Influence of Sediment DOM on Environmental Factors in Shallow

Environ Earth Sci (2017) 76:142 DOI 10.1007/s12665-017-6427-x

ORIGINAL ARTICLE

Inﬂuence of sediment DOM on environmental factors in shallow eutrophic lakes in the middle reaches of the Yangtze River in China

1,4 1,2 1,3 1 1 Yiwen Wu • Yingjie Li • Jingjing Lv • Beidou Xi • Lieyu Zhang • 1,4 1 1 1,4 Tianxue Yang • Guowen Li • Caole Li • Hongliang Liu

Received: 23 March 2016 / Accepted: 17 January 2017 / Published online: 10 February 2017 Ó Springer-Verlag Berlin Heidelberg 2017

Abstract Both three-dimensional excitation–emission environments. Components C1, C2, and C4 were correlated matrix fluorescence spectroscopy and parallel factor anal- positively with PC1 axis (factor 1), while C3 was opposite. ysis were utilized to identify dissolved organic matter Component C1 and C3 showed positive correlations with (DOM) components in sediments of thirty eutrophic shal- PC2 axis (factor 2). The CCA and Pearson correlation low lakes in the middle reaches of the Yangtze River in analysis showed that sediment DOC was correlated sig- China. Four distinct DOM components were identified: two nificantly with sediment variables (TN, OM, and conduc- 3- main components regarding terrestrial humic-like materials tivity), water quality variables (TN, PO4 , and Chl a), and (C1 and C2), one about terrestrial fulvic/humic-like mate- DOM components C1 and C3. All DOM components were rials (C3), and one related to autochthonous tryptophan- related significantly to different environment variables, like materials (C4). The dominance of terrestrial fluores- including water quality variables especially nutrient ele- - cence materials in DOM indicated that terrestrial inputs ments (NO3 , TN, Chl a, and DOC) and sediment variables ? have critical effects on the surveyed lakes. The Principal (OM, NH4 , and HCl–P). The relationship between DOC Component Analysis (PCA) and Canonical Corresponding and the lake comprehensive nutritive index TLI was mar- Analysis (CCA) were applied to analyze DOM components ginally significant. and their relationship with the environmental variables. PCA graphs showed that there were spatial differences in Keywords DOM Á Environmental variables Á PCA Á CCA Á DOM fluorophores among lakes with various Shallow eutrophic lakes

& Beidou Xi Introduction [email protected] & Lieyu Zhang As the largest substance pool in lake ecosystems, sediment [email protected] receives organic matter (OM) primarily from terrene and 1 State Environmental Protection Key Laboratory of residues of aquatic organisms (Zhu and Chen 2001). Dur- Simulation and Control of Groundwater Pollution, Chinese ing the process of organic matter mineralization, a large Research Academy of Environmental Sciences, amount of oxygen was consumed, and nutrient elements No. 8 Dayangfang, Beiyuan Road, Chaoyang District, such as carbon (C), nitrogen (N), phosphorus (P), and Beijing 100012, People’s Republic of China 2 sulfur (S) were released into water column, which could College of Resource and Environmental Engineering, cause serious deterioration of water quality (e.g., water Wuhan University of Technology, Wuhan 430070, People’s Republic of China eutrophication) (D’Angelo and Reddy 1994). In deep lakes, 85% of the OM was oxidized before it reached the ther- 3 School of Energy and Environment, Zhongyuan University of Technology, Zhengzhou 450007, People’s Republic of China mocline (Eadie et al. 1984) and the organic particles in deep lake sediments were not necessarily proportional to 4 CNHOMELAND Environmental Protection Water Pollution Governance Academician Workstation, Guangzhou 510000, those in the surface water, so the influence of OM on water People’s Republic of China quality in deep lakes is not critical (Xu et al. 2015). But in 123 142 Page 2 of 13 Environ Earth Sci (2017) 76:142 shallow lakes, there is no thermocline, and the substance River region have been under mesotrophic or eutrophic can be readily released from sediment to overlying water conditions (Wang et al. 2005a). Previous studies focused by wind, waves, and bioturbation (Kenney et al. 2016). mainly on the eutrophication process in one or two lakes in Thus, the OM in sediments plays an important role in this region. However, the trophic status in many other lakes controlling biogeochemical processes and influencing of this region is still poorly understood, and we know less water pollution in shallow lakes. about how DOM composition varies spatially across the As an active part of the OM, dissolved organic matter different lakes and what abiotic factors determine the (DOM) is comprised of a series of OM which could dis- spatial DOM variation in the Yangtze River region. As the solve in water and pass through 0.45 lm filter membrane aims of this study were: (1) to investigate the trophic status (Kalbitz et al. 2000). DOM includes organic acids carbo- of the shallow lakes in the middle reaches of the Yangtze hydrates, free amino acid, humic substances, phenolic, River region; (2) to analyze the sediment DOM compo- carbonyl, enzymes and so on (Hur et al. 2011; Jones and nents in eutrophic lakes using EEMs–PARAFAC method; Bryan 1998). DOM played important roles in influencing (3) to determine the relationship between environmental global C, N, and P bioavailability (Inamdar et al. 2012), variables and sediments DOM in the lakes of Yangtze altering metal speciation and transportation (Louis et al. River region. 2014; Neculita et al. 2011), and accepting electrons from microbial respiration (Lovley et al. 1996) in lake ecosystems. Residues and metabolites of microorganisms such as Methods phytoplankton and bacteria in water column provided the autochthonous resources for DOM in sediments (Birdwell Sample origin and Engel 2010), while terrigenous materials such as plants’ residues and humic substances are the allochtho- Ninety water samples and sixty sediment samples were nous inputs for DOM components (Li et al. 2015). Sedi- collected between August and September in 2014 from ments seems to be the main place for producing DOM thirty shallow lakes (GPS coordinates: 108°210–116°070 E, components, and the complexing ability of humic acid 29°050–33°200 N; waterbody size [10 km2; Fig. 1; from sediments was much stronger than those from waste Table 2) in Hubei Province, which is located in middle water (Ghosh and Banerjee 1997). These components course of the Yangtze River. This region has subtropical could enter the overlying water from sediment by water monsoon climate, with average annual temperature of upwelling, diffusion, and gas bubbles rising (Malmqvist 16 °C. The investigated lakes are located along Han River and Maki 1994). DOM might also precipitate from the and the Yangtze River. Palm red soil, yellow–brown soil, water column into the sediments (Ward 1994), and this and paddy soil are the main bottom types in the middle processes might occur more often in eutrophic freshwater course plain of the Yangtze River (Wang and Dou 1998). systems (Thomas 1997). Phytoplankton assemblages in the lakes were composed Three-dimensional excitation–emission matrix (EEM) mainly of Anabaena flos-aquae, Merismopedia glauca, fluorescence spectroscopy combined with parallel factor Microcystis aeruginosa,Cyclotella hubeiana, Synedra acus, analysis (PARAFAC) has been widely used to analyze the Cryptomonas ovata, etc.(Yan et al. 2015). Zizania aquatica DOM from numerous types of freshwater system in recent L, Potamogeton distinctus, Hydrilla verticillata, Nelumbo decade (Birdwell and Valsaraj 2010; Carstea et al. 2014). nucifera were the common aquatic macrophytes in these Fluorescence spectroscopy can provide sufficient infor- lakes (Wang and Dou 1998). mation on DOM source and composition with only small amount of sample and little sample isolation or preparation Water and sediment collections and physiochemical (Coble 1996; Birdwell and Valsaraj 2010; Guo et al. 2014). analysis PARAFAC is a powerful statistical method, which can effectively decompose fluorescence EEMs into different The water samples were taken from 0.5 m below the water independent groups of fluorescent compounds (Singh et al. surface by a 1.0 L plastic water sampler, and the surface 2010). Thus, utilization of both EEM and PARAFAC could sediment samples were collected by a 1/16 Peterson give a better understanding of DOM characteristics in the dredger. In the field, pH and dissolved oxygen (DO) in sediment of shallow lakes. water column were measured by the YSI ProDSS (digital Due to the economic and social development in the sampling system) handheld multiparameter meter (YSI- Yangtze River region, a lot of nutrient elements associated 556MPS, YSI) and water transparency (Sacchi depth, SD) with sewage waste were anthropogenically released into was measured by the Sacchi disk. Water and sediment the rivers and lakes in the Yangtze River region. Most of samples were kept at 4 °C and taken immediately to the the lakes in the middle and lower reaches of the Yangtze laboratory for chemical analysis. Then, sediments samples 123 Environ Earth Sci (2017) 76:142 Page 3 of 13 142

Fig. 1 Map of the thirty surveyed lakes in Hubei Province. Lake number-name: 1-Bao’an Lake, 2-Baoxie Lake, 3-Ce Lake, 4-Chidong Lake, 5- Chong Lake, 6-Daye Lake, 7- Dongxicha Lake, 8-Futou Lake, 9-Hanyang Dong Lake, 10- Hong Lake, 11-Hou Lake, 12- Huama Lake, 13-Liangzi Lake, 14-Longgan Lake, 15-Lu Lake, 16-Niulang Lake, 17-Niushan Lake, 18-San Lake, 19-Sanshan Lake, 20-Shangjin Lake, 21- Shangshe Lake, 22-Taibai Lake, 23-Tangxun Lake, 24-Tongjia Lake, 25-Wu Lake, 26-Xiliang Lake, 27-Yezhu Lake, 28- Chang Lake, 29-Zhangdu Lake, 30-Zhupo Lake

were freeze dried and filtered through 100 lm meshes 0.45 lm glass fiber filter (Whatman GF/F, burned at sieve in the laboratory, and stored at 4 °C for later use. 450 °C for 4 h) for later analysis. The dissolved organic In laboratory, chlorophyll a (Chl a) in water samples carbon (DOC) content in DOM leaching liquor (sedi- was determined using 90% of the hot ethanol extraction ments: water 1:10) was determined by a total organic method (Papista et al. 2002). Ammonium molybdate carbon analyzer (Analytik Jena AG-Germany, multi N/C spectrophotometry method (Spears et al. 2008) and alka- 2100S). line potassium persulfate digestion-UV spectrophotomet- ric method (D’Elia et al. 1977) were applied to test total Evaluation of the trophic status of the shallow lakes phosphorus (TP) and total nitrogen (TN) in water samples. Acidic potassium permanganate method was used to Trophic Level Index (TLI) has been widely used to assess determine chemical oxygen demand (COD )(GB Mn the eutrophication level of freshwater lakes (Jin et al. 1990; 11892-89 1990). As for sediment samples, TN and TP Xiang et al. 2015). Five parameters were used to calculate were determined using the CuSO –Se Kjeldahl procedure 4 the TLIs: Chl a, TP, TN, COD , and SD. Below are the and colorimetric method with H SO –HClO digestion. Mn 2 4 4 formulae for calculating TLI indices (Jin et al. 1990): Sediment pH and conductivity were determined by a pH meter (Multi 350i, WTW) in soil–water slurry at ratios of TLIðÞ¼ TN 10ðÞð 5:453 þ 1:694 ln TN 1Þ 1:1 and 1:5, respectively. Sediment OM was measured as TLIðÞ¼ TP 10ðÞð 9:436 þ 1:624 ln TP 2Þ the weight difference in the percentage of OM before and TLI Chl a 10 2:5 1:086 ln Chl a 3 after soil ignition at 550 °C using loss-on-ignition (LOI) ðÞ¼ðÞðþ Þ analysis (Lukawska-Matuszewska et al. 2014). We mea- TLIðÞ¼ SD 10ðÞð 5:118 À 1:94 ln SD 4Þ - sured nitrate nitrogen (NO3 ) and ammonium nitrogen ? TLIðÞ¼ CODMn 10ðÞ 0:109 þ 2:661 ln CODMn : ð5Þ (NH4 ) following the method described by Bao et al. (2000). The NaOH-extractable P (NaOH–P) and HCl-ex- The comprehensive TLI of the lakes was calculated tractable P (HCl–P) concentration of sediment were using the following equations: measured according to the Standards Measurements and X Xm Testing Program of the European Commission (SMT) TLI ¼ Wj Â TLIðjÞð6Þ (Ruban et al. 2001). Sediment DOM was extracted in j¼1 soil–water slurry at a ratio of 1:10 and kept in dark in a r2 P ij shaking table with 150 r/min at 20 °C for 24 h. After Wj ¼ m : ð7Þ r 2 extraction, the DOM leaching liquor was filtered through j¼1 ij

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Table 1 Correlation between Chl a and other indicators inﬂuencing XN TLI (Song et al. 2013) xijk ¼ ainbjnckn þ eijk nÀ1 ð8Þ Chl a TN TP SD COD i ¼ 1; 2; ...; i; j ¼ 1; 2; ...; j; k ¼ 1; 2; ...; k: rij 1 0.82 0.84 -0.83 0.83 2 Xijk stands for the ﬂuorescence intensity values of the rij 1 0.7056 0.6724 0.6889 0.6889 i th sample, under the emission wavelength (j) and exci-

tation wavelength (k); ain, bjn, and ckn means matrix A, B, In which TLI(j) is the jth composite indicator with the and C, respectively; eijk stands for residual matrix E corresponding weight Wj, the rij value represents the cor- (i 9 j 9 k), which means unexplained variance in fluo- relation coefficient between the reference Chl a concentra- rescence intensity; n stands for the matrix column number. tion and other indicators (Table 1). A set of 60 EEMs datasets of the thirty lakes sediments PThe trophic status of the lakes was categorized using the samples was generated by PARAFAC. The proper number TLI as follows (Xiang et al. 2015): of components was determined by the split-half analysis X and random initialization (Stedmon and Bro 2008). The Oligotrophic: TLI \ 30 X four-component model was chosen based on the percentage Mesotrophic: 30 TLI 50 of variance explained. All analyses were conducted in X MATLAB 2009 and plots conducted in Origin 8.0. Light eutrophic: 50 \ TLI 60 X Statistical analyses Mid-eutrophic: 60 \ TLI 70 X Principal Component Analysis (PCA) was utilized on log- Hypereutrophic: TLI [ 70: transformed sediments DOM components. PCA can identify the principal compounds which accounted for the EEMs–PARAFAC analysis majority of the fluorescence spectra variation of DOM (Guo et al. 2014; Cuss and Gue´guen 2013). Canonical EEMs–PARAFAC method was applied to study the Correspondence Analysis (CCA) and Pearson’s correlation relationship between eutrophication and the sediments test were conducted, relating the four EEMs–PARAFAC DOM in shallow lakes. Three-dimensional fluorescence components as response variables to fifteen environmental spectra of sediments DOM was conducted by fluorescence factors as explanatory variables. The relative importance of spectrum analyzer (Hitachi/F-7000), with the following variable could be obtained using forward selection. The parameter settings: the scanning wavelength from 200 to significance of relation of the variables was test with Monte 450 nm and emission wavelength from 280 to 550 nm Carlo permutation tests (999 permutations). The PCA and with the length of step of 5 nm for both wavelengths, CCA were conducted in CANOCO 4.5, and difference scanning spectrum and response time were set as default analysis and Pearson’s correlation test in SPSS 19.0. with the slit width of 5 nm and scanning speed of 2400 nm/min. Spectral data were read by Hitachi FL Solutions. Result Delaunay triangulation interpolation method was applied to make scatterplots and smooth the EEMs’ curves (Yao et al. Descriptions of the lakes 2013). Parallel factor analysis (PARAFAC) was utilized to analyze the three-dimensional fluorescence spectrum Among investigated lakes, five lakes have a surface area larger 2 2 (Colin and Rasmus 2008). The PARAFAC is a mathe- than 100 km , fifteen is between 20 and 100 km , and ten 2 matical model based on the theory of three-line break and lakes between 10 and 20 km . As for water chemical char- alternating least squares algorithm (Stedmon and Bro 2008; acteristics, all the lakes (Table 2) are alkaline (pH: 7.78–9.47). Carstea et al. 2014), which can conduct ‘‘mathematical Nine lakes have DO more than 10 mg/L, including Hanyang separation’’ in fluorescent substances of DOM. The fluo- Dong Lake, Hong Lake, Liangzi Lake, Huama Lake, Niulang rescence spectrum data were constructed as an i 9 j 9 k Lake, Sanshan Lake, Shangjin Lake, Tangxun Lake, and matrix, in which i is the number of samples and j and k Chang Lake. DO of Luoma Lake was only 5.895 mg/L, the represent the number of the excitation wavelength and lowest among the surveyed lakes. DO concentration of the emission wavelength of samples, respectively. PARAFAC other lakes was between 7 and 10 mg/L. With regard to sed- can break the matrix down to matrix A, B, and C, according iment, pH range was 6.75–7.44, and conductivity was from to the following decomposition model: 1.633 to 12.96 mS/cm, with highest value in Bao’an Lake.

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Table 2 Environmental characteristics of the surveyed lakes and lakes locations Number Name DOC (g/kg) Sediment pH Sediment conductivity Longitude Latitude Area (km2) (mS/cm)

1 Bao’an lake 10.65 6.88 12.96 114.7250 30.25833 48.0 2 Baoxie lake 10.43 7.44 1.99 114.5492 30.41611 25.8 3 Ce lake 10.5 6.86 3.31 115.1569 30.25306 11.8 4 Chidong lake 10.07 7.16 3.24 115.3853 30.14361 26.8 5 Chong lake 24.58 7.25 7.38 112.2744 29.92166 13.9 6 Daye lake 10.8 7.26 1.77 115.1389 30.09056 68.7 7 Dongxicha lake 26.19 6.95 6.71 113.6633 30.81417 24.3 8 Futou lake 20.27 7.12 5.78 114.2011 30.05056 114.7 9 Hanyang Dong lake 10.65 6.88 3.41 114.0528 30.54444 34.4 10 Hong lake 16.12 7.07 4.62 113.3375 29.83611 344.4 11 Hou lake 8.71 7.11 2.18 114.2800 30.7367 16.2 12 Huama lake 7.93 7.13 2.17 115.0425 30.29388 10.3 13 Liangzi lake 19.09 7.12 4.06 114.5408 30.2575 304.3 14 Longgan lake 7.21 7.20 3.92 116.2492 29.98578 316.2 15 Lu lake 14.29 7.17 7.72 114.1947 30.20278 40.2 16 Niulang lake 7.69 6.89 4.80 111.9662 29.81888 15.9 17 Niushan lake 32.7 6.77 4.28 114.5152 30.33794 41.67 18 San lake 19.09 6.88 9.75 113.9534 29.9436 13.8 19 Sanshan lake 31.38 7.10 6.58 114.7681 30.31952 24.3 20 Shangjin lake 6.64 7.09 2.51 112.5001 29.64345 18.6 21 Shangshe lake 10.08 6.93 2.06 114.2103 30.13587 11.9 22 Taibai lake 6.94 7.11 1.63 115.8092 29.99222 25.1 23 Tangxun lake 7.59 7.15 3.30 114.3814 30.40472 36.6 24 Tongjia lake 8.19 7.22 2.07 114.1658 30.80556 14.4 25 Wu lake 6.06 7.38 2.36 114.4906 30.78667 21.2 26 Xiliang lake 22.47 7.06 5.16 114.0989 29.97778 72.1 27 Yezhu lake 8.97 6.75 2.44 114.0781 30.86083 26.6 28 Chang lake 19.65 7.30 3.74 112.3806 30.42553 129.1 29 Zhangdu lake 5.06 7.19 2.38 114.6903 30.63306 35.2 30 Zhupo lake 11.13 7.36 2.90 115.4006 29.83778 17.7

Trophic status of the lakes PARAFAC analysis identiﬁed four DOM components: Component 1 (C1) which represented terrestrial humic-like TP and TN in the lakes’ water ranged from 0.035 to materials (Table 4); Component 2 (C2) which was humic- 0.354 mg/L and from 0.718 to 2.688 mg/L, respectively like materials (Peaks A and C); Component 3 (C3) which (Table 3). Chl a and SD changed from 4.480 to was related to terrestrial fulvic/humic-like materials; 228.644 lg/L and from 0.27 to 1.37 m among lakes, Component 4 (C4) which was autochthonous protein-like respectively. CODMn varied between 1.888 and 21.418 O2, materials, in which peak S (Ex/Em 235/340) indicated low mg/L. According to the Trophic Level Index (TLI) we excitation regime tryptophan-like materials and peak T calculated, the thirty investigated lakes were divided into meant strong excitation regime tryptophan-like materials four categories: mesotrophic, light eutrophic, mid-trophic, (Fig. 2). and hypereutrophic lakes. Spatial variation in DOM components EEMs–PARAFAC analysis among the thirty lakes

Two peaks (S, T) were observed in the UV region at C1 and C2, representing humic-like materials, occupied 340 nm of emission, while others (A, M and C) were in the 62.1% of the DOM. The protein-like materials (C4) visible region at 400–458 nm of emission. EEMs– accounted for 20.3% of the DOM components. Fulvic/

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Table 3 Trophic statues of the Lake number TP (mg/L) TN (mg/L) Chl a (lg/L) SD (m) COD (O , mg/L) TLI Type shallow lakes Mn 2 1 0.05 1.12 52.99 0.45 3.95 58.85 2 2 0.035 1.07 4.48 0.46 2.63 48.53 1 3 0.07 1.38 112.12 0.38 7.85 66.14 3 4 0.08 1.72 75.45 0.46 8.82 65.77 3 5 0.09 1.62 30.08 0.73 9.49 62.24 3 6 0.07 1.90 35.21 0.50 9.42 63.46 3 7 0.07 0.75 8.52 1.27 6.71 52.62 2 8 0.04 1.18 23.47 0.38 4.28 57.00 2 9 0.25 2.69 228.64 0.31 21.42 79.19 4 10 0.11 1.53 145.91 0.43 7.44 67.89 3 11 0.16 2.08 140.14 0.27 9.66 72.41 4 12 0.05 1.94 29.19 0.70 3.09 55.54 2 13 0.04 0.81 12.61 0.51 3.38 52.38 2 14 0.06 0.72 41.54 0.39 5.58 60.34 3 15 0.07 1.12 47.56 0.60 7.05 61.35 3 16 0.11 1.13 99.22 0.32 8.78 68.28 3 17 0.09 0.92 31.11 0.84 9.35 60.72 3 18 0.04 1.59 20.07 0.95 3.16 52.43 2 19 0.05 1.50 24.23 0.72 2.57 53.16 2 20 0.14 1.66 130.71 0.28 12.21 72.49 4 21 0.16 1.82 99.21 0.42 12.62 70.95 4 22 0.01 1.32 100.07 0.35 5.35 65.30 3 23 0.09 1.63 153.65 0.47 6.64 66.87 3 24 0.04 1.64 18.47 1.22 1.89 48.25 1 25 0.09 1.54 34.38 1.23 10.51 61.08 3 26 0.05 1.33 33.61 1.37 3.37 53.03 2 27 0.11 2.04 68.07 0.3 11.33 69.74 3 28 0.11 2.08 113.94 0.32 11.89 71.26 4 29 0.07 0.98 9.17 0.62 2.71 51.38 2 30 0.13 1.77 107.83 0.3 14.08 72.27 4 humic-like materials (C3) was the least (only 17.6%) in the Fig. 2 Fluorescence spectra and excitation/emission profiles of fourc DOM components and C3 fluorescent intensity did not factors from parallel factor method. Contour plots (a–d: C1–C4) present spectral shapes of excitation and emission. Line plots (e–h: exist in Lake 3, 4, 12, 15 and 21 (Fig. 3a). There was no C1–C4)atright side of contour plots present split-half validation significant difference in the proportions of sediment DOM results for four component components among the four eutrophic lakes (Fig. 3b), but the content of DOC was obviously different, with an increase from the average of 9.31 g/kg in mesotrophic Factor 1 0:792C1 0:454C2 0:99C3 0:509C4 lakes to 18.01 g/kg in light eutrophic lakes and then with a ¼ þ À þ decrease to 11.14 g/kg in hypereutrophic lakes (Table 4). ð9Þ Factor 2 ¼ 0:288C1 À 0:855C2 þ 0:142C3 þ 0:591C4: Principle Component Analysis ð10Þ The Principle Component Analysis (PCA) showed that the The components C1, C2, and C4 showed a positive eigenvalues of PC1 and PC2 axes (factor 1 and factor 2) correlation with factor 1, while C3 was related negatively were 0.72 and 0.192, respectively (Fig. 4a). Each PCA to factor 1. Meanwhile, factor 2 was correlated positively factor is a linear combination of four PARAFAC compo- with C1, C3 and C4, and negatively with C2 (Fig. 4b). nents where the measured factors are dimensionless and PCA plots indicated that the thirty lakes could be grouped can be either positive or negative: into two main clusters, one cluster with 17 lakes and the

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0.30 ae Excitation Emission 0.25

0.20

0.15

0.10 Component1 loading

0.05

0.00 200 250 300 350 400 450 500 550 Wavelength(nm)

bf0.30 Excitation Emission 0.25

0.20

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0.10 Component2 loading

0.05

0.00 200 250 300 350 400 450 500 550 Wavelenth(nm)

c 0.50 g Excitation 0.45 Emission 0.40

0.35

0.30

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0.20

0.15 Component3 loading 0.10

0.05

0.00 200 250 300 350 400 450 500 550 Wavelength(nm)

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0.40 d h Excitation 0.35 Emission

0.30

0.25

0.20

0.15 Component4 loading 0.10

0.05

0.00 200 250 300 350 400 450 500 550 Wavelength(nm)

Fig. 2 continued

Fig. 3 Proportions of DOM EEMs-PARAFAC components. b Type 1 lakes indicated mesotrophic lakes; Type 2 lakes indicated light eutrophic lakes; Type 3 lakes indicated mid-trophic lakes; Type 4 lakes indicated hypereutrophic lakes

Table 4 Peak positions of ﬂuorophores in sediments samples Peak Ex (nm) Em (nm) Fluorescence type Reference Probable source

A 250 400 Humic-like materials Stedmon et al. (2003), Wu et al. (2012) Terrestrial M 319 400 Humic-like materials Coble (1996), Wu et al. (2012) Terrestrial A 265 458 Humic-like materials Coble (1996), Ishii and Boyer (2012) Terrestrial C 375 458 Humic-like materials Coble (1996), Ishii and Boyer (2012) Terrestrial A/C 225 400 Fulvic/humic-like materials Coble (1996) Terrestrial S 235 340 Tryptophan-like materials, protein-like materials Hudson et al. (2007), Jiang et al. (2008) Autochthonous T 280 340 Tryptophan-like materials, protein-like materials Coble (1996), Yamashita and Tanoue (2003) Autochthonous

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Fig. 4 Property–property plots of PCA factor loadings. a PCA graph of the four sediment DOM components; b Property–property plots of PCA factor scores of all samples

C3 (p \ 0.01). DOM component C1 was related signifi- - cantly and positively to water quality variables (NO3 and Chl a)(p \ 0.05), and negatively to DOM component C3 and DOC (p \ 0.01). DOM component C2 was correlated significantly with sediment OM (p \ 0.05), positively with ? sediment NH4 and negatively with component C3 (p \ 0.01). DOM component C3 was associated negatively with water TN, Chl a and component C4 (p \ 0.05) and negatively with sediment DOC (p \ 0.05), and highly - significant correlated with water NO3 (p \ 0.01). DOM component C4 was correlated negatively with sediment HCl–P (p \ 0.05) and negatively with sediment conductivity (p \ 0.01). The lake comprehensive nutritive index TLI was associated negatively with sediment TN and OM Fig. 5 CCA of the FARAFAC with 4 components and environment (p \ 0.05), positively with sediment IP (p \ 0.05), posi- 3- factors of all the lakes. STN represented TN in sediments, SNO3–N - ? tively with water TN, TP, PO4 (p \ 0.01) (Table 5). represented NO3 in sediments, SNH3–N represented NH4 in sediments, SOP represented OP in sediments, STP represented TP in sediments, SIP represented IP in sediments, SNaOH–P represented NaOH–P in sediments, SHCl–P represented HCl–P in sediments Discussion other with 12 lakes, with the exception of Lake 19 which Characteristics of DOM fluorescence components was very different from the two clusters. in the lakes

Correlations between DOM components In this study, ﬂuorescence intensity of C1, C2, and C4 was and environmental variables detected in all the thirty lakes, and was at least 65% of the DOM components in each of the thirty lakes. Even in Lake Our results showed there was a high correlation between 3, 4, 12, 15, and 21, the three DOM components proportion ? water nutrient variables (TN, NH4 , and Chl a), sediment reached 100%, and C1 had the biggest proportion. Yao OP, and CCA1 axis. CCA2 axis (CCA factor 2) was highly et al. (2011) found that the dynamics of colored dissolved related to sediment variables (TN, IP, NaOH–P, OM) and organic matter (CDOM) in Lake Taihu were controlled by TP in water (Fig. 5). the geology and associated land use. That might be an Pearson’s correlation analysis showed that sediment interpretation of our result. And the result might also DOC was correlated signiﬁcantly with sediment variables suggest that C1 and C3 had a similar molecular structure, (TN, OM, conductivity, and DOM component C1), water or they could transform into each other in certain 3- quality variables (TN, PO4 , and Chl a) and component conditions. 123 142 Page 10 of 13 Environ Earth Sci (2017) 76:142

Table 5 Correlations of DOM components and environmental variables

- 3- TN TP NO3 PO4 Chl a STN IP OP HCl–P

C1 0.330 0.205 0.422* 0.267 0.387* -0.332 0.164 -0.044 0.218 C2 0.315 -0.078 0.209 -0.072 0.293 0.316 -0.086 -0.006 0.140 C3 -0.427* -0.086 -0.493** -0.150 -0.432* 0.093 -0.001 0.059 -0.110 C4 0.015 -0.017 0.229 0.039 -0.044 -0.191 -0.175 -0.078 -0.385* DOC -0.383* -0.324 -0.226 -0.363* -0.375* 0.707** -0.322 0.298 -0.170 TLI 0.633** 0.810** 0.059 0.773** 0.318 -0.389* 0.363* -0.133 0.198

À SNH3–N SNO3 À N OM Cond. C1 C2 C3 C4 DOC

C1 -0.301 0.269 -0.231 -0.333 1 -0.023 -0.746** 0.236 -0.666** C2 0.630** 0.060 0.404* 0.043 -0.023 1 -0.569** -0.116 0.100 C3 -0.162 -0.344 -0.047 0.359 -0.746** -0.569** 1 -0.416* 0.454* C4 -0.073 0.376* -0.141 -0.485** 0.236 -0.116 -0.416* 1 -0.188 DOC 0.323 0.090 0.589** 0.464** -0.666** 0.100 0.454* -0.188 1 TLI -0.185 -0.055 -0.404* -0.270 0.232 -0.070 -0.097 -0.053 -0.304

- ? STN represented TN in sediments, SNO3–N represented NO3 in sediments, SNH3–N represented NH4 in sediments

Some studies reported that fluorescence intensities of components in the investigated lakes, which could be protein-like and fulvic-like materials were higher than that generalized into a conclusion that exogenous inputs may of humic-like materials in sewage-impacted water. Simi- greatly influence the lakes sediment environment. In larly, the protein-like fluorescence peaks were dominant in summary, the DOM in surveyed shallow lakes might not be the samples collected from agricultural waste water (Baker directly influenced by human-induced pollutions, but the and Inverarity 2004; Yao et al. 2011). In our surveyed large proportion of terrestrial humic-like materials in DOM lakes, DOM fluorescence intensities of sediments were could imply that its indirect effect has been becoming composed mainly of terrestrial humic-like materials, which increasingly severe. indicated that terrestrial inputs should be the primary source of DOM in sediments. These inputs might not Using CCA to identify the relationship originate from the sewage-impacted water or agricultural between PARAFAC components and environment waste water and could come from other sources on land. variables

PCA of the DOM–PARAFAC components CCA and Pearson’s correlation analysis showed that the CCA axis 1 was represented mainly by the water body Our results showed that components C1, C3, and C4 were environment variables, the axis 2 primarily associated with associated positively with the PCA factor 1 and 2. PCA sediment environment factors of lakes (Fig. 5 and Table 5). factor 1 represented humic-like substances due to great Our results showed that there was a significant correla- contributions of C1 and C3 to the factor 1. The high cor- tion between sediment DOC and sediment TN, OM, which relation between C1 (humic-like materials) and C4 (tryp- are consistent with previous studies that as sediment OM tophan-like materials) suggested that they might have a increased, the sorption of DOC increased with it (Wang common source and a similar transport process (Yao et al. et al. 2007). DOM in sediments could strongly promote the 2011). Our results are consistent with the previous study in P sorption in sediment (Wang et al. 2007). This is in Lake Erhai (Fu et al. 2004). Besides weak dispersive force accordance with our result that DOC was correlated sig- 3- (Romera-Gastillo et al. 2014) and the hydrophobic inter- nificantly and negatively with PO4 in water. DOM in action of the components surface (Mei et al. 2009), Wang sediments possibly changed the P sorption isotherms of the et al. (2015) suggested that humic-like components could sediments, and therefore enhanced the sorption process and form molecular assemblies with protein-like components, the efficiency of P sorption in the sediments, without any which could lead to fluorescence quenching. effect on the P sorption kinetics (Wang et al. 2005b). There PCA results showed that thirty lakes can be grouped into were two potential mechanisms for DOM promoting sed- two clusters and humic-like materials (C1 and C2) have iment P adsorption. One might be that DOM exchanged – large loadings on PCA factor 1 and 2. This possibly indi- OH group within sediment (Kaiser and Zech 1998), which cated that humic-like materials played a main role in DOM could change sediment pH and lead to release of P and the

123 Environ Earth Sci (2017) 76:142 Page 11 of 13 142 dissolution of iron and aluminum ion into water, thereby Conclusions affecting the phosphorus adsorption in sediment (Deten- beck and Brezonik 1991). The other reason could be that In our study, four components of sediments DOM in sur- the complexation and chelation of DOM improved OM veyed lakes were identified by EEMs–PARAFAC method: content in sediment (Huang et al. 2002) and changed the two components regarding terrestrial humic-like materials characteristics of electric charge on the sediment surface, (C1 and C2), one about terrestrial fulvic/humic-like mate- which can affect the P absorption (Wang et al. 2005a). rials (C3), and one related to autochthonous tryptophan- For DOM–PARAFAC components, C1 and C2 were like materials (C4). PCA plot showed that there were both humic-like materials, their high correlations with spatial variations in DOM fluorophores among lakes with DOC, N, OM, and Chl a indicated that humic-like various environments. For the thirty shallow lakes, ter- materials in DOM determined these environmental vari- rigenous inputs strongly affected lake sediments, and other ables. Positive relationship between humic-like materials exogenous factors also explained some variations in DOM. (C1) and Chl a indicated that humic-like fluorophores DOM composition were different possibly due to the dif- could be produced by bacteria using organic matters ferent lake geology locations. The CCA and Pearson tests which came from phytoplankton (Rochelle-Newall and showed that the content and composition of DOM were Fisher 2002). C3 represented terrestrial fulvic/humic-like related to the lake nutrient elements.P The significant cor- materials, and its negative correlations with water TN and relation between DOC content and TLI indicated that the Chl a and positive relationships with DOC and water content of sediments DOM might influence eutrophication - NO3 implied that C3 is an important component in in lakes of Yangzt River region. DOM, and its conversion process was distinct from C1 and C2. C4 (autochthonous tryptophan-like materials) was Acknowledgements This research was supported by the Mega-pro- correlated positively with sediment NO - and negatively jects of Science Research for Water Environment Improvement of 3 China (Program No. 2012ZX07101-002). with HCl–P. Some studies also found the tryptophan-like materials had a positive interaction with a microbial enzyme named extracellular leucine aminopeptidase References activity (AMA). 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