Characteristics of antibiotic pollution and assessment of ecological risk of lake water in typical urban landscape in the context of a epidemic

Characteristics of antibiotic pollution and assessment of ecological risk of lake water in typical urban landscape in the context of a epidemic | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Characteristics of antibiotic pollution and assessment of ecological risk of lake water in typical urban landscape in the context of a epidemic Sheng Xu, Lei Ren, Wei Wu, Peng Zheng, Jialong Liu, Xiaoxiong Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6508163/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract As widely used drugs, antibiotics pose a serious threat to humans and the ecosystem. However, the outbreak of the COVID-19 pandemic significantly changed patterns of human activity and antibiotic use. The characteristics of antibiotic pollution in lakes in the urban landscape and their ecological risks due to the pandemic are still unclear. In this study, the levels, distributions, sources, and ecological risks of antibiotics in Xingqing Lake before and after the COVID-19 pandemic were investigated. The results revealed that macrolide antibiotics dominated in the environmental matrices. The synergistic effect of the pandemic outbreak and the lake renovation was the main factor driving the differences in the distribution of antibiotics. The positive matrix factorization model indicated that the potential sources of antibiotics in the water were domestic drainage, hospital discharge, and livestock drainage. The ecological risk assessment revealed that antibiotics posed a medium-high risk (RQ > 0.1) to algae. Notably, azithromycin, clarithromycin, and sulfadiazine presented higher risk values for crustaceans than for other aquatic organisms. A toxicity assessment of a single species may severely underestimate the actual ecological risks of antibiotics. This study provides a scientific basis identifying and controlling the sources of antibiotics in lakes in the urban landscape. Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Antibiotics Urban-landscape lake Source identification Ecological risk assessment COVID-19 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction As emerging environmental chemical substances, antibiotics play an irreplaceable and crucial role in the field of human medicine. They are also widely used in the livestock and aquaculture industries to promote animal growth and prevent and treat diseases (González-Pleiter et al., 2013 ; Kovalakova et al., 2020 ; Yi et al., 2018 ). As antibiotic production technologies have developed, the cost of antibiotics has significantly decreased, making them easily accessible (Bielen et al., 2017 ; Kümmerer 2008a , b ). The global consumption of antibiotics increased by 65% from 2000 to 2015. Of this increase, human consumption accounted for 48%, and animal consumption accounted for 52% (Qian et al., 2015). Antibiotics are difficult to completely absorb and metabolize by humans or animals (excreted through urine and feces) (Carvalho and Santos 2016 ; Ju et al., 2014; Xu et al., 2015 ), and conventional sewage and sludge treatment cannot completely remove these antibiotics. This leads to a large amount of antibiotics being discharged into the natural environment in their original forms or as active metabolites, and they are often detected in water bodies, sediments, soils, and even aquatic organisms (Cha and Cupples 2009 ; Lyu et al., 2020 ; Tran et al., 2018 ). Antibiotic residues can cause ecological toxicity and damage ecological health (Jessica et al., 2016 ; Wang et al., 2017b ). Aquatic ecosystems serve as major reservoirs of various antibiotics (Zhao et al., 2022 ), and residual antibiotics in their aquatic environment have adverse effects on nontarget activities, such as inhibiting algal growth; affecting chloroplast replication, transcription, and translation; interfering with the physiological functions of aquatic animals; and disrupting the N cycle involved in microorganisms (Carvalho and Santos 2016 ; Kümmerer 2008a ; Yin et al., 2017 ). The main sources of antibiotics in aquatic environments include (a) medical wastewater; (b) domestic sewage; (c) aquaculture; (d) soil infiltration for animal husbandry; (e) sediment release; (f) surface water runoff; (g) groundwater migration; and (h) atmospheric deposition (Han et al., 2023). At present, methods of source analysis include mainly the absolute principal component score–multiple linear regression model (APCS–MLR), Unmix model (Tong et al., 2023 ) and positive matrix factorization model (PMF) (Yuan et al., 2022). Compared with other traceability models, the PMF model has the advantages of a greater degree of fit and less influence on the number of samples and has been applied to analyze antibiotic sources (Lamine et al., 2021 ; Yuan et al., 2023). China is one of the major consumers of antibiotics (Qian et al., 2015). From 2013–2018, the annual production of antibiotics in China reached 193,000 tons, and the per capita consumption was 10 times greater than that of the United States (US) (Matteo et al., 2017 ). Since 2005, research on antibiotics in China has continued to increase. At present, research on antibiotics has focused mainly on their effects on natural water bodies, such as the Yellow River, the Yangtze River, the Liao River and the Pearl River (Dong et al., 2016 ; Ling et al., 2013 ; Rui et al., 2017; Wu et al., 2014 ), and the objects used for risk assessment are single algae. As important landscape systems in cities, urban lakes are not only landscape projects but also host tourist resorts. These lakes have many functions, such as flood control and storage, flood control and disaster reduction, and improvement of the urban ecological environment. Lakes in urban landscapes usually feature poor water fluidity and weak self-purification ability. Coupled with the dense population in the surrounding areas and the development of the tourism industry, the amount of antibiotics input to these lakes is relatively high (Hui et al., 2017). The outbreak of the COVID-19 pandemic has significantly changed the patterns of human activity and antibiotic use, and the relationships between the characteristics of antibiotic pollution in lake water bodies and the COVID-19 pandemic are still unclear. Owing to differences in the geographical location, functional positioning, and pollution input, the evolutionary process and current situation of pollution of water bodies in urban shallow lakes are quite different from those of lakes far from cities. Therefore, it is necessary to study the characteristics of antibiotic pollution and conduct risk assessments of lake water bodies against the backdrop of the COVID-19 pandemic. Xingqing Lake is has undergone the integration of the modern urban landscape and historical and cultural accumulation in Xi'an, and it is also an ecological lake for which recreation and sightseeing are integrated. It is a typical urban lake. The concentrations of antibiotics collected in the influent area, central area and outlet area of Xingqing Lake were investigated before and after the pandemic. The purpose of this study was to 1) analyze the spatiotemporal heterogeneity of antibiotics in the surface water of Xingqing Lake against the background of a pandemic; 2) analyze the source of antibiotic pollution via the PMF model; and 3) assess the ecological risks of antibiotics in water to many aquatic organisms to provide a scientific basis for identifying and controlling antibiotic sources in lake water in the urban landscape. 2. Materials and methods 2.1. Target antibiotics, chemical reagents, and solvents In this study, 44 antibiotics from seven different classes were investigated, and ultimately 19 target antibiotics were detected. These antibiotics are as follows: [1] Sulfonamides: sulfadiazine (SDZ); sulfamethoxypyridazine (SMP); sulfamethoxazole (SMX); sulfafurazole (SIZ); sulfaquinoxaline (SQX); sulfadimethoxine (SMM); sulfadoxine (SDX); sulfaphenazolum (SPZ); [2] Quinolones: ofloxacin (OFL); ciprofloxacin (CIP); danofloxacin (DAN); lomefloxacin (LOM); sarafloxacin (SAR); [3] Macrolides: azithromycin (ATM); clarithromycin (CTM); erythromycin (ERY); roxithromycin (ROX); [4] Lincosamides: lincomycin (LIN); [5] Tetracyclines: doxycycline (DXC). The information for the 19 antibiotics is listed in Table S1 . All the target antibiotic standards were purchased from the Sigma‒Aldrich Company (MO, USA) and Dr. Ehrenstorfer Company (Germany) and were > 99% pure. The external standard method was used in this study. Each target antibiotic standard sample had a configured gradient solution of 5–500 µg/L, and methanol or ultrapure water was used. The other chemicals used in this study were of LC‒MS grade. 2.2. Study area and sample collection Xingqing Lake is located in Xingqing Palace Park, Xi'an city, Shaanxi Province, China. Its water area is approximately 4.6×104 m 2 , and its length, width, average water depth and hydraulic retention time are approximately 915 m, 214 m, 1.6 m and 310 d, respectively, acting as a buffer used for flood control in the eastern suburbs of Xi'an city. Xingqing Lake is the subject of the integration of the modern urban landscape and historical and cultural growth in Xi'an. Moreover, it is an ecological lake for which leisure, entertainment for citizens and sightseeing tourism are combined, and these functions play a crucial role in the ecological environment and economic development of Xi'an, as a typical urban lake. The sampling sites are shown in Fig. 1 (We was obtained for the collection of water samples from Xingqing Lake from the park administration department. And we confirm that all sampling was performed with permission). A total of several sampling points were selected from the influent area (S1), the central area (S2) and the outlet area (S3) of Xingqing Lake, and samples were collected twice, i.e., once in January 2020 (before the pandemic) and once in January 2024 (after the pandemic). A glass water sampler was used to collect a 1 L water sample 0.5 m below the water surface at each sampling point (in addition, 2 water samples were collected at each site as a control). The collected samples were stored in the dark (4.0°C) and transported back to the laboratory for extraction and analysis as soon as possible (Bi et al., 2012 ; Liang et al., 2018 ; Zou et al., 2018 ). 2.3. Sample extraction and instrumental analysis After a 1 L water sample was filtered through a 0.45 µm fiber filter membrane (Whatman, UK), 6.0 g of Na 2 -EDTA was added, the mixture was shaken until it was fully dissolved, and the pH of the water sample was adjusted to 2 with hydrochloric acid. An autosolid phase extraction (SPE) instrument (Auto SPE-06C, USA) with an Oasis hydrophile lipophilic balance (HLB) SPE column (6 mL, 200 mg, Waters, USA) was used to extract antibiotics from the water samples. The extraction column was activated with 5 mL of deionized water, 5 mL of methanol and 5 mL of acidic water (pH = 2). When approximately 2 mL of deionized water remained in the extraction column, all the water samples were passed through the extraction column at flow rates of 10 to 15 mL/min. The column was then rinsed with 5 mL of deionized water and purged with nitrogen for 20 minutes to dry the extraction column. Afterward, 10 mL of methanol was added, the mixture was eluted at a flow rate of 5 mL/min, and the eluent was collected. The eluent was concentrated to near dryness using a nitrogen blowing instrument and diluted to 1.0 mL with the initial mobile phase. The mixture was filtered through a 0.22 µm organic filter membrane and was ready for testing. Before instrumental analysis, 10 µL of the internal standard mixture (in a 4 ng/µL methanol solution) was added to the final sample. The antibiotics in groundwater were analyzed using a Waters ACQUITY UPLC H-Class system coupled with a Waters Xevo-TQ-S Triple Quadrupole MS/MS spectrometer equipped with an electrospray ionization (ESI) source (Waters, MA, USA). A Waters ACQUITY UPLC BEH C18 (1.9 µm 2.1×50 mm) at 40°C was used. The analysis was carried out in positive electrospray ionization mode for the target antibiotics (Tran et al., 2019 ). The mobile phase contained 0.1% aqueous formic acid (A) and methanol/acetonitrile (B, 1/1, V/V, with 0.1% formic acid) at a 0.4 mL/min flow rate. Table S2 lists the conditions of the electrospray ionization mobile phase and gradient elution. Table S3 and table S4 list the mass spectrometry and ion selection parameters. 2.4. Quality assurance and quality control Stringent laboratory controls were implemented for all laboratory operations. The quality assurance and quality control of the laboratory analysis followed the US EPA methods 8270D and 8000B. All the samples from the two groups were analyzed in duplicate. The external standard method was used to quantify the concentrations of the target antibiotics in the water samples. The method for determining samples in the external standard experiment was the same as the pretreatment method for antibiotics in water samples (including filtration, complexation with Na₂-EDTA, acidification, and SPE enrichment treatment), and the correlation coefficient of the standard curve was greater than 99.8%. For our analysis, surrogate standards were added to the samples before extraction to evaluate the analytical recovery rate (Table S5). The instrumental quantification limits (IQLs) were calculated as S/N = 10, and the instrumental detection limits (IDLs) were calculated as S/N = 3 (Table S5). 2.5. Environmental risk assessment According to the method for environmental risk assessment in the technical guidance document of the European Union, the effects of exposure to chemicals are expressed as risk quotients (RQs) (Rafiquel et al., 2021 ). The environmental risk of antibiotics to aquatic organisms (i.e., algae, crustaceans, fish, etc.) is calculated as the ratio between the maximum measured environmental concentration (MEC) in water and the predicted no effect concentration (PNEC) for each contaminant, as shown in Eq. (1) (Kötke et al., 2019 ). The PNEC value was obtained from the 50% effective or lethal concentration or no observed effect concentration (EC 50 or LC 50 or NOEC) of the pharmaceutical by dividing by a safety factor (AF = 100–1000), as shown in Eq. (2) (Tran et al., 2019 ). Antibiotics have different toxicities to aquatic organisms. Therefore, in this study, five common aquatic plants were selected to evaluate the ecological risks caused by antibiotics. The EC 50 , LC 50 or NOEC values and AF values were obtained from the Environmental Protection Agency and other studies, as shown in Table S6. $$\:{RQ}_{i}=\frac{{MEC}_{i}}{{PNEC}_{i}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ $$\:{PNEC}_{i}=\frac{{L\left(E\right)C}_{50}\:or\:L\left(N\right)OEC}{{AF}_{i}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)$$ RQ>1 represents a highecological riskto aquatic organisms, 0.1<RQ<1 indicates a medium ecological risk, and RQ<0.1 reveals a low ecological risk. 3. Results and discussion 3.1. Occurrence and concentrations of antibiotics Figure 2 show the concentrations and detection frequencies (DFs) of antibiotics in the surface water of Xingqing Lake. A total of 19 antibiotics were found, with the DFs ranging from 16.67–100%, and they exhibited different degrees of pollution. High detection frequencies (100%) of ERY, OFL, ROX, and CIP were detected in the samples (Fig. 2 ), whereas 25 antibiotics were not detected in any of the samples. The concentrations of antibiotics varied significantly, ranging from below the instrumental detection limit (IDL) to nearly 100 ng/L. ERY and OFL had relatively high pollution levels, with average concentrations of 55.87 ng/L and 31.09 ng/L, respectively, followed by SMM (10.63 ng/L) and ATM (16.46 ng/L), and the other antibiotics had relatively low pollution levels. These findings indicate that ERY and OFL are the dominant antibiotics in Xingqing Lake, accounting for more than 80% of the total antibiotics. High levels of ERY and OFL have also been detected in other natural and lake water ecosystems in China, such as Taihu Lake, the Haihe River and Chaohu Lake waters (Li et al., 2012 ; Matthew et al., 2018 ; Satoshi et al., 2007 ; Tamtam et al., 2007 ). In addition, a similar phenomenon was shown in a study of antibiotics in waters in Vietnam and France (Satoshi et al., 2007 ; Tamtam et al., 2007 ). We found that MAs are the dominant antibiotics in the environment, accounting for more than 60% of all antibiotics, followed by QNs (26%). As effective drugs for the treatment and prevention of various tissue infections, macrolide antibiotics have expanded in variety and quantity since their introduction in 1952 and have been widely used in poultry and aquaculture as growth agents, in addition to their use in human medicine. The average concentration of sulfonamides (SAs) is significantly lower than that of MAs and QNs because the use of SAs in Southwest China is lower than the production of MAs and QNs (Qian et al. 2015). Notably, tetracycline classes (TCs) were found at low concentrations in aquatic environments, and only doxycycline (DXC) was detected. This is due to the unstable nature of tetracyclic antibiotics in aquatic environments (Liu et al., 2015 ; Qi and R 2008), which easily accumulate in sediments. The absorption of DXC in soil is relatively weak, which makes it easier for DXC to quickly enter the aquatic environment through soil pores via erosion by rainfall. In addition, DXC has better chemical stability, a stronger ability to resist interference from environmental factors, can be relatively stable in environments such as water bodies and is more easily detected. For LIs, LIN was detected at concentrations of not detected (ND)–9.3 ng/L, which was lower in Xingqing Lake than in Yuehu (31.5 ~ 209.0 ng/L) (Rui et al., 2018), Xianghu (19.2 ~ 49.7 ng/L) and Yao Lakes (ND ~ 54.7 ng/L) (Hui et al., 2017). 3.2. Spatial and temporal distributions of antibiotics The temporal variation in antibiotics in water bodies is caused by a variety of factors, such as precipitation, temperature, current, photosensitivity, and biodegradation (Karthikeyan and Meyer, 2005 ; Paíga et al., 2016 ; Qian et al., 2011 ). Temporally, the overall antibiotic concentration in 2020 was significantly higher than that in 2024 (Fig. 3 ), which was attributed mainly to the water ecological treatment of Xingqing Lake (2020) and the project to improve the stormwater pipe network (2023). Water renewal, water purification, water circulation, artificial wetlands and other measures were adopted in the lake ecological treatment project, which caused the antibiotics in the water need to reaccumulate and accelerate their degradation ( Loftin et al., 2008 ; Oksana et al., 2014 ). Because of the construction of a special water supply pipeline for Xingqing Lake, the water supply for Xingqing Lake has separated from the municipal rainwater pipeline, thus reducing the amount of exogenous antibiotic pollution. Notably, azithromycin (ATM), clarithromycin (CTM), and sarafloxacin (SAR) concentrations were higher in 2024 than in 2020, especially ATM. The reason is that Xi'an experienced two severe COVID-19 epidemics before and after the two sampling periods. During the epidemics period, due to the heavy use of medicines in hospitals and households, some antibiotics flowed into the aquatic environment through urine and feces, resulting in increased antibiotic concentrations in the water. ATM and CTM are macrolide antibiotics and broad-spectrum antibiotics and are often used for respiratory infections. The World Health Organization noted that there was an overuse of antibiotics, especially broad-spectrum antibiotics, such as azithromycin and ceftriaxone, for the treatment of COVID-19 (Ying et al., 2025 ). Spatially, the concentrations of various antibiotics tended to decrease from S1 to S3 in 2020, which was mainly related to the distribution of the three sampling points in the Xingqing Lake area. S1 was located downstream of the sedimentation tank at the Xingqing Lake inlet. Generally, after the water entering Xingqing Lake is precipitated in the sedimentation tank, the sediment and insoluble particles can be removed, and soluble pollutants and fine particles that do not easily precipitate gradually enter the lake as the influent water flow. However, at S1, the cross-section is small and the water depth is shallow and the pollutants have weak dilution and diffusion capacities and the contents of various pollutants are high. Therefore, the rate of detection of antibiotics in the water body at S1 was the highest, and the antibiotic content was relatively high. S2 is located in the main lake area, and S3 is near the outlet of Xingqing Lake. As the influent water in Xingqing Lake gradually migrates, diffuses, and undergoes continuous degradation through biochemical and physicochemical processes (Wei et al., 2024 ), the concentration of antibiotic pollutants in the lake has steadily decreased.Therefore, the rates of detection and antibiotic contents at S2 and S3 gradually decreased. However, S2 in the main lake area presented a relatively high level of antibiotics over 2024, which may be due to the construction of special water supply pipelines to reduce water pollution. In addition, the area of S2 in the main lake district is the largest and has greater potential pollution sources, such as garbage discarded by visitors, fertilizers and pesticides used for park management, and dead leaves of plants in the park. In addition, duck farming on the island in the middle of the lake at S2 also increased antibiotic pollution. 3.3. Clustering on the basis of concentration patterns Cluster analyses of antibiotics and sample sites were conducted by hierarchical clustering with the between-groups linkage method and Euclidean distance matrices. The dendrogram and heatmap in Fig. 4 show the reclassification of two distinct clusters of sample points and three distinct clusters of 19 antibiotics. The sample sites were clearly divided into prepandemic sites with low concentrations and postpandemic sites with high concentrations, which is fully consistent with the actual situation. In addition, the three antibiotic clusters presented different concentration patterns. Group A contains six antibiotics, including CTM, ATM, DXC, etc. These antibiotics are the main contaminants at postpandemic (T2) compared with prepandemic (T1). The second cluster (B in Fig. 4 ), containing 10 antibiotics, such as OFL, ERY, and ROX, had significantly higher concentrations from 2020 at S1 compared to the concentrations at other sampling sites (p < 0.05), which is consistent with the results described in 3.2. Moreover, principal component analysis confirmed these results (Figure S1 ). The last cluster (C in Fig. 4 ) contained three antibiotics, including SDZ, SMP, and SIZ, which did not differ significantly between the two samples, indicating that they were also present in similar concentrations at the sample sites and had the same source of contamination (Corey and Damian, 2018 ; Paul et al., 2017 ). 3.4. Source identification of antibiotics In this study, the PMF model was used to analyze the sources of 19 antibiotics. In the PMF model is running, an important parameter for finding the best solution is the number of factors (Jia et al., 2022). According to the results of principal component analysis (PCA) of standardized antibiotic concentrations, three principal components were obtained, and the cumulative interpretative variance exceeded 92% (Table S7), which was the same as the above clustering results. The PMF model was debugged and three factors were validated as the best solution. The PMF model was used to identify the main sources of antibiotics in water bodies, and three factors were selected (Fig. 5 a, 5 b). The results revealed that SDZ, SMP and SIZ were the primary loadings on Factor 1, with contribution rates of 85.9%, 88.6% and 67.2%, respectively. SDZ, SMP and SIZ are sulfonamide antibiotics that are widely used in livestock feed and medicine and treat livestock diseases and promote growth (Qian et al., 2022 ). Antibiotics ingested by animals have a low absorption rate in the gastrointestinal tract. Approximately 80–90% of antibiotics are excreted intact in urine and feces and are often detected in animal feces (Sarmah et al., 2006 ) and various aquatic environments (Laura et al., 2022 ; Weiyan et al., 2022 ). SDZ, SMP and SIZ are used to treat bacterial infections of the respiratory tract, digestive tract and urinary tract in animals (Hao et al., 2023 ; Sonkar et al., 2024 ). Animal breeding in parks (birds in forests and ducks and geese on islands in the lake), tourism (pet waste) and pet hospital discharge may be the main sources of these antibiotics in the water. Therefore, factor 1 is considered the livestock drainage source. ATM (91.7%), DXC (82.6%) and CTM (73.4%) are the main loadings on Factor 2. ATM and CTM belong to the macrolide class of antibiotics, which are broad-spectrum antibacterials that are often used to treat respiratory infections, and they have been detected at concentrations of nano- and micrograms per liter in various regions of the world because of their high utilization in the treatment of human diseases (Wang et al., 2019 ; Xiang et al., 2015), especially during the COVID-19 pandemic. DXC is used to treat upper respiratory tract infections, tonsillitis, bronchitis, and mycoplasma pneumonia caused by gram-positive and gram-negative bacteria in humans. There are many colleges, universities and areas with high population densities near Xingqing Lake, and the high antibiotic content of the lake is affected by various pollution sources, such as university experimental wastewater and domestic garbage and sewage in the surrounding environment (Yu et al., 2017). This finding indicates that factor 2 may be domestic drainage. OFL, SMM, LIN, SMX and ROX were dominant in Factor 3, with contribution rates of 86.6%, 86.4%, 85.9%, 84.7% and 81.0%, respectively. Interestingly, OFL has a wide range of clinical applications (Fan et al., 2025), with the detection frequency of OFL in sludge from hospital sewage treatment plants reaching 100% (Ajibola et al., 2020). The concentrations of OFL in hospital wastewater in Vietnam and Italy are 800–7.4×10 3 ng/L and 2.5×10 4 × 3.7×10 4 ng/L (Ashfaq et al., 2016; Lien et al., 2016), respectively. Both SMM and SMX are SAs that are used to treat mainly respiratory tract, intestinal tract and urinary tract infections. LIN is used mainly to treat infections in the respiratory tract, skin soft tissue, abdominal cavity and other infections caused by gram-positive bacteria and some anaerobic bacteria. ROX is a new generation of macrolide antibiotics that are often used to treat respiratory tract infections, skin soft tissue infections, and urinary and reproductive system infections and is released mainly from hospital discharge (Dinh et al., 2017; Ling et al., 2023). Notably, there are many anorectal and other hospitals and clinics near Xingqing Lake, and they are the main sources of these antibiotics. Therefore, Factor 3 was classified as hospital discharge. 3.5. Ecological risk assessment The ecotoxic effects and health risks caused by antibiotic residues have become important issues in the environmental field (Jessica et al., 2016 ; Wang et al., 2017b ). Studies have shown that residual antibiotics in aquatic environments not only adversely affect nontarget organisms but also threaten ecosystem stability through food chain transmission. In this study, antibiotics were frequently detected in water samples collected from lakes in the urban landscape. Therefore, it is necessary to estimate the ecological risks of these antibiotics. The RQs of various aquatic organisms (algae, crustaceans, fish, invertebrates and plants) are shown in Fig. 6 . Antibiotic risk assessment revealed that OFL, SMX, CIP, ERY and ROX posed moderate (0.1 < RQ 1) risks to algae. OFL posed a moderate risk to plants. ERY posed a moderate risk to invertebrates, and crustaceans, fish and other organisms were generally at low risk levels, indicating that algae are highly sensitive to antibiotic pollution (Wei et al., 2024 ). This result is consistent with previous studies of the South Yellow Sea (Du et al., 2017 ), Laizhou Bay (Shuang et al., 2021 ) and karst rivers (Huang et al., 2019 ). Antibiotics pose a moderate risk to plants (OFL) and invertebrates (ERY). ATM and CTM present relatively low risk, despite their relatively high concentrations in the water column. This is due mainly to their higher risk threshold, i.e., relatively high PNEC values (Wei et al., 2024 ). Notably, ATM, CTM, and SDZ presented higher risk values for crustaceans than for other aquatic organisms, such as algae, indicating that the harm of different antibiotics to aquatic organisms is different. Therefore, the toxicology of antibiotics to individual aquatic organisms, such as algae, cannot be considered only in studies assessing the ecological risks of antibiotics, even though algae are the basis of the food chain. In the actual environment, the combined effect of pollution may further amplify the ecotoxicity of antibiotics (Qadeer et al., 2023 ; Tian et al., 2023). Wang et al (Wang et al., 2017a ) revealed that combined exposure to norfloxacin (NOX) and sulfamethoxazole (SMX) significantly inhibited the reproductive ability of Brachydanio rerio. The synergistic effect of tetracycline drugs may exacerbate damage to aquatic plants (Brain et al., 2005 ). At present, ecological risk assessment based on a single pollutant can provide an only preliminary risk understanding, and the antagonistic/synergistic mechanism of multicomponent pollutants in the actual environment still needs to be further explored. Future studies should focus on comprehensive risk calculations when multiple antibiotics coexist and include a model for the effects of the interactions of pollutants to improve the accuracy of ecological risk assessment. 4. Conclusions In this work, the temporal and spatial variability, potential sources and ecological risks of antibiotics in surface water in typical lakes in the urban landscape in the COVID-19 pandemic were identified via a PMF model. The results indicate that all 19 target antibiotics were detected in the study area. Macrolide antibiotics (MAs) were predominant in environmental matrices, accounting for more than 60% of the total detected concentration of antibiotics, followed by fluoroquinolones (QNs, 26%). ERY and OFL were the dominant antibiotics in Xingqing Lake. The synergistic effect of the pandemic and lake renovation was the main factor driving the differences in the distribution of antibiotics. In summary, lake reconstruction projects generally reduce the concentration of antibiotics, and epidemics increase the pollution of related antibiotics, such as ATM, and CTM. The PMF model indicated that the potential sources of antibiotics in water were domestic discharge, medical discharge, and livestock farming. The ecological risk assessment revealed that antibiotics posed a medium–high risk (RQ > 0.1) to algae. OFL posed a moderate risk to plants. ERY posed a moderate risk to invertebrates, and crustaceans, fish and other organisms were generally at low risk levels. Notably, ATM, CTM and SDZ presented higher risk values for crustaceans than for other aquatic organisms, such as algae, indicating that different antibiotics are harmful to aquatic organisms. A toxicity assessment of a single species may severely underestimate the actual ecological risk of antibiotics, and it is necessary to establish a comprehensive system for evaluating polytrophic organisms. In addition, more attention should be given to the influence of environmental factors on antibiotic occurrence in future studies. This work provides a scientific basis for identifying and controlling antibiotic sources in the lake water of urban landscapes. Declarations Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution Sheng Xu: Writing – original draft, Formal analysis. Lei Ren: Writing – review & editing, Data curation. Wei WU: Methodology, Projiect administration. Peng Zheng: Software, Visualization. Jialong Liu: Investigation, Conceptualization. Xiaoxiong Zhang: Investigation, Methodology. Acknowledgments This work was supported by the National Natural Science Foundation of China (No. U2243242); Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No. 23JY060). Data Availability The datasets used and analysed during the current study available from the corresponding author on reasonable request. References Ajibola, A. S., Amoniyan, O. A., Ekoja, F. O. & Ajibola, F. O. QuEChERS Approach for the Analysis of Three Fluoroquinolone Antibiotics in Wastewater: Concentration Profiles and Ecological Risk in Two Nigerian Hospital Wastewater Treatment Plants. Arch. Environ. Contam. Toxicol. 1–13. (2020). Ashfaq, M. et al. Occurrence and ecological risk assessment of fluoroquinolone antibiotics in hospital waste of Lahore. Pakistan Environ. Toxicol. Pharmacol. 42 , 16–22 (2016). Bi, E., Liu, Y., He, J., Wang, Z. & Liu, F. Screening of Emerging Volatile Organic Contaminants in Shallow Groundwater in East China. Groundw. Monit. 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Expanded Target-Chemical Analysis Reveals Extensive Mixed-Organic-Contaminant Exposure in U.S. Streams. Environ. Sci. Technol. 51 (9), 4792–4802 (2017). Qadeer, A. et al. A mega study of antibiotics contamination in Eastern aquatic ecosystems of China: occurrence, interphase transfer processes, ecotoxicological risks, and source modeling. J. Hazard. Mater. 458 , 131980 (2023). Qian-Qian, Z., Guang-Guo, Y., Chang-Gui, P., You-Sheng, L. & Jian-Liang, Z. Comprehensive evaluation of antibiotics emission and fate in the river basins of China: source analysis, multimedia modeling, and linkage to bacterial resistance. Environ. Sci. Technol. 49 (11), 6772–6782 (2015). Qian, L. et al. Degradation of sulfonamides in aquaculture wastewater by laccase–syringaldehyde mediator system: Response surface optimization, degradation kinetics, and degradation pathway. J. Hazard. Mater. 432 , 128647–128647 (2022). Qian, S., Jun, H., Shubo, D., Weiwei, C. & Gang, Y. 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A global perspective on the use, sales, exposure pathways, occurrence, fate and effects of veterinary antibiotics (VAs) in the environment. Chemosphere 65 (5), 725–759 (2006). Satoshi, M., Ayako, M., Hideshige, T., Cach, T. B. & H, C. N. Distribution of macrolides, sulfonamides, and trimethoprim in tropical waters: ubiquitous occurrence of veterinary antibiotics in the Mekong Delta. Environ. Sci. Technol. 41 (23), 8004–8010 (2007). Shuang, L. et al. Profiling of the spatiotemporal distribution, risks, and prioritization of antibiotics in the waters of Laizhou Bay, northern China. J. Hazard. Mater. 424 (PB), 127487–127487 (2021). Sonkar, V., Venu, V., Nishil, B. & Thatikonda, S. Review on antibiotic pollution dynamics: insights to occurrence, environmental behaviour, ecotoxicity, and management strategies. Environ. Sci. Pollut Res. Int. 31 (39), 1–33 (2024). Tamtam, F. et al. Occurrence and fate of antibiotics in the Seine River in various hydrological conditions. Environ. Sci. Technol. 393 (1), 84–95 (2007). Tianzhi, L. et al. Occurrence, distribution, and potential ecological risks of antibiotics in a seasonal freeze-thaw basin. J. Hazard. Mater. 459 , 132301–132301 (2023). Tong, Z., Qiuying, A., Ling, Z., Ce, W. & Changzhou, Y. Phytoremediation for antibiotics removal from aqueous solutions: A meta-analysis. Environ. Res. 240 (P1), 117516–117516 (2023). Tran, N. H. et al. Occurrence and risk assessment of multiple classes of antibiotics in urban canals and lakes in Hanoi. Vietnam Environ. Sci. Technol. 692 , 157–174 (2019). Tran, N. H., Reinhard, M. & Gin, K. Y. H. Occurrence and fate of emerging contaminants in municipal wastewater treatment plants from different geographical regions-a review. Water Res. 133 , 182–207 (2018). Wang, S. et al. Fate of antibiotics in three distinct sludge treatment wetlands under different operating conditions. Environ. Sci. Technol. 671 , 443–451 (2019). Wang, X., Liu, S. & Guo, H. A nosocomial-pathogens-infections model with impulsive antibiotics treatment on multiple bacteria. Appl. Math. Comput. 296 , 64–87 (2017a). Wang, Z. et al. Occurrence and ecological hazard assessment of selected antibiotics in the surface waters in and around Lake Honghu, China. Environ. Sci. Technol. 609 , 1423–1432 (2017b). Wei, C. et al. Spatiotemporal distribution and potential risks of antibiotics in coastal water of Beibu Gulf, South China Sea: Livestock and poultry emissions play essential effect. J. Hazard. Mater. 466 , 133550 (2024). Weiyan, D., Hongwu, C., Xinyu, J. & Xiao, H. Occurrence and ecotoxicity of sulfonamides in the aquatic environment: A review. Environ. Sci. Technol. 820 , 153178–153178 (2022). Wu, C. et al. Occurrence of pharmaceuticals and personal care products and associated environmental risks in the central and lower Yangtze river, China. Ecotoxicol. Environ. Saf. 106 , 19–26 (2014). Xiangjuan, Y., Zhimin, Q., Weiwei, B., Bing, Z. & Jiuhui, Q. Distribution, mass load and environmental impact of multiple-class pharmaceuticals in conventional and upgraded municipal wastewater treatment plants in East China. Environ. sci. Processes impacts . 17 (3), 596–605 (2015). Xu, J. et al. Occurrence of antibiotics and antibiotic resistance genes in a sewage treatment plant and its effluent-receiving river. Chemosphere 119 , 1379–1385 (2015). Yi, X., Lin, C., Ong, E. J. L., Wang, M. & Zhou, Z. Occurrence and distribution of trace levels of antibiotics in surface waters and soils driven by non-point source pollution and anthropogenic pressure. Chemosphere 216 , 213–223 (2018). Yin, G. et al. Effects of multiple antibiotics exposure on denitrification process in the Yangtze Estuary sediments. Chemosphere 171 , 118–125 (2017). Ying, H. et al. Single-cell transcriptome-wide Mendelian randomization and colocalization reveals immune-mediated regulatory mechanisms and drug targets for COVID-19. EBioMedicine 113, 105596. (2025). Yuanan, H., Sen, Y., Hefa, C. & Shu, T. Systematic Evaluation of Two Classical Receptor Models in Source Apportionment of Soil Heavy Metal(loid) Pollution Using Synthetic and Real-World Datasets. Environ. Sci. Technol. 56 (24), 17604–17614 (2022). YuanMeng, S. et al. Source Apportionment and Source-specific Risk of Typical Antibiotics in Baiyangdian Lake. Environ. Sci. 44 (9), 4927–4940 (2023). Yuyi, Y., Chen, X., Xinhua, C., Hui, L. & Jun, W. Antibiotic resistance genes in surface water of eutrophic urban lakes are related to heavy metals, antibiotics, lake morphology and anthropic impact. Ecotoxicol. (London England) . 26 (6), 831–840 (2017). Zhao, C., Qian, D., Zhiguo, Y., Xia, H. & Yanchen, L. Fate characteristics, exposure risk, and control strategy of typical antibiotics in Chinese sewerage system: A review. Environ. Inter . 167 , 107396–107396 (2022). Zou, S., Huang, F., Chen, L. & Liu, F. The occurrence and distribution of antibiotics in the Karst river system in Kaiyang, Southwest China. Water Sci. Technol. : Water Supply . 18 (6), 2044–2052 (2018). Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6508163","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":457485025,"identity":"cfcef75d-9905-4aac-abb9-41b0ed31daad","order_by":0,"name":"Sheng Xu","email":"","orcid":"","institution":"Xi'an University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Sheng","middleName":"","lastName":"Xu","suffix":""},{"id":457485029,"identity":"0360f107-b741-4e85-862e-db7f1eab10c3","order_by":1,"name":"Lei Ren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYHACxgMMBjZQNhuReoBa0oAUM0laGA6ToMW8vffAgQ8F5xP7Z+QfYPhQdpiBf3YDfi0yZ84lHJxhcNtY4kYyA+OMc4cZJO4cwK9FQiLH4DCPwW05BqAWZt62wwwGEgkEtMi/MTj8x+AcjzxIy1+itEjwGACVHZAzAGlhJEoLT47BwR6DZGPDM4+BjHPpPBI3CGlhP2P44Mcfu8R5xxMfPvhRZi3HP4OAFhRwAIh5SFA/CkbBKBgFowAXAABlNEF2q5uMhAAAAABJRU5ErkJggg==","orcid":"","institution":"Xi'an University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Ren","suffix":""},{"id":457485031,"identity":"1f459479-1fd0-482c-91af-39895165b063","order_by":2,"name":"Wei Wu","email":"","orcid":"","institution":"Xi'an University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wu","suffix":""},{"id":457485032,"identity":"8b528f81-305a-4f60-9079-b63268b67254","order_by":3,"name":"Peng Zheng","email":"","orcid":"","institution":"Xi'an University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Zheng","suffix":""},{"id":457485033,"identity":"2beea93c-5cf3-442d-b2cf-931a47de1fdc","order_by":4,"name":"Jialong Liu","email":"","orcid":"","institution":"Xi'an University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jialong","middleName":"","lastName":"Liu","suffix":""},{"id":457485034,"identity":"1cbba956-66bf-489c-929e-dd3c0225eef0","order_by":5,"name":"Xiaoxiong Zhang","email":"","orcid":"","institution":"Xi'an University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxiong","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-04-23 02:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6508163/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6508163/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83041215,"identity":"c4d5c6da-fdbf-42b7-ad0c-8e78c1a01cb1","added_by":"auto","created_at":"2025-05-19 10:44:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":608540,"visible":true,"origin":"","legend":"\u003cp\u003eMap of study area and water sampling sites.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6508163/v1/b476889d4e8373944f073919.png"},{"id":83038839,"identity":"e16166de-aa59-47ea-9329-5a25a6b64353","added_by":"auto","created_at":"2025-05-19 10:28:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":174319,"visible":true,"origin":"","legend":"\u003cp\u003eConcentration distribution and detection frequencies of antibiotics.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6508163/v1/ba485af9ddeb7c12ffd14261.png"},{"id":83038845,"identity":"791bbaec-3a06-46a7-9b9f-93073bf23770","added_by":"auto","created_at":"2025-05-19 10:28:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":169475,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution and temporal variation of five antibiotic types.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6508163/v1/63e1b518c645bcc9620113d7.png"},{"id":83038846,"identity":"1cb9a323-414a-4b89-814d-48e613168bc9","added_by":"auto","created_at":"2025-05-19 10:28:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":399260,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of 19 antibiotics clustered by concentration profiles in all samples. The colour of each cell represents the detection concentrations of standardization.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6508163/v1/affd50b56a417890bfcd99ed.png"},{"id":83039325,"identity":"5266bb12-1aa3-475b-8a9d-4f997106246a","added_by":"auto","created_at":"2025-05-19 10:36:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":385054,"visible":true,"origin":"","legend":"\u003cp\u003ePMF model results of antibiotics (a) and the three sources (b).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6508163/v1/50595d0e5335438aa86409b0.png"},{"id":83039326,"identity":"15953d9d-a4c6-4328-9a29-70ef90ec2e5f","added_by":"auto","created_at":"2025-05-19 10:36:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":127919,"visible":true,"origin":"","legend":"\u003cp\u003eRisk quotients (RQ) for the antibiotics detected of Pre-COVID (a) and Post-COVID (b).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6508163/v1/e411a4fc4545f1b6adfcd24d.png"},{"id":102397816,"identity":"a9b19c05-f5eb-4c9d-a360-f6f323fce8d1","added_by":"auto","created_at":"2026-02-11 10:19:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2664296,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6508163/v1/4a6238bb-4235-4363-b9b4-240f41704ddc.pdf"},{"id":83039324,"identity":"2bc3f4a0-83a2-48ee-b9a7-91d4b3048f42","added_by":"auto","created_at":"2025-05-19 10:36:38","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":405836,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6508163/v1/24d9009a4ef6a4bcfd7aebcb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characteristics of antibiotic pollution and assessment of ecological risk of lake water in typical urban landscape in the context of a epidemic","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAs emerging environmental chemical substances, antibiotics play an irreplaceable and crucial role in the field of human medicine. They are also widely used in the livestock and aquaculture industries to promote animal growth and prevent and treat diseases (Gonz\u0026aacute;lez-Pleiter et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kovalakova et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yi et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As antibiotic production technologies have developed, the cost of antibiotics has significantly decreased, making them easily accessible (Bielen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; K\u0026uuml;mmerer \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008a\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003eb\u003c/span\u003e). The global consumption of antibiotics increased by 65% from 2000 to 2015. Of this increase, human consumption accounted for 48%, and animal consumption accounted for 52% (Qian et al., 2015). Antibiotics are difficult to completely absorb and metabolize by humans or animals (excreted through urine and feces) (Carvalho and Santos \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ju et al., 2014; Xu et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and conventional sewage and sludge treatment cannot completely remove these antibiotics. This leads to a large amount of antibiotics being discharged into the natural environment in their original forms or as active metabolites, and they are often detected in water bodies, sediments, soils, and even aquatic organisms (Cha and Cupples \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lyu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tran et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAntibiotic residues can cause ecological toxicity and damage ecological health (Jessica et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e). Aquatic ecosystems serve as major reservoirs of various antibiotics (Zhao et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and residual antibiotics in their aquatic environment have adverse effects on nontarget activities, such as inhibiting algal growth; affecting chloroplast replication, transcription, and translation; interfering with the physiological functions of aquatic animals; and disrupting the N cycle involved in microorganisms (Carvalho and Santos \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; K\u0026uuml;mmerer \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008a\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The main sources of antibiotics in aquatic environments include (a) medical wastewater; (b) domestic sewage; (c) aquaculture; (d) soil infiltration for animal husbandry; (e) sediment release; (f) surface water runoff; (g) groundwater migration; and (h) atmospheric deposition (Han et al., 2023). At present, methods of source analysis include mainly the absolute principal component score\u0026ndash;multiple linear regression model (APCS\u0026ndash;MLR), Unmix model (Tong et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and positive matrix factorization model (PMF) (Yuan et al., 2022). Compared with other traceability models, the PMF model has the advantages of a greater degree of fit and less influence on the number of samples and has been applied to analyze antibiotic sources (Lamine et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yuan et al., 2023).\u003c/p\u003e \u003cp\u003eChina is one of the major consumers of antibiotics (Qian et al., 2015). From 2013\u0026ndash;2018, the annual production of antibiotics in China reached 193,000 tons, and the per capita consumption was 10 times greater than that of the United States (US) (Matteo et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Since 2005, research on antibiotics in China has continued to increase. At present, research on antibiotics has focused mainly on their effects on natural water bodies, such as the Yellow River, the Yangtze River, the Liao River and the Pearl River (Dong et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ling et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rui et al., 2017; Wu et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and the objects used for risk assessment are single algae. As important landscape systems in cities, urban lakes are not only landscape projects but also host tourist resorts. These lakes have many functions, such as flood control and storage, flood control and disaster reduction, and improvement of the urban ecological environment. Lakes in urban landscapes usually feature poor water fluidity and weak self-purification ability. Coupled with the dense population in the surrounding areas and the development of the tourism industry, the amount of antibiotics input to these lakes is relatively high (Hui et al., 2017). The outbreak of the COVID-19 pandemic has significantly changed the patterns of human activity and antibiotic use, and the relationships between the characteristics of antibiotic pollution in lake water bodies and the COVID-19 pandemic are still unclear. Owing to differences in the geographical location, functional positioning, and pollution input, the evolutionary process and current situation of pollution of water bodies in urban shallow lakes are quite different from those of lakes far from cities. Therefore, it is necessary to study the characteristics of antibiotic pollution and conduct risk assessments of lake water bodies against the backdrop of the COVID-19 pandemic.\u003c/p\u003e \u003cp\u003eXingqing Lake is has undergone the integration of the modern urban landscape and historical and cultural accumulation in Xi'an, and it is also an ecological lake for which recreation and sightseeing are integrated. It is a typical urban lake. The concentrations of antibiotics collected in the influent area, central area and outlet area of Xingqing Lake were investigated before and after the pandemic. The purpose of this study was to 1) analyze the spatiotemporal heterogeneity of antibiotics in the surface water of Xingqing Lake against the background of a pandemic; 2) analyze the source of antibiotic pollution via the PMF model; and 3) assess the ecological risks of antibiotics in water to many aquatic organisms to provide a scientific basis for identifying and controlling antibiotic sources in lake water in the urban landscape.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Target antibiotics, chemical reagents, and solvents\u003c/h2\u003e \u003cp\u003eIn this study, 44 antibiotics from seven different classes were investigated, and ultimately 19 target antibiotics were detected. These antibiotics are as follows:\u003c/p\u003e \u003cp\u003e[1] Sulfonamides: sulfadiazine (SDZ); sulfamethoxypyridazine (SMP); sulfamethoxazole (SMX); sulfafurazole (SIZ); sulfaquinoxaline (SQX); sulfadimethoxine (SMM); sulfadoxine (SDX); sulfaphenazolum (SPZ);\u003c/p\u003e \u003cp\u003e[2] Quinolones: ofloxacin (OFL); ciprofloxacin (CIP); danofloxacin (DAN); lomefloxacin (LOM); sarafloxacin (SAR);\u003c/p\u003e \u003cp\u003e[3] Macrolides: azithromycin (ATM); clarithromycin (CTM); erythromycin (ERY); roxithromycin (ROX);\u003c/p\u003e \u003cp\u003e[4] Lincosamides: lincomycin (LIN);\u003c/p\u003e \u003cp\u003e[5] Tetracyclines: doxycycline (DXC).\u003c/p\u003e \u003cp\u003eThe information for the 19 antibiotics is listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. All the target antibiotic standards were purchased from the Sigma‒Aldrich Company (MO, USA) and Dr. Ehrenstorfer Company (Germany) and were \u0026gt;\u0026thinsp;99% pure. The external standard method was used in this study. Each target antibiotic standard sample had a configured gradient solution of 5\u0026ndash;500 \u0026micro;g/L, and methanol or ultrapure water was used. The other chemicals used in this study were of LC‒MS grade.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Study area and sample collection\u003c/h2\u003e \u003cp\u003eXingqing Lake is located in Xingqing Palace Park, Xi'an city, Shaanxi Province, China. Its water area is approximately 4.6\u0026times;104 m\u003csup\u003e2\u003c/sup\u003e, and its length, width, average water depth and hydraulic retention time are approximately 915 m, 214 m, 1.6 m and 310 d, respectively, acting as a buffer used for flood control in the eastern suburbs of Xi'an city. Xingqing Lake is the subject of the integration of the modern urban landscape and historical and cultural growth in Xi'an. Moreover, it is an ecological lake for which leisure, entertainment for citizens and sightseeing tourism are combined, and these functions play a crucial role in the ecological environment and economic development of Xi'an, as a typical urban lake. The sampling sites are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (We was obtained for the collection of water samples from Xingqing Lake from the park administration department. And we confirm that all sampling was performed with permission).\u003c/p\u003e \u003cp\u003eA total of several sampling points were selected from the influent area (S1), the central area (S2) and the outlet area (S3) of Xingqing Lake, and samples were collected twice, i.e., once in January 2020 (before the pandemic) and once in January 2024 (after the pandemic). A glass water sampler was used to collect a 1 L water sample 0.5 m below the water surface at each sampling point (in addition, 2 water samples were collected at each site as a control). The collected samples were stored in the dark (4.0\u0026deg;C) and transported back to the laboratory for extraction and analysis as soon as possible (Bi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Liang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zou et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Sample extraction and instrumental analysis\u003c/h2\u003e \u003cp\u003eAfter a 1 L water sample was filtered through a 0.45 \u0026micro;m fiber filter membrane (Whatman, UK), 6.0 g of Na\u003csub\u003e2\u003c/sub\u003e-EDTA was added, the mixture was shaken until it was fully dissolved, and the pH of the water sample was adjusted to 2 with hydrochloric acid. An autosolid phase extraction (SPE) instrument (Auto SPE-06C, USA) with an Oasis hydrophile lipophilic balance (HLB) SPE column (6 mL, 200 mg, Waters, USA) was used to extract antibiotics from the water samples. The extraction column was activated with 5 mL of deionized water, 5 mL of methanol and 5 mL of acidic water (pH\u0026thinsp;=\u0026thinsp;2). When approximately 2 mL of deionized water remained in the extraction column, all the water samples were passed through the extraction column at flow rates of 10 to 15 mL/min. The column was then rinsed with 5 mL of deionized water and purged with nitrogen for 20 minutes to dry the extraction column. Afterward, 10 mL of methanol was added, the mixture was eluted at a flow rate of 5 mL/min, and the eluent was collected. The eluent was concentrated to near dryness using a nitrogen blowing instrument and diluted to 1.0 mL with the initial mobile phase. The mixture was filtered through a 0.22 \u0026micro;m organic filter membrane and was ready for testing. Before instrumental analysis, 10 \u0026micro;L of the internal standard mixture (in a 4 ng/\u0026micro;L methanol solution) was added to the final sample.\u003c/p\u003e \u003cp\u003eThe antibiotics in groundwater were analyzed using a Waters ACQUITY UPLC H-Class system coupled with a Waters Xevo-TQ-S Triple Quadrupole MS/MS spectrometer equipped with an electrospray ionization (ESI) source (Waters, MA, USA). A Waters ACQUITY UPLC BEH C18 (1.9 \u0026micro;m 2.1\u0026times;50 mm) at 40\u0026deg;C was used. The analysis was carried out in positive electrospray ionization mode for the target antibiotics (Tran et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The mobile phase contained 0.1% aqueous formic acid (A) and methanol/acetonitrile (B, 1/1, V/V, with 0.1% formic acid) at a 0.4 mL/min flow rate. Table S2 lists the conditions of the electrospray ionization mobile phase and gradient elution. Table S3 and table S4 list the mass spectrometry and ion selection parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Quality assurance and quality control\u003c/h2\u003e \u003cp\u003eStringent laboratory controls were implemented for all laboratory operations. The quality assurance and quality control of the laboratory analysis followed the US EPA methods 8270D and 8000B. All the samples from the two groups were analyzed in duplicate. The external standard method was used to quantify the concentrations of the target antibiotics in the water samples. The method for determining samples in the external standard experiment was the same as the pretreatment method for antibiotics in water samples (including filtration, complexation with Na₂-EDTA, acidification, and SPE enrichment treatment), and the correlation coefficient of the standard curve was greater than 99.8%. For our analysis, surrogate standards were added to the samples before extraction to evaluate the analytical recovery rate (Table S5). The instrumental quantification limits (IQLs) were calculated as S/N\u0026thinsp;=\u0026thinsp;10, and the instrumental detection limits (IDLs) were calculated as S/N\u0026thinsp;=\u0026thinsp;3 (Table S5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Environmental risk assessment\u003c/h2\u003e \u003cp\u003eAccording to the method for environmental risk assessment in the technical guidance document of the European Union, the effects of exposure to chemicals are expressed as risk quotients (RQs) (Rafiquel et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The environmental risk of antibiotics to aquatic organisms (i.e., algae, crustaceans, fish, etc.) is calculated as the ratio between the maximum measured environmental concentration (MEC) in water and the predicted no effect concentration (PNEC) for each contaminant, as shown in Eq.\u0026nbsp;(1) (K\u0026ouml;tke et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The PNEC value was obtained from the 50% effective or lethal concentration or no observed effect concentration (EC\u003csub\u003e50\u003c/sub\u003e or LC\u003csub\u003e50\u003c/sub\u003e or NOEC) of the pharmaceutical by dividing by a safety factor (AF\u0026thinsp;=\u0026thinsp;100\u0026ndash;1000), as shown in Eq.\u0026nbsp;(2) (Tran et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Antibiotics have different toxicities to aquatic organisms. Therefore, in this study, five common aquatic plants were selected to evaluate the ecological risks caused by antibiotics. The EC\u003csub\u003e50\u003c/sub\u003e, LC\u003csub\u003e50\u003c/sub\u003e or NOEC values and AF values were obtained from the Environmental Protection Agency and other studies, as shown in Table S6.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{RQ}_{i}=\\frac{{MEC}_{i}}{{PNEC}_{i}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{PNEC}_{i}=\\frac{{L\\left(E\\right)C}_{50}\\:or\\:L\\left(N\\right)OEC}{{AF}_{i}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eRQ\u0026gt;1 represents a highecological riskto aquatic organisms, 0.1\u0026lt;RQ\u0026lt;1 indicates a medium ecological risk, and RQ\u0026lt;0.1 reveals a low ecological risk.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Occurrence and concentrations of antibiotics\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show the concentrations and detection frequencies (DFs) of antibiotics in the surface water of Xingqing Lake. A total of 19 antibiotics were found, with the DFs ranging from 16.67\u0026ndash;100%, and they exhibited different degrees of pollution. High detection frequencies (100%) of ERY, OFL, ROX, and CIP were detected in the samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), whereas 25 antibiotics were not detected in any of the samples. The concentrations of antibiotics varied significantly, ranging from below the instrumental detection limit (IDL) to nearly 100 ng/L. ERY and OFL had relatively high pollution levels, with average concentrations of 55.87 ng/L and 31.09 ng/L, respectively, followed by SMM (10.63 ng/L) and ATM (16.46 ng/L), and the other antibiotics had relatively low pollution levels. These findings indicate that ERY and OFL are the dominant antibiotics in Xingqing Lake, accounting for more than 80% of the total antibiotics. High levels of ERY and OFL have also been detected in other natural and lake water ecosystems in China, such as Taihu Lake, the Haihe River and Chaohu Lake waters (Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Matthew et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Satoshi et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Tamtam et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In addition, a similar phenomenon was shown in a study of antibiotics in waters in Vietnam and France (Satoshi et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Tamtam et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe found that MAs are the dominant antibiotics in the environment, accounting for more than 60% of all antibiotics, followed by QNs (26%). As effective drugs for the treatment and prevention of various tissue infections, macrolide antibiotics have expanded in variety and quantity since their introduction in 1952 and have been widely used in poultry and aquaculture as growth agents, in addition to their use in human medicine. The average concentration of sulfonamides (SAs) is significantly lower than that of MAs and QNs because the use of SAs in Southwest China is lower than the production of MAs and QNs (Qian et al. 2015). Notably, tetracycline classes (TCs) were found at low concentrations in aquatic environments, and only doxycycline (DXC) was detected. This is due to the unstable nature of tetracyclic antibiotics in aquatic environments (Liu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Qi and R 2008), which easily accumulate in sediments. The absorption of DXC in soil is relatively weak, which makes it easier for DXC to quickly enter the aquatic environment through soil pores via erosion by rainfall. In addition, DXC has better chemical stability, a stronger ability to resist interference from environmental factors, can be relatively stable in environments such as water bodies and is more easily detected. For LIs, LIN was detected at concentrations of not detected (ND)\u0026ndash;9.3 ng/L, which was lower in Xingqing Lake than in Yuehu (31.5\u0026thinsp;~\u0026thinsp;209.0 ng/L) (Rui et al., 2018), Xianghu (19.2\u0026thinsp;~\u0026thinsp;49.7 ng/L) and Yao Lakes (ND\u0026thinsp;~\u0026thinsp;54.7 ng/L) (Hui et al., 2017).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Spatial and temporal distributions of antibiotics\u003c/h2\u003e \u003cp\u003eThe temporal variation in antibiotics in water bodies is caused by a variety of factors, such as precipitation, temperature, current, photosensitivity, and biodegradation (Karthikeyan and Meyer, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Pa\u0026iacute;ga et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Qian et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Temporally, the overall antibiotic concentration in 2020 was significantly higher than that in 2024 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which was attributed mainly to the water ecological treatment of Xingqing Lake (2020) and the project to improve the stormwater pipe network (2023). Water renewal, water purification, water circulation, artificial wetlands and other measures were adopted in the lake ecological treatment project, which caused the antibiotics in the water need to reaccumulate and accelerate their degradation ( Loftin et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Oksana et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Because of the construction of a special water supply pipeline for Xingqing Lake, the water supply for Xingqing Lake has separated from the municipal rainwater pipeline, thus reducing the amount of exogenous antibiotic pollution. Notably, azithromycin (ATM), clarithromycin (CTM), and sarafloxacin (SAR) concentrations were higher in 2024 than in 2020, especially ATM. The reason is that Xi'an experienced two severe COVID-19 epidemics before and after the two sampling periods. During the epidemics period, due to the heavy use of medicines in hospitals and households, some antibiotics flowed into the aquatic environment through urine and feces, resulting in increased antibiotic concentrations in the water. ATM and CTM are macrolide antibiotics and broad-spectrum antibiotics and are often used for respiratory infections. The World Health Organization noted that there was an overuse of antibiotics, especially broad-spectrum antibiotics, such as azithromycin and ceftriaxone, for the treatment of COVID-19 (Ying et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpatially, the concentrations of various antibiotics tended to decrease from S1 to S3 in 2020, which was mainly related to the distribution of the three sampling points in the Xingqing Lake area. S1 was located downstream of the sedimentation tank at the Xingqing Lake inlet. Generally, after the water entering Xingqing Lake is precipitated in the sedimentation tank, the sediment and insoluble particles can be removed, and soluble pollutants and fine particles that do not easily precipitate gradually enter the lake as the influent water flow. However, at S1, the cross-section is small and the water depth is shallow and the pollutants have weak dilution and diffusion capacities and the contents of various pollutants are high. Therefore, the rate of detection of antibiotics in the water body at S1 was the highest, and the antibiotic content was relatively high. S2 is located in the main lake area, and S3 is near the outlet of Xingqing Lake. As the influent water in Xingqing Lake gradually migrates, diffuses, and undergoes continuous degradation through biochemical and physicochemical processes (Wei et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the concentration of antibiotic pollutants in the lake has steadily decreased.Therefore, the rates of detection and antibiotic contents at S2 and S3 gradually decreased. However, S2 in the main lake area presented a relatively high level of antibiotics over 2024, which may be due to the construction of special water supply pipelines to reduce water pollution. In addition, the area of S2 in the main lake district is the largest and has greater potential pollution sources, such as garbage discarded by visitors, fertilizers and pesticides used for park management, and dead leaves of plants in the park. In addition, duck farming on the island in the middle of the lake at S2 also increased antibiotic pollution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Clustering on the basis of concentration patterns\u003c/h2\u003e \u003cp\u003eCluster analyses of antibiotics and sample sites were conducted by hierarchical clustering with the between-groups linkage method and Euclidean distance matrices. The dendrogram and heatmap in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show the reclassification of two distinct clusters of sample points and three distinct clusters of 19 antibiotics. The sample sites were clearly divided into prepandemic sites with low concentrations and postpandemic sites with high concentrations, which is fully consistent with the actual situation. In addition, the three antibiotic clusters presented different concentration patterns. Group A contains six antibiotics, including CTM, ATM, DXC, etc. These antibiotics are the main contaminants at postpandemic (T2) compared with prepandemic (T1). The second cluster (B in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), containing 10 antibiotics, such as OFL, ERY, and ROX, had significantly higher concentrations from 2020 at S1 compared to the concentrations at other sampling sites (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which is consistent with the results described in 3.2. Moreover, principal component analysis confirmed these results (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The last cluster (C in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) contained three antibiotics, including SDZ, SMP, and SIZ, which did not differ significantly between the two samples, indicating that they were also present in similar concentrations at the sample sites and had the same source of contamination (Corey and Damian, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Paul et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Source identification of antibiotics\u003c/h2\u003e \u003cp\u003eIn this study, the PMF model was used to analyze the sources of 19 antibiotics. In the PMF model is running, an important parameter for finding the best solution is the number of factors (Jia et al., 2022). According to the results of principal component analysis (PCA) of standardized antibiotic concentrations, three principal components were obtained, and the cumulative interpretative variance exceeded 92% (Table S7), which was the same as the above clustering results. The PMF model was debugged and three factors were validated as the best solution.\u003c/p\u003e \u003cp\u003eThe PMF model was used to identify the main sources of antibiotics in water bodies, and three factors were selected (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The results revealed that SDZ, SMP and SIZ were the primary loadings on Factor 1, with contribution rates of 85.9%, 88.6% and 67.2%, respectively. SDZ, SMP and SIZ are sulfonamide antibiotics that are widely used in livestock feed and medicine and treat livestock diseases and promote growth (Qian et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Antibiotics ingested by animals have a low absorption rate in the gastrointestinal tract. Approximately 80\u0026ndash;90% of antibiotics are excreted intact in urine and feces and are often detected in animal feces (Sarmah et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and various aquatic environments (Laura et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Weiyan et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). SDZ, SMP and SIZ are used to treat bacterial infections of the respiratory tract, digestive tract and urinary tract in animals (Hao et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sonkar et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Animal breeding in parks (birds in forests and ducks and geese on islands in the lake), tourism (pet waste) and pet hospital discharge may be the main sources of these antibiotics in the water. Therefore, factor 1 is considered the livestock drainage source.\u003c/p\u003e \u003cp\u003eATM (91.7%), DXC (82.6%) and CTM (73.4%) are the main loadings on Factor 2. ATM and CTM belong to the macrolide class of antibiotics, which are broad-spectrum antibacterials that are often used to treat respiratory infections, and they have been detected at concentrations of nano- and micrograms per liter in various regions of the world because of their high utilization in the treatment of human diseases (Wang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xiang et al., 2015), especially during the COVID-19 pandemic. DXC is used to treat upper respiratory tract infections, tonsillitis, bronchitis, and mycoplasma pneumonia caused by gram-positive and gram-negative bacteria in humans. There are many colleges, universities and areas with high population densities near Xingqing Lake, and the high antibiotic content of the lake is affected by various pollution sources, such as university experimental wastewater and domestic garbage and sewage in the surrounding environment (Yu et al., 2017). This finding indicates that factor 2 may be domestic drainage.\u003c/p\u003e \u003cp\u003eOFL, SMM, LIN, SMX and ROX were dominant in Factor 3, with contribution rates of 86.6%, 86.4%, 85.9%, 84.7% and 81.0%, respectively. Interestingly, OFL has a wide range of clinical applications (Fan et al., 2025), with the detection frequency of OFL in sludge from hospital sewage treatment plants reaching 100% (Ajibola et al., 2020). The concentrations of OFL in hospital wastewater in Vietnam and Italy are 800–7.4×10\u003csup\u003e3\u003c/sup\u003e ng/L and 2.5×10\u003csup\u003e4\u003c/sup\u003e × 3.7×10\u003csup\u003e4\u003c/sup\u003e ng/L (Ashfaq et al., 2016; Lien et al., 2016), respectively. Both SMM and SMX are SAs that are used to treat mainly respiratory tract, intestinal tract and urinary tract infections. LIN is used mainly to treat infections in the respiratory tract, skin soft tissue, abdominal cavity and other infections caused by gram-positive bacteria and some anaerobic bacteria. ROX is a new generation of macrolide antibiotics that are often used to treat respiratory tract infections, skin soft tissue infections, and urinary and reproductive system infections and is released mainly from hospital discharge (Dinh et al., 2017; Ling et al., 2023). Notably, there are many anorectal and other hospitals and clinics near Xingqing Lake, and they are the main sources of these antibiotics. Therefore, Factor 3 was classified as hospital discharge.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Ecological risk assessment\u003c/h2\u003e \u003cp\u003eThe ecotoxic effects and health risks caused by antibiotic residues have become important issues in the environmental field (Jessica et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e). Studies have shown that residual antibiotics in aquatic environments not only adversely affect nontarget organisms but also threaten ecosystem stability through food chain transmission. In this study, antibiotics were frequently detected in water samples collected from lakes in the urban landscape. Therefore, it is necessary to estimate the ecological risks of these antibiotics. The RQs of various aquatic organisms (algae, crustaceans, fish, invertebrates and plants) are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Antibiotic risk assessment revealed that OFL, SMX, CIP, ERY and ROX posed moderate (0.1\u0026thinsp;\u0026lt;\u0026thinsp;RQ\u0026thinsp;\u0026lt;\u0026thinsp;1) to high (RQ\u0026thinsp;\u0026gt;\u0026thinsp;1) risks to algae. OFL posed a moderate risk to plants. ERY posed a moderate risk to invertebrates, and crustaceans, fish and other organisms were generally at low risk levels, indicating that algae are highly sensitive to antibiotic pollution (Wei et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This result is consistent with previous studies of the South Yellow Sea (Du et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Laizhou Bay (Shuang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and karst rivers (Huang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Antibiotics pose a moderate risk to plants (OFL) and invertebrates (ERY). ATM and CTM present relatively low risk, despite their relatively high concentrations in the water column. This is due mainly to their higher risk threshold, i.e., relatively high PNEC values (Wei et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Notably, ATM, CTM, and SDZ presented higher risk values for crustaceans than for other aquatic organisms, such as algae, indicating that the harm of different antibiotics to aquatic organisms is different. Therefore, the toxicology of antibiotics to individual aquatic organisms, such as algae, cannot be considered only in studies assessing the ecological risks of antibiotics, even though algae are the basis of the food chain.\u003c/p\u003e \u003cp\u003eIn the actual environment, the combined effect of pollution may further amplify the ecotoxicity of antibiotics (Qadeer et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tian et al., 2023). Wang et al (Wang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e) revealed that combined exposure to norfloxacin (NOX) and sulfamethoxazole (SMX) significantly inhibited the reproductive ability of Brachydanio rerio. The synergistic effect of tetracycline drugs may exacerbate damage to aquatic plants (Brain et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). At present, ecological risk assessment based on a single pollutant can provide an only preliminary risk understanding, and the antagonistic/synergistic mechanism of multicomponent pollutants in the actual environment still needs to be further explored. Future studies should focus on comprehensive risk calculations when multiple antibiotics coexist and include a model for the effects of the interactions of pollutants to improve the accuracy of ecological risk assessment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eIn this work, the temporal and spatial variability, potential sources and ecological risks of antibiotics in surface water in typical lakes in the urban landscape in the COVID-19 pandemic were identified via a PMF model. The results indicate that all 19 target antibiotics were detected in the study area. Macrolide antibiotics (MAs) were predominant in environmental matrices, accounting for more than 60% of the total detected concentration of antibiotics, followed by fluoroquinolones (QNs, 26%). ERY and OFL were the dominant antibiotics in Xingqing Lake. The synergistic effect of the pandemic and lake renovation was the main factor driving the differences in the distribution of antibiotics. In summary, lake reconstruction projects generally reduce the concentration of antibiotics, and epidemics increase the pollution of related antibiotics, such as ATM, and CTM. The PMF model indicated that the potential sources of antibiotics in water were domestic discharge, medical discharge, and livestock farming. The ecological risk assessment revealed that antibiotics posed a medium\u0026ndash;high risk (RQ\u0026thinsp;\u0026gt;\u0026thinsp;0.1) to algae. OFL posed a moderate risk to plants. ERY posed a moderate risk to invertebrates, and crustaceans, fish and other organisms were generally at low risk levels. Notably, ATM, CTM and SDZ presented higher risk values for crustaceans than for other aquatic organisms, such as algae, indicating that different antibiotics are harmful to aquatic organisms. A toxicity assessment of a single species may severely underestimate the actual ecological risk of antibiotics, and it is necessary to establish a comprehensive system for evaluating polytrophic organisms. In addition, more attention should be given to the influence of environmental factors on antibiotic occurrence in future studies. This work provides a scientific basis for identifying and controlling antibiotic sources in the lake water of urban landscapes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSheng Xu: Writing \u0026ndash; original draft, Formal analysis. Lei Ren: Writing \u0026ndash; review \u0026amp; editing, Data curation. Wei WU: Methodology, Projiect administration. Peng Zheng: Software, Visualization. Jialong Liu: Investigation, Conceptualization. Xiaoxiong Zhang: Investigation, Methodology.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. U2243242); Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No. 23JY060).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAjibola, A. S., Amoniyan, O. A., Ekoja, F. O. \u0026amp; Ajibola, F. O. QuEChERS Approach for the Analysis of Three Fluoroquinolone Antibiotics in Wastewater: Concentration Profiles and Ecological Risk in Two Nigerian Hospital Wastewater Treatment Plants. \u003cem\u003eArch. Environ. Contam. Toxicol.\u003c/em\u003e 1\u0026ndash;13. 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Technol. : Water Supply\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e (6), 2044\u0026ndash;2052 (2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Antibiotics, Urban-landscape lake, Source identification, Ecological risk assessment, COVID-19","lastPublishedDoi":"10.21203/rs.3.rs-6508163/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6508163/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs widely used drugs, antibiotics pose a serious threat to humans and the ecosystem. However, the outbreak of the COVID-19 pandemic significantly changed patterns of human activity and antibiotic use. The characteristics of antibiotic pollution in lakes in the urban landscape and their ecological risks due to the pandemic are still unclear. In this study, the levels, distributions, sources, and ecological risks of antibiotics in Xingqing Lake before and after the COVID-19 pandemic were investigated. The results revealed that macrolide antibiotics dominated in the environmental matrices. The synergistic effect of the pandemic outbreak and the lake renovation was the main factor driving the differences in the distribution of antibiotics. The positive matrix factorization model indicated that the potential sources of antibiotics in the water were domestic drainage, hospital discharge, and livestock drainage. The ecological risk assessment revealed that antibiotics posed a medium-high risk (RQ\u0026thinsp;\u0026gt;\u0026thinsp;0.1) to algae. Notably, azithromycin, clarithromycin, and sulfadiazine presented higher risk values for crustaceans than for other aquatic organisms. A toxicity assessment of a single species may severely underestimate the actual ecological risks of antibiotics. This study provides a scientific basis identifying and controlling the sources of antibiotics in lakes in the urban landscape.\u003c/p\u003e","manuscriptTitle":"Characteristics of antibiotic pollution and assessment of ecological risk of lake water in typical urban landscape in the context of a epidemic","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-19 10:28:34","doi":"10.21203/rs.3.rs-6508163/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2759476e-b6da-4138-87d7-0a21eb7bf388","owner":[],"postedDate":"May 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48621109,"name":"Earth and environmental sciences/Ecology"},{"id":48621110,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2026-02-10T10:22:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-19 10:28:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6508163","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6508163","identity":"rs-6508163","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}