Inhomogeneous antibiotic distribution in sediment profiles in anthropogenically impacted lakes: Source apportionment, fate drivers, and risk assessment

Comprehensive evaluation of antibiotic pollution in a typical tributary of the Yellow River, China: Source-specific partitioning and fate analysis

The partitioning and migrating of antibiotic residues pose a considerable pollution to the river environment. However, a source-specific approach for quantifying the fate of antibiotics is lacking. To further elucidate the migration behavior of antibiotics from different pollution sources in aquatic environments, we introduced a source-specific partition coefficient (S-Kp) based on Positive Matrix Factorization (PMF) model to improve the multimedia model. This study identified six sources of antibiotic pollution in the water and sediment of the Fenhe River. Farmland drainage contributed 2.6 times more antibiotics to sediment than to surface water, whereas livestock sources contributed 0.3 times less to sediment than to water. High S-Kp values were primarily obtained from livestock, aquaculture, and farmland drainage pollution sources, with an average S-Kp value exceeding 200 L/kg. Sulfaquinoxaline (SQX) in sulfonamides (SAs) from livestock sources exhibited the highest S-Kp value of 34,740.04 L/kg. The predicted environmental concentrations indicated that almost 99 % of the antibiotics from the six sources remained in the water phase, with the highest contribution (99.9 %) of azithromycin (AZM) from livestock, pharmaceutical wastewater, and domestic sewage. This study provides novel insights into the migration of antibiotics from source-specific partitions in multimedia environments of watersheds.

Distribution and relationship of heavy metals, microbial communities and antibiotic resistance genes in the riparian soils of Daye Lake, China

Heavy metals pose ecological and resistome risks to aquatic systems. To comprehensively assess the health status of aquatic ecosystems, it is necessary to quantify the ecological risks of heavy metals in riparian soils and their associations with microbial communities and antibiotic resistance genes (ARGs), yet related evidence was scarce. This study evaluated the potential ecological risk of heavy metal-contaminated riparian soils of Daye Lake, revealed the distribution of bacterial communities and ARGs by high-throughput sequencing techniques, and explored the association between heavy metals and bacterial communities and ARGs. The results showed that As, Cd, Cu, Pb, and Se were the primary polluting metals in the riparian soils of Daye Lake. Microbial community analysis presented that Proteobacteria (31.5%), Actinobacteria (30.3%), and Acidobacteria (14.1%) appeared to be the top three prevalent phylums, and seven pathogenic genera were identified based on VFDB. Correlation analysis showed that 17 bacterial communities among the top 50 bacterial genera had significant negative associations with heavy metals (r < -0.5; P < 0.05), and 10 bacterial communities had significant positive associations with heavy metals (r > 0.5; P < 0.05), indicating that heavy metals could exert co-selection forces on the microbial community. ARGs analysis presented that vancomycin, multidrug, and aminoglycoside resistance genes were the dominant ARGs. The co-occurrence of ARGs, virulence factor genes (VFGs), and mobile genetic elements (MGEs) (r > 0.8; P < 0.05) suggested high transmission risk of ARGs in environments. The significant correlations of heavy metals and ARGs (P < 0.05), co-occurrence of the resistance genes (MRGs) and ARGs (r > 0.8; P < 0.05), and significant associations between the geochemical enrichment of heavy metals and ARGs (P < 0.05) consistently indicated important impacts of heavy metals on environmental resistome risks. This research firstly revealed the associations between heavy metals and microbial communities and ARGs in riparian soils, which offers valuable insights into risk prevention and pollution control of heavy metals in the environment.

Cross-sectional-dependent microbial assembly and network stability: Bacteria sensitivity response was higher than eukaryotes and fungi in the Danjiangkou Reservoir

Water depth variation can lead to the vertical structure change of microbial communities in reservoirs, and then affect the relationship between the microbial communities along the depth gradient, profoundly affecting the stability of the aquatic ecosystems. However, the interspecific dynamics of microbial communities across different water layers in deep-water low-nutrient drinking water reservoirs remain not well understood. Thus, we assessed microbial communities' dynamic changes in different water layers in this study. The physical and chemical parameters and different planktonic microbial of the surface, middle, and bottom layers were studied from July 2022 to August 2023 in the Danjiangkou Reservoir, China. Based on high-throughput sequencing technology, model analysis and network analysis, the diversity of microbial communities in different water layers, community construction process and co-occurrence network differences were studied. The results showed that the diversity of bacterial communities in the Danjiangkou reservoir was significantly higher than that of fungi and eukaryotic microorganisms in different water depths. The dominant taxa of the bacterial communities in different water depths were Actinobacteriota, Bacteroidota, Proteobacteria and Cyanobacteria. The dominant phyla were Ascomycota, unclassified_k__Fungi and Chytridiomycota. The relative abundance of vertical dominant species in eukaryotic communities was slightly different, including Cryptophyta, Chlorophyta, Dinophyta and Metazoa. Different microbial communities shared the main dominant species on the vertical stratification. The neutral model showed that random processes significantly affected the assembly process of microbial communities in different water layers, and the mobility of fungal communities was much lower than that of bacteria and eukaryotes. The co-occurrence network analysis showed that the number of nodes and edges of the bacterial community was the highest, indicating that the network scale of the bacterial community was the largest. In addition, the map density and average clustering coefficient of bacterial and eukaryotic communities in surface water were the highest, indicating that the surface microbial species had a high degree of connectivity, can better transfer materials and exchange information, and Sensitive to changes in the external environment. In contrast, in fungal communities, microbial interactions were the most complex at the bottom. The interactions between microbial communities in different water depths were mainly positive, and the negative correlation of microbial communities in the middle and bottom water was greater than that in the surface water, indicating that the competition between species increased with the increase of depth. Correlation analysis showed that the key species of microbial community were significantly correlated with TP, PO43--P, NO3--N and ORP. In summary, by analyzing water depth changes' impacts on the spatial distribution pattern, community assembly process and symbiotic network stability of microbial communities in the Danjiangkou Reservoir, we found that bacterial communities were more sensitive to water depth than eukaryotes and fungi. This study revealed the response mechanism of microbial communities to water depth in low-nutrient reservoirs, which is helpful to reflect aquatic ecological processes and provide a theoretical basis for the construction of subsequent reservoir ecological models.

Antibiotics in ambient fine particulate matter from two metropolitan cities in China: Characterization, source apportionment, and health risk assessment

Excessive production and widespread application of antibiotic has led to residues in environmental matrices worldwide. There is limited knowledge of the concentrations of antibiotics bound to ambient fine particulate matter (PM2.5) and their health risks. We investigated the occurrence, sources, environmental driving factors, and health risks of antibiotics in PM2.5 samples collected from Beijing and Shijiazhuang, China, during periods of high air pollution. Using ultra-high performance liquid chromatography-tandem mass spectrometry, 25 antibiotics were detected in PM2.5 at concentrations ranging from undetectable to 774.7 pg/m3. These compounds were predominantly tetracyclines and macrolides. The positive matrix factorization model was used to pinpoint the main sources of these antibiotics as pharmaceutical and medical waste, sewage treatment plants, and livestock emissions, with contributions of 39.1 %, 31.7 %, and 29.2 % respectively, to the total concentrations. Crucial environmental driving factors were determined using a linear mixed-effects model and random forest model. Most antibiotics showed a positive correlation with gaseous pollutants and a negative correlation with meteorological factors. PM2.5, PM10, and CO had the highest influence. The estimated daily intake and hazard quotient (HQ) were calculated to assess the human inhalation exposure risks for these antibiotics, and children aged 0-6 years had the highest intake of 102.8 pg/kg/day. Although the calculated health risk of antibiotic inhalation was low (HQ < 1), considering that exposure to antibiotics via inhalation occurs over long periods and these compounds accumulate, further attention should be given to health risks associated with this exposure. Our results provide valuable insight for environmental planning and policymaking concerning antibiotic pollution and its associated health risks.

A distributed and process-based model coupling water-sediment-antibiotic interactions to simulate dynamic source-transport-fate of antibiotics at catchment scale

A lack of hydro-biogeochemical models for catchment-scale antibiotic dynamics limits our mechanistic understanding of the transport and fate of antibiotics. This study addresses this gap by developing a distributed and process-based model that focuses on the complex water-sediment-antibiotic interactions. We applied the model to a typical agricultural catchment and selected tetracyclines (TCs) as the target antibiotics. Parameter sensitivity analysis demonstrated that source distribution, groundwater discharge, and water-soil/sediment partitioning were crucial processes. The multi-site performance evaluation generally proved the model's validity, though some overestimation of riverine concentration dynamics was observed. The grid-based distribution of the annual source inputs of the summation of the four TCs (∑4TCs) highly varied in space (μ = 3494.92 mg·ha-1·yr-1, σ = 4761.20 mg·ha-1·yr-1). About 99 % of the source inputs were retained in soil, with mixing layer as the largest reservoir and degradation as the primary loss pathway. Daily terrestrial discharged loading of ∑4TCs peaked with rainfall events. Surface runoff contributed more than 50 % of the terrestrial load of ∑4TCs in summer, while groundwater discharge dominated in other seasons. These results imply that the catchment-scale TCs dynamics are transport-limited rather than source-limited. Our model offers new insights into the high-resolution sources-transport-fate of antibiotics, aiding in developing strategies to mitigate antibiotic contamination.

Combining chemical analysis and toxicological methods to access the ecological risk of complex contamination in Daye Lake

As one of the nine primary non-ferrous metal smelting bases in China, Daye Lake basin was polluted due to diverse human activities. But so far the pollution status and related ecological risks of this region have not been detailly investigated. In current study, pollutants including heavy metals, polycyclic aromatic hydrocarbons (PAHs) and organochlorine pesticides (OCPs) in eight sediment samples from Daye Lake were quantified. 18S rRNA gene sequencing was employed to profile the nematode community structure within these sediments. Model organism Caenorhabditis elegans (C. elegans) were further applied for a comprehensive ecological risk assessment of Daye Lake. Notably, Cadmium (Cd) was identified as a key driver of ecological risk, reaching an index of 1287.35. At sample point S4, OCPs particularly p,p'-DDT, displayed an extreme ecological risk with a value of 23.19. Cephalobidae and Mononchida showed strong sensitivity to pollutant levels, reinforcing their suitability as robust bioindicators. The composite pollutants in sampled sediments caused oxidative stress in C. elegans, with gene Vit-2 and Mtl-1 as sensitive biomarkers. By employing the multiple analysis methods, our data can offer valuable contributions to environmental monitoring and health risk assessment for composite polluted areas.

Distribution and ecological risk assessment of antibiotics in different freshwater aquaculture ponds in a typical agricultural plain, China

Freshwater aquaculture serves as a significant focal point for antibiotic contamination, yet understanding antibiotic distribution across different aquaculture models and stages remains limited. This study evaluated antibiotic pollution in three distinct freshwater aquaculture models: rice-crayfish coculture, fish aquaculture, and crab-crayfish aquaculture, during various aquaculture stages. Of the 33 target antibiotics, 16 antibiotics were detected, with the total concentrations ranging from 111.81 ng/L to 15,949.05 ng/L in water and 10.11 ng/g to 8986.30 ng/g in sediment. Among these antibiotics, erythromycin and lomefloxacin are prohibited for use in Chinese aquaculture. Dominant antibiotics in water included lincomycin, enrofloxacin, and enoxacin, whereas in sediment, oxytetracycline and erythromycin were predominant. Notably, lincomycin emerged as a dominant antibiotic in aquaculture for the first time. The concentrations of these dominant antibiotics were high compared to other aquaculture settings and exhibited elevated ecological risk. Critical periods for antibiotic contamination in water and sediment were found to be incongruent, occurring during the rainy season in July for water and the dry season in October for sediment. Notably, the rice-crayfish coculture model exerts a good effect in reducing antibiotic pollution. Overall, these findings offer valuable evidence for the healthful and sustainable advancement of aquaculture.

Land use and spatial contiguity are key driven factors of antibiotic multimedia patterns in the megacity river network

The widespread spread of antibiotics in the environment poses a growing threat to human health. This study investigated the distribution and fate of antibiotics concerning land use characteristics, hydrological conditions, and spatial contiguity within a megacity river network. Temporally, the average concentrations of twenty antibiotics in water (354 ng/L), suspended particulate matter (SPM) (46 ng/L), and sediment (151 ng/g) during dry season were notably higher than that in the corresponding environment media (water: 127 ng/L, SPM: 2 ng/L, and sediment: 49 ng/g) during the wet season. Moreover, the inter-annual variation of antibiotics in water showed a decreasing trend. Spatially, substantial antibiotic contamination was observed in a human-intensive watershed, particularly in the upstream and central city sections. The macrolides in water were most affected by land use types and hydrological processes. Antibiotic contamination in water exhibited a stronger spatial autocorrelation compared to other media. Nevertheless, the interconnectedness of antibiotic contamination in sediments during the wet season warrants attention, and relevant authorities should enhance environmental monitoring in watersheds with pollution hotspots. Certain antibiotics, such as sulfamethoxazole, enrofloxacin, and florfenicol, were transported via urban rivers to the ocean, potentially posing environmental risks to coastal water quality. Local sources accounted for the predominant portion (>50 %) of most antibiotics in various media. The correlation distances of antibiotics in waters during the wet season could screen ecological risk prioritization in aquatic environments.

Insight into environmental adaptability of antibiotic resistome from surface water to deep sediments in anthropogenic lakes by metagenomics

The escalating antibiotic resistance threatens the long-term global health. Lake sediment is a vital hotpot in transmitting antibiotic resistance genes (ARGs); however, their vertical distribution pattern and driving mechanisms in sediment cores remain unclear. This study first utilized metagenomics to reveal how resistome is distributed from surface water to 45 cm sediments in four representative lakes, central China. Significant vertical variations in ARG profiles were observed (R2 = 0.421, p < 0.001), with significant reductions in numbers, abundance, and Shannon index from the surface water to deep sediment (all p-values < 0.05). ARGs also has interconnections within the vertical profile of the lakes: twelve ARGs persistently exist all sites and depths, and shared ARGs (e.g., vanS and mexF) were assembled by diverse hosts at varying depths. The 0-18 cm sediment had the highest mobility and health risk of ARGs, followed by the 18-45 cm sediment and water. The drivers of ARGs transformed along the profile of lakes: microbial communities and mobile genetic elements (MGEs) dominated in water, whereas environmental variables gradually become the primary through regulating microbial communities and MGEs with increasing sediment depth. Interestingly, the stochastic process governed ARG assembly, while the stochasticity diminished under the mediation of Chloroflexi, Candidatus Bathyarcaeota and oxidation-reduction potential with increasing depth. Overall, we formulated a conceptual framework to elucidate the vertical environmental adaptability of resistome in anthropogenic lakes. This study shed on the resistance risks and their environmental adaptability from sediment cores, which could reinforce the governance of public health issues.

Algae-Mediated Removal of Prevalent Genotoxic Antibiotics: Molecular Perspective on Algae-Bacteria Consortia and Bioreactor-Based Strategies

Antibiotic pollution poses a potential risk of genotoxicity, as antibiotics released into the environment can induce DNA damage and mutagenesis in various organisms. This pollution, stemming from pharmaceutical manufacturing, agriculture, and improper disposal, can disrupt aquatic ecosystems and potentially impact human health through the consumption of contaminated water and food. The removal of genotoxic antibiotics using algae-mediated approaches has gained considerable attention due to its potential for mitigating the environmental and health risks associated with these compounds. The paper provides an in-depth examination of the molecular aspects concerning algae and bioreactor-driven methodologies utilized for the elimination of deleterious antibiotics. The molecular analysis encompasses diverse facets, encompassing the discernment and profiling of algae species proficient in antibiotic degradation, the explication of enzymatic degradation pathways, and the refinement of bioreactor configurations to augment removal efficacy. Emphasizing the significance of investigating algal approaches for mitigating antibiotic pollution, this paper underscores their potential as a sustainable solution, safeguarding both the environment and human health.

Urban agglomerations as an environmental dimension of antibiotics transmission through the "One Health" lens

The spatiotemporal distributions of antibiotics in different media have been widely reported; however, their occurrence in the environmental dimension of the Chinese urban agglomerations has received less attention, especially in bioaccumulation and health risks of antibiotics through the "One Health" lens. The review presents the current knowledge on the environmental occurrence, bioaccumulation, as well as health exposure risks in urban agglomerations through the "One Health" lens, and identifies current information gaps. The reviewed studies suggested antibiotic concentrations in water and soil were more sensitive to social indicators of urban agglomerations than those in sediment. The ecological risk and resistance risk of antibiotics in water were much higher than those of sediments, and the high-risk phenomenon occurred at a higher frequency in urban agglomerations. Erythromycin-H2O (ETM-H2O), amoxicillin (AMOX) and norfloxacin (NFC) were priority-controlled antibiotics in urban waters. Tetracyclines (TCs) posed medium to high risks to soil organisms in the soil of urban agglomerations. Health risk evaluation based on dietary intake showed that children had the highest dietary intake of antibiotics in urban agglomerations. The health risk of antibiotics was higher in children than in other age groups. Our results also demonstrated that dietary structure might impact health risks associated with target antibiotics in urban agglomerations to some extent.

NCRD: A non-redundant comprehensive database for detecting antibiotic resistance genes

Antibiotic resistance genes (ARGs) are emerging pollutants present in various environments. Identifying ARGs has become a growing concern in recent years. Several databases, including the Antibiotic Resistance Genes Database (ARDB), Comprehensive Antibiotic Resistance Database (CARD), and Structured Antibiotic Resistance Genes (SARG), have been applied to detect ARGs. However, these databases have limitations, which hinder the comprehensive profiling of ARGs in environmental samples. To address these issues, we constructed a non-redundant antibiotic resistance genes database (NRD) by consolidating sequences from ARDB, CARD, and SARG. We identified the homologous proteins of NRD from Non-redundant Protein Database (NR) and the Protein DataBank Database (PDB) and clustered them to establish a non-redundant comprehensive antibiotic resistance genes database (NCRD) with similarities of 100% (NCRD100) and 95% (NCRD95). To demonstrate the advantages of NCRD, we compared it with other databases by using metagenome datasets. Results revealed its strong ability in detecting potential ARGs.