Optical properties as tracers of riverine dissolved organic matter biodegradation in a headwater tributary of the Yangtze

Journal of Hydrology 582 (2020) 124497

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Optical properties as tracers of riverine dissolved organic matter biodegradation in a headwater tributary of the Yangtze Maofei Nia,b,c, Siyue Lia,

T

⁎

a

Key Laboratory of Reservoir Aquatic Environment, Research Center for Ecohydrology, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China b University of Chinese Academy of Sciences, Beijing 100049, China c College of Eco-Environmental Engineering, Guizhou Minzu University, Guiyang 550025, China

A R T I C LE I N FO

A B S T R A C T

This manuscript was handled by G. Syme, Editor-in-Chief

The optical properties can characterize the chemical composition, bioavailability and origins of dissolved organic matter (DOM), and thus indicate its biodegradability in natural waters. However, there remains a large gap in how DOM optical properties (UV–visible and fluorescence spectra) trace and predict the dissolved organic carbon biodegradation (%BDOC) in river systems. Over a 56-day laboratory incubation experiment, we examined the variations of %BDOC and DOM optical properties in a headwater tributary of the upper Yangtze River. We found that DOC biodegradability was significantly higher in the tributaries (20 °C: 38.9 ± 19.8%; 30 °C: 58.6 ± 19.5%) than that in the main stem (20 °C: 26.1 ± 18.2%: 30 °C: 41.8 ± 16.3%) (p < 0.001). The %BDOC, exhibiting coincident peaks in the agricultural regions and troughs in the ecologically fragile regions, increased by a factor of two-fold with a temperature gradient of 10℃. The optical properties SUVA254 and S275-295 decreased, while the S350-400, FI and BIX increased after the incubations, implying the synchronous biodegradation and production of young DOMs via microbial respiration. The %BDOC:DOC tightly linked to a254, S275-295, S350-400 and BIX, illustrating that optical properties could predict the riverine %BDOC. The findings demonstrated that optical properties could be regarded as the useful tracers of DOM biodegradation in river systems.

Keywords: Optical properties Biodegradation Chemical compositions Fluorescence spectra UV–visible spectra

1. Introduction Dissolved organic matter (DOM) is widely distributed in freshwater ecosystems and largely changes the carbon cycling in globe (Williams et al., 2016; Li et al., 2018). It is known that DOM is a heterogeneous mixture and comprises conjugated aliphatic and aromatic structures, its chemical components and bioactivities could determine how it reacts and mobilizes in running waters (Dalmagro et al., 2017). Natural DOM vary on both temporal and spatial scales, which can trace its sources and behaviours of environmental processing (Hansen et al., 2016; Li et al., 2020). Fluvial DOMs are derived from terrigenous inputs, root exudates and microbial biomass, and will be selectively decomposed by aquatic microbes (Saadi et al., 2006). The bioavailability of organic compounds is influenced by intrinsic molecular properties (e.g. aromaticity, aliphaticity and proteome) (Koehler et al., 2015), resulting in highly variable mineralizing effect in aquatic environments (Williams et al., 2013).

Natural DOM mobilizes and transports chemicals, supporting bacterial respiration and aquatic food webs (Pollard, 2013). In particular, DOM biodegradation typically depletes labile dissolved organic carbon (DOC) (De Falco et al., 2016), leading to the rapid loss of biodegradable fractions (biodegradable dissolved organic carbon, BDOC) and relative accumulation of refractory materials (recalcitrant DOC; e.g. lignin and humic acids) (Hu et al., 2018). Hence, the variations of chemical compositions indicate the fate of aquatic DOM, and also associate with the bioavailability and microbial respiration (Zhang et al., 2009; Catalan et al., 2017). Prior studies reported the strong linkages between the DOM intrinsic properties and aquatic microbes, suggesting all major bacterial clades were co-varied with DOM abundance and composition (Lin et al., 2012; Amaral et al., 2016). In this context, DOM properties not only characterize the riverine biodegradability, but also trace the biodegradation processes (Moody and Worrall, 2017) in river systems. Different characterization methods (e.g. Fourier transform ion cyclotron resonance mass spectrometry) have been used to explore DOM

⁎ Corresponding author at: Chongqing Institute of Green and Intelligent Technology (CIGIT), Chinese Academy of Sciences (CAS), 266, Fangzheng Avenue, Shuitu High-tech Park, Beibei, Chongqing 400714, China. E-mail address: [email protected] (S. Li).

https://doi.org/10.1016/j.jhydrol.2019.124497 Received 14 October 2019; Received in revised form 18 December 2019; Accepted 19 December 2019 Available online 23 December 2019 0022-1694/ © 2019 Elsevier B.V. All rights reserved.

Journal of Hydrology 582 (2020) 124497

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the Dry-hot Valley Region (an ecologically fragile region) (Duan et al., 2016), which is characterized by the prominent environmental features of the intensive agricultural practices, serious soil erosion and fragile ecosystem (poor ability of ecological self-recovery).

structures (Kellerman et al., 2014), among them optical measurement is regarded as a reliable method due to its molecular-level understandings of DOM dynamics (Spencer et al., 2008). For instance, the non-normalized (a254) and DOC-normalized absorbance coefficient at wavelength of 254 nm (SUVA254) indicate the abundance of aromatic fractions and aromaticity in DOMs, respectively (Weishaar et al., 2003). Spectral slopes at the wavelength ranges of 275–295 nm (S275-295) and 350–400 nm (S350-400) decipher the DOM molecular weight, and their slope ratio (S275-295/S350-400, SR) indicates the DOM sources in waters (Helms et al., 2008). The fluorescence index (FI) and biological index (BIX) identify the microbial sources and young DOMs in waters (Kolic et al., 2014). Natural biodegradation occurs in photic and aphotic zone, resulting in the alteration of the DOM components, molecular weight, bioavailability and fate (Yamada et al., 2012). Optical properties are effective in explaining DOM features and sources at a molecular level, and thus can underscore the DOM biodegradation processes in freshwater (Wu et al., 2007). While optical properties were widely employed to demonstrate spatio-temporal patterns of DOM components and origins (Seidel et al., 2016), few studies have examined how optical properties trace the DOM biodegradation in rivers. The river continuum concept theorized that DOM diversity would rapidly decrease in the upstream within a river system (Vannote et al., 1980), suggesting a high potential of riverine DOC biodegradation (% BDOC) in headwater rivers (Ejarque et al., 2017). We thus hypothesized that (1) optical properties can trace the DOM biodegradation processes because they decipher the variations of DOM characterization (Hansen et al., 2016); (2) riverine %BDOC can be predicted by initial DOM properties because they largely explain the DOC biodegradability (Wickland et al., 2012). In testing our hypotheses, we examined riverine %BDOC and optical measurements in the Longchuan River, a headwater tributary of the upper Yangtze River. The objectives of this study were to: (1) reveal how the optical properties trace the DOM biodegradation, and (2) explore the couplings of riverine %BDOC and DOM optical parameters (SUVA254, S275-295, S350-400, SR, FI and BIX). Our results indicated that initial DOM components could predict the biodegradability of DOC, and the variability of optical properties largely characterized the DOM biodegradation processes in the Longchuan River. This study hopes to supplement the knowledge gaps of linkages between optical properties and DOM biodegradation.

2.2. Sample collection and measurement Sampling expedition was carried out in September 2017, and a total of 15 and 21 grab samples were collected in the main stem and the major tributaries, respectively. The sampling sites were selected to incorporate full spectrum of stream order, depending on the spatial representativeness, and thus gave a typical representation of regional characteristics. Samples taken at a depth of ∼10 cm were filtered through glass microfiber filters (GF/ F 47 mm, 0.7-μm, Whatman), and then preserved in the 100 mL highdensity polyethylene (HDPE) plastic containers. The filtrates were kept in refrigerators at 4 °C, and the BDOC incubation and optical measurements were immediately performed once the samples were transported to the laboratory. The DOC and dissolved inorganic carbon (DIC) were determined by multi N/C 2100S (Analytik Jena, Germany). The Carbon dioxide (CO2) in the headspace was detected using a gas chromatography-flame ionization detector (GC-FID) (PerkinElmer Clarus 580, USA) with external standard method. The UV–visible absorption spectra (200–700 nm) was measured using a double-beam scanning spectrophotometer (UV-5500PC, Shanghai). The Excitation-emission matrices (EEMs) fluorescence spectra (excitation wavelengths: 220–450 nm, 2 nm intervals; emission wavelengths: 250–550 nm, 2 nm intervals) was scanned using a fluorescence spectrophotometer (F-7000 FL Spectrophotometer, Japan). The optical measurements were blank subtracted using the ultrapure water at room temperature of 25 °C with an acid-cleaned 1 cm quartz cuvette. The description and calculation of the extracted optical parameters in this study were summarized in Table S1. The modified laboratory incubations were conducted using biochemical incubators (Model SPX-250B-Z, Shanghai) in dark for 56 days at the temperatures of 20 °C and 30 °C (automatically controlled by the incubators), following the description that reported by Wickland et al. (2012). In order to simulate in-situ microbes, inoculation was not used and 0.7-μm filters were employed in this study (most microbes could pass through a pore size of 0.7 μm) (Mao and Li, 2018). Three replicates of the 50-mL filtered water and duplication of blanks (ultrapure water, 50 mL) were incubated in 150-mL glass bottle, and then capped with Teflon-lined stoppers and crimp seals. Twenty milliliters gas samples in the headspace were collected in gas sampling bags (Dalian Delin Gas Packing Co., Ltd, Liaoning) on day 7, 14, 28, 42 and 56 and immediately detected by GC-FID. To maintain aerobic condition, the incubation bottles were shaken every 2 days and introduced into fresh air for 10 min after gas sampling. By the end of BDOC incubations, the final DIC concentration in each bottle was measured and the cumulative CO2 production (deducted the blanks) in the headspace was calculated (Fig. S1). The total CO2 production was defined as the differences of initial DIC, final DIC and the cumulative headspace CO2 production. The % BDOC was expressed using the following Eq. (1):

2. Materials and methods 2.1. Study area The Longchuan River (latitude 24°45′–26°51′N, longitude 100°56′102°02′E) is one of the most important headwater tributaries of the upper Yangtze River, and has a catchment area of 5475.3 km2 and a total length of 244.9 km. It originates from the Puzao pond in the east of the Nanhua, flows through seven counties and cities, and then joins into Jinshajiang River in the Jiangbian village (Yuanmou County) (Fig. 1). The Qingling River is the major tributary. Despite the low-latitude monsoon climate, this region exhibits a tropical savanna-like ecosystem with the major vegetation type of tropic bushveld (Yang et al., 2017). The elevation ranges from 940 to 3657 m with an average of 1992 m, the annual mean temperature in the rainy season and dry season are 29.8 ± 3.96℃ 20.3 ± 5.43 °C, respectively. About 86–94% of annual precipitation (560.97–1081.6 mm) occurs from May to October, and the mean evaporation is 1613.4 mm. The average discharge at the lower gauging station (Xiaohuanghuayuan Station) is ∼7.7 × 108 m3/y. The catchment area geology is dominated by limestone (carbonates), sandstone (quartz and felspar), shale (quartz and calcite) and basalt (siliciclastic rocks). The main land uses were forest (60.8%), farmland (21.3%) and grass (16.8%). Particularly, the percentages of water and urban were 0.58% and 0.54%, respectively (Landsat 8 OLI in 2015 and Digital elevation model data). The lower Longchuan River belongs to

%BDOC = (total CO2 − C production/initial DOC−C) × 100%

(1)

2.3. Quality control The samplings and chemical analysis in this study followed the standard procedures that proposed by the American Public Health Association (1985) (Brindha and Kavitha, 2015). The UV–visible spectra measurements were repeated, the different readings (outliers were picked out) suggested the Coefficient of variance (CV) of SUVA254, S275-295, S350-400 and SR were below 2%. Fluorescent scans were corrected for instrument optics, Raman area normalization and Raman 2

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Fig. 1. Map of sampling locations in the Longchuan River, China.

2.4. Statistical analyses

Table 1 Initial dissolved organic carbon (DOC), dissolved inorganic carbon (DIC) concentrations and the total carbon dioxide (CO2) production in the headspace.

Min Max Median Mean Std. Dev

DOC (mg/L)

DIC (mg/L)

CO2 production at 20 °C (mg/L)

CO2 production at 30 °C (mg/L)

2.41 8.62 5.01 4.93 1.49

9.0 54.1 24.8 26.6 8.67

0.19 4.22 1.43 1.58 0.96

0.82 5.10 2.22 2.48 1.09

Prior to data analysis, Kolmogorov-Smirnov and Levene's test were employed for normality and homogeneity tests, respectively. Data that disobeyed a normal distribution were converted using the natural log transform. The Mann-Whitney U test, Analysis of variance (ANOVA) and Turkey HSD test were conducted to identify the significant differences of %BDOC and DOM optical parameters. The outliers (< 5th and > 95th percentiles) for the data were assessed using boxplot method (Schwertman et al., 2004). The relationships between %BDOC and optical properties were examined using simple linear regressions. All statistical analyses were performed using SPSS statistical package 19.0. All the figures were prepared using SigmaPlot 11.0 and OriginLab OriginPro 2017.

normalized blank subtraction (O'Donnell et al., 2016). Particularly, the inner-filter effect of the fluorescence intensities was corrected following the previous reports by non-linear function fits (Chen et al., 2011; Xu et al., 2016). The standard CO2 gas was employed for GC-FID calibration (CV < 3%), and each gas sample in the headspace was determined in duplicates. The DIC and DOC concentrations were determined in triplicate, the outliers were picked out and the average of readings was record (CV < 2% with the majority being < 1%). Prior observations implied that some uncertainties for BDOC incubations could be attributed to DOC biodegradation in transit (Begum et al., 2017). In this study, water samples were cryopreserved (4 °C) and the BDOC incubation was carried out once the samples were transported to the laboratory, which minimized deterioration of DOC quantity and DOM optical properties. The influence from diurnal fluctuation on samples could be ignored according to the River Continuum Concept, because riverine continuous gradient of physical and chemical conditions led to a continuum of biotic adjustments and consistent patterns of %BDOC and DOM loadings (Statzner and Higler, 1985). Meanwhile, the diurnal variability was also corrected by all-day sampling (9:00 am-18:00 pm). The uncertainties of riverine %BDOC were ∼10% due to the systematic error (CV < 2%) of DIC and DOC analysis.

3. Results 3.1. CO2 production and spatial patterns of %BDOC Initial dissolved carbon (DOC and DIC) concentrations and the total CO2 production at 20 °C and 30 °C are listed in Table 1. Riverine DOC concentrations ranged between 2.41 mg/L and 8.62 mg/L with an average of 4.93 ± 1.49 mg/L. The DIC concentrations varied from 9.0 mg/L to 54.1 mg/L (with a mean of 26.6 ± 8.67 mg/L), and were 5.4-fold higher than the DOC concentrations. The total CO2 production levels were respectively 1.58 ± 0.96 mg/L at 20 °C and 2.48 ± 1.09 mg/L at 30 °C (p < 0.001, Mann-Whitney U test), resulting in a two-fold variation between %BDOC20 and %BDOC30 (Fig. 2). Similarly, the %BDOC levels exhibited the pronounced spatial patterns in the Longchuan River (p < 0.001, Mann-Whitney U test), which were higher in the tributaries (20 °C: 38.9 ± 19.8%; 30 °C: 58.6 ± 19.5%) than that in the main stem (20 °C: 26.1 ± 18.2%: 30 °C: 41.8 ± 16.3%) (Fig. 2). Particularly, the coincident peaks occurred in several sampling sites (e.g. sites 18, 24, 26 and 28), and the troughs were observed in the largest tributary (Qingling River, sites 34–36) with the average %BDOC levels of 21.4 ± 2.69% (20 °C) and 3

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Fig. 2. The spatial patterns of riverine %BDOC20 and %BDOC30 along the flow direction in the Longchuan River (main stem: sites 1–15; tributaries: sites 16–36).

Continuum Concept (Vannote et al., 1980; Hotchkiss et al., 2015). Besides, terrigenous organic carbon inputs and biological DOC exudation are strong determinants of riverine BDOC pools (Sachse et al., 2005). Extensive observations suggested the larger allochthonous and autochthonous BDOC inputs in streams compared to rivers (Hotchkiss et al., 2015), substantiating a greater proportion of %BDOC in the tributaries (Fig. 2). Particularly, agricultural practices enhanced the land–water connectivity and thus organic carbon inputs, which promoted the in-situ biomineralization (West and Marland, 2002) and resulted in the high % BDOC levels (62.2–79.7%) in the agricultural regions (site 18, 26 and 28). However, excessive tillage and crop harvesting would stimulate soil respiration and reduce soil carbon pools (Arneth et al., 2018), which decreased lateral BDOC inputs over geologic timescales and explained low %BDOC levels (∼30%) in the ecologically fragile regions (Qingling River) (Zhao and Zhao, 2007). The optical properties reflected the DOM sources and bioavailability in aquatic environments (Zhang et al., 2009). For instance, the initial SUVA254 values averaged 4.43 ± 2.22 L mg−1 m−1 (Fig. 3), indicating the high abundance of aromatic (recalcitrant) DOM (Piirsoo et al., 2012). The high SR values (∼1.5; Fig. 3) suggested the dominance of autochthonous DOMs, which was similar to the prior reports in the Yangtze River (Chen et al., 2011). The FI (2.35 ± 0.07) and BIX values (0.83 ± 0.05) implied that the DOMs were derived from the microbial origins (or autochthonous), but not recently produced (Xu et al., 2016). Besides, a previous study found the significant relationships between DOM optical properties and aquatic bacteria, suggesting that DOMs could explain the bacterial groups (Amaral et al., 2016). In fact, bacteria adapted to the surrounding environment and tended to co-vary with specific DOM properties (Lin et al., 2012). For example, the high loadings of endogenous DOMs probably originated from the abundant

37.3 ± 5.84% (30 °C). 3.2. Variations in optical properties Biodegradation largely changed the optical properties in the Longchuan River (Fig. 3). The SUVA254 values decreased significantly from 4.43 ± 2.22 L mg−1 m−1 to 3.31 ± 1.78 L mg−1 m−1 (20 °C) and 3.07 ± 1.33 L mg−1 m−1 (30 °C) (p < 0.05 by ANOVA). The S275values slightly decreased from 0.015 ± 0.004 nm−1 to 295 0.013 ± 0.003 nm−1 at 30 °C, and the S350-400 increased from 0.010 ± 0.002 nm−1 to 0.011 ± 0.002 nm−1 at 20 °C. The SR values maintained within 1.40–1.53 after 56-day incubations (p > 0.05 by ANOVA), whereas the fluorescence parameters varied with the incubation temperatures. For example, the FI increased from 2.35 ± 0.07 initially to 2.43 ± 0.09 at 20 °C and 2.50 ± 0.08 at 30 °C after incubation (p < 0.001), and the BIX exhibited the similar trend (increasing from 0.83 ± 0.05 to 0.88 ± 0.07 at 20 °C and 0.91 ± 0.08 at 30 °C, p < 0.001) (Fig. 3). 4. Discussion Riverine %BDOC exhibited a two-fold variation at a temperature gradient of 10 °C (Fig. 2), which was similar to the observations in the other subtropical rivers (Mao and Li, 2018), indicating that the elevated temperature stimulated aquatic microbial respiration (Fernandes et al., 2014). The %BDOC was observed higher in the tributaries when it compared to the main stem, suggesting the rapid depletion of labile DOC in the headwaters (Ejarque et al., 2017). Indeed, small rivers and streams generally have highly variable metabolism, and the main stem is inclined to more metabolically balanced according to the River 4

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Fig. 3. The variations of optical parameters (SUVA254, S275-295, S350-400, SR, FI and BIX) after 56-day incubations (the black line, red line, lower edge, upper edge, bars, dots in or outside the boxes refer to median and mean values, 25th and 75th, 5th and 95th, and < 5th and > 95th percentiles of all data, respectively). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

cleavage of high molecular weight DOMs could explain the increasing trend of S350-400 values at 20 °C (Hansen et al., 2016). On the other hand, the S275-295 values decreased after DOC incubations at 30 °C (Fig. 3), suggesting that the high temperature (30 °C) could promote the decomposition of low molecular weight DOMs (some of them were the broken DOMs as mentioned above) (Fellman et al., 2009).These apparently conflicting observations suggested that high molecular weight DOMs also gained incomplete degradation (or even mineralization) after incubations, and the microbial processes synchronously consumed and produced the DOMs (Amaral et al., 2016). Consistent with our first hypothesis, the DOM optical properties also traced the biodegradation

heterotrophic and autotrophic bacterial species (Kaplan and Bott, 2006; Landa et al., 2014), suggesting a high potential of DOM consumption and production in the Longchuan River. We highlighted the decreased SUVA254 values with DOC incubations (Fig. 3). This was rarely reported in the short-term laboratory incubations (Saadi et al., 2006), yet the finding was consistent with the longterm biodegradation (Dicorcia et al., 1994), indicating that the recalcitrant fractions in DOMs were not accumulated but were degraded (Reardon et al., 2002). The results supported that biodegradation was a continuous process, and aromatics gained biodegradation after 56-day incubations (Koehler et al., 2015). The disaggregation and bond

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30

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: y=-10.84ln(x)+43.466 R2=0.46, p < 0.05

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Fig. 4. Linear regressions of %BDOC:DOC against (a) SUVA254 (20 °C: R2 = 0.01, p > 0.05; 30 °C: R2 = 0.01, p > 0.05); (b) S275-295 (20 °C: R2 = 0.35, p < 0.05; 30 °C: R2 = 0.46, p < 0.05); (c) S350-400 (20 °C: R2 = 0.42, p < 0.05; 30 °C: R2 = 0.39, p < 0.05); (d) SR (20 °C: R2 = 0.006, p > 0.05; 30 °C: R2 = 0.004, p > 0.05); (e) FI (20 °C: R2 = 0.01, p > 0.05; 30 °C: R2 = 0.05, p > 0.05); (f) BIX (20 °C: R2 = 0.35, p < 0.05; 30 °C: R2 = 0.53, p < 0.05).

5. Conclusion

processes in natural waters. The FI values that reflected the terrestrial and microbial sources of DOMs (Chen et al., 2011) increased after DOC incubations (Fig. 3), illustrating that the elevated temperatures stimulated the production of autochthonous DOMs (Mao and Li, 2018). Similarly, the BIX values exhibited the increasing trend after incubations (Fig. 3), supporting the more recently produced DOMs in waters (Kolic et al., 2014). According to the second hypothesis, we found the strong linkages between %BDOC:DOC and DOM optical properties (Fig. 3), suggesting that the initial DOM components predicted the DOC biodegradability (Saadi et al., 2006). For example, the %BDOC:DOC was negatively correlated with a254 values (Fig. S2), indicating that higher abundance of aromatic fractions led to lower bioavailability in riverine DOMs (Mao et al., 2017). Negative %BDOC:DOC-spectral slopes correlations (Fig. 4) illustrated that DOC biodegradability increased with the increasing DOM molecular weight in the Longchuan River. This suggested the consumption of partial high molecular weight DOMs during long-term DOC incubations, and was supported by the decreasing SUVA254 and the increasing S350-400 values (Fig. 3). Our results implied the rapid consumption of labile low molecular fractions and accumulation of refractory fractions in recently produced DOMs, which established the negative links between the BIX values and %BDOC:DOC (Fig. 4). However, previous studies have observed that the labile moieties could be dominant in young DOMs in the Amazonian rivers (Mayorga et al., 2005). These inconsistent observations could be explained by the distinct regional biodegradability. In fragile ecosystem of the Longchuan River catchment, the intensive agricultural practices and serious soil erosion probably further picked specific microbes that decomposed and produced the recalcitrant DOMs in rivers (Kirchman, 2001). However, the insignificant spatial variability of DOM origins masked the correlations between %BDOC:DOC and origin-related optical parameters (SUVA254, SR and FI) (Ni et al., 2019). The linkages between %BDOC and heterotrophic bacterial groups should be called for caution.

Optical properties generally reflect the components, sources and bioavailability of aquatic DOM, showing a high potential in tracing biodegradation processes. In this study, we attempted to explore the interrelations between optical properties and DOM biodegradation. Riverine %BDOC levels were higher in the tributaries than in the main stem because of the rapid depletion of labile factions in the headwaters. The variations of optical properties (i.e., decreases in SUVA254 and S275295, and increases in S350-400, FI and BIX) suggested the co-existence of DOM biodegradation and production with incubation, and further indicated that the biodegradation stimulated autochthonous and recently produced DOMs in waters. The %BDOC:DOC exhibited the significant relationships with the optical properties, suggesting that DOM chemical compositions showed profound effects on the biodegradability, and thus predicted the riverine %BDOC well. Our results highlighted that optical properties could trace the DOM biodegradation in rivers. CRediT authorship contribution statement Maofei Ni: Data curation, Investigation, Methodology, Writing original draft. Siyue Li: Conceptualization, Funding acquisition, Investigation, Methodology, Supervision, Writing - review & editing. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This study was financially supported by the National Natural Science Foundation of China (NSFC grant no. 31670473) and “the 6

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Hundred-Talent Program” of the Chinese Academy of Sciences (granted to Dr. Li). We are grateful to Dr. Rong Mao, Tianyang Li, as well as Mr. Jiachen Luo for their help in the field trips. Special thanks are given to the editor, Prof. Geoff Syme and two anonymous reviewers for their constructive comments and suggestions.

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