Deciphering the occurrence, distribution, and source apportionment of antibiotics in the Ningxia section of the Yellow River

Deciphering the occurrence, distribution, and source apportionment of antibiotics in the Ningxia section of the Yellow River | 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 Research Article Deciphering the occurrence, distribution, and source apportionment of antibiotics in the Ningxia section of the Yellow River Yajunjie Liu, Shiquan Wang, Yu Jiang, Meiping Zhou, Yinlong Zhu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8804872/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The pervasive release of antibiotics into aquatic environments necessitates a thorough understanding of their fate. This study systematically investigated the occurrence, sources, and controlling factors of antibiotic pollution in the Ningxia section of the Yellow River. Across four major cities, 44 target antibiotics were monitored. Fluoroquinolones and macrolides were the predominant groups, with tylosin, enrofloxacin, and ciprofloxacin as the most abundant compounds. Concentrations exhibited significant spatiotemporal variation, peaking in the dry season (up to 150.97 ng/L) and clustering near intensive livestock areas and wastewater outfalls. Source apportionment via PCA-MLR quantified major contributions from combined medical/aquaculture and livestock sources (50.8%) and livestock farming alone (29.8%). Structural Equation Modeling identified livestock activity as the principal spatial driver, with significant positive paths to NH₄⁺-N and altitude, collectively explaining 68.2% of concentration variance. Furthermore, a stable predictive model (adj. R² = 0.833) was established, highlighting swine density as a key positive predictor and residential land proportion as a major negative predictor. This work concludes that antibiotic contamination is primarily anthropogenic, driven by livestock production with seasonal shifts between point and non-point pathways. The integrated methodology and mechanistic insights offer a valuable framework for developing targeted mitigation strategies in similar semi-arid river basins. Antibiotics Ningxia section of the Yellow River Spatial-Temporal variations Source Apportionment Driving factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Antibiotics are extensively used in human medicine and animal husbandry for disease treatment and growth promotion (Carvalho and Santos, 2016 ; Ferri et al., 2017 ). However, a significant proportion of these compounds enters the environment due to incomplete removal by conventional wastewater treatment processes (Li et al., 2024 ). Consequently, antibiotics have been frequently detected as emerging contaminants in various aquatic matrices worldwide, including surface water, groundwater, and even oceans (D. Fan et al., 2025 ; C. Wang et al., 2023 ). Their persistence poses direct risks to aquatic ecosystems (Patel et al., 2019) and, through bioaccumulation and drinking water exposure, potential threats to human health (Y. Wang et al., 2023 ). More critically, the widespread release of antibiotics exerts selective pressure, facilitating the proliferation and dissemination of antibiotic resistance genes (ARGs), thereby undermining clinical efficacy and intensifying the global public health crisis of antimicrobial resistance (Aversa et al., 2021 ; Shu et al., 2024 ). China ranks among the largest global consumers of antibiotics (Lin et al., 2020 ), leading to their ubiquitous occurrence in its river systems. Notable spatial heterogeneity exists, with contamination profiles varying significantly across major basins—such as the Haihe, Yangtze, and Pearl Rivers—reflecting differences in regional socioeconomic activities, hydrological conditions, and pollution sources (Y. Liu et al., 2024 ; Xu et al., 2013 ). While several studies have documented antibiotic pollution in the mainstream of the Yellow River (e.g., Su et al., 2023 ), its critical Ningxia section remains notably under-investigated. This knowledge gap is particularly concerning given the region's acute dependence on the Yellow River for agricultural irrigation and as a vital ecological barrier in arid northwest China (Lin et al., 2025 ). Understanding the contamination status here is thus imperative for local water security and ecosystem health. Large rivers, integrating inputs from diverse point and non-point sources, serve as key sentinels for assessing regional anthropogenic impacts (Gao et al., 2024 ; Ruff et al., 2015 ). Therefore, investigating the Ningxia section of the Yellow River offers a strategic opportunity to decipher the complex interplay between antibiotic emissions and environmental processes. To address this gap, the present study conducted a comprehensive seasonal monitoring of 44 target antibiotics across 20 sampling sites. The primary objectives were to: (1) elucidate the spatiotemporal distribution patterns of antibiotics; (2) identify and apportion their potential sources using principal component analysis-multiple linear regression (PCA-MLR); (3) quantify the key drivers influencing antibiotic concentrations through structural equation modeling (SEM) and multifactor analysis; and (4) develop a predictive model for antibiotic levels based on environmental variables. This work provides a mechanistic understanding of antibiotic pollution dynamics in a semi-arid fluvial system and offers a science-based framework for targeted pollution control in the Yellow River Basin. 2. Materials and methods 2.1 Chemicals and reagents A total of 44 antibiotics spanning six major classes were targeted for analysis: sulfonamides (SAs, n = 21), macrolides (MLs, n = 6), quinolones (QNs, n = 8), tetracyclines (TCs, n = 4), lincosamides (LMs, n = 2), and chloramphenicols (CPs, n = 3). The complete list with full names, abbreviations, and suppliers is provided in Table S1 . All chemical standards were of high-performance liquid chromatography (HPLC) grade or higher. Additionally, conventional water quality parameters, including chemical oxygen demand (COD), total phosphorus (TP), and ammonia nitrogen (NH₄⁺-N), were analyzed. Key instruments employed are listed in Table S4. 2.2 Sample collection Water sampling was conducted seasonally to capture hydrological and anthropogenic activity variations: July 2023 (summer, high-flow period), September 2023 (autumn, moderate-flow period), November 2023 (winter, irrigation period), and March 2024 (spring, low-flow period). Surface water samples were collected from 20 predetermined sites along the Ningxia section of the Yellow River (Fig. 1 ; site details in Table S2). At each site, triplicate grab samples were taken at a depth of approximately 0.5 m below the surface using a pre-cleaned stainless-steel water sampler. The triplicates were then combined in equal volumes to form a composite sample, representing the site conditions. Sampling at site S6 in September was omitted due to inaccessible conditions. Immediately after collection, all sample bottles (pre-rinsed with methanol and ultrapure water) were preserved with methanol (10 mL per liter of sample) to inhibit microbial degradation. Samples were transported on ice to the laboratory, stored at 4°C, and processed within 48 hours. Physicochemical parameters (pH, NH₄⁺-N, TP) were measured within three days post-sampling. 2.3 Sample pre-treatment Water samples were pretreated using solid-phase extraction (SPE), following a modified protocol from Wu et al. ( 2021 ). Briefly, each 1 L water sample was filtered through a 0.45 µm glass fiber filter (Whatman GF/C), with the initial 10–15 mL filtrate discarded. Subsequently, 6 g of ethylenediaminetetraacetic acid disodium salt (EDTA-2Na) was added and dissolved. The sample pH was adjusted to 2.0 ± 0.1 using diluted HCl or NaOH. An Oasis HLB cartridge (200 mg/6 mL, Waters) was preconditioned sequentially with 5 mL of methanol, 5 mL of deionized water, and 5 mL of acidified deionized water (pH 2.0). The prepared water sample was then loaded onto the cartridge at a flow rate of 10–15 mL/min. After loading, the cartridge was washed with 5 mL of deionized water and dried under a nitrogen stream for 20 min. Analytes were eluted with 10 mL of methanol at 5 mL/min. The eluate was concentrated to near dryness under a gentle nitrogen stream and reconstituted in 1.0 mL of initial mobile phase for HPLC-MS/MS analysis. The final extract was filtered through a 0.22 µm organic membrane prior to injection. 2.4 Antibiotic analysis and quality control Antibiotics were quantified using high-performance liquid chromatography coupled with tandem triple quadrupole mass spectrometry (HPLC-MS/MS, Agilent 1200 series). Separation was achieved on a GL Sciences Inert Sustain AQ-C18 column (1.9 µm, 2.1 × 50 mm) maintained at 40°C. The mobile phase consisted of (A) 0.1% formic acid in water and (B) 0.1% formic acid in a 1:1 (v/v) methanol/acetonitrile mixture. A gradient elution program was applied: 30% B (0 min), increased to 40% B (3 min), then to 95% B (5 min), held for 1.5 min (6.5 min), and returned to 15% B in 0.1 min, followed by re-equilibration. The flow rate was 0.4 mL/min. MS detection employed positive electrospray ionization (ESI+). Optimized MS parameters for each compound are listed in Table S5. Quantification was based on an external standard calibration curve (5, 10, 50, 100, 250, 500 µg/L). Method detection limits (MDLs) ranged from 0.1 to 1.0 ng/L. Recoveries were evaluated by spiking analyte-free water samples, yielding rates between 63.0% and 106.1% (Table S6). Although tetracyclines exhibited slightly lower recoveries (63.0-65.9%), consistent with literature reports due to matrix complexity, the results were deemed acceptable. Rigorous QA/QC measures were implemented. For every batch of 20 samples, two procedural blanks and one duplicate were processed. All target antibiotics in blanks were below MDLs. The relative standard deviation for duplicates was < 20%. Additionally, a continuing calibration verification standard (100 µg/L) was injected after every ten samples to monitor instrumental stability. 2.5 Statistical Analysis Basic statistical analyses were performed using R (v3.6.1). Data normalization was applied prior to multivariate analysis. Principal component analysis (PCA) was conducted to explore patterns in antibiotic composition across sites and seasons. Differences in antibiotic concentrations among seasons or sites were assessed using one-way analysis of variance (ANOVA) followed by Tukey's honest significant difference post-hoc test, with a significance level of p < 0.05. 2.6 Structural equation model and multifactor model To identify potential pollution sources, PCA coupled with multiple linear regression (PCA-MLR) was performed using IBM SPSS Statistics 24.0. Details of this approach are provided in Text S2. To further elucidate the key drivers of antibiotic distribution, a structural equation model (SEM) was constructed using IBM SPSS Amos 22.0 (maximum likelihood estimation). The conceptual model (Fig. S1 ) incorporated variables from five groups: (1) geophysical factors (elevation, latitude), (2) water quality parameters (e.g., NH₄⁺-N, COD), (3) socioeconomic indices (population density, livestock/poultry densities at buffer radii of 1, 5, 10, 20 km; Gross Regional Product), and (4) land use proportions (cropland, forest, grassland, urban). The selection of buffer radii was based on previous studies (Song et al., 2020). Model fit was evaluated using standard indices (Fig. S1 b). A detailed description of variable selection and model evolution is in Text S3. Finally, to predict antibiotic concentrations based on environmental variables, multifactor regression models were developed via forward stepwise selection. Variation partitioning analysis was conducted using the `vegan` package in R to assess the individual and combined effects of different variable groups. The relative importance of predictors in the final model was calculated using the `relaimpo` package. The environmental factors included in the modeling are abbreviated as follows: LD₁ (Livestock Density in 1-km buffer), GRP₁ (Gross Regional Product per capita in 1-km buffer), GP₁ (Grassland Proportion in 1-km buffer), HD₅ (Pig Density in 5-km buffer), GP₂₀ (Grassland Proportion in 20-km buffer), RLP₂₀ (Residential Land Proportion in 20-km buffer). 3. Results 3.1 Levels of antibiotic concentrations in water Among the 44 target antibiotics, 34 were detected in the water samples, with a maximum individual concentration of 89.07 ng/L. As shown in Figure S3, up to 16 different antibiotics were co-detected at a single sampling site. FQs were the predominant antibiotic class, with concentrations ranging from not detected (n.d.) to 89.07 ng/L (Fig. 2 ). Their prevalence is consistent with reports from the Wei River (11.09–216.12 ng/L) (Cao et al., 2024 ) and surface waters across China (0.03–27,011.01 ng/L) (Yi et al., 2025 ), attributable to their extensive production and use in China (Wan et al., 2021 ; Yang, 2023 ), coupled with high stability and solubility in aquatic environments which hinder hydrolysis and slow photolytic or microbial degradation (Sarmah et al., 2006 ; Zhang et al., 2019 ). MLs were the second most prevalent class (n.d. – 35.46 ng/L), aligning with their frequent detection in Chinese surface waters (Yi et al., 2025 ) and significant contamination reported in the Wei River (Ge et al., 2024 ). This can be explained by their global high consumption (Senta et al., 2017) and hydrophilic nature (Hu et al., 2023 ). In contrast, SAs and TCs were present at relatively lower concentrations (up to 5.24 and 7.01 ng/L, respectively). However, specific compounds within these classes, such as STX1, STX2, SMX, TER, and TET, exhibited high detection frequencies (42.37%–48.99%). This discrepancy between concentration and detection rate likely stems from their widespread application in livestock and poultry farming (Dame-Korevaar et al., 2025 ; Gao et al., 2025 ), long environmental half-lives (Pan et al., 2025 ), low photolysis efficiency (for TER and TET) (Ouyang et al., 2022 ; A. Peng et al., 2022 ), and persistence in conventional wastewater treatment processes (Arun et al., 2022; C. Li et al., 2025 ; Li et al., 2024 ). At the individual compound level, tylosin (TYL, a ML), enrofloxacin (ENR, a FQ), and ciprofloxacin (CIP, a FQ) showed the highest mean concentrations (12.90, 7.35, and 7.28 ng/L, respectively) and detection rates (64.41%–69.62%). Their prominence is corroborated by regional studies: TYL is reported as a dominant antibiotic in rivers of Changchun and the Yangtze River Delta (Du et al., 2022 ; Zhang et al., 2023 ); CIP shows high levels in the Weihe River (Cao et al., 2024 ); and ENR is commonly detected in the Yangtze River Basin (D. Fan et al., 2025 ). The extensive use of these compounds drives their environmental occurrence: TYL is employed against mycoplasmosis and respiratory infections (Kanci Condello et al., 2023 ; Taiyari et al., 2021 ), ENR treats respiratory and dermatophytic infections in animals (Anh et al., 2021 ; Dessus-Babus et al., 1998 ), and CIP is used for respiratory and digestive infections in both humans and animals (Cios et al., 2014 ; Mumcuoglu et al., 2010 ). Consequently, the elevated concentrations of TYL, ENR, and CIP directly contribute to the high overall levels observed for the FQ and ML classes in this study. 3.2 Temporal differences of antibiotics The total concentration of antibiotics exhibited significant seasonal variation (p < 0.05), with medians (and ranges) of 17.41 (12.52–29.00), 52.13 (26.13–104.18), 45.80 (5.80–135.52), and 89.99 (43.99–150.97) ng/L in July, September, and November 2023, and March 2024, respectively (Fig. 2 ). The highest concentrations occurred in March 2024. This peak is attributed to environmental conditions that favor antibiotic persistence: low temperatures (1.8–4.3°C) and solar irradiance (Fig. S4) decelerate hydrolysis and photolysis rates, thereby extending environmental half-lives (Bueno et al., 2023 ; Habibi et al., 2025 ). Concurrently, lower river discharge during this period reduces the dilution capacity for pollutants (Abily et al., 2021 ). In contrast, the lowest concentrations were observed in July 2023, resulting from the opposing effects of higher temperatures and irradiance (enhancing degradation) (Fig. S4) and peak river flow (providing maximal dilution) (Wang et al., 2021 ). Notably, the number of detected antibiotic species also varied seasonally, peaking in September 2023 (34 species) compared to July 2023 (22), November 2023 (30), and March 2024 (25) (Fig. S3). This pattern diverges from the total concentration trend and likely reflects a peak in agricultural antibiotic application in early autumn (Lu and Lu, 2020 ), introducing a diverse array of compounds. The moderate temperatures in September (17.0–19.4°C) may not have been sufficiently high to rapidly degrade all introduced antibiotics, allowing for broader detection despite ongoing microbial and chemical degradation processes (Achermann et al., 2018 ; Mitchell et al., 2014 ). The compositional profile of the antibiotic cocktail shifted markedly between seasons. From July to September 2023, MLs dominated, constituting 76.1–81.4% of the total concentration, with TYL alone contributing 69.7–76.1%. This period coincides with warmer temperatures (17.0–25.9°C) and higher humidity, which can increase the incidence of livestock respiratory and intestinal diseases (e.g., caused by Pasteurella and Mycoplasma), leading to elevated ML use (Kamathewatta et al., 2024 ; Taiyari et al., 2021 ). Furthermore, substantial rainfall (19.9–90.5 mm) during these months facilitates the runoff of MLs (e.g., TYL) from farms into the river network (Hu et al., 2023 ).Conversely, FQs became the predominant class in the colder months, comprising 70.0–87.6% of the total in November 2023 and March 2024, driven by high concentrations of ciprofloxacin (CIP, 262.16–310.34 ng/L) and enrofloxacin (ENR, 116.67–455.27 ng/L). This shift correlates with the heavy use of these broad-spectrum antibiotics in livestock, poultry, and aquaculture, especially during periods of temperature fluctuation or decline in late autumn and early spring, when disease outbreaks are common (Hal and El-Barbary, 2021 ; Yun et al., 2023 ; Y. Wei et al., 2024 ). In summary, the temporal patterns of antibiotics are shaped by the interplay of seasonal use practices (e.g., agricultural and veterinary applications) and environmental fate processes (degradation and dilution). The distinct seasonal dominance of MLs (warm/wet season) versus FQs (cold/dry season) strongly indicates that antibiotic contamination in the studied area is primarily driven by dynamic anthropogenic sources, with environmental conditions modulating their subsequent persistence and transport. 3.3 Characterization of the spatial distribution of antibiotic sites Contrary to the frequently reported downstream accumulation pattern in river systems (Hu et al., 2023 ), the total antibiotic concentration in this study did not exhibit a consistent increasing trend from upstream (S1) to downstream (S20). Instead, spatial heterogeneity was pronounced, characterized by distinct concentration peaks at specific sites that varied across sampling campaigns. The spatial heterogeneity was marked by distinct concentration peaks at specific sites during different sampling campaigns. For instance, site S12 in September 2023 showed a significantly higher total concentration (p < 0.05) than other sites, which was largely attributed to roxithromycin (ROX, 61.77 ng/L, accounting for 59.29% of the total). This anomaly is likely linked to the discharge from the adjacent Yinchuan First Recycled Water Plant, as ROX is known to be poorly removed by conventional wastewater treatment processes (Novo et al., 2013 ; Watkinson et al., 2007 ). Concurrently, the seasonal prevalence of respiratory infections and associated ROX usage in autumn may have amplified this localized input (Ogimi et al., 2021 ; Yan et al., 2025 ). Another example is the high concentration of ciprofloxacin (CIP) observed at site S5, which aligns with the surrounding land-use characteristics featuring high population density, intensive cattle and sheep farming, and extensive arable land (Fig. 3 a, b, d). These factors collectively promote the widespread use and subsequent environmental release of this antibiotic, which is applied in both human and veterinary medicine (Cios et al., 2014 ; Mumcuoglu et al., 2010 ). These cases underscore the critical role of discrete pollution sources—such as wastewater treatment plants, livestock farms, and agricultural runoff—in creating spatial hotspots (Li et al., 2024 ; Wen et al., 2023 ; Shao et al., 2018 ). Furthermore, the overall spatial pattern of antibiotics in this study differed notably from the downstream accumulation trend frequently reported in the literature (Hu et al., 2023 ). To reconcile this discrepancy, the contrasting environmental fate processes of different antibiotic classes must be considered. Macrolides, such as tylosin (TYL) which dominated the profile at the upstream site S1 (10.71–35.46 ng/L), are susceptible to photochemical degradation (Xu et al., 2023 ) and are subject to significant dilution at tributary confluences (Wang et al., 2021 ). These attenuation processes effectively offset potential downstream accumulation. In contrast, sulfonamide antibiotics (SAs) exhibited a gradual increasing trend from upstream to downstream. This class-specific behavior can be attributed to the high solubility and environmental persistence of SAs (Li et al., 2018 ; Liu et al., 2019 ), which facilitates their transport and gradual accumulation along the flow path, despite their relatively lower overall consumption (Zhang et al., 2015 ). In summary, the spatial distribution of antibiotics is governed by the interplay between spatially heterogeneous anthropogenic inputs and compound-specific environmental fate processes (e.g., degradation, dilution, and persistence). The overall pattern thus reflects the superposition of concentration hotspots from multiple point sources and the differential transport behaviors of various antibiotic classes. 3.4 Characterization of the regional spatial distribution of antibiotics The composition of antibiotics exhibited considerable spatial variation across the Ningxia section of the Yellow River basin (Fig. 3 c). This heterogeneity can be primarily linked to regional disparities in anthropogenic pressures, notably population density and the structure of livestock and poultry production within the four cities (Shizuishan, Wuzhong, Yinchuan, and Zhongwei) encompassed by the study area (Fig. 3 a, b). Variations in these factors likely drive differences in local antibiotic use patterns (Fan et al., 2025 ; C. Wei et al., 2024 ), which are subsequently reflected in the aquatic environment. A key determinant is the distinct composition of food animal farming in each city. For instance, while Wuzhong showed the highest total livestock production, Zhongwei presented a more balanced distribution among cattle (25.38%), pigs (34.84%), and sheep (39.78%). In contrast, Wuzhong and Yinchuan were predominantly characterized by cattle and sheep farming. Given that different animal species have distinct antibiotic consumption rates and spectra (e.g., global average: pigs 172, chickens 148, cattle 45 mg/kg; Tiseo et al., 2020 ), these regional farming structures directly influence the types and quantities of antibiotics introduced into the local environment (Hu et al., 2023 ; Krishnasamy et al., 2015). Temporally, the regional prevalence of specific antibiotics also shifted. Tylosin (TYL) and norfloxacin (NOR) were characteristic of the warmer months (July and September 2023), whereas ciprofloxacin (CIP) and enrofloxacin (ENR) dominated in the cooler periods (November 2023 and March 2024). This pattern aligns with seasonal animal management practices in Ningxia’s major livestock industry. Higher temperatures in summer can induce heat stress, while significant temperature fluctuations in spring and autumn may compromise animal immunity (Betote et al., 2024 ; Jin et al., 2024 ; X. Fan et al., 2025 ), both scenarios potentially leading to prophylactic or therapeutic antibiotic use. The application of manure containing antibiotic residues to soil further acts as a secondary source, exacerbating the environmental load of these representative compounds (X. Fan et al., 2025 ; S. Peng et al., 2022 ). Despite these spatial and temporal variations in driving factors, the suite of major antibiotics detected across the region showed relatively limited geographic fluctuation. This consistency suggests a pervasive influence of a common, dominant source. Cattle farming, prevalent across all studied cities, is a likely major contributor to the regional antibiotic footprint (Hu et al., 2023 ). To quantitatively apportion the sources and identify key drivers behind the observed distribution patterns, we employed Principal Component Analysis-Multiple Linear Regression (PCA-MLR) and Structural Equation Modeling (SEM). These analyses helped elucidate why, despite underlying regional differences, the profile of representative antibiotics remained broadly consistent across the study area. 3.5 Source allocation To elucidate the drivers behind the observed spatiotemporal variations, we applied Principal Component Analysis-Multiple Linear Regression (PCA-MLR) to quantify and apportion the sources of antibiotics. Three principal components (PCs) were extracted, collectively explaining 100% of the total variance (Table S8). PC1, explaining 41.83% of the variance, exhibited strong loadings on a diverse suite of antibiotics, including sulfonamides (STX, SQX, SMLD, SFPZ, SMZ, ALF), fluoroquinolones (ENR, OFL), macrolides (AZI, TYL), and clindamycin. The presence of antibiotics widely used in both human medicine and veterinary practice (e.g., ENR, TYL, AZI) identifies PC1 as a mixed source from livestock production and domestic wastewater. PC2 (29.83% variance) was predominantly associated with veterinary antibiotics, including specific sulfonamides (SQX, SFPZ, SMP), fluoroquinolones (NOR, DAF, SAR, ENR), and tetracyclines (AUR, TET). This profile strongly suggests PC2 represents direct input from livestock and poultry farming. PC3 (28.34% variance) was highly correlated with ciprofloxacin (CIP), pefloxacin (PEF), ofloxacin (OFL), and sulfamethazine (SMA). The dominance of high-consumption human and veterinary fluoroquinolones indicates PC3 is primarily influenced by medical wastewater and aquaculture emissions. The MLR model estimated the average contributions of PC1, PC2, and PC3 to the total antibiotic concentration at 19.48%, 29.75%, and 50.77%, respectively. This indicates that combined pollution from medical/aquaculture sources and livestock farming constitutes the primary source (approximately 80%) in the Ningxia Yellow River section. This combined contribution is higher than the estimated livestock contribution in the Pearl River Basin (42.1%) (Zhu et al., 2024 ), likely due to more intensive integrated farming practices and less efficient wastewater treatment in our study area. Seasonal dynamics of source contributions were pronounced (Fig. 4 ). PC3 (medical/aquaculture) dominated during the cold, dry months of November (87.39%) and March (65.85%). The high contribution in March was driven by elevated levels of OFL and CIP. OFL inputs may be linked to spring snowmelt transporting residual antibiotics, while CIP’s prevalence is consistent with its heavy use in both human and veterinary sectors during periods of temperature fluctuation. Conversely, PC1 (mixed livestock/domestic wastewater) was the dominant source in the warm, wet months of July (45.52%) and September (39.45%), with ENR and TYL as key markers. This shift correlates with increased antibiotic use for livestock disease control in summer and, crucially, with enhanced transport via non-point source runoff during the rainy season. The significantly higher COD concentration in September (41.05 and. 7.58–18.62 mg/L in other months) further supports the dominance of diffuse agricultural runoff as the key pathway during this period. In conclusion, source apportionment reveals that antibiotic pollution is primarily from anthropogenic sources, with the dominant pathway shifting seasonally: point sources (medical/livestock wastewater) prevail in the dry season, while non-point sources (agricultural and livestock runoff) dominate in the wet season. This finding, which contrasts with patterns in some humid basins (e.g., peak in wet season), underscores the necessity for tailoring pollution control strategies to local hydrological and agricultural practices. 3.6 Drivers of antibiotic distribution Structural Equation Modeling (SEM) was employed to elucidate the key factors controlling the spatial distribution of antibiotic concentrations. The model (Fig. 5 , with fit indices meeting recommended criteria) explained 68.2% of the observed variance, identifying four significant direct drivers: NH₄⁺-N concentration (λ = 0.558, p < 0.05), per capita Gross Regional Product (GRP) (λ = -0.461, p < 0.05), latitude (λ = -0.327, p < 0.05), and altitude (λ = 0.221, p < 0.05). Notably, NH₄⁺-N exhibited the strongest positive direct effect, underscoring its role as a robust chemical indicator co-emitted with antibiotics from animal production systems. This relationship was confirmed by a significant positive correlation between NH₄⁺-N and total antibiotic concentrations (r = 0.445, p < 0.05). Furthermore, a multiple linear regression revealed that livestock density, rather than urban population density, was the primary predictor of NH₄⁺-N levels (β = 0.944, p 0.05), consolidating the link between waterborne antibiotics and livestock farming. The significant negative effect of per capita GRP provides critical socio-economic insight. Higher GRP in the study area correlates with economic transition away from intensive, high-antibiotic-use livestock sectors like swine farming, and toward sheep grazing (Table S11), which typically has a lower antibiotic consumption intensity per head. This economic-geographic pattern helps explain the lack of strong regional variation in antibiotic profiles noted earlier, as sheep farming is widespread across the region. The negative effect of latitude aligns with the northward decline in total livestock output within the study area, directly reducing antibiotic loading. Conversely, the positive effect of altitude appears counterintuitive given the potential for enhanced photodegradation at higher elevations. However, this is overridden by the strong positive correlation between altitude and swine farming density (λ = 0.45, p < 0.05; Table S11). The high emission factor associated with swine farms effectively makes altitude a proxy for localized, intensive point-source pollution from this sector. In summary, the SEM analysis reveals that the spatial distribution of antibiotics is not primarily driven by natural environmental gradients, but by the spatial heterogeneity of anthropogenic activity, specifically livestock farming. The positive effects of NH₄⁺-N and altitude, and the negative effects of latitude and per capita GRP, all converge to identify the intensity and type of animal agriculture as the fundamental driver shaping the antibiotic contamination landscape in the Ningxia section of the Yellow River, powerfully corroborating the source apportionment conclusions from Section 3.5 . 3.7 A multifactorial model for predicting antibiotic concentrations To enable the estimation of antibiotic concentrations based on accessible environmental and socio-economic variables, we developed a multiple linear regression model (Eq. 1). The model demonstrated strong performance in predicting total antibiotic concentrations, with an adjusted R² of 0.833 (p < 0.001). Its robustness was confirmed by a leave-one-out cross-validation R² of 0.756 (difference < 15%), along with low mean absolute error (MAE) and root mean square error (RMSE) values, indicating excellent stability and predictive capability (Fig. 5 c). $$\:Conc\left(ng/L\right)=0.673\:{HD}_{5}-110.724\:{RLP}_{20}+43.112\:{GP}_{20}-32.175\:{GP}_{1}-0.04\:{LD}_{1}+0.007\:{GRP}_{1}+20.369$$ $$\:({R}^{2}=0.833\:,\:p<0.001)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ The model identified several key predictors, with independent explanatory rates ranging from 9.7% to 25.8% (Fig. 5 d). `HD₅` (swine density within a 5-km radius) showed a strong positive effect, which aligns directly with its high antibiotic emission factor and its identification as a key point source in our SEM and source apportionment analyses. Conversely, `RLP₂₀` (residential land proportion within a 20-km radius) exhibited the strongest negative effect. This is logically explained by the association of populated areas with wastewater treatment plants, which provide dilution and standardized treatment that reduces the net antibiotic load per unit of effluent, consistent with the lower contribution from domestic sources quantified earlier. The effects of `GP₂₀` and `GP₁` (grassland proportion at 20-km and 1-km radii) were contrasting. The positive effect of `GP₂₀` suggests that at a broader landscape scale, grasslands integrate non-point source runoff from upstream agricultural and pastoral activities, acting as a diffuse source conduit. In contrast, the negative effect of `GP₁` at the immediate local scale may indicate a dilution or retention effect in riparian grassland buffers. The similar magnitudes of the explanatory rates for these factors underscore that antibiotic fate is governed by the interplay of multiple, concurrent processes—including concentrated point sources (`HD₅`), diffuse agricultural runoff (`GP₂₀`), and attenuation through infrastructure (`RLP₂₀`) and landscape features (`GP₁`). We also constructed class-specific models for FQs and MLs, which were the only ones to achieve stability (adj. R² = 0.655 and 0.869, respectively; Figs. 5 e, f). The key predictors for these models were largely consistent with those in the overall model (Fig. S11), reinforcing the conclusion that the distribution of these dominant antibiotic classes is driven by the same set of integrated environmental and anthropogenic factors. 4. Conclusions This study provides a comprehensive assessment of antibiotic contamination in the Ningxia section of the Yellow River. The key findings are synthesized as follows: (1) Pollution Profile: A total of 34 antibiotics were detected, confirming widespread contamination. Fluoroquinolones (FQs) and macrolides (MLs) were the dominant pollutant classes. Concentrations exhibited significant spatiotemporal variability, with peak levels occurring during the dry season (March) when reduced river flow diminished dilution capacity. (2) Sources and Drivers: The contamination originated primarily from integrated livestock and medical wastewater sources, accounting for over 50% of the total burden. Spatial patterns were shaped by a combination of point sources (e.g., wastewater treatment plants, intensive farms) and non-point sources (agricultural runoff). Structural equation modeling quantified the key drivers, revealing that antibiotic concentrations were positively influenced by elevation (linked to high-density farming) and ammonium-nitrogen (a co-indicator of livestock pollution), but negatively correlated with latitude and per capita gross regional product. (3) Predictive Framework and Implications: A robust multivariate regression model was developed, successfully predicting antibiotic concentrations based on easily accessible environmental variables (Adj. R² = 0.833). This model integrates the major drivers—including livestock density, land use, and treatment infrastructure—into a practical management tool. Collectively, these findings delineate a clear pathway from emission sources to environmental concentrations in the river. They underscore that controlling antibiotic pollution in this agriculturally intensive basin requires targeted management of livestock farming and wastewater treatment. The mechanistic understanding and predictive tool established here offer a replicable framework for assessing and mitigating antibiotic pollution in similar arid and semi-arid river systems worldwide. Declarations Acknowledgements I would like to express my sincere gratitude to my supervisor, Professor Shiquan Wang, for his invaluable guidance, continuous support, and patience throughout my graduate study. His insightful comments and encouragement have been instrumental in the completion of this work. Funding This work was supported by the Ningxia Nature Fund Ningxia Key R&D Program Project (2023BEG02047) and Ningxia Nature Fund Ningxia Key R&D Program (2021BEB04051). Authors ’ Contributions Shiquan Wang : Writing – review & editing, Funding acquisition, Conceptualization. Yajunjie Liu : Writing – original draft, Formal analysis, Data curation. Yu Jiang ：Methodology. Meiping Zhou : Writing – review & editing. Yinlong Zhu : Methodology. Xuan Yang : Writing – review & editing Ethical Approval All procedures performed in studies involving human participants were in accordance with theethical standards of the institutional and/or national research committee and with the 1964Helsinki Declaration and its later amendments or comparable ethical standards. Consent to Participate All authors disclosed no relevant relationships. Consent to Publish I, Yajunjie Liu, hereby declare that I agree to the publication of the research findings presented in this manuscript entitled "Deciphering the occurrence, distribution, and source apportionment of antibiotics in the Ningxia section of the Yellow River" in the ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH. I have carefully read and understood the submission guidelines and requirements of the journal.I certify that the data, figures, and images presented in this manuscript are original or have been authorized and licensed legally. I hereby declare that I have made every effort to avoid errors and misconduct in this research and that the results presented in this manuscript are truthful and reliable. I also declare that I will not be held responsible for any negative consequences resulting from the publication of this manuscript. Competing Interests No potential conflict of interest was reported by the authors Data Availability Statement Data sets generated during the current study are available from the corresponding author on reasonable request. Meteorological data can be obtained from local statistical yearbooks, which were used under license for the current study, and so are not publicly available. Mandatory if your study involves humans and/or animals This article does not include any human and/or animals participant research conducted by the author. References Abily, M., Acuña, V., Gernjak, W., Rodríguez-Roda, I., Poch, M., Corominas, L., 2021. 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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-8804872","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598185408,"identity":"26a7fe80-a5df-4f52-a8e2-bdeed80ca51d","order_by":0,"name":"Yajunjie Liu","email":"","orcid":"","institution":"Ningxia University School of Ecology and Environment","correspondingAuthor":false,"prefix":"","firstName":"Yajunjie","middleName":"","lastName":"Liu","suffix":""},{"id":598185409,"identity":"295a83cb-3f2b-4fb5-a106-93279c69f2a9","order_by":1,"name":"Shiquan Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYPACGwjFQ4KWNNK1HCZBi8Hxs4df/ig7n2dwI4Hxwds2BnlzglrO5KVZ85y7XSw5I4HZcG4bg+HOBgJazA7kmBkztt1O7JdIYJPmbWNIMDhASMv5N2aGP9vOJbZJJLD/Jk7LjRzjB7xtB8C2MBOlxf7GGzNmnnPJiTN7HjZLzjknYbiBkBbJ/hzjjz/K7BI3HE8++OFNmY08QVuAgE2CgQ1EMzYACQnC6oGA+QNEyygYBaNgFIwCHAAAM6tARDFS7pYAAAAASUVORK5CYII=","orcid":"","institution":"Ningxia University School of Ecology and Environment","correspondingAuthor":true,"prefix":"","firstName":"Shiquan","middleName":"","lastName":"Wang","suffix":""},{"id":598185410,"identity":"c0640e11-159a-4233-956c-e019071ac51e","order_by":2,"name":"Yu Jiang","email":"","orcid":"","institution":"Ningxia University School of Ecology and Environment","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Jiang","suffix":""},{"id":598185411,"identity":"1fe279ca-c691-4e5a-8d8d-ae768a7526c9","order_by":3,"name":"Meiping Zhou","email":"","orcid":"","institution":"Ningxia University School of Ecology and Environment","correspondingAuthor":false,"prefix":"","firstName":"Meiping","middleName":"","lastName":"Zhou","suffix":""},{"id":598185412,"identity":"1c7429b4-ffbf-47e0-b4fa-8c6482e0e24e","order_by":4,"name":"Yinlong Zhu","email":"","orcid":"","institution":"Ningxia University School of Ecology and Environment","correspondingAuthor":false,"prefix":"","firstName":"Yinlong","middleName":"","lastName":"Zhu","suffix":""},{"id":598185413,"identity":"7a7d3352-53cf-4c5f-8af7-857e44383c32","order_by":5,"name":"Xuan Yang","email":"","orcid":"","institution":"Ningxia University School of Ecology and Environment","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2026-02-06 09:08:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8804872/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8804872/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103937523,"identity":"152fd308-260d-4ba8-836e-9b8da13d7e0b","added_by":"auto","created_at":"2026-03-04 18:12:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":271232,"visible":true,"origin":"","legend":"\u003cp\u003eA map showing the sampling locations (S1-S20)along the Ningxia section of the Yellow River. Table S2 provides detailed information on the 20 sampling sites.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8804872/v1/1389ed91fd1ec78819bdada5.png"},{"id":104401534,"identity":"4c3bc2da-1c9d-474a-887a-db5c785de365","added_by":"auto","created_at":"2026-03-11 12:12:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":149202,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of antibiotic concentrations in water samples from the Ningxia section of the Yellow River in July, September, and November 2023 and March 2024\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8804872/v1/be4347c9c1bdf8f3da653b43.png"},{"id":104402190,"identity":"c4421826-dc90-4726-b7b5-7f91ab99bf88","added_by":"auto","created_at":"2026-03-11 12:14:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":418986,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution characteristics of residential activities in the Yellow River Basin. (a) Distribution pattern of population density. Areas with high population density near the sampling sites are marked with yellow circles. (b) Spatial distribution of livestock and poultry output. City-level production density (tons/km²) is color-coded, while bar charts represent cattle, pig, and sheep population densities. (c) Variations in antibiotic composition in water samples from Zhongwei, Wuzhong, Yinchuan, and Shizuishan as shown in bar charts. (d) Land use distribution in the Ningxia section of the Yellow River. The proportions of different land use types in each city are presented in pie charts.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8804872/v1/a2f0c7d898b9b9b5257f6d81.png"},{"id":104401875,"identity":"817d144b-c0e9-4980-b2df-30d12849bfce","added_by":"auto","created_at":"2026-03-11 12:13:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":222358,"visible":true,"origin":"","legend":"\u003cp\u003eSource contributions to antibiotic concentrations identified by PCA-MLR in (a) July 2023, (b) September 2023, (c) November 2023, and (d) March 2024. Gray dots indicate the total antibiotic concentrations at each sampling site. The calculation method for source-specific contributions (PC1, PC2, and PC3) is detailed in Text S2.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8804872/v1/20d8c76d31f4459d88b3f682.png"},{"id":104402029,"identity":"66c29df6-7613-4461-9867-375c4d6a82cb","added_by":"auto","created_at":"2026-03-11 12:14:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":170120,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships among influencing factors and performance evaluation of multifactor models for antibiotic distribution. (a) Structural equation model: solid arrows represent the normalized path coefficients ( \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;), with values indicated next to each arrow. Dashed arrows indicate non-significant normalized path coefficients, while red and gray represent positive and negative relationships, respectively. R² values represent the proportion of variance explained for each endogenous variable. * indicates statistical significance at p \u0026lt; 0.05. The model demonstrated a good fit, with all parameters conforming to the specified standards in Fig. S1. (b) Standardized effects of predictive factors on antibiotic concentrations. (c) Correlation between predicted and measured total antibiotic concentrations based on multifactorial modeling. (d) Relative contribution of each factor in the prediction model. (e, f) Correlation between predicted and measured values for dominant antibiotic classes: (e) FQs and (f) MLs.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8804872/v1/58e1963d5dad09994a155d00.png"},{"id":104408810,"identity":"2218f621-dd19-4624-b902-5521a367e372","added_by":"auto","created_at":"2026-03-11 12:43:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1914236,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8804872/v1/27ca5f90-f55e-427a-be8c-3d5a62805e92.pdf"},{"id":104402338,"identity":"60e4a191-9379-4a50-a31b-06888016bea9","added_by":"auto","created_at":"2026-03-11 12:15:05","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":4831091,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8804872/v1/9e696522320f0c6db17597fe.docx"}],"financialInterests":"","formattedTitle":"Deciphering the occurrence, distribution, and source apportionment of antibiotics in the Ningxia section of the Yellow River","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAntibiotics are extensively used in human medicine and animal husbandry for disease treatment and growth promotion (Carvalho and Santos, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ferri et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, a significant proportion of these compounds enters the environment due to incomplete removal by conventional wastewater treatment processes (Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, antibiotics have been frequently detected as emerging contaminants in various aquatic matrices worldwide, including surface water, groundwater, and even oceans (D. Fan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; C. Wang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Their persistence poses direct risks to aquatic ecosystems (Patel et al., 2019) and, through bioaccumulation and drinking water exposure, potential threats to human health (Y. Wang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). More critically, the widespread release of antibiotics exerts selective pressure, facilitating the proliferation and dissemination of antibiotic resistance genes (ARGs), thereby undermining clinical efficacy and intensifying the global public health crisis of antimicrobial resistance (Aversa et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shu et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChina ranks among the largest global consumers of antibiotics (Lin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), leading to their ubiquitous occurrence in its river systems. Notable spatial heterogeneity exists, with contamination profiles varying significantly across major basins\u0026mdash;such as the Haihe, Yangtze, and Pearl Rivers\u0026mdash;reflecting differences in regional socioeconomic activities, hydrological conditions, and pollution sources (Y. Liu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). While several studies have documented antibiotic pollution in the mainstream of the Yellow River (e.g., Su et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), its critical Ningxia section remains notably under-investigated. This knowledge gap is particularly concerning given the region's acute dependence on the Yellow River for agricultural irrigation and as a vital ecological barrier in arid northwest China (Lin et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Understanding the contamination status here is thus imperative for local water security and ecosystem health.\u003c/p\u003e \u003cp\u003eLarge rivers, integrating inputs from diverse point and non-point sources, serve as key sentinels for assessing regional anthropogenic impacts (Gao et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ruff et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Therefore, investigating the Ningxia section of the Yellow River offers a strategic opportunity to decipher the complex interplay between antibiotic emissions and environmental processes. To address this gap, the present study conducted a comprehensive seasonal monitoring of 44 target antibiotics across 20 sampling sites. The primary objectives were to: (1) elucidate the spatiotemporal distribution patterns of antibiotics; (2) identify and apportion their potential sources using principal component analysis-multiple linear regression (PCA-MLR); (3) quantify the key drivers influencing antibiotic concentrations through structural equation modeling (SEM) and multifactor analysis; and (4) develop a predictive model for antibiotic levels based on environmental variables. This work provides a mechanistic understanding of antibiotic pollution dynamics in a semi-arid fluvial system and offers a science-based framework for targeted pollution control in the Yellow River Basin.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Chemicals and reagents\u003c/h2\u003e \u003cp\u003eA total of 44 antibiotics spanning six major classes were targeted for analysis: sulfonamides (SAs, n\u0026thinsp;=\u0026thinsp;21), macrolides (MLs, n\u0026thinsp;=\u0026thinsp;6), quinolones (QNs, n\u0026thinsp;=\u0026thinsp;8), tetracyclines (TCs, n\u0026thinsp;=\u0026thinsp;4), lincosamides (LMs, n\u0026thinsp;=\u0026thinsp;2), and chloramphenicols (CPs, n\u0026thinsp;=\u0026thinsp;3). The complete list with full names, abbreviations, and suppliers is provided in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. All chemical standards were of high-performance liquid chromatography (HPLC) grade or higher. Additionally, conventional water quality parameters, including chemical oxygen demand (COD), total phosphorus (TP), and ammonia nitrogen (NH₄⁺-N), were analyzed. Key instruments employed are listed in Table S4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample collection\u003c/h2\u003e \u003cp\u003eWater sampling was conducted seasonally to capture hydrological and anthropogenic activity variations: July 2023 (summer, high-flow period), September 2023 (autumn, moderate-flow period), November 2023 (winter, irrigation period), and March 2024 (spring, low-flow period). Surface water samples were collected from 20 predetermined sites along the Ningxia section of the Yellow River (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; site details in Table S2).\u003c/p\u003e \u003cp\u003eAt each site, triplicate grab samples were taken at a depth of approximately 0.5 m below the surface using a pre-cleaned stainless-steel water sampler. The triplicates were then combined in equal volumes to form a composite sample, representing the site conditions. Sampling at site S6 in September was omitted due to inaccessible conditions. Immediately after collection, all sample bottles (pre-rinsed with methanol and ultrapure water) were preserved with methanol (10 mL per liter of sample) to inhibit microbial degradation. Samples were transported on ice to the laboratory, stored at 4\u0026deg;C, and processed within 48 hours. Physicochemical parameters (pH, NH₄⁺-N, TP) were measured within three days post-sampling.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Sample pre-treatment\u003c/h2\u003e \u003cp\u003eWater samples were pretreated using solid-phase extraction (SPE), following a modified protocol from Wu et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Briefly, each 1 L water sample was filtered through a 0.45 \u0026micro;m glass fiber filter (Whatman GF/C), with the initial 10\u0026ndash;15 mL filtrate discarded. Subsequently, 6 g of ethylenediaminetetraacetic acid disodium salt (EDTA-2Na) was added and dissolved. The sample pH was adjusted to 2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 using diluted HCl or NaOH.\u003c/p\u003e \u003cp\u003eAn Oasis HLB cartridge (200 mg/6 mL, Waters) was preconditioned sequentially with 5 mL of methanol, 5 mL of deionized water, and 5 mL of acidified deionized water (pH 2.0). The prepared water sample was then loaded onto the cartridge at a flow rate of 10\u0026ndash;15 mL/min. After loading, the cartridge was washed with 5 mL of deionized water and dried under a nitrogen stream for 20 min. Analytes were eluted with 10 mL of methanol at 5 mL/min. The eluate was concentrated to near dryness under a gentle nitrogen stream and reconstituted in 1.0 mL of initial mobile phase for HPLC-MS/MS analysis. The final extract was filtered through a 0.22 \u0026micro;m organic membrane prior to injection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Antibiotic analysis and quality control\u003c/h2\u003e \u003cp\u003eAntibiotics were quantified using high-performance liquid chromatography coupled with tandem triple quadrupole mass spectrometry (HPLC-MS/MS, Agilent 1200 series). Separation was achieved on a GL Sciences Inert Sustain AQ-C18 column (1.9 \u0026micro;m, 2.1 \u0026times; 50 mm) maintained at 40\u0026deg;C. The mobile phase consisted of (A) 0.1% formic acid in water and (B) 0.1% formic acid in a 1:1 (v/v) methanol/acetonitrile mixture. A gradient elution program was applied: 30% B (0 min), increased to 40% B (3 min), then to 95% B (5 min), held for 1.5 min (6.5 min), and returned to 15% B in 0.1 min, followed by re-equilibration. The flow rate was 0.4 mL/min. MS detection employed positive electrospray ionization (ESI+). Optimized MS parameters for each compound are listed in Table S5.\u003c/p\u003e \u003cp\u003eQuantification was based on an external standard calibration curve (5, 10, 50, 100, 250, 500 \u0026micro;g/L). Method detection limits (MDLs) ranged from 0.1 to 1.0 ng/L. Recoveries were evaluated by spiking analyte-free water samples, yielding rates between 63.0% and 106.1% (Table S6). Although tetracyclines exhibited slightly lower recoveries (63.0-65.9%), consistent with literature reports due to matrix complexity, the results were deemed acceptable.\u003c/p\u003e \u003cp\u003eRigorous QA/QC measures were implemented. For every batch of 20 samples, two procedural blanks and one duplicate were processed. All target antibiotics in blanks were below MDLs. The relative standard deviation for duplicates was \u0026lt;\u0026thinsp;20%. Additionally, a continuing calibration verification standard (100 \u0026micro;g/L) was injected after every ten samples to monitor instrumental stability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eBasic statistical analyses were performed using R (v3.6.1). Data normalization was applied prior to multivariate analysis. Principal component analysis (PCA) was conducted to explore patterns in antibiotic composition across sites and seasons. Differences in antibiotic concentrations among seasons or sites were assessed using one-way analysis of variance (ANOVA) followed by Tukey's honest significant difference post-hoc test, with a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Structural equation model and multifactor model\u003c/h2\u003e \u003cp\u003eTo identify potential pollution sources, PCA coupled with multiple linear regression (PCA-MLR) was performed using IBM SPSS Statistics 24.0. Details of this approach are provided in Text S2.\u003c/p\u003e \u003cp\u003eTo further elucidate the key drivers of antibiotic distribution, a structural equation model (SEM) was constructed using IBM SPSS Amos 22.0 (maximum likelihood estimation). The conceptual model (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) incorporated variables from five groups: (1) geophysical factors (elevation, latitude), (2) water quality parameters (e.g., NH₄⁺-N, COD), (3) socioeconomic indices (population density, livestock/poultry densities at buffer radii of 1, 5, 10, 20 km; Gross Regional Product), and (4) land use proportions (cropland, forest, grassland, urban). The selection of buffer radii was based on previous studies (Song et al., 2020). Model fit was evaluated using standard indices (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb). A detailed description of variable selection and model evolution is in Text S3.\u003c/p\u003e \u003cp\u003eFinally, to predict antibiotic concentrations based on environmental variables, multifactor regression models were developed via forward stepwise selection. Variation partitioning analysis was conducted using the `vegan` package in R to assess the individual and combined effects of different variable groups. The relative importance of predictors in the final model was calculated using the `relaimpo` package. The environmental factors included in the modeling are abbreviated as follows: LD₁ (Livestock Density in 1-km buffer), GRP₁ (Gross Regional Product per capita in 1-km buffer), GP₁ (Grassland Proportion in 1-km buffer), HD₅ (Pig Density in 5-km buffer), GP₂₀ (Grassland Proportion in 20-km buffer), RLP₂₀ (Residential Land Proportion in 20-km buffer).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Levels of antibiotic concentrations in water\u003c/h2\u003e \u003cp\u003eAmong the 44 target antibiotics, 34 were detected in the water samples, with a maximum individual concentration of 89.07 ng/L. As shown in Figure S3, up to 16 different antibiotics were co-detected at a single sampling site. FQs were the predominant antibiotic class, with concentrations ranging from not detected (n.d.) to 89.07 ng/L (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Their prevalence is consistent with reports from the Wei River (11.09\u0026ndash;216.12 ng/L) (Cao et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and surface waters across China (0.03\u0026ndash;27,011.01 ng/L) (Yi et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), attributable to their extensive production and use in China (Wan et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yang, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), coupled with high stability and solubility in aquatic environments which hinder hydrolysis and slow photolytic or microbial degradation (Sarmah et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMLs were the second most prevalent class (n.d. \u0026ndash; 35.46 ng/L), aligning with their frequent detection in Chinese surface waters (Yi et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and significant contamination reported in the Wei River (Ge et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This can be explained by their global high consumption (Senta et al., 2017) and hydrophilic nature (Hu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In contrast, SAs and TCs were present at relatively lower concentrations (up to 5.24 and 7.01 ng/L, respectively). However, specific compounds within these classes, such as STX1, STX2, SMX, TER, and TET, exhibited high detection frequencies (42.37%\u0026ndash;48.99%). This discrepancy between concentration and detection rate likely stems from their widespread application in livestock and poultry farming (Dame-Korevaar et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), long environmental half-lives (Pan et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), low photolysis efficiency (for TER and TET) (Ouyang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; A. Peng et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and persistence in conventional wastewater treatment processes (Arun et al., 2022; C. Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the individual compound level, tylosin (TYL, a ML), enrofloxacin (ENR, a FQ), and ciprofloxacin (CIP, a FQ) showed the highest mean concentrations (12.90, 7.35, and 7.28 ng/L, respectively) and detection rates (64.41%\u0026ndash;69.62%). Their prominence is corroborated by regional studies: TYL is reported as a dominant antibiotic in rivers of Changchun and the Yangtze River Delta (Du et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); CIP shows high levels in the Weihe River (Cao et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); and ENR is commonly detected in the Yangtze River Basin (D. Fan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The extensive use of these compounds drives their environmental occurrence: TYL is employed against mycoplasmosis and respiratory infections (Kanci Condello et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Taiyari et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), ENR treats respiratory and dermatophytic infections in animals (Anh et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Dessus-Babus et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), and CIP is used for respiratory and digestive infections in both humans and animals (Cios et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mumcuoglu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Consequently, the elevated concentrations of TYL, ENR, and CIP directly contribute to the high overall levels observed for the FQ and ML classes in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Temporal differences of antibiotics\u003c/h2\u003e \u003cp\u003eThe total concentration of antibiotics exhibited significant seasonal variation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with medians (and ranges) of 17.41 (12.52\u0026ndash;29.00), 52.13 (26.13\u0026ndash;104.18), 45.80 (5.80\u0026ndash;135.52), and 89.99 (43.99\u0026ndash;150.97) ng/L in July, September, and November 2023, and March 2024, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The highest concentrations occurred in March 2024. This peak is attributed to environmental conditions that favor antibiotic persistence: low temperatures (1.8\u0026ndash;4.3\u0026deg;C) and solar irradiance (Fig. S4) decelerate hydrolysis and photolysis rates, thereby extending environmental half-lives (Bueno et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Habibi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Concurrently, lower river discharge during this period reduces the dilution capacity for pollutants (Abily et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, the lowest concentrations were observed in July 2023, resulting from the opposing effects of higher temperatures and irradiance (enhancing degradation) (Fig. S4) and peak river flow (providing maximal dilution) (Wang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Notably, the number of detected antibiotic species also varied seasonally, peaking in September 2023 (34 species) compared to July 2023 (22), November 2023 (30), and March 2024 (25) (Fig. S3). This pattern diverges from the total concentration trend and likely reflects a peak in agricultural antibiotic application in early autumn (Lu and Lu, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), introducing a diverse array of compounds. The moderate temperatures in September (17.0\u0026ndash;19.4\u0026deg;C) may not have been sufficiently high to rapidly degrade all introduced antibiotics, allowing for broader detection despite ongoing microbial and chemical degradation processes (Achermann et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mitchell et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe compositional profile of the antibiotic cocktail shifted markedly between seasons. From July to September 2023, MLs dominated, constituting 76.1\u0026ndash;81.4% of the total concentration, with TYL alone contributing 69.7\u0026ndash;76.1%. This period coincides with warmer temperatures (17.0\u0026ndash;25.9\u0026deg;C) and higher humidity, which can increase the incidence of livestock respiratory and intestinal diseases (e.g., caused by Pasteurella and Mycoplasma), leading to elevated ML use (Kamathewatta et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Taiyari et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, substantial rainfall (19.9\u0026ndash;90.5 mm) during these months facilitates the runoff of MLs (e.g., TYL) from farms into the river network (Hu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).Conversely, FQs became the predominant class in the colder months, comprising 70.0\u0026ndash;87.6% of the total in November 2023 and March 2024, driven by high concentrations of ciprofloxacin (CIP, 262.16\u0026ndash;310.34 ng/L) and enrofloxacin (ENR, 116.67\u0026ndash;455.27 ng/L). This shift correlates with the heavy use of these broad-spectrum antibiotics in livestock, poultry, and aquaculture, especially during periods of temperature fluctuation or decline in late autumn and early spring, when disease outbreaks are common (Hal and El-Barbary, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yun et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Y. Wei et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn summary, the temporal patterns of antibiotics are shaped by the interplay of seasonal use practices (e.g., agricultural and veterinary applications) and environmental fate processes (degradation and dilution). The distinct seasonal dominance of MLs (warm/wet season) versus FQs (cold/dry season) strongly indicates that antibiotic contamination in the studied area is primarily driven by dynamic anthropogenic sources, with environmental conditions modulating their subsequent persistence and transport.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Characterization of the spatial distribution of antibiotic sites\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eContrary to the frequently reported downstream accumulation pattern in river systems (Hu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the total antibiotic concentration in this study did not exhibit a consistent increasing trend from upstream (S1) to downstream (S20). Instead, spatial heterogeneity was pronounced, characterized by distinct concentration peaks at specific sites that varied across sampling campaigns.\u003c/p\u003e \u003cp\u003eThe spatial heterogeneity was marked by distinct concentration peaks at specific sites during different sampling campaigns. For instance, site S12 in September 2023 showed a significantly higher total concentration (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) than other sites, which was largely attributed to roxithromycin (ROX, 61.77 ng/L, accounting for 59.29% of the total). This anomaly is likely linked to the discharge from the adjacent Yinchuan First Recycled Water Plant, as ROX is known to be poorly removed by conventional wastewater treatment processes (Novo et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Watkinson et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Concurrently, the seasonal prevalence of respiratory infections and associated ROX usage in autumn may have amplified this localized input (Ogimi et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Another example is the high concentration of ciprofloxacin (CIP) observed at site S5, which aligns with the surrounding land-use characteristics featuring high population density, intensive cattle and sheep farming, and extensive arable land (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b, d). These factors collectively promote the widespread use and subsequent environmental release of this antibiotic, which is applied in both human and veterinary medicine (Cios et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mumcuoglu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). These cases underscore the critical role of discrete pollution sources\u0026mdash;such as wastewater treatment plants, livestock farms, and agricultural runoff\u0026mdash;in creating spatial hotspots (Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wen et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shao et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, the overall spatial pattern of antibiotics in this study differed notably from the downstream accumulation trend frequently reported in the literature (Hu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To reconcile this discrepancy, the contrasting environmental fate processes of different antibiotic classes must be considered. Macrolides, such as tylosin (TYL) which dominated the profile at the upstream site S1 (10.71\u0026ndash;35.46 ng/L), are susceptible to photochemical degradation (Xu et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and are subject to significant dilution at tributary confluences (Wang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These attenuation processes effectively offset potential downstream accumulation. In contrast, sulfonamide antibiotics (SAs) exhibited a gradual increasing trend from upstream to downstream. This class-specific behavior can be attributed to the high solubility and environmental persistence of SAs (Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which facilitates their transport and gradual accumulation along the flow path, despite their relatively lower overall consumption (Zhang et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn summary, the spatial distribution of antibiotics is governed by the interplay between spatially heterogeneous anthropogenic inputs and compound-specific environmental fate processes (e.g., degradation, dilution, and persistence). The overall pattern thus reflects the superposition of concentration hotspots from multiple point sources and the differential transport behaviors of various antibiotic classes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Characterization of the regional spatial distribution of antibiotics\u003c/h2\u003e \u003cp\u003eThe composition of antibiotics exhibited considerable spatial variation across the Ningxia section of the Yellow River basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). This heterogeneity can be primarily linked to regional disparities in anthropogenic pressures, notably population density and the structure of livestock and poultry production within the four cities (Shizuishan, Wuzhong, Yinchuan, and Zhongwei) encompassed by the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b). Variations in these factors likely drive differences in local antibiotic use patterns (Fan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; C. Wei et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which are subsequently reflected in the aquatic environment.\u003c/p\u003e \u003cp\u003eA key determinant is the distinct composition of food animal farming in each city. For instance, while Wuzhong showed the highest total livestock production, Zhongwei presented a more balanced distribution among cattle (25.38%), pigs (34.84%), and sheep (39.78%). In contrast, Wuzhong and Yinchuan were predominantly characterized by cattle and sheep farming. Given that different animal species have distinct antibiotic consumption rates and spectra (e.g., global average: pigs 172, chickens 148, cattle 45 mg/kg; Tiseo et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), these regional farming structures directly influence the types and quantities of antibiotics introduced into the local environment (Hu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Krishnasamy et al., 2015).\u003c/p\u003e \u003cp\u003eTemporally, the regional prevalence of specific antibiotics also shifted. Tylosin (TYL) and norfloxacin (NOR) were characteristic of the warmer months (July and September 2023), whereas ciprofloxacin (CIP) and enrofloxacin (ENR) dominated in the cooler periods (November 2023 and March 2024). This pattern aligns with seasonal animal management practices in Ningxia\u0026rsquo;s major livestock industry. Higher temperatures in summer can induce heat stress, while significant temperature fluctuations in spring and autumn may compromise animal immunity (Betote et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jin et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; X. Fan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), both scenarios potentially leading to prophylactic or therapeutic antibiotic use. The application of manure containing antibiotic residues to soil further acts as a secondary source, exacerbating the environmental load of these representative compounds (X. Fan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; S. Peng et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these spatial and temporal variations in driving factors, the suite of major antibiotics detected across the region showed relatively limited geographic fluctuation. This consistency suggests a pervasive influence of a common, dominant source. Cattle farming, prevalent across all studied cities, is a likely major contributor to the regional antibiotic footprint (Hu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To quantitatively apportion the sources and identify key drivers behind the observed distribution patterns, we employed Principal Component Analysis-Multiple Linear Regression (PCA-MLR) and Structural Equation Modeling (SEM). These analyses helped elucidate why, despite underlying regional differences, the profile of representative antibiotics remained broadly consistent across the study area.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Source allocation\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo elucidate the drivers behind the observed spatiotemporal variations, we applied Principal Component Analysis-Multiple Linear Regression (PCA-MLR) to quantify and apportion the sources of antibiotics. Three principal components (PCs) were extracted, collectively explaining 100% of the total variance (Table S8). PC1, explaining 41.83% of the variance, exhibited strong loadings on a diverse suite of antibiotics, including sulfonamides (STX, SQX, SMLD, SFPZ, SMZ, ALF), fluoroquinolones (ENR, OFL), macrolides (AZI, TYL), and clindamycin. The presence of antibiotics widely used in both human medicine and veterinary practice (e.g., ENR, TYL, AZI) identifies PC1 as a mixed source from livestock production and domestic wastewater. PC2 (29.83% variance) was predominantly associated with veterinary antibiotics, including specific sulfonamides (SQX, SFPZ, SMP), fluoroquinolones (NOR, DAF, SAR, ENR), and tetracyclines (AUR, TET). This profile strongly suggests PC2 represents direct input from livestock and poultry farming. PC3 (28.34% variance) was highly correlated with ciprofloxacin (CIP), pefloxacin (PEF), ofloxacin (OFL), and sulfamethazine (SMA). The dominance of high-consumption human and veterinary fluoroquinolones indicates PC3 is primarily influenced by medical wastewater and aquaculture emissions.\u003c/p\u003e \u003cp\u003eThe MLR model estimated the average contributions of PC1, PC2, and PC3 to the total antibiotic concentration at 19.48%, 29.75%, and 50.77%, respectively. This indicates that combined pollution from medical/aquaculture sources and livestock farming constitutes the primary source (approximately 80%) in the Ningxia Yellow River section. This combined contribution is higher than the estimated livestock contribution in the Pearl River Basin (42.1%) (Zhu et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), likely due to more intensive integrated farming practices and less efficient wastewater treatment in our study area.\u003c/p\u003e \u003cp\u003eSeasonal dynamics of source contributions were pronounced (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). PC3 (medical/aquaculture) dominated during the cold, dry months of November (87.39%) and March (65.85%). The high contribution in March was driven by elevated levels of OFL and CIP. OFL inputs may be linked to spring snowmelt transporting residual antibiotics, while CIP\u0026rsquo;s prevalence is consistent with its heavy use in both human and veterinary sectors during periods of temperature fluctuation. Conversely, PC1 (mixed livestock/domestic wastewater) was the dominant source in the warm, wet months of July (45.52%) and September (39.45%), with ENR and TYL as key markers. This shift correlates with increased antibiotic use for livestock disease control in summer and, crucially, with enhanced transport via non-point source runoff during the rainy season. The significantly higher COD concentration in September (41.05 and. 7.58\u0026ndash;18.62 mg/L in other months) further supports the dominance of diffuse agricultural runoff as the key pathway during this period.\u003c/p\u003e \u003cp\u003eIn conclusion, source apportionment reveals that antibiotic pollution is primarily from anthropogenic sources, with the dominant pathway shifting seasonally: point sources (medical/livestock wastewater) prevail in the dry season, while non-point sources (agricultural and livestock runoff) dominate in the wet season. This finding, which contrasts with patterns in some humid basins (e.g., peak in wet season), underscores the necessity for tailoring pollution control strategies to local hydrological and agricultural practices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Drivers of antibiotic distribution\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStructural Equation Modeling (SEM) was employed to elucidate the key factors controlling the spatial distribution of antibiotic concentrations. The model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, with fit indices meeting recommended criteria) explained 68.2% of the observed variance, identifying four significant direct drivers: NH₄⁺-N concentration (λ\u0026thinsp;=\u0026thinsp;0.558, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), per capita Gross Regional Product (GRP) (λ = -0.461, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), latitude (λ = -0.327, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and altitude (λ\u0026thinsp;=\u0026thinsp;0.221, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eNotably, NH₄⁺-N exhibited the strongest positive direct effect, underscoring its role as a robust chemical indicator co-emitted with antibiotics from animal production systems. This relationship was confirmed by a significant positive correlation between NH₄⁺-N and total antibiotic concentrations (r\u0026thinsp;=\u0026thinsp;0.445, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, a multiple linear regression revealed that livestock density, rather than urban population density, was the primary predictor of NH₄⁺-N levels (β\u0026thinsp;=\u0026thinsp;0.944, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and β\u0026thinsp;=\u0026thinsp;0.128, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), consolidating the link between waterborne antibiotics and livestock farming.\u003c/p\u003e \u003cp\u003eThe significant negative effect of per capita GRP provides critical socio-economic insight. Higher GRP in the study area correlates with economic transition away from intensive, high-antibiotic-use livestock sectors like swine farming, and toward sheep grazing (Table S11), which typically has a lower antibiotic consumption intensity per head. This economic-geographic pattern helps explain the lack of strong regional variation in antibiotic profiles noted earlier, as sheep farming is widespread across the region. The negative effect of latitude aligns with the northward decline in total livestock output within the study area, directly reducing antibiotic loading. Conversely, the positive effect of altitude appears counterintuitive given the potential for enhanced photodegradation at higher elevations. However, this is overridden by the strong positive correlation between altitude and swine farming density (λ\u0026thinsp;=\u0026thinsp;0.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table S11). The high emission factor associated with swine farms effectively makes altitude a proxy for localized, intensive point-source pollution from this sector.\u003c/p\u003e \u003cp\u003eIn summary, the SEM analysis reveals that the spatial distribution of antibiotics is not primarily driven by natural environmental gradients, but by the spatial heterogeneity of anthropogenic activity, specifically livestock farming. The positive effects of NH₄⁺-N and altitude, and the negative effects of latitude and per capita GRP, all converge to identify the intensity and type of animal agriculture as the fundamental driver shaping the antibiotic contamination landscape in the Ningxia section of the Yellow River, powerfully corroborating the source apportionment conclusions from Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e3.5\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.7 A multifactorial model for predicting antibiotic concentrations\u003c/h2\u003e \u003cp\u003eTo enable the estimation of antibiotic concentrations based on accessible environmental and socio-economic variables, we developed a multiple linear regression model (Eq.\u0026nbsp;1). The model demonstrated strong performance in predicting total antibiotic concentrations, with an adjusted R\u0026sup2; of 0.833 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Its robustness was confirmed by a leave-one-out cross-validation R\u0026sup2; of 0.756 (difference\u0026thinsp;\u0026lt;\u0026thinsp;15%), along with low mean absolute error (MAE) and root mean square error (RMSE) values, indicating excellent stability and predictive capability (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Conc\\left(ng/L\\right)=0.673\\:{HD}_{5}-110.724\\:{RLP}_{20}+43.112\\:{GP}_{20}-32.175\\:{GP}_{1}-0.04\\:{LD}_{1}+0.007\\:{GRP}_{1}+20.369$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:({R}^{2}=0.833\\:,\\:p\u0026lt;0.001)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe model identified several key predictors, with independent explanatory rates ranging from 9.7% to 25.8% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). `HD₅` (swine density within a 5-km radius) showed a strong positive effect, which aligns directly with its high antibiotic emission factor and its identification as a key point source in our SEM and source apportionment analyses. Conversely, `RLP₂₀` (residential land proportion within a 20-km radius) exhibited the strongest negative effect. This is logically explained by the association of populated areas with wastewater treatment plants, which provide dilution and standardized treatment that reduces the net antibiotic load per unit of effluent, consistent with the lower contribution from domestic sources quantified earlier.\u003c/p\u003e \u003cp\u003eThe effects of `GP₂₀` and `GP₁` (grassland proportion at 20-km and 1-km radii) were contrasting. The positive effect of `GP₂₀` suggests that at a broader landscape scale, grasslands integrate non-point source runoff from upstream agricultural and pastoral activities, acting as a diffuse source conduit. In contrast, the negative effect of `GP₁` at the immediate local scale may indicate a dilution or retention effect in riparian grassland buffers. The similar magnitudes of the explanatory rates for these factors underscore that antibiotic fate is governed by the interplay of multiple, concurrent processes\u0026mdash;including concentrated point sources (`HD₅`), diffuse agricultural runoff (`GP₂₀`), and attenuation through infrastructure (`RLP₂₀`) and landscape features (`GP₁`).\u003c/p\u003e \u003cp\u003eWe also constructed class-specific models for FQs and MLs, which were the only ones to achieve stability (adj. R\u0026sup2; = 0.655 and 0.869, respectively; Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee, f). The key predictors for these models were largely consistent with those in the overall model (Fig. S11), reinforcing the conclusion that the distribution of these dominant antibiotic classes is driven by the same set of integrated environmental and anthropogenic factors.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThis study provides a comprehensive assessment of antibiotic contamination in the Ningxia section of the Yellow River. The key findings are synthesized as follows: (1) Pollution Profile: A total of 34 antibiotics were detected, confirming widespread contamination. Fluoroquinolones (FQs) and macrolides (MLs) were the dominant pollutant classes. Concentrations exhibited significant spatiotemporal variability, with peak levels occurring during the dry season (March) when reduced river flow diminished dilution capacity. (2) Sources and Drivers: The contamination originated primarily from integrated livestock and medical wastewater sources, accounting for over 50% of the total burden. Spatial patterns were shaped by a combination of point sources (e.g., wastewater treatment plants, intensive farms) and non-point sources (agricultural runoff). Structural equation modeling quantified the key drivers, revealing that antibiotic concentrations were positively influenced by elevation (linked to high-density farming) and ammonium-nitrogen (a co-indicator of livestock pollution), but negatively correlated with latitude and per capita gross regional product. (3) Predictive Framework and Implications: A robust multivariate regression model was developed, successfully predicting antibiotic concentrations based on easily accessible environmental variables (Adj. R\u0026sup2; = 0.833). This model integrates the major drivers\u0026mdash;including livestock density, land use, and treatment infrastructure\u0026mdash;into a practical management tool. Collectively, these findings delineate a clear pathway from emission sources to environmental concentrations in the river. They underscore that controlling antibiotic pollution in this agriculturally intensive basin requires targeted management of livestock farming and wastewater treatment. The mechanistic understanding and predictive tool established here offer a replicable framework for assessing and mitigating antibiotic pollution in similar arid and semi-arid river systems worldwide.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI would like to express my sincere gratitude to my supervisor, Professor Shiquan Wang, for his invaluable guidance, continuous support, and patience throughout my graduate study. His insightful comments and encouragement have been instrumental in the completion of this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by\u0026nbsp;the Ningxia Nature Fund Ningxia Key R\u0026amp;D Program Project (2023BEG02047) and Ningxia Nature Fund Ningxia Key R\u0026amp;D Program (2021BEB04051).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e’\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShiquan Wang\u003c/strong\u003e: Writing\u0026nbsp;–\u0026nbsp;review \u0026amp; editing, Funding acquisition, Conceptualization. \u003cstrong\u003eYajunjie Liu\u003c/strong\u003e: Writing – original draft, Formal analysis, Data curation.\u003cstrong\u003eYu Jiang\u003c/strong\u003e：Methodology. \u003cstrong\u003eMeiping Zhou\u003c/strong\u003e: Writing – review \u0026amp; editing. \u003cstrong\u003eYinlong Zhu\u003c/strong\u003e: Methodology.\u003cstrong\u003e\u0026nbsp;Xuan Yang\u003c/strong\u003e: Writing – review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with theethical standards of the institutional and/or national research committee and with the 1964Helsinki Declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors disclosed no relevant relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI, Yajunjie Liu, hereby declare that I agree to the publication of the research findings presented in this manuscript entitled \"Deciphering the occurrence, distribution, and source apportionment of antibiotics in the Ningxia section of the Yellow River\" in the ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH. I have carefully read and understood the submission guidelines and requirements of the journal.I certify that the data, figures, and images presented in this manuscript are original or have been authorized and licensed legally. I hereby declare that I have made every effort to avoid errors and misconduct in this research and that the results presented in this manuscript are truthful and reliable. I also declare that I will not be held responsible for any negative consequences resulting from the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData sets generated during the current study are available from the corresponding author on reasonable request. Meteorological data can be obtained from local statistical yearbooks, which were used under license for the current study, and so are not publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMandatory if your study involves humans and/or animals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not include any human and/or animals participant research conducted by the author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbily, M., Acu\u0026ntilde;a, V., Gernjak, W., Rodr\u0026iacute;guez-Roda, I., Poch, M., Corominas, L., 2021. Climate change impact on EU rivers\u0026rsquo; dilution capacity and ecological status. Water Res. 199, 117166.\u003c/li\u003e\n\u003cli\u003eAchermann, S., Bianco, V., Mansfeldt, C.B., Vogler, B., Kolvenbach, B.A., Corvini, P.F.X., Fenner, K., 2018. Biotransformation of Sulfonamide Antibiotics in Activated Sludge: The Formation of Pterin-Conjugates Leads to Sustained Risk. Environ. Sci. 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Mater. 476, 135046.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Antibiotics, Ningxia section of the Yellow River, Spatial-Temporal variations, Source Apportionment, Driving factors","lastPublishedDoi":"10.21203/rs.3.rs-8804872/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8804872/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe pervasive release of antibiotics into aquatic environments necessitates a thorough understanding of their fate. This study systematically investigated the occurrence, sources, and controlling factors of antibiotic pollution in the Ningxia section of the Yellow River. Across four major cities, 44 target antibiotics were monitored. Fluoroquinolones and macrolides were the predominant groups, with tylosin, enrofloxacin, and ciprofloxacin as the most abundant compounds. Concentrations exhibited significant spatiotemporal variation, peaking in the dry season (up to 150.97 ng/L) and clustering near intensive livestock areas and wastewater outfalls. Source apportionment via PCA-MLR quantified major contributions from combined medical/aquaculture and livestock sources (50.8%) and livestock farming alone (29.8%). Structural Equation Modeling identified livestock activity as the principal spatial driver, with significant positive paths to NH₄⁺-N and altitude, collectively explaining 68.2% of concentration variance. Furthermore, a stable predictive model (adj. R\u0026sup2; = 0.833) was established, highlighting swine density as a key positive predictor and residential land proportion as a major negative predictor. This work concludes that antibiotic contamination is primarily anthropogenic, driven by livestock production with seasonal shifts between point and non-point pathways. The integrated methodology and mechanistic insights offer a valuable framework for developing targeted mitigation strategies in similar semi-arid river basins.\u003c/p\u003e","manuscriptTitle":"Deciphering the occurrence, distribution, and source apportionment of antibiotics in the Ningxia section of the Yellow River","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-04 18:12:17","doi":"10.21203/rs.3.rs-8804872/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-03-16T21:56:49+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-27T13:52:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2026-02-24T13:34:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-16T05:29:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2026-02-12T09:23:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4df7415a-ab7a-434a-aebb-734ff8a675b2","owner":[],"postedDate":"March 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-04T18:12:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-04 18:12:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8804872","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8804872","identity":"rs-8804872","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}