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Article

Analysis of the Distribution and Influencing Factors of Antibiotic Partition Coefficients in the Fenhe River Basin

by
Jing Zhao
1,
Hailong Yin
1,* and
Linfang Wang
2
1
Shanxi Ecological Environment Monitoring and Emergency Response Centre, Shanxi Academy of Eco-Environmental Sciences, Taiyuan 030027, China
2
Sorghum Research Institute, Shanxi Agricultural University/Shanxi Academy of Agricultural Sciences, No. 238, Yuhuaxi Street, Jinzhong 030600, China
*
Author to whom correspondence should be addressed.
Water 2024, 16(19), 2793; https://doi.org/10.3390/w16192793
Submission received: 28 August 2024 / Revised: 21 September 2024 / Accepted: 24 September 2024 / Published: 30 September 2024
(This article belongs to the Special Issue Basin Non-point Source Pollution)
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Abstract

:
Affected by point and non-point source pollution, the Fenhe River Basin faces significant environmental challenges. This study aimed to analyze the distribution characteristics and influencing factors of antibiotics in the water and sediments of the Fenhe River Basin. Samples were collected from 23 sites within the basin, and 26 antibiotics from five different classes were detected and analyzed using high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS/MS). The water–sediment partition coefficient (Kp) was calculated, and spatial analysis was conducted using geographic information system (GIS) technology. The results showed that 25 antibiotics were detected in the water, with concentrations ranging from 130 to 1615 ng/L, and 17 antibiotics were detected in the sediments, with concentrations ranging from 121 to 426 μg/kg. For quinolones (QNs), except for ofloxacin, all others could be calculated with overall high values of Kp ranging from 692 to 16,106 L/kg. The Kp values for QNs were generally higher in the midstream, with considerable point source pollution from industries and non-point source pollution from developed agriculture. The distribution of Kp is closely associated with risk. This study found that the Kp values of the antibiotics were influenced by various factors such as temperature, water flow, and the physicochemical properties of sediments. Correlation analysis revealed significant relationships between Kp and parameters such as river width, water depth, water quality (total nitrogen, total phosphorus, and chemical oxygen demand), and sediment pH and clay content.

1. Introduction

The widespread use of antibiotics has significantly improved quality of life by treating bacterial infections and reducing mortality rates [1]. However, it has also led to pervasive overuse, which poses serious consequences [2,3]. Large quantities of antibiotics enter aquatic environments through sewage treatment plants, surface runoff, and infiltration into groundwater [4,5,6]. Studies have shown that the rivers worldwide are generally polluted by antibiotics, with the highest concentration of 1900 ng/L [7,8]. Researchers have been investigating antibiotics in major river basins in China, such as the Yangtze, Yellow, Huaihe, Haihe, Liaohe, and Zhuhai rivers [9,10,11]. Among them, Haihe’s content was the highest, with an average value as high as 494 µg/L [12,13,14,15]. These antibiotics can persist in watersheds, affecting microbial communities and aquatic organisms [16,17]. Additionally, antibiotics can disrupt ecosystems and enter the human body through the food chain, posing risks to both ecological and human [18]. This highlights the urgent need for sustainable practices to mitigate these risks and protect ecosystems and human health.
Antibiotics in water can enter sediments through adsorption and deposition, where they bind to sediment particles and settle at the bottom [19]. When watershed conditions change, such as during storms, floods, or alterations in water flow, these sediments can resuspend and release pollutants back into surface waters, contributing to ongoing contamination [20]. The interactions between the aqueous and sediment phases play a crucial role in the persistence and stability of pollutants within the watershed, influencing how long and how widely antibiotics can affect the environment [21,22]. To better describe the dynamics of antibiotics in water and sediments, scientists use the partition coefficient (Kp). Studies have shown that the Kp of antibiotics varies greatly, with quinolones as high as 114.2–169,324 L/kg [23,24,25] and macrolides as high as 57.7–11,377 L/kg [25]. By quantifying these interactions, researchers can develop more effective strategies for managing and mitigating antibiotic contamination in aquatic environments.
The partition coefficient (Kp) provides a clearer understanding of the dynamic processes of antibiotics within watersheds, highlighting how these substances distribute between the water and sediment phases [26,27,28]. Influenced by factors such as temperature, water flow, and flow rate, the partition coefficient can exhibit significant variability, resulting in differing migration and accumulation processes [29]. Traditionally, Kp is derived from the octanol–water partition coefficient (Kow), serving as a general predictor of compound behavior [16,30]. However, in specific study areas, Kp is influenced by numerous local factors, such as sediment composition, organic matter content, and seasonal changes, often deviating substantially from ideal conditions. This variability means that Kp can differ greatly across different regions and times. Despite this, current research tends to overlook the spatiotemporal variability of Kp, which is crucial for accurately assessing the environmental impact of antibiotics [24,31]. Addressing this gap in understanding could lead to better management practices for antibiotic pollution in diverse aquatic environments.
Additionally, the partition coefficient is significantly impacted by the physicochemical parameters of sediments, such as grain size, composition, pH, metal content, organic carbon content, nutrient content, and ionic strength [23,32,33]. The physicochemical properties of antibiotics, including molecular weight, hydrophobicity, and functional groups, also play a crucial role [26,33,34]. Some researchers have established linear models of Kp with the physicochemical properties of sediments using regression equations. Others have found that Kp shows a linear relationship with logKow (octanol–water partition coefficient) and molecular weight [33]. However, these models often focus on only a subset of sediment parameters, such as pH, organic carbon, ionic strength, and grain size [35]. In reality, the distribution behavior of antibiotics is influenced not only by the physicochemical properties of sediments but also by the physicochemical factors in the water.
This study, based on measured data of antibiotics in the water and sediments of the Fenhe River Basin, investigated the distribution characteristics of antibiotics within the basin. It analyzed the spatiotemporal variability of the partition coefficient to elucidate the migration pathways and fate of antibiotics in the Fenhe River Basin. Using geographic information system (GIS) spatial analysis techniques, this study examined the spatial variability of the partition coefficient and identified the influencing factors. This study is essential for accurately studying the behavior and fate of antibiotics in basins.

2. Materials and Methods

2.1. Study Area

The Fenhe River, a primary tributary of the Yellow River, is located in the ecologically fragile region of the Loess Plateau (Figure 1). Originating in Ningwu County in northern Shanxi, it flows through 34 counties and cities across six municipalities, eventually joining the Yellow River in Hejin. The Fenhe River stretches 716 km with a watershed area of 39,741 km2. As the mother river of Shanxi, the Fenhe River not only serves as a major source of drinking water for the region but also receives substantial amounts of industrial and domestic wastewater. Research has shown that antibiotics are widely distributed throughout the Fenhe River Basin.

2.2. Sample Collection and Experimental Analysis

Samples were collected from 23 sampling points in the Fenhe River Basin, including the river source, downstream of major cities, reservoirs, and densely populated livestock farming areas, the confluence with the Yellow River, and major tributaries (Figure 1). Surface water and sediment samples were gathered during the dry season (November 2019) for analysis.
Water samples were collected from the surface using a plexiglass water sampler and stored in 1 L amber glass bottles. During collection, 0.5 g of Na2-EDTA was added, and the pH was adjusted to 4.0 using 0.1 M sulfuric acid, followed by 10 mL of methanol. Sediment samples from the 0–20 cm layer were collected using a Peterson grab sampler, with approximately 1 kg placed in 1 L amber glass bottles, to which 0.5 g of Na2-EDTA was added. All samples were kept in a 4 °C sampling box and transported to the laboratory on the same day. Sediment samples were immediately transferred to a −20 °C freezer, freeze-dried, and sieved through a 2 mm mesh before being stored for analysis. Water samples were filtered through 0.45 μm glass fiber filters, concentrated using HLB solid-phase extraction cartridges, eluted with methanol, and concentrated by nitrogen blowdown with internal standards added for volume determination before analysis. Sediment samples were extracted using a high-pressure solvent extractor, with the extracts concentrated by nitrogen blowdown and internal standards added for volume determination before analysis.
The sample extracts were then analyzed by liquid chromatography–tandem mass spectrometry using an Agilent 1260-6460 Triple Quadrupole system (Agilent Technologies, Santa Clara, CA, USA), which was tuned before detection. The dissolved oxygen concentration in each water sample was determined using an LH-D9 pen-type dissolved oxygen meter (Hangzhou Yingao Instrument Co., Ltd., Hangzhou, China). The pH and temperature of the water at each sampling point were measured on-site using a PHB-4 portable pH meter (Shanghai Yidian Scientific Instrument Co., Ltd., Shanghai, China). Other physicochemical parameters were measured using the methods described in the Chinese Environmental Quality Standards for Surface Water (GB3838-2002) [36]. The particle composition of the sediment was determined using the weighing method.
The analytes included 26 compounds from five categories of antibiotics (ANTs): (1) sulfonamide antibiotics (SAs): sulfamethoacetamide (SAAM), sulfamethoxazole (SDZ), sulfamethoxazole (STZ), sulfamethoxazole (SPD), sulfamethoxazole (SMR), sulfamethoxazole (SX), sulfamethoxazole (SDM), sulfamethoquinoxaline (SQX), trimethoprim (TMP), and sulfamethoxazole (SMX); (2) quinolone antibiotics (QNs): enoxacin (ENO), norfloxacin (NOR), ofloxacin (OFL), ciprofloxacin (CIP), and enrofloxacin (ENRO); (3) tetracycline antibiotics (TCs): doxycycline (DOX), aureomycin (CTC), tetracycline (TC), and oxytetracycline (OTC); (4) chloramphenicol antibiotics (CAs): chloramphenicol (CHL), thiamphenicol (THI), and florfenicol (FF); (5) macrolide antibiotics (MLs): azithromycin (AZM), clarithromycin (CTM), roxithromycin (RTM), and erythromycin (ETM).

2.3. Quality Control

The fitting equations of the standard curves for the antibiotics had R2 values greater than 0.995. The detection limits for surface water ranged from 0.01 to 6.25 μg/L and for sediment from 0.01 to 7.89 μg/kg. During the detection process, intermediate points on the standard curves were rechecked, and blank, parallel, and matrix-spiked samples were included for quality control. The concentrations of blank samples were below the detection limits, and the concentration deviations of the rechecked points did not exceed 20% to verify instrument stability. The relative deviation of the parallel samples’ measurements should be less than 30%, and the recovery rates of the matrix-spiked samples should be controlled between 40% and 150%.

2.4. Partition Coefficient

The formula for the partition coefficient (Kp) is as follows:
Kp = Cs/Cw
where Kp represents the water–sediment partition coefficient (L/kg); Cs is the concentration of antibiotics in the sediment phase (µg/L); and Cw is the concentration of antibiotics in the water phase (µg/kg). During analysis, if both phases do not show detectable levels, the partition coefficient is not studied. If one phase does not show detectable levels, the concentration is considered to be half of the detection limit.
The Kp values for SMR, SX, SDM, and CTM were not calculated because these antibiotics were not detected in the surface water or sediment. The Kp values for SDZ, STZ, SPD, TMP, SMX, and FF were set to zero because these antibiotics were not detected in the sediment. Kp values for DOX and OFL were not calculated because DOX was not detected in surface water, and OFL was detected in sediment from only one sampling site.

2.5. Statistical Analysis

To better reveal the spatial distribution characteristics of the partition coefficient and explore its regional differences, GIS spatial analysis technology was further applied based on the calculation of Kp. Then, correlation analysis was further conducted to discover the influencing factors.
The spatial distribution maps were drawn using ArcGIS 10.2 (ESRI, Redlands, CA, USA). Correlation analyses were performed using IBM SPSS Statistics 22.0 (IBM, Armonk, NY, USA), and Kolmogorov–Smirnov tests were performed to determine whether the data had normal distributions. If a dataset had a normal distribution, Pearson correlation coefficients were used to identify correlations between the Kp values and physicochemical variables. If a dataset did not have a normal distribution, Spearman correlation coefficients were used to identify correlations. Each antibiotic concentration lower than the method detection limit was replaced with zero before statistical analysis was performed. Each antibiotic concentration higher than the method detection limit but lower than the method quantification limit was replaced with a value of half of the method quantification limit before statistical analysis was performed. Antibiotics detected in <10% of the samples were not subjected to statistical analysis.

3. Results and Discussion

3.1. Antibiotic Concentration and Partition Coefficient

During the dry season, 25 out of 26 types of antibiotics were detected in the surface water, with the exception of sulfisoxazole. The concentration ranged from 130 to 1615 ng/L (Table 1). In the sediments, 17 types of antibiotics were detected during the dry season, with concentrations ranging from 121 to 426 μg/kg and an average of 172 μg/kg. The antibiotics that were not detected in the sediments included sulfadiazine, sulfapyridine, sulfamethazine, sulfisoxazole, sulfadimethoxine, trimethoprim, sulfamethoxazole, oxytetracycline, and clarithromycin.
Since the number of SAs detected in the sediments was limited, only the Kp of sulfamethazine and sulfaquinoxaline could be calculated, but their values were very high, reaching up to 3640 L/kg and 3201 L/kg, respectively. For QNs, except for ofloxacin, all others could be calculated with overall high values of Kp ranging from 692 to 16,106 L/kg. For TCs, only doxycycline did not have a calculated value, with the Kp of the others being relatively low, ranging from 17.4 to 843 L/kg. The Kp of CAs ranged from 865 to 2972 L/kg, and that of MLs ranged from 307 to 908 L/kg.
This might be because most SAs are highly hydrophilic, making it difficult for them to enter sediments [24]. Sulfaquinoxaline, however, has a higher hydrophobicity, leading to its higher Kp. Sulfamethazine, used extensively as an eye medication for conjunctivitis and keratitis and as a postoperative anti-infective drug [37], accumulates in sediments over time due to prolonged use. The higher Kp values for ofloxacin and trimethoprim could be attributed to the reduced water flow during the dry season, allowing more time for these substances to exchange into sediments, resulting in higher concentrations in the sediments. QNs contain positively charged nitrogen atoms and more ionic functional groups [38], giving them strong hydrophobicity and more ionic functional groups within the molecules [26]. Additionally, QNs in water are easily photodegraded [24,39] and thus exhibit strong adsorption properties upon entering sediments [40,41,42,43], resulting in high Kp values. The detection frequency of ofloxacin was low, but the detected concentrations were high. This could be due to ofloxacin being a third-generation quinolone antibiotic primarily used in human medicine, making it less prone to photodegradation and hydrolysis compared to other quinolone antibiotics [24]. It is more persistent in the environment [44], leading to more ofloxacin entering sediments and resulting in higher Kp values compared to other substances and regions. Compared to other areas, the overall Kp in the Fenhe River Basin was lower, possibly due to the higher sediment load in the basin, facilitating the transport of antibiotic substances, and resulting in the highest sediment concentrations downstream.
Compared to the other areas, the Kp values for QNs were generally higher [24,32,45]. This may be because the Fenhe River, as a main tributary of the Yellow River, has a high sediment content. QNs, as esterophilic compounds, are more likely to adsorb in suspended matter and then enter sediments. Their concentrations may be lower in the Fenhe River Basin than those found in other areas [25]. However, the concentration of TCs is lower than in other regions. As an animal disease prevention drug and animal feed additive, the large quantities used result in high concentrations in the water. However, since TCs are easily hydrolyzed and photolyzed after entering water, their sediment content is lower [46].

3.2. Spatial Distribution

The highest and lowest values of Kp for sulfamethazine were observed at S7 and S8, respectively (Figure 2). This might be because S7 is a concentrated discharge area, but the corresponding sediment did not allow for timely exchange, resulting in the lowest Kp. However, at the nearby S8, sulfamethazine, sulfaquinoxaline, and azithromycin all exhibited the highest Kp, likely due to their delayed effect in the water, causing higher sediment concentrations after entering the sediment despite the relatively low surface water discharge at this location. The Kp values for QNs were generally higher in the midstream, possibly because these areas are major entry points for antibiotics. The Fenhe River, located on the Loess Plateau, experiences low temperatures and low surface water flow in November. Quinolones, being lipophilic [25], more readily enter sediments, leading to higher Kp values in the midstream. Doxycycline, as a second-class broad-spectrum tetracycline antibiotic, showed minimal variation in usage across the entire basin, resulting in small differences in Kp. The overall distribution of chlortetracycline was similar to QNs, with higher values in the Xiao River (S6) and Ciyao River (S13) tributaries. As a main tetracycline antibiotic, it is extensively used in the livestock and poultry industries. MLs had the lowest Kp at S15, where there is a significant amount of farming activity. The relatively high water discharge rate and fast flow speed at this location prevented timely sediment entry, resulting in lower Kp values. Tetracycline, oxytetracycline, roxithromycin, and erythromycin exhibited relatively small differences across the entire basin.
The results showed that the Kp values for antibiotics varied greatly, ranging from 0.020 to 16,106 L/Kg. The distribution of Kp values is closely associated with risk. When the value of Kp is large, it indicates that antibiotics are prone to accumulate in sediments and cause secondary pollution as the disturbance enters the water again [47]. It will become a persistent source of pollution, bringing ongoing risks to both the environment and human health. However, the Kp value was small, meaning the substance remains in the water and is transported downstream with the water flow, resulting in an increase in antibiotic content and posing greater risks to the downstream ecology and human health [23].

3.3. Analysis of Factors Influencing the Partition Coefficient

Based on correlation analysis, Kp showed a strong correlation with river width (W), water depth (H), and water quality parameters such as total nitrogen (W-TN), total phosphorus (W-TP), chemical oxygen demand (CODcr), permanganate index (CODMn), sediment pH (S-pH), and clay content (S-NL) (Table 2). Additionally, there were strong correlations with water pH (W-pH), conductivity (W-conductivity), total organic carbon (W-TOC), ammonia nitrogen (W-NH3-N), organic matter in the sediment (S-organic matter), nitrate nitrogen in the sediment (S-NO3-N), and suspended solids content (SS).
The Kp values for enoxacin and chloramphenicol were negatively correlated with CODcr and CODMn. Both TP and TN showed a negative correlation with Kp, indicating that the concentration of nutrients in the water plays a significant role in the interaction between antibiotics in sediments and the water phase, and this influence varies by region [24]. The non-point source (NPS) pollution problem in the Fenhe River Basin was more prominent, and TP and TN were mainly of NPS [48]. The pH of the sediment is a crucial influencing factor. For instance, during the dry season, sulfamethazine, chloramphenicol, and roxithromycin exhibited strong correlations with sediment pH. This could be because pH can alter the ionic state in the sediment, with ion exchange possibly being a key factor affecting the adsorption process [49,50]. Additionally, the particle size content of sediment also affects the partition coefficient to some extent, as the grain size and composition of different types of sediments can influence their partitioning [33]. Besides the physicochemical properties of sediment and water, the surrounding environment of the sampling site also impacts the partition coefficient. For example, the Kp value for erythromycin during the dry season was positively correlated with water temperature, possibly because higher temperatures enhance the biodegradation of antibiotics [51]. Azithromycin, chloramphenicol, and florfenicol were more susceptible to hydrological conditions, showing a negative correlation with river width and depth, indicating that favorable hydrological conditions facilitate their retention in the water and subsequent migration.
The results showed that Kp is significantly influenced by the physicochemical properties of watersheds [33,52]. The interaction between surface water and sediment is crucial for the persistence and stability of antibiotics in these environments [22]. Therefore, understanding and determining the regional Kp is essential for accurately studying the behavior and fate of antibiotics in basins.

4. Conclusions

This study provided a comprehensive analysis of the distribution and influencing factors of antibiotics in the Fenhe River Basin. For QNs, except for ofloxacin, all others could be calculated with overall high values of Kp ranging from 692 to 16,106 L/kg. The Kp values for QNs were generally higher in the midstream, possibly because these areas are major entry points for antibiotics. Our findings demonstrated that, affected by point and non-point source pollution, the partition coefficients (Kp) of antibiotics were significantly affected by both the physicochemical properties of the sediment and water quality parameters. Specifically, river width, water depth, total nitrogen, total phosphorus, chemical oxygen demand, sediment pH, and clay content were all strongly correlated with Kp values. The application of GIS technology for spatial analysis revealed distinct patterns in the distribution of antibiotics across the basin. These patterns are essential for understanding the migration pathways and the ultimate fate of these contaminants. The distribution of Kp values is closely associated with risk. When the Kp value is large, it indicates that antibiotics are prone to accumulate in sediments and become a continuous source of pollution, bringing further ongoing risks to the environment and human health. However, when the Kp is small, the substance is retained in the water and is transported downstream with the water flow, bringing greater risks to the downstream ecology and human health. This study underscores the need for targeted strategies to mitigate antibiotic pollution in the Fenhe River Basin. By highlighting the key factors influencing antibiotic distribution, our research provides valuable insights that can inform effective environmental management practices and policy decisions aimed at reducing the impact of antibiotics on both ecological and human health.

Author Contributions

Methodology, J.Z.; Software, L.W.; Resources, H.Y.; Writing—original draft, J.Z.; Writing—review & editing, L.W.; Funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Natural Science Foundation of China (42377379).

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.

Acknowledgments

The authors thank the editors and anonymous reviewers for their valuable comments and suggestions.

Conflicts of Interest

The authors declare no conflict of interest.

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Water 16 02793 g001
Figure 1. Locations of the study area and sample sites.
Figure 1. Locations of the study area and sample sites.
Water 16 02793 g001
Water 16 02793 g002aWater 16 02793 g002bWater 16 02793 g002cWater 16 02793 g002dWater 16 02793 g002e
Figure 2. Spatial distribution of partitioning coefficients in the dry season of the Fenhe River.
Figure 2. Spatial distribution of partitioning coefficients in the dry season of the Fenhe River.
Water 16 02793 g002aWater 16 02793 g002bWater 16 02793 g002cWater 16 02793 g002dWater 16 02793 g002e
Table 1. Statistics of antibiotics in the water and sediment in the dry season.
Table 1. Statistics of antibiotics in the water and sediment in the dry season.
ANTsMinMaxStdev
SAs_water (ng/L)1.77986.58291.90
QNs_water (ng/L)36.68245.5046.73
TCs_water (ng/L)5.95154.2236.31
CAs_water (ng/L)59.9088.038.07
MLs_water (ng/L)19.04168.6844.40
ANTs_water (ng/L)130.411614.88391.99
SAs_sediment (μg/kg)1.7060.7516.89
QNs_sediment (μg/kg)32.99241.9355.69
TCs_sediment (μg/kg)5.227.360.55
CAs_sediment (μg/kg)62.21190.7626.17
MLs_sediment (μg/kg)16.0529.864.33
ANTs_sediment (μg/kg)120.98426.1580.28
Table 2. Correlation analysis of the partitioning coefficients and physical–chemical indexes in the dry season.
Table 2. Correlation analysis of the partitioning coefficients and physical–chemical indexes in the dry season.
SDZSPDSQXTMPSMXENONORCIPDOXOTCCHLFFRTMETM
W-PH −0.778 * −0.446 *0.600 **
W-conductivity−0.512 * −0.722 ** −0.511 *−0.754 **
W-TOC 0.630 ** 0.724 **
W-TN −0.561 * −0.538 ** −0.714 * −0.663 **−0.469 *
W-TP −0.621 ** −0.538 **
W-CODcr 0.531 ** 0.680 **
W-CODMn 0.424 * −0.592 **
W-NH3-N −0.509 * −0.615 ** −0.592 **
W-NO3-N−0.486 * −0.515 * −0.449 * −0.675 **
S-PH−0.809 ** 0.600 ** −0.459 *
S-organic matter0.490 * 0.726 ** 0.434 *
S-NL0.697 ** −0.491 *
W-m −0.555 **
H-m −0.523 *
T-°C −0.423 *
SS 0.431 *
Notes: W-pH, pH of the water; W-conductivity, water conductivity (S/m); W-TOC, total organic carbon content in water (mg/L); W-TN, total nitrogen in water (mg/L); W-TP, total phosphorus in water (mg/L); W-CODcr, chemical oxygen demand in water (mg/L); W-CODMn, permanganate index of water (mg/L); NH3-N, ammonia nitrogen content in water (mg/L); NO3-N, nitrate nitrogen content in water (mg/L); S-pH, pH of the sediment; S-organic matter, organic matter content in sediment (g/kg); S-NL, clay content in sediment (g/g); W, river width (m); H, water depth (m); T, water temperature (°C); SS, suspended solids content (mg). ** Correlation is significant at the 0.01 level (two-tailed); * correlation is significant at the 0.05 level (two-tailed).
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Zhao, J.; Yin, H.; Wang, L. Analysis of the Distribution and Influencing Factors of Antibiotic Partition Coefficients in the Fenhe River Basin. Water 2024, 16, 2793. https://doi.org/10.3390/w16192793

AMA Style

Zhao J, Yin H, Wang L. Analysis of the Distribution and Influencing Factors of Antibiotic Partition Coefficients in the Fenhe River Basin. Water. 2024; 16(19):2793. https://doi.org/10.3390/w16192793

Chicago/Turabian Style

Zhao, Jing, Hailong Yin, and Linfang Wang. 2024. "Analysis of the Distribution and Influencing Factors of Antibiotic Partition Coefficients in the Fenhe River Basin" Water 16, no. 19: 2793. https://doi.org/10.3390/w16192793

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