SUPPORTING INFORMATION:

Emission and fate of antibiotics in the Dongjiang River Basin, China:

Implication for antibiotic resistance risk

Shao-Xuan Zhang1, 2, Qian-Qian Zhang1, 2 *, You-Sheng Liu1,2, Xiao-Ting Yan1,2, Bing

Zhang1, 2, 3, Cheng Xing1, 2, Jian-liang Zhao1, 2, Guang-Guo Ying1, 2, *

1 SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of

Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical

Chemistry of Environment, South China Normal University, Guangzhou 510006,

China

2 School of Environment, South China Normal University, University Town,

Guangzhou 510006, China

3 State Key Laboratory of Organic Geochemistry, Guangzhou Institute of

Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China

* Corresponding author. Tel.: +86 020 39310796

Email address: guangguo.ying@m.scnu.edu.cn (GGY)

This file includes:

Text S1-S3

Tables S1-S8

Figures S1-S3

Text S1: Equations used in emission calculation Detailed equations reflect the calculation processes of antibiotic emissions from

different sources were showed in following equations:

(1)

(2)

(3)

(4)

(5)

(6)

(7)

where Tp(rural_wwtp) and Tp(rural_direct) represent the antibiotic emissions from rural

population with and without treatment by WWTP (unit: kg/a), respectively; Tp(urban_wwtp)

and Tp(urban_direct) represent the antibiotic emissions (unit: kg/a) from urban population

with and without treatment by WWTP (unit: kg/a), respectively; Ta_pig and Ta_chicken,

Ta_others represent the direct discharge amount of antibiotics by pigs and chickens (unit:

kg/a), respectively; Ta_others represent the direct discharge amount of antibiotics by

freshwater products, cattle, sheep and rabbit (unit: kg/a); mhuman, mpig and mchicken

represents the average antibiotic use by humans, pigs, and chickens (unit: g/a),

respectively; mothers represent total amount of antibiotics used by freshwater products,

cattle, sheep and rabbit in the province (unit: t/a); ε represent “other” accounts for the

proportion of “other” products in the province; Prural, Purban, Ppig and Pchicken are the

numbers of rural population, urban population, pig and chicken (unit: per),

respectively; μ represents the local rural sewage treatment rate; k represents the local

urban sewage treatment rate; i indicates an antibiotic user; Ei represents the total

excretion ratio of maternal and glucuronic acid conjugates of the target antibiotic by

users of category i antibiotics; η is the ratio of antibiotics taken by human, which is

2/3. Detailed data refers to Table S1 ~ S3.

Text S2: Parameter explanation for GREAT-ER model

The detailed parameters and calculation process of the model can be seen in the

manual: "GREAT-ER Desktop Manual (Wagner and Koormann, 2011)". It should be

noted that: in this paper, antibiotics without wastewater treatment plant include the

following parts: (1) antibiotics contained in urban domestic wastewater not collected

by WWTP; (2) antibiotics contained in domestic wastewater not collected by WWTP

in rural areas; (3) antibiotics contained in livestock farm wastewater not treated by

sewage treatment facilities with the loss of manure and urine. Among them, (1) and

(2) are not treated by the WWTP, which are embodied in the "Treated fraction"

parameter in the model and the effective collection rate of the WWTP, which can be

calculated by equation (8).

(8)

Treated fraction represents a fraction of population connected to the sewage

treatment. Prural and Purban are the numbers of rural population and urban population

(unit: per), respectively.

The input parameter about the discharging amount of target antibiotic are calculated

based on the following assumption. According to the data from Guangdong Statistical

Yearbook (2014), the large scale farming of livestock breeding in Guangdong

Province is more than 80%, with less than 20% in scatter breeding. Thus we labled all

livestock farms as point sources in our model. Based on a survey for the loss rate of

livestock manure around China, approximately 20% of excrement from those large

scale livestock farms are discharging into the receiving environment, and the rest 80%

is taken as fertilizer to enter the farmland (Wu, 2005). These fertilizers are processed

and sold, which may be used locally or in other regions. Considering the uncertainty

of the total amount of manure returned to the field, only 20% of the excrement into

rivers were takn into consideration.

Text S3 Instrumental conditions and quality assurance and quality control

(QA/QC)

The target antibiotics were analyzed using UPLC–MS/MS (Agilent Ultra Performance

Liquid Chromatography 1200 series UPLC system coupled to an Agilent 6460 triple

quadrupole MS equipped with an electrospray ionization (ESI) source (Agilent, Palo

Alto, CA, USA)) in multiple-reaction monitoring (MRM) mode. The chromatographic

separation of each group was performed on an Agilent SB-C18 column (2.1 ×100

mm, 1.8 μm) with a UPLC in-line filter kit (2.1 mm, 0.2 μm filter) (Germany). The

column temperature was maintained at 40 °C. The chromatographic mobile phases

were run at a flow rate of 0.3 mL/min. The mobile phase for analysis of the antibiotics

consisted of (A) 0.2% formic acid and 2 mM ammonium acetate and (B) acetonitrile,

with the gradient programmed as follows: 0 min, 10 % B; 5 min, 15 % B; 7 min, 20%

B; 11 min, 40% B; 15 min, 60% B and 16 min, 95% B, hold for 9 min. The mass

spectrometer was operated in positive electrospray ionization mode (ESI+) with

optimized parameters as follows: drying gas temperature 325°C and flow rate 6

L/min, capillary voltage 3500 V, nozzle voltage 0 V, nebulizing gas pressure 45 psi,

sheath gas temperature 350°C and flow rate 11 L/min. Nitrogen gas was used as the

drying and collision gas. The injection volume was 10 μL. The instrumental

parameters for each antibiotic (retention time, precursor ion and production ions,

fragmentor and collision energy) has been reported by us before (Zhao et al., 2015).

Quality assurance/quality control (QA/QC) procedures were followed during the

sampling, extraction and analysis. Blanks and control samples were run with every 10

samples to check for any carryover, background contamination, precision and

accuracy of the recovery. No target antibiotics were detected in blank samples.

Table S1. Population (per) and sewage treatment rate of each district or county in

Dongjiang River Basin.

Study area Purban a k Prural

a μDongguan CityChang'an Town 593915 0.92 b 75285 0.22 c

Changping Town 345681 0.92 b 43819 0.22 c

Chashan Town 139693 0.92 b 17708 0.22 c

Dalang Town 279208 0.92 b 35393 0.22 c

Dalingshan Town 250364 0.92 b 31736 0.22 c

Daojiao Town 127623 0.92 b 16178 0.22 c

Dongcheng Street 440910 0.92 b 55890 0.22 c

Dongkeng Town 124073 0.92 b 15728 0.22 c

Fenggang Town 284266 0.92 b 36034 0.22 c

Gaobu Town 193564 0.92 b 24536 0.22 c

Guancheng Street 147148 0.92 b 18653 0.22 c

Hengli Town 183713 0.92 b 23288 0.22 c

Hongmei Town 52451 0.92 b 6649 0.22 c

Houjie Town 392719 0.92 b 49781 0.22 c

Huangjiang Town 207764 0.92 b 26336 0.22 c

Humen Town 571728 0.92 b 72473 0.22 c

Liaobu Town 375324 0.92 b 47576 0.22 c

Machong Town 106411 0.92 b 13489 0.22 c

Nancheng Street 265096 0.92 b 33604 0.22 c

Qiaotou Town 149366 0.92 b 18934 0.22 c

Qingxi Town 279829 0.92 b 35471 0.22 c

Qishi Town 109074 0.92 b 13826 0.22 c

Shatian Town 159839 0.92 b 20261 0.22 c

Shijie Town 220278 0.92 b 27923 0.22 c

Shilong Town 127268 0.92 b 16133 0.22 c

Shipai Town 144219 0.92 b 18281 0.22 c

Songshanhu Town 47570 0.92 b 6030 0.22 c

Tangxia Town 432213 0.92 b 54788 0.22 c

Wangniudun Town 76503 0.92 b 9698 0.22 c

Wanjiang Street 219301 0.92 b 27799 0.22 c

Xiegang Town 89105 0.92 b 11295 0.22 c

Zhangmutou Town 119458 0.92 b 15143 0.22 c

Zhongtang Town 125315 0.92 b 15885 0.22 c

Study area Purban a k Prural

a μGanzhou CityAnyuan County 161728 0.85 d 218272 0.22 c

Dingnan County 89376 0.87 d 120624 0.22 c

Xunwu County 131936 0.85 d 178064 0.22 c

Guangzhou City

Zengcheng District 896870 0.94 d 154930 0.44 e

Heyuan City

Dongyuan County 99400 0.91 f 353600 0.22 c

Heping County 108200 0.84 f 277200 0.22 c

High-tech Development Zone 200000 g 0.93 h - -

Lianping County 115900 0.91 f 230900 0.22 c

Longchuan County 200400 0.86 f 515300 0.22 c

Yuancheng Distric 470700 0.93 d 8100 0.22 c

Zijin County 240200 0.76 f 417700 0.22 c

Huizhou City

Boluo County 551169 0.92 f 506331 0.22 c

Daya Bay 183995 0.95 i 15005 0.22 c

Huicheng District 961994 0.95 d 225506 0.22 c

Huidong County 503385 0.91 f 421615 0.22 c

Huiyang District 438804 0.95 f 148696 0.22 c

Longmen County 113589 0.90 f 201411 0.22 c

Shaoguan CityXinfeng County 104902 0.50 f 105998 0.22 c

Shenzhen City

Longgang Distric 1944700 0.97 j - -

a: Population data from regional statistical yearbooks, 2013.b: Zheng et al., 2017.c: http://www.chinacace.org/news/view?id=7974. (2019.2.1)d: China Urban Construction Statistical Yearbook, 2016.e: http://www.gzepb.gov.cn/zwgk/gs/fzgh/201702/P020170822531798286495.pdf. (2019.2.1)f: China County Construction Statistics Yearbook, 2016.g: http://www.baike.com/wiki/%E6%B2%B3%E6%BA%90%E9%AB%98%E6%96%B0%E6%8A%80%E6%9C%AF%E5%BC%80%E5%8F%91%E5%8C%BA. (2019.2.1)h: Consistent with the Yuancheng City.i: Consistent with the Huiyang City.j: http://www.waterchina.com/content/detail/id/12115. (2019.2.1)Notes: Purban represent the numbers of urban population (unit: per); k represent the local urban sewage treatment rate; Prural represent the numbers of rural population (unit: per); µ represent the local rural sewage treatment rate.

Table S2. Year-end live pig/chisken stock and Others meat products of each district or county in Dongjiang River Basin.

Study area Year-end live pig stock (per) a

Year-end chicken stock (per) a

Others a, b

Total (t) ε c

Dongguan CityDalang Town 32989 640000 32859 0.0090Zhangmutou Town 32989 640000 32859 0.0090Ganzhou CityAnyuan County 167010 1054100 9441 0.0038Dingnan County 423825 840500 9140 0.0037Xunwu County 124990 1774000 10867 0.0043Guangzhou CityZengcheng District 172712 3410000 49835 0.0137Heyuan CityDongyuan county 88924 1673957 9590 0.0026Heping County 56112 1438244 3753 0.0010Lianping County 207955 1202582 5809 0.0016Longchuan County 248265 2435637 14976 0.0041Yuancheng County 73353 1114792 1409 0.0004Zijin County 124417 1733837 10758 0.0029Huizhou CityBoluo County 513225 4614386 28287 0.0078Daya Bay 23490 74338 660 0.0002Huicheng District 196469 2001941 20057 0.0055Huidong County 301374 1995275 9650 0.0026Huiyang District 20861 1013931 4591 0.0013Longmen County 72726 768788 6052 0.0017Shaoguan CityXinfeng County 62783 611800 4684 0.0013a: Data from regional statistical yearbooks, 2013.b: Others: include freshwater products, cattle, sheep and rabbit meat.c: ε represent “other” accounts for the proportion of “other” products in the province.

Table S3. Per capita use of antibiotics and individual metabolic ratio.

Compound AbbrAverage usage (g) in Guangdong, 2013 a

Others-total usage (t) in Guangdong, 2013 a

Average usage (g) in Jiangxi, 2013 a, b

Others-total usage (t) in Jiangxi, 2013 a, b

Ehuman a, c

Epig a, d

Echicken a,

e

Human Pig Chicken Human Pig Chicken Eurine Efeces

Sulfadiazine SDZ 0.1463 1.1236 0.0176 11.3656 0.1490 1.3536 0.0396 7.1075 52.30% 42.40% 1.60% 28.80%

Sulfamethazine SMZ 0.0547 0.7469 0.0150 7.3640 0.0343 0.7363 0.0185 4.2511 65.90% 24.50% 0.90% 13.90%

Sulfamethoxazole SMX 0.0012 0.3439 0.0069 2.8957 0.0012 0.3576 0.0095 2.0008 15.20% 99.00% 0.90% 28.80%

Sulfathiazole STZ 0.0004 0.0929 0.0011 0.5881 0.0005 0.0801 0.0025 0.4220 51.34% 88.10% 3.40% 28.80%

Sulfachlorpyridazine SCP 0.0000 0.6336 0.0101 4.9541 0.0000 0.7181 0.0223 3.8674 0.00% 35.30% 1.40% 28.80%

Sulfameter SM 0.0085 0.4854 0.0110 5.0627 0.0082 0.5981 0.0151 3.0387 51.34% 35.30% 1.40% 28.80%

Sulfamonomethoxine SMM 0.0061 2.4266 0.0434 20.4619 0.0060 3.0559 0.0856 15.9645 10.00% 4.60% 0.20% 20.00%

Sulfaquinoxaline SQX 0.0000 0.0000 0.1278 10.9501 0.0000 0.0000 0.1891 9.8584 0.00% 35.60% 1.40% 28.80%

Sulfaguanidine SG 0.0860 0.1085 0.0025 0.7543 0.0332 0.0713 0.0019 0.4930 51.34% 35.60% 1.40% 28.80%

Trimethoprim TMP 0.4000 0.2720 0.0049 4.1231 0.2131 0.3128 0.0099 1.7188 60.50% 32.30% 1.20% 28.80%

Ormetoprim OMP 0.0000 0.0000 0.0001 0.5306 0.0000 0.0000 0.0001 0.3050 0.00% 34.70% 1.30% 28.80%

Oxytetracycline OTC 0.1060 1.5696 0.0374 13.0724 0.1056 1.4063 0.0284 8.1722 66.70% 59.00% 28.10% 52.50%

Tetracycline TC 0.8559 0.2525 0.0055 2.2693 0.6973 0.2149 0.0062 1.1529 57.90% 52.50% 25.00% 52.50%

Chlortetracycline CTC 0.0267 0.3418 0.0106 1.9944 0.0290 0.2332 0.0050 1.4368 46.00% 55.80% 26.50% 52.50%

Doxycycline DC 0.2081 4.4381 0.0804 33.6621 0.1346 4.5929 0.1101 21.2150 47.00% 55.80% 26.50% 52.50%

Methacycline MT 0.0355 0.0137 0.0004 0.0575 0.0450 0.0093 0.0002 0.0326 36.50% 55.80% 26.50% 52.50%

Norfloxacin NFX 0.8100 4.3457 0.1093 57.6590 0.6599 5.3539 0.1078 27.1926 61.50% 30.00% 27.50% 53.00%

Ciprofloxacin CFX 0.3920 4.2010 0.0966 50.0841 0.2395 6.8022 0.1549 32.7923 53.80% 36.80% 33.70% 53.00%

Ofloxacin OFX 0.9491 3.7594 0.0946 32.0640 1.0310 5.0945 0.1353 26.7284 75.80% 27.80% 25.50% 53.00%

Lomefloxacin LFX 0.1824 1.2529 0.0227 8.5530 0.1314 1.1113 0.0286 7.1324 71.00% 27.80% 25.50% 53.00%

Enrofloxacin EFX 0.0000 5.3556 0.1699 66.1097 0.0000 6.4515 0.1741 45.0889 0.00% 21.00% 19.20% 53.00%

Fleroxacin FL 0.0805 0.0933 0.0030 1.0675 0.0715 0.1323 0.0040 0.6676 76.00% 27.80% 25.50% 53.00%

Pefloxacin PEF 0.1477 2.0373 0.0615 23.1851 0.1153 2.5099 0.0581 16.2350 10.60% 27.80% 25.50% 53.00%

Compound AbbrAverage usage (g) in Guangdong, 2013 a

Others-total usage (t) in Guangdong, 2013 a

Average usage (g) in Jiangxi, 2013 a, b

Others-total usage (t) in Jiangxi, 2013 a, b

Ehuman a, c

Epig a, d

Echicken a,

e

Human Pig Chicken Human Pig Chicken Eurine Efeces

Difloxacin DIF 0.0000 0.7277 0.0234 7.4791 0.0000 0.6814 0.0202 5.1604 75.00% 23.60% 67.40% 53.00%

Leucomycin LCM 0.1263 1.8133 0.0292 16.5051 0.1338 1.5191 0.0414 9.0795 9.59% 3.50% 5.00% 67.00%

Clarithromycin CTM 0.0446 0.1540 0.0081 3.4391 0.0429 0.3361 0.0128 1.8263 33.70% 12.10% 17.50% 67.00%

Roxithromycin RTM 0.1246 0.1727 0.0077 2.0072 0.1154 0.2448 0.0106 0.8613 66.70% 24.00% 34.70% 67.00%

Tylosin TYL 0.0000 4.1670 0.0958 49.6686 0.0000 6.7472 0.1772 36.5868 0.00% 0.40% 38.60% 67.00%

Erythromycin- H2O ETM 0.8415 2.4411 0.0514 26.5027 0.6856 3.3082 0.0888 18.7998 35.00% 12.60% 18.20% 67.00%

Florfenicol FF 0.0000 11.0510 0.2938 106.0172 0.0000 13.3121 0.3019 69.4174 0.00% 47.50% 15.00% 42.00%

Chloramphenicol CAP 0.1853 0.7444 0.0428 8.3676 0.1078 1.4675 0.0479 5.9335 10.10% 26.50% 40.60% 54.10%

Lincomycin LIN 0.7987 8.3703 0.2357 63.5016 0.7007 9.4872 0.2242 53.3570 51.00% 5.50% 16.00% 66.20%

Amoxicillin AMOX 1.7026 14.5430 0.4255 120.4000 1.2271 14.3335 0.2886 75.2760 70.00% 65.00% 10.00% 70.00%

Penicillin PEN 0.7329 7.8454 0.1866 64.8952 0.4593 8.0838 0.1557 32.4585 50.00% 50.00% 7.70% 50.00%

Cefazolin CFZ 0.0027 0.0029 0.0001 0.0575 0.0016 0.0028 0.0002 0.0384 91.00% 91.00% 0.00% 91.00%

Cephalexin CPX 2.6577 0.1608 0.0039 1.4766 1.2737 0.1663 0.0041 0.7750 91.00% 91.00% 0.00% 91.00%

a: Zhang et al., 2015.b: Used to calculate regional antibiotic emissions in Xunwu County, Anyuan County, and Dingnan County.c: Ehuman: the total excretion ratio of maternal and glucuronic acid conjugates of the target antibiotic by human.d: Epig: the total excretion ratio of maternal and glucuronic acid conjugates of the target antibiotic by pig.e: Echicken: the total excretion ratio of maternal and glucuronic acid conjugates of the target antibiotic by chicken.

Table S4 Calibration and evaluation for monthly streamflow in the Dongjiang River Basin a

Station R2 R2 rating NSE NSE rating

calibration

Heyuan 0.71 satisfactory 0.29 unsatisfactory

Lingxia 0.93 satisfactory 0.85 satisfactory

Boluo 0.95 satisfactory 0.89 satisfactory

validation

Heyuan 0.62 satisfactory 0.04 unsatisfactory

Lingxia 0.93 satisfactory 0.63 satisfactory

Boluo 0.89 satisfactory 0.69 satisfactorya: Zhang et al., 2019.

Table S5. Daily treatment capacity, service population, and treatment process of each wastewater treatment plant (W).

WWTP ID Name Daily treatment capacity (t/d)

Service population (per)

Treatment process

W1 Xunwu county 20000 690000 ASW2 Dingnan county 10000 210000 ASW3 Heping county 15000 120000 ASW4 Lianping county 15000 130000 ASW5 Xinfeng county 5000 210900 ASW6 Longchuan county 16800 136000 ASW7 Dongyuan county 11600 130000 ASW8 Yuancheng district 18000 80000 ASW9 Heyuan city south 22700 100000 ASW10 Zijin county 17500 120000 ASW11 Longmen county 10000 70900 ASW12 Baitang town 5000 60000 ASW13 Huidong county 39600 230000 ASW14 Longgang_shangyang 148900 430000 ASW15 Daya bay 29800 212000 ASW16 Xinxu town 10000 20000 TFW17 Huiyang city 70000 300000 AS

W18 Huiyang economic development zone 20000 40000 TF

W19 Jinshan county 102600 300000 ASW20 Huizhou no4 26200 110000 ASW21 Huizhou no5 41500 131000 ASW22 Huizhou jiangbei 23300 183000 ASW23 Huizhou meihu 300000 810000 ASW24 Boluo county 44400 200000 ASW25 Huizhou no6 50000 100000 ASW26 Tangxiashiqiaotou 40000 482100 ASW27 Zhangmutou 52500 132800 ASW28 Changpingdongbu 64200 250000 ASW29 Qishi 48200 121700 ASW30 Boluo huzhen 7400 30000 ASW31 Boluolongxi 6200 50000 ASW32 Huanjiang town 42700 118900 ASW33 Dalang 50700 37700 ASW34 Changpingxibu 45300 250000 ASW35 Boluoyuanzhou town 52200 200000 ASW36 Boluoshiwan town 21600 51000 ASW37 Liaobuzhuyuan 100000 418600 ASW38 Henglidongkeng 117400 204800 ASW39 Chashan 50000 156500 ASW40 Shijie 60000 247000 AS

WWTP ID Name Daily treatment capacity (t/d)

Service population (per)

Treatment process

W41 Gaobu town 50000 217400 ASW42 Zengcheng_xintang 100400 410000 ASData from: http://www.water8848.com/news/201703/15/91681.html (2019.2.5)

Table S6. Wastewater treatment rate and treated fraction of livestock farms (L).Livestock Farms ID Name Wastewater Treatment Rate Treated fractionL1 Xunwu county 0 1L2 Dingnan county 0 1L3 Heping county 0 1L4 Lianping county 0 1L5 Xinfeng county 0 1L6 Longchuan

county0 1

L7 Dongyuan county

0 1

L8 Yuancheng district

0 1

L9 Zijinxian county 0 1L10 Longmen county 0 1L11 Huidong county 0 1L12 Huiyang district 0 1L13 Huicheng district 0 1L14 Boluo county 0 1L15 Dongguan

district 10 1

L16 Dongguan district 2

0 1

L17 Xingtang district 0 1

Assuming that all wastewater from livestock farms is discharged directly into rivers without sewage treatment plants.

Table S7. Estimated predicted no-effect concentrations (PNECs) for 36 antibiotics (μg/L).

Category Chemicals Abbr N a

Observed lowest MIC

Size-adjusted lowest MIC

PNEC (resistance selection)

Ref.

Sulfonamides

Sulfadiazine SDZ 2 256000 16000 2 (mg/L) b

Sakharkar et al., 2009; http://antibiotics.toku-e.com

Sulfamethazine SMZ / / / 16 c Chen et al., 2018

Sulfamethoxazole SMX 8 1000 125 16 Bengtsson-Palme

and Larsson, 2016Sulfathiazole STZ 2 3200 2000 256 Karen et al., 1991Sulfachlorpyridazine SCP 1 400000 8000 1 (mg/L) b Karen et al., 1991

Sulfameter SM 3 40.9 2 0.25 Nakahata et al., 2016

Sulfamonomethoxine SMM 67 780 1000 128 Asawa et al., 1995

Sulfaquinoxaline SQX / / / 256 /

Sulfaguanidine SG 1 400000 8000 1 (mg/L) b Tsuchida et al.,

2014

Trimethoprim TMP 22 16 8 0.5 Bengtsson-Palme and Larsson, 2016

Ormetoprim OMP 1 1260 32 4http://antibiotics.toku-e.com

Tetracyclines

Oxytetracycline OTC 2 125 4 0.5 Bengtsson-Palme

and Larsson, 2016

Tetracycline TC 66 16 16 1 Bengtsson-Palme and Larsson, 2016

Chlortetracycline CTC 32 20 16 2

http://antibiotics.toku-e.com

Doxycycline DC 29 32 16 2 Bengtsson-Palme and Larsson, 2016

Methacycline MT 5 40 4 0.5http://antibiotics.toku-e.com

Fluoroquinolones

Norfloxacin NFX 15 16 4 0.5 Bengtsson-Palme and Larsson, 2016

Ciprofloxacin CFX 70 2 1 0.064 Bengtsson-Palme and Larsson, 2016

Ofloxacin OFX 26 8 4 0.5 Bengtsson-Palme and Larsson, 2016

Lomefloxacin LFX 29 4 2 0.25http://antibiotics.toku-e.com

Enrofloxacin EFX 4 8 0.5 0.064 Bengtsson-Palme and Larsson, 2016

Fleroxacin FL 15 25 8 1http://antibiotics.toku-e.com

Pefloxacin PEF 1 4000 64 8 Bengtsson-Palme and Larsson, 2016

Difloxacin DIF 1 500 16 2http://antibiotics.toku-e.com

Macrolides Leucomycin LCM 24 400 256 32 Omura and

Nakagawa 1977Clarithromycin

CTM 15 8 2 0.25 Bengtsson-Palme and Larsson, 2016

Category Chemicals Abbr N a

Observed lowest MIC

Size-adjusted lowest MIC

PNEC (resistance selection)

Ref.

Roxithromycin RTM 14 32 8 1 Bengtsson-Palme

and Larsson, 2016

Tylosin TYL 1 2000 32 4 Bengtsson-Palme and Larsson, 2016

Erythromycin- H2O

ETM 39 16 8 1 Bengtsson-Palme and Larsson, 2016

Chloramphenicols

Florfenicol FF 9 125 16 2 Bengtsson-Palme and Larsson, 2016

Chloramphenicol CAP 29 125 64 8 Bengtsson-Palme

and Larsson, 2016Lincomycin Lincomycin LIN 2 500 16 2 Bengtsson-Palme

and Larsson, 2016

β-Lactamsa (Penicillins and Cephalosporins)

Amoxicillin AMOX 29 4 2 0.25 Bengtsson-Palme and Larsson, 2016

Penicillin PEN 1019 4 4 0.5

http://antibiotics.toku-e.com

Cefazolin CFZ 18 32 8 1 Bengtsson-Palme and Larsson, 2016

Cephalexin CPX 10 250 32 4 Bengtsson-Palme and Larsson, 2016

Note: Values in red color mean that those MIC was for the silver complex with the sulfameter.Values in blue color mean no available values in publications and the average PNEC among Sulfonamides was selected.a: The number of tested species and the lowest MIC values observed for any species were collected from EUCAST database or antimicrobial index knowledgebase or publications.b: The estimated PNEC was larger than the largest EUCAST testing scale in the unit of µg/L, a unit of mg/L was used.c: There were available PNECs for resistance selection from publications.

Table S8. Modeling results (PECinitial and PECcatchment) of 36 antibiotics

AntibioticsDry Season Wet SeasonPECinitial (ng/L) PECcatchment (ng/L) PECinitial (ng/L) PECcatchment (ng/L)

SDZ 574 13.5 97.8 4.19SMZ 218 48.8 44.9 12.6SMX 192 50.7 34.3 9.84STZ 40.7 15.2 7.8 2.52SCP 192 84.2 31.9 12.8SM 157 57.5 25.7 8.5SMM 316 14.7 48 4.09SQX 346 11 44.8 3.33SG 163 63.3 35.8 15.6TMP 508 62.9 90.6 18.3OMP 4.03 1.54 0.644 0.231OTC 892 41.7 145 12.2TC 1030 54.7 196 16.1CTC 142 12.1 23.4 3.35DC 2530 953 476 156MT 49.7 20 9.68 3.85NFX 2650 85.4 478 26.6CFX 3320 99 544 29.9OFX 3540 92.9 531 27.2LFX 593 32.8 114 12EFX 1680 35.7 339 12.1FL 221 91.1 44 18.1PEF 882 340 169 55.5DIF 388 177 72 29.9LCM 347 127 52.8 18CTM 147 57.2 25.7 9.17RTM 348 92.7 70.9 23.5TYL 1920 140 308 39ETM 1790 161 269 44.6FF 4840 893 761 203CAP 639 243 119 38.9LIN 2990 170 509 51AMOX 8570 371 1430 111PEN 3450 148 600 45.5CFZ 6.95 0.217 1.19 0.0682CPX 2720 367 598 108

Figure S1

Figure S1 The points of antibiotic discharge in Dongjiang River Basin, includes 42 wastewater treatment plants (WWTPs, mark with W) and 17 livestock farms (LFs, mark with L).

Fig S2-1

Fig S2-2

Fig S2-3

Fig S2-4

Fig S2-5

Fig S2-6

Figure S2 Resistance risk assessment for 23 out of the 36 antibiotics.

Figure S3

Figure S3 High-risk map of antibiotic resistance in the Dongjiang River Basin. The cumulative frequency was defined as a full basin risk assessment of 36 antibiotics, and each river segment was assessed as the cumulative number of potential risks.

Note: We evaluated the risk of antibiotic resistance of 36 antibiotics in Dongjiang River Basin, and found that 23 antibiotics showed different levels of risk. Based on this, when an antibiotic is considered to cause the risk of antibiotic resistance, we count the number of times that each reach is considered to be likely to cause the risk of antibiotic resistance.

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