Science of the Total Environment最新文献

{"title":"Clustering of emission–dispersion dynamics in a state space defined by pollution sources and meteorological variables","authors":"Yuval ,&nbsp;Yoav Levi ,&nbsp;Pavel Khain ,&nbsp;David M. Broday","doi":"10.1016/j.scitotenv.2025.180051","DOIUrl":"10.1016/j.scitotenv.2025.180051","url":null,"abstract":"<div><div>The complex relationship between air pollution sources, the meteorological conditions governing their dispersion, and the resulting pollutant concentrations is an interesting scientific topic with significant implications for air resource management. A widely adopted approach to exploring these interactions is the interpretation of statistical models which simulate them. Dispersion conditions can vary markedly—from cold, calm nights with stable atmosphere to stormy periods characterised by intense turbulence and convection. Deeper insights can be obtained by interpreting statistical models trained on data subsets that correspond to specific emission–dispersion conditions. To achieve such distinct subsets, we present a robust and reproducible methodology for partitioning the multidimensional state space, defined by pollution sources and meteorological variables, into a large number of clusters at each monitoring location. Our methodology ensures a systematic and objective analysis across multiple stations. We analyse four years of hourly data, integrating high-resolution meteorological outputs from a numerical weather prediction model with traffic volume data used as proxies for emissions or precursors of NO, NO₂, NO_x, PM_2.5, and O₃ concentrations. These pollutants were observed at 85 air quality monitoring stations across Israel. The resulting clusters capture sub-daily temporal patterns that are indicative of distinct emission–dispersion scenarios in the region. We demonstrate that statistical models trained on these clustered subsets consistently outperform models trained on the full-period datasets. This highlights the value of our clustering approach in improving both predictive performance and scientific understanding of air pollution dispersion dynamics.</div></div>","PeriodicalId":422,"journal":{"name":"Science of the Total Environment","volume":"995 ","pages":"Article 180051"},"PeriodicalIF":8.2,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variation in patterns of production and water-use efficiency among agroecosystems","authors":"Olivia L. Hajek ,&nbsp;Nicole E. Kaplan ,&nbsp;Shefali Azad ,&nbsp;Philip A. Fay ,&nbsp;Makki Khorchani ,&nbsp;Amanda M. Nelson ,&nbsp;Adam P. Schreiner-McGraw ,&nbsp;Lori J. Abendroth ,&nbsp;Claire Baffaut ,&nbsp;John Baker ,&nbsp;Brandon T. Bestelmeyer ,&nbsp;Elizabeth H. Boughton ,&nbsp;Dawn M. Browning ,&nbsp;Bryan R. Carlson ,&nbsp;Michel A. Cavigelli ,&nbsp;Patrick E. Clark ,&nbsp;Curtis Dell ,&nbsp;Yuxi Guo ,&nbsp;John Hendrickson ,&nbsp;David Huggins ,&nbsp;David L. Hoover","doi":"10.1016/j.scitotenv.2025.180115","DOIUrl":"10.1016/j.scitotenv.2025.180115","url":null,"abstract":"<div><div>Across agroecosystems, water is a key driver of primary production, and the relationship between precipitation and production (i.e., water-use efficiency; WUE) provides an important indicator for evaluating agroecosystem resilience to changes in water availability. While this relationship has been well-characterized in relatively unmanaged, native ecosystems, cross-site syntheses spanning diverse agroecosystems and climate gradients are lacking. We leveraged the USDA's Long-Term Agroecosystem Research (LTAR) network to assess the relationship between annual precipitation and aboveground net primary production (ANPP) across an extensive set of climate conditions and agroecosystems, representing native rangelands, croplands, and pasturelands and various management intensities. We utilized long-term ANPP data (mean = 17 years) from fifteen sites spanning a large precipitation gradient (265 to 1347 mm yr⁻¹). We observed a positive relationship between annual precipitation and productivity across precipitation gradients; however, this nonlinear pattern differed from native ecosystems and varied by agroecosystem type. Rangeland ANPP was strongly coupled to annual precipitation, increasing nearly 20% for every 100 mm of precipitation. Croplands and pasturelands showed significantly decreased sensitivity, although grouping crops by photosynthetic pathway and crop type revealed some significant patterns. Underlying these patterns in sensitivity were large differences in overall ANPP among agroecosystems; cropland ANPP was up to 6.7-fold greater than rangelands and 2.6-fold greater than pasturelands, despite overlapping precipitation gradients. While agroecosystem type captured much of the variability in the precipitation-production relationship at the continental scale, understanding the more subtle differences in precipitation sensitivities will be fundamental for identifying production vulnerabilities and adapting to changing water resources.</div></div>","PeriodicalId":422,"journal":{"name":"Science of the Total Environment","volume":"995 ","pages":"Article 180115"},"PeriodicalIF":8.2,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of soil cover chemical pollution using satellite data: A case study of Kharkiv region, Ukraine","authors":"M. Solokha ,&nbsp;O. Melnyk ,&nbsp;N. Cannon ,&nbsp;M. Horton ,&nbsp;O. Datsko ,&nbsp;D. O'Connor","doi":"10.1016/j.scitotenv.2025.180105","DOIUrl":"10.1016/j.scitotenv.2025.180105","url":null,"abstract":"<div><div>Chemical contamination of soils due to military activity has become an urgent global concern, particularly in conflict-affected regions such as Ukraine. This study presents a methodology for the rapid identification and assessment of contaminated agricultural land using an integrated approach that combines open-source intelligence, geographic information systems (GIS), and remote sensing. The authors focused on military debris as a primary vector of soil contamination (by Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) and utilized publicly available digital resources to geolocate fragments of destroyed equipment across farmlands in Kharkiv Oblast.</div><div>To compensate for the limited availability of high-resolution, time-specific satellite imagery, the study employed free datasets from the European Space Agency. The emergence of soil tracks and field roads was used as a proxy indicator of ground disturbance and potential chemical impact. These signals guided field sampling campaigns, during which soil samples were collected in proximity to identified debris and analyzed in laboratory conditions for chemical pollutants. Analytical studies revealed that heavy metals at the impact site exceed maximum permissible concentrations and exhibit an increasing trend, particularly for cadmium, copper, lead, and zinc, with exceedances reaching up to 62 times the regulatory limits.</div><div>All findings were systematically documented using a custom-designed GIS database, allowing the classification of contamination severity and spatial distribution. The resulting maps aim to support local authorities and farmers in decision-making and prioritising remediation measures. This approach provides a replicable framework for post-conflict environmental assessment and contributes to strengthening soil resilience in war-affected agricultural zones.</div></div>","PeriodicalId":422,"journal":{"name":"Science of the Total Environment","volume":"995 ","pages":"Article 180105"},"PeriodicalIF":8.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bacteria-mediated sulfur cycle and cadmium stabilization in an anaerobic-aerobic coupled system","authors":"Yuanjie Xie ,&nbsp;Qunwei Dai ,&nbsp;Weifu Wang ,&nbsp;Weiqi Du ,&nbsp;Jun Guo ,&nbsp;Linbao Han ,&nbsp;Yulian Zhao ,&nbsp;Jiangrong Cai ,&nbsp;Zihang Chen","doi":"10.1016/j.scitotenv.2025.180107","DOIUrl":"10.1016/j.scitotenv.2025.180107","url":null,"abstract":"<div><div>Although cadmium stabilization through dissimilatory sulfate reduction (DSR), mediated by sulfate-reducing bacteria (SRB) has been extensively studied, the mutual interactions between cadmium and sulfur-cycle systems co-regulated by SRB and sulfur-oxidizing bacteria (SOB) remain poorly characterized. We examined physiological responses (growth kinetics, sulfur valence transitions, Cd²⁺ tolerance) to determine how bacterially mediated bidirectional sulfur cycling influences cadmium speciation. Results demonstrate that SRB (<em>Enterobacter quasihormaechei</em>) immobilizes Cd²⁺ anaerobically, forming CdS, as confirmed by XRD, FTIR, and SEM/EDS analyses. At 40 mg/L Cd²⁺ over 7 days, Cd immobilization efficiency reached 75 %. Conversely, SOB (<em>Pseudomonas protegens</em>) aerobically oxidizes S₂O₃²⁻ and S²⁻ to replenish SO₄²⁻ for DSR while inducing limited dissolution of SRB-mineralized products. Treatment with 66.67 mg/L biogenic CdS for 7 days yielded only 18 % Cd²⁺ dissolution. Crucially, at 40 mg/L Cd²⁺, the actual stoichiometric ratios were SO₄²⁻:S²⁻ = 2:1 (reduction) and S²⁻:SO₄²⁻ = 1.29:1 (oxidation), enabling mutual substrate replenishment: SRB provides S²⁻ to SOB while SOB supplies SO₄²⁻ to SRB. This synergistic loop ultimately stabilizes cadmium as CdS. This study establishes a groundbreaking bioremediation strategy for aquatic cadmium contamination.</div></div>","PeriodicalId":422,"journal":{"name":"Science of the Total Environment","volume":"995 ","pages":"Article 180107"},"PeriodicalIF":8.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chronic in-situ exposure to pesticide-contaminated water bodies impairs the metabolism, growth, and performance of the freshwater fish Prochilodus lineatus","authors":"Carlos Eduardo Delfino Vieira,&nbsp;Maria Eduarda Tesser,&nbsp;Paulo Cesar Meletti,&nbsp;Claudia Bueno dos Reis Martinez","doi":"10.1016/j.scitotenv.2025.180085","DOIUrl":"10.1016/j.scitotenv.2025.180085","url":null,"abstract":"<div><div>Anthropogenic contamination of freshwater ecosystems by complex pesticide mixtures is a growing concern, particularly in agricultural regions such as Southern Brazil. This study evaluated biomarker responses in juvenile <em>Prochilodus lineatus</em> under chronic in situ exposure to water bodies impacted by agricultural runoff. Fish were caged for 120 days at two sites with contrasting contamination levels and sampled at six time points (5, 15, 30, 60, 90, and 120 days). The reference site (RFS) had minimal anthropogenic influence, while the agricultural site (AGS), located in an area of intensive farming, presented elevated levels of pesticides and metals. A multidisciplinary approach was used to assess hematological, metabolic, neurotoxic, and histopathological biomarkers, along with ecologically relevant endpoints including somatic growth, Fulton's condition factor, hepatosomatic index, and swimming endurance. Principal Component Analysis revealed site- and time-dependent physiological responses. Energy metabolism markers—liver and muscle glycogen, plasma glucose, and hepatosomatic index—were key in differentiating site effects. The Integrated Biomarker Response (IBR) index was consistently higher in fish at the AGS site, with peaks at 15, 30, 90, and 120 days, suggesting sustained physiological stress. In contrast, fish at the RFS site exhibited a moderate IBR increase at 30 and 60 days, followed by recovery. Findings indicate that chronic exposure to agricultural contaminants induces adaptive and compensatory responses that elevate energy demands to maintain homeostasis. This energetic trade-off may impair growth and overall organic fitness, potentially affecting population dynamics of the fish in impacted freshwater environments.</div></div>","PeriodicalId":422,"journal":{"name":"Science of the Total Environment","volume":"995 ","pages":"Article 180085"},"PeriodicalIF":8.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long term assessment of SARS-CoV-2 in wastewater and the transition to evaluate additional viral targets","authors":"Ayaaz Amirali ,&nbsp;Mark E. Sharkey ,&nbsp;Shruti Choudhary ,&nbsp;Kristina M. Babler ,&nbsp;Cynthia C. Beaver ,&nbsp;Pratim Biswas ,&nbsp;Kate R. Bowie ,&nbsp;Taylor Burke ,&nbsp;Benjamin B. Currall ,&nbsp;George S. Grills ,&nbsp;Hannah G. Healy ,&nbsp;Alexander G. Lucaci ,&nbsp;Christopher E. Mason ,&nbsp;Michaela McGuire ,&nbsp;Rosemarie Ramos ,&nbsp;Madelena Ruedaflores ,&nbsp;Natasha Schaefer Solle ,&nbsp;Stephan C. Schürer ,&nbsp;Bhavarth S. Shukla ,&nbsp;Mario Stevenson ,&nbsp;Helena M. Solo-Gabriele","doi":"10.1016/j.scitotenv.2025.180096","DOIUrl":"10.1016/j.scitotenv.2025.180096","url":null,"abstract":"<div><div>The COVID-19 pandemic caused by the SARS-CoV-2 virus dramatically impacted society over five years ago and continues to have an impact today. Since the beginning of the pandemic there have responses and strategies implemented to maintain the public safety of communities affected by SARS-CoV-2. This study is a unique opportunity to analyze nearly four years of SARS-CoV-2 wastewater-based surveillance (WBS) data, obtained from five different laboratories, combined with four years of human health data from three adjacent regions of a large urban community (Miami-Dade County). The objective of this study was to analyze that data and evaluate longitudinal and geographic trends of SARS-CoV-2 levels in wastewater (WW) during the extensive time frame of this study. Additionally, WBS data were analyzed for multiple targets (influenza A/B, norovirus, RSV, HMPV, PMMoV) other than SARS-CoV-2 to assess the potential for expanding WBS to a wider range of targets. We found that SARS-CoV-2 levels correlated strongest with clinical positivity rates across all three geographic regions (Spearman <em>r</em> = 0.81 for the entire period of record), with the most geographically restricted region showing higher correlations (South, <em>r</em> = 0.86) than the region with populations with higher geographic mobility (North, <em>r</em> = 0.69). Stronger correlations (0.80 &lt; <em>r</em> &lt; 0.97) were observed when correlations were established by variant waves rather than single or multiple year time frames (0.73 &lt; <em>r</em> &lt; 0.88). When analyzing the data for targets beyond SARS-CoV-2, results show promise as two laboratories detected norovirus, influenza A/B, RSV, and HMPV at statistically not different frequencies (Chi-squared≥0.6). Overall, results suggest that the clinical metrics used (e.g., positivity), geography, and the time frames of data analyses influence the ability of WBS to predict disease prevalence in a community. The consistency among the laboratories supports that the measurement of a wider range of viral targets can be disaggregated among different laboratories providing flexibility for building national-level WBS programs.</div></div>","PeriodicalId":422,"journal":{"name":"Science of the Total Environment","volume":"995 ","pages":"Article 180096"},"PeriodicalIF":8.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to: \"Transcriptome responses to single and combined stressors in seagrass populations from pristine and impacted sites reveal local adaptive features and core stress-response genes\" [Sci. Total Environ. 987 (2025) 179623].","authors":"Hung Manh Nguyen, Beery Yaakov, Pedro Beca-Carretero, Gabriele Procaccini, Guannan Wang, Maheshi Dassanayake, Gidon Winters, Simon Barak","doi":"10.1016/j.scitotenv.2025.180034","DOIUrl":"https://doi.org/10.1016/j.scitotenv.2025.180034","url":null,"abstract":"","PeriodicalId":422,"journal":{"name":"Science of the Total Environment","volume":" ","pages":"180034"},"PeriodicalIF":8.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144717216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Edible mushrooms for improved human health, food security and environmental sustainability: A critical review","authors":"Anuradha Mishra ,&nbsp;Shiv Shankar","doi":"10.1016/j.scitotenv.2025.180093","DOIUrl":"10.1016/j.scitotenv.2025.180093","url":null,"abstract":"<div><div>Edible mushrooms (EMs) are gaining recognition as a sustainable and nutrient-dense food source. They have emerged as a super nutritious food item with remarkable role in promotion of global food security, nutritional security, and environmental sustainability. This review critically examines the multifaceted benefits of EMs, highlighting their rich nutritional profile, which includes proteins, vitamins, minerals, and bioactive compounds such as polysaccharides and antioxidants. These components contribute to disease prevention, immune support, and overall well-being, making mushrooms a valuable dietary addition. From a food security perspective, mushrooms offer a sustainable solution, as they can be cultivated on agricultural waste, require minimal resources, and have a low environmental footprint. Their production supports circular economies by recycling organic waste into nutritious food (Grimm et al., 2018). Environmentally, mushrooms contribute to sustainability through bioremediation, soil enrichment, and carbon sequestration. Despite their potential, challenges such as limited consumer awareness, underutilization of diverse species, and scalability issues persist. This review underscores the need for increased research, policy support, and public education to integrate EMs into global food systems.</div></div>","PeriodicalId":422,"journal":{"name":"Science of the Total Environment","volume":"995 ","pages":"Article 180093"},"PeriodicalIF":8.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving disaster resilience with causal machine learning for flood damage estimation","authors":"Mujungu L. Museru ,&nbsp;Rouzbeh Nazari ,&nbsp;Mohammad Reza Nikoo ,&nbsp;Maryam Karimi","doi":"10.1016/j.scitotenv.2025.180121","DOIUrl":"10.1016/j.scitotenv.2025.180121","url":null,"abstract":"<div><div>Machine Learning (ML) models have become a pivotal tool in the scientific community, successfully addressing complex problems across various domains, including flood risk management. Despite these advancements, traditional data-driven models often struggle when training data is scarce and primarily rely on correlation rather than causal relationships, making them vulnerable to shifts in data distribution. This paper introduces a Causally Informed Neural Network (CINN) framework that integrates causal prior knowledge as an inductive bias to improve flood damage predictions for residential properties and address these limitations. The proposed approach enhances model adaptability to unseen data distributions—an essential requirement for flood damage modeling. First, Deep End-to-End Causal Inference (DECI) is used to discover causal relationships and estimate their average treatment effects. These causal insights are then embedded into the neural network through causal weight initialization and causal regularization. The framework is validated using an enhanced National Flood Insurance Program (NFIP) claims dataset from Hurricane Katrina, and its performance is benchmarked against six widely used ML models from previous studies. Results show that the discovered causal relationships align with domain knowledge, reinforcing the approach's credibility. The proposed CINN model achieves an average 22 % error reduction compared to traditional ML models, demonstrating its superior robustness and predictive accuracy. Additionally, a feature attribution experiment confirms that the model's decision-making process is consistent with the identified causal relationships, increasing interpretability and trust in its predictions. These findings highlight the potential of integrating causality into ML-based flood damage modeling, paving the way for more resilient and generalizable disaster risk assessment frameworks.</div></div>","PeriodicalId":422,"journal":{"name":"Science of the Total Environment","volume":"995 ","pages":"Article 180121"},"PeriodicalIF":8.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of food sources and trace elements in the acquisition of antimicrobial resistance in Escherichia coli isolated from white stork nestlings (Ciconia ciconia)","authors":"Teresa Cardona-Cabrera ,&nbsp;Javier Pineda-Pampliega ,&nbsp;Sandra Martínez-Álvarez ,&nbsp;Alberto Sánchez-Cano ,&nbsp;Yolanda Muela-Trujillo ,&nbsp;Marta Herrero-Villar ,&nbsp;Rafael Mateo ,&nbsp;Carmen Torres ,&nbsp;Ursula Höfle","doi":"10.1016/j.scitotenv.2025.180098","DOIUrl":"10.1016/j.scitotenv.2025.180098","url":null,"abstract":"<div><div>Foraging in landfills enhances the probability of acquiring antimicrobial resistance (AMR) in wildlife and increases exposure to pollutants like metal(loid)s, pharmaceuticals and caffeine. Exposure to metal(loid)s in the environment may cause selective pressure on bacteria, inducing metal resistance genes that drive antimicrobial resistance genes (ARGs) through co-resistance and cross-resistance mechanisms. Hence, white storks fed in landfills could increase AMR acquisition through the combined effect of urban-waste and pollutants. Using a novel approach combining stable isotopes, trace elements and microbiological analysis, our study investigates the influence of the degree of anthropization of the diet and the presence of metal(loid)s in the occurrence of AMR/ARGs in <em>E. coli</em> in nestlings. Cloacal swabs, blood samples, and contour feathers were collected from 86 white stork nestlings from five breeding colonies. ARGs in <em>E. coli</em> were previously studied in cloacal swabs. Plasma was analysed for veterinary pharmaceuticals and caffeine and feathers for carbon/nitrogen isotope ratios and concentrations of metals and arsenic. Isotopic signature classified nestlings into natural or urban-waste diet, relating Al/Ni/Co/Cr/Pb and caffeine to urban-waste diet, while As/Hg/Cu/Zn were more related to natural diet. No pharmaceuticals were detected in the plasma of nestlings. The probability of acquisition of AMR and some ARGs (those conferring resistance to phenicols, tetracyclines and ampicillin) was higher in nestlings fed with urban-waste diet, but no effect of metal(loid) pollution was observed. This shows AMR in wildlife can occur even without direct contact with antibiotics, highlighting the complexity and challenges of addressing the threat of bacterial resistance in the environment.</div></div>","PeriodicalId":422,"journal":{"name":"Science of the Total Environment","volume":"995 ","pages":"Article 180098"},"PeriodicalIF":8.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}