Seasonal Characterization of Cdom for Lakes in Semi-arid Regions of Northeast China Printer-friendly Version Interactive Discussion Seasonal Characterization of Cdom for Lakes in Semi-arid Regions of Northeast China Using Excitation-emission Matrices Fluorescence and Parallel Factor Analysis (eem-pa

This discussion paper is/has been under review for the journal Biogeosciences (BG). Please refer to the corresponding final paper in BG if available. Abstract The seasonal characteristics of fluorescence components in CDOM for lakes in the semi-arid region of Northeast China were examined by excitation-emission matrices fluorescence and parallel factor analysis (EEM-PARAFAC). Two humic-like peaks C1 (Ex/Em = 230, 300/425 nm) and C2 (Ex/Em = 255, 350/460 nm) and two protein-like B 5 (Ex/Em = 220, 275/320 nm) and T (Ex/Em = 225, 290/360 nm) peaks were identified using PARAFAC. The average fluorescence intensity of the four components differed −1 10 in February and reduced to the lowest 1.57 ± 0.55 nm −1 in April 2014. In general, the fluorescence intensity was dominated by peak C1, indicating that most part of CDOM for inland waters being investigated in this study was originated from phytoplankton degradation. The lowest C2 represents only a small portion of CDOM from terrestrial imported organic matter to water bodies through rainwash and soil leaching. The two 15 protein-like intensities (B and T) formed in situ through microbial activity have almost the same intensity. Especially, in August 2013 and February 2014, the two protein-like peaks showed obviously difference from other seasons and the highest C1 (1.02 nm −1) was present in February 2014. Components 1 and 2 exhibited strong linear correlation (R 2 = 0.633). There were significantly positive linear relationships between CDOM 20 absorption coefficients a(254) (R 2 = 0.72, 0.46, p < 0.01), a(280) (R 2 = 0.77, 0.47, p < 0.01), a(350) (R 2 = 0.76, 0.78, p < 0.01) and F max for two humic-like components (C1 and C2), respectively. A close relationship (R 2 = 0.931) was found between salinity and DOC. However, almost no obvious correlation was found between salinity and EEM-PARAFAC extracted components except for C3 (R 2 = 0.469). Results from this 25 investigation demonstrate that the EEM-PARAFAC technique can be used to evaluate the seasonal dynamics of CDOM fluorescence components for inland waters in semi-arid regions of Northeast China.


Introduction
Dissolved organic matter (DOM), a heterogeneous mixture of humic acids, proteins and carbohydrates, plays an important role in aquatic ecosystems (Zhang et al., 2010).Chromophoric dissolved organic matter (CDOM), the colored fraction of DOM, absorbs light energy in the ultraviolet (UV) and visible region of the spectrum and inhibits the propagation of UV radiation.CDOM in waters also affects the transport and bio-availability of materials such as trace metals and other pollutants (Song et al., 2013), so it can be used as a proxy of water quality.In natural waterbodies, CDOM originates from the degradation of plant materials and other organisms and terrestrially imported substances, which varies in time and space and is controlled by its structure and composition (Stedmon et al., 2003).CDOM is compositionally complex, making it difficult to isolate hydrophobic from hydrophilic acids using Amberlite XAD ionexchange resins (Aiken et al., 1992;Spencer et al., 2010).Nonetheless, some optically active components of CDOM can emit fluorescence after absorbing light at certain wavelengths (Zhang et al., 2010); therefore, fluorescence spectroscopic techniques can be used to provide detailed information about the source and concentration of CDOM.Traditional fluorescence techniques, including fluorescence emission spectrometry and synchronous fluorescence scanning, applied to examine CDOM components have the drawback that the output was restricted to a linear scan (Hudson et al., 2007).

Y. Zhao et al.: Seasonal characterization of CDOM for lakes in semiarid regions of Northeast China
Recently, excitation-emission matrix (EEM) fluorescence spectroscopy has been applied to identify CDOM components because of its ability to produce synchronous scan spectra in the form of contours (Stedmon et al., 2003;Zhang et al., 2010).EEM spectroscopy is considered the most effective technique for studying the composition of fluorophores given its high selectivity and sensitivity to CDOM in water columns (Zhang et al., 2010).In recent years, EEM spectroscopy has been widely used to investigate the dynamics of marine, freshwater and ice meltwater ecosystems as well as snowmelt water (Barker et al., 2006(Barker et al., , 2009(Barker et al., , 2010(Barker et al., , 2013;;Coble, 2007;Fellman al., 2010;Guo et al., 2010;Hudson et al., 2007;Stedmon et al., 2007).Moreover, the EEM spectroscopy can also be used to distinguish allochthonous and autochthonous CDOM sources in aquatic environments (Coble et al., 1998;Mayer et al., 1999;Yamashita et al., 2008Yamashita et al., , 2010;;Zhang et al., 2013).Based on the peak positions in the EEM, two main fluorescent components, i.e., humiclike and protein-like substances, have been identified and investigated (Del Castillo et al., 1999;Jaffé et al., 2004).However, overlapped fluorophores of CDOM EEM could make this traditional "peak-picking" method unreliable to evaluate CDOM dynamics in aquatic ecosystems (Coble, 1996;Stedmon et al., 2003).Recently, the combined EEM-PARAFAC (parallel factor analysis) technique has been shown to effectively decompose EEM of CDOM into independent fluorescent components and assess the source of CDOM and relationships with other water quality parameters.A number of investigators have used EEM-PARAFAC to characterize DOM in freshwater and marine aquatic environments (Borisover et al., 2009;Cory et al., 2005;Guo et al., 2010;Stedmon et al., 2003;Stedmon and Markager, 2005;Yamashita, 2008;Zhang et al., 2010Zhang et al., , 2011Zhang et al., , 2013)).Stedmon et al. (2003) introduced PARAFAC and identified five distinct DOM components for a Danish estuary and its catchment.In coastal environments, Yamashita et al. (2008) reported on seven components using the combined EEM-PARAFAC technique and assessed the dynamic of individual fluorophores and their relationship with salinity in Ise Bay.Zhang et al. (2011) also found three different components by PARAFAC modeling and analyzed the correlations between the fluorescent components and absorption coefficients of CDOM for Lake Tianmu and its catchment.
The Songnen Plain is a fluvial plain with semiarid climate, in which many fresh and brackish waters are distributed according to its geomorphological characteristics (Song et al., 2013).Dissolved organic carbon (DOC) characteristics of these fresh and brackish waters across the Songnen Plain have been studied by Song et al. (2013); the results indicated that a huge amount of DOC was stored in these waters.In particular, brackish waters would exhibit high average DOC concentrations and significantly contributed to the carbon budget to inland waters (Duarte et al., 2008;Song et al., 2013;Tranvik et al., 2009).However, little study has been done on the detailed information of DOC sources for these waters in the Songnen Plain.Therefore, it motivated us to investigate the components in CDOM for both fresh and brackish waters in the semiarid region.In the present study, the absorption and fluorescence of CDOM were determined for the water samples collected from seven lakes in the western part of the province of Jilin, which varied in different seasons.The specific objectives of this study are to (1) characterize CDOM components contained in these lakes using the EEM and their origins through the EEM-PARAFAC method; (2) assess the dynamic of individual fluorescent components of CDOM under seasonal variation; and most importantly (3) link CDOM fluorescence intensities, absorption coefficients, DOC concentrations and salinity to each other, in order to establish proxies for CDOM bioavailability and photoreactivity in waters.

Lakes and water sampling
The waterbodies investigated in this study were located in the western part of Jilin, which belongs to the semiarid part of the Songnen Plain (Song et al., 2013).Two groups of lakes were investigated, i.e., the Chagan lake group and the Yuelianghu lake group.The Chagan lake group is made up of Lake Chagan (CGL), Xinmiaopao (XMP), Xindianpao (XDP) and Kulipao (KLP).The Yuelianghu lake group mainly includes Lake Yueliang (YLL), Talahong (TLH) and Xinhuangpao (XHP) (Fig. 1).The two groups are about 60 km away from each other, each of which includes both fresh and brackish waters.The primary economic value for these lakes is fisheries, agricultural irrigation and recreation.The average annual precipitation is about 391 mm, but the average evaporation is up to 1790 mm, resulting in water scarcity.Due to the area dominated by saline-alkali soil, the rainfall flush and agricultural catchment land use can result in an increase of lake salinities.These seven lakes are endowed with similar geological, hydrological and climatic settings; thus we presume that similar processes may control the CDOM components.In order to characterize the CDOM fluorescent components under seasonal variation using EEM-PARAFAC, 67 water samples were collected from the surface of the seven lakes in 1 L acid-cleaned plastic bottles during four field campaigns in June and August 2013 as well as in February and April 2014, respectively.These samples were collected during the ice-covering period using an ice drilling auger.The under-ice surface water came up when a hole was drilled in the ice layer by the auger.The ice shavings were collected in plastic bags and the under-ice surface water was collected in plastic bottles.The collected samples were held on ice and immediately transported to the laboratory in the city of Changchun in Jilin within 3-5 h.In the laboratory, these samples were filtered within 24 h and then kept at 4 • C until analysis within 2 days.Latitude and longitude of each sample location were recorded in situ using a Trimble Global Positioning System (GPS).

Analytical procedures
To characterize the basic parameters of water quality, salinity was measured by a DDS-307 electrical conductivity meter in the laboratory.Salinity was expressed on the basis of the UNESCO practical salinity unit.The pH was measured using a PHS-3C pH meter at room temperature (20 ± 2 • C) in the laboratory.Water turbidity was determined using the Shimadzu UV-2600PC UV-visible dual beam spectrophotometer with matching 3 cm quartz cells at room temperature (20 ± 2 • ) with Milli-Q water as the reference (UV Talk Letter, 2013).To determine DOC concentrations, water samples were filtered through 0.45 µm filters and then measured using a Shimadzu TOC-5000 analyzer and a 1.2 % Pt on silica catalyst at 680 • C. Potassium hydrogen phthalate was used as a reference.The reproducibility of the analytical procedure was within 2-3 % for the current study (APHA/AWWA/WEF, 1998; Song et al., 2011).

Absorption measurement
In the laboratory, all the samples were filtered at low pressure, first through a precombusted Whatman GF/F filter (0.7 µm), and then through a prerinsed 25 mm Millipore membrane cellulose filter (0.22 µm) into glass bottles.Absorption spectra of the samples were measured between 200 and 800 nm at 1 nm increments using the Shimadzu UV-2600PC UV-visible dual beam spectrophotometer with a 1 cm quartz cuvette and Milli-Q water as a reference.The absorption coefficient a CDOM was calculated from the measured optical density (OD) of the sample using Eq. ( 1): where γ is the cuvette path length (0.01 m) and the factor 2.303 converts from base 10 to base natural logarithm transformation.Some fine particles possibly remained in the filtered solution (Babin et al., 2003;Bricaud et al., 1995); therefore it was necessary to correct for scattering by fine particles and in this case, OD (null) is the average optical density over 740-750 nm where the absorbance of CDOM can be assumed to be zero.A CDOM absorption spectrum (a CDOM (λ)) can be expressed as an exponential function (Babin et al., 2003;Bricaud et al., 1995): where a CDOM (λ i ) is the CDOM absorption at a given wavelength λ i , a CDOM (λ r ) is the absorption estimate at the reference wavelength λ r (440 nm) and S is the spectral slope of the CDOM absorption.According to Helms et al. (2008), S is calculated by fitting a linear model to the data over a wavelength range of 275 to 295 nm (S1) or 350 to 400 nm (S2).
To eliminate the inter-laboratory variability, the slope ratio S R = S1/S2 is defined to indicate the molecular weight and photobleaching of CDOM (Helms et al., 2008;Zhang et al., 2010).

Three-dimensional fluorescence measurement
The EEM analysis of CDOM was conducted using a Hitachi F-7000 fluorescence spectrometer (Hitachi High-Technologies, Tokyo, Japan) with a 700-voltage xenon lamp.
The scanning ranges were 200-450 nm for excitation, and 250-500 nm for emission.Readings were collected in the ratio mode at 5 nm intervals for excitation, and at 1 nm intervals for emission, using a scanning speed of 2400 nm min −1 .The band passes were 5 nm for both excitation and emission.
The inner-filter effect, which results from reabsorption and excitation of the fluorescence itself, can reduce the fluorescence intensity by 5 % (Larsson et al., 2007;McKnight et al., 2001).In order to eliminate the inner-filter effect, the EEM was corrected for absorbance by multiplying each value in the EEM by a correction factor based on the assumption that the average path length of absorption of the excitation and emission light is one half length of the cuvette (McKnight et al., 2001;Zhang et al., 2010).The correction function is expressed as follows: where F Corr and F obs are the corrected and uncorrected fluorescence intensities and A ex and A em are the absorbance values at the respective excitation and emission wavelengths.
The measured fluorescence intensity is dependent on the concentration of the dissolved fluorophores in waterbodies.Finally, the fluorescence intensities of all samples' EEMs were normalized to the area under the Milli-Q water Raman peak (λex = 350 nm, λem = 371-428 nm) measured daily (Lawaetz and Stedmon, 2009).The contour figures of the EEMs were plotted using the MATLAB 10.0 software package (MathWorks, Natick, Massachusetts, United States).

PARAFAC modeling
PARAFAC, a three-way method, is applied to divide the CDOM fluorescence into separate fluorescent signals (Andersen and Bro, 2003;Stedmon and Bro, 2008).According to Stedmon and Bro (2008), a similar PARAFAC analysis is carried out in the present study using the DOMFluor toolbox in MATLAB with the "N-way toolbox for MAT-LAB" (Andersson and Bro, 2000).Before PARAFAC modeling, the excitation wavelengths from 200 to 220 nm and the emission wavelengths from 250 to 300 nm were deleted because of their poor quality.In order to remove the effect of Rayleigh scatter on PARAFAC modeling, the miss- ing values (NaN -"not a number") were inserted in the regions (Ex−20 Em Ex+20 and 2Ex−20 Em 2Ex+20; unit: nm) which are significantly influenced by the firstand second-order scattering from the measured spectroscopic data (Hua et al., 2007;Stedmon and Bro, 2008).
To determine the appropriate number of PARAFAC components, the split-half validation procedure was executed to verify whether the model was valid by comparing the emission and excitation loadings from each half (Stedmon and Bro, 2008).Split-half analysis is the most effective method for implementing the PARAFAC models, in which the EEM is randomly divided into four groups of equal size, and then analyzed for two split halves (1-2 and 3-4 half) respectively.If the correct number of components is chosen, the excitation and emission loadings from the two groups should show the same shape and size (Bro, 1997(Bro, , 1999)).The fluorescence intensity of every component was represented by F max (Raman unit: nm −1 ) (Stedmon and Markager, 2005).

Statistical analysis
Statistical analysis was conducted using the SPSS 16.0 software package (Statistical Program for Social Sciences).Regression and correlation analysis was used to describe the relationship between the CDOM absorption coefficient, DOC concentration, salinity and F max .A model II-ANOVA (analysis of variance) was performed to determine whether the seasonal variability is higher than between-lake variability.The difference is considered to be statistically significant when p values are less than or equal to 0.05.

Water quality conditions
The water quality parameters, i.e., pH, salinity, and turbidity, for the 67 water samples collected from June 2013 to April 2014 in the western part of Jilin are displayed in Table 1.1).

EEM characterization of CDOM
Based on the EEM "peak picking" technique, the key fluorescence peaks can be observed in 67 water samples: two humic-like and two protein-like substances (Coble, 1996;Stedmon et al., 2003).The humic-like components are the mixture of the humic-like acids of aromatic and aliphatic compounds from terrestrial substances, and aquatic humiclike substances of phytoplankton origin.With respect to the protein-like components (i.e., tyrosine-like and tryptophanlike substances), they mainly consist of dissolved amino acids.As an example, Fig. S1  In our study, four separate fluorescent components (Fig. 2a-d) and the excitation and emission loadings (e-h) of the four components identified by EEM-PARAFAC are summarized in Fig. 2 and Table 2.The first fluorescent component (C1) was a biological degradation humic-like component comparable to humic-like peaks (M and N) in marine and in phytoplankton degradation experiments for inland waters (Coble, 1996;Zhang et al., 2009).Component 2 was consistent with the humic-like peaks (A and C) defined by Coble (1996).Component 3 resembles the tryptophan-like (T) component as found by Baker et al. (2004) and Hudson et al. (2007).Component 4 is likely related to the tyrosine-like component (B) (Hudson et al., 2007).Components 3 and 4 represent autochthonous semi-labile CDOM associated with bacteria activity and phytoplankton degradation (Borisover et al., 2009;Stedmon et al., 2003).In particular, there was a shoulder (i.e., deflection but not peak) at the excitation wavelength 310-330 nm in component 4 and 330-340 nm in component 3, which may be due to the residual Raman peaks in some water samples (Fig. 2c-d).In this study, not all of the four components were present in all of the samples.

Temporal distribution of PARAFAC components
These fresh and brackish water in Jilin in Northeast China are endowed with similar geological, hydrological and climatic settings; thus it is presumed that similar processes may control the CDOM components.When a model II-ANOVA using season and lake as random effect factors was performed, it shows that the seasonal variability (F > F crit , p < 0.05) is higher than between-lake variability.Therefore, the water www.biogeosciences.net/13/1635/2016/Biogeosciences, 13, 1635-1645, 2016 Fluorescence peaks were named as Coble and Zhang components by Coble et al. (1996Coble et al. ( , 1998) ) and Zhang et al. (2010Zhang et al. ( , 2011)), while as Stedmon and Markager components by Stedmon and Markager (2005).
samples from different lakes for every season were pooled together in order to study the seasonal variation of the fluorescent components.As shown in Fig. 3a, the average fluorescence intensity of the four components had seasonal variation.When all the water samples in different seasons were pooled together, the average value of total fluorescence intensity was 2.05 ± 0.93 nm −1 , corresponding to the intensities of 0.71 ± 0.32 (C1), 0.33 ± 0.11 (C2), 0.50 ± 0.24 (C3) and 0.51 ± 0.26 (C4) nm −1 for different components.These results can demonstrate that the fluorescence intensity was dominated by C1, implying most of the CDOM for the seven inland lakes originated from the degradation of phytoplankton and microorganisms.The protein-like components (C3 and C4), related to bioavailability and microbial activity of CDOM, had almost the same magnitude.In all four seasons, the fluorescent component C2, which was terrestrially imported to waterbodies, contributed less to total fluorescence than the other three.The total fluorescence intensity differed under seasonal variation, varying from 2.54 ± 0.68 nm −1 in June to 1.93 ± 0.70 nm −1 in August 2013, and then increased to 2.34 ± 0.92 nm −1 in February and reduced to the lowest intensity of 1.57 ± 0.55 nm −1 in April 2014 (Fig. 3c).The intensities of four fluorescent components (i.e., 0.75 ± 0.17 (C1), 0.32 ± 0.06 (C2), 0.69 ± 0.24 (C3) and 0.77 ± 0.20 (C4) nm −1 ) (Fig. 3d) from the samples collected in June 2013 exhibited similar trends to that for the pooled data set.These values were higher than the seasonal average except C2 (0.32 ± 0.06 nm −1 ).This can be explained by enhanced activities from plant degradation and microbial activities, but fewer terrestrial substances were imported to the waterbodies in June; therefore, the fluorescence intensity of C2 was lower than the seasonal average.Compared to the fluorescence intensity in June, the three fluorescence intensities (0.65 ± 0.14 (C1), 0.33 ± 0.16 (C3) and 0.52 ± 0.36 (C4) nm −1 ) from the samples collected in August 2013 were reduced, but an increased value was recorded for C2 (0.42 ± 0.05 nm −1 ) (Fig. 3d).Particularly, the fluorescence intensities of two protein-like components showed an obvious difference.This can be attributed to substantially increased precipitation up to 180 mm in July from June to August 2013 (Fig. 3b); therefore, floods occurred when rainfall continued to increase in August.Gradually, DOM contained in terrestrial CDOM was flushed by rainfall to the lakes; therefore, the C2 (0.42 ± 0.05 nm −1 ) fluorescence intensity became higher.In accordance with Cheng et al. (2010), the rainwater CDOM for this study was largely characterized by protein-like components (Cheng et al., 2010).The fluorescence intensity of the rainwater CDOM was very weak, and the rainwater CDOM also had a much lower humic-like concentration (Fig. S2b).The intensities of the other three components decreased because of dilution resulting from heavy rain and relatively weak microbial decomposition of plants.
The highest C1 (1.02 ± 0.38 nm −1 ), presented in February 2014, and the C2 (0.39 ± 0.12 nm −1 ) intensity remained almost the same as that in August 2013.However, the proteinlike components indicated that the C3 (0.57 ± 0.25 nm −1 ) intensity was higher than the C4 (0.35 ± 0.17 nm −1 ) intensity, which was opposite to the results from other months (Fig. 3d).In cold winters, the surface waters formed a thick layer of ice covering the lake waters.Because the ice cover reduced light penetration and restricted gas exchange between the underlying water and atmosphere, vigorous biological activity in the lakes would be reduced at low temperature and low light level (Thomas, 1983;Uusikiv et al., 2010;Wharton Jr., et al., 1993).Although the biological activity was very weak, there could still be a bit of production of C1 and C3 in lake water.Also, dissolved materials were left in the underlying surface waters and little terrestrial matter was imported to the lakes once covered by ice (Stedmon et al., 2007).Therefore, the C1 and C3 in the water of the lakes beneath the ice layers would have been produced and accumulated simultaneously, whereas the C2 remained the same.Obviously, the fluorescence intensity of component 1 reached the highest value for the winter samples.As shown in Fig. S2a, another striking feature for the winter samples was that the fluorescence of CDOM in the ice was dominated by the tyrosine-like C4 component, which is consistent with the findings of Barker et al. (2009Barker et al. ( , 2013) ) and Stedmon et al. (2007).It showed that the C4 component was left in the ice cover when the lakes were frozen.Therefore, it is not surprising that the intensity of component C4 for water beneath ice layers was reduced and the concentrated C3 showed a much higher fluorescence intensity.In April 2014, the intensities of four fluorescent compo- nents (0.47 ± 0.17 (C1), 0.25 ± 0.08 (C2), 0.40 ± 0.16 (C3) and 0.45 ± 0.13 (C4) nm −1 ) (Fig. 3d) exhibited similar seasonal trends, though these values were much lower than the average.Our interpretation is that the ice CDOM was characterized by the tyrosine-like component (C4) (Fig. S2a), and the fluorescence intensity of C4 contributed by the ice meltwater was very weak.However, the underlying lake CDOM included both humic-like (C1 and C2) and protein-like (C3 or C4) components.When the ice in the lakes melted into water during warming weather and biological degradation, and human activity was weak, the lake CDOM was diluted by the ice meltwater and the fluorescence intensity reached its lowest value in early spring.

CDOM vs. EEM-PARAFAC-extracted components
The concentration of DOC, CDOM absorption coefficients and the slope ratio S R are shown in Table 3.The DOC concentrations ranged from 835.83 to 7345.83 µmol L −1 , with an average value of 3133.05 ± 1504.14 µmol L −1 during the period from June 2013 to April 2014, demonstrating seasonal dynamics that can be attributed to hydrological, climatic and landscape variations (Song et al., 2013).The highest aver-age DOC concentration (4587.03± 1666.83 µmol L −1 ) was present in February 2014 (ice-covered period); whereas relatively constant values of approximate 2500 µmol L −1 were observed in the ice-free season.The relative high DOC concentration in the ice-free season was caused by the evapocondensed effect due to the prolonged sunshine duration for the lakes in the Songnen Plain.The higher DOC concentration in winter can be attributed to the accumulated DOC left in the liquid phase when ice formation took place, resulting in the higher DOC concentration in the underlying water (unpublished material).Generally, the absorption coefficient a( 350) is used as a proxy for characterizing CDOM concentration (Guo et al., 2010;Zhang et al., 2011); a(280) is related to DOC biodegradation (McDowell et al., 2006), and a(254) can be used to characterize the optical properties of DOC aromaticity (Jaffé et al., 2004;Weishaar et al., 2003).The highest averaged CDOM absorption coefficients, a(350), a(280) and a(254), were also present in February 2014, corresponding to the highest DOC concentration.The S R values of the two wavelength ranges (275-295 over 350-400 nm) were used to represent DOM molecular weight (Helms et al., 2008).The lowest mean of S R was present in August 2013, suggesting that the relatively weak microbial decomposition of plants and lots of terrestrially imported substances through rainwash resulted in the higher average molecular weight of DOC.
When the whole data set (N = 67) was pooled together, there were significantly positive linear relationships between a(254), a(280), a(350) and F max for two humic-like components (C1 and C2), respectively, but mostly, such correlations were not observed for the protein-like components (Fig. 4a and b, Table 3).These results were in accordance with previous investigations (Zhang et al., 2010(Zhang et al., , 2011)).Components 1 and 2 were strongly linearly correlated with each other (R 2 = 0.628) (Fig. 4c), indicating that the concentrations of the two humic-like components were controlled by common sources (Baker and Spencer, 2004).There was a weak relationship (R 2 = 0.051) between the protein-like components (C3 and C4), possibly because of a complex origin of CDOM such as rainfall in summer, ice in winter and organic pollutants derived from domestic, agricultural and industrial sewerage, which represent the complex origins of CDOM.However, there was almost no correlation between the humic-like and protein-like components.The linkage of a fluorescence signal to DOC was very complicated because of the seasonal impacts, i.e., increased rainfall, algal blooms and ice cover, which affect the DOC concentration.Due to both steady and labile CDOM fluorescent components in DOC, the fluorescent signal changed with the ratio of fluo- rescent and non-fluorescent CDOM components (Henderson et al., 2009).A weak relationship (R 2 = 0.411) (Fig. 4d) was found between DOC and component 3, likely from the decay of plants through microbial activity or the pollution from human and animal waste.
Different from the findings by Yamashita et al. (2008) for ocean water, this study did not find obvious correlation between salinity and EEM-PARAFAC-extracted components, with the exception of C3 (R 2 = 0.469) (Table 4 and Fig. 4f).The most important finding for the water samples collected in different seasons from the Songnen Plain is a significant relationship (R 2 = 0.930) between salinity and DOC (Fig. 4e).This is because DOC is evapo-condensed from spring to autumn and freeze-accumulated in winter in the semiarid region.A prolonged sunshine duration can result in an evapocondensed DOC concentration in the ice-free season.On the other hand, the DOC accumulates when the lakes freeze in winter, leaving DOC in the liquid phase.

Conclusions
A model II-ANOVA using season and lake as random effect factors shows that the seasonal variability (F > F crit , p < 0.05) is higher than between-lake variability.In this study, the application of EEM-PARAFAC to characterize four fluorescent components under seasonal variation in CDOM was presented with 67 water samples collected from June 2013 to April 2014 in the semiarid region of the Songnen Plain.Two humic-like components and protein-like components were identified using the PARAFAC modeling.The average fluorescence intensity of the four components differed under seasonal variation from June 2013 to April 2014.The highest C1 1.02 nm −1 was presented in February 2014, probably due to the condensed CDOM caused by ice formation in winter.Especially in summer, when rainfall occurs, and in winter when water is frozen, the fluorescence intensity is dominated by tyrosine-like components in rain and ice meltwater.Components 1 and 2 exhibited a strong linear correlation (R 2 = 0.628).There were significantly positive linear relationships between F max and CDOM absorption coefficient a(254) (R 2 = 0.72, 0.46, p < 0.01), a(280) (R 2 = 0.77, 0.47, p < 0.01) and a(350) (R 2 = 0.76, 0.78, p < 0.01) for two humic-like components (C1 and C2), respectively.A weak relationship (R 2 = 0.411) was found between DOC and component 3 from the decay of plants through microbial activity or the pollution from human and animal waste.However, almost no obvious correlation was found between salinity and EEM-PARAFAC-extracted components, except C3 (R 2 = 0.469), though the correlation was not as strong as with DOC concentration.Most importantly, a significant relationship (R 2 = 0.930) was found between salinity and DOC.In order to understand the biogeochemical effects on the aquatic ecosystem, further study is required to identify the source of CDOM and assess physical/chemical, bioavailable and photoreactive transformation in various lakes with larger saline gradients in the semiarid region of Northeast China.

Figure 2 .
Figure 2. The PARAFAC modeling output shows the contour plots of the four PARAFAC fluorescent components (a-d) and excitation (black) and emission (red) loadings (e-h) of each component.Fluorescence is in Raman units: nm −1 .

Figure 3 .
Figure 3. (a) Seasonal average of F max for EEM-PARAFAC components (C1, C2, C3 and C4) for lakes in the western part of Jilin; (b) monthly variation of rainfall for the lakes in the western part of Jilin from April 2013 to February 2014; (c) seasonal variation of the total fluorescence intensity in different seasons; (d) seasonal variation of the four EEM-PARAFAC components in different seasons.The error bars represent 1 standard deviation.

Table 1 .
Mean value of water quality parameters from June 2013 to April 2014.Turb denotes water turbidity; N denotes sampling numbers.When the set of samples from various field trips was pooled together, the waters had high pH values and high salt contents.The highest salinity was present when the lakes were frozen in February 2014, whereas relatively constant values (around 0.40) were exhibited in the other three seasons.In addition, the waterbodies were highly turbid.The highest turbidity was present in June 2013, and then reduced in August 2013, and the lowest value was recorded in February 2014.Compared with February 2014, the turbidity had almost no change in April 2014 (Table

Table 2 .
Positions of the fluorescence maximum peaks of the four components identified by PARAFAC modeling in the present study compared with those previously identified.Secondary excitation maxima are given in brackets.

Table 3 .
Mean values of DOC concentration and CDOM absorption coefficients groups in different seasons.S R : the slope ratio of S 275-295 nm : S 350-400 nm .

Table 4 .
Correlation coefficients (R) and significance levels (p) of the linear relationships between CDOM absorption, DOC, salinity and fluorescent components.