3.1. Distribution characteristics of antibiotics in water environment
3.1.1. Distribution of antibiotics in surface water
The concentration range and detection rate of antibiotics in surface water are shown in Table 3. In September, the total concentration range was 14.45-160.60ng/L, the average concentration was 69.26ng/L, and the detection rate ranged from 13% to 100%. The concentration of SMX was the highest, the range of concentration was 5.50-77.76ng/L, the average concentration was 29.67ng/L, the detection rate was 100%. The detection rates of SMZ (13%) and SIZ (38%) were low, and the detection rates of other species were 100%. In November, the total concentration range of the detection was 11.08-109.34ng/L, the average concentration was 61.84ng/L, and the detection rate ranges from 0% to 100%. Among them, the concentration of SMX was the highest, SMZ was not detected, and the detection rate of other species was 100%. Overall, the seven sulfonamide antibiotics were seen, the concentration was at the level of ng/L, and the total concentration was similar in autumn and winter. SMX was the primary antibiotic species, which was consistent with previous studies (Kolpin et al. 2002). Due to its unique occurrence characteristics, SMX in water samples can produce resistance among microorganisms and persist in the ecosystem (Prasannamedha et al. 2020). This section's SMX concentration in surface water was higher (Chen et al. 2002). The low detection rate of SIZ in September and the high rate in November may be due to a source of such antibiotics in the vicinity of the river, which should be taken seriously.
Table 3 Antibiotic concentrations in surface water of the Yitong River
|
Antibiotics
|
Range(ng/L)
|
Mean(ng/L)
|
Freq(%)
|
September(n=8, ng/L)
|
SMZ1
|
1.10-4.03
|
2.50
|
100%
|
SMZ
|
ND-0.36
|
0.05
|
13%
|
SMX
|
5.50-77.76
|
29.67
|
100%
|
SIZ
|
ND-2.64
|
0.73
|
38%
|
STZ
|
1.38-10.32
|
5.89
|
100%
|
SDZ
|
5.78-36.73
|
14.50
|
100%
|
SPD
|
0.69-28.76
|
15.92
|
100%
|
November(n=8, ng/L)
|
SMZ1
|
1.91-4.88
|
3.54
|
100%
|
SMZ
|
ND-ND
|
0.00
|
0%
|
SMX
|
5.98-36.31
|
20.28
|
100%
|
SIZ
|
ND-13.16
|
9.27
|
100%
|
STZ
|
1.49-6.78
|
3.90
|
100%
|
SDZ
|
1.36-22.80
|
11.61
|
100%
|
SPD
|
0.34-25.41
|
13.24
|
100%
|
Note: ND means not detected.
The spatial distribution of antibiotic concentration in the surface water is shown in Fig. 2. The total concentration of the S7 site was the highest in September, which was 149.98ng/L. The concentrations of the S3-S6 sites were high, the range was 81.35-90.30ng/L, and the rest were low, ranging from 16.06 to 23.64ng/L. In November, the total concentration of the S7 site was the highest (97.94ng/L), S4-S6 sites concentrations were high, the range was 73.81-89.37ng/L, the rest were low, ranging from 22.87 to 52.42ng/L. Generally speaking, the total concentration of S1, S2, S8 points were low; meanwhile, the total concentration of S3-S7 point was high, which may be related to the surrounding environment of the river section. The surrounding of S3-S7 point is densely populated and has a sewage outlet. While S1 is the outlet of Xinlicheng Reservoir, S2 and S8 are located around parks with a good environment. SMX, SDZ, and SPD concentrations were high, which were the main antibiotics in the reach, indicating that the three antibiotics were primarily used in the surrounding environment. However, the concentration of SIZ at each site was significantly high in November, meaning there was a potential source of this antibiotic around the area.
3.1.2. Distribution of antibiotics in surface sediments
The concentration range and detection rate of antibiotics in surface sediments are shown in Table 4. In September, the total concentration ranged from 3.36 to 85.68ng/g, the average concentration was 18.78ng/g, and the detection rate ranged from 25% to 100%. SMX was the main component in surface sediment, the same as the main antibiotics in surface water. The concentration ranged from 3.01 to 54.20ng/g, the average concentration was 12.41ng/g, and the detection rate was 100%. Except for SMZ1 and SPD, the detection rates of other species were lower. The total concentration range of November was ND-24.31ng/g, the average concentration was 15.17ng/g, and the detection rate ranged from 0% to 88%, among which the concentration of SIZ was the highest, meanwhile, SMZ and STZ were not detected. Overall, the seven sulfonamides antibiotics were all detected, the concentration was at the level of ng/g, and the total concentration in autumn and winter was similar. Same as surface water, SMX was the primary type of antibiotic. SMX and SDZ had higher concentrations than other parallel rivers.
The spatial distribution of antibiotic concentrations in surface sediments is shown in Fig. 3. In September, the total concentration of the S1 site was the highest, thus 70.60ng/g, S3 site was the higher concentration, 40.21ng/g. The rest of the site concentration was low, and the range was 4.06-9.70 ng/g. In November, the concentration of each point was low; the range was ND-21.73ng/g. Generally speaking, SMX was the main antibiotic in September, and SIZ was dominant in November, respectively. Different levels of antibiotic pollution can reflect the characteristics of antibiotic use and discharge in the surrounding area of the urban river. The results in Fig. 3 show that the antibiotic concentration of most points was low, which indicated that the antibiotic pollution in the surface sediment of this section was relatively light. However, the potential sources of various antibiotics still need to be paid attention to.
Table 4 Concentration of antibiotics in surface sediments of the Yitong River
|
Antibiotics
|
Range(ng/g)
|
Mean(ng/g)
|
Freq(%)
|
September(n=8,ng/g)
|
SMZ1
|
0.31-3.62
|
0.92
|
100%
|
SMZ
|
ND-14.73
|
2.72
|
25%
|
SMX
|
3.01-54.20
|
12.41
|
100%
|
SIZ
|
ND-8.23
|
1.46
|
50%
|
STZ
|
ND-1.49
|
0.37
|
38%
|
SDZ
|
ND-2.38
|
0.54
|
38%
|
SPD
|
0.04-1.03
|
0.36
|
100%
|
November(n=8,ng/g)
|
SMZ1
|
ND-2.12
|
1.00
|
88%
|
SMZ
|
ND-ND
|
0.00
|
0%
|
SMX
|
ND-3.70
|
2.46
|
88%
|
SIZ
|
ND-12.48
|
9.41
|
88%
|
STZ
|
ND-ND
|
0.00
|
0%
|
SDZ
|
ND-3.71
|
1.39
|
75%
|
SPD
|
ND-2.30
|
0.91
|
88%
|
3.1.3. Ecological risk assessment of antibiotics
The Risk Quotients method proposed by Hernand et al. was used to evaluate the risk of antibiotics (Hernando et al. 2006). According to the EU standards, the specific calculation formula is as follows (Garnier-Laplace et al. 2008):
RQs=MEC/PNEC(1)
In the formula: MEC is the measured environmental concentration (ng/L), considering the worst risk situation, choose the maximum concentration as the risk evaluation value; PNEC is the predicted no-effect concentration (ng/L).
PNEC=NOEC/AF=EC50/AF (2)
NOEC is the no observed effect concentration of the most sensitive species (mg/L); EC50 is the concentration for 50% of maximal effect (mg/L); AF is the assessment factor.
Acute toxicity data were used in this study, where AF was 1000 (Park and Choi 2008). The value of PNEC was obtained using the EC50 toxicity data obtained in the literature; details are shown in Table 5 (Białk-Bielińska et al. 2011). In addition, according to the RQs classification method proposed by Hernando et al., the risk assessment was divided into four levels: no risk (<0.01), low risk (0.01-0.1), medium risk (0.1-1), and high risk (>1) (Chen et al. 2020).
Table 5 Toxicological data of antibiotics for sensitive species
Antibiotics
|
Species
|
Toxic type
|
Evaluation factors
|
EC50(mg/L)
|
PNEC(ng/L)
|
SMZ1
|
Lemna minor
|
acute
|
1000
|
0.68
|
680
|
SMZ
|
Lemna minor
|
acute
|
1000
|
1.74
|
1740
|
SMX
|
Lemna minor
|
acute
|
1000
|
0.21
|
210
|
SIZ
|
Lemna minor
|
acute
|
1000
|
0.62
|
620
|
STZ
|
Lemna minor
|
acute
|
1000
|
4.89
|
4890
|
SDZ
|
Lemna minor
|
acute
|
1000
|
0.07
|
70
|
SPD
|
Lemna minor
|
acute
|
1000
|
0.46
|
460
|
Fig. 4 shows the RQs of the target antibiotics in surface water in autumn and winter. The results showed that SMX and SDZ showed the moderate acute risk to the sensitive species in river water in September, SPD showed low acute risk, and others showed no risk. In November, SMX and SDZ showed moderate acute risk, SIZ and SPD showed low acute risk, and others showed no risk. Generally, there was a specific problem of antibiotic pollution in the water environment of the target river, but it had not reached the high risk. Therefore, the control and management should be strengthened to reduce the ecological risk.
3.2. Distribution characteristics of resistance genes in water environment
Three sulfonamide resistance genes and 16S rRNA internal control genes in water environment samples were detected in the autumn and winter. The relative quantitative analysis of target gene abundance distribution was used to avoid the difference caused by DNA extraction efficiency and environmental microbial background value (Yang et al. 2016). The sampling points were S3, S5, and S7, and the sample names were NW9, ZW9, SW9, NW11, ZW11, and SW11.
3.2.1. Distribution of resistance genes in surface water
The absolute abundance of 16S rRNA in the surface water is shown in Fig. 5. The absolute abundance range was 1.92×105-1.29×106copies/μL in September and 1.87×105-2.57×105copies/μL in November. In general, the total bacterial abundance was similar in autumn and winter, in the range of 105-106. The relative abundance of that three sulfonamide resistance genes is shown in Fig. 6. The detection rate was 100% in autumn and winter, and the total relative abundance range was 1.43×10-2-9.98×10-2copies/16S rRNA in September and 5.91×10-2-1.19×10-1copies/16S rRNA in November. Overall, sul1 was the main resistance gene in September, and sul2 was the main resistance gene in November. The total relative abundance was similar in the 10-2-10-1 order of magnitude. In terms of temporal distribution, the relative abundance of resistance genes was November (2.63×10-1 copies/16S rRNA)>September (1.50×10-1 copies/16S rRNA). Again, in terms of spatial distribution, the relative abundance of sites ranged from 10-3-10-2 orders of magnitude in September to 10-3-10-1 orders of magnitude in November. Overall, the relative abundance of each site in autumn and winter was sul2 (2.51×10-1 copies/16S rRNA) > sul1(1.31×10-1 copies/16S rRNA) > sul3 (3.23×10-2 copies/16S rRNA). The high detection rate and high abundance level of ARGs indicated that the widespread use of the corresponding types of antibiotics around the river and other human activities and pollution source distribution had caused some environmental risks in the region (Yang et al. 2017a). There were many people around these three sites and drainage ports, hospitals, and other construction facilities. Therefore, they received sewage treatment plants, hospitals, and domestic sewage discharge, resulting in strong ARGs pollution.
3.2.2. Distribution of resistance genes in surface sediments
The absolute abundance of 16S rRNA in surface sediments is shown in Fig. 7. The absolute abundance range of 16S rRNA was 7.45×109-1.45×1011copies/g in September and 6.83×108-3.48×1010copies/g in November. The difference in bacterial abundance between autumn and winter was large, in the range of 108-1011. The relative abundance of that three sulfonamide resistance genes is shown in Fig. 8. The detection rate was 100% in autumn and winter, and the total relative abundance range was 1.03×10-2 - 6.02×10-2 copies/16S rRNA in September and 5.00×10-3-5.27×10-2 copies/16S rRNA in November. In general, sul1 was the main resistance gene in autumn and winter, and the relative abundance was similar in the range of 10-3-10-2. Based on temporal distribution, the relative abundance of resistance genes in September (1.13×10-1 copies/16S rRNA)>November (9.59×10-2 copies/16S rRNA). In autumn and winter, sul1 was the main resistance gene, and the relative abundance was in the range of 10-2-10-1. In the spatial distribution, the relative abundance of each point in autumn and winter was in the range of 10-5-10-2 orders of magnitude. Overall, the relative abundance of each site was sul1(1.70×10-1 copies/16S rRNA)>sul2(3.84×10-2 copies/16S rRNA)>sul3(3.44×10-4 copies/16S rRNA). The high detection rate and high abundance level of ARGs indicated that the resistance gene pollution in the target river was relatively serious. A similar thread was seen in the survey results of resistance genes in surface sediments of many lakes and rivers in the Haihe River Basin and the middle and lower reaches of the Yangtze River. Therefore, indicating that surface sediments of many lakes and rivers in China have become an important reservoir of resistance genes (Luo et al. 2010; Yang et al. 2017b).
3.3. Correlation analysis between antibiotic and resistance gene
As the antibiotics and resistance genes reservoir, the water environment plays an essential role in their storage and transmission. Studies have found a correlation between antibiotics and resistance genes (Guo et al. 2018). The Pearson correlation analysis was used to analyze the correlation between antibiotic concentration and the abundance of the corresponding resistance genes, as shown in Fig. 9 and Fig. 10. In surface water, sul1 was significantly negatively correlated with sul2 and positively correlated with sul3, while sul2 was significantly positively correlated with sul3. sul1 and sul3 were significantly positively correlated with SMZ and STZ, sul2 was significantly positively correlated with SIZ and was weakly correlated with other antibiotics. This, thus, indicated that SMZ, STZ, and SIZ promoted the formation of sul1, sul3, and sul2, respectively. SMZ was significantly positively correlated with STZ, and SMZ1 was significantly positively correlated with SPD, which indicated that they had homology with each other. SIZ was significantly negatively correlated with SMZ, STZ, and SPD, while the other antibiotics showed a weak correlation. In the surface sediments, sul1 and sul2 showed a significant positive correlation, which indicated that sul1 and sul2 had some homology.
Meanwhile, sul1 was weakly correlated with all the antibiotics tested, indicating other main influencing factors, such as environmental factors or pollutants, of resistance gene generation besides corresponding antibiotics in the water environment. sul3 was significantly negatively correlated with SMZ, SMX, and SMZ1, indicating that these antibiotics inhibited the production of sul3. On the other hand, SDZ was significantly positively correlated with SPD and significantly negatively correlated with most antibiotics. In addition, there was a significant correlation between most of the antibiotics, which indicated that they influenced each other immensely. Therefore, it could provide some reference for controlling and managing environmental pollution of sulfonamide antibiotics in urban rivers.
3.4. Correlation between antibiotic resistance genes and water quality parameter
The distribution of antibiotic resistance genes in the water environment is affected by antibiotics and is related to the water quality parameter (Li et al. 2018). Table 6 shows the test data of water quality parameter of the Yitong River. Furthermore, the correlation analysis results between water quality parameters and antibiotic concentration and resistance gene abundance are shown in Table 7 and Table 8.
Table 6 Water quality parameter data
|
Index
|
Min
|
Max
|
Mean
|
September
|
pH
|
7.239
|
8.404
|
7.765
|
WT(℃)
|
11.9
|
19.2
|
15.6
|
DO(mg/L)
|
6.20
|
12.60
|
9.86
|
SD(m)
|
0.15
|
1.02
|
0.48
|
CODCr(mg/L)
|
8.28
|
48.91
|
24.46
|
TN(mg/L)
|
1.52
|
6.71
|
3.52
|
NH3-N(mg/L)
|
0.60
|
3.58
|
1.04
|
TP(mg/L)
|
0.12
|
0.78
|
0.31
|
Chl-a(mg/L)
|
0.18
|
4.42
|
2.20
|
November
|
pH
|
7.791
|
9.116
|
8.125
|
WT(℃)
|
0.0
|
4.0
|
1.8
|
DO(mg/L)
|
4.41
|
13.16
|
9.38
|
SD(m)
|
0.10
|
0.75
|
0.34
|
CODCr(mg/L)
|
24.83
|
74.50
|
46.18
|
TN(mg/L)
|
0.47
|
2.32
|
0.94
|
NH3-N(mg/L)
|
0.09
|
0.33
|
0.15
|
TP(mg/L)
|
0.15
|
0.51
|
0.27
|
Chl-a(mg/L)
|
0.09
|
0.57
|
0.35
|
Table 7 shows that pH had a weak correlation with antibiotics; WT, DO, SD, and CODCr strongly correlated with some antibiotics. TN was significantly positively correlated with SMX, STZ, and SDZ and significantly negatively correlated with SIZ. NH3-N was significantly positively correlated with SMX and SDZ; TP was significantly positively correlated with SMX and SDZ. Chl-a was significantly positively correlated with SMX, STZ, and SDZ and significantly negatively correlated with SIZ. Therefore, it was concluded that the nutrient greatly affected the antibiotics in the water environment. Table 8 shows that sul1 was not associated with all the water quality parameters; sul2 was significantly positively correlated with pH and CODCr, and significantly negatively correlated with WT and TN. sul3 was significantly negatively correlated with pH and CODCr. In addition, the correlation between water quality parameters and resistance genes was weak, which indicated that water quality parameters had little effect on resistance genes in the water environment of this section of the Yitong River.
Table 7 Correlation between water quality parameter and antibiotic concentration
|
SMZ1
|
SMZ
|
SMX
|
SIZ
|
STZ
|
SDZ
|
SPD
|
pH
|
0.106
|
-0.256
|
-0.071
|
0.448
|
-0.349
|
-0.343
|
-0.041
|
WT(℃)
|
-0.323
|
0.301
|
0.335
|
-0.862**
|
0.541*
|
0.222
|
0.288
|
DO(mg/L)
|
0.477
|
0.300
|
0.382
|
0.067
|
0.433
|
0.364
|
0.590*
|
SD(m)
|
0.285
|
0.066
|
0.554*
|
-0.228
|
0.778**
|
0.465
|
0.551*
|
CODCr(mg/L)
|
0.418
|
-0.188
|
0.391
|
0.596*
|
0.010
|
0.420
|
0.430
|
TN(mg/L)
|
-0.061
|
0.313
|
0.717**
|
-0.679**
|
0.622*
|
0.659**
|
0.481
|
NH3-N(mg/L)
|
-0.103
|
0.049
|
0.697**
|
-0.464
|
0.239
|
0.729**
|
0.236
|
TP(mg/L)
|
0.045
|
0.045
|
0.707**
|
-0.076
|
0.169
|
0.788**
|
0.394
|
Chl-a(mg/L)
|
-0.165
|
-0.003
|
0.765**
|
-0.579*
|
0.532*
|
0.588*
|
0.478
|
Note: * indicates significant correlation at the level of 0.05, and ** indicates significant correlation at the level of 0.01.
Table 8 Correlation between water quality parameter and resistance genes abundance
|
sul1
|
sul2
|
sul3
|
pH
|
-0.524
|
0.827*
|
-0.920**
|
WT(℃)
|
0.551
|
-0.950**
|
0.734
|
DO(mg/L)
|
0.394
|
-0.021
|
-0.176
|
SD(m)
|
-0.262
|
-0.553
|
0.250
|
CODCr(mg/L)
|
-0.684
|
0.869*
|
-0.870*
|
TN(mg/L)
|
0.430
|
-0.850*
|
0.403
|
NH3-N(mg/L)
|
0.145
|
-0.594
|
-0.123
|
TP(mg/L)
|
-0.026
|
-0.129
|
-0.361
|
Chl-a(mg/L)
|
-0.015
|
-0.784
|
0.228
|
Note: * indicates significant correlation at the level of 0.05, and ** indicates significant correlation at the level of 0.01.