Molecular Chemodiversity of Dissolved Organic Matter in Paddy Soils

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Molecular chemodiversity of dissolved organic matter in paddy soils Xiaoming Li, Guo-Xin Sun, Songcan Chen, Zhi Fang, Hai-Yan Yuan, Quan Shi, and Yong-Guan Zhu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b00377 • Publication Date (Web): 04 Jan 2018 Downloaded from http://pubs.acs.org on January 4, 2018

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Environmental Science & Technology

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Molecular chemodiversity of dissolved organic matter in

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paddy soils

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Xiao-Ming Li1,4; Guo-Xin Sun1; Song-Can Chen1,4; Zhi Fang2; Hai-Yan Yuan1,4; Quan

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Shi2; Yong-Guan Zhu1,3*

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1

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Eco-Environmental Sciences, Chinese Academy of Sciences, Shuangqing Road, No.

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18, Haidian District, Beijing 100085, People’s Republic of China

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State Key Laboratory of Urban and Regional Ecology, Research Center for

State Key Laboratory of Heavy Oil Processing, China University of Petroleum, 18

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Fuxue Road, Changping, Beijing 102249, China

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Chinese Academy of Sciences, Jimei Road, No. 1799, Jimei District, Xiamen 361021,

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People’s Republic of China

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4

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District, Beijing 100049, People’s Republic of China

Key Laboratory of Urban Environment and Health, Institute of Urban Environment,

University of Chinese Academy of Sciences, Yuquan Road, No. 19A, Shijingshan

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ACS Paragon Plus Environment


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*Corresponding author. Phone: 86-10-62936940, E-mail: [email protected].

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Abstract:

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Organic matter (OM), and dissolved organic matter (DOM), have a major

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influence upon biogeochemical processes; most significantly, the carbon cycle. To

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date, very few studies have examined the spatial heterogeneity of DOM in paddy soils.

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Thus, very little is known about the DOM molecular profiles and the key

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environmental factors that underpin DOM molecular chemodiversity in paddy soils.

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Here, Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS)

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was applied to unambiguously resolve 11,361 molecular formulae in 16 paddy soils;

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thereby elucidating the molecular characteristics of paddy soil DOM. Soil pH, iron

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complexing index (Fep/FeR) and C/N ratio were established to be key factors

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controlling DOM profiles. Polycyclic aromatics (derived from combustion) and

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polyphenols (derived from plants) increased with increasing pH, while polyphenols

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molecules, pyrogenic aromatics and carboxylic compounds decreased with increasing

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iron complexing index. Patterns in molecular profiles indicated DOM in paddy soils

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to become more recalcitrant at higher soil C/N ratio and higher pH. Furthermore,

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plant-derived polyphenols and pyrogenic DOM were retained favorably by iron and

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the chemodiversity of DOM in paddy soil increased with increasing soil C/N ratios.

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This study provides critical information about DOM characteristics at a molecular

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level and will inform better global management of soil carbon in paddy soil

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ecosystems.

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TOC / Abstract Art

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Introduction

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Dissolved organic matter (DOM) is ubiquitous in terrestrial ecosystems and the

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cycling of this carbon is of pivotal importance in global biogeochemistry and

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greenhouse gas emissions1, 2. Thus, characterizing DOM will allow a better

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understanding of the global carbon cycling3; and, other biogeochemical cycles linked

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to it4. Given the importance of this topic to environmental and ecological fields5, 6 the

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molecular characterization of DOM has become a priority. While efforts have been

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made to evaluate DOM quality7, its extreme complexity and heterogeneity presents

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challenges to understanding its molecular characteristics and reactivity8, 9.

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Recent progress in advanced mass spectrometry and data processing has enabled

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detailed characterization of DOM molecular profiles and has allowed the influence of

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broad-scale environmental gradients on DOM chemodiversity to be defined10,

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Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) is a

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robust approach through which to obtain detailed information on individual molecules

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present in complex mixtures, such as natural DOM12-14. FT-ICR MS facilitates the

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assignment of formulae to thousands of DOM moieties; thus providing a detailed

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profiling of DOM chemodiversity15. Thus, the information generated by FT-ICR MS

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provides new molecular understanding regarding sources and processing of DOM.

11

.

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While this approach has recently been successfully used to characterize DOM

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from several environments (e.g. ocean water11, 16-18, lake water12, soil pore water19,

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sediment20, 21 and the atmosphere20), the molecular diversity of soil DOM has rarely

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been characterized9, 22; this is particularly true of paddy soils. Paddy soils are of global 6


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significance as they have been identified as one of the major sources of greenhouse

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gases3 (emitting between 60 and 100 Mt methane per year4). This information, if

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sustained, could be used to regulate and forecast the geochemical processes involving

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DOM in paddy soils. About 30.3 Mha of paddy soil cover ~27% of the cropped area

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in China (FAOSTAT, 2014)23. Thus, China presents an ideal model to establish the

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influence of environmental gradients on chemodiversity profiles of DOM in paddy

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soils.

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We hypothesize that, the molecular composition and diversity of paddy soil

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DOM are related to entrained soil chemical attributes. To elucidate the influence of

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environmental gradients on DOM chemodiversity sixteen representative paddy soils

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were collected from across China (from northeastern to southeastern regions - latitude

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20.55 - 47.58 and longitude 105.29 - 132.74). Across this gradient fundamental soil

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attributes varied: soil pH (5.0-8.1), soil C/N ratio (8.5-15.4) and dissolved organic

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carbon (DOC) concentration (105-485 mg kg-1) (Table S1). Trough interrogation of

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these samples using FT-ICR MS, we elucidate: 1) DOM chemodiversity profiles in

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paddy soils and how it varies across a broad environmental gradient; 2) the main

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factors driving DOM molecular chemodiversity in paddy soils, and; 3) specific

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chemo-marker molecules that can be used to ascribe fundamental differences in DOM

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composition.

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Methods

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Soil sampling

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Samples were collected in October 2014 from 16 paddy fields distributed across 7



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China (Fig. S1) at 5 cm depth. Samples were transported to the laboratory, on ice, in

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sterile plastic bags. Soil chemical properties were analyzed according to standard

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methods24. Soil pH was measured using a digital pH meter (PHS-3C, Shanghai Lida

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Instrument Company, China) at a soil:deionized-water ratio of 1:2.5. Total carbon and

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total nitrogen (TC, TN) were determined via dry combustion in an element analyzer

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(Vario EL III - Elementar, Germany)25. The concentration of DOC was measured

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using a TOC analyzer (Liquic TOC - Elementar, Germany). Nitrate and ammonium

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were extracted from soil using KCl (2 mol·L-1) and measured using a continuous flow

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analyzer (SAN++, Skalar, Holand)26.

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Quantitative mineralogical analysis of the main mineral elements in soil samples

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(such as, Fe, Al, Mn, Ca, Mg and Si) were carried out by selective dissolution

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procedures and quantified by Inductively Coupled Plasma-Optical Emission

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Spectrometer (ICP-OES; Optimal 2000 DV, Perkin-Elmer, USA)27. To determine the

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total mineral elements (Fetot, Altot, Mntot, Catot, Mgtot and Sitot) in the soils, samples were

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wet-digested using methods described previously28. Quantitation of reactive Fe

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minerals (FeR) was performed using a citrate-bicarbonate-dithionite (CBD) solution

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(0.1 mol·L-1)29. Short-range ordered (SRO) minerals were determined following

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extraction (4 h) with ammonium oxalate solution (0.2 mol·L-1 at pH 3)22. The

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organically complexed minerals (Fep, Alp, Mnp, Cap, Mgp and Sip) were prepared by

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extraction with sodium-pyrophosphate (0.1 mol·L-1) for 16 h and analyzed by

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ICP-OES24.

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DOM sample preparation for FT-ICR MS using solid-phase extraction 8


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Water was used to extract DOM that was, thereafter, isolated with cartridges

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obtained from Agilent Technologies (Bond Elut PPL, 500 mg, 6 mL)30. Milli-Q 18

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mΩ water (DDI; Millipore, Bedford, MA, USA) was used throughout all experiments.

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Soil (12 g) was placed in polypropylene centrifuge tubes and Milli-Q water (60 mL;

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i.e. 1:5 w/v) added. Samples were then shaken (170 rpm, at 25 ± 1°C) for 8 h31.

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Thereafter, the tubes were centrifuged (at 2,800 × g) for 10 min32. The supernatant

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was filtered through a 0.45 µm mixed cellulose ester membrane.

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HPLC methanol (20 mL) was passed through PPL cartridges to clean them; these

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were then conditioned with acidified (pH 2) Milli-Q water (20 mL)33. To increase the

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subsequent extraction efficiency of organic molecules loaded onto the PPL cartridges,

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soil derived DOM water extracts were acidified to pH 2 (with HCl, p.a. grade, Merck,

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Germany)34. Sufficient volume of sample (to deliver 100 µg C; informed by the

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predetermined DOC content of the sample) was then loaded on to the PPL Cartridges

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under gravity11. The cartridges were then rinsed with 20 mL of acidified Milli-Q water

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(pH = 2)5 and ultrapure N2 gas blown onto the cartridges to completely dried them.

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DOM was collected from the cartridges by eluting them using HPLC grade methanol

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(5 mL; Merck, Germany). The elutes were stored in acid washed (and combusted)

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glass vials and kept in the dark in the refrigerator (-20°C) prior to FT-ICR MS

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measurement. The scheme for DOM sample preparation is provided in Figure S3.

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FT-ICR MS analysis

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Bruker Apex Ultra FT-ICR MS (equipped with a 9.4 T superconducting magnet5

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interfaced with negative-ion mode electrospray ionization35) was used to perform 9



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ultra-high resolution mass spectrometry analysis of DOM samples. Diluted Suwannee

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River Natural Organic Matter solution (50 mg L-1) (obtained from IHSS, USA) was

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used for calibration. To compare the relative intensities of MS spectra in various

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samples deuterated stearic acid (C18D35H1O2; Sigma-Aldrich, USA) was added as an

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internal standard to the samples13. Ammonium hydroxide was added prior to

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electrospray to increase the ionization efficiency34. Samples (180 µL h-1) were

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injected into the electrospray source (voltages of the capillary and spray shield were,

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respectively, 4 kV and 3.5 kV). Spectra were acquired over 128 scans (with an ion

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accumulation time of 0.6 s; ion flight time into the ICR cell of 0.0012 s, and a m/z

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range of 150 - 1,200). PPL extraction blanks and solvent blanks were prepared and

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analyzed to check for contamination and carry-over5; peaks found in these blank were

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removed the profiles obtained for the DOM samples33.

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Molecular formulas of DOM were calculated using a custom software; the data

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processing is described elsewhere13, 35. Briefly, detected mass peaks with S/N greater

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than 6 were exported to datasheets for processing. Elemental combinations were

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limited to molecular formulas containing

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mass measurement errors less than 1 13. Each assigned class species was extended in

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double bond equivalence (DBE) values (with a mass interval of 2 Da) and carbon

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numbers (CH2 unit, with a mass interval of 14 Da) by an automatic retrieval within a

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tolerance of ± 0.001 Kendrick mass defect (KMD)33. Through the application of these

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criteria formulae were ascribed with a high level of confidence; and, only formulae

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that were physically possible were permitted35. Element P were excluded because no

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C0-100, 1H0-200,

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N0-4,

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O0-30,

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S0-2 and

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systematic trends in P-containing compounds were observed across the samples.

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Where mass peaks were detected in more than two of the three replicates for

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each sample they were retained for further data analysis17. Relative abundance of

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moieties was determined through normalization of the sum of all FT-ICR MS signal

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intensities of each mass of the respective DOM formulae. Numerous studies have

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evidenced quantitative reproducibility of FT-ICR MS approaches36. Compound

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groups were delineated by elemental ratios, aromaticity index (AI)11,

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bond equivalence (DBE)40 and H/C cutoffs37: polycyclic aromatics originating from

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combustion (AI > 0.66), polyphenols derived from plants (0.66 ≥ AI > 0.50), highly

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unsaturated and phenolic compounds (AI ≤ 0.50 and H/C < 1.5), aliphatic compounds

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(2.0 ≥ H/C ≥ 1.5)12, lipids (O/C = 0−0.3, H/ C = 1.5−2.0), protein/amino sugars (O/C

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= 0.3−0.67, H/C = 1.5−2.2, N/C ≥ 0.05), carbohydrates (O/C = 0.67−1.2; H/C =

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1.5−2), unsaturated hydrocarbons (O/C = 0−0.1, H/C = 0.7−1), lignins (O/C =

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0.1−0.67, H/C = 0.7−1.5, AI < 0.67), condensed aromatics (O/C = 0−0.67, H/C =

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0.2−0.7, AI ≥ 0.67) and tannins (O/C = 0.67−1.2, H/C = 0.5−1.5, AI < 0.67)41, 42.

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Statistical analyses of DOM chemodiversity

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, double

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Moiety accumulation curve, ranked abundance and redundancy analysis were

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performed in R software using the normalized intensities of all compounds12. Moiety

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accumulation curve was calculated as the number of unique compounds by adding the

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samples in a random order using the R package vegan. Confidence interval (95%) of

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the molecule accumulation curve was estimated by 1,000 permutations. By this

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approach a confidence interval for the number of unique compounds detected with 11



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each additional sample was obtained. Regarding the ranked abundance curves color

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was used to depict the proportion (%) of samples in which a compound was acquired.

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The rank abundance curve was obtained using the R package BiodiversityR43.

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RDA was carried out using relative peak abundances (matrix: Bray-Curtis

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dissimilarities) in R using package vegan44. Geography and soil chemistry variables

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were fitted to each ordination using ‘envfit’ function implemented in package vegan,

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with P-values reckoned over 999 permutations.

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Correlation of DOM chemodiversity and environmental attributes

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Soil chemistry associated molecular patterns were evaluated using Spearman’s

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rank correlation coefficients. Where the formulae could be unambiguously assigned,

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spearman correlation was performed between environmental parameters and the

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sum-normalized intensity of peaks, following the method described previously12. The

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environmental parameters covered soil chemistry (DOC, pH, total carbon, total

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nitrogen, mineral elements (Fe, Mn, Al, Mg, Ca, Si)) and geography (elevation,

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latitude and longitude). Van Krevelen45 diagrams were then plotted for each

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environmental parameter, major patterns of chemodiversity were visually identified,

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and variables were grouped based on patterns of similar correlation.

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Reproducibility and duplicate analysis

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The same amount of DOM was extracted from each sample in triplicates. All

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samples were solid phase extracted following the same protocol in a random order

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and analyzed on the same FT-ICR MS with the same settings, which has previously

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shown high reproducibility of replicates12. Soil from MY and WC as representatives 12


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were extracted in triplicate for quality control (Table S6). The variation in the

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replicates was negligible. Compared with applying presence/absence-based methods,

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using normalized peak intensities broadened the range of statistical tools available for

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interpreting molecular patterns of DOM46.

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Index of chemodiversity

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The chemodiversity index was calculated using the Chao 1 metric47 in R,

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wherein the individual molecule was assigned as the “species” and the relative

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intensity of the peaks used to ascribe species abundance12. Spearman’s correlation was

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calculated between environmental parameters and Chao 1 index establish possible

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associations between DOM chemodiversity, soil chemistry attributes and geography

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variables. Automatic linear modeling was performed (95% confidence level) using

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IBM SPSS Statistics for Mac (21.0)48. Finally, Spearman’s correlation was carried out

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to identify molecules that were associated with Chao 1. These associations were

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plotted in a van Krevelen diagrams to support further interpretation of the

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relationships between DOM molecules and chemodiversity.

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Linear discriminant analysis effect size (LEfSe)

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DOM molecules established to be most likely to explain differences between

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samples were determined by linear discriminant analysis effect size (LEfSe)17, 49. The

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threshold on the LDA score for discriminative features was set at 2.0. Kruskal–Wallis

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tests were performed at a significance level of P < 0.05

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LEfSe was performed in the Galaxy framework49 with online analysis tools

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(http://huttenhower.sph.harvard.edu/lefse/). 13


17

. An implementation of


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Results

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Dominant DOM molecular compounds in paddy soils.

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Individual molecules differed among samples with respect to their presence and

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signal intensity indicating that paddy soil DOM molecules were observed to be very

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complex and very diverse. Across the 16 paddy soils a total of 11,361 molecular

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formulae were assigned. The identified formulae ranged in mass from 152.07 to

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799.39 Da and had weighed average masses per sample between 312.1 and 355.3 Da

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(average 320.8 Da). Statistical analyses of the classified groups for the formulae

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showed that CHO (44.3%) and CHON (42.9%) molecules were dominant, followed

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by CHOS (12.4%) (Table S4). N-containing compounds were present in paddy soil

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DOM in large amounts; and, most moieties contained two or more N atoms. Van

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Krevelen analysis revealed that paddy soil DOM has the greatest proportion of lignin

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components and aromatic component; these can be ascribed as soil-derived

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“humics”38. The relative abundance of lignin and aromatic components increased with

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soil pH (Fig. 1).

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Distribution of organic molecules in paddy soils across China.

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Moiety accumulation curve and rank abundance were derived to characterize the

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molecular distribution across the dataset. A saturation of compounds was observed in

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molecular accumulation across the samples, with 95% of the molecular richness being

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reached in 13 out of 16 paddy soils (Fig. 2a). The chemodiversity of paddy soil DOM

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was captured within a relatively few number of sample sites. Accordingly, fewer

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previously undetected compounds would be identified with each additional sample. 14


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Thus, we are confident that the variability of paddy soil DOM molecular composition

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was well defined in this research.

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Rank-abundance analysis is a well-known and informative method of analyzing

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patterns of diversity43. Rank abundance for the DOM molecular profiles, for all

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samples, showed that compounds with normalized intensities in the highest 30% were

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present in at least 80% of the samples (Fig. 2b). This observation suggests that there

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exists a fundamental foundation of DOM compounds that, while they varying in their

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abundance, are ubiquitous across the large environmental gradient captured in this

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research.

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Linking DOM molecular profiles to environmental factors

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Redundancy analysis (RDA) was used to evaluate relationships between the

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intensities of all chemical moieties and environmental attributes (Fig. 3). Our results

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indicated that DOM molecular profile composition depended, to a large extent, upon

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soil parameters such as pH, soil minerals, carbon and nitrogen contents (Fig. 3). pH

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emerged as the most important driver influencing the DOM compound profiles (Fig.

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4a). Our analysis highlighted that soil pH resulted in divergent chemical composition.

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Similarly, Fep/FeR and (Alp+Fep) also appeared as important drivers. This was

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congruent with the opposing correlation observed with soil C/N ratio and NO3¯-N

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concentration (Fig. 3).

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Van Krevelen diagrams45, that cross-plot hydrogen to carbon (H/C) ratio with

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oxygen to carbon (O/C) ratio of DOM moieties assist in the visualization of DOM

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profile chemodiversity. The van Krevelen diagrams also facilitate the assignment of 15



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molecular formulae into different broad molecular groups, for example: aliphatics,

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polyphenols and polycyclic aromatics. Our analysis, of van Krevelen plots (Fig. 4),

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suggested that all DOM moieties were significantly correlated (P < 0.05) with either

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pH, iron complexing index (Fep/FeR), or soil C/N ratio. Spearman rank,

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sum-normalized, compound intensities were observed to correlate with environmental

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variables, including pH, Fep/FeR, and C/N ratio. Across pH, Fep/FeR and C/N ratio,

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clear trends were observed in the DOM moiety profiles (Fig. 4a-c). In general, the

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relative abundance of polyphenols (derived from plants) and polycyclic aromatics

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(derived from combustion) increased in prevalence in paddy soils with higher pH (Fig.

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4a). Compounds with higher H/C ratio and O/C ratio were observed to be represented

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in greater proportions in paddy soil with lower pH; a similar distribution pattern was

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observed for soil C/N ratio, and; an inverse pattern observed for Fep/FeR (Fig. 4b-c).

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DOM moieties that were positively correlated with pH had low H/C ratios and low

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O/C ratios, and were clearly differentiated from moieties that were negatively

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correlated with pH (Fig. 4a).

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DOM molecular associations with soil pH

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Spearman correlations between the intensity of individual moieties and

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environmental factors (Fig. 4) highlighted that variability in the DOM chemodiversity

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profiles was driven mainly by soil pH. Roughly half of all detected molecular

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formulas (~4,904) varied significantly across paddy soils. Chemical moieties

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positively correlated to pH included polycyclic aromatics (derived from combustion),

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polyphenols (derived from plants) and carboxyl-containing aliphatic molecule. Seven 16


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different magnitude-weighted parameters (H/C)w, (O/C)w, (MW)w, (DBE)w,

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(DBE/C)w, (DBE/O)w and (DBE-O)w18 were used to obtain more detailed

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characterization of molecular chemodiversity profiles. The highest positive

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correlation of pH with (DBE)w, (DBE/C)w, (DBE/O)w and (DBE-O)w, and the highest

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negative correlation with (H/C)w and (O/C)W, indicated that at higher pH, the DOM

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molecules were more aromatic (Table S5). The positive correlation between pH and

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(DBE-O)w suggests that more C=C unsaturation existed with higher pH values.

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Consequently, in more acidic paddy soils DOM displayed more carbon saturated and

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N-containing compounds (e.g. fatty acids and proteinaceous materials), which were

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more labile and inherently degradable.

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Marker molecules of DOM in paddy soils

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LEfSe was performed to determine markers in DOM composition under different

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soil pH. LEfSe analysis pin-pointed 564 marker molecules that defined differences in

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DOM composition at contrasting soil pH (Fig. 5). DOM in paddy soils of pH > 5.63

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had 340 marker components compared to 224 for DOM in paddy soils of pH < 5.63

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(Fig. 5). In general, at elemental composition level CHO compounds were dominant

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in high pH group, whereas the CHON compounds are major components in low pH

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group. At the compound level, DOM were marked by recalcitrant component (shown

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in black) in high pH soil, such as polycyclic aromatics (derived from combustion) and

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polyphenols (derived from plants), while labile compounds (shown in red), such as

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proteins, amino sugars, carbohydrates, and phenolic compounds were marker

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components in low pH soil. This result suggested that soil pH has a strong effect on 17



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the DOM molecular distribution. Most significantly, our findings suggest that DOM

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in paddy soils will become more labile with further soil acidification.

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DOM molecular associations with iron complexing index (Fep/FeR)

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About 32.4% of the molecules that have been unambiguously identified showed

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a significant correlation with iron complex index (Fep/FeR). For all there was

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consistency in molecular-level chemodiversity patterns within DOM moieties, highly

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unsaturated compounds and phenolic compounds (with formulae typical of aromatic

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and condensed aromatic compounds), lignin and tannin, show a significant shift in

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chemodiversity profiles associated with pH and Fep/FeR (Fig. 4a-b). Polycyclic

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aromatics (derived from combustion) and polyphenols (derived from plants)

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compounds were negatively correlated to Fep/FeR, with an inverse relationship with

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pH. In addition, a large number of N-containing compounds, most containing two or

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more N-atoms, were significantly correlated to iron complexing index.

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DOM molecular associations with soil C/N ratio

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Soil C/N ratio is a recognized indicator of soil quality56. Our results highlight

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that soil C/N ratio has a close relationship with the DOM compounds and is also a

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good predictor of the molecular-level DOM distribution (Fig. 4c). Molecules that

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were positively correlated with C/N ratio formed a cluster and mainly plot in the

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aromatic and condensed aromatics regions. Low soil C/N ratio was associated with

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molecules with higher H/C ratios. Higher H/C ratios, indicative of greater saturation,

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are characteristic for compounds that are labile (e.g. proteinaceous materials and

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carbohydrates)37. A majority (69.4%) of nitrogenous compounds in our dataset were 18


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positively correlated with soil C/N ratio. This suggests that the inputs of organic

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matter with high C/N ratios (e.g. charred residues, organic fertilizer and returned

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straw) under intensive management favor accumulation of recalcitrant compounds,

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and aromatic structures may prevail in the DOM of old paddies.

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Chemodiversity of DOM in paddy soil

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Chao 1 index was used to qualify DOM molecular chemodiversity in paddy soils

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(Table S3). The chemodiversity ranged from 3,258 to 3,853, with an average diversity

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index of 3,607, whereas the mean chemodiversity of sediment DOM ranged from

337

1,848

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While DOM composition reflects variations in pH, Fep/FeR, and soil C/N ratio,

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chemodiversity was primarily predicted by soil C/N ratio. Automatic linear modeling

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indicated soil C/N ratio to be the most important predictor of DOM chemodiversity

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(Fig. S2). Paddy soils established to have relatively low DOM chemodiversity were

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noted to contained less aromatic but more aliphatic compounds (Fig. 4d). Compounds

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established to have with a H/C ratio of less than 1 (primarily polyphenols and

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polycyclic aromatics) were synonymous with higher chemodiversity. Through the

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application of numerical analysis, more traditionally applied to biological species

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biodiversity assessment (e.g. Chao 1 index)11, 12, we highlight the opportunity to apply

347

these methods to evaluate the chemodiversity profiles of DOM in soils across; and to

348

use this analysis to qualify the influence of broad environmental gradients on DOM

349

transformation.

350

Discussion

14

to 1,919 50, indicating a larger spatial heterogeneity of DOM in paddy soils.

19



351

Here we present an original characterization of DOM chemodiversity in paddy

352

soil. The number of molecular formulae (11,361) was observed to be much higher in

353

paddy soil than in other environments, for example, agricultural soil DOM (1,237

354

formulae)9, sediment (1,848

355

and boreal rivers (4,109 formulae)51. With combination of multivariate analysis, we

356

demonstrated that the molecular distribution of paddy soil DOM was closely related

357

to soil pH, C/N ratio and iron complexing index (Table S7).

358

The effects of pH on paddy soil DOM distribution

14

to 1,919

50

formulae), boreal lakes (7,122 formulae)12,

359

It has been well documented that rice cultivation and increasing inputs of

360

chemical nitrogen fertilizers generally lead to more acidic and oligotrophic soil

361

conditions52-54, with changes in pH affecting organically complexed iron55, 56, along

362

with changes in soil C/N ratio56, ultimately affecting the distribution of DOM

363

chemical diversity. It has been reported that soil pH has decreased significantly in

364

major Chinese croplands over the last 25 years54, and specifically paddy soil has been

365

reported to have acidified (with an average pH decline of 0.59) over this period57.

366

Thus, with the pH decrease in paddy soils, the abundance of polyphenols (derived

367

from plant) and polycyclic aromatics (derived from combustion) decreased, while the

368

abundance of labile components such as aliphatic, highly unsaturated and phenolic

369

compounds have increased (Fig. 4a). Our results highlight the vulnerability of paddy

370

soil DOM to degradation, and, in particular, the antagonism acidification has with

371

respect to DOM degradation.

372

Iron-DOM complexing effects on paddy soil DOM distribution 20


Page 20 of 40

Page 21 of 40


373

The importance of solid iron in the cycling of organic carbon has long been

374

known29, 58. However, direct field evidence linking the biogeochemical cycles of

375

DOM with iron, at the molecular level, has to date been lacking. Using FT-ICR MS,

376

we observed chemodiversity of DOM to be reduced as iron complexing index

377

(Fep/FeR) increased; our results further suggesting that organically complexed iron

378

selectively traps polyphenols (derived from plants) and polycyclic aromatics (derived

379

from combustion) (Fig. 4b). This observation could be explained by the fractionation

380

of DOM induced by iron oxyhydroxides. Iron oxyhydroxides complex with carboxyl

381

groups of DOM compounds21, 59 (Table S5, Spearman correlation: -0.347, P < 0.05),

382

resulting in the precipitation of phenolic and aromatic compounds with iron40 and

383

consequential release of hydrocarbons and amino acids into soil solutions. Iron is

384

ubiquitous in paddy soils61 and our results highlight its importance for SOM storage

385

as iron associated DOM. Our results also reveal that 32.4% of the DOM molecules in

386

paddy soils were significantly correlated with iron; this finding may have far-reaching

387

implications for the understanding of global cycling and the long-term storage of

388

SOM in paddy soils.

389

C/N ratio effects on paddy soil DOM distribution

390

Intensive inputs of chemical N fertilizer and simultaneous decrease in

391

applications of organic fertilizer have caused the decrease in soil C/N ratio in paddy

392

soils, as well as soil acidification. Paddy soils with high C/N ratio contain the most

393

diverse mixture of DOM molecules. DOM composition is more recalcitrant and

394

diverse at high soil C/N ratio. The abundance of polyphenols (derived from plants) 21



395

and polycyclic aromatics (derived from combustion) decreased with decreasing soil

396

C/N ratio. The decreased abundance of polyphenols and polycyclic aromatics would

397

reduce the recalcitrant soil organic carbon and nitrogen pool; potentially leading to

398

soil degradation40 and a reduction in food security52. Furthermore, decreased

399

recalcitrant pyrogenic compounds could reduce the stability of soil organic carbon

400

pool; thus, potentially increase greenhouse gas emissions.

401

Paddy soils are of prominent importance to the global carbon cycling as they have

402

a pivotal role to play in carbon fixation, its respiration and methane emissions. Our

403

results have revealed that qualitative changes in DOM were mainly driven by soil pH,

404

iron bonding and C/N ratio in paddy soils. The applications of organic and chemical N

405

fertilizers significantly affect these factors, especially excess inputs of N fertilizer to

406

soil. These results present a new framework for understanding the distribution of

407

DOM in paddy soils and will facilitate predictions on how paddy soil DOM

408

chemodiversity and recalcitrance responds to soil management and environmental

409

gradients.

410

Notes

411

Declaration: We, the authors, have no competing financial interest linked to this

412

research and its findings.

413 414

Acknowledgements

415

This research was financially supported by the State Key Program of Natural Science

416

Foundation of China (No. 41430858), the National Natural Science Foundation of

417

China (No. 41371459) and the National Basic Research (973) Program of China (No. 22


Page 22 of 40

Page 23 of 40


418

2014CB441102).

419 420

Supporting Information

421

Details on supportive methods and discussion; sampling sites, predictive importance

422

of chemical variables, schematic diagram for the preparation of SPE-DOM,

423

magnitude-weighted parameters, molecular characteristics, spearman correlation

424

analysis.

425 426

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33



643

Figure legends

644

Figure 1. Comparison of relative abundance (%) of van Krevelen diagram-derived

645

classification classes from the FT-ICR MS analysis of the DOM components for 16

646

paddy soils.

647

Figure 2. Molecular distributions of FT-ICR MS assigned molecules in sixteen DOM

648

samples across China. (a) Number of unique molecules with each added sample. The

649

black dotted line indicates 95% of compounds. (b) Rank abundance of the compounds

650

across all paddy soil DOM samples. Molecular compounds are color coded by the

651

percentage of samples in which they occurred.

652

Figure 3. Multivariate analysis of molecular data and drivers using RDA. Ordinations

653

are based on Bray-Curtis, which utilizes relative compound abundances information.

654

Environmental variables were fit to the ordination. Grey-shaded circles are DOM

655

compounds, whereas black circles indicate the site. Variables with a significance level

656

of < 0.05 (blue), < 0.01 (light green) and < 0.001 (purple) are shown. FeR, the

657

citrate-bicarbonate-dithionite extracted reactive Fe. FeP, the sodium pyrophosphate

658

extracted organic complexed Fe. FeP/FeR, this ratio represents the iron complexing

659

index. (Alp+ Fep), the sum of organic complexed Al and Fe.

660

Figure 4. Molecular-level DOM patterns across 16 paddy fields. Significant

661

Spearman rank correlation coefficients (P < 0.05) of individual molecules with (a) pH,

662

(b) Fep/FeR, (c) C/N and (d) the chemodiversity index (Chao 1). The color scale

663

indicates Spearman correlations between the intensity of individual molecules and pH,

664

Fep/FeR, C/N and chemodiversity index (red, positive; blue, negative). Circles indicate 34


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665

compounds without N and diamonds indicate N-containing compounds. Compound

666

groups include combustion-derived polycyclic aromatics (Aromaticity index (AI) >

667

0.66), vascular plant-derived polyphenols (0.66 ≥ AI > 0.50), highly unsaturated and

668

phenolic compounds (AI ≤ 0.50 and H/C < 1.5, and aliphatic compounds (2.0 ≥ H/C ≥

669

1.5). Compound category labels for delineation in panels (d) also apply to delineated

670

regions in (a) (c) and (d). Lines separating compound categories on van Krevelen

671

diagrams are for visualization only and exact categorization may slightly differ. The

672

number of significant correlations is (a) n=4,904; (b) n=3,676; (c) n=2,498; (d)

673

n=2,714

674

Figure 5. Linear discriminant effect size analysis (LEfSe) results on DOM molecules.

675

van Krevelen of linear discriminant analysis (LDA) scores computed for DOM

676

molecules, differently abundant across paddy soils. Data are displayed as molecular

677

hydrogen-to-carbon versus oxygen-to-carbon ratio. Circles indicate individual

678

molecule compounds. Red indicate the molecules that are enriched in paddy soils (pH

679

< 5.63) and black indicate the molecules that are enriched in paddy soils (pH > 5.63).

680

Molecule compounds with no significant differences are not shown.

681 682

35


Relative abundance(%)

100


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80 60 40 20 0 TY AH YT

LZ JSJ JX WC JS

Tannins Ligins Carbohydrates Lipids

HL SY

BJ DH MY AX

JL

Condensed Aromatics Unsaturated Hydrocarbons Protein ACS Paragon Plus Environment

BC

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F ig u r e 5

Page 40 of 40

2 .5

p H < 5 .6 3 n = 2 2 4 p H > 5 .6 3 n = 3 4 0

H /C

2 .0 A lip h a tic

1 .5

H ig h ly u n s a tu r a te d a n d p h e n o lic c o m p o u n d s

1 .0

V a s c u la r p la n t- d e r iv e d p C o m b u o ly p h e n s tio n - d o ls e r iv e d p o ly c y c lic a r o m a tic s

0 .5

0 .0 0 .0

0 .2

0 .4

0 .6

O /C


0 .8

1 .0

1 .2

Molecular Chemodiversity of Dissolved Organic Matter in Paddy Soils

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