Introduction
Dissolved organic matter (DOM) is widely distributed in natural aquatic
ecosystems and plays a key role in the global carbon cycle (Massicotte et
al., 2017). Chromophoric dissolved organic matter (CDOM), widely known as
the light-absorbing constituent of DOM, can absorb light from ultraviolet to
visible (UV–vis) wavelengths (Bricaud et al., 1981). Owing to its
light-absorbing properties, CDOM is important in biological processes
(Seekell et al., 2015; Thrane et al., 2014), photochemical processes (Helms
et al., 2013; Vaehaetalo and Wetzel, 2004), and the energy budget (Hill and
Zimmerman, 2016; Pegau, 2002) in natural water bodies.
Compared to the aquatic environments, there were only limited studies
evaluating DOM in the cryosphere. Whereas the global glacier ecosystem is a
large organic carbon pool and exports approximately 1.04±0.18 TgC yr-1
of dissolved organic carbon into freshwater and marine
environments (Hood et al., 2015). In addition, the glacier-derived DOM shows
high bioavailability and can be a source of labile organic matter for
downstream ecosystems (Hood et al., 2009; Lawson et al., 2014; Singer et
al., 2012). The DOM in snow and ice originates from in situ processes
(autochthonous) such as microbial activity (Anesio et al., 2009) and
is imported from the surrounding terrestrial environments (allochthonous),
including soil, vegetation (Bhatia et al., 2010), and anthropogenic
activity (Stubbins et al., 2012).
Snowfall is an important carbon and nutrient input for land ecosystems
(Mladenov et al., 2012) and a crucial freshwater reservoir (Jones, 1999).
Snowpack is also an active field for photochemical (Beine et al.,
2011; Domine et al., 2013) and biological processes (Liu et al., 2009; Lutz
et al., 2016). Unlike the aquatic environments, high surface albedo is the
most obvious physical property of snow (IPCC, 2013). Once light-absorbing
impurities are deposited on the snow surface, the albedo can be significantly
reduced, and the regional and global climate are further affected (Hadley
and Kirchstetter, 2012). Several field campaigns covering the Arctic,
Russia, North America, and northern China have been conducted to measure
insoluble light-absorbing particles (ILAPs) in snow, for instance black
carbon (BC), insoluble organic carbon (ISOC), and mineral dust (MD) (Doherty
et al., 2010, 2014, 2015; Huang et al., 2011; Pu et al., 2017; Wang et al.,
2013, 2015, 2017; Warren and Wiscombe, 1980; Ye et al., 2012; Y. Zhou et
al., 2017). However, these studies neglected CDOM, which is rarely studied
in snow but has been proven to be an effective light absorber whether in the
atmosphere (i.e., brown carbon, BrC) (Hecobian et al., 2010) or in water bodies
(Bricaud et al., 1981). To constrain the photochemistry of snow soluble
chromophores, Anastasio and Robles (2007) first quantified the light
absorption of dissolved chromophores in melted snow samples from the Arctic
and Antarctica. They found that in addition to
NO3- and H2O2, approximately half of the
light absorption at 280 nm and above was due to unknown chromophores,
probably organics. After that, Beine et al. (2011) analyzed more than 500
snow samples collected in Alaska. They exhibited slight contributions of
H2O2 and
NO3- to the total absorption within
300–450 nm (combined <9 %), while humic-like
substances (HULIS), which are a type of macromolecular organic matter defined
for aerosol with certain similar chemical properties to terrestrial and
aquatic humic and fulvic substances (Graber and Rudich, 2006), and unknown
chromophores each accounted for approximately half of the total absorption.
Recently, several studies have started to focus on the optical properties
and radiative forcing of CDOM in glaciers on the Tibetan Plateau. Yan et
al. (2016) measured the mass absorption cross section (MAC) of CDOM in snow
(1.4±0.4 m2 g-1 at 365 nm) at Laohugou Glacier, northern
Tibetan Plateau, and further calculated the radiative forcing of CDOM, which
accounted for approximately 10 % relative to that of BC. Niu et al. (2018)
showed quite a high MAC value of CDOM (6.31±0.34 m2 g-1 at
365 nm) in snow and ice samples collected on Mt. Yulong, southeastern
Tibetan Plateau. Moreover, it is surprising that the light absorption of
CDOM within 330 to 400 nm was approximately 4 times higher than that of BC,
although with high uncertainty. In above studies, CDOM showed significant
effects on the energy budget of surface snow and ice on glaciers. Until now,
the study of CDOM in snow and ice is still in its infancy, and much more
work is imperative to improve our understanding of it. In northern China,
snowpack is affected by more anthropogenic activities or sunlight than those
at higher elevation or latitude, thus the effects of CDOM may be more
remarkable. Therefore, we conducted a large field campaign to investigate
the CDOM in seasonal snow of northwestern China from January to
February 2012.
UV–vis absorption and fluorescence spectroscopies are both rapid and
effective methods for characterizing the optical properties and sources of
CDOM. The absorption coefficient at a certain wavelength within the UV band,
for instance, 254, 280, or 350 nm (Spencer et al., 2012; Zhang et al.,
2010, 2011), usually serves as an indicator of CDOM abundance. The
absorption spectrum of CDOM decreases approximately exponentially with
increasing wavelength (Helms et al., 2008), and is usually described by the
spectral slope (S) (Twardowski et al., 2004). Helms et al. (2008) used the
spectral slope between 275 and 295 nm (S275-295) to
investigate the molecular weight and sources of CDOM (terrestrial or marine
origin), i.e., lower S275-295 corresponds to CDOM
with a higher molecular weight and a more obviously terrestrial characteristic.
Fluorescence excitation–emission matrix (EEM) has been widely used to
identify the sources and compositions (humic-like or protein-like) of
fluorescent DOM (FDOM) in natural waterbodies (Birdwell and Engel, 2010;
Coble, 1996; Zhao et al., 2016), rainwater (Y. Q. Zhou et al., 2017), fog
water (Birdwell and Valsaraj, 2010), and aerosols (Duarte et al., 2004; Lee
et al., 2013; Mladenov et al., 2011). To precisely extract useful
information from the large data set of EEMs, Bro (1997) successfully applied
parallel factor (PARAFAC) analysis to decompose EEMs into several
independent fluorescent components. Due to the great advantages of PARAFAC
analysis in interpreting the results of EEMs, this has been the mainstream
approach in recent natural CDOM studies (Murphy et al., 2013). However, the
application of EEM combined with PARAFAC analysis in the cryosphere is
scarce. Therefore, we try to employ it to characterize the snow CDOM.
In this study, for the first time, with the aim of presenting a
comprehensive understanding of CDOM in seasonal snow across northwestern
China, UV–vis absorption and fluorescence spectroscopies along with chemical
analysis were applied to investigate the abundances, optical properties, and
potential sources of CDOM as well as their spatial distributions.
(a) Location of study area and sample site distribution across
northwestern China. The site numbers and regional groupings are shown in
panel (b) for Xinjiang and (c) for Qinghai. Sample sites are divided into
five groups indicated by different symbols, and the land cover types
are represented in different colors, as shown in the legend in panel (a).
The D indicates that the sample was collected from a snow drift, and the
F indicates that the sample was fresh snow. The elevation is shown in
the contour plot.
Material and methods
Sample collection
During January to February 2012, snow samples were collected at 7 sites in
Qinghai and 32 sites in Xinjiang, northwestern China. The distribution of
sample sites, which are numbered chronologically, is shown in Fig. 1. Based
on Pu et al. (2017), these sites were separated into five regions by their
geographical distribution to investigate the spatial variations of light
absorption and fluorescence properties, as well as the potential sources of
CDOM. Region 1 is in the southeastern part of Qinghai at high altitude,
and other regions are in Xinjiang. Region 2 is along the Tianshan Mountains;
region 3 is located to the north of the Tianshan Mountains and close to the
industrial city belt in central Xinjiang. Regions 4 and 5 are in
northwestern and northeastern Xinjiang, and both are along the
border of China.
Pictures of typical sample sites.
The sample sites were chosen to be upwind and far enough away from roads,
railways, cities, and villages to minimize the effects of local pollution.
Hence, the collected samples can be representative of a wide range of
areas. Pictures of several sample sites are shown in Fig. 2. Snow samples
were collected every 5 cm from top to bottom at each site. If there was a
melt layer or fresh snow on the top layer, such a sample was collected
individually. A pair of two adjacent vertical profiles of snow (left and
right samples) to assess the variability of the same snowpack
and to enhance the accuracy of the measurements. During this campaign, 13
fresh snow samples that had fallen during the sampling time were collected.
In addition, at some sites, the snow was thin and patchy and the wind was
strong; hence, these samples were gathered from snow drifts and were potentially
influenced by the deposition of local soil dust (Ye et al., 2012). More
details on the sampling methods have been reported previously (Doherty et
al., 2010; Wang et al., 2013; Ye et al., 2012).
After being returned to the laboratory in Lanzhou University, all the
samples were stored in a freezer at -20 ∘C or lower for subsequent
analyses. However, some previous studies indicated that the freeze–thaw
process may lead to biases of the optical properties for DOM samples. For
instance, Fellman et al. (2008) reported that there was a decrease in
specific ultraviolet absorbance (SUVA) for stream water DOM after frozen,
with a median of approximately 8 %. A study of peatland DOM found that the
change in light absorption at 254 nm after freeze and thaw was less than
5 % of the median (Peacock et al., 2015). Thieme et al. (2016) assessed the
changes in fluorescence properties for several types of DOM sample. The
results showed the decreased relative percentages of terrestrial humic-like
fluorophores (-3 % on average) and humification index (HIX, -2 % on
average), and the increased percentage of fluvic-like fluorophore (+6 %
on average). Other studies have also shown that the optical properties
(light absorption and fluorescence) of several types of DOM were not
affected significantly by freezing, such as those in ocean water, pore
water, spring, and cave water (Birdwell and Engel, 2010; Del Castillo and
Coble, 2000; Otero et al., 2007; Yamashita et al., 2010). As discussed
above, the freeze–thaw process may influence the relative contributions of
PARAFAC components slightly, while the effects on
aCDOM(280) and fluorescence indices can be
neglected.
Fluorescence measurement
The snow samples were first melted under room temperature. Then, the meltwater samples were filtrated using 0.22 µm PTFE syringe filters
(Jinteng, Tianjin, China) and stored in pre-baked glass vials
(450 ∘C
for 4 h) at 4 ∘C in a freezer. All the samples were
measured for UV–vis and fluorescence spectroscopies within 24 h after
filtration. The ultrapure water (18.2 MΩ cm) filtrated by
the PTFE syringe filters exhibited no clear fluorescence signal.
The EEMs (n=78) of surface snow samples were measured by an Aqualog
spectrofluorometer system (Horiba Scientific, NJ, USA) in a 1 cm quartz
cell. The scanning ranges were 240 to 600 nm in 5 nm intervals for
excitation and 250 to 825 nm in 4.65 nm (8 pixels) intervals for emission,
with the integrating time of 5 s. An ultrapure water blank was subtracted to
remove the water Raman scatter peaks.
The inner filter effect (IFE) of EEM was corrected using the method shown in
Kothawala et al. (2013). The fluorescence intensities were calibrated by the
Raman peak of ultrapure water reference at a 350 nm excitation wavelength
following the method presented by Lawaetz and Stedmon (2009). The Rayleigh
scatter peaks were addressed by the EEMscat MATLAB toolbox (version 3) using
an interpolation algorithm (Bahram et al., 2006).
PARAFAC is a multi-way method for modeling the data with three- or
higher-order arrays (Murphy et al., 2013). For EEMs, the three dimensions
are samples, excitation, and emission wavelengths. PARAFAC analysis can
decompose the EEMs into several components with clear chemical
interpretations. The details about the theory of PARAFAC analysis can be
found in the Supplement. In this study, PARAFAC analysis was performed using
the DOMFluor toolbox (version 1.7, Stedmon and Bro, 2008) in MATLAB. In
addition, because the emission signals were mainly in the range of
250–650 nm, those at longer wavelengths were weak and more likely to be
noises; hence, the emission wavelengths longer than 650 nm were not considered in
the model. According to the analysis of residual error, split-half method,
and visual inspection, the three-component PARAFAC model was selected. The
residual error decreased distinctly when the component number increased from
two to three and from four to five (Fig. S1). Combined with the split-half analysis for
2- to 7-component models, only 2- and 3-component models were validated with
the S4C4T2 split scheme (Murphy et al., 2013). Therefore,
the 3-component model was chosen here. The fluorescence intensity of each
fluorescent component was expressed as Fmax in Raman
unit (RU) (Stedmon and Markager, 2005b). The relative contributions of
intensities for three components to the total fluorescence are given as
%C1–%C3 hereinafter. In addition, three fluorescence-derived indices
are widely used to identify the potential sources of CDOM. Zsolnay et
al. (1999) presented HIX to describe the relative humification of DOM. The
fluorescence index (FI) is used to identify the sources of DOM from
terrestrial or microbial origins (McKnight et al., 2001), and the biological
index (BIX) can be an indicator of autochthonous productivity (Huguet et
al., 2009). These three indices are calculated by the following formulas:
FI=I(Ex=370,Em=450)/I(Ex=370,Em=499),BIX=I(Ex=310,Em=379)/I(Ex=310,Em=430),HIX=I(Ex=255,Em=434-480)/I(Ex=255,Em=300-345),
where I is the fluorescence intensity, and Ex and Em are short for the
excitation and emission wavelengths. We note that the
wavelengths used in the calculation were changed slightly (1 nm or less) due
to different instruments.
UV–vis absorption measurement
The UV–vis absorption spectra (n=78) of snow samples were derived from
240 to 600 nm in 5 nm intervals, while the fluorescence measurements were
conducted by an Aqualog spectrofluorometer system, and an ultrapure water
blank was used as a reference. The absorbance of CDOM was assumed to be zero
above 550 nm, and the average absorbance between 550 and 600 nm was subtracted
from the whole spectrum to correct the baseline shifts and scattering
effects of the measurement. The absorbances of samples were converted to
absorption coefficients using the following equation:
aCDOM(λ)=ln(10)⋅A(λ)/L,
where A is the absorbance, λ is the wavelength, L is the
path length of cuvette (0.01 m), and aCDOM is the
absorption coefficient (m-1). The abundance of CDOM is presented by the
absorption coefficient at 280 nm,
aCDOM(280) (Zhang et al., 2010). The
S275-295 was determined both by a log-transform
linear regression and an exponential regression. The variation of these two
methods was approximately 3 % on average. Linear regression has been
frequently used to calculate S275-295 (Fichot and
Benner, 2012; Helms et al., 2008; Yang et al., 2013), and in this study,
showed higher R2 values than exponential regression. Therefore, the
results of linear regression were adopted here. Additionally, if the
difference in S275-295 between the linear and
exponential methods was higher than 10 %, indicating a high uncertainty
for absorption measurement, such data were removed. The absorption
Ångström exponent (AAE) is used to describe the wavelength
dependence of light absorption for aerosol (Bond, 2001), which has also been
applied to characterize the ILAPs and CDOM in snow and ice (Doherty et al.,
2010; Niu et al., 2018; Wang et al., 2013; Yan et al., 2016). The AAEs were
calculated using power-law regression in the wavelength range of 240 to 550 nm,
as follows:
aCDOMλ=K⋅λ-AAE,
where K is a constant related to DOM concentration. The R2 of all
regressions (S275-295 and AAE) were higher than 0.9
and most of them were higher than 0.95.
Because the light absorption within the visible wavelengths of some samples
were below the detection limit of the spectrophotometer, 19 of 39 samples
were available for the calculations of AAE.
Note that the left samples of sites 51b and 58, which showed abnormal
absorption and fluorescence spectra compared to other samples, were supposed
to be contaminated, and thereby these two samples were not considered for
the absorption and fluorescence analyses.
Soluble ions
The major soluble ions of meltwater samples were analyzed with an
ion chromatograph (Dionex, Sunnyvale, CA, USA) using an AS11 column for the
anions SO42-, NO3-, Cl-, and
F- and a CS12 column for the cations
Na+, K+,
Ca2+, Mg2+, and
NH4+. The soluble ions showed no
obvious differences between filtered and unfiltered samples (Pu et al.,
2017). According to Pio et al. (2007), the K+ can be
separated into three fractions: sea salt (ss), dust, and others (the fraction
not related to sea salt and mineral dust, nss-ndust). The
nss-ndust-K+ is a good marker for biomass burning
(Pio et al., 2007). The Ca2+ concentrations of our
samples were mostly higher than that of Na+,
leading to much larger mass ratios of
Ca2+/Na+ than that in seawater (0.038) (Pio et al., 2007). Therefore, Ca2+
is dominated by the dust fraction and not corrected to
nss-Ca2+ in this study.
nss-ndust-K+ is calculated using the following
formulas (Pio et al., 2007):
nss-ndust-K+=K+-ss-K+-dust-K+,ss-K+=0.038×ss-Na+,ss-Na+=Na+-0.14×Ca2+,dust-K+=0.028×Ca2+.
In Eq. (7), 0.038 is the mass ratio of
K+/Na+ in seawater (Pio et al., 2007). In Eq. (8), the lowest mass ratio of
Na+/Ca2+ of our samples (0.14) is used to evaluate the dust fraction of
Na+. Similarly, the lowest mass ratio of
K+/Ca2+ (0.028) is used in
Eq. (9) to calculate the dust fraction of K+.
Hierarchical cluster analysis
A hierarchical cluster analysis was used to classify the samples based on
the relative abundances of three PARAFAC components. Euclidean distance was
used to estimate the distances between samples. Before determining the
clustering method, the cophenetic correlation coefficients, criteria for
assessing the efficiency of clustering methods (Saracli et al.,
2013), for the cluster trees created by different methods were calculated,
including unweighted average, weighted average, centroid, farthest neighbor,
shortest neighbor, weighted center of mass, and Ward's methods. Finally, the
unweighted average method was chosen due to the highest cophenetic
correlation coefficients. A total of four clusters were determined and
labeled as clusters A–D.
Air mass backward trajectories and active-fire data
Air mass backward trajectory has been widely used to identify the sources of
air pollution (Stein et al., 2015) and also successfully applied to
studies of impurities in snow (Hegg et al., 2010; Wang et al., 2015; Zhang
et al., 2013). In this study, 72 h air mass backward trajectories were
conducted by the HYbrid Single-Particle Lagrangian Integrated Trajectory
(HYSPLIT) model (version 4, http://ready.arl.noaa.gov/HYSPLIT.php, last access:
5 April 2018). The
model was run at 500 m above ground level (a.g.l.) 4 times a day for a period of
30 days preceding the sampling date at a given site. Using a satellite map
of fire locations, the backward trajectories that pass through the satellite-derived fire locations can be
identified as the sources of biomass-burning particles to the receptor
sites (Antony et al., 2014; Hegg et al., 2010; Zhang et al., 2013). We used the
active-fire data from the Moderate Resolution Imaging Spectroradiometer
(MODIS) Collection 6 (MCD14DL, 2018) and the Visible Infrared Imaging Radiometer
Suite (VIIRS) (VNP14IMGTDL_NRT, 2018) to capture potential fire
location distributions. The data are available online:
http://earthdata.nasa.gov/firms (last access: 2 October 2018).
aCDOM(280) and
S275-295 for sites in (a, c) Xinjiang and
(b, d) Qinghai. The five regions are indicated by
different symbols (same as Fig. 1).
Results and discussion
The absorption characteristics of CDOM
(aCDOM(280), S275-295,
and AAE)
The distributions of aCDOM(280) and
S275-295 are shown in Fig. 3, and the corresponding
values are summarized in Table 1. aCDOM(280)
ranged widely from 0.15 to 10.57 m-1 with an average of 1.69±1.80 m-1 (mean ± standard deviation). The highest value appeared
at site 67 (10.57 m-1), followed by sites 53, 79, and 47
(5.25, 3.13, and 3.11 m-1). Most of
these samples were collected from snow drifts. These values were higher than
the aCDOM(280) of CDOM in snow, ice, and
cryoconite on the Tibetan Plateau (typically lower than 2.0 m-1)
(Feng et al., 2016, 2017). The lowest value was found at site 66 (0.15 m-1),
followed by sites 70, 82, 73, and 83 (0.21, 0.23, 0.30, and 0.31 m-1), and these values were comparable
with the absorption of soluble species in Alaskan snow with typical values
of 0.1–0.15 m-1 at 250 nm (Beine et al., 2011). Some of these samples
comprised freshly fallen snow and some were collected at remote sites that
were far from pollution sources (Pu et al., 2017). The values of
S275-295 ranged from 0.0129 to 0.0389 nm-1 with
an average of 0.0243±0.0073 nm-1.
S275-295 has never been reported in the terrestrial snow
and ice samples before but is widely measured in the aquatic environments.
For example, Hansen et al. (2016) summarized the
S275-295 for oceanic and terrestrial water systems.
The values are in the range of 0.020–0.030 nm-1 for ocean,
0.010–0.020 nm-1 for coastal water, and 0.012–0.023 nm-1 for
rivers and wetlands. The S275-295 in this study
covered the typical values in different types of natural water bodies,
indicating complex compositions and sources of CDOM in seasonal snow across
northwestern China. The AAEs of 19 CDOM samples are also shown in Table 1,
which ranged from 4.41 to 8.91 with an average of 5.55±1.11. This
value is comparable with the average AAE of HULIS extracted from Alaskan
snow (6.11, from 300 to 550 nm) (Voisin et al., 2012).
Statistics on absorption and fluorescence parameters for
snow CDOM
at each site. Note that N.A. stands for no data.
aCDOM (280)
S275-295
Site
Lat. (∘ N)
Long. (∘ E)
(m-1)
(nm-1)
AAE
HIX
BIX
FI
47
35.54
99.49
3.11
0.0174
4.66
2.24
0.51
1.31
48
34.85
98.13
1.32
0.0212
5.12
1.30
0.59
1.40
49
35.22
98.95
2.14
0.0206
5.20
2.24
0.47
1.29
50
34.80
99.05
2.38
0.0194
4.91
2.08
0.48
1.27
51a
33.89
99.80
2.44
0.0183
4.87
2.44
0.44
1.21
51b
33.89
99.80
2.02
0.0175
4.91
2.57
0.44
1.28
52
34.92
100.89
2.71
0.0170
4.63
2.63
0.53
1.23
53
43.07
86.81
5.25
0.0178
4.53
3.20
0.48
1.25
54
43.08
85.82
0.41
0.0350
N.A.
0.28
0.82
1.76
55
43.51
83.54
2.52
0.0178
5.13
1.90
0.61
1.30
56
43.66
82.75
1.38
0.0192
5.77
1.39
0.61
1.31
57
43.64
82.11
2.66
0.0168
5.31
1.75
0.59
1.33
58
43.52
81.13
0.42
N.A.
N.A.
0.54
0.70
1.42
59
44.49
81.15
0.54
0.0357
N.A.
0.59
0.65
1.43
60
44.96
82.63
2.39
0.0174
8.91
1.38
1.24
1.43
61
44.38
83.09
1.66
0.0189
6.06
1.49
0.59
1.36
62
44.57
83.96
0.80
0.0194
N.A.
0.52
1.47
1.63
63
45.58
84.29
0.81
0.0264
N.A.
0.71
0.78
1.41
64
46.68
83.54
2.01
0.0194
5.54
1.96
0.49
1.21
65
46.49
85.04
0.64
0.0291
N.A.
0.77
0.82
1.39
66
46.88
85.92
0.15
N.A.
N.A.
0.39
0.84
1.61
67
47.26
86.71
10.57
0.0169
4.41
1.47
0.63
1.30
68
48.15
86.56
0.57
0.0301
N.A.
0.59
1.10
1.54
69
47.86
86.29
1.41
0.0221
7.70
1.02
1.04
1.48
70
48.33
87.13
0.21
0.0351
N.A.
0.41
0.62
1.39
71
48.07
87.03
0.61
0.0255
N.A.
0.66
1.01
1.52
72
47.79
87.56
1.49
0.0259
N.A.
1.05
1.20
1.41
73
47.55
88.61
0.30
0.0381
N.A.
0.54
1.12
1.45
74
47.63
88.40
0.55
0.0337
N.A.
0.63
1.20
1.49
75
47.58
88.78
0.81
0.0376
N.A.
0.74
0.79
1.34
76
47.17
88.70
1.21
0.0389
N.A.
0.89
0.97
1.47
77
47.27
89.97
1.50
0.0301
N.A.
0.80
0.71
1.25
78
46.85
90.32
2.32
0.0221
5.52
1.93
0.48
1.25
79
43.53
89.74
3.13
0.0221
5.52
1.65
0.46
1.20
80
44.10
87.49
0.54
0.0292
N.A.
0.49
0.74
1.66
81
43.60
87.51
0.72
0.0255
N.A.
0.45
0.80
1.65
82
44.09
84.80
0.23
N.A.
N.A.
0.16
0.88
1.92
83
43.93
85.41
0.31
N.A.
N.A.
0.27
0.77
1.60
84
43.93
86.76
1.65
0.0129
6.66
0.95
0.79
1.52
The detailed results of each region are discussed below. Region 1 (sites
47–52) is located in the eastern Tibetan Plateau, which is typically higher
than 4000 m above sea level. In this region, the snowpack is usually patchy
and thin (Fig. 2a). During windy weather, local soil can be blown and deposited
on the snow surface, which had been observed by previous studies (Pu et al.,
2017; Ye et al., 2012). Moreover, the filters for samples in this region
were in yellow due to the high loading of soil dust. The average
aCDOM(280) was the highest among all five
regions (2.30±0.52 m-1), and the S275-295
fell in the range of 0.0170–0.0212 nm-1 (0.0188±0.0015 nm-1),
which shows similar values of leaching for permafrost on the
Tibetan Plateau (Wang et al., 2018).
In region 2 (sites 53–59, 61, and 79), snow at some sites was patchy (e.g.,
sites 53, 57, 61, and 79, Fig. 2b), and some sites were farmland (e.g.,
sites 55 and 56, Fig. 2c), which can all be influenced by local soil due to
strong wind. The aCDOM(280) values of these
sites were in the range of 1.38–5.25 m-1. Several sites in this region
showed lower aCDOM(280) (0.41–0.54 m-1,
e.g., sites 54, 58, and 59) might result from new-fallen snow or fewer
human activities due to their high altitude. Overall, region 2 showed a high
average aCDOM(280) (2.00±1.50 m-1),
and the average S275-295 was 0.0229±0.0073 nm-1.
Region 3 (sites 60, 62, 63, and 80–84) is the most developed part of
Xinjiang, and major industrial cities are located here (e.g., Urumqi,
Shihezi, Kuytun, and Karamay). Therefore, human activities may dominate the
contribution of snow CDOM in this region. However, the
aCDOM(280) values were mostly less than 1.0 m-1
except at sites 60 and 84, with a low average of 0.93±0.68 m-1.
Because samples of these sites were almost new-fallen snow, the
deposition of pollutants to the snowpack can be quite slight. Sites 60 and
84 were both close to industrial cities (Fig. 1 in Pu et al., 2017), and
locally anthropogenic pollutants may be responsible for the high
aCDOM(280) (2.39 and 1.65 m-1). The average S275-295 was 0.0218±0.0057 nm-1 in this region.
In region 4 (sites 64–71), the maximum and minimum
aCDOM(280) values of the entire campaign
were found here, i.e., 10.57 m-1 (site 67, snow drift, Fig. 2d) and
0.15 m-1 (site 66, new snow). Generally, the
aCDOM(280) was in the range of 0.50–2.00 m-1
with an average of 0.80±0.62 m-1 (excluding site 67),
which was the lowest of the values in all regions. The mean value
of S275-295 (0.0255±0.0060 nm-1) was
higher than those in regions 1–3.
In region 5 (sites 72–78), the average value of
aCDOM(280) was 1.17±0.63 m-1,
which was intermediate among five regions. S275-295
was typically higher than 0.0300 nm-1 and showed the highest average of
0.0324±0.0060 nm-1.
Fluorescent components identified by PARAFAC analysis.
The fluorescence characteristics of CDOM
PARAFAC components
The EEMs of snow samples were analyzed by PARAFAC model, and three
fluorescent components (C1–C3) were identified (Fig. 4). The corresponding
excitation and emission loading spectra of each component are shown in
Fig. S2. The excitation–emission (Ex–Em) wavelengths of each component's
fluorescence peaks are summarized in Table 2.
Descriptions of fluorescent components identified by PARAFAC
analysis. The secondary peaks are shown in parentheses.
Component
Excitation
Emission
number
maximum
maximum
wavelength (nm)
wavelength (nm)
Descriptions
References
C1
<240 (305)
453
Terrestrial humic-like substances
Stedmon and Markager (2005b),
Stedmon et al. (2003)
C2
<240 (300)
393
Microbial, anthropogenic, or
Murphy et al. (2011),
terrestrial humic-like substances
Stedmon and Markager (2005b),
Stedmon et al. (2003)
C3
<240 (270)
315
Tyrosine-like fluorophore
Yu et al. (2015)
C1 showed a primary peak at <240/453 nm for Ex–Em, which was
similar to the component 1 reported by Stedmon and Markager (2005b)
(Ex–Em = <250/448). This kind of fluorophore absorbs light mainly in the
UVC band and shows a broad emission peak, which is usually identified as a
terrestrial FDOM (Stedmon et al., 2003). The appearance of a secondary peak
at longer excitation wavelength (Ex–Em = 305/453 nm) may indicate that C1
is more aromatic and has a higher molecular weight (Coble et al., 1998). C1
also resembled another terrestrial fluorophore, namely component 4 in
Stedmon and Markager (2005b) (Ex–Em = <250(360)/440), which has
been widely found in natural freshwater environments and even
water-extracted organic matter in aerosols (Chen et al., 2016; Mladenov et
al., 2011; Zhang et al., 2009; Zhao et al., 2016).
C2 had a primary (secondary) peak at <240(300)/393 nm (Ex–Em),
which was first measured in the oceanic system by Coble (1996).
Subsequently, Stedmon et al. (2003) found a similar fluorophore (component 4
therein) in a terrestrially dominated estuary region. The following studies
suggested that the C2-like components are also linked to microbial activity
and phytoplankton degradation in natural aquatic systems (Yamashita et al.,
2008; Zhang et al., 2009) or DOM in waste water from anthropogenic sources
(Stedmon and Markager, 2005b).
C3 is a typical fluorophore that is categorized as tyrosine-like FDOM and
that exhibits Ex–Em pairs of <240(270)/315 nm. C3 reflects
autochthonously labile DOM produced by biological processes (Stedmon et al.,
2003) and has been commonly reported in previous studies of natural water
bodies and water extraction of aerosols (Chen et al., 2016; Murphy et al.,
2008; Stedmon and Markager, 2005a).
Variations of fluorescent components among regions. The box plots
show the Fmax of different components. The boxes
denote the 25th and 75th quantiles, and the horizontal lines
represent the 50th quantiles (medians), the averages are shown as dots;
the whiskers denote the maximum and minimum data within 1.5 times the
interquartile range, and the data points out of this range are marked with
crosses (+). The pie charts show the regionally average relative contributions
of three components. C1, C2, and C3 are represented in red, yellow, and
blue, both for the box plots and pie charts. The percentages on
the left of the panel are the averages of %C1–%C3 for the whole
data set.
Regional variation in PARAFAC components
Figure 5 shows the variations of three fluorescent components among regions,
including intensities and relative contributions. Overall, C2 was the most
intense fluorophore and accounted for 42 % on average of the total
fluorescence intensity of all samples, followed by C3 (38 % on average)
and C1 (20 % on average). Compared to glacial snow and ice samples, which
were dominated by protein-like substances (Dubnick et al., 2010; Feng et
al., 2016), the seasonal snow samples in this study showed fewer microbial
characteristic. According to Thieme et al. (2016), although we might
underestimate %C1 (approximately 3 %) and overestimate %C2
(approximately 6 %) due to the preservation artifacts, it only slightly
changes the results shown here.
In Qinghai (region 1), the most obvious feature was that C1 accounted for
approximately 35 % of the total fluorescence intensity on average. This
value was significantly higher than that of the other regions. In contrast,
%C3 was quite low (24 % on average). This result was mainly due to the
high Fmax (C1) in region 1, since the regional
variation of Fmax (C3) was slight (Fig. 5).
In Xinjiang (regions 2–5), %C1 varied by region, while %C2 and %C3
were roughly equal. In region 2, %C1 was also high (25 % on average).
However, %C1 showed the lowest value (9 % on average) in region 3,
where most of the samples were new-fallen snow (7 of 8 sites). The great
difference between %C1 and %C2 in this region indicated different
sources of these two humic-like components. In regions 4 and 5, %C1 were
nearly double of that in region 3 (both were approximately 17 % on
average).
At sites 54 and 82, the relative abundance of C3 exceeded 70 %, which was
approximately two-fold higher than the average of the whole data set (38 %).
This result can be explained in two ways: (1) lower inputs of C1
and C2, and (2) greater biological activities being available in the snowpack
at these sites. We found lichens near these two sites (Fig. S3), providing
evidence for the latter.
At site 67, the fluorescence intensities were highest among all samples
(0.30, 0.39, and 0.38 RU for C1, C2, and C3), especially for
C3. The average Fmax (C3) was 0.10 RU for all samples
excluding site 67, with a low standard deviation of 0.02 RU, and this value
was approximately one-fourth of that at site 67. Therefore, rather than
owing to biological activity alone, the extremely high
Fmax (C3) of site 67 may be due to other sources, for
instance, some organic compounds released from diesel combustion may show
similar spectra (Mladenov et al., 2011).
Hierarchical cluster analysis based on the relative contributions
of fluorescent components.
To assess the similarities and differences between samples, a hierarchical
cluster analysis based on the relative intensities of fluorescent components
was conducted (Fig. 6). The snow samples were separated into four clusters
(clusters A–D) (Fig. S4). Samples classified into clusters A and B were
dominant. The high %C1, which was 34 % on average, was the most
remarkable feature of cluster A and led to a low %C3 (26 % on average).
All samples in region 1 and most samples in region 2 were assigned to
cluster A. For cluster B, %C1 was low (13 % on average), and %C3
(47 % on average) was slightly higher than %C2 (40 % on average). For
the sites in northern Xinjiang (regions 4 and 5), most samples were
classified into cluster B. The samples assigned to cluster C, including
those of sites 60, 62, 69, 72, 76, and 84, showed the dominant contribution of
C2 (57 % on average). Half of these samples were found in region 3, and
the others were dispersed in regions 4 and 5. Cluster D contained only two
samples from sites 54 and 82. The difference between cluster D and the
others was an extremely high contribution of protein-like component C3
(73 % on average), which indicated the high bioavailability of snow CDOM.
Variations of HIX (red), BIX (blue), and FI (green) among regions.
The meaning of each part of the box is same as that in Fig. 5.
Fluorescence-derived indices
The regional variations of three established fluorescence-derived indices
are shown in Fig. 7 and the values of each site are listed in Table 1. The
HIX values fell into the range of 0.16–3.20, with an average of 1.21±0.78. The highest HIX appeared in region 1 (2.21±0.42), demonstrating
a high degree of humification of snow CDOM. The lowest value was found in
region 3 (0.62±0.37), which suggests that the CDOM was fresh. This
finding is easily explained by the fact that nearly all snow samples in
this region were new-fallen snow. Compared to other types of samples
(Table 3), the HIX of snow CDOM across northwestern China was higher than that of
spring water (Birdwell and Engel, 2010); comparable to those of cryoconite
in glaciers from the Tibetan Plateau (Feng et al., 2016), inland lakes
(Zhang et al., 2010), and North Pacific Ocean water (Helms et al., 2013);
lower than those of cave water (Birdwell and Engel, 2010), estuarine water
(Huguet et al., 2009), fog water (Birdwell and Valsaraj, 2010), groundwater
(Huang et al., 2015), water extraction of alpine aerosol (Xie et al., 2016),
and urban aerosol (Mladenov et al., 2011).
The fluorescence-derived indices in this study and comparison with
those of natural water bodies and water extractions of aerosols reported by
other studies. Note that average values are shown in parentheses.
Study area
Sample type
HIX
BIX
FI
References
Northwestern China
Seasonal snow
0.16–3.20 (1.21)
0.44–1.47 (0.76)
1.20–1.92 (1.42)
This study
Tibetan Plateau
Cryoconite in glaciers
1.11–1.37 (1.27)
0.65–0.93 (0.80)
3.12–3.44 (3.24)
Feng et al. (2016)
Yungui Plateau, China
Inland lakes
0.23–6.00 (1.57)
0.60–1.54 (0.93)
1.14–1.80 (1.37)
Zhang et al. (2010)
Frasassi Caves, Italy
Cave water
1.79–3.28 (2.32)
0.80–1.12 (0.95)
∼1.80
Birdwell and Engel (2010)
Springs in the USAa
Spring water
0.36–1.21 (0.76)
0.64–1.13 (0.87)
1.92–2.28 (2.09)
Birdwell and Engel (2010)
Gironde Estuary, France
Estuary
∼4–17
0.6–0.8
1.14–1.22
Huguet et al. (2009)
North Pacific Ocean
Ocean water
0.92–1.80 (1.49)
0.87–1.38 (1.00)
1.54–1.77 (1.66)
Helms et al. (2013)
Tai Mountain, China
Fog water
3.23–6.79 (4.80)
0.64–1.02 (0.87)
1.42–1.83 (1.63)
Birdwell and Valsaraj (2010)
Jianghan Plain, China
Ground water
2.71–7.49 (5.26)
0.88–0.97 (0.94)
–
Huang et al. (2015)
Colorado, USA
Aerosol in alpine sites
0.72–4.75 (2.42)
0.54–0.75 (0.65)
1.18–1.57 (1.40)
Xie et al. (2016)
Granada, Spain
Urban aerosol
2.79–4.89 (3.83)
–
1.48–1.64 (1.55)
Mladenov et al. (2011)
a Including Jemez Spring, NM; Sharon
Springs, NY; Sulfur Springs,
IN; White Sulphur Springs, LA (Birdwell and Engel, 2010).
According to McKnight et al. (2001) and Huguet et al. (2009), the values of
FI>1.9 or BIX>1.0 indicate microbially derived
DOM. The BIX and FI for the snow samples were typically below 1.0 and 1.9, implying an unremarkably autochthonous characteristic. The
regional distributions of BIX and FI corresponded with that of HIX. The
samples with highest average BIX and FI were in region 3 (0.93±0.25
and 1.60±0.15), and the samples in region 1 exhibited
the lowest average values (0.49±0.05 and 1.29±0.05). The BIX and FI of different types of samples changed little,
while the only exception was the FI of cryoconite in glaciers from the
Tibetan Plateau (Feng et al., 2016), which was approximately twice as high
as those of the other samples.
Source attribution of CDOM
Source identification of PARAFAC components
As mentioned in Sect. 3.1, the snowpack in Qinghai was strongly influenced
by local soil dust, which was confirmed by the lowest
S275-295, leading to a high %C1. This result
implied that the terrestrial fluorophore C1 was mainly from the soil HULIS,
and demonstrated the invariably terrestrial source of the C1-like
fluorophores, regardless of whether in the natural water bodies, aerosol
water extraction, or snow.
Linear relationships between intensities of (a) C1 and C2, (b) C1
and C3, and (c) C2 and C3. The red dashed lines show the fits of the entire
data set, and the blue solid lines show the fits of the data excluding site 67
(shown as markers in red). The corresponding fitting parameters are
exhibited in the same color.
Correlation analyses were conducted to assess the potential sources of C2.
The mutual relationships between PARAFAC components were shown in Fig. 8.
The Fmax (C3) of site 67 was much higher than that of
any other sample (shown as red markers in Fig. 8), which can strongly
influence the results of the correlation analysis. When excluding the data of
site 67, the R2 between Fmax (C1) and
Fmax (C3) fell from 0.316 to 0.082, and the linear
relationship became nonsignificant (Fig. 8b). Therefore, we used the data set
that excludes site 67 in the analysis, and the results are shown below.
Fmax (C1) and Fmax (C2) were
linearly correlated with each other (R2=0.332, p<0.001);
however, the R2 value was much lower than those in previous studies of
natural water, for instance R2=0.63 for inland lakes (Zhao et al.,
2016) and R2=0.88 for inland rivers (Zhang et al., 2011). This
result indicated that soil dust only partly accounted for the source of C2.
Meanwhile, a significant linear relationship (R2=0.364, p<0.001)
was found between Fmax (C2) and
Fmax (C3), which implied a potential microbial source
for C2 and was consistent with the finding of Yamashita et al. (2008). Not
surprisingly, Fmax (C1) and
Fmax (C3) showed no correlation (R2=0.082,
p>0.05). Furthermore, the correlation coefficients of
Fmax and three major ions were calculated. The
results are shown in Table 4. Fmax (C2) showed
significant and positive correlations with these ions (p<0.001).
The secondary ions SO42- and
NO3- are commonly considered to be the
markers of anthropogenic emissions from the burning of fossil fuel, such as
oil and coal (Doherty et al., 2014; Oh et al., 2011; Pu et al., 2017), and
nss-ndust-K+ is a good tracer of biomass
burning (Pio et al., 2007). Therefore, C2 may also originate from
anthropogenic pollution and biomass burning. Overall, there were four
potential sources of snow CDOM in our study. Since the contribution of
microbial-derived C3 to aCDOM(280) was
relatively low compared to C1 and C2 (Fig. S5), three major sources were
identified, i.e., soil dust, biomass burning, and anthropogenic pollution.
Regional averages of the ratios for (a) Fmax (C2)
and Fmax (C1), (b) (SO42-
+ NO3-)
and nss-ndust-K+.
Pearson's correlation coefficients (r) of major ions and
Fmax for fluorescent components when excluding the
data from site 67; the results for the entire data set are shown in
parentheses. Note that ∗ denotes p<0.001.
SO42-
NO3-
nss-ndust-K+
Fmax (C1)
0.01 (0.14)
-0.10 (-0.04)
0.23 (0.48)
Fmax (C2)
0.70∗ (0.72)
0.60∗ (0.57)
0.57∗ (0.69)
Fmax (C3)
0.44 (0.42)
0.34 (0.23)
0.29 (0.68)
The ratios of intensities for PARAFAC components can be a useful tool for
tracing the CDOM sources (Murphy et al., 2008). In this study, the ratio of
Fmax (C2) and Fmax (C1) was
applied to assess the relative contributions of soil and nonsoil (i.e.,
biomass burning and anthropogenic pollution) sources for snow CDOM
(Fig. 9a). An analysis of variations (ANOVA) was used to test the differences
among regions. Regions 1 and 2 showed low ratios of
Fmax (C2) and Fmax (C1)
(1.20±0.14 and 1.76±0.82), indicating the strong
influence from local soil dust. The values of
Fmax (C2)/Fmax (C1) for regions
3, 4, and 5 were significantly higher (ANOVA, p<0.05) with averages
of 5.57±2.26, 3.17±1.47, and 3.02±1.22.
This result implied that the sources of snow CDOM in these regions were
different from those in regions 1 and 2, and were mainly from nonsoil sources.
Regional variations
The regional variations of CDOM sources are discussed below using analyses
of absorption and fluorescence characteristics, chemical species, and air
mass backward trajectories. In addition, the sources of CDOM in snow are
also compared with those of particulate light absorption of ILAPs.
In Qinghai (region 1), the lowest regional average and slight variation in
S275-295 indicated the dominant contribution of
terrestrial sources for snow CDOM (e.g., local soil dust) (Fichot and
Benner, 2012; Helms et al., 2008). This result was also verified by the
fluorescence properties: the highest HIX and %C1
and the lowest
Fmax (C2)/Fmax (C1). Although
some trajectories to site 47 passed through the active fires (Fig. 10a),
compared to strongly local soil input, the influence of long-range
transportation of biomass-burning aerosol was much smaller. According to the
low value of
(SO42-+NO3-)/nss-ndust-K+
(Fig. 9b), the CDOM produced by anthropogenic pollution was negligible.
Above all, soil dust is clearly the primary source in region 1.
72 h air mass backward trajectories at 500 m a.g.l. with
the initial positions at representative sites (shown as yellow
pentagrams) in each region. Trajectories were calculated 4 times per day
for a period of 30 days preceding the sampling date at a given site by
HYSPLIT (version 4, NOAA) except for panel (c). Since the snow was fresh at
site 84, the trajectories were derived for 5 days preceding the sampling
date. The red lines show the air masses that passed through the active fires
before reaching the receptor sites, and the blue lines are those did not
pass the fires. The white dots represent the typical industrial cities in
Xinjiang, i.e., Karamay, Kuytun, Shihezi, and Urumqi from west to east.
In region 2, the contribution of soil dust to snow CDOM was also remarkable,
which was proven by the high average %C1 and low average
Fmax (C2)/Fmax (C1). Along the
paths of the air masses to site 55 (Fig. 10b), very few trajectories encountered
fires. Additionally, the average of
(SO42-+NO3-)/nss-ndust-K+
was also low, which showed the insignificant role of anthropogenic pollution.
Therefore, in region 2, a major source of soil is found.
In region 3, the averages of
Fmax (C2)/Fmax (C1) and
(SO42-+NO3-)/nss-ndust-K+
were significantly higher than those in other regions (ANOVA, p<0.05), implying a strong influence of anthropogenic pollution. The mass
ratio of Cl- and Na+ (2.48, Fig. S6) was approximately 2 times
higher than that in seawater (1.18, Hara et al., 2004), which indicated
that Cl- might originate from other sources in addition to sea salt
such as coal combustion (Wang et al., 2008; Y. Wang et al., 2006),
while the values in other regions were comparable to 1.18. This result
again confirmed that the CDOM from pollution was dominant in region 3 but
inapparent in other regions. The backward trajectories also showed
consistent results (Fig. 10c). Most of the trajectories to site 84 came from
the northwest and passed through the cities with heavy industry (e.g.,
Karamay and Shihezi). Therefore, air pollutants can be transported to the
sample area and deposited on surface snow.
In regions 4 and 5, the nonsoil sources of snow CDOM were predominant due
to the high regional averages of
Fmax (C2)/Fmax (C1). In region
4, many of the air masses, which originated from central Asia (west),
Siberia (north), and central Xinjiang (south), passed through the active
fires and strongly influenced this region (Fig. 10d). The ratios of
(SO42-+NO3-)
and nss-ndust-K+ were significantly lower
than those in region 3 (ANOVA, p<0.05), indicating a major source
of biomass burning. Coincidentally, in region 5, the value of
(SO42-+NO3-)/nss-ndust-K+
was comparable with that of region 4, which suggested CDOM from biomass
burning rather than pollution. The respectable amount of air mass which
encountered fires (Fig. 10e) can explain this finding. Furthermore, the low
mass ratios of Cl- and Na+ in regions 4 and 5 also implied the
slight influence of anthropogenic pollution. Overall, biomass burning is the
dominant source both in regions 4 and 5.
Pu et al. (2017) used a positive matrix factorization (PMF) model to
identify the sources of particulate light absorption of ILAPs (denoted as
CBCmax) in snow during the same field
campaign. The comparison of CBCmax and
aCDOM(280) among regions is shown in Fig.
S7. In regions 1, 2, and 5, there was no significant correlation between
CBCmax and
aCDOM(280), which indicated the entirely
different sources of CDOM and particulate absorption of ILAPs. As reported
by Pu et al. (2017), the major sources of
CBCmax were biomass burning in regions
1–2 and industrial pollution in region 5, while those of CDOM in this study
were soil dust and biomass burning. Robust linear correlations
were found in regions 3 and 4 (R2 is 0.95 and 0.75),
which implied high consistency for sources of CDOM and particulate
absorption of ILAPs (i.e., anthropogenic pollution and biomass burning,
respectively).
Relative absorption contributions of CDOM and BC at
(a) 400 nm
and (b) 500 nm.
Comparing the light absorption by CDOM and BC
Figure 11 shows the relative contributions of CDOM and BC to light
absorption. As mentioned above, light absorption within visible wavelengths
was available for 19 samples. The BC concentrations in surface snow were
obtained from Pu et al. (2017), and the MAC and AAE of BC used in the
calculation were 6.3 m2 g-1 (550 nm) and 1.1 (Pu et
al., 2017).
Most of these sites were assigned to cluster A, except sites 60, 69, and 84.
As discussed in Sect. 3.2.2, sites of cluster A exhibited high values of
%C1, indicating that CDOM mainly originated from soil dust. At sites 50, 52,
and 79, the light absorptions of CDOM and BC were roughly equal at 400 nm.
It was not only due to the high abundances of CDOM but also the relatively
low BC mixing ratios in snow (approximately 30 ng g-1, Pu et al.,
2017). Sites 60, 69, and 84, where the fluorescence intensities were
dominated by C2, were the only three sites assigned to cluster C. Biomass
burning and anthropogenic pollution (e.g., fossil fuel combustion) are both
major sources of fluorophore C2 and BC. Therefore, the BC mixing ratios were
approximately 300 ng g-1 at these sites (Pu et al., 2017), leading to
quite low ratios of light absorption due to CDOM and BC (approximately 0.03
at 400 nm). At other sites, this value was typically in the range of 0.1 to
0.4. In summary, the light absorption of CDOM was 0.34±0.34 times
that for BC at 400 nm. At 500 nm, this value decreased quickly to
0.10±0.11 due to the stronger wavelength dependence of CDOM
absorption. This finding is quite different from the results for snow
samples collected at Barrow, Alaska. As presented by Doherty et al. (2013),
the mixing ratio of BC in Barrow snow ranged between 10 and 30 ng g-1;
however, the equivalent BC mixing ratio of CDOM absorption was only 0.14 ng g-1
at 400 nm and 0.07 ng g-1 at 550 nm (Dang and Hegg, 2014).
Hence, the absorption of CDOM in Alaskan snow can be safely ignored, but
this does not appear reasonable for some areas across northwestern China.
Previous studies have focused on the insoluble particles (e.g., BC, ISOC,
and MD) in seasonal snow (Doherty et al., 2010, 2014; Pu et al., 2017;
Wang et al., 2013). The above discussion indicates that in some specific
areas of northwestern China, the absorption of CDOM in snow was remarkable.
In addition to the results of cluster analysis, we summarized several
absorption- and fluorescence-related indices of these sites. The average
S275-295 (0.0187±0.0022) of these 19 sites was
lowest compared to those of regions 1–5. The averages of BIX (0.60±0.20), FI (1.31±0.09), and
Fmax (C2)/Fmax (C1)
(1.66±1.03) were lower than those of region 2, in which the influence of local
soil dust was obvious. Besides, the averages of HIX (1.87±0.57) and
%C1 (30 %) were higher than those of region 2. These results confirmed
that the CDOM of these sites was undoubtedly from terrestrial origins (e.g.,
wind-blown soil dust). Hence, we suggest that the absorption by CDOM in the
snowpack, which is heavily affected by soil, cannot be ignored.
Conclusions
Seasonal snow samples were collected across northwestern China from January
to February 2012. The aCDOM(280) and
S275-295 of snow CDOM ranged from 0.15 to 10.57 m-1
and 0.0129 to 0.0389 nm-1. The average value of
aCDOM(280) (1.69±1.80 m-1) was
approximately 10 times higher than that in Alaska (Beine et al., 2011).
Samples in Qinghai (region 1) exhibited the highest average
aCDOM(280) (2.30±0.52 m-1) and
the lowest average S275-295 (0.0188±0.0015 nm-1),
resulting from the strong influence of local soil dust. Lower
average aCDOM(280) appeared in central
Xinjiang (region 3, 0.93±0.68 m-1), where almost all the samples
were collected from new-fallen snow, and northwestern Xinjiang (region 4,
0.80±0.62 m-1 when excluding site 67), which was far from
industrial areas. In the Tianshan Mountains (region 2) and northeastern
Xinjiang (region 5), the average values of
aCDOM(280) were 2.00±1.50 m-1
and 1.17±0.63 m-1. For all sites in Qinghai and
some sites in Xinjiang (19 of 39 sites), the light absorption of CDOM
cannot be neglected and was even remarkable (0.34±0.34 times relative
to BC at 400 nm) due to the high contribution of CDOM from soil dust. Hence,
we suggest that the CDOM absorption in visible wavelengths at such sites
should be taken into consideration in future studies.
Based on PARAFAC analysis, two humic-like fluorophores (C1 and C2) and one
protein-like fluorophore (C3) were identified. In Qinghai, %C1 (35 % on
average) was much higher than those of the other regions; besides, the
highest HIX, the lowest BIX and FI were also found. In Xinjiang (regions 2–5),
%C1 varied among the regions. In region 2, C1 accounted for approximately
25 % to the total fluorescence, followed by regions 4 and 5 (both 17 %
on average). In region 3, C1 contribution was the lowest (9 % on average), and
the values of fluorescence-derived indices also showed consistent
results (the lowest HIX, the highest BIX and FI). A hierarchical cluster
analysis was used to classify samples into four clusters (A–D) based on the
relative intensities of three fluorescent components. All samples in region
1 and most samples in region 2 were assigned to cluster A (a high
contribution of C1). The number of samples assigned to cluster B (roughly
equal contributions of C2 and C3) and cluster C (a dominant contribution of
C2) were nearly even in region 3. For regions 4 and 5, most samples were
classified into cluster B. Only two samples were assigned to cluster D due
to the dominant contribution of C3.
According to the correlation analysis between Fmax (C2) and three major ions
(SO42-, NO3-, and
nss-ndust-K+), as well as the mutual
relationships among three fluorescent components, C2 exhibited potential
sources of soil dust, microbial activity, anthropogenic pollution, and
biomass burning. Furthermore, the regional distribution of CDOM sources was
assessed by using variations
of Fmax (C2)/Fmax (C1),
(SO42-+NO3-)/nss-ndust-K+,
Cl-/Na+, and air mass backward trajectory analysis. The major
sources were soil dust for regions 1–2, anthropogenic pollution for region
3, and biomass burning for regions 4–5.
This study investigated the optical characteristics and potential sources of
CDOM in seasonal snow across northwestern China. Future studies should focus
on the molecular characteristics of snow CDOM and the relationship with
optical properties, which is of great importance to the energy budget of
snowpack and the global carbon cycle.