Introduction
Chromophoric dissolved organic matter (CDOM) is the colored component of
dissolved organic matter (DOM) in the natural waters environment. The rivers
serve to connect the terrestrial cycling of carbon with the marine carbon
cycle (Alvarez-Cobelas et al., 2012; Guo et al., 2012; Para et al., 2010).
Terrestrial allochthonous inputs are the dominant CDOM source in aquatic
ecosystems (Nelson and Siegel, 2013; Zhou et al., 2015). Phytoplankton
excretion, zooplankton, and bacterial metabolism are the major autochthonous
CDOM sources (Coble, 2007). As an important constituent of DOM, which is the
largest reservoir of organic carbon on Earth, CDOM plays a vital role in the
global carbon cycle (Gonnelli et al., 2013; Mopper and Kieber, 2002). CDOM
is one of the major light-absorbing constituents in natural waters; it can
absorb solar radiation in the ultraviolet (UV) and visible ranges of the light spectrum to
shield biota from harmful UV radiation. As a consequence of its optical
behavior, CDOM is also largely responsible for the bio-optical properties of
natural water, and has a potential effect on the productivity of the water
column (Organelli et al., 2014). The absorption characteristics of CDOM also
influences the inversion accuracy of remote sensing of chlorophyll a (chl a) and
other suspended solids (Siegel et al., 2005; Song et al., 2014).
Spectral analysis of CDOM (absorption and fluorescence) has been used to
trace its origin, chemical composition, and photochemical reaction (Stedmon
et al., 2000; Vodacek et al., 1997; Xie et al., 2014). Understanding the
spectral characteristics of CDOM could help to understand DOM cycling in
aquatic ecosystems. According to previous studies, the light absorption of
CDOM often decreases in a near-exponential manner with increasing optical
wavelength (Coble, 2007; Zhang et al., 2010). In order to characterize the
properties of CDOM from absorption spectra, several spectral indices have
been previously developed. The ratio of absorption at 250 to 365 nm
(E250:365) is used to track changes in the size of DOM molecules (De
Haan and De Boer, 1987); specific UV absorbance (SUVA254) is found to
have strong correlation with DOM aromaticity as measured by
C nuclear magnetic resonance (13CNMR) spectroscopy (Weishaar et al., 2003). Two optical parameters, the absorption
coefficient at specific wavelengths λ nm (aCDOMλ) and
CDOM spectral slopes (S), are generally recognized proxies of CDOM
concentration and molecular origin (Helms et al., 2008). Furthermore, S may
also correlate with the ratio of fulvic acid (FA) to humic acid (HA). In
fact, the use of S is dependent on the calculated wavelength intervals, and
the ratio of the slope (Sr), as a dimensionless parameter, could avoid
the limitations of spectral wavelength measurements (Helms et al., 2008;
Spencer et al., 2012). The analysis of these spectral indices is therefore
useful for understanding spatial and temporal CDOM variations in the aquatic environment.
Recent studies have proven that CDOM in aquatic ecosystems exerts an impact
on ecosystem productivity, optical properties of water, and biochemical
processes (Zhang et al., 2007). However, regional CDOM characteristics are
still not thoroughly understood in diverse aquatic environments because of
various physicochemical parameters of water (Findlay and Sinsabaugh,
2003). Many water quality parameters have been proposed to affect the
temporal and spatial variation of CDOM, and some correlations among them
have been established; a significant positive correlation was found between
dissolved organic carbon (DOC) and CDOM absorption coefficients, and a
series of different models were established based on this correlation in
Lake Taihu (Zhang et al., 2007), the Yangtze River (Zhang et al., 2005), and
six rivers in Georgia (Yacobi et al., 2003). CDOM strongly absorbs in
the blue spectral region, which interferes with the determination of chl a
concentration by remote image sensing (Siegel et al., 2005), therefore the
relationship between the spectral characteristics of CDOM with chl a
concentration has received widespread attention. A significant linear
relationship between aCDOM(300) and chl a concentrations was identified in
the central eastern Mediterranean Basin (Bracchini et al., 2010), the
Atlantic Ocean (Kitidis et al., 2006), and the Baltic Sea (Kowalczuk et al.,
2006), but aCDOM(440) was loosely related to pigment concentrations in
the 0–400 m depth layer of the NW Mediterranean Sea (Organelli et al., 2014).
Furthermore, the relationship between aCDOM(λ) and other
physicochemical parameters of water, such as total nitrogen (TN), total
phosphorous (TP), salinity, and extracellular enzyme activities (Gonnelli et
al., 2013; Kowalczuk et al., 2006; Niu et al., 2014; Phong et al., 2014),
were all investigated in different aquatic environments. However, these
studies reached different conclusions due to regional variations in water quality.
Studies published to date have focused on the relationship between CDOM
properties and environmental factors; results indicate that salinity, solar
radiation, and watershed characteristics all have important effects on CDOM
optical properties (Graeber et al., 2012; Gueguen et al., 2011; Mavi et al.,
2012; Song et al., 2013b). The properties of CDOM in plateau water at high
altitude have attracted interest due to the unique natural environmental and
climatic features of these waters. The DOM composition in two Tibetan alpine
lakes showed a limited terrigenous DOM and exhibited a high biolability of
DOC (Spencer et al., 2014). The analysis of CDOM parameters in three
intermontane plateau rivers in the western USA indicated that autochthonous DOM
or DOM derived from anthropogenic sources dominated the DOM pool (Spencer et
al., 2012). However, the relationship between CDOM and environmental factors
in plateaus area has been less well studied. Analysis of these
optical–physicochemical correlations is critical for understanding the
source and distribution of CDOM in plateau water environments and evaluating
the potential influence of water quality factors on non-water light absorption.
The Inner Mongolia Plateau in China is located in an arid and cold climate
zone with sparse annual rainfall. The plateau is covered with numerous lakes
surrounded by vast grasslands and forest. These lakes are located far away
from the ocean and are supplied with water by precipitation and river
runoff, and most of them are noncontributing lakes (Tao et al., 2015). The
unique geographical environment and climatic factor in arid and cold plateau
regions altered CDOM properties, when compared with the bulk of inland
water. Moreover, over 80 % of the lakes are saline, which allows higher
carbon storage levels than freshwater lakes (Duarte et al., 2008; Song et
al., 2013b). The plateau lakes therefore play an important role in global
carbon balance estimation.
Based on the above studies, we address the following issues. (1) Do
CDOM properties in this cold plateau region differ within the plains? Is this
difference related to the water quality of the terminal lakes?
(2) Under the unique climatic and hydrological conditions of the boreal plateau,
what is the main non-water light absorption component in water? We expect
that the information obtained in this study can answer all of these questions.
Answers to these questions can enhance our understanding of the non-water
light absorption characteristics of inland waters in arid and cold plateau
regions. The results of CDOM source and component analysis are also helpful
for carbon storage estimation in these catchments. The
optical–physicochemical correlation analysis could contribute to an improved
understanding and interpretation of satellite remote sensing imagery for this area.
Material and methods
Study sites
The Inner Mongolia autonomous region is located in the north of China with
an area of about 1.18 million km2 (37∘24′–53∘23′ N,
97∘12′–126∘04′ E). The average altitude of the whole region is over 1000 m, and
it is basically a plateau landform composed of the Hulun Buir, Xilingol,
Ulanqab, Bayannur, Alxa, and Erdos plateaus. Rivers, lakes, reservoirs, and
other surface water areas account for 0.8 % of the whole area. All of the
water samples collected were taken from the Hulun Buir plateau (Fig. 1). The
Hulun Buir plateau is located in the northeast of the Inner Mongolia plateau,
with the topography being high in the east and low in the west. The east
side of the plateau is connected to Daxing'anling mountains, and the geology
composition consists mainly of Paleozoic granite, Mesozoic andesite, quartz
trachyte, and tuff. Most of the overlying rock has weathered away into
loess. The central regions are undulating rolling plains, and are made up of
loose river and lacustrine sedimentary sand. The western regions are
mainly low hills composed of volcanics. The physicogeographical zone of the
whole Hulun Buir plateau is divided into a chernozem zone with forest and
steppe, and a chestnut earth zone with steppe. This plateau is characterized by
a typical semi-humid and semiarid continental monsoon climate with
intensive solar radiation throughout the year. The Hulun Buir plateau has
distinct seasons with a dry spring, a hot and rainless summer, a windy and
short autumn, and a cold dry winter (Zheng et al., 2015). Based on long-term
meteorological data (1961–2010), the average annual temperature is
0.8 ∘C. The average annual wind speed is 3.5 m s-1. The average annual
rainfall is 273.9 mm, 70–80 % of which falls in May–August (Bai et
al., 2008). There are greater than three-quarters of days each year with
direct sunlight. In this area, the average sunlight per day is over 8.2 h.
The average annual evaporation is 1615.3 mm, which is far greater than
precipitation, resulting in water scarcity. The soils of the area are
Mollisols, and the topography consists of gently rolling hills and tablelands.
Study area location and sampling station distribution.
The plateau is dotted with numerous lakes surrounded by vast grasslands and
forests, and the two largest freshwater lakes (Hulun Lake and Buir Lake) of
Inner Mongolia are located in this area. Most lakes in this area are inland
terminal lakes. Several rivers flow through Hulun Buir plateau, including
the Kerulen, Ergun, Wuerxun, Hailar, and Zhadun rivers. Wuerxun River
originates from Buir Lake's northern shore, flows north, and empties into
Hulun Lake. Kerulen River flows east through Hulun Lake, and finally into
the Ergun River. The Zhadun River flows into the Hailar River, flowing north
to join the Ergun River. A total of 46 surface waters were collected in this
study with respect to both watershed characteristics and lake size.
Water sampling and water quality measurement
Water samples were taken from the Hulun Buir plateau, China, during
September 2012 (Fig. 1), and the sampling numbers for each water body are
listed in Table 1 and marked in Fig. 1. Based on the salinity and electrical conductivity
(EC)(salinity threshold value = 0.5 PSU, EC threshold value = 1000 ms cm-1),
these waters were divided into 22 river waters and 24 saline waters.
In particular, the saline waters were collected from lakes without outflow or
the terminal flow of rivers. Hulun Lake and Buir Lake are connected with
rivers, so the water samples collected from these lakes are classified as
river water samples in the subsequent analysis in this study. The saline
lakes' size in this study ranged from 1 to 42.5 km2, with an
average depth of 0.4–2.8 m. The related hydrological data of rivers and
freshwater lakes are shown in Table 2, including the names of rivers (or freshwater
lakes), sampling numbers, basin area, width, length, maximum water depth, and
elevation. The surface water (0.5–1 m) was collected in a sample bottle
with at least 4 L in every sampling point, and kept in a portable
refrigerator before they were returned to a laboratory. Chemical and physical
parameters, e.g. pH, total dissolved solid (TDS), and EC were determined by sampling in situ using a portable
multi-parameter water quality analyzer (YSI6600, US). Concentrations of
DOC, TN, and TP were measured with unfiltered water samples by a standard
procedure (APHA/AWWA/WEF, 1998). Total suspended matter (TSM) was determined
by gravimetrical analysis (Song et al., 2013a). Water turbidity was
determined by a UV spectrophotometer in 680 nm (Shangfen, 7230) with Milli-Q
water as a reference at room temperature (20 ± 2∘).
Chlorophyll a (chl a) was extracted from water samples by 90 % buffered acetone
solution, and the concentrations were determined with a UV spectrophotometer
(Shimadzu, UV-2600PC) by the method detailed in Song et al. (2013a).
Water quality and CDOM absorption parameters of water samples
collected in the Hulun Buir plateau.
River water (n = 22)
Terminal lakes (n = 24)
Mean
Min–max
Mean
Min–max
DOC
25.99 ± 6.64
8.44–39.74
83.83 ± 68.79
23.03–300.50
TN
1.33 ± 0.63
0.64–3.51
4.58 ± 3.80
1.39–19.03
TP
0.11 ± 0.04
0.06–0.23
1.52 ± 1.87
0.12-6.31
TAlk
156.22 ± 53.60
48.00–298.56
652.70 ± 642.15
96.00–2906.40
EC
325.95 ± 141.64
106.70–745.00
5729.69 ± 9715.26
1236–41 000.00
TDS
163.07 ± 70.62
53.40–372.00
743.59 ± 483.34
93.10–1505.00
Turbidity
20.21 ± 20.80
2.19–83.84
273.79 ± 608.75
1.75–2521.20
Chl a
4.62 ± 3.95
0.04–11.06
6.27 ± 11.06
0–41.07
aCDOM335
18.29 ± 9.87
4.71–40.07
36.16 ± 30.27
10.47–158.24
aCDOM440
2.68 ± 1.68
0.60–7.14
5.60 ± 5.10
0.83–26.21
E250:365
7.80 ± 2.30
5.43–12.30
8.02 ± 3.48
5.47–20.73
SUVA254
2.74 ± 1.08
1.08–4.79
1.90 ± 0.57
0.79–3.74
S275-295
0.019 ± 0.004
0.015–0.027
0.02 ± 0.004
0.015–0.031
SR
1.00 ± 0.17
0.73–1.35
1.05 ± 0.09
0.91–1.25
TN, TP, TDS, TSM, TAlk, and DOC represent total nitrogen, total phosphorus,
total dissolved solids, total suspended matter, total alkalinity, and
dissolved organic carbon concentration, respectively (mg L-1). EC represents the electrical
conductivity of water samples (µs cm-1). Chl a is
chlorophyll a concentration (µg L-1). The unit of SUVA254 is L mg C-1 m-1. The unit of turbidity is NTU, nephelometric turbidity unit.
Names of rivers (or freshwater lakes), sampling numbers, basin area,
width, length, maximum water depth, and elevation in the Inner Mongolia Plateau.
Name
Number
Area
Max
Elevation
Width
Length
Defined
(km2)
depth
(m)
(m)
(km)
type
(m)
Kerulen River
1, 11
7153
1.9
–
60–70
1264
River
Hulun Lake
5–9
2339
33
545.9
30 000–40 000
–
River
Hailar River
21, 31
54 500
1
1100
30–130
1430
River
Yimin River
22–23
22 725
2.5
–
20–50
390
River
Buir Lake
24–28
609
21.6
583
20 000
–
River
Ergun River
29
151 184
> 2
–
200–300
1666
River
Moegele River
36
150
2
–
1–6
319
River
Zhadun River
30, 41
3100
4
675
2–8
River
Wulannuor wetland
34
710
3.5
540
–
–
River
Qingkai River
38
–
2
580
1–10
River
“–” denotes without data.
Non-water light absorption analysis
CDOM was extracted from the water samples collected by filtering through a
0.7 µm glass fiber membrane (Whatman, GF/F 1825-047) and then was
further filtered through a 0.22 µm polycarbonate membrane (Whatman,
110606). The filtering process was finished within 2 days in dim light in
order to avoid alteration by microbial activity. Filtered samples were kept
refrigerated and were warmed at room temperature at the time of the analysis.
CDOM absorption was analyzed within 12 h using a UV-2600 spectrophotometer
equipped with a 1 cm quartz cuvette. Absorbance scans were performed from
200 to 800 nm, and Milli-Q water was used as a reference. Between each sample, the
quartz cuvette was flushed with Milli-Q water, and the cleanliness was
checked according to the optical density of reference water. Bubbles
were avoided during the measurement. In order to eliminate the internal
backscattering, the absorbance at 700 nm was used to correct absorption
coefficients (Bricaud et al., 1981). The absorption coefficient (aCDOM)
was calculated from the measured water optical density (OD) following Eq. (1).
aCDOM(λ′)=2.303×OD/L,
where L is the cuvette path length (0.01 m) and 2.303 is the conversion
factor. ODλ is the average optical density. The absorption coefficients at wavelengths
335 nm (aCDOM335) and 440 nm (aCDOM440) were selected to express
the CDOM concentration (Miller, 1998). The CDOM absorption ratio (E250:365)
was calculated using absorbance at 250 and 365 nm. SUVA254 values
were calculated by dividing the UV absorbance at 254 nm by the DOC
concentration (mg L-1) (Weishaar et al., 2003).
CDOM spectral slopes (S275-295 and S350-400) between wavelengths
275–295 and 350–400 nm were both calculated using a nonlinear fit of an
exponential function to the absorption spectrum according to Eq. (2) by
Origin 8.0 software (Bricaud et al., 1981; Jerlov, 1968).
aCDOM(λ)=aCDOMλ0×eSλ0-λ,
where aCDOM(λ) is the CDOM absorption at a given wavelength, and
aCDOM(λ0) is the absorption at a reference wavelength (440 nm). The spectral slope
ratio (Sr) was calculated as the ratio of S275-295 to S350-400.
Particulate absorption was determined by a quantitative membrane filter
technique (Cleveland and Weidemann, 1993). A certain volume of water
was filtered through a 0.7 µm glass fiber membrane (Whatman, GF/F 1825-047),
and the filter membrane was subsequently stored in the laboratory at
-80∘ until analysis. The light absorption of total particulate
trapped on the filter membrane was determined by UV spectrophotometry
(Shimadzu, 2660) from 280 to 800 nm with a virgin wet membrane as a reference.
After correction of the path length using the path length amplification factor (β),
the measured optical densities were transformed into total
particulate absorption coefficients according to Eq. (3) (Bricaud and Stramski, 1990).
aPB(λ)=2.303×SV×ODS(λ),
where aPB(λ) is the total particulate absorption at a given wavelength (nm), S is the
effective area of the deposited particle on the fiber membrane (m2),
and V is the volume of the filtered water (m3). OD(λ) is the
optical density at the given wavelength (nm).
The above fiber membranes loaded with total particulate were soaked in the
sodium hypochlorite solution in order to remove the pigments, and the light
absorption coefficient of non-algal particles (aNAPλ) was
determined and calculated as the aPB(λ). The phytoplankton
light absorption coefficient (aphyλ) was the difference
between aPB(λ) and aNAP(λ) according to Eq. (4).
aphy(λ)=aPB(λ)-aNAP(λ)
Statistical analysis
The contributions of CDOM, phytoplankton, and non-algal particles (NAP) to
non-water light absorption at 440 nm were calculated using Origin 8.0
software (Ortega-Retuerta et al., 2010). The variation of water quality
parameters in different sampling locations was assessed by principal
component analysis (PCA) using CANOCO 4.5 for Windows with centered and
standardized variables. Correlations between water quality parameters and
light absorption characteristics were determined by redundancy analysis (RDA)
using CANOCO 4.5, light absorption characteristics were defined as
species variables, and water quality parameters were selected as explanatory
variables. The Pearson correlation coefficient (rp) was calculated
using SPSS 5.0. Because the responding variables may exist in the high
autocorrelation, they were first screened through canonical correspondence
analysis using CANOCO 4.5 to remove the variables with an inflation
coefficient greater than 20 (Leps and Smilauer, 2003). A Monte Carlo
permutation test was conducted with CANOCO 4.5 and indicated that the
selected environmental variables were significantly related to light
absorption characteristics (499 permutations under the reduced model, p ≤ 0.05).
Correlation between DOC and alkalinity, EC, TN, and TP in Hulun
Buir plateau water.
Results
Water quality
The collected river and terminal lake samples exhibited large variations in
water quality (Table 1). A significant difference of DOC was also observed
between these two water types (p < 0.001). The DOC concentration
ranged from 8.44–39.74 mg L-1 in river waters, and exhibited higher
values in the terminal lakes (23.03–300.5 mg L-1). Further, the
terminal lakes also had higher alkalinity and EC than river waters. We have
recalculated the statistics after log transformation of the data and a normal
distribution test. A positive relationship between DOC and alkalinity was
established in the Hulun Buir plateau waters (Fig. 2a, R2 = 0.87).
Regression analyses were also conducted, and a linear relationship between
EC and DOC was shown based on the collected data (Fig. 2b; R2 = 0.72).
The average nutrient concentrations for TN (1.33 ± 0.63 mg L-1)
and TP (0.11 ± 0.04 mg L-1) in river waters were both
lower than in terminal lakes, and significant differences were observed for
TN (p < 0.001) and TP (p < 0.01). Strong linear relationships
were shown between TN and DOC in the Hulun Buir plateau (Fig. 2c; R2 = 0.67).
A positive correlation between DOC and TP was found in surface water
in this area (Fig. 2c; R2 = 0.66).
PCA was performed for all the sampling locations with 10 water environment
variables (Fig. 3a). The first two principal components (PCs) of the PCA
explained 61.0 % of the variability in all the selected variables (PC1,
36.4 %; PC2, 24.6 %). Relatively high loadings on PC1 were TSM and turbidity,
whereas DOC and CDOM showed high negative loadings. The second PCA axis
revealed gradients of nutrients (TN and TP). These all had positive loadings
on PC2. Furthermore, TDS and chl a showed high negative loadings on PC2. A
clear difference was found between river waters and terminal lakes (Fig. 3b).
Terminal water samples clustered in close proximity to each other and
were distributed on the negative side of PC2 (with the exception of one
point) in Fig. 3b, and river waters clustered almost exclusively on the
positive side of PC2.
PCA of the physicochemical characteristics of all waters
collected, (a) loading data of factors, and (b) sample scores. • represents
terminal lakes, and ⧫ represents river waters.
Spectral characteristics of CDOM
CDOM absorption spectra of the waters collected from the Hulun Buir plateau
decreased in a classical near-exponential manner, with increasing
wavelengths from the ultraviolet to the visible spectral region. This
near-exponential CDOM absorption spectra have been observed in many natural
waters (Bricaud et al., 1981; Spencer et al., 2009; Xie et al., 2014). The
comparative analysis was conducted in two types of sampling waters in the
study, and the mean values of aCDOM(335) and aCDOM(440) both
showed that the terminal lakes exhibited significantly higher CDOM light
absorption than river waters (Table 1).
E250:365 values in the waters examined ranged from 5.43 to 20.73, and
the mean values were 7.80 ± 2.30 and 8.02 ± 3.48 in the river
and terminal lakes respectively. The majority of the SUVA254 values
in the river waters ranged from 1.09 to 3.56 L mg C-1 m-1, and the
mean SUVA254 was clearly higher in river waters (2.74 ± 1.08 L mg C-1 m-1)
than the terminal lakes (1.90 ± 0.57 L mg C-1 m-1),
and this was significant (p < 0.01). In order to confirm
the source and composition of CDOM in different types of waters, the
spectral slopes in the 275–295 nm (S275-295) and 350–400 nm
(S350-400) ranges were both calculated as the indicators (Table 1).
S275-295 values showed a wide variation in the river water samples,
ranging from 14.80 × 10-3 to 26.79 × 10-3 nm-1
(mean = 19.25 ± 4.05 × 10-3 nm-1), and the majority of river waters in the study exhibited
S275-295 between 17.11 and 17.82 × 10-3 nm-1. There
was not a significant difference when compared with terminal lakes.
S350-400 values also showed no significant difference between the two
types of waters. Furthermore, the mean values of S275-295 and
S350-400 in Hulun Lake were both lower than Buir Lake.
Relative contributions of CDOM, phytoplankton, and non-algal
particles to total non-water light absorption at 440 nm.
Light absorption of CDOM and particulates
Detailed knowledge regarding the relative contributions of CDOM,
phytoplankton, and non-algal particles to the total non-water light
absorption is essential in bio-optical and biogeochemical models, and the
relative contributions at 440 nm are shown in Fig. 4. There was no obvious
difference in the relative contributions of CDOM, phytoplankton, and
non-algal particles between river waters and terminal lakes (p > 0.5).
At all the sampling locations, the mean contribution of CDOM to the
total non-water light absorption was 52.78 %, with the range varying from
2.87 to 97.23 %, and the relative contribution of non-algal particles
was on average 39.84 %, ranging from 2.01 % to 97.13 %. Phytoplankton
absorption played a minor role in total non-water light absorption, with a
mean of 7.61 %. In most water samples examined in this study, CDOM was the
dominant non-water light-absorbing substance.
To assess the distribution of light absorption in the waters of the Hulun Buir
plateau, levels of light absorption due to CDOM, phytoplankton, and non-algal
particles were plotted based on the numbers of sampling locations and their
contributions to total light absorption at 440 nm using a Pareto–Lorenz
curve (Lorenz, 1905). The relative contributions were arranged from high to
low. Subsequently, the cumulative sampling points are represented on the
abscissa axis, and the cumulative contributions are plotted on the vertical
axis. The more the curve deviated from the theoretical perfect evenness line
(45∘ diagonal), the more inhomogeneous light contributions were
observed (Fig. 5). According to the Pareto principle, the value of the vertical
axis was in accordance with 20 % abscissa axis, which was used to interpret
the Pareto–Lorenz curves. From the degree of curve deviation (Fig. 5), it
was observed that the light absorption of optically active substances in the
Hulun Buir plateau area presented inhomogeneous phenomena. Among them, CDOM
absorption was the most representative relative to other non-water
absorption components. CDOM light absorption by 20 % of the samples
corresponded with 5.03 % of the cumulative CDOM contributions to non-water
absorption. For non-algal particles and phytoplankton, 20 % of the samples
corresponded with 1.46 and 0.51 % of cumulative light absorption
contributions, respectively. Thus, for all the non-water absorption types,
it was observed that CDOM light absorption was numerically dominant compared
with non-algal particles and phytoplankton.
Pareto–Lorenz curves derived from the total non-water light
absorption at 440 nm.
Pearson correlation coefficients for general water quality and light
absorption properties.
aCDOM(335)
aCDOM(440)
aPB(440)
aphy(440)
aNAP(440)
S275-2295
TN
0.574**
0.548**
0.288*
0.377**
0.264
0.164
TP
0.508**
0.401**
0.078
0.194
0.062
0.151
TDS
0.483**
0.534**
-0.048
0.178
-0.068
0.015
DOC
0.527**
0.411**
-0.007
0.151
-0.024
0.377**
pH
0.192
0.129
-0.121
0.026
-0.131
0.567**
Chl a
0.021
0.084
-0.056
0.224
-0.083
0.089
TSM
0.021
0.045
0.985**
0.515**
0.985**
-0.073
EC
-0.024
-0.083
0.055
0.081
0.050
0.506**
* p < 0.05; ** p < 0.01.
Units of DOC, TN, TP, TDS, TSM, and DOC concentrations are mg L-1; the
unit of chl a concentrations is µg L-1; the EC unit is µs cm-1.
Correlations between water quality parameters and light absorption
The RDA data showed that the forward selected explanatory variables could
explain the variability of light absorption characteristics with
species–environment correlations of 0.781 (Fig. 6). The first two axes of
RDA explained 43.7 % of total variability in light absorption
characteristics of all the water samples collected (axis one, 34.3 %; axis
two, 9.4 %). Coefficients between environmental variables with axes in RDA
indicated that TSM, TN, and EC had a strong correlation with light
absorption characteristics, followed by TDS and chl a. TDS, TP, and DOC were
most closely corrected to CDOM light absorption (Fig. 6). TSM, TN, and chl a were
best correlated to light absorption of phytoplankton, non-algal
particulates, and total particulates at 440 nm (Fig. 6). EC and pH were
related to the CDOM spectral slope (S275-295) (Fig. 6).
RDA of CDOM adsorption data and water quality parameters (n = 44).
The Pearson correlation coefficients (rp) between water quality and
light absorption characteristics presented in Table 3 indicate that CDOM
light absorption (αCDOM335 and αCDOM440) showed a
significantly positive correlation with TN, TP, TDS, and DOC (p < 0.01),
but had no correlation with chl a concentration (p > 0.05,
n = 46). There was also no correlation between S275-295 and chl a
concentration. However, S275-295 presented a significantly positive
correlation with DOC, pH, and EC in this plateau water (p < 0.01, n = 46).
Light absorption of pigments at 440 nm showed a significantly
positive correlation with TN (rp = 0.377, p < 0.01, n = 46)
and TSM (rp = 0.515, p < 0.01, n = 46), and there was also
no linear relationship with chl a concentration. The light absorption at 440 nm
of total particulates and non-algal particulates both had a significant
positive correlation with TSM (rp = 0.985, p < 0.01, n = 46).
Discussion
Dissolved organic carbon in river and terminal lakes
Previous studies have shown that DOC concentrations in inland waters always
decrease with the prolongation of water residence times due to
biodegradation and photobleaching in humid regions (Curtis and Adams,
1995). However, terminal lakes with long water residence times exhibited
higher DOC values than the river waters in this study. The most likely
explanation for the opposite pattern of DOM concentration is that the most
refractory DOC is diluted in humid regions and evapoconcentrated in
semiarid regions (Song et al., 2013b). Further, the higher alkalinity and
EC in the terminal lakes compared with river waters may explain the inverse
pattern (Table 1). The sodicity of water could also increase DOM solubility.
Increasing EC (salt concentration) would result in decreased osmotic
potential, which has negative effects on microbial activity (Mavi et al.,
2012). DOM, along with other nutrients, comes from soil via runoff and
leaching, and can accumulate in terminal lakes due to lower microbial
activity. Furthermore, the average DOC concentration in rivers
(25.99 ± 6.64 mg L-1) was higher than in many rivers reported in other
studies (Alvarez-Cobelas et al., 2012; Evans et al., 2005; Findlay and
Sinsabaugh, 2003; Song et al., 2013b; Spencer et al., 2010, 2012;
Worrall and Burt, 2004). DOC levels in rivers are linked to climate
and watershed landscape characteristics (Alvarez-Cobelas et al., 2012; Jiang
et al., 2014). The elevated DOC concentrations in these plateau rivers could
be attributed to evaporation, which would be expected to be extreme in the
arid environment of the Inner Mongolia Plateau (Hao et al., 2007).
Furthermore, the Inner Mongolia region is located in a semiarid climatic zone with low
rainfall, and the impoundment of these plateau waters mainly depended on
surface runoff. The land use types around the sampling locations were mainly
grassland and forest (Bai et al., 2008). The high DOC concentration in the
waters highlights the organic-rich nature of these ecosystems (Zheng et al., 2015).
Most monitoring data indicate that DOC concentration in rivers shows a
tendency to increase year by year, potentially due to recovery from acid
deposition (Evans et al., 2005; Monteith et al., 2007). DOC concentrations
in surface water are depressed when acid anion concentrations are high, and
increase as acidic anion concentrations decrease (Evans et al., 2005). The
response of water parameters to acid deposition are apparent in the
alkalinity measurements. In this study, the positive relationship between
DOC and alkalinity indicated that an empirical model might be established in
Hulun Buir plateau waters for estimating DOC storage based on water
alkalinity, with calibration by a comprehensive data set. Within semiarid
regions, DOC is always related to salinity, which could reflect the water
residence times and DOM accumulation (Curtis and Adams,
1995; Song et al., 2013b). A positive correlation between DOC and EC was
established based on the collected data (Fig. 2b; R2 = 0.72). More
than 80 % of lakes in the Inner Mongolia Plateau are saline lakes, and
prior research has shown that inland saline lakes always contain higher
concentrations of DOC than freshwater lakes in a semiarid region (Arts et
al., 2000). There are probably several reasons: saline lakes are
hydrologically terminal; organic matter received and produced by saline
lakes largely remains within the basin and is not exported downstream; DOC
in the saline lakes accumulated with much higher rates than that in
freshwaters; and saline lakes commonly support highly active biological communities,
which can actively break down refractory organic matter into DOC.
The relationship between CDOM properties and nutrients (TN and TP) may be used
to track the plant-derived source fraction. Strong linear relationships were
shown between nutrients (TN and TP) and DOC in the surface waters (Fig. 2c and d).
The types of land use around the water sampling locations may be a
crucial to the nutrient levels in the waters. The main land types in the Hulun
Buir plateau were grassland and forest, with high nitrogen and organic matter
export rates. A similar relationship between TN and DOC was also shown
during rainfall in agricultural and forested wetlands in the Shibetsu
watershed, Japan (Jiang et al., 2014). Studies have indicated that DOC
concentration in natural water environments is closely related to phytoplankton production and biological activity. Phytoplankton can convert
dissolved inorganic carbon (mainly CO2) to DOM through
photosynthesis, and part of the DOM in water can be degraded to
CO2 by heterotrophic microorganisms. TN and TP concentrations affect
the respiration and reproduction of microbes and phytoplankton, which could
have a pronounced influence on the conversion between DOC and CO2. The
respiration is often assumed to be conducted according to the Redfield ratio
(C : N : P = 106 : 16 : 1) under aerobic conditions (Redfield et al., 1963). When
C : N > 20 and C : P > 100, the biodegradation of DOC
cannot be fully performed. In this study, the values of C : N and C : P in most
the of water samples satisfy the degradation condition of DOC. We suspect
the above relationships (Fig. 2c and d) may be connected with the escape
of CO2 from the waters. Furthermore, CO2 flux in lakes is
negatively correlated with lake size (Raymond et al., 2013). If the flow and
size of lakes in the study are reduced by the construction of reservoirs,
irrigation, and land use, higher carbon emissions may develop. Furthermore,
DOC concentration and CO2 escape from the waters are both affected by
season and watershed characteristics (Organelli et al., 2014; Tranvik et
al., 2009; Riera et al., 1999).
PCA was performed in order to explain the variations in water quality in the
different sampling waters (Fig. 3). From the locations of the variables in
Fig. 3a, PC1 could be involved in the non-water light absorption, which may
be one important factor that distinguishes particulate light absorption from
CDOM light absorption. TN and TP with positive loadings on PC2 indicated
that PC2 may be related to anthropogenic nutrient disturbance. The close
juxtaposition of TDS and chl a shown in Fig. 3a indicated that TDS
concentration may be linked to phytoplankton metabolism. The PCA also
indicated that non-water light absorption and anthropogenic nutrient
disturbance might be the causes of the diversity of water quality in
different sampling locations.
The surface area of global inland waters is 3 624 000 km2; based on the
calculation of Raymond et al. (2013), that is 2.47 % of the Earth's land
surface (Raymond et al., 2013). They play a substantial role in the global
carbon (C) cycle, and about 2.9 Pg C yr-1 migrates, is transformed, and stored
via the inland water ecological system (Tranvik et al., 2009). DOM is the major
component of organic matter during the process of terrestrial organic matter
transport to lakes and the coastal zone, and it represents an essential link
between terrestrial and aquatic ecosystems (Cole et al., 2007; Harrison et
al., 2005). Based on the revision of the “active pipe” hypothesis, the total
current emissions from inland waters to atmosphere as CO2 and CH4
may be as high as 1.4 Pg C yr-1, the carbon burial in inland waters sediments
may amount to 0.6 Pg C yr-1, and the annual transport from inland
waters to the ocean is 0.9 Pg C (Tranvik et al., 2009). The inland waters area in the Inner
Mongolian Plateau covers about 9843 km2, which is 0.27 % of the global
inland water surface; so a rough count in this study would be 3.8 Tg C for
annual emissions, 1.6 Tg C for annual sediment burial, and 2.4 Tg C for the
annual transport from Inner Mongolian Plateau inland waters. Furthermore,
over 80 % of lakes in Inner Mongolian Plateau are saline lakes and salt
lakes. Previous studies have shown that saline lakes emit more
substantial carbon to the atmosphere and contain higher DOC concentrations
than freshwater (Anderson and Stedmon, 2007; Duarte et al., 2008; Osburn et
al., 2011). The dissolved inorganic C concentrations are about 10-15 times
greater than in freshwater lakes (Cole et al., 2011; Duarte et al., 2008;
Tranvik et al., 2009). Therefore the above estimation possibly
underestimated the contribution of Inner Mongolian Plateau inland waters to
the global carbon cycle. The mean CO2 emission rate from saline lakes
of the world is 81–105 mmol m-2 day-1 (Duarte et al., 2008). When
applied to the inland waters area in the Inner Mongolian Plateau of 9843 km2,
the calculated CO2 emissions to the atmosphere from these
inland waters amount to 3.49–4.53 Tg C yr-1.
Analysis of CDOM spectral characteristics
Terminal lakes exhibited significantly higher CDOM light absorption than
river waters (Table 1). Terminal lakes in the study all had high EC values
(> 1000 µs cm-1) (Song et al., 2013b). Researchers have
reported that the structure and composition of DOM alters obviously after
flowing into saline lakes (Waiser and Robarts, 2000). The use of
E250:365 for the tracking of changes in CDOM molecule size has been
practically demonstrated by many researchers (Helms et al., 2008; Song et
al., 2013b). Increasing E250:365 values indicate a decrease in
aromaticity and molecular weight (MW) of CDOM, and the results of this study
showed that CDOM in river waters had higher aromaticity and MW than terminal
lakes. The relatively low CDOM MW in terminal lakes implied that
chromophores associated with high MW CDOM were destroyed by photolysis with
the prolongation of hydraulic retention time and irradiation. In terminal
lakes, the change of molecular structure in high MW CDOM, caused by bond
cleavage, resulted in its transformation to a low MW pool. Furthermore,
previous studies have shown that the bulk of E250:365 mean values in
30 US rivers ranged from 5.00 to 6.50 (Spencer et al., 2012), and in the
Elizabeth River and Chesapeake Bay estuary ranged from 4.33 to 6.23 (Helms
et al., 2008). Compared with the reported river waters, the plateau rivers
in the study presented significantly higher mean E250:365 values. The
intense solar irradiance in this region potentially enhances the
photochemical degradation of allochthonous DOM and high MW CDOM, causing an
increase in the E250:365 values with the production of low MW CDOM.
Two rivers in the intermontane plateaus of the western USA with intense
solar irradiance also presented higher E250:365 values (9.05 ± 1.47,
7.38 ± 0.84) than other plain rivers (Spencer et al., 2012).
SUVA254 values in the river waters in this study were lower than the
following rivers. Mean SUVA254 values in 30 US rivers examined ranged
from 1.31 to 4.56 L mg C-1 m-1 (Spencer et al., 2012), while
SUVA254 in the Songnen Plain waters ranged from 2.3 (±0.14 SD)
to 8.7 (±2.8 SD) (Song et al., 2013b), and in the tropical Epulu river
ranged from 3.08 to 3.57 L mg C-1 m-1 (Spencer et al., 2010). A
possible driver of this CDOM characteristic in these plateau rivers is
coupled evapoconcentration, photo-degradation, and photobleaching, with strong
plateau ultraviolet radiation (Spencer et al., 2014; Spencer et al., 2009).
SUVA254 values have been proven to have a correlation with DOM
aromaticity as determined by 13C-NMR (Weishaar et al., 2003). In this
study, the lower SUVA254 measurements in terminal lakes indicated that
the aromatic moieties of CDOM in this environment were lower compared within
river waters due to the effect of photodegradation and microbial degradation,
with prolonged water residence times. From the conclusions of some studies
on SUVA254 and hydrophobic organic acid fraction (HPOA), the
SUVA254 values were always comparable to HPOA, and the conjecture could
be reached that low SUVA254 values indicate that the aquatic systems
with little vascular plant input, and the autochthonous sources (algal or
microbial) dominated the organic matter content (Spencer et al., 2008;
Weishaar et al., 2003). Conversely, high SUVA254 values indicated that
the organic matter in aquatic systems was dominated by allochthonous sources
with significant vascular plant inputs (Cory et al., 2007; Spencer et al.,
2012). In this study, the SUVA254 revealed that the contribution of
vascular plant matter to DOM in rivers might be greater than the terminal
lakes, and the high MW DOM was more abundant in freshwater lakes than terminal
lakes. Shorter residence time of DOM in river waters and the quick exchange
rates of flow water shortened the photo-oxidation of DOM, which could be
responsible for the phenomenon (Song et al., 2013b; Spencer et al., 2012).
The majority of river waters in the study exhibited significantly higher
S275-295 values than allochthonous-dominated freshwaters which include
the majority of US rivers (13.00–16.50 × 10-3 nm-1)
(Spencer et al., 2012), and the Congo River (12.34 × 10-3 nm-1)
(Spencer et al., 2009), which indicated the proportion of
autochthonous sources of CDOM and photolysis of allochthonous CDOM in plateau
waters was higher than in freshwater rivers. The ratio of spectral slopes
(SR), an indicator of CDOM molecular weight and source (Helms et al.,
2008; Spencer et al., 2010), indicated that river water samples with lower
SR values contained greater allochthonous and higher MW DOM than
terminal lakes. Previous studies have proven that S values were inversely
proportional to CDOM MW, with a steeper spectral slope signifying decreasing
aromaticity, and a shallower spectral slope signifying an increasing aromatic
content (Gonnelli et al., 2013; Helms et al., 2008). S values in this study
indicated that the percentage of high MW humic acid in CDOM in Hulun Lake
was greater than in Buir Lake, whereas the proportion of fulvic acid and
aromatic compounds showed the reverse trend. Furthermore, S275-295
could be used as indicator for terrigenous DOC percentage in bodies of
water (Gonnelli et al., 2013). Our results indicate that the percentage of
terrigenous DOC is higher in Hulun Lake than Buir Lake. From the known
geological history of the region, Buir Lake is a throughput lake with inflow
from the Halaha River and outflow from the Wuerxun River to Hulun Lake.
Also, the land use pattern in Buir Lake watersheds shows potential
desertification. Hulun Lake not only receives the Wuerxun River, flowing from
Buir Lake, but also receives water from the Kerulun River. Natural
grassland with fresh organic rich layers was dominant in Hulun Lake
watersheds. The geographical location and land use pattern together account
for the larger percentage of terrigenous DOM in Hulun Lake.
Correlations between water quality parameters and light absorption
Strong positive correlations between CDOM absorption coefficients and TN,
TP, and DOC concentrations in all water samples indicated that CDOM light
absorbance could be explained by variations in nutrients and DOC
concentration to a greater extent. Previous studies have shown that CDOM
absorption in a range of spectra could be used as an proxy for DOC in many
inland water bodies, including the Kolyma River basin (Griffin et al.,
2011), the Epulu River (Spencer et al., 2010), as well as many US rivers
(Spencer et al., 2012); and our results once again support this relationship
in the aquatic environment of the Hulun Buir plateau. S275-295 and chl a
concentration had no correlation; a similar phenomenon has been identified
in the Ligurian Sea (BOUSSOLE site) and the Mediterranean Sea (central
eastern basin) (Bracchini et al., 2010; Organelli et al., 2014). These
results indicated that CDOM in natural waters did not originate entirely
from the release and dissociation of the phytoplankton, and that terrestrial
input and microbial activities all play an important role in the generation
and properties of CDOM (Ogawa et al., 2001; Rochelle-Newall and Fisher,
2002). Furthermore, strong solar radiation in the plateau area and the open
ocean enhanced the photobleaching of CDOM, resulting in variation in the
structural composition of CDOM. The Inner Mongolian Plateau has high levels of
wind and dust, and a number of lakes in the region have shrunk remarkably in
recent decades (Tao et al., 2015). The shrinkage and resuspension of lakes
as a result of climatic conditions may seriously influence the optical
characteristics and chl a concentration. The significant positive correlation
between light absorption with TSM may be related to the unique climate of the
Hulun Buir plateau with alternating windy, rainless, and frigid
conditions, which need to be further studied.
Contribution of CDOM to light absorption
At all the sampling locations, phytoplankton absorption played a minor role
on total non-water light absorption (Fig. 4). The low levels of
phytoplankton in the Hulun Buir plateau lakes with higher salinity may be
responsible for this phenomenon. Previous studies have also shown that light
absorption by non-algal particles often exceeds that of phytoplankton in
shallow inland lakes and coastal waters (Carder et al., 1991; Frenette et
al., 2003). In most water samples examined in this study, CDOM was the
dominant light-absorbing substance even when the CDOM absorption was minimal
due to photobleaching in summer. The large contribution of CDOM to total
absorption (approximately 50 % at 440 nm in the surface layer) was also
shown in the Sepik River (Parslow et al., 1998). The large contribution of
CDOM was also identified in other water environments, such as a fluvial lake
(Frenette et al., 2003), the equatorial Pacific area (Bricaud et al., 2002),
and the Ligurian Sea (Organelli et al., 2014). The above analysis indicated
that the waters in the Hulun Buir plateau were classified as Case 2 water, with CDOM present in
all the water samples (Morel and Prieur, 1977). According to Morel and Prieur (1977),
Case 2 water describes the case in which non-algal particles play a major role in actual
absorption, and phytoplankton absorption is of comparatively minor importance. According to the optical
classification of surface waters (Prieur and Sathyendranath, 1981), the
majority of the river and terminal water samples collected in the Hulun Buir
plateau could be classified as “CDOM-type” water, and others were
“NAP-type”. Different catchment properties and water quality parameters
could be responsible for the variation in optical classification of these
waters. Other studies have shown that CDOM absorption is related to the EC
of water (Sieczko and Peduzzi, 2014). EC values in rivers and lakes of the Hulun Buir plateau showed a wide range, which may affect the agglomeration
or dissociation of particles and CDOM in the waters and indirectly influence
light absorption. In addition, in lakes located near the paddy field and
built-up areas, water quality is greatly influenced by human activities
(Graeber et al., 2012).
The light absorption of optically active compounds (OACs) determines the
inherent optical properties of waters. In our opinion, the pattern shown in
Fig. 4 is not invariable, and it may change with season and some extreme
climate events. First, as a constituent of DOM, CDOM inputs to lakes are a
mixture of allochthonous organic substances delivered by river discharge and
metabolites produced by metabolic activities of autochthonous heterotrophic
bacteria (Dillon and Molot, 1997; Zhou et al., 2015). Terrestrial CDOM
leached from the soil to the rivers and flowing to the lakes is subject to
diverse processes: physical flocculation and adsorption, chemical
photobleaching, and microbial degradation (Li et al., 2014). It is widely
recognized that CDOM loading and composition in aquatic environments are
regulated by ambient hydrology, landscape features, climate, and aquatic
organisms' activity, and vary seasonally and interannually (Dillon and Molot,
2005; Spencer et al., 2012; Worrall and Burt, 2004; Griffin et al., 2011).
For example, studies have indicated that highly seasonal variability of DOC
has been observed in high-latitude rivers, characterized by rising
concentration significantly with increasing discharge of these rivers;
spring snowmelt and winter freeze both have an effect on DOC concentration
(Raymond et al., 2007). Photobleaching in summer dramatically altered the
optical properties of the surface waters, with the CDOM absorption and
fluorescence lost through photo-oxidation (Vodacek et al., 1997). The Hulun Buir plateau is
characterized by a typical semi-humid and semiarid continental monsoon
climate with intensive solar radiation (especially in summer) and a long
frozen period. The temperature, snowmelt, solar radiation, water quality,
and plankton and microbe activity all have a non-negligible effect on CDOM
photo-absorption characteristics. Secondly, in shallow inland lakes, light
absorption of non-algal particles often exceeds that of phytoplankton. The
light absorption of CDOM and non-algal particles often decreases in a
near-exponential manner with increasing optical wavelength, which is not
beneficial for the growth of phytoplankton. In the Hulun Buir plateau,
phytoplankton growth is very slow even in the warm season; the high pH,
salinity, and alkalinity of water may be responsible for this phenomenon.
Therefore, the relative contributions of phytoplankton to non-light absorption in the Hulun
Buir plateau may be difficult to improve due to the depressed algae growth.
Non-algal particles' concentration is related to the TSM (Table 2), the
sediment suspension caused by strong winds in late autumn and winter, the
increase of surface runoff in spring with snowmelt, and the change of land
use pattern; these factors may cause the increase of TSM concentration,
resulting in the increase of light absorption by non-algal particles.
Above all, many factors could affect the relative contributions of OACs to
total non-water light absorption, and the issue should be discussed in relation to the
local environment and climate.
Pareto–Lorenz curve analysis indicated that in the Hulun Buir plateau and
similar geographical aquatic environments, we could randomly select 20 % of
the water samples collected to analyze the light absorption. The
contributions of optically active substances can be estimated based on
these absorption values and the cumulative contributions in this study; then
the estimated value could be used to identify water type and evaluate the
regional homogeneity of non-water light absorption.