35762-76-6 · web view[22] k.w. thomulka, d.j. mcgee, j.h. lange, use of the bioluminescent...

Supporting Information

Characterization of pharmaceutically active compounds in Beijing,

China: Occurrence pattern, spatiotemporal distribution and its

environmental implication

Ruixue Ma a, Bin Wang a *, Lina Yin a,b, Yizhe Zhang a, Shubo Deng a, Jun Huang a, Yujue Wang a, Gang Yu a

a Beijing Key Laboratory of Emerging Organic Contaminants Control, State Key Joint Laboratory of Environmental Simulation and Pollution Control, Collaborative Innovation Center for Regional Environmental Quality, School of Environment, Tsinghua University，Beijing, 100084, China

bSchool of Environmental and Municipal Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China

* Corresponding author： Tel: +86-10-62795315; Fax: +86-10-62794006; E-mail: [email protected]

Supporting Information includes the following:

S1 Information on study area and Sampling strategy

S2 Sample preparation and analysis

Table S1 Summary of characteristics and analytical conditions for target analytes

Table S2 Information of sampling sites

Table S3 Analysis condition and method validation

Table S4 Toxicity data of analytes

Table S5 Comparison of occurrence data among areas

Table S6 Hazard quotient of target compounds in rivers of Beijing in 2013 and 2015

Table S7 Detection frequency and HQs of antibiotics in two seasons in 2015

Table S8 Statistical analysis of seasonal variation in 2015

S1 Information on study area and sampling strategy

The sampling strategy was to select 34 representative sites along the key

tributaries to the main stream Beiyun Rive in flood season and dry season,

respectively. The four tributaries (Qing River, Ba River, Tonghui River and Liangshui

River) located from northwest to southeast cover the most populated area of Beijing,

representing 70% of Beijing’s population. The Beiyun River basin is the sole receiving

water system for treated and untreated wastewater in Beijing with hundreds of

sewage drain outlets along the rivers, taking over 90% drainage tasks. The Qing River,

Ba River and Tonghui River are predominantly influenced by urban inputs, whereas

the Liangshui River is mainly accepted for suburban inputs. Qing River and Ba River

contributed not only most of the discharged pollutants loads ，but also 70% of the

water flow from the effluent of municipal WWTPs. Therefore, PPCP pollution and

environmental load in these watershed could be probably massive.

The sampling strategy was considered for the representativeness and operability.

The sites were selected at large catchment area from upstream to downstream. Since

Beiyun River and its tributaries were drainage rivers with WWTPs effluent and

discharged sewage as their main water sources, the flux was almost steady except for

rainfall incidents. Sampling intervals were decided based on the precipitation. The

period from June to September is the typical high water season of Beijing, accounting

for over 80% of the annual amount, and from November to April is the typical dry

season. For the uneven distribution of raining, the precipitation in July accounted

nearly 40% of the total annual amount, moreover, the heaviest rainfalls occur in July

in recent years. The average water temperature in November was 5.6 ℃, much

lower than 27.4 ℃ in July, this could possibly influence the PhACs occurrence in

these two seasons. Thus, two sampling campaigns were conducted in 2015 in June

and November, respectively, to investigate the spatiotemporal variation of PhACs in

the study area.

The information on study area and precipitation were shown in the tables below.

watershed Area(km2) PopulationWater

flow(m3/s)WWTPs

Population serviced

Daily flow(m3)

Beiyun River (main stream)

4250 15×106 65.6 BSWTP - 1×105

Qing River 210 3×106 6.7QWTPXJWTP

XHMWTP

2×106

0.5×106

2.4×106

4×105

0.8×105

6×105

Ba River 158 3.8×106 4.4JXWTP

BXHWTP0.5×106 2×105

1×105

Tonghui River 258 2.9×106 2.4 GBDWTP 2.4×106 10×105

Liangshui River 624 4.5×106 13.2BWTPYJWTP

0.4×106

-0.8×105

0.1×105

year

Precipitation (mm) precipitation days

in Jun-Sep(d)

percentage in Jun-Sep

of the whole year(%)

Jan-May Jun-Sep

2013 35 457 66 90

2014 63 356 49 88

2015 82 447 54 92

Annual average 67 384 51 89

S2 Sample preparation and analysis

Water samples were collected in amber glass bottles prewashed by methanol and

ultra-pure water. They were filtered through prebaked (400 , 4 h) glass fiber filters℃

(GF/F, Whatman) immediately after transported to laboratory. After adjusting the pH

of water samples to 4 with 1 mol/L HCl solutions. Solid phase extraction was

conducted on a Supelco Visipre SPE Vacuum Manifold (USA). First, the Oasis HLB

cartridges (200 mg, 6 mL, Waters, U.K.) was conditioned with 10mL methanol and 6

mL ultra-pure water and followed by 6 mL ultra-pure water (pH=4). Then, 500 mL

water sample was introduced to the cartridge at a flow rate of 5―10 mL/min. After

being washed with 10 mL ultra-pure water, the cartridge was dried under vacuum for

2 h. Analytes were extracted and eluted with 5.5 mL of MeOH and 2.5 mL

MeOH/Acetone (1:1, v/v). The extracts were evaporated to dryness and finally

reconstituted in 0.5 mL of MeOH containing 0.025% formic acid, then 25 μL of the

mixture of internal standards (2 mg/L for each IS) was added. All samples were

filtered through 0.22 µm PTFE filters (Whatman, Puradisc, 13 mm) prior to

instrumental analysis.

Procedural blanks were set as controls to evaluate contamination arising from

sampling and laboratory operating. Bottles of ultrapure water from milli-Q system

was carried during sampling campaign, after transported to lab, they were pretreated

exactly the same way with samples and randomly injected for analysis. The

concentrations of target analytes detected in procedural blanks were below LOQ.

Matrix-spiked samples (mixed standards spiked into pre-extracted water sample)

were performed and analyzed together with the water sample collected at the same

site.

Tuning and optimization of the MS/MS parameters (declustering potential (DP),

collision energy (CE),) were performed for each analyte and IS by direct infusion of

standards in solvent. Mass spectrometric source conditions: CUR (Curtain Gas), CAD

(Collision Gas), IS (Ion Spray Voltage), TEM (source temperature), GS1 and GS2 (Ion

Source gases) were adjusted to obtain maximum sensitivity under the

chromatographic conditions. The optimized mass spectrometric conditions were as

follows: CUR (N2) 20, CAD 5, IS -4500 V, TEM 500 , GS1 40 and GS2 60 for negative℃

mode and CUR 20, CAD 5, IS 5500 V, TEM 500 , GS1 40 and GS2 60 for positive℃

mode. Two precursor ion/product ion transitions were monitored with the most

abundant used for quantification and the second for confirmation.

The injection volume was 10 μL, and the column temperature was 30°C. The LC

conditions were as follows: the flow rate was 0.3 ml/min. Methanol and water with

0.1% (v/v) formic acid were used for analysis of compounds of No.1-8 in Table S3

Supporting Information under ESI+ mode. For ESI-, methanol and water with 2

mmol/L ammonium acetate were used for analysis of compounds of No.7-16.

Acetonitrile and water with 0.1% (v/v) formic acid were used for analysis of

compounds of No.17-33 under ESI+ mode. Data acquisition was carried out using

Analyst 1.6 software.

Table S1 Summary of properties and analytical conditions for target analytes

Class Compound Acronym CAS numberConsumption(ton/year) a pKa logKow Ref

Lipid regulators

Gemfibrozil GF 25812-30-0 1.7 4.8 4.77 d [1]

Bezafibrate BF 41859-67-0 2.8 3.6 4.25 d [1]

Clofibric acid CA 882-09-7 -c 2.57 2.6 d [2]

Anti-inflammatory

Diclofenac DF 15307-86-5 278 4.15 4.51 d [3]

Indomethacin IM 53-86-1 312 4.5 4.27 d [4]

Mefenamic acid MA 61-68-7 220 4.2 5.12 d [5]

Ketoprofen KP 2207-15-4 49 4.5 3.12 d [5]

acetaminophen ATP 103-90-2 3303b 9.39 0.46 d [3]

Beta-blockerMetoprolol MTP 37350-58-6 0.6 9.6 1.88 d [5]

Propranolol PHO 525-66-6 -c 9.42 3.48 d [6]

Psychiatric drugsCarbamazepine CBZ 298-46-4 78 13.9 2.45 d [3]

Sulpiride SP 15676-16-1 49 9.1 0.57 d [7]

Stimulant Caffeine CF 58-08-2 1679 10.4 -0.07 d [3]

RepellentN,N-diethyl-meta-

toluamideDEET 134-62-3 -c <2.0 2.18 d [3]

Sulfonamide

Sulfadiazine SD 68-35-9 899.274b 6.5 -0.13 [8, 9]

Sulfathiazole ST 72-14-0 -c 2.2/7.24 0.05 d [9]

Sulfamerazine SMR 127-79-7 -c 2.06/6.9 0.44 [10]

Sulfisoxazole SIX 127-69-5 -c - 1.01 d - d

Sulfisomidin SIM 515-64-0 -c 7.6 - [11]

Sulfamethoxypyridazine SMP 80-35-3 -c 6.7 0.32 d [11]

Sulfaquinoxaline SQX 967-80-6 -c 5.1 1.68 d [11]

Sulfamethazine SMT 35762-76-6 479.803 2.79/7.59 0.89 [8, 12]

Sulfadimethoxine SDM 122-11-2 -c 2.1/6.3 1.63 d [10]

Sulfamethoxazole SMX 723-46-6 1331.85 1.9/5.7 0.89 d [3]

Sulfamethizole SMZ 144-82-1 -c 1.9/5.3 0.54 d [9]

Sulfamonomethoxine SM 1220-83-3 -c 2.0/6.0 0.7 [10, 13]

Trimethoprim TP 738-70-5 959 7.1 0.91 d [3]

Macrolide

Erythromycin EM 7704-67-8 379.617 8.88 3.06 d [8]

Clarithromycin CAM 81103-11-9 220.784 8.99 3.16 d [14]

Tylosin tartrate TS 74610-55-2 -c 7.73 1.63 [13, 15]

Other antibiotics

Chloramphenicol CP 56-75-7 744 5.5 1.14 d [14]

Nalidixic acid NA 389-08-2 -c 8.6 1.59 d [8]

Penicilline G PCG 6130-64-9 -c 2.71 1.76 d [15]a data from the Chinese Medical Statistical Yearbook (CMEIN, 2008).b data from Besse et al.(2008) for 2004 in France c Not availabled experimental data drawn from the EPI database

Table S2 Information of sampling sites

SiteLocation Date

(m/d/y)Site

Locationdate

longitude latitude longitude latitude

Q1 116°19.034′ 40°01.143′ 2015/07/17,11/18

W1 116°20.343′ 40°07.965′ 2015/07/17,11/18

Q2 116°28.898′ 40°04.870′ 2015/07/17,11/18

W2 116°24.689′ 40°09.080′ 2015/07/17,11/18

Q3 116°25.401′ 40°03.404′ 2015/07/17,11/18

W3 116°26.647′ 40°08.854′ 2015/07/17,11/18

Q4 116°28.898′ 40°04.870′ 2015/07/17,11/18

W4 116°29.378′ 40°06.903′ 2015/07/17,11/18

B1 116°31.912′ 39°58.437′ 2015/07/17,11/18

W5 116°32.666′ 40°03.401′ 2015/07/17,11/18

B2 116°33.463′ 39°58.149′ 2015/07/17,11/18

W6 116°34.304′ 40°01.837′ 2015/07/17,11/18

B3 116°35.532′ 39°58.046′ 2015/07/17,11/18

W7 116°38.431′ 39°59.900′ 2015/07/17,11/18

B4 116°37.663′ 39°57.398′ 2015/07/17,11/18

W8 116°38.507′ 39°59.214′ 2015/07/17,11/18

T1 116°31.414′ 39°54.402′ 2015/07/17,11/18

W9 116°40.440′ 39°54.569′ 2015/07/17,11/18

T2 116°33.219′ 39°54.313′ 2015/07/17,11/18

W10 116°43.632′ 39°53.314′ 2015/07/17,11/18

T3 116°34.247′ 39°54.505′ 2015/07/17,11/18

W11 116°46.553′ 39°48.877′ 2015/07/17,11/18

T4 116°39.054′ 39°54.637′ 2015/07/17,11/18

W12 116°47.199′ 39°47.560′ 2015/07/17,11/18

L1 116°27.688′ 39°47.835′ 2015/07/17,11/18

W13 116°52.675′ 39°45.989′ 2015/07/17,11/18

L2 116°32.284′ 39°45.718′ 2015/07/17,11/18

A1 116°39.52′ 39°56.35′ 2015/07/17,11/18

L3 116°38.465′ 39°47.501′ 2015/07/17,11/18

A2 116°40.13′ 39°59.12′ 2015/07/17,11/18

L4 116°41.933′ 39°50.596′ 2015/07/17,11/18

Y1 116°40.89′ 39°55.3′ 2015/07/17,11/18

L5 116°45.795′ 39°48.257′ 2015/07/17,11/18

Y2 116°43.78′ 39°54.95′ 2015/07/17,11/18

Table S3 Analysis condition and method validation

Number

Analyte

Precursor io

n(m/z)

Product ion (m/z)

Ionization

mode

DP(V

)CE(V)

AverageRecover

y(%)

MDL(ng/

L)

Matrix effect (%) a

1 NA 233.2215.2/187.

2+ 41 17/33 121 1.7 17.5

2 TP 291.6123.2/230.

2+ 56 31/21 110.2 2.0 4.6

3 PHO 260.3 116.1/56.1 + 46 25/43 120.6 1.9 20.6

4 CBZ 237.2194.2/192.

3+ 51 23/31 100.1 0.9 5.9

5 DEET 192.3 119.1/91.1 + 51 23/39 99 2.5 14.3

6 SP 342.4112.1/214.

2+ 66 33/43 73.9 5.2 19.2

7 MTP 268.3 116.1/77.1 + 46 23/71 112.7 2.0 10.6

8 CF 195.2138.2/110.

1+ 56 23/31 66.6 2.1 12.9

9 CP 320.9152.0/121.

0- -35

-26/-48

79.1 1.1 16.7

10 DF 293.9 250.0 - -15 -12 68.4 1.8 6.3

11 IM 356.0312.2/297.

2- -15

-10/-16

75.5 2.4 3.7

12 MA 239.6196.0/193.

9- -35

-22/-26

72.4 2.0 9.3

13 KP 253.0209.1/197.

0- -40 -8/-6 82.7 1.0 14.2

14 BF 360.0274.0/154.

1- -30

-16/-40

74.3 1.4 13.6

15 CA 212.8 126.9 - -15 -20 70.9 0.8 16.5

16 GF 249.3121.0/127.

1- -25

-18/-12

95.2 0.5 11.9

17 SD 251.1 156.2/92.1 + 46 19/33 86.7 2.2 12.4

18 ST 256.1 156.2/92.1 + 41 19 82.3 0.9 11.0

19 SMR 265.1 108.1/92.1 + 51 37/33 109.0 1.6 16.6

20 SIX 268.2 156.2/92.1 + 46 17/35 82.4 1.5 14.9

21 SIM 279.1 124.1/92.1 + 46 29/43 70.3 1.3 10.3

22 SMP 281.1 156.2/92.0 + 46 21/37 115.8 1.4 15.7

23 SQX 301.2 156.1/92.0 + 41 21/41 108.2 1.7 12.2

24 SMT 279.1186.2/124.

2+ 56 21/29 117.9 0.3 18.6

25 SDM 311.1 156.2/92.1 + 61 25/43 118.3 2.0 14.4

26 SM 281.1 156.2/92.2 + 46 21/39 110.0 2.7 17.5

27 SMX 254.2 156.2/92.0 + 46 19/35 113.2 2.4 20.7

28 SMZ 271.1 156.1/92.1 + 41 17/35 109.2 2.7 9.7

29 PCG 335.2 217.2/91.1 + 81 17/61 89.9 5.1 10.4

30 EM 734.4158.2/116.

1+ 66 37/55 77.4 0.4 17.9

31 CAM 748.4 158.3/83.1 + 56 37/77 76.2 1.5 26.2

32 TS 916.5174.2/101.

1+ 96 49/65 77.1 4.1 8.3

33 ATP 152.1 110.1/65.0 + 46 21/39 89.5 5.9 8.9

Table S4 Toxicity data of analytes

AnalyteEC50 (LC50 )

(mg L-1)Tested organisms Reference

Acetaminophen1

30.1549.7

FishDaphniaBacteria

[17]

[18]

[18]

Bezafibrate18.05.325

AlgaeFish

Daphnia

[19][20][20]

Caffeine0.01580546

AlgaeFish

Daphnia

Predicted by ECOSAR[20][20]

Carbamazepine0.42101111

-a

FishDaphnia

[21][20][20]

Chloramphenicol4.000.16365

AlgaeBacteriaDaphnia


Clarithromycin0.0468.16

AlgaeDaphnia

[24][25]

Clofibric acid86.053

293

AlgaeFish

Daphnia

[26][20][20]

DEET388235108

AlgaeFish

Daphnia

[27][28][29]

Diclofenac0.1532

5057

-a

FishDaphnia

[21][20][20]

Erythromycin0.0020.22

AlgaeDaphnia

[30][25]

Gemfibrozil4.000.96

AlgaeFish

Daphnia

[19][20][20]

Indomethacine18.03.926

AlgaeFish

Daphnia

[20][20][20]

Ketoprofen179.5

32248

AlgaeFish

Daphnia


Mefenamic acid4.330.43

AlgaeBacteria

[31][32]

Metoprolol 7.30116

AlgaeFish

[33][20]

8 Daphnia [20]

Nalidixic acid22.9

0.19995Algae

Bacteria[34][35]

Penicilline G0.006>256

AlgaeBacteria

[15][36]

Propranolol0.66829.52.3

AlgaeFish

Daphnia

[37][20][20]

Sulfadiazine0.110212

AlgaeDaphnia

[38][39]

Sulfadimethoxine2.30270

AlgaeDaphnia

[40][39]

Sulfamerazine11.90.68

AlgaePlant

[41][42]

Sulfamethazine1.56

344.7174.4

AlgaeBacteriaDaphnia

[39][18][18]

Sulfamethizol0.1031113

5

AlgaeFish

Daphnia

[10][20][20]

Sulfamethoxazole0.0300.2178.1

AlgaeDaphniaBacteria

[43][25][18]

Sulfamethoxypyridazine3.8226.4

589.3

AlgaeBacteria

Fish

[41][18][18]

Sulfamonomethoxine8.5648

>1000

AlgaeDaphnia

Fish


Sulfathiazole16.3

149.3Algae

Daphnia[45][18]

Sulfisoxazole18.90.62

Algaeplant

[42][42]

Sulfaquinoxaline0.45131

AlgaeDaphnia

[42][39]

Sulpiride99.8>100>100

AlgaeDaphnia

Fish

[46][47][47]

Trimethoprim

2.60120.7165.1795

AlgaeDaphniaBacteria

Fish

[17][18][18][20]

Tylosin0.034680

AlgaeDaphnia

[15][48]

a Not refered.

Table S5 Comparison of occurrence data among areas

Compound Concentration(ng/L) Location Reference

NA9.5750

nd-34.2

Tone River basin, JapanRiver water, Australia

Beiyun River basin, Beijing, China

[49][50]

This study

TP

3-1211-94

8.1-6053.5-110.4Nd-140.6

Danube river,ItalyHan River, KoreaPearl River, ChinaMiño River,Spain


[51][52][53][54]

This study

PHO

4.2-62.6<0.06-0.24

Nd-22.5Nd-4.5

Miño River,SpainSurface water,in Hanoi,Vietnam

Dong Lake, ChinaBeiyun River basin, Beijing, China

[49][55][56]

This study

CBZ

56-160Nd-402-160

10.1-199.5

Han River, KoreaDanube river, Italy

Hai River, ChinaBeiyun River basin, Beijing, China

[52] [51] [57]This study

DEET

36120-190

4-5902.5-1356.1

Tennessee River,USAHan River, KoreaHai River, China


[58][52][57]

This study

SP170

73-7194.4-127.3

Tennessee River,USARivers, Beijing, China


[58][59]

This study

MTP

1.8-2611.3-105.5

LOD-1.655.3-495.2

Madrid, SpainPearl River, ChinaDong Lake, China


[43][60][56]

This study

CF

210018-230

29-500031.3-2714.1

Tennessee River,USALake Michigan,USHai River, China


[58][61][57]

This study

CP<2-40Nd-13

1.1-22.5

Taff and Ely River, UKDanube river,Italy


[62][51]

This study

DF

Nd-16628-464-190

1.8-121.6

Danube river, ,ItalyMiño River,SpainHai River, China


[51][54][57]

This studyIM Nd-7.7 Danube river, ,Italy [51]

2323Nd-74.9

Rivers, Costa RicaBeiyun River basin, Beijing, China

[63]This study

MA

190-4390.6-3.7

<0.3-1692.0-7.3

Bangkok, ThailandDong Lake, China

Taff and Ely River, UKBeiyun River basin, Beijing, China

[64][56][62]

This study

KP

Nd-580.3-99153-249Nd-65.0

Danube river,ItalyMadrid,Spain

River, Beiing, ChinaBeiyun River basin, Beijing, China

[51][43][59]

This study

BF

<10-76170

8-1701.4-42.9

Taff and Ely River, UKTone River basin,Japan

Hai River, ChinaBeiyun River basin, Beijing, China

[62][49][57]

This study

CA<0.05-0.35

Nd-4.32.3-11.8

Surface water,Hanoi, VietnamDanube river, Italy


[55][51]

This study

GF

1703612-46

Nd-13.5Nd-8.1

Rivers, Costa RicaHan River, Korea

Yellow River, ChinaBeiyun River basin, Beijing, China

[63][52][65]

This study

SD<MDL-3.8

1-30.52.2-157.4

Lake Michigan,USLiao River, China


[61][66]

This study

ST

401.5-332

2<MDL-8.5

Nd-2.1

River water, AustraliaSegre,Llobregat, and Anoia River, Spain

Mess River, LuxembourgLiao River, China


[50][67][68][66]

This study

SMR0.03-0.8Nd-5.3

Huangpu River,ChinaBeiyun River basin, Beijing, China

[69]This study

SIX0.5-2.8Nd-3.4

Segre,Llobregat, and Anoia River, SpainBeiyun River basin, Beijing, China

[67]This study

SIM0.01-64.4

Nd-1.3Plana de Vic and LaSelva,Catalonia,Spain

Beiyun River basin, Beijing, China[70]

This study

SQX0.08-0.9Nd-1.7


[69]This study

SMT

220Nd-1230.21-3.4

<MDL-26.4Nd-87.9

Cache La Poudre, USAHan River, Korea

Panjiakou Reservoir, ChinaLiao River, China


[71][72][73][66]

This studySDM 50-90

<7.1Cache La Poudre, USA

Lake Michigan,US[71][61]

Nd-801

Nd-2.0

Han River, KoreaLiao River, China


[72][66]

This study

SMX

2.5-30Nd-270

<0.11-7.23Nd-276.2

Danube river,ItalyHan River, Korea

Panjiakou Reservoir, ChinaBeiyun River basin, Beijing, China

[51][72][73]

This study

PCG250

Nd-499River water,Australia

Beiyun River basin, Beijing, China[50]

This study

EM1.5-24.6Nd-1320


[69]This study

CAM 600-2330<MDL

Nd-96.9

Arc River, FranceLake Michigan,US


[74][61]

This study

TSNd-39

1.3Nd-4.1

Danube river,ItalyHuangpu River,China


[51][69]

This study

ATP25-1163Nd-6402Nd-3577

SingaporeJiyun River, Hai River and Duliu River ,

ChinaBeiyun River basin, Beijing, China

[75][76]

This study

Table S6 Hazard quotient of target compounds in rivers of Beijing in 2013 and 2015

compound PNEC(ng/L)

Beiyun River Qing River Ba River Tonghui River Liangshui River

2013 2015 2013 2015 2013 2015 2013 2015 2013 2015

DF(%) HQ DF(%) HQ DF(%) HQ DF(%) HQ DF(%) HQ DF(%) HQ DF(%) HQ DF(%) HQ DF(%) HQ DF(%) HQ

NA 22900 100 0.003 81 0.001 100 0.002 100 0.001 75 0.004 100 0.000 100 0.005 100 0.001 100 0.004 100 0.001

TP 2600 100 0.025 100 0.027 100 0.015 100 0.030 100 0.023 100 0.005 100 0.020 100 0.021 75 0.053 100 0.029

PHO 668 80 0.018 88 0.003 75 0.003 100 0.003 100 0.003 100 0.003 100 0.005 100 0.003 75 0.110 100 0.005

CBZ 420b 100 0.423 100 0.435 100 0.037 100 0.159 100 0.157 100 0.118 100 0.199 100 0.192 100 0.289 100 0.262

DEET 388000 100 0.002 100 0.002 100 0.000 100 0.001 100 0.000 100 0.001 100 0.000 100 0.001 100 0.000 100 0.001

SP 99800 100 0.003 100 0.001 100 0.001 100 0.001 100 0.003 100 0.000 100 0.004 100 0.000 100 0.002 100 0.001

MTP 7300 100 0.025 100 0.058 100 0.013 100 0.038 100 0.026 100 0.021 100 0.052 100 0.047 100 0.061 100 0.063

CF 15 100 160 100 112 100 117 100 135 100 352 100 120 100 56.9 100 25.6 100 360 100 78.7

CP 267000 87 0.000 54 0.000 75 0.000 63 0.000 75 0.000 63 0.000 75 0.000 88 0.000 75 0.000 70 0.000

DF 100b 100 0.791 100 0.878 100 0.458 100 0.645 100 1.070 100 0.350 100 1.700 100 0.739 100 1.240 100 1.076

IM 18000 100 0.003 65 0.001 100 0.001 50 0.001 100 0.003 50 0.001 100 0.004 63 0.001 100 0.002 70 0.002

MA 4330 80 0.002 69 0.012 75 0.001 88 0.010 75 0.002 63 0.004 75 0.002 88 0.005 75 0.002 100 0.015

KP 179455 100 0.000 100 0.000 100 0.000 100 0.000 100 0.000 100 0.000 100 0.000 100 0.000 100 0.000 100 0.000

BF 18000 100 0.002 100 0.002 100 0.001 100 0.001 100 0.001 100 0.001 75 0.001 100 0.000 100 0.002 100 0.002

GF 4000 100 0.004 100 0.002 100 0.001 100 0.001 100 0.002 100 0.001 75 0.002 100 0.000 100 0.003 100 0.002

CA 86000 — — 100 0.000 — — 100 0.000 — — 100 0.000 — — 100 0.000 — — 100 0.000

ATP 1000 — — 73 0.134 — — 100 2.034 — — 88 0.567 — — 88 0.048 — — 90 0.332

Table S7 Detection frequency and HQs of antibiotics in two seasons in 2015

compound PNECa

(ng/L)

Beiyun River Qing River Ba River Tonghui River Liangshui River

November July November July November July November July November July

DF(%) HQ DF(%) HQ DF(%) HQ DF(%) HQ DF(%) HQ DF(%) HQ DF(%) HQ DF(%) HQ DF(%) HQ DF(%) HQ

SD 110 100 0.747 100 0.648 100 0.393 100 0.276 100 0.151 100 0.217 100 0.321 100 0.305 100 1.43 100 0.678

ST 16320 15 0.000 69 0.000 50 0.000 0 0.000 0 0.000 0 0.000 25 0.000 25 0.000 40 0.000 60 0.000

SMR 11900 54 0.000 54 0.000 100 0.000 25 0.000 0 0.000 0 0.000 100 0.000 100 0.000 60 0.000 20 0.000

SIX 18980 54 0.000 46 0.000 50 0.000 50 0.000 50 0.000 50 0.000 50 0.000 50 0.000 60 0.000 40 0.000

SIM 1 23 1.31 62 1.310 25 1.31 50 1.310 50 1.31 50 1.31 0 0.000 0 0.000 0 0.000 100 1.31

SMP 3820 69 0.000 31 0.000 100 0.001 50 0.000 75 0.000 75 0.000 75 0.000 75 0.000 80 0.000 60 0.000

SQX 450 77 0.004 62 0.004 50 0.004 75 0.004 50 0.004 50 0.004 25 0.004 25 0.004 80 0.004 60 0.004

SMT 1563 100 0.056 92 0.015 50 0.004 0 0.000 75 0.003 75 0.005 100 0.009 100 0.008 100 0.039 100 0.007

SDM 2300 31 0.001 31 0.001 50 0.001 75 0.001 0 0.000 0 0.001 0 0.000 0 0.001 20 0.001 60 0.001

SMX 30 100 5.06 100 5.34 100 9.21 100 4.794 100 0.991 100 3.09 75 3.67 75 7.264 100 6.34 100 6.02

SMZ 103 23 0.026 38 0.026 50 0.026 50 0.026 25 0.026 25 0.026 50 0.026 50 0.026 20 0.026 60 0.026

SM 8562 92 0.002 92 0.001 0 0.000 25 0.000 0 0.000 0 0.000 50 0.000 50 0.000 100 0.001 100 0.000

PCG 6 85 83.1 38 0.845 50 56.3 50 0.845 25 29.2 25 0.845 100 36.7 100 0.845 100 58.8 100 0.845

EM 2000 100 0.615 100 0.090 75 0.530 100 0.095 50 0.184 50 0.029 100 0.660 100 0.472 80 0.510 100 0.030

CAM 46 0 0.000 92 0.343 0 0.000 100 2.108 0 0.000 0 0.254 0 0.000 0 0.784 0 0.000 100 1.72

TS 34 69 0.120 85 0.120 25 0.120 50 0.120 100 0.120 100 0.120 50 0.120 50 0.120 20 0.120 80 0.120

Table S8 Statistical analysis of seasonal variation in 2015

Class Compound Acronym P value

Lipid regulators

Gemfibrozil GF 0.015

Bezafibrate BF 0.014

Clofibric acid CA 0.042

Anti-inflammatory

Diclofenac DF 0.000

Indomethacin IM 0.000

Mefenamic acid MA 0.306

Ketoprofen KP 0.037

acetaminophen ATP 0.009

Beta-blockerMetoprolol MTP 0.150

Propranolol PHO 0.025

Psychiatric drugsCarbamazepine CBZ 0.131

Sulpiride SP 0.000

Stimulant Caffeine CF 0.975

Repellent N,N-diethyl-meta- toluamide DEET 0.000

Sulfonamide

Sulfadiazine SD 0.013

Sulfathiazole ST 0.100

Sulfamerazine SMR 0.058

Sulfisoxazole SIX 0.513

Sulfisomidin SIM 0.000

Sulfamethoxypyridazine SMP 0.002

Sulfaquinoxaline SQX 0.715

Sulfamethazine SMT 0.000

Sulfadimethoxine SDM 0.021

Sulfamethoxazole SMX 0.198

Sulfamethizole SMZ 0.282

Sulfamonomethoxine SM 0.185

Trimethoprim TP 0.220

Macrolide

Erythromycin EM 0.000

Clarithromycin CAM 0.000

Tylosin tartrate TS 0.000

Other antibiotics

Chloramphenicol CP 0.000

Nalidixic acid NA 0.623

Penicilline G PCG 0.000

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