This study aimed to detect antibiotics in water, particulate, plant, and sediment in the Tigris River within Baghdad City, in addition to their spatiotemporal variations, and related physicochemical parameters. Five sites were selected in the river. Three target antibiotics (tetracycline, gentamycin, and ciprofloxacin) were detected in water, particulate, plant, and sediment of the river at all selected sites. The results clearly showed that the concentrations of target antibiotics were sediment > water > plant > particulate. Site 3 is considered as a risk site where high concentrations of all antibiotics during the wet and dry seasons were recorded. Tetracycline was recorded as a high concentration among other antibiotics in the river. Spearman's correlation and principal component analysis showed only a weak correlation between dissolved oxygen and the electric conductivity of the river's sediment with target antibiotics. To our knowledge, this was the first study of antibiotic traces in the Tigris River ecosystem. Lack of wastewater management, absence of antibiotic treatment, and less public awareness of antibiotic consumption are the main causes of antibiotic risk in the river ecosystem.

  • Antibiotics were highly accumulated in the river's sediment, and then in water, plant, and particulate.

  • The concentration of antibiotics in the river's sediment is as follows: tetracycline > ciprofloxacin > gentamycin.

  • A spatiotemporal variation of antibiotics was noticed.

  • A positive correlation between antibiotics and physicochemical parameters was reported in both seasons.

Day by day, antibiotic residues in aquatic systems have significantly increased, raising considerable concern (Riaz et al. 2018). The massive dissemination of drugs and pharmaceuticals affects the water system, human health, and animals. It extends to the target and non-target organisms and emerging antimicrobial resistance (Lin et al. 2018). Several synthetic antibiotics have been used for infection treatment in humans and animals, and most are used by pharmaceutical and farming industries to inhibit or prevent bacterial infections (Bitchava & Nengas 2010). Antibiotics are also used as growth promoters in many countries such as China and India, although their use has been prohibited in Europe since 2006 (Anh et al. 2021).

Worldwide, the consumption of antibiotics is remarkably increasing each year. It was reported that there was a rapid increase in the uptake of antibiotics from 21 to approximately 35 billion doses per day between 2000 and 2015 (Scaria et al. 2021), and an upsurge of utilization may continue to 200% by 2030 (Klein et al. 2018).

Antibiotics can be released into the environment via many sources, such as municipal wastewater treatment plants, hospitals, farms and agriculture, aquaculture, and pharmaceutical manufacturers (Kovalakova et al. 2020). It was reported that the pharmaceutical industries are considered the primary source of antibiotics in water, as their wastewater is discharged after treatment in pharmaceutical treatment plants; however, a small portion of antibiotics can be eliminated through the wastewater treatment plants of manufacturers (Lin & Tsai 2009). A small amount of antibiotics are absorbed in water, most of which are discharged into the environment, particularly in wastewater and aquaculture areas, and then accumulated in the surrounding sediments via absorption (Rico et al. 2013). In addition, some antibiotics are delivered to the environment through animal feces and urine, especially those used in livestock and poultry. In most cases, they enter the rivers and lakes and are absorbed by plants after being accumulated in the soil (Liu et al. 2021).

Tetracyclines (TCs) are broad-spectrum antibiotics commonly used worldwide, known for their ability to kill bacteria by protein synthesis inhibition due to their ring system with various functional groups such as hydroxyl, methyl, keto, and dimethylamine side chain (Granados-Chinchilla & Rodríguez 2017). TCs are water-soluble drugs and exist in three different forms in water depending on pH values (pKa1, pKa2, and pKa3), and log Kow values ranged from −2.2 to −1.3, whereas Kd value ranged from 300 to 200 l kg−1. These speciation forms make them easily absorbed in soils and persist in water systems (Zhao et al. 2011). Moreover, TCs can accumulate in the food chain, affecting microbial populations and enhancing the distribution of antibiotic resistance due to their slow environmental degradation. This phenomenon could lead to ecological imbalance. It also disrupts the intestinal flora of humans, especially when they are detected in drinking water and irrigation water (Monahan et al. 2022).

Aminoglycosides are highly polarized compounds in surface water and are passed to drinking water by leaking groundwater (Guevara-Almaraz et al. 2015). Gentamycin is one of the widest spectrum antibiotics used for the treatment of infectious diseases, is highly soluble in water and less soluble in organic solvents (Arhoutane et al. 2019), and has an electrochemical activity due to the hydroxyl and amino groups. Ivkovic et al. (2023) reported that evaporation, light scattering, and pulsed electrochemical detection methods were employed to detect gentamycin in water.

The increasing use of fluoroquinolones (FQs) leads to the emergence of pathogenic drug resistance, which may threaten human and animal health. Ciprofloxacin (CIP) has been one of the most frequently used in recent years as a new antibacterial drug. It exerts strong chemical interference and high toxicity for microorganisms and is difficult for microbes to absorb and utilize. Although many technologies have been studied to treat and reduce the concentration of CIP, a gradual increase in CIP concentration in water has been detected (Nannapaneni et al. 2005). In addition, CIP is considered the most effective antibiotic among FQs, CIP concentration in water ranged from ng l−1 to μg l−1 and increased remarkably to more than mg l−1 as the absorption method did not remove or degrade CIP (Wang et al. 2021).

In this study, we attempt to detect the presence of antibiotics in the Tigris River, understand their fate in the river's ecosystem matrices, and assess spatial and temporal variations.

Study area

Five sites alongside the Tigris River were selected to detect and determine the fate of antibiotics. The selected sites represented the Tigris River's up-reach, mid-reach, and down-reach (Figure 1). The nature and characteristics of selected sites are variable. The first site is considered an agricultural area, and the growth of many aquatic plants is noticeable. The second site is an agricultural area with public tourist activities. The third site represents the city center site in Baghdad City. The area is experiencing significant pollution primarily because of its proximity to ageing residential structures and an outdated urban sewer system. Consequently, waste is being directly discharged into the river. The fourth site includes several institutional buildings and is highly populated. The last site is a typical industrial region, highly populated, and near the main sewage treatment plant in Baghdad City.
Figure 1

Map illustrating the selected sites along the Tigris River.

Figure 1

Map illustrating the selected sites along the Tigris River.

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Sample collection

Samples from water, particulates, sediments, and plants were collected monthly from five sites during the study period from November 2020 to May 2021, from 8:30 am to 2:30 pm. All physicochemical analyses were performed during 24 h of collection. The water samples from the surface layer were collected at 20–30 cm depth in 1-l stopper-fitted clean polyethylene bottles. In this study, the submerged Ceratophyllum demersum was selected and collected as a whole plant from each site, as this plant is available throughout the year (including the wet and dry seasons). The plant was collected in plastic bags and rinsed with tap water and then with distilled water before being dried by a freeze-dryer. The samples were stored at –20 °C for further use. The sediment sample from each site was collected with a depth of 15 cm by Ekman Grab tools and placed in polyethylene bags. All samples were dried by a freeze-dryer and kept at –20 °C for further use. Particulates were collected by filtration, in which 3 l form water for each site was filtered through Millipore filter paper (0.45 μm). Filter papers were dried by a freeze-dryer and stored in closed bottles at –20 °C for further use. Sampling collection was done in triplicate. The results were expressed by wet and dry seasons based on the humidity percentage (RH %). The seasons at a humidity of more than 50% are represented by wet seasons and less than 50% RH; the seasons are considered dry seasons (Khalaf et al. 2019) (Supplementary material S1).

Determination of physical and chemical parameters

Several physicochemical parameters were determined in water and sediment samples, such as air temperature (AT), pH, conductivity (EC), and water temperature (WT). In addition, total alkalinity, total hardness, turbidity, total nitrogen, total organic carbon, dissolved oxygen (DO), salinity, and biological oxygen demand (BOD) were determined. All parameters were measured for dry and wet seasons. APHA (2012) methods were used (Supplementary material S2).

Detection of antibiotics

Three antibiotics were selected in this study, including tetracycline, gentamycin, and ciprofloxacin, based on their extensive local use (Personal Communication). The detection of antibiotics was performed in the water, sediment, particulates, and plant samples according to EPA (2007) with some modifications using AutoTrace SP280 with HLB Oasis (Dionex, USA).

Extraction of antibiotics from water samples

TC was extracted using a modified method by Shama et al. (2016). Briefly, water samples were collected from the surface at 30 cm depth in polyethylene. The tetracycline was extracted by adding 10 ml of citrate buffer. The mixture was vortexed for 5 min and incubated for 5 min at room temperature (RT). The mixture was centrifuged at 3,500 rpm for 10 min, the extraction was repeated, and the supernatant was loaded on a pre-conditioned SPE cartridge. The tetracycline was eluted by washing the cartridge with 5 ml of water and then filtered through 0.45-μm Millipore filter paper. 100 μl of the filtered aliquot was injected into the HPLC system.

Gentamycin and ciprofloxacin were extracted according to the method described by Salah et al. (2015). Briefly, the samples were kept in the dark for 30 min at RT, and antibiotics were extracted by adding 10 ml phosphate buffer in a tightly sealed tube. The tubes were shaken vigorously for 10 min and centrifuged at 4,000 rpm for 10 min. The supernatant was transferred to a clean tube containing 10 ml phosphate buffer with vigorous shaking for 10 min. The supernatant was centrifuged again, and the pH was adjusted to 7.5–8.0. The supernatant was loaded on a pre-conditioned SPE cartridge.

Extraction of antibiotics from particulate and sediment samples

2.0 g of each particulate and sediment sample was stored at 4 °C for 24 h to extract antibiotics from particulate and sediment samples. 10 ml of citrate buffer (pH 3.0) and acetonitrile were added to the tubes containing samples, and the mixture was vortexed for 40 s. The mixtures were subjected to sonication for 15 min before centrifugation at 3,500 rpm for 10 min. The extraction process was repeated twice, and the resulting supernatants were collected in a 100-ml container for evaporation. The supernatants were evaporated at 55 °C on a rotary evaporator to remove organic solvents and diluted with Milli-Q water. The SPE cartridge was supplemented with Tandem SAX and HLB cartridge, and all supernatants were loaded on the HLB cartridge at a flow rate of 3–5 ml/min. The cartridge was washed with 10 ml milli-Q water (pH 3.0) and vacuum-dried for 30 min. The antibiotics were eluted by the addition of 10 ml of methanol and evaporated, followed by the addition of 1.0 ml of methanol and Milli-Q water at a ratio of (v/v, 1:1), then stored at –20 °C until analysis (Yang et al. 2020).

Extraction of antibiotics from plant samples (C. demersum)

To extract antibiotic residues from the plant, 0.5 g of collected dried plant samples were kept in the dark for 24 with the standard solution. The samples were swirled for 1 min after adding 5% acetonitrile-acetic and 0.1 M EDTA (v/v, 1:1, pH 4.0). The supernatant was collected by centrifugation at 8,000 rpm for 10 min and then transferred to tubes containing (1 g) NaCl and (4 g) Na2SO4 to extract the settled solid. All tubes were shaken vigorously, centrifuged at 5,000 rpm for 5 min, and manipulated with (150 mg) C18 and (900 mg) Na2SO4. The mixture was vortexed for 1 min, centrifuged at 4,000 rpm for 5 min, and evaporated. The resulting supernatant was filtered through 0.22 μm Millipore filter paper after being re-dissolved with a mixture of 0.8 ml of acetonitrile and (0.1%) formic acid at a ratio of (v/v, 20:80) (Tang et al. 2021).

Quality assurance and quality control

Three different replicates from five different sites were performed from each site. All physicochemical analyses were done according to the standard methods of APHA (APHA 2012) in triplicate with a standard curve for each factor. The analytical methods were done using HPLC; the mobile phase = Methanol: DW (70:30), column = C18-ODS (25 cm*4.6 mm). The fluorescent detector is 320nm, Em: 420nm, flow rate: 1.2 ml min−1. Antibiotic standard solutions (0.05, 0.15, 0.25, and 0.5 μg ml−1) were used to generate standard curves for antibiotic extraction, and the single peak for each single antibiotic was blotted against the resulting peak (Gros et al. 2006).

Statistical analysis

To analyze the association between antibiotics and environmental characteristics, statistical analysis was carried out using R-statistical programming packages (Love et al. 2019), and Canonical Correlation Analysis (CAA) was performed using the CANOCO software (Ter Braak & Smilauer 2002). As well as using the PCA (principal components analysis) approach and Spearman's Correlation.

Physicochemical parameters

In this study, environmental parameters were measured for each site during the wet and dry seasons. The physical parameter results showed a variation in the values of each factor in the dry and wet seasons. In contrast, there were no obvious variations between both seasons for each site. The statistical analysis revealed significant differences between the dry and wet seasons (Table 1).

Table 1

The physical parameters in the dry and wet seasons for all studied sites

FactorsRange (Dry)
Mean ± SDRange (Wet)
Mean ± SD
Minimum valueMaximum valueMinimum ValueMaximum Value
Air Temperature (AT°C) 20 35 29.26 ± 6.26 11 22 18.05 ± 3.23 
Water Temperature (WT°C) 14 27 22.73 ± 4.25 13 24 17.30 ± 3.51 
Electrical Conductivity (EC) (μS cm−1871 1,143 1,041.06 ± 85.12 363 1,192 996.55 ± 226.46 
Turbidity (NTU) 27 141 63.20 ± 31.17 146.0 34.85 ± 33.95 
Salinity 0.54 0.710 0.01 ± 0.05 0.22 0.740 0.60 ± 0.13 
EC Sediment (μS cm−1213 1,640 535.53 ± 367.39 174 1,394 529.75 ± 325.70 
FactorsRange (Dry)
Mean ± SDRange (Wet)
Mean ± SD
Minimum valueMaximum valueMinimum ValueMaximum Value
Air Temperature (AT°C) 20 35 29.26 ± 6.26 11 22 18.05 ± 3.23 
Water Temperature (WT°C) 14 27 22.73 ± 4.25 13 24 17.30 ± 3.51 
Electrical Conductivity (EC) (μS cm−1871 1,143 1,041.06 ± 85.12 363 1,192 996.55 ± 226.46 
Turbidity (NTU) 27 141 63.20 ± 31.17 146.0 34.85 ± 33.95 
Salinity 0.54 0.710 0.01 ± 0.05 0.22 0.740 0.60 ± 0.13 
EC Sediment (μS cm−1213 1,640 535.53 ± 367.39 174 1,394 529.75 ± 325.70 

The results from Table 1 revealed that the physical factors in the dry and wet seasons were variable in all studied sites, as the minimum values of AT ranged from 20 to 11 °C and the maximum from 35 to 22 °C in dry and wet seasons, respectively. On the other hand, the WT showed a slight variation between the dry and wet seasons, as the minimum values were 14–13 °C, and the maximum values were 27–24 °C. In addition, other parameters did not show significant differences between both seasons and within the study sites.

The physical parameters were measured in each site, and there were significant differences between each factor within each site (Figure 2). The EC values were significantly different in water and sediments and within sites, as the higher values of EC were recorded in water in the wet season for all sites (Figure 2(a)). For sediment, site 3 recorded a higher value (1,640 μS cm−1) in the wet season, followed by site 5 in the dry season (Figure 2(b)). Moreover, the lowest value of turbidity was at site 3 (7 UNT) in the dry season, and the highest one was at site 4 (144 UNT) in the wet season (Figure 2(c)). In contrast, site 2 showed the lowest salinity value (0.22) in the wet season and the highest values of 0.71 in sites 1, 2, 3, and 5 in the dry season (Figure 2(d)).
Figure 2

The physical parameters of water and sediment. Physical factors were measured in all sites and at both seasons; dry and wet seasons. (a) Electrical conductivity in water; (b) electrical conductivity in sediment; (c) turbidity; (d) salinity. In all cases, the X axis refers to the sites, and the Y axis refers to the values of each factor, all sites are represented by a particular color. All measurements are done in triplicate.

Figure 2

The physical parameters of water and sediment. Physical factors were measured in all sites and at both seasons; dry and wet seasons. (a) Electrical conductivity in water; (b) electrical conductivity in sediment; (c) turbidity; (d) salinity. In all cases, the X axis refers to the sites, and the Y axis refers to the values of each factor, all sites are represented by a particular color. All measurements are done in triplicate.

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Chemical parameters

Chemical factors of water and sediment are measured in the dry and wet seasons for all studied sites (Table 2). It is obvious from Table 2 that there was a difference in the values of DO in the dry and wet seasons, whereas there were no significant differences in total hardness and a slight difference in BOD in both seasons. In addition, total organic carbon recorded maximum values in the dry season, and obvious variations were within the studied sites. Similarly, total phosphorous and total nitrogen values were higher in the dry season than in the wet season.

Table 2

The values of chemical factors of studied sites in dry and wet seasons

FactorsRange (dry)
MeanRange (wet)
Mean
Minimum valueMaximum valueMinimum ValueMaximum Value
Dissolved oxygen (mg l−16.60 ± 0.50 7.90 ± 0.85 
Alkalinity (mg l−178 91 83.80 ± 4.16 63 138 103.55 ± 24.63 
Total hardness (mg l−1362 490 415.40 ± 31.14 264 441 996.55 ± 226.46 
pH Water 7.66 ± 0.48 7.15 ± 0.36 
Biological oxygen demand (BOD) (mg l−14.93 ± 0.70 6.25 ± 0.71 
Total phosphorous (mg l−10.41 6.81 1.36 ± 1.75 0.01 1.53 0.44 ± 0.41 
Total nitrogen (mg l−10.76 1.49 1.21 ± 0.25 0.53 0.91 0.66 ± 0.08 
Total organic carbon (mg l−10.01 0.29 0.10 ± 0.08 0.59 0.14 ± 0.15 
Total organic compound 0.03 0.42 0.16 ± 0.11 0.71 0.20 ± 0.19 
pH Sediment 7 ± 0 6.80 ± 0.41 
FactorsRange (dry)
MeanRange (wet)
Mean
Minimum valueMaximum valueMinimum ValueMaximum Value
Dissolved oxygen (mg l−16.60 ± 0.50 7.90 ± 0.85 
Alkalinity (mg l−178 91 83.80 ± 4.16 63 138 103.55 ± 24.63 
Total hardness (mg l−1362 490 415.40 ± 31.14 264 441 996.55 ± 226.46 
pH Water 7.66 ± 0.48 7.15 ± 0.36 
Biological oxygen demand (BOD) (mg l−14.93 ± 0.70 6.25 ± 0.71 
Total phosphorous (mg l−10.41 6.81 1.36 ± 1.75 0.01 1.53 0.44 ± 0.41 
Total nitrogen (mg l−10.76 1.49 1.21 ± 0.25 0.53 0.91 0.66 ± 0.08 
Total organic carbon (mg l−10.01 0.29 0.10 ± 0.08 0.59 0.14 ± 0.15 
Total organic compound 0.03 0.42 0.16 ± 0.11 0.71 0.20 ± 0.19 
pH Sediment 7 ± 0 6.80 ± 0.41 

The chemical factors in each site were measured, as shown in Figure 3. In all sites, DO values were constant during the dry and wet seasons, except for sites 1 and 2, where the value was slightly increased (7.9 mg l−1) in the dry season (Figure 3(a)). On the other hand, the alkalinity values in the dry season were higher than those recorded for the wet season in all sites (Figure 3(b)).
Figure 3

The chemical parameters of water and sediment. Chemical factors were measured in all sites and at both seasons; dry and wet seasons. (a) Dissolved oxygen; (b) alkalinity; (c) total hardness; (d) biological oxygen demand; (e) pH of water; (f) pH for sediment; (g) total phosphorous; (h) total nitrogen; (i) total organic carbon; (j) total organic compound. In all cases, the X axis refers to the sites, and the Y axis refers to the values of each factor, all sites are represented by a particular color. All measurements are done in triplicate. The data shown are the mean and SD from three independent replicates. The statistical analysis was done by GraphPad Prism 8.

Figure 3

The chemical parameters of water and sediment. Chemical factors were measured in all sites and at both seasons; dry and wet seasons. (a) Dissolved oxygen; (b) alkalinity; (c) total hardness; (d) biological oxygen demand; (e) pH of water; (f) pH for sediment; (g) total phosphorous; (h) total nitrogen; (i) total organic carbon; (j) total organic compound. In all cases, the X axis refers to the sites, and the Y axis refers to the values of each factor, all sites are represented by a particular color. All measurements are done in triplicate. The data shown are the mean and SD from three independent replicates. The statistical analysis was done by GraphPad Prism 8.

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Unlike alkalinity, total hardness demonstrates different patterns in the dry and wet seasons within the site. The highest value was detected at site 5 (490 mg l−1 in the wet season) (Figure 3(c)). Furthermore, most BOD values were above 6.5 mg l−1 in almost all sites, particularly in the dry season (Figure 3(d)).

In addition, pH is among the chemical parameters measured in water and sediment for all sites, and the values ranged from 7 to 8 among all sites in both seasons (figure 3(e) and 3(f)). Phosphorus is another important factor that may affect the occurrence of antibiotics in water. In this study, sites 3 and 4 exhibit the highest values of 0.61 and 0.75 mg l−1 in the wet season and remarkably decrease in sites 3 and 5 in the dry season. In contrast, the total nitrogen values increased dramatically during the wet season in all sites, compared to the dry season (Figure 3(h)).

The TOC and OM concentrations were variable within the sites and during both seasons, as the lowest value for both factors was reported at site 5 during the wet season (Figure 3(i) and 3(j)). At the same time, the concentration of OM in all sites was higher than the TOC (Figure 3(j)).

Detection of antibiotics

In this study, three types of antibiotics (tetracycline, gentamycin, and ciprofloxacin) were selected according to their extensive use in all selected sites (Table 3). For each site, four samples from water, particulate matter, plant, and sediment were collected to detect antibiotic residues. The HPLC analysis of each antibiotic is shown in Figure 4.
Table 3

The physicochemical properties of antibiotics used in this study

GroupCompoundAcronymMolecular MassLog kowapKaaMolecular FormulaReferences
Tetracyclines Tetracycline TC 444.43 −1.37 3.3 C22H24N2O8 Yang et al. (2011)  
Aminoglycoside Gentamycin GEN 477.596 −4.1 12.5 C21H43N5O7 O'Neil et al., (2013)  
Fluoroquinolones Ciprofloxacin CIP 331.34 0.28 6.09 C17H18FN3O3 Qiang & Adams (2004)  
GroupCompoundAcronymMolecular MassLog kowapKaaMolecular FormulaReferences
Tetracyclines Tetracycline TC 444.43 −1.37 3.3 C22H24N2O8 Yang et al. (2011)  
Aminoglycoside Gentamycin GEN 477.596 −4.1 12.5 C21H43N5O7 O'Neil et al., (2013)  
Fluoroquinolones Ciprofloxacin CIP 331.34 0.28 6.09 C17H18FN3O3 Qiang & Adams (2004)  
Figure 4

The HPLC analysis of detected antibiotics. Samples of water, particulate, plant, and sediment were subjected to HPLC for antibiotic detection. (a) Tetracycline; (b) gentamycin; (c) ciprofloxacin.

Figure 4

The HPLC analysis of detected antibiotics. Samples of water, particulate, plant, and sediment were subjected to HPLC for antibiotic detection. (a) Tetracycline; (b) gentamycin; (c) ciprofloxacin.

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Antibiotics in water

As previously mentioned, samples were collected during the dry and wet seasons and subjected to HLB Oasis to unequivocally determine the destination of antibiotics in water or whether they will be passed through particulate matter, plants, and sediment.

The results revealed that the concentration of all selected antibiotics in water was higher in site 3 compared to other sites at both seasons. The concentration of tetracycline residues in site 3 was 43.57 and 29.86 μg ml−1 for wet and dry seasons, followed by site 5 (22.52 and 9.16 μg ml−1) in wet and dry seasons (Table 4). The statistical analysis showed highly significant differences (***p < 0.001) between 3 and 5 sites compared to other sites.

Table 4

The mean antibiotic concentrations for all studied sites during the dry and wet seasons

Antibiotic concentration – mean
AntibioticsSitesWater (μg ml−1)
Particulate (μg kg−1)
Plant (μg kg−1)
Sediments (μg kg−1)
DryWetDryWetDryWetDryWet
Tetracycline S1 2.20 5.55 0.46 0.72 11.40 18.20 21.25 40.20 
S2 6.96 16.66 0.70 1.02 15.13 30.25 23 45.72 
S3 29.86 43.57 1.30 2.32 56.36 79.75 158.05 246.15 
S4 1.60 5.47 0.16 0.42 5.33 10.20 18.65 23.45 
S5 9.16 22.52 0.70 0.97 34.93 65.32 60.80 97.12 
Gentamycin S1 1.86 2.95 0.16 0.30 9.56 12.85 18.30 22.40 
S2 3.20 5.32 0.50 0.67 10.46 12.29 10.50 15.80 
S3 10.96 12.50 1.30 1.40 29.86 32.32 43.35 48.90 
S4 2.36 3.10 0.06 0.20 4.96 6.92 9.15 12.02 
S5 8.06 10.95 0.60 0.82 20.36 21.15 21.55 29.02 
Ciprofloxacin S1 9.86 11.22 1.50 1.37 13.56 23.15 40.90 48.87 
S2 8.86 9.55 1.10 1.17 17.20 21.32 41.55 42.75 
S3 22.50 23.40 2.70 3.35 63.76 70.07 91.35 99.42 
S4 11.30 9.97 0.50 0.62 15.36 19.07 27.35 32.40 
S5 15.70 16.57 1.16 1.12 41.50 42.97 83.25 84.45 
Antibiotic concentration – mean
AntibioticsSitesWater (μg ml−1)
Particulate (μg kg−1)
Plant (μg kg−1)
Sediments (μg kg−1)
DryWetDryWetDryWetDryWet
Tetracycline S1 2.20 5.55 0.46 0.72 11.40 18.20 21.25 40.20 
S2 6.96 16.66 0.70 1.02 15.13 30.25 23 45.72 
S3 29.86 43.57 1.30 2.32 56.36 79.75 158.05 246.15 
S4 1.60 5.47 0.16 0.42 5.33 10.20 18.65 23.45 
S5 9.16 22.52 0.70 0.97 34.93 65.32 60.80 97.12 
Gentamycin S1 1.86 2.95 0.16 0.30 9.56 12.85 18.30 22.40 
S2 3.20 5.32 0.50 0.67 10.46 12.29 10.50 15.80 
S3 10.96 12.50 1.30 1.40 29.86 32.32 43.35 48.90 
S4 2.36 3.10 0.06 0.20 4.96 6.92 9.15 12.02 
S5 8.06 10.95 0.60 0.82 20.36 21.15 21.55 29.02 
Ciprofloxacin S1 9.86 11.22 1.50 1.37 13.56 23.15 40.90 48.87 
S2 8.86 9.55 1.10 1.17 17.20 21.32 41.55 42.75 
S3 22.50 23.40 2.70 3.35 63.76 70.07 91.35 99.42 
S4 11.30 9.97 0.50 0.62 15.36 19.07 27.35 32.40 
S5 15.70 16.57 1.16 1.12 41.50 42.97 83.25 84.45 

Similarly, gentamycin and ciprofloxacin were detected mainly in site 3 and site 5 with significant differences at **p < 0.05, compared to other sites during both seasons (Figure 5, Table 4).
Figure 5

The concentration of antibiotic residues in water for all selected sites during the wet and dry seasons. Tetracycline, gentamycin, and ciprofloxacin were detected in water samples for each site at two seasons. The black bar represents the proportion of tetracycline within five selected sites; the grey bar: refers to gentamycin; the white bar: refers to ciprofloxacin. In all cases, the data shown are the mean and SD from three independent replicates. The statistical significance was determined by multiple Student t-test (**p < 0.05, ***p < 0.001). The statistical analysis was done by GraphPadPrism 8.0.

Figure 5

The concentration of antibiotic residues in water for all selected sites during the wet and dry seasons. Tetracycline, gentamycin, and ciprofloxacin were detected in water samples for each site at two seasons. The black bar represents the proportion of tetracycline within five selected sites; the grey bar: refers to gentamycin; the white bar: refers to ciprofloxacin. In all cases, the data shown are the mean and SD from three independent replicates. The statistical significance was determined by multiple Student t-test (**p < 0.05, ***p < 0.001). The statistical analysis was done by GraphPadPrism 8.0.

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Antibiotics in particulate matter

The selected antibiotics were extracted from particulate matter for all sites to track the presence of antibiotics from the source to their fate (Figure 6). The figure clearly shows that the concentration of all antibiotics was lower than that in water (as shown in Figure 4).
Figure 6

Antibiotic residues in particulate for all selected sites within two seasons. All antibiotics were detected from particulate for each site at two seasons. The black bar represents the proportion of tetracycline within five selected sites; the grey bar: refers to gentamycin; the white bar: refers to ciprofloxacin. In all cases, the data shown are the mean and SD from three independent replicates. The statistical significance was determined by multiple Student t-test (**p < 0.05, ***p < 0.001). The statistical analysis was done by GraphPadPrism 8.0.

Figure 6

Antibiotic residues in particulate for all selected sites within two seasons. All antibiotics were detected from particulate for each site at two seasons. The black bar represents the proportion of tetracycline within five selected sites; the grey bar: refers to gentamycin; the white bar: refers to ciprofloxacin. In all cases, the data shown are the mean and SD from three independent replicates. The statistical significance was determined by multiple Student t-test (**p < 0.05, ***p < 0.001). The statistical analysis was done by GraphPadPrism 8.0.

Close modal

Most noted that ciprofloxacin was the most abundant drug in particulate matter, particularly in site 3, where the concentration was 3.35 and 2.70 μg kg−1 for wet and dry seasons, respectively, followed by tetracycline (2.32, 1.30 μg kg−1). On the other hand, gentamycin was detected at lower concentrations in the same site (1.40, 1.30 μg kg−1) (Table 4). Although sites 1, 2, 4, and 5 did not exhibit high concentrations of antibiotics in particulate, the statistical analysis showed significant differences between site 3 and other sites.

Antibiotics in C. demersum

To precisely assess the fate of antibiotics in water and particulate matter and whether the residues reach the sediment by plant, C. demersum from all studied sites was collected and extracted as previously mentioned. The scenario of all antibiotics is similar to water and particulate, where the higher concentration of antibiotics was in site 3, followed by site 5, and less in sites 1, 2, and 4 (Figure 7). At site 3, tetracycline recorded a higher concentration than others at both seasons (79.75 and 56.36 μg kg−1) with high significant differences at ***p < 0.001, and 65.32 and 34.93 μg kg−1 at site 5 during the wet and dry seasons.
Figure 7

Concentration of antibiotic residues in plant for all selected sites at two seasons. Antibiotics were detected from plant samples for each site at two seasons. The black bar represents the proportion of tetracycline within five selected sites; the grey bar: refers to gentamycin; the white bar: refers to ciprofloxacin. In all cases, the data shown are the mean and SD from three independent replicates. The statistical significance was determined by multiple Student t-test (**p < 0.05, ***p < 0.001). The statistical analysis was done by GraphPadPrism 8.0.

Figure 7

Concentration of antibiotic residues in plant for all selected sites at two seasons. Antibiotics were detected from plant samples for each site at two seasons. The black bar represents the proportion of tetracycline within five selected sites; the grey bar: refers to gentamycin; the white bar: refers to ciprofloxacin. In all cases, the data shown are the mean and SD from three independent replicates. The statistical significance was determined by multiple Student t-test (**p < 0.05, ***p < 0.001). The statistical analysis was done by GraphPadPrism 8.0.

Close modal

Most notably, the plant exerts a good environment for tetracycline, which was the most abundant pollutant in the studied sites, followed by ciprofloxacin and less gentamycin (Figure 6) (Table 4). The concentration of ciprofloxacin in the wet and dry seasons was slightly variable in all sites, except for site three, which exhibits highly significant differences (***p < 0.001) compared with others in the wet season and at **p < 0.05 in the dry season. Site 4 has no statistical differences from sites 1, 2, 3, and 5.

On the other hand, the concentrations of gentamycin decrease in sites 3 and 5 and much less in sites 1, 2, and 4 (Figure 7).

Antibiotic residues in sediment

To determine the final destination of antibiotics in the river, sediment samples from all selected sites were collected and manipulated for antibiotic extraction and detection (Figure 8).
Figure 8

Antibiotic residues in sediment for all selected sites within two seasons. All antibiotics were extracted and detected in sediment for each site at two seasons. The black bar represents the proportion of tetracycline within five selected sites; the grey bar: refers to gentamycin; the white bar: refers to ciprofloxacin. In all cases, the data shown are the mean and SD from three independent replicates. The statistical significance was determined by multiple Student t-test (**p < 0.05, ***p < 0.001). The statistical analysis was done by GraphPadPrism 8.0.

Figure 8

Antibiotic residues in sediment for all selected sites within two seasons. All antibiotics were extracted and detected in sediment for each site at two seasons. The black bar represents the proportion of tetracycline within five selected sites; the grey bar: refers to gentamycin; the white bar: refers to ciprofloxacin. In all cases, the data shown are the mean and SD from three independent replicates. The statistical significance was determined by multiple Student t-test (**p < 0.05, ***p < 0.001). The statistical analysis was done by GraphPadPrism 8.0.

Close modal

Interestingly, the figure revealed that the concentration of tetracycline residues in sediment was higher than that in water, particulate matter, and plants in the wet and dry seasons at all studied sites.

Similarly, site 3 recorded a higher concentration of all selected antibiotics than other sites. In the wet season, the concentration of tetracycline, gentamycin, and ciprofloxacin residues was 246.15, 48.90, and 99.42 μg kg−1. In comparison, their concentration in the dry season was 158.05, 43.35, and 91.35 μg kg−1 at site 3 (Table 4).

Our findings suggest that the antibiotic residues in the water sample were unstable and may have accumulated in the sediment through particulate matter and plants.

The correlation between physicochemical parameters and antibiotics

Spearman's Correlation was performed to unequivocally determine the correlation between physical and chemical factors and the presence of antibiotics in water, particulate, plant, and sediment (Supplementary material) (Figure 9).
Figure 9

Data analysis of CCA of antibiotics and environmental factors in Tigris River during the study period.

Figure 9

Data analysis of CCA of antibiotics and environmental factors in Tigris River during the study period.

Close modal

The results indicated that most physical and chemical factors did not affect the occurrence of antibiotics during the wet and dry seasons, except for DO and EC sediment, where there was a slight effect on antibiotics.

Canonical correspondence analysis (CCA) results showed a positive correlation between two antibiotics (ciprofloxacin and gentamycin) and the environmental factors (WT, EC, and total hardness) in particulate matter. In contrast, a negative correlation was with other matrixes (plant, water, and sediment). For tetracycline, the environmental factors show a negative correlation in particulate and a positive correlation in other matrixes (Figure 9).

The principal component analysis (PCA) was done, and the presence of six-factor groups was determined: RC1- RC6 (Table 5) (Figure 10). The eigenvalues ranged from 4.704 to 1.030 for RC1 and RC6, respectively. All groups are related negatively, as shown in the Components correlation matrix below (Supplementary material).
Table 5

The principal component analysis of the correlation between antibiotics and physicochemical factors

Component characteristics
Unrotated solution
Rotated solution
EigenvalueProportion var.CumulativeSum Sq. loadingsProportion var.Cumulative
Component 1 4.704 0.248 0.248 3.330 0.175 0.175 
Component 2 3.284 0.173 0.420 3.120 0.164 0.339 
Component 3 2.703 0.142 0.563 2.546 0.134 0.473 
Component 4 1.883 0.099 0.662 2.084 0.110 0.583 
Component 5 1.297 0.068 0.730 1.920 0.101 0.684 
Component 6 1.030 0.054 0.784 1.901 0.100 0.784 
Component characteristics
Unrotated solution
Rotated solution
EigenvalueProportion var.CumulativeSum Sq. loadingsProportion var.Cumulative
Component 1 4.704 0.248 0.248 3.330 0.175 0.175 
Component 2 3.284 0.173 0.420 3.120 0.164 0.339 
Component 3 2.703 0.142 0.563 2.546 0.134 0.473 
Component 4 1.883 0.099 0.662 2.084 0.110 0.583 
Component 5 1.297 0.068 0.730 1.920 0.101 0.684 
Component 6 1.030 0.054 0.784 1.901 0.100 0.784 
Figure 10

Principal component analysis of the correlation between antibiotics and environmental factors in the Tigris River during the study period.

Figure 10

Principal component analysis of the correlation between antibiotics and environmental factors in the Tigris River during the study period.

Close modal

Since water is one of the most essential sources of all living organisms worldwide, it can be used for municipal, industrial, and agricultural purposes. The need for clean and pure water is increasing daily due to the increase in the demand for water sources in many countries, which leads to water shortages worldwide (Noor et al. 2022). The rapid increase in population density and world development enhanced the contamination of rivers and surface water, which have a diverse effect on human and animal health worldwide (Al-Sudani 2021). Due to anthropogenic and industrial activities, the physicochemical parameters of rivers have remarkably deteriorated (Majeed et al. 2022). Tigris River is the main source of surface water in Iraq, and the contamination of Tigris River is brought about mainly by the direct discharge of industrial and human waste into the river without proper treatment plants, as well as the leaching of organic compounds and nutrient from soil, the atmospheric process of evapotranspiration, and hydrological factors leads to changing the physical and chemical parameters of Tigris River (Ali et al. 2018).

Iraq is well known for its climate properties: hot and dry in summer and cold and rainy in winter; this may explain the variation of water and air temperatures during wet and dry seasons (Al-Ansari 2021). In addition, the improper discharge of industrial and agricultural wastewater into the river increases the evaporation rate, particularly in the dry season, which in turn increases the electrical conductivity and salinity (Hassan et al. 2010). Here we showed that the EC values increased in the wet season than in the dry season, which may be attributed to the role of discharge from runoff and precipitation in the increased values of EC in the wet season than the dry season, as several dissolved salts and inorganic materials such as chlorides, alkalis, and sulfides are delivered into the water by surface runoff, irrigation, and human activity, and also to increase soil salinity in the wet season (Al-Ani et al. 2019). In the studies by Majeed et al. (2022) and Noor et al. (2022), they found that the EC values increased from 788.33 to 942.5 μS cm−1 and salinity from 0.504 to 0.603 and from 877 to 1,192 μS cm−1 in the wet season.

Interestingly, the variation in turbidity values during wet and dry seasons is due to the amount of wastewater discharged to the close area without proper treatment and runoff discharge (Majeed et al. 2022). Furthermore, it was reported that DO is held by cold water more than warm water; however, a higher value of DO in the dry season and a dense macrophytes growth were noticed, which may suggest that temperature is inversely correlated with DO values, as low temperature leads to a decrease in the DO ions and inactivation of the molecules in water (Rashad et al. 2020).

The study area showed a high amount of alkalinity during the dry season due to its geographical distribution alongside the Tigris River, thus explaining the high values of alkalinity in particular sites (CCME 2001). Most notably, Moyel & Hussain (2015) stated the deleterious impact of water TH on human health when it was above 300 mg l−1. The excessive concentrations of cations and anions discharged into the water increase the hardness of water, which is attributed to geographical structure, waste disposal, domestic waste, and untreated sewage (Mitra et al. 2018; Majeed et al. 2022). Several factors affect the pH value of water, such as respiration and photosynthesis, and the variation of pH value mainly depends on carbon dioxide and hydrogen ions, where the latter decreases the acidity of water caused by carbon dioxide, the Iraqi aquatic ecosystems are very known as alkaline (Al-Ani et al. 2019). On the other hand, the production of dissolved carbon dioxide by the degradation of organic molecules may also decrease the pH of water (Xu et al. 2021).

Additionally, microorganisms' biodegradation of organic matter directly affects the amount of oxygen in water, which is expressed as BOD. Increasing BOD values refer to an increase in bacteria's consumption of oxygen during the dry season, which ultimately leads to oxygen depletion (APHA 2012).

As a part of chemical parameters, TN and TP concentrations were measured during the dry and wet seasons; however, the concentration of these nutrients was increased remarkably during the wet season, as the activity of microorganisms is mostly in the hot season rather than in the dry season (WHO 2004; Al-Ani et al. 2019). TOC and OM are considered pollution indicators; the existence of OM in soil or water sediments leads to the interaction with metal ions, which in turn results in the formation of soluble and insoluble complexes to produce particles and, therefore, contamination of surface water (WHO 2004; Lazar et al. 2012). The result of Al-Ani et al. (2019) also reported that high values of TOC were in the dry season and high values of OM were in the wet season.

Antibiotics in water

The presence and persistence of antibiotics in the environment have been extensively studied worldwide due to the increase in the consumption of different types of antibiotics for medicinal, agricultural, and industrial uses. The extensive and improper usage of these drugs resulted in their accumulation in many environmental areas. The spatial and temporal distribution of detected antibiotics in all sites varies remarkably, and their concentrations vary from water, sediment, particulates, and plants due to each site's disposal methods, industrial structure, and livestock industry (Zhang et al. 2019).

In addition, many physicochemical parameters can influence the concentration of antibiotics in the environment, especially in water, resulting in an inconsistency in their concentrations (Zhang et al. 2014; Tang et al. 2015). Our results were inconsistent with the studies of Kim & Carlson (2007), Jiang et al. (2011), and Yang et al. (2011); they all stated that the frequency of antibiotics in surface water was higher in the dry season than in the wet season.

The concentration of antibiotics was seasonally varied. The average concentration was slightly higher in the wet season than in the dry season, which may be attributed to the composition and structure of these antibiotics and their abundance in the environment (Ben et al. 2013). On the other hand, low flow conditions and low temperature may increase the persistence of antibiotics in water, resulting in higher concentrations and higher frequencies of drugs in the wet season than in the dry season (Yan et al. 2013).

In general, a high concentration of all antibiotics was observed in a particular site because many pharmaceutical industries and hospitals are abundant in this area, a large amount of antibiotic residues are discharged directly, and the absence of a wastewater treatment plant leads to high antibiotic pollution (Bao et al. 2021).

The results show that tetracycline was the predominant drug in water, particulate sediment, and plants within all sites during both seasons. TC is the third drug used mostly in animal farming and human disease treatment in Brazil after quinolones and penicillin, as approximately 2,500 tons are used yearly for animal health in Europe (Lundström et al. 2016; Xu et al. 2021). The detectable concentrations of TC in water were high because TC is released to the environment in a natural form due to its strong stability, low adsorption capacity, and weak metabolism in human and animal bodies, resulting in water contamination (Tang et al. 2019). In Turkey, it was found that about 33% of total TC was consumed in poultry and animal farming (Liu et al. 2019; Woźniak-Biel et al. 2018), whereas, in the UK, USA and South Korea, a huge amount of TC was used every year (240, 3,230, and 732 tons), respectively (Kim et al. 2011; Liu et al. 2019). Therefore, the contamination of aqua systems with TC is due mainly to the excessive consumption of TC in either human or animal livestock, as it is used as a growth enhancer in agriculture (Xu et al. 2021). Our results also declare that site 3 exhibits a higher concentration of TC, and this is attributed to the large consumption in hospitals, pharmaceutical manufacturing, and animal management facilities in developed regions (Burke et al. 2016; Hou et al. 2016), and direct release to the river (Karthikeyan & Meyer 2006).

Gentamycin, another selected antibiotic, belongs to the aminoglycoside group and is used increasingly in livestock and meat production (Economou & Gousia 2015). Gentamycin is indirectly delivered to the water surface from human wastewater, sewage sludge, and biosolids (Coates et al. 2022). Although gentamycin is used for human treatment, a low concentration of gentamycin was detected in water, and the maximum concentration was at site 3 during the dry and wet seasons. The study by Zhang et al. (2021) declared that a high concentration of antibiotics was detected in the wet season in Karst River. This increase is due mainly to the seasonal effect on human health and the emergence of many diseases that require medication throughout seasonal occurrence time. In addition, Löffler & Ternes (2003) stated that the concentration of gentamycin in hospital wastewater ranged between 0.4 and 7.6 mg l−1 in Germany. In addition, Tahrani et al. (2016) stated that only 19 ng ml−1 of gentamycin was detected in wastewater effluent in Tunisia.

FQs are the third major group of drugs in the world, exhibiting different antibacterial activity due to the differences in their structure and constitution (Riaz et al. 2018). The existence of high concentrations of many antibiotics (quinolones, TCs, and macrolides) in the Wangyu River during the wet season has been stated by Zhang et al. (2022). CIP can be released into the environment as a non-metabolized compound, and about 70% of FQs persist in the environment for a long time due to their strong sorption capacity and their resistance to microbial degradation (Ramos Payán et al. 2011). According to the route of administration, the effluent of hospitals and industrial, domestic, and urban wastewater is the main source of CIP in water at site 3. Nevertheless, rainfall runoff from agricultural farms contaminated with sludge or fertilized manure can be another route of exposure (He et al. 2015). CIP was detected in wastewater (Teglia et al. 2019), drinking water treatment plants (Mahmood et al. 2019), source water, and finished water (Heidari et al. 2013). Another study revealed that FQs were the most detectable antibiotics in the effluent of wastewater treatment plants in China and Sweden (Lindberg 2006). Similarly, CIP was detected at a high concentration (up to 6.5 mg l−1) in the effluent of common wastewater plants in India, thus resulting in surpassing drug contamination of ground, surface, and drinking water (Liu et al. 2023). Moreover, the detection and quantification of five selected antibiotics in the wastewater channels of Islamabad, Pakistan, was conducted. The results showed that CIP was detected at the highest concentration (332.154 μg ml−1), followed by Ofloxacin, Ampicillin, Levofloxacin, and Sulfamethoxazole (Zafar et al. 2021). The presence of such a high concentration of CIP was attributed to the extensive consumption of CIP compared to others, which may result in the presence of resistant bacteria and increase the spread of antibiotic resistance.

Antibiotics in particulate and plants

Antibiotic residues (tetracycline, gentamycin, and ciprofloxacin) in the particulate matter were low compared to the water surface. Many factors affect the presence of antibiotics in particulate matter; particle size is one of the most important factors affecting the adsorption of antibiotics. It was reported that high adsorption capacity for tetracycline with particle size ranged from 63 to 150 μm, and low capacity when the size was above 300 μm. On the other hand, the physicochemical properties of suspended particles, such as cation exchange capacity, organic matter content, and surface area, directly influence the adsorption capacity (Luo et al. 2019). According to our results, the concentration of TC was slightly higher in the wet season than in the dry season in all sites, which may be because the degradation of antibiotics in water, soil, and sediment is decreased or stopped at low temperatures (Mahmood et al. 2019).

Generally, antibiotics have different exposure routes, especially when used for animal treatment; they will be released directly into the soil and land. Although the concentration of antibiotics in aquatic ecosystems is lower than those in manure or fertilized soil, some antibiotics are degraded or eliminated rapidly over time by the activity of microorganisms in the soil, and others may remain for a long time. On the other hand, many antibiotics strongly interact with sewage sludge particles. However, some of them remain active (Al-Haideri et al. 2021).

In addition, all three antibiotics were detected in C. demersum at very high concentrations at all sites during the wet and dry seasons. The presence of TC, gentamycin, and CIP at high concentrations in plants may be due to their half-lives, resistance, and adsorption in soil. The half-life of TC is relatively long for at least 30 days, so it persists in soil for a long time (Albero et al. 2018), whereas the physicochemical properties of CIP affect its half-life in soil because it possesses a quinolone ring system (Schaumann & Rodloff 2007). Many studies reported that antibiotics can be delivered to crops, vegetables, aquatic plants, and animals from water (Eggen et al. 2011; Dong et al. 2012; Na et al. 2013; Sun et al. 2021). The ability of plants to uptake drugs is different from part to part, for example, carrot (Daucuc corota L.) and lettuce (Lactuca sative L.) were able to uptake tetracycline and amoxicillin from irrigation water even at low concentrations. TC was found in all parts of carrot and lettuce at concentrations ranging from 4.4 to 28.3 ng g−1 and 12.0 to 36.8 ng g−1, respectively (Azanu et al. 2016). Ahmed et al. (2015) demonstrated that the accumulation of chlortetracycline hydrochloride in lettuce root was 1.16 mg kg−1. On the other hand, a study reported that CIP accumulated in the root of carrot and barley (Hordeum vulgare), with an accumulation factor higher than that of leaves (Eggen et al. 2011). Moreover, seasonal changes are another factor affecting the ability of plants to uptake drugs, as in the dry season, the concentration of Tc, gentamycin, and CIP was higher than in the wet season. The accumulation of Tc, oxytetracycline, and chlortetracycline in coriander leaves increased more in winter than in summer, with approximate concentrations ranging from 78 to 330 μg kg−1, 92 to 481 μg kg−1, and 1.9 to 5.6 μg kg−1, respectively (Hu et al. 2010). Furthermore, many antibiotics were detected to be accumulated in radish leaves, such as sulfadoxine (0.2–0.6 μg kg−1), sulfachloropyridazine (0.1–0.5 μg kg−1), chloramphenicol (8–30 μg kg−1), and sulfamethoxazole (0.9–2.7 μg kg−1) (Al-Haideri et al. 2021). In celery, the concentrations of oflaxacin (1.7–3.6 μg kg−1), pefloxacin (1.1 μg kg−1), and lincomycine (5–20 μg kg1) were higher in leaves than in other parts (Li et al. 2013).

Detection of antibiotics in sediment

The concentration of all selected antibiotics in sediment was higher than in water at all sites; this may be due to the strong ability of particles to adsorb antibiotics (Yang et al. 2010) and resulted in antibiotic accumulation in sediment (Mangalgiri & Blaney 2017). In addition, the concentration of antibiotics in sediment is higher than in water because they are sensitive to external environmental factors like adsorption of particles, dilution, and photodegradation in surface water (Yang et al. 2020). The adsorption properties of sediment are different due to different water environments, which lead to spatially and geographically heterogeneous dissemination of antibiotics in sediment (Liu et al. 2021). In our study, sites 3 and 5 exhibit a high concentration of all selected antibiotics in sediment, which may be attributed to the external environment, as the antibiotics-harbouring sediment may be flushed by external currents, releasing adsorbed antibiotics into the water environment, causing secondary pollution (Radke et al. 2009). Moreover, the concentration of antibiotics in water's sediment can be influenced by sediment contents, which are highly complex and variable in different water bodies (Liu et al. 2021).

Most noted that the residues of TC in sites 3 and 5 were higher than those of gentamycin and CIP because of its extensive use in poultry farming and veterinary treatment, as well as its chemical properties like being highly persistent, highly adsorptive, easily accumulated, and hardly degradable in soils and sediments (Chee-Sanford et al. 2009; Jiang et al. 2011). The spatial–temporal distribution of selected antibiotics in this study revealed that the distribution of antibiotics in sediment was higher in the wet season than in the dry season. This is because in the wet season, the efficiency of biodegradation and photodegradation is low (Cheng et al. 2014). In addition, TCs were used extensively for respiratory infection treatment and diarrhea, which remarkably developed in the wet rather than dry season (Matsui et al. 2008). Furthermore, the concentration of antibiotics was higher in sediment than in water, particulates, and plants because the release of antibiotics into the water is mediated by sediment, which acts as an alternative source of antibiotics and gives protection from the potential degradation process by strong bonding to the sediment part. Ultimately, antibiotics will persist longer (Cheng et al. 2014). The study of Awad et al. (2014) indicates that the detectable concentrations of TC and other types of TC in sediment and soils were much higher in winter than in summer, which may reflect the importance of low temperature on the degradation ability of tetracycline in water, sediment, and soil, and depends on the emergence of pathogens and their related disease in the wet season. Moreover, the waste of pharmaceutical industries is the main source of TC in sediment, and the residues are disseminated to unsuitable places for waste products without proper treatment management, therefore, TC remains longer in soil or water sources in its degradable form (Mahamallik et al. 2015).

On the other hand, Lu et al. (2018) demonstrated that the concentration of three types of TCs (tetracycline, oxytetracycline, and chlortetracycline) in the longshore sediments in China was higher in the summer season than in the winter season. This was attributed to the use of these drugs in winter and different seasonal variations of Gorges Reservoir.

Here, our results also indicate that CIP was detected at high concentrations in site 3 and site 5 during both seasons, which is due to the properties of the sediment of both sites and seasonal variation. The presence of CIP in sediment is mediated by animal manure and direct discharge of livestock (Valdés et al. 2021). It was reported that CIP can be detected at different latitudes in soil with concentrations ranging from 0.1 to 370 μg kg−1 with slow degradation, long persistence, and adsorption capacity (Rosendahl et al. 2012; Wu et al. 2019). The adsorption capacity of CIP in sediment is affected by physicochemical parameters like pH and OM and decreases with the increases in analyte concentration. The pH of the soil is essential for CIP to establish the chemical form of the molecule, and the availability of OM content is also important for CIP persistence. This suggests that CIP adsorption efficiency is related to the interaction of soil-component and pollutants (Cycoń et al. 2019) and thus may justify the presence of a high concentration of CIP in sites three and five.

The emergence of antibiotics in aquatic ecosystems has become a global issue regarding human and animal health. Most residues can be detected beyond water, like in sediment, plants, and particulate matter, as shown in the results of the present study. Two sites exhibit higher levels of antibiotic contamination due to their infrastructures, hospitals, industries, and animal farms. The dissemination of antibiotics in particulate, plant, and sediment from water without proper management leads to the accumulation of antibiotic residues in the environment. The results of the present study remarkably indicate that tetracycline, gentamycin, and ciprofloxacin reached the plant and sediment, and a few were adsorbed to particulate. TC exists in very high concentrations, followed by ciprofloxacin and gentamycin, and sites 3 and 5 were the most polluted areas with antibiotics. In conclusion, our study suggests that improper use of antibiotics without control, absence of proper sanitary treatment, and direct waste disposal into the river lead to the accumulation of antibiotics in the river's ecosystem and increase the risk of river pollution.

No funding received.

F.H. and H.A.-H. wrote the original draft; wrote, reviewed, and edited the article. S.S. did data curation. F.H., H.A.-H., and M.M.E.-S. wrote, reviewed, and edited the article.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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