45, 115-131.

586

41. Zbytniewski, R., Buszewski, B. Characterization of natural organic matter (NOM)

587

derived from sewage sludge compost. Part 2: multivariate techniques in the study

588

of compost maturation. Bioresource Technology, 2005, 96, 479-484. 27



589

Page 28 of 37

42. Walker, D. J., Clemente, R., Roig, A., Bernal, M. P. The effects of soil

590

amendments on heavy metal bioavailability in two contaminated mediterranean

591

soils. Environmental Pollution, 2003, 122, 303-312.

592

43. Cronan, C.S., Lakshman, S., Patterson, H.H. Effects of disturbance and soil

593

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594

floors. Journal of Environmental Quality, 1992, 21, 457-463.

595 596 597 598 599 600 601 602 603 604

Table

605 606 607 608

Table 1 Sign of each cross-peak in the synchronous (Φ) and asynchronous (Ψ, in the

609

brackets) maps from the ex-loadings of the DOM PARAFAC components with

610

mercury binding.

611 612

Components

C1

C2 28


C3

C4

Page 29 of 37


C1

C2

C3

C4

Peak T1

Peak T1

Peak T2

Peak A1

Peak A2

Peak B1

Peak B2

Peak Z1

Peak Z2

+

-(-)

-(+)

+(-)

-(-)

+(-)

-(+)

+(-)

+

+(-)

-(+)

+(-)

-(+)

+(-)

-(+)

+

-(-)

+(+)

-(-)

+(+)

-(-)

+

-(-)

+(+)

-(-)

+(+)

+

-(+)

+(-)

-(+)

+

-(+)

+(-)

+

-(-)

Peak T2 Peak A1 Peak A2 Peak B1 Peak B2 Peak Z1 Peak Z2

+

613 614 615 616 617 618 619 620 621

Figure captions:

622

Fig. 1 Changes in the relative distribution (Fmax %) of the PARAFAC-modeled

623

components from soil amended with 30% compost and unamended soil (CK) during

624

the 50 days of incubation.

625 626

Fig. 2 Synchronous and asynchronous hetero-spectral two-dimensional correlation

627

spectrum during incubation. Red represents the positive correlations, and blue 29



628

represents the negative correlations.

629 630

Fig. 3 The fate of each DOM PARAFAC component during the incubation of the

631

compost-amended soil during the incubation.

632 633

Fig. 4 Structural equation models of the treatments (7 d, 14 d and 42 d), representing

634

the hypothesized causal relationships among different TOC, TN, bacterial community

635

compositions, and DOM PARAFAC components (C1, C2, C3 and C4). The arrows

636

depict causal relationships. The red lines indicate positive effects, and the black lines

637

indicate negative effects. The continuous and dashed arrows indicate significant and

638

insignificant relationships, respectively. The arrow widths are proportional to the r

639

values. The proportion of variance explained for each variable is denoted by an r2

640

values. The significance levels are indicated as follows: ∗P < 0.05, ∗∗P < 0.01, and

641

∗∗∗P < 0.001.

642 643

Fig.5 Projection values of the fluorescence spectra parameters of DOM PARACA

644

components from different compost-amended soils.

645 646 647 648 649 30


Page 30 of 37

Page 31 of 37


650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665

Fig. 1

666

31



667 668 669 670 671 672 673 674 675 676 677 678

Fig. 2

679 32


Page 32 of 37


Synchronous map

C2/Wavelength (nm)

680

Asynchronous map

5.0x10-3

250

-3

4.0x10

3.0x10-3

300

2.0x10-3

350

-3

1.0x10 0.0

400

-1.0x10-3

450

-2.0x10-3

C2/Wavelength (nm)

Page 33 of 37

1.2x10-3

250 8.0x10-4 300 4.0x10-4 350 0.0 400 -4.0x10-4

450

-8.0x10-4

-3.0x10-3

450

400

350

300

250

C1/Wavelength (nm)

250 2.0x10-3 300 1.0x10-3 350 0.0 400 -1.0x10-3

450

400

350

300

250

C1/Wavelength (nm)

3.0x10-3

C3/Wavelength (nm)

C3/Wavelength (nm)

681

450

1.0x10-3

250

7.5x10-4

300

5.0x10-4

350

2.5x10-4

400

0.0

450

-2.5x10-4

-2.0x10-3 450

400

350

300

C1/Wavelength (nm)

450

250 2.0x10-3 300 1.0x10-3 350 0.0

400

-1.0x10-3

450

400

350

300

250

C1/Wavelength (nm)

3.0x10-3

C4/Wavelength (nm)

C4/Wavelength (nm)

682

-5.0x10-4

250

2.0x10-3

250

1.5x10-3

300

1.0x10-3

350

5.0x10-4

400

0.0

450

-5.0x10-4

-2.0x10-3 350

300

C3/Wavelength (nm)

250

450

2.0x10

300

-3

1.5x10-3 1.0x10-3

350

5.0x10-4 0.0

400

-5.0x10 450

-4

-1.0x10-3 400

350

300

250

400

350

300

250

C1/Wavelength (nm)

3.0x10-3 2.5x10-3

450

-1.0x10-3

250

C1/Wavelength (nm)

683

684

400

4.0x10-4

C4/Wavelength (nm)

450

-1.5x10-3

C2/Wavelength (nm)

250

3.0x10-4

300

2.0x10-4

350

1.0x10-4

400

0.0

450

-1.0x10-4 -2.0x10-4 450

400

350

300

250

C2/Wavelength (nm)

33



Page 34 of 37

3.0x10-4

-3

1.6x10

1.2x10-3

300

8.0x10-4

350

-4

4.0x10 0.0

400

-4.0x10-4

450

-8.0x10-4

C4/Wavelength (nm)

C4/Wavelength (nm)

2.0x10-3

250

250

2.5x10-4 2.0x10-4

300

1.5x10-4 1.0x10-4

350

5.0x10-5 0.0

400

-5.0x10-5 -1.0x10-4

450

-1.5x10-4 -2.0x10-4

-3

-1.2x10

450

400

350

300

250

450

C2/Wavelength (nm)

685

350

300

250

C2/Wavelength (nm)

250

1.2x10-3

300

8.0x10

350

-4

4.0x10-4

400

0.0

450

-4.0x10-4 -8.0x10 400

350

300

3.0x10-4

C4/Wavelength (nm)

C4/Wavelength (nm)

1.6x10-3

450

250

2.5x10-4 2.0x10-4

300

1.5x10-4 1.0x10-4

350

5.0x10-5 0.0

400

-5.0x10-5 450

-1.0x10-4

-4

250

-1.5x10-4 450

C3/Wavelength (nm)

686

400

350

300

250

C3/Wavelength (nm)

687 688 689 690 691 692 693 694 695

400

Fig. 3

696

34


Page 35 of 37


697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713

Fig. 4 7d

14d 35


42d


714

715

716

717 718 719 720 721 722 723 724 725 726 727

Fig. 5

36


Page 36 of 37

Page 37 of 37


728

729 730 731 732

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