Trophic transfer of antibiotics in the benthic-pelagic coupling foodweb in a macrophyte-dominated shallow lake: The importance of pelagic-benthic coupling strength and baseline organism.

In lake ecosystems, pelagic-benthic coupling strength (PBCS) is closely related to foodweb structure and pollutant transport. However, the trophic transfer of antibiotics in a benthic-pelagic coupling foodweb (BPCFW) and the manner in which PBCS influences the trophic magnification factor (TMFs) of antibiotics is still not well understood in the whole lake. Herein, the trophic transfer behavior of 12 quinolone antibiotics (QNs) in the BPCFW of Baiyangdian Lake were studied during the period of 2018-2019. It was revealed that 24 dominant species were contained in the BPCFW, and the trophic level was 0.42-2.94. Seven QNs were detected in organisms, the detection frequencies of ofloxacin (OFL), flumequine (FLU), norfloxacin (NOR), and enrofloxacin (ENR) were higher than other QNs. The ∑QN concentration in all species was 11.3-321 ng/g dw. The TMFs for ENR and NOR were trophic magnification, while for FLU/OFL it was trophic dilution. The PBCS showed spatial-temporal variation, with a range of 0.6977-0.7910. The TMFs of ENR, FLU, and OFL were significantly positively correlated with PBCS. Phytoplankton and macrophyte biomasses showed indirect impact on the TMFs of QNs by directly influencing the PBCS. Therefore, the PBCS was the direct influencing factor for the TMFs of chemicals.

Antibiotic resistance genes and mobile genetic elements in different rivers: The link with antibiotics, microbial communities, and human activities.

Rivers as a critical sink for antibiotic resistance genes (ARGs), and the distribution and spread of ARGs are related to environmental factors, human activities, and biotic factors (e.g. mobile genetic elements (MGEs)). However, the potential link among ARGs, microbial community, and MGEs in rivers under different antibiotic concentration and human activities remains unclear. In this study, 2 urban rivers (URs), 1 rural-urban river (RUR), and 2 rural rivers (RRs) were investigated to identify the spatial-temporal variation and driving force of ARGs. The total concentration of quinolones (QNs) was 160.1-2151 ng·g-1 in URs, 23.34-1188 ng·g-1 in RUR, and 16.39-85.98 ng·g-1 in RRs. Total population (TP), gross domestic production (GDP), sewage, industrial enterprise (IE), and IEGDP appeared significantly spatial difference in URs, RUR, and RRs. In terms of ARGs, 145-161 subtypes were detected in URs, 59-61 subtypes in RURs, and 46-79 subtypes in RRs. For MGEs, 55-60 MGEs subtypes were detected in URs, 29-30 subtypes in RUR, and 29-35 subtypes in RRs. Significantly positive correlation between MGEs and ARGs were found in these rivers. More ARGs subtypes were related to MGEs in URs than those in RUR and RRs. Overall, MGEs and QNs showed significantly direct positive impact on the abundance of ARGs in all rivers, while microbial community was significantly positive impact on the ARGs abundance in URs and RUR. The ARGs abundance in URs/RUR were directly positive influenced by microbial community/MGEs/socioeconomic elements (SEs)/QNs, while those in RRs were directly positive influenced by QNs/MGEs and indirectly positive impacted by SEs. Most QNs resistance risk showed significantly positive correlation with the abundance of ARGs types. Therefore, not only need to consider the concentration of antibiotics, but also should pay more attention to SEs and MGEs in antibiotics risk management and control.

[Source Analysis and Health Risk Assessment of Heavy Metals in the Groundwater of Shijiazhuang, a Typical City in North China Plain].

Increasing attention has been paid to the heavy metal pollution in groundwater. The source analysis and risk assessment of heavy metals will provide data and method support for the targeted control of heavy metal pollution in groundwater. In this study, 20 sampling sites were selected in Shijiazhuang City. The APCS-MLR model and health risk model were applied to analyze and evaluate the pollution sources and health risks of 10 types of heavy metals in the groundwater of Shijiazhuang. The results showed that ① the mean concentration of heavy metals in groundwater followed the order of Fe>Zn>Mn>Cu>Al>Pb>Cr>As>Cd>Hg, and the mean ρ(Fe) and ρ(Pb) were 260.3 µg·L-1 and 10.01 µg·L-1, respectively. According to the results of the single factor and Nemerow index, Pb, Fe, and Cd primarily contributed to the heavy metal pollution in the groundwater. ② The concentration of heavy metals ranged from 47.30 to 2560 µg·L-1. In terms of spatial distribution, the highest concentration appeared at S3 (2560 µg·L-1), whereas the lowest concentration was at S9 (47.30 µg·L-1). ③ Source analysis results showed that industrial and agricultural activities, transportation emission, and geological background were the major heavy metal sources, among which the contribution of industrial and agricultural activities was the highest (47.83%). ④ The industrial-agricultural activities posed a potential threat to adults (HI>1); however, the non-cancer and the cancer risks of other sources for both adults and children were at an acceptable level (HI<1) and potential threat level, respectively; industrial-agricultural activities were the major source of non-cancer (adults:52.46%, children:52.45%) and cancer risks (adults:65.22%, children:65.69%), among which Cd and As showed high cancer risk. Therefore, to ensure the safety of the groundwater environment, strictly controlling the pollution sources and further strengthening the risk control of heavy metal pollution in groundwater are necessary.

[Source Apportionment and Source-specific Risk of Typical Antibiotics in Baiyangdian Lake].

The current situation of antibiotic pollution in lakes is critical. At present, most of the previous studies on antibiotics in lakes have focused on the spatiotemporal distribution and risk assessment, while less attention has been paid to the source apportionment. Ultra-high performance liquid chromatography-mass spectrometry was used to determine the concentration of tetracyclines (TCs), sulfonamides (SAs), and quinolones (QNs) in the samples. The source apportionment and source-specific risk of typical antibiotics in the study area were analyzed using the combination of a PMF model and risk quotients (RQ). The results showed that ① the total concentrations of target antibiotics (Σ antibiotics) ranged from ND to 2635 ng·L-1 for surface water and from ND to 259.8 ng·g-1 for sediments. ② The spatial distribution of QNs in surface water decreased from west to east, SAs decreased from middle to north and south, and TCs increased from middle to north and south. In the sediment, QNs decreased from middle to east and west, whereas SAs and TCs increased from east to west. ③ Aquaculture was the major antibiotic source, accounting for the highest proportion (33.2%), followed by sewage treatment plants (29.2%), livestock activities (18.9%), and domestic sewage (18.7%). ④ The ecological risk assessment results showed that enrofloxacin and flumequine were at a medium-high risk level. ⑤ For the spatial distribution of source-specific risk, the results showed that the aquaculture at S1 was at a high risk level, whereas the source-specific risks for other sites were at a medium-low risk level. In terms of source types, aquaculture was at a medium-high risk level, whereas the other sources were at a medium-low risk level. Therefore, considering the major sources and source-specific risk level of antibiotics, more precise and scientific antibiotic risk control should be adopted in Baiyangdian Lake.

[Spatial-temporal Changes and Driving Factors of Soil Microbial Communities in a Typical City of North China Plain].

The structure and function of microbial communities are affected by several environmental factors. To clarify the spatial-temporal changes and main influencing factors of soil microbial communities in a typical pharmaceutical city, it is urgent to study the spatial-temporal changes in microbial communities in soils for typical cities. Shijiazhuang City was selected as the study area, and 12 sampling sites were selected. The topsoil was collected in June (summer) and September (autumn) of 2021. The 16S rRNA high-throughput sequencing technology was used to study the structure and function of microbial communities in the soil and explore their spatial-temporal changes. Concurrently, Pearson correlation analysis was applied to establish the correlation between the microbial community and environmental factors, and identify the main driving factors of temporal and spatial changes in the microbial community. The results showed that:① Actinobaciota and Proteobateria were the main dominant bacteria in the surface soil of Shijiazhuang City; at the phylum level, the relative abundance of Actinobacteria and Proteobateria decreased from summer to autumn; at the genus level, the dominant genera were Arthrobacter and unknown genera in summer and Arthrobacter and Candidatus_Nitrocosmicus in autumn, which showed significant seasonal differences (P<0.05). ② For seasonal variation, the mean values of the Simpson, Ace, and Chao indices increased, whereas the mean values of OTU decreased; for spatial variation, the Shannon and Simpson indices showed significant spatial difference (P<0.01 and P<0.05). ③ There were no significant spatial-temporal differences in various functional genes; thereinto, the relative abundances of energy production and transformation functional genes were the highest (24.06%-24.84% in summer and 24.63%-25.98% in autumn, respectively). ④ The compositions of microbial community, diversity index, and functional genes were significantly correlated with quinolone antibiotics (QNs), total phosphorus (TP), and nitrate nitrogen (NO3--N), most significantly correlated with QNs (|r|>0.900), which indicated that antibiotics were the main driving factor of soil microbial communities. Therefore, to ensure the stability of microbial community structure and function in urban soil, the comprehensive management and control of antibiotic pollution in soil should be further strengthened.

[Spatial-temporal Distribution and Risk Assessment of Quinolones Antibiotics in Soil of Shijiazhuang City].

Due to their importance in human medicine, quinolones (QNs), as a typical class of antibiotics, are considered to be the "highest priority critically important antimicrobials" by the World Health Organization (WHO). In order to clarify the spatial-temporal variation and risk of QNs in soil, 18 representative topsoil samples were respectively collected in September 2020 (autumn) and June 2021 (summer). The contents of QNs antibiotics in soil samples were determined using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS), and the ecological risk and resistance risk were calculated using the risk quotient method (RQ). The results showed that:① the average content of QNs decreased from autumn to summer (the average contents of QNs were 94.88 µg·kg-1in autumn and 44.46 µg·kg-1 in summer); the highest values appeared in the middle area. ② The average proportion of silt was without change, whereas the average proportion of clay and sand was increased and decreased, respectively; the average contents of total phosphorus (TP), ammonia nitrogen (NH4+-N), and nitrate nitrogen (NO3--N) also decreased. ③ The content of QNs was significantly correlated with soil particle size, nitrite nitrogen (NO2--N), and nitrate nitrogen (NO3--N) (P<0.05). ④ The combined ecological risk of QNs showed high risk level (RQsum>1), whereas the combined resistance risk of QNs showed medium risk level (0.1

The antibiotic resistance and risk heterogeneity between urban and rural rivers in a pharmaceutical industry dominated city in China: The importance of social-economic factors.

Rivers are important environmental sources of human exposure to antibiotic resistance. Many factors can change antibiotic resistance in rivers, including bacterial communities, human activities, and environmental factors. However, the systematic comparison of the differences in antibiotics resistance and risks between urban rivers (URs) and rural rivers (RRs) in a pharmaceutical industry dominated city is still rare. In this study, Shijiazhuang City (China) was selected as an example to compare the differences in antibiotics resistance and risks between URs and RRs. The results showed higher concentrations of total quinolones (QNs) antibiotics in both water and sediment samples collected from URs than those from RRs. The subtypes and abundances of antibiotic resistance genes (ARGs) in URs were significantly higher than those in RRs, and most emerging ARGs (including OXA-type, GES-type, MCR-type, and tet(X)) were only detected in URs. The ARGs were mainly influenced by QNs in URs and social-economic factors (SEs) in RRs. The composition of the bacterial community was significantly different between URs and RRs. The abundance of antibiotic-resistant pathogenic bacteria (ARPBs) and virulence factors (VFs) were higher in URs than those in RRs. Therein, 371 and 326 pathogen types were detected in URs and RRs, respectively. Most emerging ARGs showed a significantly positive correlation with priority ARPBs. Variance partitioning analysis revealed that SEs were the main driving factors of ARGs (80 %) and microbial communities (92 %) both in URs and RRs. Structural equation models indicated that antibiotics (QNs) and microbial communities were the most direct influence of ARGs in URs and RRs, respectively. The cumulative resistance risk of QNs was high in URs, but relatively low in RRs. Enrofloxacin and flumequine posed the highest risk in water and sediment, respectively. This study could help us to better manage and control the risk of antibiotic resistance in different rivers.

[Distribution Coefficient of QNs in Urban Typical Water and Its Main Environmental Influencing Factors].

Sediment is the main storage medium of antibiotics in a water environment, and a growing body of research has focused on the distribution behavior of antibiotics in water-sediment. However, most of the previous studies were based on laboratory simulation, and less attention was paid to the distribution behavior of antibiotics in a natural water environment and its correlation with environmental factors. Thus, the surface water and sediment in Shijiazhuang were taken as the research object for this study. The temporal and spatial distribution characteristics of quinolone (QNs) antibiotics in Shijiazhuang water were analyzed by using high performance liquid chromatography-tandem mass spectrometry (HPLC-MS), calculating the distribution coefficients of quinolone (QNs) antibiotics in water and sediment, and confirming the main environmental factors influencing the distribution coefficient in natural water. The results showed that:① the content of ΣQNs in water and sediment ranged from 8.0 to 4.4×103 ng·L-1 and 16 to 2.2×103 ng·g-1 in Shijiazhuang water, whereas the primary QNs in water and sediment were enrofloxacin (ENR) and ofloxacin (OFL), respectively. ② The total concentrations of ΣQNs in Shijiazhuang water showed a tendency of being higher in December (1.0×104 ng·L-1) than in April (5.5×103 ng·L-1), and QNs in sediment were also higher in December (7.8×103 ng·g-1) than in April (6.2×103 ng·g-1). ③ The distribution coefficient of QNs in Shijiazhuang water varied from 34 to 2.9×105 L·kg-1 and showed a trend of being greater in December than in April. ④ The results of correlation analysis showed that total nitrogen (TN), nitrate nitrogen (NO3--N), nitrite nitrogen (NO2--N), and ammonia nitrogen (NH4+-N) were significantly correlated with most distribution coefficients of QNs[OFL, norfloxacin (NOR), ENR, difloxacin (DIF), and oxolinic acid (OXO)], whereas temperature (T), total organic carbon (TOC), and total dissolved solids (TDS) were significantly correlated with individual distribution coefficients of QNs[marbofloxacin (MAR) and DIF]. Therefore, the eutrophication level of water affects the distribution behavior of antibiotics in water-sediment.

[Spatial Distribution of Quinolone Antibiotics and Its Correlation Relationship with Microbial Community in Soil of Shijiazhuang City].

Microbial communities are an important component of soil ecosystems. Long-term low content antibiotic pollution will affect the structure and function of microbial communities in soil. Therefore, Shijiazhuang City was selected as the study area, in which twelve sample points were set up in September 2020. These sample sites were divided into four areas (S1, S2, S3, and S4) according to spatial orientation. Ultra-high performance liquid chromatography-mass spectrometry (HPLC-MS/MS) was applied to determine the content of typical antibiotic-quinolones (QNs) in the soil. 16S rRNA high-throughput sequencing technology was used to study the microbial community structure and diversity in the soil. The results showed that:① the total detected contents of QNs in the four areas were S3 (313.5 µg·kg-1)>S4 (65.54 µg·kg-1)>S1 (46.19 µg·kg-1)>S2 (12.63 µg·kg-1). The content of norfloxacin (NOR) was the highest (91.99 µg·kg-1), whereas the content of oxolinic acid (OXO) was the lowest (0.4486 µg·kg-1). ② For grain size, the proportion of powder (2-50 µm) was the highest (66.7%-93.2%), whereas the proportion of clay (less than 2 µm) was the lowest (2.50%-9.10%). For physical and chemical parameters, total phosphorus (TP) and ammonia nitrogen (NH4+-N) showed non-significant spatial differences, whereas nitrate nitrogen (NO3--N), nitrite nitrogen (NO2--N), and grain size showed significant spatial differences. ③ For microbial community composition, there were six dominant bacteria phyla and five dominant bacteria genera, among which Actinobacteriota (18.3%-34.6%) and Proteobacteria (13.6%-34.1%) were the dominant bacteria phyla, and Arthrobacter (3.24%-8.61%) and Nitrososphaeraceae (2.93%-9.46%) were the dominant bacteria genera. The diversity results showed the highest value in the S2 area (6.48) and the lowest value in the S3 area (5.89). ④ QNs and soil physical and chemical parameters significantly changed the structural composition of microbial communities, and OXO, NO3--N, and soil particle size affected the diversity of microbial communities. FLU, NH4+-N, NO2--N, and soil particle size affected the function of the microbial community. Therefore, it is necessary to further strengthen the risk control of antibiotics in the soil of Shijiazhuang City.