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1

Supplementary Information 1

Continental-Scale Pollution of Estuaries with Antibiotic Resistance Genes 2

3

Yong-Guan Zhu1,2,#,*, Yi Zhao1,#, Bing Li3, Chu-Long Huang1, Si-Yu Zhang2, Shen 4

Yu1, Yong-Shan Chen1, Tong Zhang4, Michael R Gillings5, Jian-Qiang Su1* 5

1. Key Lab of Urban Environment and Health, Institute of Urban Environment, 6

Chinese Academy of Sciences, Xiamen 361021, China 7

2. State Key Lab of Urban and Regional Ecology, Research Center for 8

Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China 9

3. Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China 10

4. Environmental Biotechnology Laboratory, Department of Civil Engineering, 11

University of Hong Kong, Hong Kong SAR, China 12

5. Department of Biological Sciences, Genes to Geoscience Research Centre, 13

Macquarie University, Sydney, New South Wales 2109, Australia 14

15

# These two authors contributed equally to this work; 16

* Corresponding authors 17

Yong-Guan Zhu, Key Lab of Urban Environment and Health, Institute of Urban 18

Environment, Chinese Academy of Sciences, Xiamen, 361021, China 19

E-mail: [email protected], Phone: 86-592-6190997 20

Jian-Qiang Su, Key Lab of Urban Environment and Health, Institute of Urban 21

Environment, Chinese Academy of Sciences, Xiamen, 361021, China 22

E-mail: [email protected], Phone: 86-592-6190792 23

24

Continental-scale pollution of estuaries withantibiotic resistance genes

© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

SUPPLEMENTARY INFORMATIONVOLUME: 2 | ARTICLE NUMBER: 16270

NATURE MICROBIOLOGY | DOI: 10.1038/nmicrobiol.2016.270 | www.nature.com/naturemicrobiology 1

http://dx.doi.org/10.1038/nmicrobiol.2016.270

2

Supplementary Tables 25

26

Supplementary Table 1 27

Abbreviation of 18 estuaries spanning 7 provinces along the coastline of China 28

Province (Pinyin) River name (Pinyin) Abbreviation

Liaoning (Liáoníng) Biliu Jiang (Bìlíu Jiāng) LN-BLH

Liaoning (Liáoníng) Liao He (Liáo Hé) LN-LH

Hebei (Héběi) Luan He (Lúan Hé) HB-LH

Tianjin (Tiānjīn) Yongdingxin He (Yǒngdìng Hé) TJ-YDXH

Zhejiang (Zhéjiāng) Qiantang Jiang (Qiántáng Jiāng) ZJ-QTJ

Zhejiang (Zhéjiāng) Yong Jiang (Yǒng Jiāng) ZJ-YJ

Zhejiang (Zhéjiāng) Jiao Jiang (Jiāo Jiāng) ZJ-JJ

Zhejiang (Zhéjiāng) Ou Jiang (ōu Jiāng) ZJ-OJ

Fujian (Fújìan) Huotong Xi (Huótóng Xī) FJ-HTX

Fujian (Fújìan) Min Jiang (Mǐn Jiāng) FJ-MJ

Fujian (Fújìan) Jin Jiang (Jìn Jiāng) FJ-JJ

Fujian (Fújìan) Jiulong Jiang (Jiǚlóng Jiāng) FJ-JLJ

Guangdong (Gǔangdōng) Han Jiang (Hàn Jiāng) GD-HJ

Guangdong (Gǔangdōng) Long Jiang (Lóng Jiāng) GD-LJ

Guangdong (Gǔangdōng) Zhu Jiang (Zhū Jiāng) GD-ZJ

Guangxi (Guāngxī) Nanliu Jiang (Nánlíu Jiāng) GX-NLJ

Guangxi (Guāngxī) Qin Jiang (Qín Jiāng) GX-QJ

Guangxi (Guāngxī) Fangcheng Jiang (Fángchéng Jiāng) GX-FCJ

29

3


Shannon-Weiner index for antibiotic resistance genes detected in estuarine 31

sediments 32

33

34

Sites H' Sites H'

FJ-MJ 1.83557 ZJ-QTJ 1.96679

FJ-JJ 1.94451 ZJ-YJ 1.79997

FJ-HTX 1.81241 ZJ-JJ 1.99043

FJ-JLJ 1.9361 ZJ-OJ 1.99028

GD-LJ 1.8877 TJ-YDXH 1.95365

GD-ZJ 1.93009 HB-LH 1.9515

GD-HJ 1.94814 LN-LH 1.93274

GX-FCJ 1.9185 LN-BLH 1.92948

GX-QJ 2.00862

GX-NLJ 1.98566

4


List of antibiotic resistance genes detected in all estuarine sediment samples 36

Assay ID Gene Name ARDB* gene Classfification Mechanism

aac(6')-Ib aac(6')-Ib aac6ib Aminoglycoside Antibiotic inactivation

aacC aacC aac3vi Aminoglycoside Antibiotic inactivation

aacC4 aacC4 aac3iv Aminoglycoside Antibiotic inactivation

aadA1 aadA1 ant3ia Aminoglycoside Antibiotic inactivation

aadA5-02 aadA5-02 aadA5 Aminoglycoside Antibiotic inactivation

blaoxY blaOXY bl2be_oxy1 Beta_Lactamase Antibiotic inactivation

cphA-01 cphA-01 bl3_cpha Beta_Lactamase Antibiotic inactivation

fox5 fox5 fox5 Beta_Lactamase Antibiotic inactivation

mphA-01 mphA-01 mphA MLSB Antibiotic inactivation

mphA-02 mphA-02 mphA MLSB Antibiotic inactivation

acrA-05 acrA-05 acrA Multidrug Efflux pump

CeoA ceoA ceoA Multidrug Efflux pump

floR floR cml_e3 Multidrug Efflux pump

oprD oprD oprd Multidrug Efflux pump

oprJ oprJ oprj Multidrug Efflux pump

qacEdeltal-01 qacEdelta1-02 qacEdelta1 Multidrug Efflux pump

qacH-01 qacH-01 qacH Multidrug Efflux pump

tetG-02 tetG-02 tetg Tetracycline Efflux pump

*ARDB: antibiotic resistance genes database 37

38

5


ARGs co-occurring with the clinical class 1 integron-integrase gene 40

Co-occurring ARGs Co-occurring ARGs class Spearman’s correlation

coefficient

aac6-Ib-akaaacA4 Aminoglycoside 0.61

aadA1 Aminoglycoside 0.67


aadA Aminoglycoside 0.73




aac6-II Aminoglycoside 0.7

aphA3 Aminoglycoside 0.6



blaOXA10 Beta-Lactamase 0.63


ereA MLSB 0.61

floR Multidrug 0.61

tetG Tetracycline 0.71

tetM Tetracycline 0.6

tnpA Transposase 0.67

41

42

6


ARGs that co-occurred with the hub of Module I 44

Module I Hub Resistance class Co-occurring ARG Co-occurring ARG class

aadA Aminoglycoside aadA1 Aminoglycoside

aadA2

aadA

aphA3

aadA5

aadA2

aadA2

aac6-II

aphA3

aac6-Ib-akaaacA4

aac6-Ib-akaaacA4

blaOXA10 Beta-Lactamase

blaOXA10

cmx(a) Chloramphenicol

ereA MLSB

qacEdelta1 Multidrug

floR

qacH

tetG Tetracycline

tetM

intI Integron

tnpA Transposase

45

46

7


ARGs that co-occurred with the hub of Module II 48

Module II Hub Resistance class Co-occurring ARG Co-occurring ARG class

oprD Multidrug aacC4 Aminoglycoside

aac

aadA9

fox5 Beta-Lactamase

cphA

blaCMY2

ampC

matA/mel MLSB

oleC

pikR2

oprJ Multidrug

ceoA

yceL/mdtH

mepA

mexF

acrA

acrR

acrA

emrD

yidY/mdtL

qacEdelta1

yceL/mdtH

mdtE/yhiU

pica Others

tetG Tetracycline

tetR

tetD

vanB Vancomycin

vanHB

vanC

vanTC

49

8


ARGs that co-occurred with the hub of Module III 51

Module III Hub Resistance class Co-occurring ARG Co-occurring ARG class

vanC Vancomycin blaCMY2 Beta-Lactamase

ampC

ampC

matA-mel MLSB

oleC

pikR2

erm(36)

oprD Multidrug

oprJ

ceoA

yceL/mdtH

tolC

mexF

acrA-04

acrR

emrD

yidY/mdtL

acrA

mdtE/yhiU

acrF

pica Others

tetG Tetracycline

tetR

vanB Vancomycin

vanHB

vanTC

52

53

9


The correlation between absolute abundance of 16S rRNA gene and ARGs 55

Pearson correlation (N = 90) 16S rRNA gene

Aminoglycoside R 0.927**

Sig. <0.01

Beta-Lactamase R 0.887**

Sig. <0.01

Chloramphenicol R 0.84**

Sig. <0.01

MLSB R 0.966**

Sig. <0.01

Multidrug R 0.957**

Sig. <0.01

Others R 0.84**

Sig. <0.01

Sulfonamide R 0.894**

Sig. <0.01

Tetracycline R 0.896**

Sig. <0.01

Vancomycin R 0.927**

Sig. <0.01

Total ARGs R 0.964**

Sig. <0.01

*, P<0.05; **, P<0.01.

56

57

10


Antibiotics analyzed in 14 southern estuaries 59

Class Compound Abbreviation CAS NO Solubility

(mg/L) LogKow

a pKab Mainly usage

Diaminopyrimidines Trimethoprim TMP 738-70-5 4004 0.914 7.124 Human, animal

Tetracyclines

Tetracycline TC 60-54-8 1.30 1 8.3, 10.215 animal

Oxtetracycline OTC 79-57-2

-1.221

animal

Chlortetracycline CTC 64-72-2 -0.621

animal

Doxycycline DOC 17086-28-1

0.523

animal

Sulfonamides

Sulfadiazine SDZ 68-35-9 774 -0.094 6.364 Animal

Sulfamethoxazole SMX 723-46-6 6104 0.894 1.85 5.6 10 Human, animal

Sulfamethazine SMT 57-68-1

0.891

Human, animal

Sulfamonomethoxine

sodium hydrate SSH

Human, animal

Sulfachinoxalin SCX

0.542

Human, animal

Sulfadimethoxine SDM 122-11-2

0.792

Human, animal

Sulfameter SM 651-06-9

0.252

Human, animal

Sulfaclozine sodium

monohydrate SSM

Human, animal

Sulfathiazole STZ 72-14-0 3734 0.054 7.24 Animal

Sulfamerazine SMZ 127-79-7

0.216 2.17; 6.777 Human, animal

11

Fluoroquinolones

Norfloxacin NFC 70458-96-7 178004 -1.034 3.11 6.10 8.6

10.56 10 Human, animal

Ofloxacin OFC 82419-36-1 28304 0.36 11 5.97 8.28 13 Human, animal

Ciprofloxacin CPC 85721-33-1 300002 .412 3.01 6.14 8.70

10.58 10 Animal

Difluoxacin DFC 91296-86-5

5.66; 7.247 Animal

Enrofloxacin EFC 93106-60-6 1300004 1.112 3.85 6.19 7.59

10 Animal

Macrolides

Erythromycin ETM 114-07-8 20005 3.064 8.91 Human, Animal

Roxithromycin RTM 80214-83-1

2.754 9.17 10 Human, animal

Tylosin TYL 74610-55-2 50008 1.639 7.57 Animal

60

12


The socio-economic parameters used as anthropogenic factors 62

Sites 1.Sewage

/10,000 tons

per year

2.Total

Population

/10,000

person

3.GDP /100

billion

4.Aquatic

production

/ton

5. Urban

ratio (%)

6.Meat

production /ton

7.Pork

production

/ton

8.Pigs

marketing

/10,000

9.Total

Wastewater

/10,000 tons

10.Nr. of

patient

diagnosed and

treated

/10,000 person

11.Number

of residential

patient

/person

FJ-HTX 1772.06 57.50 267.50 163077.00 56.70 32392.00 164.29 2092.05 259098.00 19203.10 5101566.00

FJ-JJ 16445.55 387,00 2087.00 57578.00 61.80 121338.00 164.29 2092.05 259098.00 19203.10 5101566.00

FJ-JLJ 36518.29 755.80 5320.28 486729.00 74.30 398486.00 164.29 2092.05 259098.00 19203.10 5101566.00

FJ-MJ 41560.42 1188.70 6380.19 571065.00 58.50 822660.00 164.29 2092.05 259098.00 19203.10 5101566.00

GD-HJ 34922.61 1250.96 3521.09 406647.00 76.50 761277.00 317.27 3527.76 1213000.00 71491.80 12157959.00

GD-LJ 5820.62 208.50 500.17 9848.00 28.91 45166.00 317.27 3527.76 1213000.00 71491.80 12157959.00

GD-ZJ 345591.00 11571.42 70812.24 4603256.00 57.00 3424041.00 317.27 3527.76 1213000.00 71491.80 12157959.00

GX-FCJ 1409.31 37.36 99.08 130301.27 46.00 24000.00 277.91 3456.70 245578.00 23189.20 6995176.00

GX-NLJ 17416.99 461.75 869.97 481961.99 14.73 576943.56 277.91 3456.70 245578.00 23189.20 6995176.00

GX-QJ 8430.86 223.51 634.11 475723.76 46.02 246881.89 277.91 3456.70 245578.00 23189.20 6995176.00

HB-LH 11982.04 1430.07 8562.05 898100.00 43.66 1510000.00 303.92 3452.00 310921.00 36791.60 8696025.00

LN-BLH 1101.93 30.94 353.81 106594.73 55.45 37735.92 257.42 2731.20 510100.00 17525.50 5504358.00

LN-LH 5611,00 2522.88 14864.17 552903.00 66.00 1476739.00 257.42 2731.20 510100.00 17525.50 5504358.00

TJ-YDXH 75641.93 1472.21 14370.16 398600.00 64.00 1245500.00 26.96 397.03 56883.00 9607.70 1321499.00

ZJ-JJ 19850.61 386.35 1864.17 438543.00 16.39 80402.00 150.14 1895.09 419100.00 45191.00 6263527.00

ZJ-OJ 25105.22 488.62 2768.74 25101.00 24.64 129864.00 150.14 1895.09 419100.00 45191.00 6263527.00

ZJ-QTJ 87380.17 1700.67 13814.59 369287.00 37.60 1065256.00 150.14 1895.09 419100.00 45191.00 6263527.00

13

ZJ-YJ 16329.32 317.82 4408.72 200425.21 62.06 108762.23 150.14 1895.09 419100.00 45191.00 6263527.00

1-6, the data were mainly collected and calculated from governmental statistical yearbooks, bulletins and reports at county and town level.

7-11, the data were mainly collected and calculated from governmental statistical yearbooks, bulletins and reports at province and county level.

2, total population is total population of permanent resident.

5, urban ratio is the ratio of urban population and total population.

6, meat production included the production of pork, beef, poultry, mutton, and rabbit meat.

4, aquatic production included freshwater and saltwater aquaculture product.

* Detailed information shown in Supplementary Discussion and Tables “Explanation of the anthropogenic factors”.

14


The co-occurring patterns between ARGs and transposases 64

Transposase Co-occurring ARGs Co-occurring ARGs class

Spearman's correlation

coefficient

IS613 aac6-II Aminoglycoside 0.68


tnpA aadA2 Aminoglycoside 0.63




strB Aminoglycoside 0.63

matA/mel MLSB 0.74

qacEdelta1 Multidrug 0.62

mdtE/yhiU Multidrug 0.66



tetR Tetracycline 0.64

tnpA strB Aminoglycoside 0.78

matA-mel MLSB 0.67

oleC MLSB 0.61

pikR2 MLSB 0.61

mexF Multidrug 0.71

acrA Multidrug 0.66

emrD Multidrug 0.64

qacEdelta1 Multidrug 0.88

pica Others 0.63



tetR Tetracycline 0.66







aac6-II Aminoglycoside 0.62







aphA3 Aminoglycoside 0.71

15


tetO Tetracycline 0.66

tetPA Tetracycline 0.66

tnpA intI Integron 0.67

65

66

16

Supplementary Figures 67

68

Supplementary Figure 1 69

Proportion of total antibiotic resistance genes detected in estuarine sediments 70

classified by resistance mechanism. 71

72

73

74

17

75


Distance-decay analysis of ARGs similarity on the gene level revealed no spatial 77

pattern of antibiotic resistance genes distribution. The line plotted with no swooping 78

concavely downward as distance along the ARGs similarity increase showed no 79

significant geographic distance decay of the ARGs similarity (coefficient r = 0.062, 80

P=0.447). This result indicated that the differences in ARGs abundance are more 81

likely driven by local environmental variables such as anthropogenic effects rather 82

than geographic distance. 83

84

18

85


(a) ARG profile from eighteen estuary sites. Each column is labeled with the site 87

name, and each row is the results from a single primer set. Values plotted are 88

normalized gene copy numbers (copies per cell). The legend denotes corresponding 89

abundance value scaled by row. All primer sets (248 detected ARGs) that showed 90

amplification in at least one sample are shown. (b) The profile of MGEs and ARGs 91

that confer resistance to each class of antibiotics. Rows and column were clustered 92

based on the Bray-Curtis distance. 93

19

94


Principal coordinate analysis (PCoA) based on Bray-Curtis distance showed the 96

overall distribution pattern of (a) ARG profiles and (b) bacterial communities among 97

estuarine sediment samples. 98

99

20

100


The concentrations of antibiotics in 14 southern estuarine sediments. Error bars 102

represent standard deviation (SD) of five sampling replicates on each site (n = 5). 103

104

21

105

106


Correlation between antibiotic concentration and abundance of each type of ARGs. 108

Each column refers to the concentration of each class of antibiotic, and each row 109

refers the abundance of ARGs. Values plotted are the Pearson’s coefficient between 110

antibiotic concentration and ARG abundance. The significant correlations were 111

marked with asterisk (*, P < 0.05; **, P < 0.01). 112

113

22

114

115


Correlation of the normalized copy numbers of antibiotic resistance genes with 117

normalized copy number of transposases or that of class 1 integron-integrase gene (P 118

< 0.01). 119

120

Normalized gene copy numbers of ARGs (log)

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0

No

rma

lize

d g

ene

co

py

nu

mbe

rs o

f in

teg

ron

(lo

g)

-4

-3

-2

-1

0

No

rmaliz

ed

ge

ne c

op

y nu

mb

ers

of

tra

nsp

osa

se

s (lo

g)

-4

-3

-2

-1

0

Class 1 integron-integrase, Pearson's r = 0.436Transposases, Pearson's r = 0.579, P < 0.01

23

121

122


Network analysis revealed the module patterns among ARGs, transposases and 124

integron-integrase genes. Nodes with the same color belong to the same module and 125

the size of nodes is proportional to the number of connections. Each connection 126

represents a significant correlation (Spearman’s r > 0.6, P < 0.01) 127

128

129

24

130


Absolute abundance (copies per gram of sediment) of 16S rRNA genes for the 132

estuarine sediment samples. Values were calculated from 5 independent samples for 133

each location, error bars represent standard deviation (SD) of five sampling 134

replicates (n = 5). 135

136

25

137


Rarefaction curves showing the diversity of bacterial communities based on 16S 139

rRNA genes (a) observed species, (b) PD whole tree metrics, (c) Chao1 estimator 140

and (d) Shannon index. Error bars were generated with standard deviation (n = 5) by 141

QIIME. 142

143

26

144

145


Alpha-diversity of bacterial communities for all the estuarine sediments samples at a 147

sequencing depth of 16,640, (a) observed species, (b) PD whole tree metrics, (c) 148

Chao1 estimator and (d) Shannon index. 149

150

27

151


Average percentages of total 16S rRNA gene sequences classified to each phylum 153

among the 18 estuaries. Phyla are displayed if they represent at least 2% of the total 154

sequences in at least one estuary. Others contain the taxa with a maximum 155

abundance of < 2% in any sample. The sites are ordered by geographic location, 156

from North to South. 157

158

159

160

28

161


Average percentages of total 16S rRNA gene sequences classified to each class with 163

at least 1% of the total sequences in at least one estuary. Only classes in three major 164

dominant phyla were shown, (a) Proteobacteria (b) Bacteroidetes and (c) Chloroflexi 165

166

29

OTU645121

OTU571223

OTU670581

OTU338223

OTU374650

OTU278785

OTU202467

OTU614077

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OTU88894

OTU66070

OTU208048

OTU505425

OTU219686 OTU593057

OTU503013

OTU470649

OTU468329

OTU680772

OTU569892

OTU251085

OTU590975

OTU232821

OTU544643

OTU502268

OTU389789

OTU536601

OTU223700OTU657072

OTU671392OTU26311

aac

aac6-Ib-akaaacA4-01

aac6-Ib-akaaacA4-02aac6-Ib-akaaacA4-03

aac6-II

aacC

aacC4

aadA-01

aadA-02

aadA1

aphA3-01

aadA-1-02

aadA2-01

aadA2-02aadA2-03

aadA5-01

aadA5-02

aadA9-01

aadA9-02

aadE

acrA-01

acrA-02

acrA-04

acrA-05

acrR-01

ampC-01

ampC-02

ampC-04

ampC-07

ampC-09

bacA-02

blaCMY2-02

bla-L1

blaOXA10-01

blaOXA10-02

blaoxY

blaPER

blaSHV-01

blaSFO

ceoA

cmx(A)

cphA-01

cphA-02

emrD

ereA

erm(36)

floR

fox5

intI-1

matA/mel

mdtE/yhiU

mefA

mepA

mexF

mphA-01

mphB

oleC

oprD

oprJ

pikR2

pica

putative-multidrug

qacEdelta1-02

qacEdelta1-01

qacH-02

strB

sulA/folP-01

tetD-02

tetG-01

tetG-02

tetM-01

tetM-02

tetO-01tetPA

tetR-02

tnpA-02

tnpA-04

tnpA-05

tnpA-06

tolc-02

vanB-01

vanC-03

vanHB

vanTC-02

vatE-01

vgb-01

yceL/mdtH-01

yceL/mdtH-03

yidY/mdtL-01

Aminoglycoside MLSB OthersChloramphenicol Multidrug

TransposaseSulfonamide IntegronTetracycline

Beta-lactams

Vancomycin Bacteria (OTU)

167

168

169


Network analysis revealing the co-occurrence patterns between ARGs and microbial 171

taxa. The microbial taxa for OTU and their co-occurrence patterns between ARGs 172

are summarized in Supplementary Fig. S15. 173

174

30

175


Heatmap showing the ARGs and their potential hosts (associated taxa). This 177

co-occurrence between some ARGs and microbial taxa was revealed by network 178

analysis with spearman coefficient 0.7 as detection limit (P < 0.01). 179

180

31

Supplementary Discussion and Tables 181

Explanation of sewage, total population, GDP, aquatic production and other 182

socio-economic parameters used as anthropogenic factors (Supplementary Table 10) in 183

this study. 184

185

Table 1 Social economic data collected or estimated according to the administrative 186

regions in every basin 187

Basin

Sewage (Municipal domestic

sewage)

×104 t/a

Total population

×104 person

GDP

×108 yuan

Aquatic production

t

FJ-HTX 1772 58 268 163077

FJ-JJ 16446 387 2087 57578

FJ-JLJ 36518 756 5320 486729

FJ-MJ 41560 1189 6380 571065

GD-HJ 34923 1251 3521 406647

GD-LJ 5821 209 500 9848

GD-ZJ 345591 11571 70812 4603256

GX-FCJ 1409 37 99 130301

GX-NLJ 17417 462 870 481962

GX-QJ 8431 224 634 475724

HB-LH 11982 1430 8562 898100

LN-BLH 1102 31 354 106595

LN-LH 5611 2523 14864 552903

TJ-YDXH 75642 1472 14370 398600

ZJ-JJ 19851 386 1864 438543

ZJ-OJ 25105 489 2769 25101

ZJ-QTJ 87380 1701 13815 369287

ZJ-YJ 16329 318 4409 200425

Notes: 188

1. The administrative regions for data collection and estimation of every basin 189

The catchment descriptors were mainly determined by the principle that if only 190

minor part of an administrative region is located in a basin, the data of this region 191

wouldn’t be considered in data collection and estimation for this basin, except that 192

the main cities of this region are located in the basin; if all or major part of an 193

administrative region is located in a basin, the data of this region were collected and 194

estimated for this basin. The following are the detailed regions considered as 195

covering areas of each basin. 196

32

FJ-HTX: Pingnan county and Ningde district in Ningde prefecture of Fujian 197

province. The boundaries of districts in Fujian province were determined in 198

December, 2002, including the districts in the following three basins in Fujian. 199

FJ-JJ: Yongchun county, Anxi county, Nan’an county, Licheng district, and Fengze 200

district in Quanzhou prefecture of Fujian province. 201

FJ-JLJ: Xinluo district and Zhangping county in Longyan prefecture; Hua’an county, 202

Nanjing county, Pinghe county, Changtai county, Longhai county, Xiangcheng 203

district and Longwen district in Zhangzhou prefecture; Xiamen prefecture. The 204

three prefectures are under the jurisdiction of Fujian province. 205

FJ-MJ: Fuzhou prefecture excluding counties of Lianjiang, Luoyuan, Pingtan, and 206

Fuqing; Gutian county in Ningde prefecture; Nanping prefecture; Sanming 207

prefecture; Dehua county in Quanzhou prefecture; Liancheng county in 208

Longyan prefecture. All the prefectures are directly governed by Fujian 209

province. 210

GD-HJ: Changting county, Wuping county, Shanghang county, and Yongding 211

county in Longyan prefecture of Fujian province; Meizhou prefecture excluding 212

Fengshun county; Shantou prefecture excluding Nan’ao county; Chaozhou 213

prefecture excluding Raoping county. Shantou and Chaozhou are prefectures of 214

Guandong province. The boundaries of districts in this basin were determined in 215

December, 2009. 216

GD-LJ: Puning county in Guandong province. 217

GD-ZJ: Qujing prefecture, Yuxi prefecture, and Honghe Hani and Yi Autonomous 218

Prefecture in Yunnan province; Guangxi Zhuang Autonomous Region excluding 219

prefectures of Yulin, Qinzhou, Fangchenggang, and Beihai; Guangzhou, Foshan, 220

Jiangmen, Zhaoqing, Shaoguan, Huizhou, Shenzhen, and Zhuhai prefectures of 221

Guangdong. 222

GX-FCJ: Fangcheng district in Fangchenggang prefecture of Guangxi province. 223

GX-NLJ: Xingye county, Bobai County, and Yuzhou district in Yulin prefecture; 224

Pubei county in Qinzhou prefecture; Hepu county in Beihai prefecture. The 225

three prefectures are located in Guangxi Zhuang Autonomous Region. 226

GX-QJ: Lingshan county, Qinnan District, and Qinbei county in Qinzhou prefecture 227

of Guangxi Zhuang Autonomous Region. 228

HB-LH: Chengde, Qinhuangdao, and Tangshan prefectures in Hebei province. 229

LN-BLH: Wolongquan town in Gaizhou county of Yingkou prefecture; Towns of 230

Guiyunhua, Hehuashan, Mingyang and Chengshan in Zhuanghe county of 231

Dalian prefecture; Towns of Shuangta, Anbo, Mopan, and Chengzitan in 232

Pulandian county of Dalian prefecture. The mentioned prefectures are located in 233

Liaoning province. 234

LN-LH: Siping prefecture of Jilin province; Tongliao and Chifeng prefectures of 235

Inner Mongolia Autonomous Region; Tieling, Shenyang, Fuxin, and Panjin 236

prefectures of Liaoning province. 237

TJ-YDXH: Tianjin municipality. 238

ZJ-JJ: Taizhou prefecture excluding counties of Wenling, Yuhuan, and Sanmen in 239

Zhejiang province. 240

33

ZJ-OJ: Yongjia county, Wenzhou city, and Lishui excluding Suichang county. 241

ZJ-QTJ: Hangzhou prefecture, Quzhou prefecture, Jinhua prefecture, Zhuji county in 242

Shaoxing prefecture, Suichang county in Lishui prefecture in Zhejiang province; 243

Huangshan prefecture in Anhui province. The basin of Xin’an river, one 244

tributary of Qiantangjiang River, covers Keemun county, Yi County, Xiuning 245

county, Tunxi District, She County, Huizhou District, Jixi county in Anhui 246

province, but mainly located in Huangshan prefecture which was regarded as the 247

Anhui part of QTJ basin in this study due to the limited data sources. 248

ZJ-YJ: Yuyao county, Fenghua county, and Ningbo city excluding Beilun District 249

and Xianxiang town. The mentioned areas are located in Ningbo prefecture of 250

Zhejiang province. 251

252

2. General Description of indicators 253

2.1. All data of indicators for each basin are derived from statistical data of the 254

administrative regions where were mainly covered by each basin, if not, the main 255

population or larger cities in administrative regions should be distributed within the 256

basin. 257

2.2. Urban domestic sewage effluent (s) is mainly estimated according to the 258

following equation, otherwise, indicated in the note of “3. The detailed sources and 259

estimation for data in Table 1”. 260

ooSs PP (1) 261

Where So, urban domestic sewage effluent of province or prefecture which 262

administers the administrative regions, see note 1 for Table 1; P, population in the 263

administrative regions in table 1; Po, population in the province or prefecture 264

administering the administrative regions in table 1. 265

2.3. Administrative region population are estimated in according with the annual 266

sample survey on population changes. 267

2.4. GDP data were recompiled in accordance with the uniform plan of NBS and 268

revised by trend approach. 269

2.5. Aquatic production includes aquaculture and capture, but the latter only 270

accounts for a small proportion of total production in most basins. 271

2.6. All the administrative regions used in this study are the latest administrative 272

divisions. 273

274

3. The detailed sources and estimation for data in Table 1 275

The data in Table 1 were sourced from the row named “Total” of Tables 2, 3, 4, 276

5, …, 19, respectively. Table 2 is the data for FJ-HTX basin, Table 3 for FJ-JJ, Table 277

4 for FJ-JLJ, Table 5 for FJ-MJ, Table 6 for GD-HJ, Table 7 for GD-LJ, Table 8 for 278

GD-ZJ, Table 9 for GX-FCJ, Table 10 for GX-NLJ, Table 11 for GX-QJ, Table 12 279

for HB-LH, Table 13 for LN-BLH, Table 14 for LN-LH, Table 15 for ZJ-JJ, Table 16 280

for ZJ-OJ, Table 17 for ZJ-QTJ, Table 18 for ZJ-YJ, Table 19 for TJ-YDXH. 281

282

283

34

284

Table 2 The data collected or estimated for FJ-HTX basin 285

Region

Urban domestic sewage

effluent

×104 t/a

Population

×104 person

GDP

×108 yuan

Aquatic

production

t

Reference

Pingnan

county 13.6a1 52.84b1 2560c1

Fujian

Provincial

Bureau of

Statistics,2014 Ningde city 43.9a2 214.67b2 160517c2

Total 1772s 57.5x 267.51y 163077z

Notes: 286

1. Region mean the administrative regions for data collection and estimation of every 287

basin, the same meaning is for “Region” in Table 3-19. 288

2. “x” is the sum of a1 and a2, “y” is the sum of b1 and b2, “z” is the sum of c1 and 289

c2, the same are the calculation methods of “x”, “y”, and “z” in the row named 290

“Total” of Table 3-6, Table 12, Table 14, Table 17. 291

3. “s” is calculated by Eq.(1), where So is urban domestic sewage effluent of Ningde 292

prefecture; P is population for FJ-HTX basin, i.e. “x” in Table 2; Po is population for 293

Ningde prefecture. So, Po are from Fujian Provincial Bureau of Statistics (2014). 294

295

Table 3 The data collected or estimated for FJ-JJ basin 296

Region


effluent

×104 t/a

Population

×104 person

GDP

×108

yuan

Aquatic

production

t

Reference

Yongchun

county 45.7 262.16 1262

Fujian

Provincial

Bureau of

Statistics,2014

Anxi county 99.5 381.23 1725

Nan’an county 145 709.99 36335

Licheng district 42 320.17 104

Fengze district 54.8 413.36 18152

Total 16446s 387x 2086.91

y 57578z

Notes: 297

1. “s” is calculated by Eq.(1), where So is urban domestic sewage effluent of 298

Quanzhou prefecture; P is population for FJ-JJ basin, i.e. “x” in Table 3; Po is 299

population for Quanzhou prefecture. 300

301

Table 4 The data collected or estimated for FJ-JLJ basin 302

Region


effluent

×104 t/a

Population

×104 person

GDP

×108 yuan

Aquatic

production

t

Reference

Xinluo

district 69.3 568.68 6172

Fujian Provincial

Bureau of

Statistics,2014 Zhangping 24 154.78 9018

35

county

Hua’an

county 16.1 80.8 3180

Zhangzhou

City 76.7 514.48 18595

Nanjing

county 33.7 173.19 13000

Pinghe

county 49.7 140.44 6962

Changtai

county 21.5 149.51 21000

Longhai

county 91.8 520.24 377267

Xiamen

prefecture 373 3018.16 31535

Total 36518s 755.8x 5320.28y 486729z

Notes: 303

1. “s” is calculated by Eq.(2). 304

ozozoxolol SSSs PPPP zl (2) 305

where Sol, Sox, Soz are urban domestic sewage effluents of Longyan, Xiamen, and 306

Zhangzhou prefectures, respectively; Pl is population for Xinluo and Zhangping in 307

FJ-JLJ basin, Pz is population for other regions excluding Xiamen in FJ-JLJ basin; 308

Pol, Poz are populations for Longyan and Zhangzhou prefectures, respectively. 309

310

Table 5 The data collected or estimated for FJ-MJ basin 311

Region


effluent

×104 t/a

Population

×104 person

GDP

×108 yuan

Aquatic

production

t

Reference

Fuzhou City 302.8 2305.16 144485

Fujian

Provincial

Bureau of

Statistics,201

4

Yongtai county 24.7 108.71 9537

Changle

county 70.2 485.06 140880

Minhou county 69.3 377.46 30762

Minqing

county 23.3 117.02 7571

Gutian county 32.6 125.36 18254

Nanping

county 362 1105.82 107909

Sanming

county 251 1477.59 93328

Dehua county 28.3 154.74 1730

Liancheng

county 24.5 123.27 16609

36

Total 41560s 1188.7x 6380.19y 571065z

Notes: 312


ololoqoqosmonpononofof SSSSSSs PPPPPPPP lqnf (3) 314

where Sof, Son, Sonp, Sosm, Soq, Sol are urban domestic sewage effluents of Fuzhou, 315

Ningde, Nanping, Sanming, Quanzhou, and Longyan prefectures, respectively; Pf is 316

population for Fuzhou City, Yongtai, Changle, Minhou, and Minqing; Pn is 317

population for Gutian; Pq is population for Dehua Pl is population for Liancheng; Pof, 318

Pon, Poq, Pol are populations for Fuzhou, Ningde, Quanzhou and Longyan prefectures, 319

respectively. 320

321

Table 6 The data collected or estimated for GD-HJ basin 322

Region


effluent

×104 t/a

Populatio

n

×104

person

GDP

×108

yuan

Aquatic

producti

on

t

Reference

Changting county 39.6a1 141.73 12130 Fujian

Provincial

Bureau of

Statistics,2014

Wuping county 27.5a2 120.63 10858

Shanghang county 37.1a3 202.28 9151

Yongding county 36a4 168.59 5308

Meizhou prefecture

excluding Fengshun

county

382.7 a5 724.39 102900

Meizhou

Municipal

Bureaus of

Statistics, 2014

Shantou prefecture

excluding Nan’ao

county

541.79 a6 1552.57 78300

Shantou

Municipal

Bureaus of

Statistics, 2014

Chaozhou city 50.77 a7 133 0 Chaozhou

Municipal

Bureaus of

Statistics, 2014

Chao’an county 135.5 a8 477.9 188000

Total 34923s 1250.96x 3521.09

y 406647z

Notes: 323

1. “s” is calculated by Eq.(1), where So is urban domestic sewage effluent of basins 324

of HJ and other Eastern rivers in Guangdong province, 6.5×108 t (Pearl River Water 325

Resources Commission, 2013); P is population for GD-HJ basin, i.e. “x” in Table 6; 326

Po is total population for basins of HJ and other Eastern rivers in Guangdong 327

province, the population for other Eastern river basins include Jieyang (6826800 328

person, Jieyang Municipal Bureau of Statistics, 2014), Shanwei (2986200 person, 329

Shanwei Municipal Bureau of Statistics, 2014), Raoping (900000 person, 330

Guangdong Provincial Bureau of Statistics, 2014), and Nan’ao (61000 person, 331

37

Guangdong Provincial Bureau of Statistics, 2014). 332

Table 7 The data collected or estimated for GD-LJ basin 333

Region Urban domestic sewage effluent

×104 t/a

Population

×104 person

GDP

×108 yuan

Aquatic production

t

Total 5821s 208.5x 500.17y 9848z

Notes: 334

1. “s” is calculated by Eq.(1), where So is urban domestic sewage effluent of basins 335

of HJ and other Eastern rivers in Guangdong province, 6.5×108 t (Pearl River Water 336

Resources Commission, 2013); P is population for GD-HJ basin, i.e. “x” in Table 7; 337

Po is total population for basins of HJ and other Eastern rivers in Guangdong 338

province, the population for other Eastern river basins include Jieyang (6826800 339

person, Jieyang Municipal Bureau of Statistics, 2014), Shanwei (2986200 person, 340

Shanwei Municipal Bureau of Statistics, 2014), Raoping (900000 person, 341

Guangdong Provincial Bureau of Statistics, 2014), and Nan’ao (61000 person, 342

Guangdong Provincial Bureau of Statistics, 2014). 343

2. x, z is calculated by Eq.(4). 344

)/)5/)((1( 20122007201220122013 PPPPP (4) 345

Where P2013 is x or z in 2013; P2012 is x or z in 2012; P2007 is x or z in 2007. 346

P2012, P2007 are sourced from Jieyang Municipal Bureau of Statistics (2013). 347

Thereinto, Population data and Aquatic production are from Jieyang Municipal 348

Bureau of Statistics (2013). 349

3. y is from Jieyang Municipal Bureau of Statistics (2014).. 350

351

Table 8 The data collected or estimated for GD-ZJ basin 352

GD-

ZJ


effluent

×104 t/a

Populatio

n

×104

person

GDP

×108

yuan

Aquatic

production

t

References

Total 345591 11571.42 70812.

24 4603256

Prefectures’ Municipal Bureau of

Statistics

Note: 353

1. The data are collected and calculated/estimated by the following administrative 354

regions in the basin: Qujing prefecture, Yuxi prefecture, and Honghe Hani and Yi 355

Autonomous Prefecture in Yunnan province; Guangxi Zhuang Autonomous Region 356

excluding prefectures of Yulin, Qinzhou, Fangchenggang, and Beihai; Guangzhou, 357

Foshan, Jiangmen, Zhaoqing, Shaoguan, Huizhou, Shenzhen, Zhuhai, Zhongshan, 358

Dongguan, Heyuan, and Qingyuan prefectures of Guangdong. The regions are all 359

prefectures, data sourced from each prefecture’s Municipal Bureau of Statistics 360

(2014). 361

2. Estimation of Beihai aquatic production: Assumed that the increase rate of aquatic 362

production is the same as fisheries output from 2012 to 2013, i.e. 5.22% (Beihai 363

38

Municipal Bureau of Statistics, 2014), then, according to aquatic production in 2012, 364

i.e. 980000 t (Beihai Municipal Bureau of Statistics, 2013), aquatic production in 365

2013 should be 1031156 t. 366

3. Aquatic production in Jiangmen is sourced from Southern Metropolis Daily 367

(2014). 368

369

Table 9 The data collected or estimated for GX-FCJ basin 370

Region


effluent

×104 t/a

Population

×104

person

GDP

×108

yuan

Aquatic

production

t

Fangcheng

county 1409s 37x 99y 130301z

Notes: 371

1. Fangcheng county refers to GX-FCJ basin here. 372

2 “s” is calculated by Eq.(1), where So is urban domestic sewage effluent of Guangxi 373

Zhuang Autonomous Region (Water Resource Department of Guangxi Zhuang 374

Autonomous Region, 2014); P is population for GX-FCJ basin, i.e. “x” in Table 9; Po 375

is population for Guangxi Zhuang Autonomous Region (Guangxi Zhuang 376

Autonomous Region Bureau of Statistics, NBS Survey Office in Guangxi, 2014b). 377

3. “x” is calculated by Eq.(5). 378

)1(2012 RPx f (5) 379

Where x is population of Fangcheng county in 2013; Pf2012 is population of 380

Fangcheng county in 2012, i.e. 370700 persons (Guangxi Zhuang Autonomous 381

Region Bureau of Statistics, 2014a); R is 0.789%, the increase rate of population for 382

Guangxi Zhuang Autonomous Region from 2011 to 2012 (Guangxi Zhuang 383

Autonomous Region Bureau of Statistics, 2014a), which is assumed to be the 384

increase rate of population for Fangcheng county from 2012 to 2013. 385

4. “y” is sourced from Fangchenggang Municipal Bureau of Statistics (2014a). 386

5. “z” is calculated by Eq.(6). 387

VApz 2012 (6) 388

Where z is aquatic production of Fangcheng county in 2013; Ap2012 is aquatic 389

production of Fangcheng county in 2012, i.e. 124571 t (Guangxi Zhuang 390

Autonomous Region Bureau of Statistics, 2014a); V is the annual increase rate of 391

aquatic production in Fangchenggang prefecture from 2012 to 2013, i.e. 4.6% 392

(Fangchenggang Municipal Bureau of Statistics, 2014b), which is assumed to be the 393

rate in Fangcheng county from 2012 to 2013. 394

395

Table 10 The data collected or estimated for GX-NLJ basin 396

Region

Urban

domestic

sewage

effluent

Populatio

n

×104

person

GDP

×108

yuan

Aquatic

production

t

Reference

39

×104 t/a

Counties of Xingye,

Bobai, Yuzhou in

2011

261 502 53303 Yulin Municipal Bureau of

Statistics, 2014a Yulin prefecture in

2011 554 1020 118003

R 0.47 0.49 0.45

Yulin prefecture in

2013 562 1198 138100

Yulin Municipal Bureau of

Statistics, 2014b

Counties of Xingye,

Bobai, Yuzhou in

2013

264 590 62381

Pubei county 92 115 28981 Guangxi Zhuang

Autonomous Region Bureau

of Statistics, 2014a Hepu county 106 165 390600

Total 17417s 462x 870y 481962z

Notes: 397


Guangxi Zhuang Autonomous Region (Water Resource Department of Guangxi 399

Zhuang Autonomous Region, 2014); P is population for GX-NLJ basin, i.e. “x” in 400

Table 10; Po is population for Guangxi Zhuang Autonomous Region (Guangxi 401

Zhuang Autonomous Region Bureau of Statistics, NBS Survey Office in Guangxi, 402

2014b). 403

2. Population, GDP, and aquatic production for GX-NLJ basin can be calculated by 404

Eq.(7), assuming that the rate of N2013XBY to N2013YL is the same as the rate of 405

N2011XBY to N2011YL . 406

HPPBYLXBYYL NNNNNN 201120112013 / (7) 407

Where N is x, y, or z for GX-NLJ basin in 2013; N2013YL is x, y, or z for Yulin 408

prefecture in 2013; N2011XBY is x, y, or z for Xinye, Bobai, Yuzhou counties in 2011; 409

N2011YL is x, y, or z for Yulin prefecture in 2011; NPB is x, y, or z for Pubei county in 410

2013; NHP is x, y, or z for Hepu county in 2013. 411

412

Table 11 The data collected or estimated for GX-QJ basin 413

Region

Urban

domestic

sewage

effluent

×104 t/a

Population

×104 person

GDP

×108 yuan

Aquatic

production

t

Reference

Qinzhou prefecture in

2012 313.33 724.48 479885

Qinzhou Municipal

Bureau of Statistics,

2013

Pubei county in 2012 91.65 114.99 28981

Counties of Lingshan,

Qinnan, Qinbei in 2012 221.68 609.49 450904

40

R 0.71 0.84 0.94

Qinzhou prefecture in

2013 315.92 753.74 506300

Qinzhou Municipal


2014

Total 8431s 223.51x 634.11y 475724z

Notes: 414


Guangxi Zhuang Autonomous Region (Water Resource Department of Guangxi 416

Zhuang Autonomous Region, 2014); P is population for GX-QJ basin, i.e. “x” in 417

Table 11; Po is population for Guangxi Zhuang Autonomous Region (Guangxi 418

Zhuang Autonomous Region Bureau of Statistics, NBS Survey Office in Guangxi, 419

2014b). 420

2. Population, GDP, and aquatic production for GX-QJ basin can be calculated by 421

Eq.(8), assuming that the rate of NLNB to NQ is the same between 2012 and 2013. 422

RNN Q 2013 (8) 423

Where N is population, GDP, and Aquatic production for GX-QJ basin in 2013; 424

N2013Q is population, GDP, and Aquatic production for Qinzhou prefecture in 2013; R 425

is calculated by Eq.(9). 426

QPBQ NNNR /)( (9) 427

Where NQ is population, GDP, or aquatic production for Qinzhou prefecture in 2012; 428

NPB is population, GDP, or aquatic production for Pubei county in 2012. 429

430

Table 12 The data collected or estimated for HB-LH basin 431

Region

Urban domestic

sewage effluent

×104 t/a

Populati

on

×104

person

GDP

×108

yuan

Aquati

c

product

ion

t

Reference

Chengde

prefecture 378.15

1272.0

9 40000

Chengde Municipal Bureau of

Statistics, 2014

Qinhuangdao

prefecture 304.52

1168.7

5 320100

Qinhuangdao Municipal Bureau of

Statistics, 2014

Tangshan

prefecture 747.4

6121.2

1 538000

Tangshan Municipal Bureau of

Statistics, 2014

Total 11982s 1430.07

x

8562.0

5y

898100z

Notes: 432

1. Assumed that the rate of urban domestic sewage effluent to total wastewater 433

discharge of Chengde, Qinhuangdao, and Tangshan in 2013 is the same as the rate of 434

urban domestic sewage effluent to total wastewater discharge of Haihe basin in 2012, 435

i.e. 15.63/79.1=19.76% (Haihe River Water Resources Commission, 2013; Ministry 436

of Environmental Protection of China, 2013), “s” is calculated by Eq.10). 437

41

HbHb PxSs /%76.19 (10) 438

Where s is urban domestic sewage effluent of HB-LH basin in 2013; SHb is total 439

wastewater discharge of Hebei province in 2013, i.e. 3109205400 t (Hebei 440

Environmental Protection Agency, 2014); x is population of HB-LH basin, i.e. 441

1430.07×104 person in Table 12; PHb is is population of Hebei province, i.e. 442

7332.61×104 person (Hebei Provincial Bureau of Statistics, 2014). 443

2. Population for Chengde or Tangshan is Household registered population. 444

445

Table 13 The data collected or estimated for LN-BLH basin 446

Region

Urban

domestic

sewage

effluent

×104 t/a

Population

10,000 person

GDP

×108

yuan

Aquatic production

t

Total 1101.93s 30.94x 353.81y 106594.73z

Notes: 447

1. Population was estimated on towns including Wolongquan, Shuangta, Mingyang, 448

Guiyunhua, Chengshan, Hehuashan, Anbo, Mopan, Chengzitan; the data was 449

sourced from http://www.baike.com/wiki/ 卧龙泉镇 , 450

http://www.dlpld.gov.cn/gov/zwgk/158541_180199.htm, 451

http://baike.baidu.com/link?url=zH2tvj45rgcdbVELU8P-IG891-SGulS51E6Pc7jR0c452

jc-i14PZGOCdNfRQZtGhazJ7MO50-CVfKo0Ensi1AGsa#5, 453

http://www.dlzh.gov.cn/zhhhsz/zhhhsz/Info/123602_132003.htm, 454

http://www.dlpld.gov.cn/gov/zwgk/158541_180220.htm, 455

http://www.dlpld.gov.cn/gov/zwgk/158541_180181.htm?d_id=157331, and 456

http://www.hilizi.com/rollingnews/2013-12/31/content_557648.htm, respectively. 457

2. x is calculated by Eq.(11), assumed that annual increasing rate of population for 458

each towns in LN-BLH basin from 2004 to 2013 is the same as that for Dalian from 459

2000 to 2010, i.e. 0.0128 (Dalian Municipal Bureau of Statistics, 2011). 460

9

1

2013 0)0128.01(i

tiPx (11) 461

Where Pi is population of each town in LN-BLH basin in reference year; t0 is 462

reference year of each town in LN-BLH basin. 463

3. y or z is calculated by Eq.(12), assumed that per capita GDP and per capita aquatic 464

production of each town in LN-BLH basin is the same as that of Dalian. 465

RPN i (12) 466

Where N is y or z; Pi is GDP or aquatic production of Dalian prefecture in 2013 467

42

(Dalian Municipal Bureau of Statistics, 2014); R is the population rate of LN-BLH 468

basin to Dalian prefecture, i.e. 5.8731% here.. 469


prefecture; P is population for basin, i.e. “x” in Table 13; Po is population for 471

prefecture. 472

dudd UUSRs / (13) 473

Where R can be seen in note 2 of Table 13; Sd is total domestic sewage effluent of 474

Dalian prefecture in 2012 (Dalian Municipal Environmental Protection Bureau, 475

2013); Uud, Ud are urban domestic water use of Dalian, domestic water use of Dalian 476

(Dalian Municipal Water Company, 2012). 477

478

Table 14 The data collected or estimated for LN-LH basin 479

Region

Urban

domestic

sewage

effluent

×104 t/a

Populatio

n

×104

person

GDP

×108 yuan

Aquatic

production

t

Reference

Siping prefecture 328.4 1210.1 6000 Siping Municipal Bureau of

Statistics, 2014

Tongliao prefecture 312.91 1811.82 0 Tongliao Municipal Bureau of

Statistics, 2014

Chifeng prefecture 430.62 1686.15 0 Chifeng Municipal Bureau of

Statistics, 2014

Tieling prefecture 301.9 1031.3 20000 Tieling Municipal Bureau of

Statistics, 2014

Shenyang prefecture 825.7 7158.6 191000 Shenyang Municipal Bureau of

Statistics, 2014

Fuxin prefecture 179.6909 615.1 7903 Fuxin Municipal Bureau of

Statistics, 2014

Panjin prefecture 143.6546 1351.1 328000 Panjin Municipal Bureau of

Statistics, 2014

Total 5611s 2522.88x 14864.17y 552903z

Notes: 480

1. Assumed that the rate of urban domestic sewage effluent to total wastewater 481

discharge of LN-LH basin in 2013 is the rate of domestic water use to total water use 482

of the basin, i.e. 3.1% (Songliao Water Resources Commission, 2013), based on total 483

wastewater discharge of LN-LH basin in 2013, i.e. 18.1×108 t (Ministry of 484

Environmental Protection of China, 2013), urban domestic sewage effluent of 485

LN-LH basin in 2013 should be 5611×104 t. 486

487

Table 15 The data collected or estimated for ZJ-JJ basin 488

Region Urban domestic sewage

effluent

Populatio

n

GDP

×108 yuan

Aquatic

production Reference

43

×104 t/a ×104

person

t

Taizhou prefecture 594.04 3153.34 1437533 Zhejiang

Provincial

Bureau of

Statistics, 2014

Wenling county 121.05 748.28 533193

Yuhuan county 42.81 400.47 250317

Sanmen county 43.83 140.42 215480

Total 19851s 386.35x 1864.17y 438543z

Notes: 489

1. x, y, or z is calculated by Eq.(14). 490

sYT NNNNNW

(14) 491

Where N is x, y, or z; NT is population, GDP, or aquatic production of Taizhou; NW is 492

population, GDP, or aquatic production of Wenling; NY is population, GDP, or 493

aquatic production of Yuhuan; NS is population, GDP, or aquatic production of 494

Sanmen. 495


)/(0 HZi PPPSs (15) 497

where So is urban domestic sewage effluent of Zhejiang province and Huangshan 498

prefecture, assumed to be urban domestic and other sewage effluent of Zhejiang 499

province, i.e. 254972×104 t (Zhejiang Provincial Bureau of Statistics, 2014); Pi is 500

population for a basin, i.e. “x” in Table 15-18; PZ is population for Zhejiang province, 501

i.e. 4826.89×104 persons (Zhejiang Provincial Bureau of Statistics, 2014); PH is 502

population for Huangshan prefecture, i.e. 135.6×104 persons (Huangshan Municipal 503

Bureau of Statistics, 2014). 504

505

Table 16 The data collected or estimated for ZJ-OJ basin 506

Region


effluent

×104 t/a

Populatio

n

×104

person

GDP

×108 yuan

Aquatic

production

t

Reference

Lishui prefecture 263.92 983.08 18414 Zhejiang

Provincial

Bureau of

Statistics, 2014

Wenzhou city 151 1578.29 4784

Yongjia county 96.95 291.91 3181

Suichang county 23.25 84.54 1278

Total 25105s 488.62x 2768.74y 25101z

Notes: 507


2. “x, y, or z” is calculated by Eq.(16). 509

SYWL NNNNN (16) 510

Where N is x, y, or z in Table 16, i.e. population, GDP, and aquatic production of 511

ZJ-OJ basin in 2013; NL is population, GDP, and aquatic production of Lishui 512

prefecture in 2013; NW is population, GDP, and aquatic production of Wenzhou city 513

44

in 2013; NY is population, GDP, and aquatic production of Yongjia county in 2013; 514

NS is population, GDP, and aquatic production of Suichang county in 2013. 515

Table 17 The data collected or estimated for ZJ-QTJ basin 516

Region

Urban domestic

sewage effluent

×104 t/a

Populatio

n

×104

person

GDP

×108 yuan

Aquatic

production

t

Reference

Huangshan

prefecture 135.6 470.3 17000

Huangshan Municipal


2014

Hangzhou

prefecture 706.61 8343.52 197992

Zhejiang Provincial


2014

Quzhou prefecture 254.21 1056.57 56844

Jinhua prefecture 473.35 2958.78 77813

Zhuji county 107.65 900.88 18360

Suichang county 23.25 84.54 1278

Total 87380s 1700.67x 13814.59y 369287z

Notes: 517


519

Table 18 The data collected or estimated for ZJ-YJ basin 520

Region

Urban

domestic

sewage

effluent

×104 t/a

Populatio

n

×104

person

GDP

×108 yuan

Aquatic

productio

n

t

Reference

Yuyao

county 83.51 749.63 27189

Zhejiang Provincial Bureau of

Statistics, 2014 Fenghua

county 48.37 290.36 143341

Ningbo city 227.59 4309.46 43517

Beilun

district 38.60 924.63 3566.56c1

Beilun District Bureau of

Statistics, 2014

Xianxiang

town 3.077a1 16.1b1

10055.23c

2

Total 16329s 317.79x 4408.72y 200425.2z

Notes: 521


2. “x, y, or z” is calculated by Eq.(17). 523

XBNFY NNNNNN (17) 524

Where N is x, y, or z in Table 18; NY is population, GDP, and aquatic production of 525

Yuyao county in 2013; NF is population, GDP, and aquatic production of Fenghua 526

county in 2013; NN is population, GDP, and aquatic production of Ningbo city in 527

45

2013; NB is population, GDP, and aquatic production of Beilun district in 2013; NX is 528

population, GDP, and aquatic production of Xianxiang town in 2013. 529

3. c1, c2 can be calculated by Eq.(18), assumed that the aquatic production rate of 530

Beilun district or Xianxiang town to Ningbo city is the same as the fisheries output 531

rate of Beilun district or Xianxiang town to Ningbo city. 532

FFPN i / (18) 533

Where N is c1 or c2 in Table18; P is aquatic production of Ningbo city, i.e. 43517t; 534

Fi is fisheries output of Beilun district or Xianxiang town, i.e. 7200i104 yuan (Beilun 535

District Bureau of Statistics, 2014), 20299×104 yuan (Agriculture Office of 536

Xianxiang town, 2014), respectively; F is fisheries output of Ningbo city, i.e. 537

87849×104 yuan (Zhejiang Provincial Bureau of Statistics, 2014). 538

4. a1 is calculated by Eq.(19), based on population of Xianxiang town in 2010, 539

assumed that its population growth rate is the same as the natural population growth 540

rate of Beilun District, 0.229% (Beilun District Bureau of Statistics, 2014). 541

201020130 )00229.01( PN (19) 542

Where N is a1; P0 is population of Xianxiang town in 2010, i.e. 30564 persons 543

(Office of Xianxiang People’s Government, 2011). 544

5. b1 is sourced from Office of Yinzhou People’s Government (2014). 545

546

Table 19 The data collected or estimated for TJ-YDXH basin 547

Regi

on


effluent

×104 t/a

Populatio

n

×104

person

GDP

×108

yuan

Aquatic

producti

on

t

Reference

Total 75642 1472 14370 398600 Tianjin Bureau of

Statistics, 2014

Notes: 548

1. “s” is sourced from Tianjin Water Authority (2013), other data from Tianjin 549

Bureau of Statistics (2014). 550

551

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48

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49

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China Book Press.2014.2.17. 735

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Yulin prefecture in 2013.2014.4.26. 737

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50

Yulin Municipal Bureau of Statistics. Economic and social development bulletin of Yulin 739

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Yunnan Provincial Bureau of Statistics. Economic and social development bulletin of 742

Yunnan province in 2013. 2014. 4.30. 743

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prefucture in 2013. 2014.10.5. http://www.tjcn.org/plus/view.php?aid=27739. 746

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Zhaoqing prefecture in 2013. 2014.10.4. 748


Zhejiang Provincial Bureau of Statistics. Zhejiang Provincial Statistical Yearbook 2014. 750

China Statistics Press and Beijing Datacom Electronic Press. 2014. 751

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2014/indexch.htm 753

Zhongshan Municipal Bureau of Statistics. Economic and social development bulletin of 754

Zhongshan prefecture in 2013. 2014.10.4. 755

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Zhuhai Municipal Bureau of Statistics, NBS Survey Office in Zhuhai. Economic and social 757

development bulletin of Zhuhai prefecture in 2013. 2014.3.28. 758

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760

Explanation of urban ratio, meat production 761

Table 20 Social economic data collected or estimated by the administrative regions 762

in every basin 763

Basin Code 5.Urban

ratio (%)

6.Meat

production

/ton

References

FJ-HTX A 56.70 32392.00

Fujian Provincial Bureau of Statistics (2014) FJ-JJ B 61.80 121338.00

FJ-JLJ C 74.30 398486.00

FJ-MJ D 58.50 822660.00

GD-HJ E 76.50 761277.00

Fujian Provincial Bureau of Statistics (2014);

Guangdong Provincial Bureau of Statistics

(2014)

GD-LJ F 28.91 45166.00

Jieyang Municipal Bureau of Statistics, 2013;

http://www.puning.gov.cn/html/zoujinpuning/201

1/1023/1053.html; Shanwei Municipal Bureau of

Statistics, 2014

GD-ZJ G 57 .00 3424041.00

National Bureau of Statistics of PRC, 2013;

PRWRC, 2013; Guangdong Provincial Bureau of

Statistics, 2013

51

GX-FCJ H 46.00 24000.00 Fangchenggang Municipal Bureau of Statistics

(2012)

GX-NLJ I 14.73 576943.56

Beihai Municipal Bureau of Statistics, 2014; Yulin

Municipal Bureau of Statistics, 2014; Qinzhou

Municipal Bureau of Statistics, 2014

GX-QJ J 46.02 246881.89 Qinzhou Municipal Bureau of Statistics, 2013;

Qinzhou Municipal Bureau of Statistics, 2014

HB-LH K 43.66 1510000.00

Chengde Municipal Bureau of Statistics,

2014;Qinhuangdao Municipal Bureau of Statistics,

2014; Tangshan Municipal Bureau of Statistics,

2014

LN-BLH L 55.45 37735.92 Dalian Municipal Bureau of Statistics, 2013; 2014;

Liaoning Statistical Information Network

LN-LH M 66.00 1476739.00 Liaoning Provincial Bureau of Statistics, 2014;

Songliao Water Resources Commission, 2013

TJ-YDXH N 64.00 1245500.00

Tianjin Municipal Bureau of Statistics, 2014;

Zhangjiakou Municipal Bureau of Statistics, 2014;

Beijing Municipal Bureau of Statistics, 2014

ZJ-JJ O 16.39 80402.00

Zhejiang Provincial Bureau of Statistics, 2014 ZJ-OJ P 24.64 129864.00

ZJ-QTJ Q 37.60 1065256.00

ZJ-YJ R 62.06 108762.23

Notes: Urban ratio is the ratio of urban population and total population, see Eq. (20), 764

unless otherwise stated; Meat production is the sum of pork, beef, poultry, mutton, 765

and rabbit meat productions, see Eq. (21), unless otherwise stated. The data in this 766

table were collected or estimated on the administrative regions covered by each 767

basin, which can be seen in the notes of Table 1 in this file. 768

100 ( / )ub ui tiR P P (20) 769

Where Rub is urban ratio in a basin; Pui is population of urban permanent residents in 770

the “i” administrative region; Pti is total population of permanent residents in the “i” 771

administrative region. The administrative regions covered each basin can be seen in 772

the notes of Table 1 in this file. 773

b i i i i iMP PK BF PL MT RB (21) 774

Where MPb is meat production in a basin; PKi, BFi, PLi, MTi, and RBi are 775

respectively the production of pork, beef, poultry, mutton, and rabbit meat in the “i” 776

administrative region in a basin. The administrative regions covered each basin can 777

be seen in the notes of Table 1 in this file. 778

779

Urban ratio and meat production of basins in Fujian, including FJ-HTX, FJ-JJ, 780

FJ-JLJ, FJ-MJ, were calculated on the data sourced from Fujian Provincial Bureau of 781

Statistics (2014). 782

783

52

F5 is based on non-agricultural population and total population in 2012, i.e. 685900 784

persons/2372800 persons= 28.91%, sourced from Jieyang Statistical Yearbook 785

(Jieyang Municipal Bureau of Statistics, 2013)； 786

F6 is based on 2012 year-end data, sourced from Jieyang Statistical Yearbook 787

(Jieyang Municipal Bureau of Statistics, 2013). 788

789

G5 is calculated by Eq. (22). 790

100 (( ) / ( )) /ub b rural urban rural bR WL P WL WL WL P (22) 791

Where Rub is urban ratio in the basin GD-ZJ; WL is total domestic water use in the 792

basin; Pb is total population in the basin; WLrural is per capita rural domestic water 793

use in the basin; WLurban is per capita urban domestic water use in the basin. WL, per 794

capita water use, total water use, WLrural, WLurban are all sourced from PRWRC 795

(2013). 796

G6 is the product of per capita pork, beef and mutton production in Guangdong 797

province (National Bureau of Statistics of PRC, 2013). 798

799

800

H5 and H6 are based on variable data of full Fangcheng prefecture in 2011. H5 is 801

calculated by Eq. (20), and Pui is calculated on urban retail sales of consumer 802

goods and per capita consumption expenditure of urban residents, sourced from 803

Fangchenggang Municipal Bureau of Statistics (2012). 804

805

J5 and J6 are respectively calculated by Eq.(20) and Eq.(21), but the variables are 806

calculated by Eq. (23). 807

2013 2012 2012( / )bi I I I (23) 808

Where i is the variables in the basin GX-QJ in 2013; I2013 is the variables in Qinzhou 809

prefecture in 2013 (Qinzhou Municipal Bureau of Statistics, 2014); Ib2012 is the 810

variables the basin GX-QJ in 2012; I2012 is the variables in Qinzhou prefecture in 811

2012. Ib2012 and I2012 are sourced from Qinzhou Municipal Bureau of Statistics 812

(2013). 813

814

K5 is calculated by Eq.(20), but urban population in Chengde prefecture are based 815

on urban ratio on 2012 and household registered population in 2013, urban 816

population in Tangshan prefecture is municipal population, urban population in 817

Qinhuangdao prefecture is based on urban ratio in Qinhuangdao prefecture in 2013 818

(50.81%, sourced from http://qhd.focus.cn/news/2014-03-20/4839560.html). 819

820

L5 is the product of non-agricultural population in Dalian prefecture in 2012 821

(3710000 persons, Dalian Municipal Bureau of Statistics, 2013) and the rate of total 822

population in the basin LN-BLH (309400 persons) and in Dalian prefecture 823

(6690432 persons) (Liaoning Statistical Information Network). 824

L6 is the product of meat production in Dalian prefecture (Dalian Municipal Bureau 825

53

of Statistics, 2014) and the rate of total population in the basin LN-BLH (309400 826

persons) and in Dalian prefecture (6690432 persons) (Liaoning Statistical 827

Information Network). 828

829

M5 is assumed as the urban ratio in Liaoning province. 830

M6 is based on the assumption that the proportion of meat production in the basin 831

LN-LH and that in Liaoning province is the proportion of total population in the 832

basin and that in the province. 833

834

References for Table 20: 835

Beihai Municipal Bureau of Statistics. Economic performance analysis of Beihai prefecture in 836

2013. 2014.2.18. 837

http://www.gxzf.gov.cn/zjgx/gxbbw/bbwbd/201402/t20140218_428866.htm. 838

Beijing Municipal Bureau of Statistics. Economic and social development bulletin of Beijing 839

municipality in 2013. 2014.3.11. http://www.sei.gov.cn/ShowArticle.asp?ArticleID=238503 840

Chengde Municipal Bureau of Statistics. Economic and social development bulletin of Chengde 841



prefecture in 2012. 2013.3.11. http://www.tjcn.org/tjgb/201303/26592.html 844



Fangcheng District Bureau of Statistics. Economic and social development bulletin of Fangcheng 847

District in 2011. 2012.9.13. http://www.tjcn.org/plus/view.php?aid=25934 848

Guangdong Provincial Bureau of Statistics, 2013. Guangdong Statistical Yearbook 2013. 849

http://gdidd.jnu.edu.cn/doc/gdtjnj/gdtjnj/2013/index.htm 850

Jieyang Municipal Bureau of Statistics, 2013. Jieyang Statistical Yearbook 2013. 851

http://www.gdjystats.gov.cn/Article/ShowInfo.asp?ID=6454 852

Liaoning Provincial Bureau of Statistics. Economic and social development bulletin of Liaoning 853

province in 2013. 2014.2.24. 854

http://www.ln.gov.cn/zfxx/tjgb2/ln/201402/t20140224_1274182.html. 855

Liaoning Statistical Information Network. The sixth census data. 856

http://www.ln.stats.gov.cn/infopub25/PubTemplet/%7B538116AD-AF2A-4978-B786-5A2857

A1621E91C%7D.asp?infoid=3320&style={538116AD-AF2A-4978-B786-5A2A1621E91C858

} 859

National Bureau of Statistics of PRC, 2013. China Statistical Yearbook 2013. 2013. Beijing: 860

China Statistics Press. http://www.stats.gov.cn/tjsj/ndsj/ 861

PRWRC (Pearl River Water Resources Commission of the Ministry of Water Resources), 2013. 862

Pearl River Water Resources Bulletin 2012. Pearl River Water Conservancy Network 863

http://www.pearlwater.gov.cn/xxcx/szygg/12gb/t20140616_59132.htm 864

Qinhuangdao Municipal Bureau of Statistics. Economic and social development bulletin of 865

Qinhuangdao prefecture in 2013. 2014.5.23. http://www.tjcn.org/plus/view.php?aid=27461. 866

Qinzhou Municipal Bureau of Statistics. Economic and social development bulletin of Qinzhou 867

prefecture in 2012. 2013.5.13. 868

54

http://www.qinzhou.gov.cn/zwgk/tjxx/tjgb/2014/02/27/09265632900.html 869

Qinzhou Municipal Bureau of Statistics. Economic and social development bulletin of Qinzhou 870

prefecture in 2013. 2014.6.18. 871


Shanwei Municipal Bureau of Statistics, 2014. Shanwei Statistical Yearbook 2014. 873

http://www.swtjj.gov.cn/2014/2.htm 874

Songliao Water Resources Commission. Songliao basin Water resources bulletin 2012. 2013. 875

http://www.slwr.gov.cn/szy2011/201312/P020131230167329834536.pdf 876

Tangshan Municipal Bureau of Statistics. Economic and social development bulletin of Tangshan 877

prefecture in 2013. 2014.9.21. http://www.tjcn.org/tjgb/201409/27460_2.html. 878

Tianjin Municipal Bureau of Statistics. Economic and social development bulletin of Tianjin 879

municipality in 2013. 2014.9.21. http://www.tjcn.org/plus/view.php?aid=27457. 880

Yulin Municipal Bureau of Statistics. Economic and social development bulletin of Yulin 881

prefecture in 2013. 2014.5.26. 882


Zhangjiakou Municipal Bureau of Statistics. Economic and social development bulletin of 884

Zhangjiakou prefecture in 2013. 2014.9.21. http://www.tjcn.org/plus/view.php?aid=27465 885

Zhejiang Provincial Bureau of Statistics. Zhejiang Provincial Statistical Yearbook 2014. China 886

Statistics Press and Beijing Datacom Electronic Press. 2014. 887

http://www.zj.stats.gov.cn/tjsj/tjnj/DesktopModules/Reports/11. 浙江统计年鉴888

2014/indexch.htm 889

890

Explanation of pork production, pig marketing, total wastewater, number of patient 891

diagnosed and treated, number of residential patient 892

The data of pork production, pig marketing, total wastewater, number of patient 893

diagnosed and treated, and number of residential patient refer to the corresponding 894

values in the province where each basin is located. 895

896

Pork production, pig marketing, and total wastewater for each basin is sourced from 897

National Bureau of Statistics of China (2014). 898

899

Number of patient diagnosed and/or treated and number of residential patient are 900

sourced from National Health and Family Planning Commission of PRC and 901

Statistical Information Center (2013). 902

903

Number of patient diagnosed and treated: refers to the number of total times that all 904

patients were diagnosed and/or treated in 2012. Statistics principles: 1) diagnosed 905

and/or treated times by all registered patients, including out-patient, emergency room 906

visits, clinic appointment, individual health check, health counseling (excluding 907

health talks). If the patients were registered one time but treated for several times, all 908

times are considered as number of patient diagnosed and treated, excluding 909

inspection, treatment, disposal, immunization, and health management services 910

55

according to the doctors’ advice; 2) number of patient diagnosed and treated will be 911

calculated on actual treatments under the following situations, i.e. that patients were 912

treated without registration, the treatment for employees of medical systems, doctors’ 913

outside visits with no registration fees (excluding out consultation). 914

915

Number of residential patients includes residential patients in university hospitals 916

MCHs, hospitals for prevention and treatment of special diseases in 2012. 917

918

References 919

National Bureau of Statistics of China, 2014. China Statistical Yearbook. Beijing: 920

China Statistics Press. http://www.stats.gov.cn/tjsj/ndsj/2014/indexch.htm. 921

National Health and Family Planning Commission of PRC, Statistical Information 922

Center, 2013. China Statistical Yearbook of Health and Family Planning 2013. 923

924

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