Introduction
Chemical weathering of rocks is the key process that links geochemical
cycling of solid earth to the atmosphere and ocean. It provides nutrients to
terrestrial and marine ecosystems and regulates the level of atmospheric
CO2. As a net sink of atmospheric CO2 on geologic timescales,
estimations of silicate chemical weathering rates and the controlling factors
are important issues that are related to long-term global climate change (e.g., Raymo
and Ruddiman, 1992; Négrel et al. 1993; Berner and Caldeira, 1997;
Gaillardet et al., 1999; Kump et al., 2000; Amiotte-Suchet et al., 2003;
Oliva et al., 2003; Hartmann et al., 2009; Moon et al., 2014). As an
important component in the Earth's critical zone (U.S. National Research
Council Committee, 2001), rivers serve as integrators of various natural
and anthropogenic processes and products in a basin, and as carriers,
transporting the weathering products from the continent to the ocean. Therefore, the
chemical compositions of river water are widely used to evaluate chemical
weathering and associated CO2 consumption rates at catchment and/or
continental scale and to examine their controlling factors (e.g., Edmond et
al., 1995; Gislason et al., 1996; Galy and France-Lanord, 1999; Huh, 2003;
Millot et al., 2002, 2003; Oliva et al., 2003; West et al., 2005; Moon et
al., 2007; Noh et al., 2009; Shin et al., 2011; Calmels et al., 2011; Li et
al., 2014).
With the intensification of human activities, human perturbations to river
basins have increased in frequency and magnitude (Raymond et al., 2008;
Regnier et al., 2013; Li and Bush, 2015). It is important to understand how
such perturbations function in the current weathering systems and to predict
how they will affect the critical zone of the future (Brantley and Lebedeva,
2011). In addition to CO2, other sources of acidity (such as
sulfuric, nitric and organic acids) can produce protons. These protons
react with carbonate and silicate minerals, thus enhancing rock chemical
weathering rates and flux compared with only considering protons deriving from
CO2 dissolution (Calmels et al., 2007; Xu and Liu, 2010). The effect
of other sourced protons (especially H+ induced by SO2 and
NOx coming from anthropogenic activities) on chemical
weathering is documented to be an important mechanism modifying atmospheric
CO2 consumption by rock weathering (Galy and France-Lanord, 1999;
Semhi et al., 2000; Spence and Telmer, 2005; Xu and Liu, 2007; Perrin et al.,
2008; Gandois et al., 2011). Anthropogenic emissions of SO2 were
projected to provide 3 to 5 times more H2SO4 to the continental
surface than the pyrite oxidation (Lerman et al.,
2007). Therefore, increasing acid precipitation due to intense human
activity nowadays could make this mechanism more prominent.
The role that acid precipitation plays on chemical weathering and
CO2 consumption has been investigated in some river catchments
(Amiotte-Suchet et al., 1995; Probst et al., 2000; Vries et al., 2003; Lerman
et al., 2007; Xu and Liu, 2010). It has been documented that silicate rocks
were more easily disturbed by acid precipitation during their weathering and
soil-leaching processes because of their low buffeting capacity (Reuss et
al., 1987; Amiotte-Suchet et al., 1995). The disturbance could be intensive
and cause a decrease in CO2 consumption by weathering by about
73 % due to acid precipitation in the Strengbach catchment (Vosges
Mountains, France), which is dominated by crystalline rocks (Amiotte-Suchet
et al., 1995). This highlights the importance of exploring the anthropogenic
impact on chemical weathering and CO2 consumption under different
backgrounds (e.g., lithology, climate, human activity intensity and basin
scale) for better constraining and estimating the effect of acid precipitation on
rock weathering. Asia, especially eastern Asia, is one of the world's major
sulfur and nitrogen emission areas. However, the effect of acid precipitation
on silicate weathering and associated CO2 consumption has not been
well evaluated in this area, especially lacks quantitative studies.
Acid precipitation affected about 30 % of the territory of China
(Fig. 1), and the seriously polluted areas are mainly located in the east,
south and center of China, where over 70 % of the cities were suffering
from acid rain (M. Zhang et al., 2007; Ministry of Environmental Protection
of China, 2009). The southeastern coastal region of China is one of the most
developed and populated areas of this country, dominated by Mesozoic magmatic
rocks (mainly granite and volcanic rocks) in lithology. Meanwhile, the
southeast coastal area has become one of the three major acid-rain areas in
China since the beginning of 1990s (Larssen and Carmichael, 2000). It is
seriously impacted by acid rain, with a volume-weighted mean value of pH
lower than 4.5 for many years (Wang et al., 2000; Larssen and Carmichael,
2000; Zhao, 2004; Han et al., 2006; Larssen et al., 2006; M. Zhang et al.,
2007; Huang et al., 2008; Xu et al., 2011). Therefore, it is an ideal area
for evaluating silicate weathering and the associated acid-rain effects. In
previous work, we have recognized and discussed the importance of sulfuric
acid on rock weathering and associated CO2 consumption in the
Qiantang river basin in this area (W. Liu et al., 2016). However, it is
difficult to infer the anthropogenic impact on chemical weathering and
CO2 consumption in the whole southeastern coastal area from the case
study of a single river basin, because of the variations on lithology, basin
scale, runoff and anthropogenic condition in the large
acid-deposition-affected area. In this study, the chemical and carbon isotope
composition of river water in this area were first systematically
investigated in order to (i) decipher the different sources of solutes and to
quantify their contributions to the dissolved loads, (ii) calculate silicate
weathering and associated CO2 consumption rates, and (iii) evaluate
the effects of acid deposition on rock weathering and CO2 consumption
flux in the whole of the SECRB.
Sketch map showing the lithology, sampling locations and sample
number of the SECR drainage basin, and regional rainwater pH ranges are
shown in the sketch map in the upper-left (modified from Zhou and Li, 2000;
Shu et al., 2009; Xu et al., 2016, rainwater acidity distribution of China
mainland is from State Environmental Protection Administration of China).
(1) Shaoxing–Jiangshan fault zone, (2) Zhenghe–Dapu fault zone and
(3) Changle–Nanao fault zone. The figure was created by CorelDraw software
version 17.1.
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Natural setting of study area
The southeastern coastal region of China, where the landscape is dominated by
mountainous and hilly terrain, lacks the conditions for developing large
rivers. The rivers in this region have dominantly small- and medium-sized
drainage areas due to the topographic limitation. Only five rivers in this region have
lengths over 200 km and drainage areas over 10 000 km2, and they
are the Qiantangjiang (Qiantang) and the Oujiang (Ou) in Zhejiang province,
the Minjiang (Min) and the Jiulongjiang (Jiulong) in Fujian province and the
Hanjiang (Han) in Guangdong province, from north to south (Fig. 1). Rivers in
this region generally flow eastward or southward and finally inject into the
East China Sea or the South China Sea (Fig. 1), and they are collectively
named Southeast Coastal Rivers (SECRs).
The Southeast Coastal River Basin (SECRB) is in the warm and humid
subtropical oceanic monsoon climate. The mean annual temperature and
precipitation are 17–21 ∘C and 1400–2000 mm, respectively. The
precipitation mainly happens during May to September, and the lowest and
highest temperatures often occur in January and July. This area is one of the
most developed areas in China, with a population more than 190 million (mean
density of ∼470 individuals km-2), but the population is
mainly concentrated in the coastal urban areas. The vegetation coverage of
these river basins is higher than 60 %, mainly subtropical
evergreen-deciduous broadleaf forest and mostly distributed in mountainous
areas. Cultivated land, industries and cities are mainly located in the plain
areas and lower reaches of these rivers.
Geologically, three regional-scale fault zones are distributed across the
SECRB region (Fig. 1). They are the sub-EW-trending Shaoxing–Jiangshan fault
zone, the NE-trending Zhenghe–Dapu fault zone and the NE-trending
Changle–Nanao fault zone (Shu et al., 2009). These fault zones dominate the
direction of the mountains ridgelines and drainages, as well as the formation
of the basins and bay. The Zhenghe–Dapu fault zone is a boundary line of the
Caledonian uplift belt and Hercynian–Indonesian depression zone. Mesozoic
magmatic rocks are widespread in the southeastern coastal region with a total
outcrop area at about 240 000 km2. Over 90 % of the Mesozoic
magmatic rocks are granitoids (granites and rhyolites), with a minor amount of basalt (Zhou and Li, 2000; Zhou et al.,
2006; Bai et al., 2014). These crust-derived granitic rocks are mainly formed
in the Yanshanian stage and may have been related to multiple collision
events between Cathaysia and Yangtze blocks and the Pacific plate (Zhou and Li,
2000; Xu et al., 2016). Among the major river basins, the proportions of
magmatic rocks outcrop are about 36 % in the Qiantang catchment, over
80 % in the Ou, the Jiaoxi and the Jin catchments and around 60 % in
the Min, the Jiulong, the Han and the Rong catchments (Shi, 2014). The
overlying Quaternary sediment in this area is composed of brown-yellow
siltstones but is rarely developed. The oldest basement complex is composed
of metamorphic rocks of greenschist and amphibolite facies. Sedimentary rocks
are categorised into two types: one is mainly composed of red clastic rocks which
cover more than 40 000 km2 in the area and the other occurs as
interlayers within volcanic formations, including varicolored mudstones and
sandstones. They are mainly distributed on the west of Zhenghe–Dapu fault
zone (FJBGMR, 1985; ZJBGMR, 1989; Shu et al., 2009).
Sampling and analytical method
A total of 121 water samples were collected from the major rivers and their tributaries
in the SECRB in July of 2010 in the high-flow period
(sample number and locations are shown in Fig. 1). For the river low-reach
samples, the sampling sites were selected as far as possible from the tide-impacted area
and the sampling was conducted during low-tide period (based
on the daily tidal time, http://ocean.cnss.com.cn/, last access: 31 July 2010) on the sampling day. Besides, the salinity of the
waters was checked by a salinometer (WS202, China) before sampling in the
field. In addition, water chemistry data were double checked to make sure
that the river samples were not contaminated by seawater. Water samples were
collected in the middle channel of the rivers from bridges or ferries or
directly from the center of some shallow streams. Temperature (T), pH and
electrical conductivity (EC) were measured in the field with a portable EC/pH
meter (YSI-6920, USA). All of the water samples for chemical analysis were
filtered in field through a 0.22 µm Millipore membrane filter, and
the first portion of the filtration was discarded to wash the membrane and
filter. One portion of filtrate was stored directly in HDPE bottles for anion
analysis and another was acidified to pH < 2 with 6 M double
sub-boiling distilled HNO3 for cation analysis. All containers were
previously washed with high-purity HCl and rinsed with Milli-Q
18.2 MΩ water.
Alkalinity was determined by phenolphthalein and methyl orange end-point
titration with dilute HCl within 12 h after sampling. The HCl consumption
volumes for phenolphthalein and methyl orange end-point titration were used
to calculate the HCO3-. Cations (Na+, K+,
Ca2+ and Mg2+) were determined using an inductively coupled
plasma atomic emission spectrometer (ICP-AES) (IRIS Intrepid II XSP, USA).
Anions (Cl-, F-, NO3- and SO42-) were
analyzed by ionic chromatography (IC) (Dionex Corporation, USA). Dissolved
silica was determined by spectrophotometry with the molybdate blue method.
Reagent and procedural blanks were measured parallel to the sample
treatment, and the calibration curve was evaluated by quality control standards
before, during and after the analyses of each batch of samples. Measurement
reproducibility was determined by duplicated sample and standards, which
showed ±3 % precision for the cations and ±5 % for the
anions. Analyzing water chemistry was conducted in the hydrochemistry and
environmental laboratory at the Institute of Geology and Geophysics, Chinese
Academy of Sciences.
Chemical and carbon isotopic compositions of river water in the
Southeast Coastal River Basin (SECRB) of China.
Rivers
Sample
Date2
pH
T
EC
Na+
K+
Mg2+
Ca2+
F-
Cl-
NO3-
SO42-
HCO3-
SiO2
TZ+
TZ-
NICB
δ13C
TDS
number
(∘C)
(µs
(µM)
(µM)
(µM)
(µM)
(µM)
(µM)
(µM)
(µM)
(µM)
(µM)
(µEq)
(µEq)
(%)
(‰)
(mg
cm-1)
L-1)
Qiantang1
1
07-08-10
7.42
28.78
190
347
197
106
473
12.0
303
62.6
147
1130
148
1703
1789
-5.0
-19.0
144
2
07-09-10
7.60
23.84
146
87.5
204
80.9
496
11.7
75.2
124
121
907
156
1446
1348
6.7
-19.8
119
3
07-09-10
7.37
27.83
308
555
233
208
698
41.8
312
223
437
1170
170
2601
2579
0.9
-17.8
204
4
07-10-10
7.27
26.28
177
176
135
116
544
15.7
151
142
170
985
175
1632
1618
0.8
-19.3
135
5
07-10-10
7.05
24.15
123
130
101
66.2
349
17.7
94.3
124
157
529
169
1061
1061
0.0
-18.7
91.2
6
07-10-10
7.24
23.75
140
97.6
69.7
81.0
451
20.0
62.1
109
204
703
164
1231
1282
-4.2
-21.3
106.6
7
07-11-10
7.40
23.23
107
92.5
70.5
68.3
327
14.9
74.9
104
147
486
156
954
960
-0.6
-21.0
82.2
8
07-11-10
7.16
27.61
281
361
87.5
128
469
26.8
245
191
239
810
179
1642
1724
-5.0
-12.9
137.5
9
07-11-10
7.02
26.48
140
275
120
60.7
319
36.2
199
150
180
437
236
1155
1146
0.8
-13.9
100.2
10
07-12-10
7.05
24.24
99
205
114
58.3
285
14.6
191
114
132
305
278
1005
874
13.1
-20.9
85.4
11
07-12-10
7.05
27.01
102
123
133
49.8
284
18.6
86.5
123
144
377
183
924
874
5.4
-19.2
79.4
12
07-12-10
7.99
24.18
260
50.0
85.4
212
993
–
66.8
153
235
1822
172
2546
2512
1.4
-17.6
205.2
13
07-12-10
7.86
24.59
231
43.5
88.4
189
859
–
55.1
97.6
169
1763
170
2228
2253
-1.1
-18.7
185.4
14
07-12-10
7.69
22.66
131
44.1
81.0
113
458
–
19.1
95.2
107
920
143
1266
1248
1.4
-18.1
106.8
15
07-12-10
7.65
24.48
106
61.1
98.3
87.9
335
–
37.2
68.3
112
663
164
1005
992
1.4
-18.6
87.3
16
07-12-10
7.46
23.68
125
64.3
108
117
406
–
25.9
75.0
174
687
164
1218
1136
6.7
-20.0
98.8
17
07-13-10
7.33
24.08
139
59.8
116
136
429
-29.6
80.4
209
752
162
1305
1281
1.9
-20.8
108.1
18
07-10-10
7.27
25.74
141
163
114
69.6
396
27.3
126
148
161
597
153
1209
1195
1.1
-21.0
101.0
Cao'e
19
07-16-10
7.17
22.27
108
212
86.3
69.4
183
5.1
151
148
114
384
216
803
912
-13.5
-21.2
79.1
20
07-16-10
7.06
26.57
182
401
77.6
145
275
18.3
269
185
245
534
215
1318
1478
-12.2
-20.5
116.9
21
07-16-10
7.14
27.26
171
333
91.3
164
362
18.1
224
194
207
658
225
1475
1490
-1.0
-20.9
123.3
22
07-16-10
7.08
27.17
173
346
94.4
168
364
18.8
247
200
211
656
222
1506
1526
-1.3
-13.0
125.2
Ling
23
07-15-10
7.07
24.14
52
164
42.9
34.9
140
4.9
40.7
61.5
68.3
277
190
558
516
7.6
-12.8
52.1
24
07-15-10
7.02
26.04
74
169
92.0
34.2
150
6.4
87.0
77.3
92.8
272
196
629
622
1.1
-20.8
59.5
25
07-16-10
7.34
25.03
92
159
80.1
47.3
235
19.3
78.0
71.4
105
455
187
804
815
-1.4
-22.5
73.9
26
07-16-10
7.40
26.75
113
216
77.8
57.1
249
20.2
133
90.0
115
494
196
905
946
-4.5
-12.7
82.8
27
07-16-10
7.39
26
89
174
86.4
56.4
209
9.0
99.3
78.4
99.9
420
199
792
798
-0.8
-14.0
72.7
28
07-15-10
6.79
22.33
75
159
82.7
44.1
143
-107
61.8
83.4
306
144
616
641
-4.1
-21.1
56.5
29
07-15-10
8.24
27.15
129
228
92.1
83.1
317
17.2
177
90.5
120
641
194
1120
1148
-2.5
-19.2
97.8
Ou
30
07-13-10
8.08
28.45
48
95.2
107
38.4
92.1
15.2
31.8
43.3
47.4
291
221
463
461
0.4
-21.7
50.6
31
07-13-10
6.71
22.97
32
60.7
106
12.6
65.0
10.8
28.9
45.0
48.9
158
169
322
329
-2.2
-23.8
36.9
32
07-13-10
7.18
27.59
73
107
127
36.2
175
4.3
57.1
111
92.0
283
210
655
634
3.2
-23.4
62.9
33
07-13-10
6.94
24.2
44
76.9
112
20.0
99.1
10.9
27.9
63.1
58.6
249
184
427
457
-7.0
-22.5
47.5
34
07-14-10
7.16
27.45
90
187
127
41.2
199.5
17.0
85.6
102
116
367
251
796
787
1.1
-22.4
76.5
35
07-14-10
6.97
24.56
54
105
50.9
29.2
122
12.2
46.1
67.8
73.1
218
193
460
478
-4.1
-22.5
47.9
36
07-14-10
6.82
21.12
31
76.4
133
12.7
74.5
7.7
20.7
36.8
49.1
192
162
383
348
9.3
-39.5
37
07-14-10
6.82
23.69
45
89.5
105
19.0
97.8
10.6
39.6
52.8
59.1
231
185
428
441
-3.0
-22.9
46.2
38
07-15-10
6.92
24.69
37
100
89.3
21.1
49.7
1.7
36.9
45.5
52.7
153
202
331
341
-2.9
-38.9
39
07-15-10
6.90
23.86
35
92.2
92.0
19.8
61.4
1.9
43.9
47.9
55.5
139
193
347
342
1.4
-22.3
38.5
40
07-15-10
7.09
25.56
47
117
112
25.7
83.4
8.0
52.4
63.1
57.4
232
193
447
462
-3.3
-22.5
48.1
41
07-14-10
6.97
24.25
53
102
107
27.6
119
13.4
43.5
59.4
73.2
277
183
502
526
-4.9
-13.7
52.3
Feiyun
42
07-17-10
7.28
25.19
38
94.0
81.7
24.0
75.6
11.4
59.9
45.7
51.9
149
151
375
358
4.5
-37.2
43
07-17-10
7.08
25.61
46
101
79.9
33.9
93.4
4.6
66.2
55.1
52.8
223
151
435
450
-3.3
-23.7
43.5
Jiaoxi
44
07-17-10
7.52
26.92
47
116
81.5
25.2
92.0
4.1
73.3
80.3
25.0
226
151
432
430
0.5
-23.4
43.0
45
07-17-10
7.45
27.46
61
152
90.2
34.2
119
– 136
59.8
53.5
238
184
548
542
1.2
-23.1
51.8
46
07-18-10
6.90
27.66
53
127
88.1
33.4
94.4
7.0
123
93.1
30.4
209
177
471
486
-3.3
-14.4
47.4
Huotong
47
07-18-10
7.34
24
43
116
78.8
26.1
58.4
5.4
68.7
49.7
20.1
197
190
364
355
2.3
-22.8
39.6
Ao
48
07-19-10
7.24
31.44
124
294
121
102
209
24.3
204
73.6
52.0
717
370
1036
1100
-6.1
-19.4
105.4
49
07-19-10
7.13
27.82
46
109
96.3
30.0
73.8
-72.0
51.3
22.5
234
236
413
402
2.6
-46.2
50
07-18-10
6.98
28.65
53
140
88.4
40.8
100
3.0
82.9
58.6
20.9
294
233
511
477
6.6
-22.3
52.2
Min
51
07-27-10
7.11
28.4
42
116
92.0
40.5
119
18.0
43.9
35.5
26.0
382
182
526
513
2.4
-19.4
52.7
52
07-27-10
7.17
30
51
102
97.9
41.7
107
4.6
29.4
45.3
35.0
350
221
496
495
0.2
-53.3
53
07-27-10
7.08
29.4
99
214
92.7
46.4
126
18.4
50.1
39.8
118
327
154
651
654
-0.4
-20.8
74.0
54
07-27-10
7.06
29.1
44
107
99.6
28.1
114
16.4
18.7
36.4
44.3
305
265
491
449
8.5
-17.6
53.6
55
07-27-10
7.42
29.4
57
139
93.7
49.8
113
3.1
67.1
56.3
26.6
384
236
558
561
-0.5
-16.4
58.6
56
07-27-10
7.12
27.8
51
103
91.0
50.8
106
4.7
82.8
35.1
63.5
249
225
507
494
2.5
-51.3
57
07-27-10
7.08
27.5
40
125
45.0
36.8
107
12.1
43.6
44.5
29.3
288
211
457
435
5.0
-21.1
47.4
58
07-27-10
6.99
27.2
52
121
98.0
42.4
115
16.7
87.1
36.6
70.9
277
228
535
542
-1.4
-11.4
55.3
59
07-27-10
6.87
29
59
154
91.4
59.4
124
16.5
77.8
36.7
88.3
272
222
612
563
8.0
-20.3
57.2
60
07-27-10
7.31
27.1
78
109
92.1
59.1
181
21.2
123
37.5
78.4
355
202
682
672
1.4
-18.7
63.1
61
07-27-10
7.22
27.8
37
122
83.3
52.8
142
17.4
111
37.3
80.4
288
221
596
597
-0.2
-22.3
58.1
62
07-27-10
7.16
28.1
58
104
83.3
59.3
163
24.0
34.6
34.5
118
294
214
632
599
5.2
-13.4
59.5
63
07-27-10
7.26
28.3
87
139
86.1
60.9
191
14.8
48.0
93.0
109
347
226
729
707
3.0
-21.4
68.6
64
07-27-10
7.00
28.8
87
127
93.1
58.7
195
6.6
59.8
81.1
60.9
480
232
729
743
-2.0
-11.0
74.0
65
07-28-10
6.97
27.9
37
163
82.1
52.2
140
20.2
53.1
60.0
106
306
221
630
632
-0.2
-61.9
66
07-13-10
7.07
27.96
59
91.9
110
40.0
127
24.8
62.0
79.3
62.3
249
228
535
515
3.8
-54.8
67
07-28-10
7.12
29.7
38
108
93.4
45.9
133
12.4
48.3
34.0
56.6
368
220
560
564
-0.7
-57.7
68
07-27-10
7.03
29.9
62
128
96.7
57.6
148
23.3
81.6
36.8
74.1
374
203
635
641
-0.9
-12.4
61.7
69
07-27-10
7.01
28.8
60
102
89.1
73.6
138
9.6
50.6
74.1
32.7
417
233
615
607
1.3
-21.0
62.3
70
07-27-10
7.06
26.5
37
93.5
93.1
34.7
87.3
-26.6
34.8
37.1
312
222
431
448
-3.9
-13.1
49.1
71
07-27-10
7.09
26.5
25
62.6
92.7
27.0
61.5
4.7
21.5
18.6
43.4
191
154
332
318
4.2
-16.0
35.3
72
07-28-10
7.07
30.1
39
76.3
87.9
35.1
87.6
7.4
43.1
36.6
35.5
266
175
409
416
-1.7
-19.4
43.5
Continued.
Rivers
Sample
Date2
pH
T
EC
Na+
K+
Mg2+
Ca2+
F-
Cl-
NO3-
SO42-
HCO3-
SiO2
TZ+
TZ-
NICB
δ13C
TDS
number
(∘C)
(µs
(µM)
(µM)
(µM)
(µM)
(µM)
(µM)
(µM)
(µM)
(µM)
(µM)
(µEq)
(µEq)
(%)
(‰)
(mg
cm-1)
L-1)
73
07-27-10
7.01
28.7
47
84.9
95.4
56.7
106
12.7
51.8
49.2
57.2
315
211
506
531
-4.8
-53.8
74
07-27-10
6.85
28.7
50
93.6
85.9
52.4
107
14.1
62.8
57.5
57.0
252
217
498
487
2.2
-19.9
50.9
75
07-27-10
7.11
29.7
69
117
85.2
73.4
159
7.6
63.7
75.2
47.4
418
230
666
652
2.2
-22.2
65.0
76
07-28-10
6.93
28.9
59
112
88.0
61.8
122
6.0
57.4
89.3
42.0
349
224
568
580
-2.2
-22.0
58.8
77
07-21-10
7.76
32.4
51.2
163
85.5
52.8
151
20.2
55.3
70.3
78.6
372
175
656
655
0.3
-12.5
61.8
78
07-28-10
7.29
26.8
106
129
75.3
84.0
321
24.0
56.2
41.0
166
599
202
1013
1028
-1.4
-16.3
90.3
79
07-21-10
7.09
26.96
56
112
87.6
37.1
129
4.5
51.5
44.9
61.9
327
276
531
547
-2.9
-22.2
59.1
80
07-21-10
7.64
33.37
83
114
96.2
60.6
151
16.7
53.0
40.6
102
371
242
633
670
-5.8
-12.8
66.2
81
07-21-10
7.83
31.27
65
131
102
52.7
141
16.1
45.3
49.7
91.8
324
239
620
603
2.8
-13.4
61.8
82
07-21-10
6.84
28.35
66
132
101
52.5
141
5.8
63.8
54.1
91.6
304
243
621
606
2.5
-22.7
61.5
83
07-21-10
7.42
30.7
98
217
113
59.2
210
18.4
98.7
63.5
84.7
496
320
868
827
4.6
-18.9
84.5
84
07-27-10
7.26
26.3
46
104
102
29.7
121
3.6
55.2
51.9
55.5
294
193
507
512
-0.9
-21.6
51.9
85
07-27-10
7.07
25.4
30
73.3
99.2
19.6
78.8
-22.9
40.0
49.2
203
170
369
365
1.3
-21.1
39.8
86
07-27-10
7.50
27.3
45
102
102
26.5
114
2.4
35.1
39.7
57.2
260
217
484
449
7.3
-15.7
49.6
87
07-27-10
7.47
26.9
51
141
100
43.6
109
7.9
79.7
42.4
57.7
311
217
547
548
-0.3
-20.1
55.6
88
07-19-10
7.99
31.74
63
167
96.5
33.5
115
8.0
105
35.5
38.1
331
218
561
548
2.3
-13.5
55.9
89
07-21-10
6.77
28.19
65
132
93.6
56.0
145
15.6
60.6
78.8
75.4
333
243
627
624
0.5
-22.6
63.3
Jin
90
07-27-10
7.36
25.8
128
126
94.8
88.9
406
22.9
51.4
39.4
229
595
208
1211
1143
5.6
-20.7
100
91
07-27-10
7.40
26.9
123
143
103
82.7
347
21.0
83.5
203
182
463
226
1105
1115
-0.9
-21.3
98.4
92
07-27-10
7.00
27.4
88
170
98.8
56.8
205
7.2
137
117
106
327
205
793
792
0.1
-22.5
71.8
93
07-27-10
7.32
28.7
73
201
116
87.1
318
20.0
93.5
41.5
189
508
267
1128
1020
9.6
-21.7
95.3
Jiulong
94
07-30-10
6.50
23.47
29
72.3
92.4
22.8
59.8
12.4
25.1
27.0
50.0
189
213
330
341
-3.4
-18.1
40.1
95
07-30-10
7.06
29.35
120
136
96.9
106
339
5.1
67.7
66.3
249
469
202
1124
1100
2.1
-20.8
94.2
96
07-30-10
7.45
27.6
104
79.5
97.5
106
363
14.4
70.7
50.0
99.9
729
184
1116
1049
6.0
-18.9
93.7
97
07-31-10
7.36
26.59
139
140
100
142
432
15.5
79.6
78.3
274
573
196
1388
1278
8.0
-19.7
108.8
98
07-31-10
7.72
26.18
88
77.6
96.2
69.0
313
19.9
39.7
34.6
63.8
731
251
938
933
0.5
-18.4
89.4
99
07-30-10
7.43
26.96
119
200
93.8
100.2
298
19.9
122
80.5
225
387
202
1091
1040
4.7
-20.5
89.5
100
07-28-10
7.41
26.66
112
173
97.9
94.4
286
46.1
118
152
201
364
207
1033
1036
-0.3
-20.9
92.2
101
07-29-10
7.16
29.35
82
151
110
55.4
178
4.9
71.2
170
53.2
385
305
727
732
-0.7
-21.2
76.1
102
07-29-10
7.10
28.9
100
222
98.3
49.4
249
3.6
126
157
52.7
532
303
917
920
-0.3
-21.7
90.0
103
07-28-10
7.20
31.15
138
339
111
81.2
277
9.2
280
285
88.6
515
317
1165
1256
-7.8
-19.0
112
104
07-28-10
7.16
27.09
101
261
95.8
81.7
235
40.3
173
80.1
174
291
136
990
892
9.9
-24.3
75.4
Zhang
105
07-28-10
8.08
30.6
93
195
96.1
61.1
167
16.8
157
193
55.2
281
288
748
741
0.9
-21.5
73.8
Dongxi
106
07-28-10
7.20
30.9
78
263
99.0
41.5
115
14.5
238
65.3
30.0
283
309
675
646
4.4
-20.8
66.7
Huangang
107
07-28-10
7.40
30.5
99
253
85.6
53.0
154
7.7
190
63.5
56.4
460
278
754
827
-9.6
-20.0
77.4
Han
108
07-31-10
7.31
27.1
68
136
61.5
45.2
195
16.1
37.7
45.3
93.7
345
218
678
615
9.2
-21.9
62.0
109
07-30-10
7.38
26.94
88
116
103
63.6
265
6.4
53.4
72.2
84.9
584
244
876
879
-0.4
-20.4
83.7
110
07-30-10
6.66
25.55
71
114
96.2
47.6
168
8.0
56.9
54.6
143
230
203
642
628
2.2
-17.9
59.7
111
07-30-10
6.66
27.76
83
135
104
63.8
203
8.6
54.5
74.9
173
302
336
774
777
-0.4
-20.6
78.7
112
07-30-10
7.31
30.81
56
168
74.0
39.1
118
13.5
62.9
44.4
81.4
237
245
556
507
8.8
-21.4
54.6
113
07-31-10
7.28
28.73
98
137
99.3
85.6
270
9.2
88.8
59.1
118
565
233
948
949
-0.1
-19.7
86.6
114
07-31-10
7.27
31.42
123
193
105
98.2
319
20.7
120
102
157
570
229
1132
1107
2.2
-19.7
98.2
115
07-30-10
7.43
29.89
85
115
97.5
65.5
244
6.5
46.5
58.6
103
511
251
832
822
1.1
-20.8
79.3
116
07-31-10
7.61
30.98
99
123
104
85.9
264
5.6
58.8
90.9
108
588
98
926
952
-2.9
-20.0
79.4
117
07-31-10
7.31
29.96
93
151
103
78.1
250
15.4
68.0
99.1
173
379
233
909
891
1.9
-21.9
81.8
118
07-31-10
7.35
28.4
2
233
84.2
101
323
12.8
84.0
101
203
460
229
1165
1051
9.8
-21.1
94.7
119
07-31-10
7.67
30.38
93
136
87.8
73.6
231
16.4
64.6
94.4
184
382
226
834
909
-9.1
-20.8
80.5
Rong
120
07-30-10
7.57
31.83
68
193
79.1
50.3
146
16.4
192
84.0
31.5
344
309
664
683
-2.8
-20.3
65.8
121
07-30-10
6.96
30.62
94
509
103
56.1
213
15.9
511
78.5
82.3
379
222
1150
1133
1.5
-20.0
94.4
TZ+ is the total cationic charge, TZ- is the total
anionic charge, NICB is the normalized inorganic charge balance and TDS is
the total dissolved solid.1 Data on major ion compositions are from L. Liu et al. (2016). 2 Date
format is mm/dd/yy.
Ternary diagrams showing (a) cations, (b) anions and dissolved
SiO2 compositions of river water in the SECRB. Chemical
compositions from case studies of rivers draining carbonate rocks (the
Wujiang and the Xijiang) are also shown for comparison (data from Han and
Liu, 2004; Xu and Liu 2007, 2010).
![]()
Samples for a carbon isotopic ratio (δ13C) of dissolved inorganic
carbon (DIC) measurements were collected in 150 mL glass bottles with
air-tight caps and preserved with HgCl2 to prevent biological
activity. The samples were refrigerated until analysis. For the
δ13C measurements, the filtered samples were injected into glass
bottles with phosphoric acid. The CO2 was then extracted and
cryogenically purified using a high vacuum line. δ13C isotopic
ratios were analyzed on Finnigen MAT-252 stable isotope mass spectrometer at
the State Key Laboratory of Environmental Geochemistry, Chinese Academy of
Sciences. The results are expressed with reference to VPDB (Vienna Pee Dee Belemnite) as follows:
δ13C=13C/12Csample/13C/12Cstandard-1×1000.
The δ13C measurement has a precision of 0.1 ‰. A number of
duplicate samples were measured, and the results show that the differences
were less than the range of measurement accuracy.
Results
The major parameter and ion concentrations of samples are given in Table 1.
The pH values of water samples ranged from 6.50 to 8.24, with an average of
7.23. Total dissolved solids (TDSs) of water samples varied from 35.3 to
205 mg L-1, with an average of 75.2 mg L-1. Compared with the
major rivers in China, the average TDS was significantly lower than the
Changjiang (224 mg L-1, Chetelat et al., 2008), the Huanghe
(557 mg L-1, Fan et al., 2014) and the Zhujiang (190 mg L-1,
S. Zhang et al., 2007). However, the average TDS was comparable to the rivers
draining silicate-rock-dominated areas, e.g., the upper Ganjiang in Ganzhou,
south China (63 mg L-1, Ji and Jiang, 2012), the Amur in northern
China (70 mg L-1, Moon et al., 2009), the Xishui in Hubei, central
China (101 mg L-1, Wu et al., 2013) and North Han River in South Korea
(75.5 mg L-1, Ryu et al., 2008). Among the major rivers in the SECRB,
the Qiantang had the highest TDS value (averaging at 121 mg L-1), and
the Ou had the lowest TDS value (averaging at 48.8 mg L-1).
Major ion compositions are shown in the cation and anion ternary diagrams
(Fig. 2a and b). In comparison with rivers (e.g., the Wujiang and Xijiang)
draining carbonate-rock-dominated areas (Han and Liu, 2004; Xu and Liu,
2010), these rivers in the SECRB had distinctly higher proportions of
Na+, K+ and dissolved SiO2. As shown in the Fig. 2,
most samples had high Na+ and K+ proportions, with an average
of more than 50 % (in µmol L-1) of the total cations, except
for samples from the Qiantang. The concentrations of Na+ and
K+ ranged from 43.5 to 555 µmol L-1 and 42.9 to
233 µmol L-1, with average values of 152 and
98 µmol L-1, respectively. The concentrations of dissolved
SiO2 ranged from 98.5 to 370 µmol L-1, with an
average of 212 µmol L-1. Ca2+ and Mg2+
accounted for about 38 % and 11.6 % of the total cation
concentrations. HCO3- was the dominant anion, with concentrations
ranging from 139 to 1822 µmol L-1. On average, it comprised
60.6 % (36 %–84.6 %) of total anions on a molar unit basis,
followed by SO42- (14.6 %), Cl- (13.1 %) and
NO3- (11.8 %). The major ionic compositions indicate that the water
chemistry of these rivers in the SECRB is controlled by silicate weathering.
Meanwhile, it is also influenced by carbonate weathering, especially for the
Qiantang catchment.
The δ13C of dissolved inorganic carbon in the rivers of the SECRB
are also given in Table 1. The δ13C of the water samples showed a
wide range, from -11.0 ‰ to -24.3 ‰ (averaging at
-19.4 ‰), and with a majority of samples falling into the range of
-15 ‰ to -23 ‰. The values are comparable to rivers
draining the Deccan Traps (Das et al., 2005).
Discussion
The dissolved solids in river water are commonly from atmospheric and
anthropogenic inputs and weathering of rocks within the drainage basin. It is
necessary to quantify the contribution of different sources to the dissolved
loads before deriving chemical weathering rates and associated CO2
consumption.
Atmospheric and anthropogenic inputs
To evaluate atmospheric inputs to river water, chloride is the most commonly
used reference. Generally, water samples that have the lowest Cl-
concentrations are employed to correct the proportion of atmospheric inputs
in a river system (Négrel et al., 1993; Gaillardet et al., 1997; Viers et
al., 2001; Xu and Liu, 2007). In pristine areas, the concentration of
Cl- in river water is assumed to be entirely derived from the
atmosphere, provided that the contribution of evaporites is negligible (e.g.,
Stallard and Edmond, 1981; Négrel et al., 1993). In the SECRB, the lowest
Cl- concentration was mainly found in the headwater of each river.
According to the geologic setting, no salt-bearing rocks were found in these
headwater areas (FJBGRM, 1985; ZJBGMR, 1989). In addition, these areas are
mainly mountainous and sparsely populated. Therefore, we assumed that the
lowest Cl- concentration of samples from the headwater of each major
river came entirely from the atmosphere.
Chemical compositions of precipitation at different sites located
within the studied area (in µmol L-1 and molar
ratio).
Province
Location
pH
F-
Cl-
NO3-
SO42-
NH4+
K+
Na+
Ca2+
Mg2+
NO3/Cl
SO4/Cl
K/Cl
Na/Cl
Ca/Cl
Mg/Cl
Reference
Zhejiang
Hangzhou
4.5
5.76
13.9
38.4
55
79.9
4.18
12.2
26
3.53
2.76
3.96
0.3
0.88
1.87
0.25
Xu et al. (2011)
Jinhua
4.54
9.05
8.51
31.2
47.6
81.1
4.73
6.27
24
1.73
3.67
5.59
0.56
0.74
2.81
0.2
M. Zhang et al. (2007)
Fujian
Nanping
4.81
0.8
5.8
26.6
18.3
38
4.9
5.4
12.9
2.7
4.59
3.16
0.84
0.93
2.22
0.47
Cheng et al. (2011)
Fuzhou
5.26
21.4
24.9
48.5
78.1
4.1
2.61
32.7
1.25
1.16
2.26
0.19
0.12
1.53
0.06
Zhao (2004)
Xiamen
4.57
15.3
23.7
22.1
31.3
37.7
3.58
36.1
21.5
4.94
0.93
1.32
0.15
1.52
0.91
0.21
Zhao (2004)
Average
2.62
3.26
0.41
0.84
1.87
0.24
The proportion of atmosphere-derived ions in river water can then be
calculated by the element / Cl ratios of the rain. Chemical compositions
of rain in the studied area have been reported at different sites, including
Hangzhou, Jinhua, Nanping, Fuzhou and Xiamen (Zhao, 2004; M. Zhang et al.,
2007; Huang et al., 2008; Cheng et al., 2011; Xu et al., 2011) (Fig. 1). The
volume-weighted mean concentration of ions and Cl-normalized molar ratios are
compiled in Table 2. Based on this procedure, 6.6 %–23.4 %
(averaging 14.3 %) of total dissolved cations in the major rivers of the
SECRB originated from rain. Among the anions, SO42- and
NO3- in the rivers are mainly from the atmospheric input, averaging
73.2 % for SO42- and 75.8 % for NO3-,
respectively.
As one of the most developed and populated areas in China, the chemistry of
river water in the SECRB could be significantly impacted by anthropogenic
inputs. Cl-, NO3- and SO42- are commonly associated
with anthropogenic sources and have been used as tracers of anthropogenic
inputs in watershed. High concentrations of Cl-, NO3- and
SO42- can be found in the lower reaches of rivers in the SECRB,
and there is an obvious increase after it had flowed through plains and
cities. This tendency indicates that the river water chemistry is affected by
anthropogenic inputs while passing through the catchments. After correcting
for the atmospheric contribution to river water, the following assumption
is needed to quantitatively estimate the contributions of anthropogenic
inputs, which is that Cl- originates from only atmospheric and
anthropogenic inputs, and the excess of atmospheric Cl- is regarded to
present anthropogenic inputs and is balanced by Na+.
Chemical weathering inputs
Water samples were plotted with Na-normalized molar ratios (Fig. 3). The
values of the world's major rivers (Gaillardet et al., 1999) are also shown
in the figure. The best correlations between elemental ratios were observed
for Ca2+/Na+ vs. Mg2+/Na+ (R2=0.95, n=120)
and Ca2+/Na+ vs. HCO3-/Na+ (R2=0.98, n=120).
The samples cluster on a mixing line, mainly between silicate and carbonate
end-members, closer to the silicate end-member and show little evaporite
contribution. This corresponds with the rock type distributions in the SECRB.
In addition, all water samples have equivalent ratios of
(Na++K+)/Cl- larger than one, indicating silicate
weathering as the source of Na+ and K+ rather than
dissolution of chloride evaporites.
Mixing diagrams using Na-normalized molar ratios: (a)
HCO3-/Na+ vs. Ca2+/Na+ and (b)
Mg2+/Na+ vs. Ca2+/Na+ for the SECRB. The samples mainly
cluster on a mixing line between silicate and carbonate end-members. Data for
the world's major rivers are also plotted (data from Gaillardet et al.,
1999).
![]()
The geochemical characteristics of the silicate and carbonate end-members can
be deduced from the correlations between elemental ratios and referred to
literature data for catchments with well-constrained lithology. After
correction for atmospheric inputs, the Ca2+/Na+,
Mg2+/Na+ and HCO3-/Na+ of the river samples ranged from
0.31 to 30, 0.16 to 6.7 and 1.1 to 64.2, respectively. According to the
geological setting (Fig. 1), there are some small rivers draining purely
silicate areas in the SECR drainage basins. Based on the elemental ratios of
these rivers, we assigned the silicate end-member for this study to
Ca2+/Na+ =0.41±0.10, Mg2+/Na+ =0.20±0.03 and HCO3-/Na+ =1.7±0.6. The ratio of
(Ca2++Mg2+)/Na+ for the silicate end-member was 0.61±0.13,
which is close to the silicate end-member for the world's rivers
((Ca2++Mg2+)/Na+ =0.59±0.17, Gaillardet et al., 1999).
Moreover, previous research has documented the chemical composition of
rivers, such as the Amur and the Songhuajiang in northern China, the Xishui
in the lower reaches of the Changjiang, and major rivers in South Korea (Moon
et al., 2009; Liu et al., 2013; Wu et al., 2013; Ryu et al., 2008; Shin et
al., 2011). These river basins have similar lithological settings to the
study area, so we could further validate the composition of the silicate
end-member with their results. Ca2+/Na+ and Mg2+/Na+
ratios of silicate end-member were reported for the Amur (0.36 and 0.22), the
Songhuajiang (0.44±0.23 and 0.16), the Xishui (0.6±0.4 and
0.32±0.18), the Han (0.55 and 0.21) and six major rivers in South
Korea (0.48 and 0.20) in the studies above, well bracketing our estimation
for the silicate end-member.
Calculated contributions (in %) from the different reservoirs to
the total cationic load for major rivers and their main tributaries in the
SECRB. The cationic loads are the sum of Na+, K+,
Ca2+ and Mg2+ from different reservoirs.
![]()
However, some samples show high concentrations of Ca2+,
Mg2+ and HCO3-, indicating the contribution of carbonate
weathering. The samples from the upper reaches (Sample 12 and 13) of the
Qiantang fall close to the carbonate end-member documented for the world's
major rivers (Gaillardet et al., 1999). In the present study, the
Ca2+/Na+ ratio of 0.41±0.10 and Mg2+/Na+ ratio of
0.20±0.03 for the silicate end-member are used to calculate the
contribution of Ca2+ and Mg2+ from silicate weathering.
Finally, residual Ca2+ and Mg2+ are attributed to
carbonate weathering.
Chemical weathering rate in the SECRB
Based on the above assumption, a forward model is employed to quantify the
relative contribution of the different sources to the rivers of the SECRB in
this study (e.g., Galy and France-Lanord, 1999; Moon et al., 2007; Xu and Liu,
2007, 2010; Liu et al., 2013). The calculated contributions of different
reservoirs to the total cationic load for major rivers and their main
tributaries in the SECRB are presented in Fig. 4. On average, the dissolved
cationic loads of the rivers in the study area originate dominantly from
silicate weathering, which accounts for 39.5 % (17.8 %–74.0 %)
of the total cationic loads in molar unit. Carbonate weathering and
anthropogenic inputs account for 30.6 % (3.9 %–62.0 %) and
15.7 % (0 %–41.1 %), respectively. Contributions from silicate
weathering are high in the Ou (55.6 %), the Huotong (54.5 %), the Ao
(48.3 %) and the Min (48.3 %) river catchments, which are dominated
by granitic and volcanic bedrock. In contrast, a high contribution from
carbonate weathering is observed in the Qiantang (54.0 %), the Jin
(52.2 %) and the Jiulong (44.8 %) river catchments. The results
manifest the lithology control on river solutes of drainage basin.
The chemical weathering rate of rocks is estimated by the mass budget, basin
area and annual discharge (data from Hydrological data of river basins in Zhejiang,
Fujian province and Taiwan region, Annual Hydrological Report of 2010,
P. R. China, Table 3), expressed in t km-2 a-1. The silicate
weathering rate (SWR) is calculated using major cationic concentrations from
silicate weathering and assuming that all dissolved SiO2 is derived
from silicate weathering (Xu and Liu, 2010), as in the equation below:
SWR=[Na]sil+[K]sil+[Ca]sil+[Mg]sil+[SiO2]riv×discharge/area.
The assumption about Si could lead to an overestimation of the silicate
weathering rate, as part of the silica may come from dissolution of biogenic
materials rather than the weathering of silicate minerals (Millot et al.,
2003; Shin et al., 2011). Thus, the cationic silicate weathering rates
(Catsil) were also calculated.
The carbonate weathering rate (CWR) is calculated based on the sum of
Ca2+, Mg2+ and HCO3- from carbonate weathering,
with half of the HCO3- coming from carbonate weathering and being
derived from the atmosphere CO2, as in the equation below:
CWR=[Ca]carb+[Mg]carb+1/2[HCO3]carb×discharge/area.
The chemical weathering rate and flux are calculated for major rivers and
their main tributaries in the SECRB, and the results are shown in Table 3.
Silicate and carbonate weathering fluxes of these rivers (SWF and CWF) range
from 0.02×106 to 1.80×106 t a-1 and from
0.004×106 to 1.74×106 t a-1, respectively. Out
of the rivers, the Min has the highest silicate weathering flux, and the
Qiantang has the highest carbonate weathering flux. On the whole SECRB scale,
3.95×106 and 4.09×106 t a-1 of dissolved solids
originating from silicate and carbonate weathering, respectively, are
transported into the East and South China seas by rivers in this region.
Compared with the largest three river basins (the Changjiang, the Huanghe and
the Xijiang) in China, the flux of silicate weathering calculated for the
SECRB is lower than the Changjiang (9.5×106 t a-1,
Gaillardet et al., 1999) but higher than the Huanghe (1.52×106 t a-1, Fan et al., 2014) and the Xijiang (2.62×106 t a-1, Xu and Liu, 2010).
The silicate and carbonate chemical weathering rates for these river
watersheds were 14.2–35.8 and 1.8–52.1 t km-2 a-1,
respectively. The total rock weathering rate (TWR) for the whole SECRB is
48.1 t km-2 a-1, higher than the world average
(24 t km-2 a-1, Gaillardet et al., 1999). The cationic silicate
weathering rates (Catsil) range from 4.7 to
12.0 t km-2 a-1 for the river watersheds in the SECRB, averaging
at 7.8 t km-2 a-1. Furthermore, a good linear correlation
(R2=0.77, n=28) is observed between the Catsil and
runoff (Fig. 5), indicating that silicate weathering rates are controlled by
runoff as documented in previous research (e.g., Bluth and Kump, 1994;
Gaillardet et al., 1999; Millot et al., 2002; Oliva et al., 2003; Wu et al.,
2013; Pepin et al., 2013).
Plots of the cationic–silicate weathering rate (Catsil)
vs. runoff for the SECRB.
![]()
Contribution of each reservoir, fluxes, chemical weathering and
associated CO2 consumption rates for the major rivers and their main
tributaries in the SECRB.
Major
Fluxes
Weathering rate
CO2 consumption rate
river
Tributaries
Location
Discharge
Area
Runoff
Contribution ( %)
(106 t a-1)
(t km-2 a-1)
(103 mol km-2 a-1)
(109 m3 a-1)
(103 km2)
(mm a-1)
Rain
Anth.
Sil.
Carb.
SWF
CWF
Catsila
SWRb
CWRb
TWRb
CSWc
CCWc
SSWd
SNSWe
Qiantang
Fuyang
43.81
38.32
1143
9
14
23
54
0.66
1.74
6.8
17.3
45.3
62.6
223
459
195
184
Fenshui
Tonglu
2.726
3.100
879
7
14
18
62
0.05
0.16
5.5
14.7
52.1
66.8
167
530
152
146
Cao'e
Huashan
2.610
3.043
858
7
23
26
44
0.06
0.11
6.8
18.2
35.4
53.5
269
369
240
229
Ling
Linhai
5.400
6.613
817
9
22
24
45
0.09
0.17
4.7
14.2
26.1
40.3
167
267
143
133
Yonganxi
Baizhiao
3.184
2.475
1286
14
15
50
21
0.06
0.03
9.1
24.2
11.7
35.9
350
119
255
216
Shifengxi
Shaduan
1.731
1.482
1168
11
19
35
36
0.03
0.04
7.6
21.4
24.5
45.9
304
249
249
227
Ou
Hecheng
20.65
13.45
1536
20
6
56
18
0.36
0.13
10.1
26.9
9.9
36.9
360
101
228
174
Haoxi
Huangdu
1.809
1.270
1447
16
8
46
30
0.04
0.02
9.9
27.9
19.0
46.9
336
192
246
210
Xiaoxi
Jupu
5.116
3.336
1534
23
0
74
4
0.09
0.01
11.4
26.4
1.8
28.2
391
18
202
125
Nanxi
Yongjiashi
1.799
1.273
1413
21
9
63
7
0.03
0.00
10.0
26.3
3.3
29.6
360
34
200
135
Huotong
Yangzhong
3.470
2.082
1667
22
18
54
5
0.06
0.00
8.3
27.3
2.1
29.4
305
24
129
59
Ao
Lianjiang
2.770
3.170
874
17
17
48
17
0.05
0.02
5.1
17.3
5.4
22.7
188
56
122
95
Min
Zhuqi
84.59
54.50
1552
15
10
48
27
1.80
0.94
10.3
33.0
17.3
50.2
390
180
292
252
Futun
Yangkou
22.53
12.67
1778
15
14
49
22
0.45
0.21
12.0
35.8
16.2
52.0
460
171
336
286
Shaxi
Shaxian
12.87
9.922
1297
13
9
42
36
0.26
0.21
8.4
26.5
21.7
48.1
315
222
249
223
Jianxi
Qilijie
24.91
14.79
1685
16
10
45
29
0.48
0.26
9.6
32.2
17.4
49.6
350
185
250
210
Youxi
Youxi
5.237
4.450
1177
15
8
46
31
0.11
0.07
7.4
24.5
15.0
39.5
272
156
205
178
Dazhangxi
Yongtai
4.205
4.034
1042
15
21
47
17
0.08
0.03
6.6
20.2
7.1
27.4
242
73
163
131
Jin
Xixi
Anxi
3.004
2.466
1218
9
10
29
52
0.06
0.10
7.9
24.4
42.2
66.6
284
430
247
232
Dongxi
Honglai
2.236
1.704
1312
12
22
28
38
0.04
0.04
6.8
22.9
25.6
48.5
226
263
178
158
Jiulong
Punan
10.20
8.49
1201
13
14
28
45
0.19
0.29
7.3
22.2
34.0
56.2
263
351
209
188
Xi'xi
Zhengdian
4.080
3.420
1193
10
32
25
33
0.10
0.11
8.0
30.7
30.9
61.6
288
317
227
203
Zhang
Yunxiao
1.011
1.038
974
16
25
29
29
0.02
0.01
5.1
21.9
14.1
36.0
174
146
114
90
Dongxi
Zhao'an
1.176
0.955
1231
16
41
26
17
0.03
0.01
5.8
28.7
10.2
38.9
187
107
93
55
Huanggang
Raoping
1.637
1.621
1010
15
30
34
21
0.04
0.02
6.0
22.8
11.1
33.9
227
115
145
112
Han
Chao'an
24.75
29.08
851
16
7
38
39
0.49
0.50
5.4
17.0
17.0
34.0
208
176
156
135
Ding
Xikou
11.14
9.228
1207
17
6
46
32
0.31
0.18
9.0
33.3
19.1
52.4
341
196
249
212
Mei
Hengshan
10.29
12.95
794
12
13
31
44
0.21
0.32
5.7
16.6
24.5
41.1
212
252
173
157
Whole SECRB
207
167
1240
3.95
4.09
7.8
23.7
24.5
48.1
287
251
218
191
Anth. noted for anthropogenic, Sil. for
silicate, and Carb. for carbonate.
a Catsil are calculated based on the sum of cations from
silicate weathering. b SWR, CWR and TWR represent silicate
weathering rates (assuming all dissolved silica is derived from silicate
weathering), carbonate weathering rates and total weathering rates,
respectively. c CO2 consumption rate with assumption that
all the protons involved in the weathering reaction are provided by carbonic
acid. d Estimated CO2 consumption rate by silicate
weathering when H2SO4 from acid precipitation was taken into
account. e Estimated CO2 consumption rate by silicate
weathering when both H2SO4 and HNO3 from acid precipitation
were taken into account.
Plots of total cations derived from (a) carbonate and silicate
weathering vs. HCO3- and (b)
HCO3-+SO42-+NO3- for river water in the
SECRB.
![]()
<inline-formula><mml:math id="M518" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">CO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> consumption and the role of sulfuric acid
To calculate atmospheric CO2 consumption by silicate weathering (CSW)
and by carbonate weathering (CCW), a charge-balanced state between rock
chemical weathering-derived alkalinity and cations was assumed (Roy et al.,
1999).
[CO2]CSW=[HCO3]CSW=[Na]sil+[K]sil+2[Ca]sil+2[Mg]sil[CO2]CCW=[HCO3]CCW=[Ca]carb+[Mg]carb
The calculated CO2 consumption rates by chemical weathering for the
rivers in SECRB are shown in Table 3. CO2 consumption rates by
carbonate and silicate weathering are from 17.9 to 530×103 mol km-2 a-1 (averaging at 206×103 mol km-2 a-1) and from 167 to 460×103 mol km-2 a-1 (averaging at 281×103 mol km-2 a-1) for major river catchments in the SECRB.
The CO2 consumption rates by silicate weathering in the SECRB are
higher than that of major rivers in the world and China, such as the Amazon
(174×103 mol km-2 a-1, Mortatti and Probst, 2003),
the Mississippi and the Mackenzie (66.8 and 34.1×103 mol km-2 a-1, Gaillardet et al., 1999), the Changjiang
(112×103 mol km-2 a-1, Chetelat et al., 2008), the
Huanghe (35×103 mol km-2 a-1, Fan et al., 2014), the
Xijiang (154×103 mol km-2 a-1, Xu and Liu, 2010), the
Longchuanjiang (173×103 mol km-2 a-1, Li et al.,
2011), the Mekong (191×103 mol km-2 a-1, Li et
al., 2014), three large rivers in eastern Tibet (103–121×103 mol km-2 a-1, Noh et al., 2009), the Hanjiang in central
China (120×103 mol km-2 a-1, Li et al., 2009) and the
Sonhuajiang in northeastern China (66.6×103 mol km-2 a-1,
Liu et al., 2013). The high CO2 consumption rates by silicate
weathering in the SECRB could be attributed to extensive distribution of
silicate rocks, high runoff and favorable climatic conditions. The regional
fluxes of CO2 consumption by silicate and carbonate weathering are
about 47.9×109 mol a-1 (0.57×1012 g C a-1) and 41.9×109 mol a-1 (0.50×1012 g C a-1) in the whole SECRB.
However, in addition to CO2, the anthropogenic-sourced proton (e.g.,
H2SO4 and HNO3) is well documented as significant proton
providers in rock weathering processes (Galy and France-Lanord, 1999; Karim and
Veizer, 2000; Yoshimura et al., 2001; Han and Liu, 2004; Spence and Telmer,
2005; Lerman and Wu, 2006; Xu and Liu, 2007, 2010; Perrin et al., 2008;
Gandois et al., 2011). Sulfuric acid can be generated by natural oxidation of
pyrite and anthropogenic emissions of SO2 from coal combustion and
subsequently dissolve carbonate and silicate minerals. The riverine nitrate
in a watershed can be derived from atmospheric deposition, synthetic
fertilizers, microbial nitrification, sewage and manure, etc. (e.g., Kendall,
1998). Although it is difficult to determine the sources of nitrate in river water, we can at least simply assume that nitrate from acid deposition is
one of the proton providers. The consumption of CO2 by rock
weathering would be overestimated if H2SO4- and HNO3-induced
rock weathering was ignored (Spence and Telmer, 2005; Xu and Liu, 2010; Shin
et al., 2011; Gandois et al., 2011). Thus, the role that anthropogenic-sourced
protons play on chemical weathering is crucial for an accurate
estimation of CO2 consumption by rock weathering.
Rapid economic growth and increased energy needs have resulted in severe air
pollution problems in many areas of China, indicated by high levels of
mineral acids (predominately sulfuric) observed in precipitation (Larssen and
Carmichael, 2000; Pan et al., 2013; L. Liu et al., 2016). The national
SO2 emissions in 2010 reached 30.8 Tg a-1 (Lu et al., 2011).
Previous studies documented that fossil fuel combustion accounts for the most
sulfur deposition (∼77 %) in China (L. Liu et al., 2016). The wet
deposition rate of nitrogen is the highest over central and southern China,
with mean values of 20.2, 18.2 and 25.8 kg N ha-1 a-1 in
Zhejiang, Fujian and Guangdong provinces, respectively (Lu and Tian, 2007).
Current sulfur and nitrogen depositions in the southeastern coastal region
are still among the highest in China (Fang et al., 2013; Cui et al., 2014;
L. Liu et al., 2016).
(a) δ13CDIC vs.
HCO3-/(Na++K++Ca2++Mg2+)* and (b)
(SO42-+NO3-)/(Na++K++Ca2++Mg2+)*
equivalent ratio in river water draining the SECRB (* noted
concentrations corrected for atmospheric and anthropogenic inputs). The plots
show that most water deviates from the three end-member mixing areas
(carbonate weathering by carbonic acid and sulfuric/nitric acid and silicate
weathering by carbonic acid).
![]()
The involvement of protons originating from H2SO4 and HNO3
in the river water can be illustrated by the stoichiometry between cations
and anions, shown in Fig. 6. In the rivers of the SECRB, the sum of the
cations released by silicate and carbonate weathering could not be balanced
by HCO3- only (Fig. 6a), but were almost balanced by the sum of
HCO3- , SO42- and NO3- (Fig. 6b). This implies
that H2CO3, H2SO4 and HNO3 are the potential
erosion agents in chemical weathering in the SECRB. The δ13C values
of the water samples showed a wide range, from -11.0 ‰ to
-24.3 ‰, with an average of -19.4 ‰. The δ13C
from soil is dominated by the relative contribution from C3 and C4
plants (Das et al., 2005). The studied areas have subtropical temperatures
and humidity, and thus C3 processes are dominant. The δ13C of
soil CO2 is derived primarily from δ13C of organic
material, which typically has a value between -24 ‰ and
-34 ‰, with an average of -28 ‰ (Faure, 1986).
According to previous studies, the average value for C3 trees and shrubs
are from -24.4 ‰ to -30.5 ‰, and most of them are lower
than -28 ‰ in southern China (Chen et al., 2005; Xiang, 2006; Dou
et al., 2013). After accounting for the isotopic effect from the diffusion of
CO2 from soil, the resulted δ13C (from the terrestrial
C3 plant process) should be ∼-25 ‰ (Cerling et al.,
1991). This mean DIC derived from silicate weathering by carbonic acid
(100 % from soil CO2) would yield a δ13C value of
-25 ‰. Carbonate rocks are generally derived from marine systems
and typically have a δ13C value close to zero (Das et al., 2005).
Thus, the theoretical δ13C value of DIC derived from carbonate
weathering by carbonic acid (50 % from soil CO2 and 50 % from
carbonate rocks) is -12.5 ‰. DIC derived from carbonate weathering
by sulfuric acid are all from carbonate rocks; thus the δ13C of the
DIC would be 0 ‰. Based on the above discussion, sources of riverine
DIC from different end-members in the SECRB were plotted in Fig. 7. Most
water samples drift away from the three end-member mixing areas (carbonate
and silicate weathering by carbonic acid and carbonate weathering by sulfuric
acid) and towards silicate weathering by sulfuric and nitric acid area,
clearly illustrating the effect of the anthropogenic-sourced protons
(sulfuric and nitric acid) on silicate weathering in the SECRB.
Considering the H2SO4 and HNO3 effects on chemical
weathering, CO2 consumption by silicate weathering can be determined
from the equation below (Moon et al., 2007; Ryu et al., 2008; Shin et al.,
2011):
[CO2]SNSW=[Na]sil+[K]sil+2[Ca]sil+2[Mg]sil-γ×[2SO4+NO3]atmos,
where γ is calculated by
cationsil / (cationsil + cationcarb).
Based on the calculation in Sect. 5.1, SO42- and NO3- in
river water were mainly derived from atmospheric inputs. Assuming that
SO42- and NO3- in river water derived from atmospheric
input (after correction for sea-salt contribution) are all from acid
precipitation and considering H2SO4 and HNO3 effects,
CO2 consumption rates by silicate weathering (SNSW) are estimated
between 55×103 and 286×103 mol km-2 a-1
for major river watersheds in the SECRB. For the whole SECRB, the actual
CO2 consumption rate by silicate is 191×103 mol km-2 a-1 when the effect of H2SO4 and
HNO3 is considered. The flux of CO2 consumption is
overestimated by 16.1×109 mol a-1 (0.19×1012 g C a-1) due to the involvement of H2SO4 and
HNO3 from acid precipitation, accounting for approximately 33.6 %
of the total CO2 consumption flux by silicate weathering in the SECRB. It
highlights the fact that the drawdown of atmospheric CO2 by silicate
weathering can be significantly overestimated if acid deposition is ignored
in long-term perspectives. The result quantitatively shows that anthropogenic
activities can significantly affect rock weathering and associated
atmospheric CO2 consumption. The quantification of this effect needs
to be well evaluated in Asia and globally, taking into account current and future
human activity.
It is noticeable that the chemical weathering and associated CO2
consumption rates for the study area were calculated by the river water
geochemistry of high-flow season. As a subtropical monsoon climate area, the
river water of the southeastern coastal rivers is mainly recharged by rain, and
the amount of precipitation in the high-flow season accounts for more than
70 % of annual precipitation in the area. The processes in the low-flow
season might be different to some extent. It is worth making further efforts to
investigate the hydrological and temperature effects on weathering rate and flux,
as well as evaluate the anthropogenic impact in different climate
regimes and hydrology seasons.