Supplement materials
Text 1: Analysis condition of ICP-MS and AFS
The analysis condition of ICP-MS was: the RF generator power was 1350 W, the velocity of flow
of plasma gas was 16.0 L·min1, the velocity of flow carrier gas was 1.12 L·min1, the residence
time was 30 ms. The pore diameter of skimmer was 0.80 mm.
Hg concentrations in the aqueous were analyzed by hydride generation atomic fluorescence
spectrometry (HG-AFS) using a Millennium Excalibur system (PSA 10.055. PS Analytical Ltd,
UK). The filtrated solution was transferred and reduced by KI–ascorbic acid solution (0.06
mol·L1 KI and 0.01 mol·L1 ascorbic acid) in 3 mol·L1 HCl. The samples were made to a certain
volume with ultrapure water and left for half an hour at room temperature. The Hg concentrations
were then analyzed.
Text S2: Generally speaking, fuzzy comprehensive assessment (FCA) includes five main stages
[1]
1) Establish assessment parameters set U as the environmental assessment parameters that were
representative of water quality.
U   u1 , u2 ,..., un  ,
(1)
where n is the number of selected parameters (ui).
2) Establish assessment criterion set V of five water quality classes was derived according to
GB/T 14848-93 standards.
V   v1 , v2 ,..., vm  ,
(2)
m is the number of water qulity criterion classes (vi). In this stduy, V is established based on GB/T
14848-93 standards. Water quality is classified into five levels: Class I, excellent; Class II, good;
Class III, ordinary; Class IV, poor; Class V, bad.
3) Establish the membership function and fuzzy matrix
Membership degree which indicates the degree of the parameters belonging to the fuzzy set is
calculated by a set of formulas below.
1
x  v1


u1 ( x)  (v2  x ) / (v2  v1 ) v1  x  v2 ,

0
x  v2

0
x  v1 or x  v3


u2 ( x )   ( x  v1 ) / (v2  v1 ) v1  x  v2 ,
 (v  x ) / ( v  v ) v  x  v
3
2
2
3
 3
(3)
(4)
0
x  v2 or x  v4


u3 ( x)  ( x  v2 ) / (v3  v2 ) v2  x  v3 ,
(v  x ) / (v  v ) v  x  v
4
3
3
4
 4
(5)
0
x  v3 or x  v5


u4 ( x)  ( x  v3 ) / (v4  v3 ) v3  x  v4 ,
(v  x ) / (v  v ) v  x  v
5
4
4
5
 5
(6)
0
x  v4


u5 ( x)  ( x  v4 ) / (v5  v4 ) v4  x  v5 ,

x  v5
1

(7)
The membership degree of each parameter is obtained from the membership function above.
The membership of all parameters establishes the fuzzy matrix R:
 r11
r
R   21


 rn1
r12
r13
r14
r22
r23
r24

rn 2

rn 3

rn 4
r15 
r25 
,
 

rn 5 
(8)
n is the number of assessment parameters.
4) Establish the weight set B
Different pollutants have their own impacts on water quality, then different weight is assigned
to each pollutant. Here the weights of all parameters are calculated using the formula below:
n
Wi  ai /  ai ,
(9)
i 1
ai  xi / si ,
(10)
where si is the average of the ith assessment criterion at each level, and xi is the real value of ith
assessment parameter (ai).
5) Comprehensive assessment
The assessment result is obtained using the fuzzy algorithm of B  R   b1 , b2 , b3 , b4 , b5  , and
the assessed subject should be put into the category with the maximum of b j ( j  1, 2,3, 4,5) .
Table S1
Sampling site information
pollution site as black triangle in
sampling site as red circles in Fig. 1
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
coordinate
N25°46.284′;
E113°09.597′
N25°47.582′;
E113°08.856′
N25°47.762′;
E113°08.648′
N25°48.115′;
E113°10.405′
N25°49.005′;
E113°09.225′
N25°49.402′;
E113°09.041′
N25°49.495′;
E113°8.547′
N25°49.548′;
E113°09.551′
N25°49.571′;
E113°09.223′
N25°49.604′;
E113°08.615′
N25°49.812′;
E113°08.599′
N25°49.877′;
E113°08.784′
N25°50.554′;
E113°08.849′
N25°51.913′;
E113°9.595′
N25°52.087′;
E113°09.194′
N25°52.289′;
E113°09.191′
N25°52.908′;
E113°09.662′
N25°52.942′;
E113°09.461′
N25°53.170′;
E113°08.758′
N25°53.657′;
E113°08.816′
well depth/m
below 1.0
1.0
1.0
1.2
1.7
2.5
1.5
1.5
2.0
1.7
1.5
1.0
1.2
below 1.0
–
1.5
1.0
below 1.0
10.0
1.0
Fig. 1
use of state
non-use from 2008
non-use from 2006
non-use from 2006
being used
being used
being used
being used
being used
being used
being used
being used
non-use from 2005
non-use from 2003
being used
non-use
being used
being used
being used
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
coordinate
N25°42.917′;
E113°10.970′
N25°43.298′;
E113°9.623′
N25°43.161′;
E113°09.997′
N25°43.906′;
E113°10.547′
N25°44.108′;
E113°10.339′
N25°46.303′;
E113°09.646′
N25°46.354′;
E113°10.349′
N25°46.354′;
E113°10.349′
N25°47.718′;
E113°09.743′
N25°47.888′;
E113°9.169′
N25°48.028′;
E113°09.676′
N25°48.054′;
E113°09.781′
N25°48.054′;
E113°09.781′
N25°48.046′;
E113°09.630′
N25°49.970′;
E113°08.457′
N25°50.463′;
E113°08.519′
N25°50.949′;
E113°8.762′
N25°51.379′;
E113°08.928′
being used
non-use from 2003
Table S2 Limit of detection (LOD), SF and RfD for each heavy metal
LOD/(ng·L1)
SF/(kg·d·mg1)
RfD /(mg·(kg d)1)
Cr
0.2
0. 5
0.003
Mn
0.2
–
0.024
Fe
30
–
0.7
Ni
0.3
–
0.02
Cu
1.0
–
0.04
Zn
2.0
–
0.3
As
0.1
1.5
0.0003
Cd
0.01
6.1
0.0005
Ba
0.2
–
0.2
Hg
0.005
–
0.00016
Pb
0.1
0.0085
0.0014
heavy metal
wet season
0.64
0.62
0.48
0.53
2.45
0.55
0.45
0.52
0.37
0.41
0.40
0.44
0.46
0.45
0.37
0.52
0.72
0.43
lognormal
0.79
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
0.89
0.83
2
3
1
distribution
0.72
2
Origin data of heavy metal in 20 well
sampling
Cr
site No.
dry season
1
0.55
Table S3
season
46.27
4.66
5.87
lognormal
1.25
12.47
5.08
2.20
6.65
5.94
6.53
6.81
232.94
723.04
5.68
6.44
1.41
30.30
1.33
2.46
35.78
21.04
150.1
74.78
93.44
lognormal
74.47
154.97
70.99
61.29
93.79
66.74
67.03
62.56
101.57
97.79
93.40
91.70
76.19
518.87
82.34
93.38
101.61
108.09
70.93
78.43
1.46
3.46
Fe
Mn
0.96
1.29
0.95
lognormal
0.54
6.76
0.96
1.04
0.72
0.95
1.06
0.86
1.49
1.00
1.14
0.96
0.78
3.09
0.72
0.69
0.58
1.05
0.68
0.64
Ni
4.70
2.01
1.65
lognormal
1.31
3.98
1.58
1.59
1.37
1.81
1.24
1.52
1.47
1.55
2.37
2.70
1.70
9.93
4.51
1.34
2.12
7.78
2.06
1.48
Cu
54.34
34.67
59.75
lognormal
37.45
86.41
51.06
76.31
53.91
61.00
61.84
66.60
43.22
62.72
100.95
116.87
70.57
208.47
73.74
46.40
58.15
53.83
57.02
53.85
Zn
16.07
0.46
3.04
lognormal
0.59
2.79
0.54
0.46
0.82
2.91
0.24
0.26
0.43
0.39
1.04
0.79
0.25
16.09
0.32
0.53
1.21
3.81
0.30
0.99
As
0.22
0.06
0.05
lognormal
0.07
0.16
0.03
0.03
0.03
0.04
0.03
0.03
0.16
0.45
0.07
0.04
0.03
0.67
0.03
0.03
0.05
0.09
0.05
0.06
Cd
115.94
109.94
103.36
lognormal
104.54
91.18
103.56
80.63
89.64
115.46
93.35
91.88
128.56
94.54
112.16
93.39
105.76
209.86
103.94
76.12
65.79
120.94
60.74
92.39
Ba
0.1
0.02
0.05
lognormal
0.02
0.04
0.01
0.01
0.05
0.03
0.02
0.04
0.06
0.05
0.07
0.09
0.14
0.82
0.01
0.01
0.01
0.03
0.02
0.02
Hg
0.28
1.27
0.79
normal
0.38
2.06
1.44
2.20
1.82
2.43
3.65
1.57
0.52
0.41
3.95
1.56
2.03
1.77
4.03
1.62
0.76
0.39
2.23
1.25
Pb
0.77
0.78
0.88
0.80
0.85
0.67
0.76
0.70
0.65
0.97
0.70
0.41
0.65
0.72
0.76
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
normal
0.76
5
distribution
0.79
4
3.24
lognormal
2.62
3.77
0.93
8.21
4.93
17.06
20.57
9.56
1471.54
3.64
5.62
4.03
42.93
0.95
4.48
17.10
113.2
97.15
lognormal
80.4
67.95
52.7
83.94
93.4
63.95
86.39
78.77
179.7
83.12
79.63
85.64
128.6
68.31
82.58
0.77
0.65
lognormal
1.02
0.79
0.79
0.80
1.19
1.16
0.86
0.67
2.00
5.89
2.62
1.18
1.00
1.01
0.91
4.25
normal
1.30
1.94
1.12
1.32
1.03
2.55
2.08
1.76
1.51
2.32
2.94
2.88
1.63
1.70
2.74
1.41
34.68
normal
41.29
40.17
33.24
31.56
56.18
40.79
43.62
33.11
34.62
45.64
50.09
30.20
32.72
23.94
26.46
40.08
2.48
lognormal
1.26
0.56
0.43
1.03
3.64
0.37
0.55
0.25
2.25
0.50
0.33
2.87
3.99
0.21
0.76
10.38
0.02
lognormal
0.08
0.02
0.03
0.03
0.05
0.04
0.03
0.04
0.45
0.07
0.02
0.23
0.05
0.02
0.03
0.22
101.54
lognormal
67.84
70.42
82.52
46.35
134.04
99.43
107.44
65.45
82.9
131.94
116.14
111.94
101.64
107.24
107.84
117.84
0.01
lognormal
0.01
0.01
0.02
0.03
0.07
0.03
0.04
0.05
0.07
0.05
0.03
0.02
0.01
0.02
0.02
0.04
normal
0.89
1.14
0.60
1.49
0.46
0.17
0.13
0.76
1.36
0.26
0.71
0.30
0.77
1.24
1.02
2.44
0.70
Table S4
season
dry
Frequency of quality of individual heavy metal in dry and wet seasons
index
Cr
Mn
Fe
Ni
Cu
Zn
As
Cd
Ba
Hg
Pb
I
1
0.9
0.9
1
1
1
0.95
0.95
0.3
0.95
1
II
0
0
0.05
0
0
0
0.05
0.05
0.7
0.05
0
III
0
0.05
0.05
0
0
0
0
0
0
0
0
IV
0
0.05
0
0
0
0
0
0
0
0
0
V
0
0
0
0
0
0
0
0
0
0
0
I
1
0.95
0.9
1
1
1
0.9
1
0.05
1
1
II
0
0
0.1
0
0
0
0.1
0
0.95
0
0
III
0
0
0
0
0
0
0
0
0
0
0
IV
0
0.05
0
0
0
0
0
0
0
0
0
V
0
0
0
0
0
0
0
0
0
0
0
season
wet
season
References
1. Huang F, Wang X, Lou L, Zhou Z, Wu J. Spatial variation and source apportionment of water
pollution in Qiantang River (China) using statistical techniques. Water Research, 2010, 44(5):
1562–1572