Non-point source fecal contamination from aging wastewater infrastructure is a primary driver of antibiotic resistance in surface waters

Microbiome and antibiotic resistome in bioelectrochemical toilets for onsite treatment of fecal sludge

Effective management of fecal sludge (FS) is essential for preventing environmental and public health risks. Developing safe and efficient FS treatment technology is crucial for reducing the health risks of onsite sanitation systems. In this study, bioelectrochemical toilets (BETs) were developed to treat FS onsite. Compared with the open-circuit BETs (OC-BETs), BETs exhibited higher removal efficiencies for total organic carbon, total nitrogen, and total phosphorus. Specifically, the enhancements in removal efficiencies were 18.82 ± 1.73 %, 7.28 ± 0.32 %, and 11.41 ± 0.05 % for urine, and 19.28 ± 4.08 %, 21.65 ± 1.23 %, and 24.68 ± 0.95 % for feces, respectively. Microbiome analysis indicated that the dominant populations were affiliated with electroactive bacteria (Desulfuromonas and Pseudomonas) in the electrode biofilm of BETs. The species co-occurrence network showed that the electrode biofilm microbiome in BETs had more complex correlations than that in OC-BETs, suggesting that a weak electrical current enhanced the microbiome stability. The relative abundance of antibiotic resistance genes in BETs and OC-BETs reduced by 59.85 ± 1.32 % and 53.01 ± 2.81 % compared with the initial FS, respectively. These findings indicate that BETs are an alternative system for enhancing onsite treatment of fecal sludge and provide a theoretical foundation for the implementation of BETs.

Do storm overflows influence AMR in the environment and is this relevant to human health? A UK perspective on a global issue

Antimicrobial resistance (AMR) is a global public health threat, and the environment has been identified as an important reservoir for resistant microorganisms and genes. Storm overflows (SOs) discharge wastewater and stormwater, and are found throughout many wastewater networks. While there are no data currently showing the impact of SOs on the environment with respect to AMR in the UK, there is a small but growing body of evidence globally highlighting the potential role of SOs on environmental AMR. This review aims to provide an overview of the current state of SOs, describe global data investigating the impact of SOs on environmental AMR, and discuss the implications of SOs regarding AMR and human health. In addition, the complexities of studying the effects of SOs are discussed and a set of priority research questions and policy interventions to tackle a potentially emerging threat to public health are presented.

Variations in antibiotic resistomes associated with archaeal, bacterial, and viral communities affected by integrated rice-fish farming in the paddy field ecosystem

Antibiotic resistance genes (ARGs) serving as a newly recognized pollutant that poses potential risks to global human health, which in the paddy soil can be potentially altered by different agricultural production patterns. To elucidate the impacts and mechanisms of the widely used and sustainable agricultural production pattern, namely integrated rice-fish farming, on the antibiotic resistomes, we applied metagenomic sequencing to assess ARGs, mobile genetic elements (MGEs), bacteria, archaea, and viruses in paddy soil. There were 20 types and 359 subtypes of ARGs identified in paddy soil. The integrated rice-fish farming reduced the ARG and MGE diversities and the abundances of dominant ARGs and MGEs. Significantly decreased ARGs were mainly antibiotic deactivation and regulator types and primarily ranked level IV based on their potential threat to human health. The integrated rice-fish farming decreased the alpha diversities and altered microbial community compositions. MGEs, bacteria, archaea, and virus exhibited significant correlations with ARGs, while integrated rice-fish farming effectively changed their interrelationships. Viruses, bacteria, and MGEs played crucial roles in affecting the ARGs by the integrated rice-fish farming. The most crucial pathway by which integrated rice-fish farming affected ARGs was through the modulation of viral communities, thereby directly or indirectly influencing ARG abundance. Our research contributed to the control and restoration of ARGs pollution from a new perspective and providing theoretical support for the development of clean and sustainable agricultural production.

Using a combination of quantitative culture, molecular, and infrastructure data to rank potential sources of fecal contamination in Town Creek Estuary, North Carolina

Estuarine water quality is declining worldwide due to increased tourism, coastal development, and a changing climate. Although well-established methods are in place to monitor water quality, municipalities struggle to use the data to prioritize infrastructure for monitoring and repair and to determine sources of contamination when they occur. The objective of this study was to assess water quality and prioritize sources of contamination within Town Creek Estuary (TCE), Beaufort, North Carolina, by combining culture, molecular, and geographic information systems (GIS) data into a novel contamination source ranking system. Water samples were collected from TCE at ten locations on eight sampling dates in Fall 2021 (n = 80). Microbiological water quality was assessed using US Environmental Protection Agency (U.S. EPA) approved culture-based methods for fecal indicator bacteria (FIB), including analysis of total coliforms (TC), Escherichia coli (EC), and Enterococcus spp. (ENT). The quantitative microbial source tracking (qMST) human-associated fecal marker, HF183, was quantified using droplet digital PCR (ddPCR). This information was combined with environmental data and GIS information detailing proximal sewer, septic, and stormwater infrastructure to determine potential sources of fecal contamination in the estuary. Results indicated FIB concentrations were significantly and positively correlated with precipitation and increased throughout the estuary following rainfall events (p < 0.01). Sampling sites with FIB concentrations above the U.S. EPA threshold also had the highest percentages of aged, less durable piping materials. Using a novel ranking system combining concentrations of FIB, HF183, and sewer infrastructure data at each site, we found that the two sites nearest the most aged sewage infrastructure and stormwater outflows were found to have the highest levels of measurable fecal contamination. This case study supports the inclusion of both traditional water quality measurements and local infrastructure data to support the current need for municipalities to identify, prioritize, and remediate failing infrastructure.

Systematic tracking of nitrogen sources in complex river catchments: Machine learning approach based on microbial metagenomics

Tracking nitrogen pollution sources is crucial for the effective management of water quality; however, it is a challenging task due to the complex contaminative scenarios in the freshwater systems. The contaminative pattern variations can induce quick responses of aquatic microorganisms, making them sensitive indicators of pollution origins. In this study, the soil and water assessment tool, accompanied by a detailed pollution source database, was used to detect the main nitrogen pollution sources in each sub-basin of the Liuyang River watershed. Thus, each sub-basin was assigned to a known class according to SWAT outputs, including point source pollution-dominated area, crop cultivation pollution-dominated area, and the septic tank pollution-dominated area. Based on these outputs, the random forest (RF) model was developed to predict the main pollution sources from different river ecosystems using a series of input variable groups (e.g., natural macroscopic characteristics, river physicochemical properties, 16S rRNA microbial taxonomic composition, microbial metagenomic data containing taxonomic and functional information, and their combination). The accuracy and the Kappa coefficient were used as the performance metrics for the RF model. Compared with the prediction performance among all the input variable groups, the prediction performance of the RF model was significantly improved using metagenomic indices as inputs. Among the metagenomic data-based models, the combination of the taxonomic information with functional information of all the species achieved the highest accuracy (0.84) and increased median Kappa coefficient (0.70). Feature importance analysis was used to identify key features that could serve as indicators for sudden pollution accidents and contribute to the overall function of the river system. The bacteria Rhabdochromatium marinum, Frankia, Actinomycetia, and Competibacteraceae were the most important species, whose mean decrease Gini indices were 0.0023, 0.0021, 0.0019, and 0.0018, respectively, although their relative abundances ranged only from 0.0004 to 0.1 %. Among the top 30 important variables, functional variables constituted more than half, demonstrating the remarkable variation in the microbial functions among sites with distinct pollution sources and the key role of functionality in predicting pollution sources. Many functional indicators related to the metabolism of Mycobacterium tuberculosis, such as K24693, K25621, K16048, and K14952, emerged as significant important factors in distinguishing nitrogen pollution origins. With the shortage of pollution source data in developing regions, this suggested approach offers an economical, quick, and accurate solution to locate the origins of water nitrogen pollution using the metagenomic data of microbial communities.

Occurrence, transmission and risks assessment of pathogens in aquatic environments accessible to humans

Pathogens are ubiquitously detected in various natural and engineered water systems, posing potential threats to public health. However, it remains unclear which human-accessible waters are hotspots for pathogens, how pathogens transmit to these waters, and what level of health risk associated with pathogens in these environments. This review collaboratively focuses and summarizes the contamination levels of pathogens on the 5 water systems accessible to humans (natural water, drinking water, recreational water, wastewater, and reclaimed water). Then, we showcase the pathways, influencing factors and simulation models of pathogens transmission and survival. Further, we compare the health risk levels of various pathogens through Quantitative Microbial Risk Assessment (QMRA), and assess the limitations of water-associated QMRA application. Pathogen levels in wastewater are consistently higher than in other water systems, with no significant variation for Cryptosporidium spp. among five water systems. Hydraulic conditions primarily govern the transmission of pathogens into human-accessible waters, while environmental factors such as temperature impact pathogens survival. The median and mean values of computed public health risk levels posed by pathogens consistently surpass safety thresholds, particularly in the context of recreational waters. Despite the highest pathogens levels found in wastewater, the calculated health risk is significantly lower than in other water systems. Except pathogens concentration, variables like the exposure mode, extent, and frequency are also crucial factors influencing the public health risk in water systems. This review shares valuable insights to the more accurate assessment and comprehensive management of public health risk in human-accessible water environments.

Microbial source tracking of fecal pollution to coral reef lagoons of Norfolk Island, Australia

Fecal pollution contributes to global degradation of water quality and requires identification of the source(s) for predicting human health risk, tracking disease, and developing management strategies. While fecal indicator bacteria are commonly used to detect fecal pollution, they cannot identify sources. Novel approaches, such as microbial source tracking (MST), can be applied to evaluate the origin of fecal pollution. This study examined fecal pollution in the coral reef lagoons of Norfolk Island, Australia where reef health decline has been related to nutrient input. The primary objective of this study was to evaluate the host sensitivity and specificity of two human wastewater-associated marker genes (Bacteroides HF183 (HF183) and cross-assembly phage (crAssphage)) and four animal feces associated marker genes targeting avian, ruminant, dog, and pig (Helicobacter-associated GFD (GFD), Bacteroides BacR (BacR), Bacteroides DogBact (DogBact), and Bacteroides Pig-2-Bac (Pig-2-Bac)) in wastewater and animal fecal samples collected from Norfolk Island. The prevalence and concentrations of these marker genes along with enterococci genetic marker (ENT 23S rRNA) of general fecal pollution and human adenovirus (HAdV), which is considered predominantly a pathogen but also a human-wastewater associated marker gene, were determined in surface, ground, and marine water resources. A secondary objective of this study was to assess the sources and pathways of fecal pollution to a sensitive marine environment under rainfall events. HF183, crAssphage, HAdV, and BacR demonstrated absolute host sensitivity values of 1.00, while GFD and Pig-2-Bac had host sensitivity values of 0.60, and 0.20, respectively. Host specificity values were > 0.94 for all marker genes. Human and animal (avian, ruminant, dog) fecal sources were present in the coral reef lagoons and surface water whereas groundwater was polluted by human wastewater markers. This study provides understanding of fecal pollution in water resources on Norfolk Island, Australia after precipitation events. The results may aid in effective water quality management, mitigating potential adverse effects on both human and environmental health.

Integrating fecal pollution markers and fluorescence analysis for water quality assessment of urban river

Human fecal contamination in urban rivers poses significant health risks, but their potential connections with other substances like dissolved organic matter (DOM) remain underexplored. In this study, five fecal pollution markers related to fecal Bacteroides or human fecal contamination (AllBac, HF183, BacH, Hum2, and Hum163) and DOM along an urban river were analyzed using quantitative polymerase chain reaction (qPCR) and three-dimensional excitation-emission (3D EEM) fluorescence spectrometry. All five markers were detected with average absolute abundance ranging from 2.51 to 6.28 lg gene copies/100 mL, showing a progressive increase along the river (R2 = 0.29-0.92, p < 0.05). Parallel factor analysis identified three dominant DOM components (humic acid-like, fulvic acid-like, and protein-like), with strong positive correlations between protein-like components and all fecal markers (R2 = 0.59-0.66, p < 0.001). Both fecal and DOM distributions consistently showed significant differences between upstream and downstream areas (p < 0.001), suggesting their complementary assessment. While DOM was more sensitive to environmental variables such as rainfall, rubber dam, and tidal dynamic, the combination of fecal pollution markers and 3D EEM analysis allowed a more comprehensive assessment of contamination levels, mitigating potential biases caused by the influence of multiple factors on a single method. Furthermore, due to the strong correlation between protein-like and fecal markers in the DOM, 3D EEM can be used as a pre-detection means for qPCR detection, reducing testing time and costs.

Unraveling the antibiotic resistome in backwater zones of large cascade reservoirs: Co-occurrence patterns, horizontal transfer directions and health risks

The widespread occurrence of antibiotic resistant genes (ARGs) throughout aquatic environments has raised global concerns for public health. However, the profiles and patterns of antibiotic resistome in backwater zone of cascade reservoirs, where water flow is slowed down, are still poorly understood. Here, we proposed a metagenomic analysis framework to comprehensively reveal the diversity, abundance, co-occurrence patterns and transfer direction of ARGs in cascade reservoirs system and evaluated their health risks through a procedure based on contigs. A total of 364 ARGs subtypes conferring resistance to different antibiotics classes were detected in our water samples, and the dominant ARGs (macB, bacA, vanRA, bcrA) were similar in different reservoirs. Meanwhile, the distribution of ARGs was influenced by the presence of biotic factors such as metal resistant genes (MRGs) and mobile genetic elements (MGEs), as well as abiotic factors such as dissolved oxygen (DO) and pH. Remarkably, ARGs (vanR, rosB, MexT) co-occurred with plasmids and virulence factor genes (VFGs), which can lead to the emergence and spread of highly virulent and antibiotic resistant bacteria in microbial communities. Overall, this study helps administrators to better understand the complex patterns of ARGs in backwater zones of large cascade reservoirs and provides a proper procedure for detecting the presence of high-risk of ARGs.

Distribution of Antibiotic Resistance in a Mixed-Use Watershed and the Impact of Wastewater Treatment Plants on Antibiotic Resistance in Surface Water

The aquatic environment has been recognized as a source of antibiotic resistance (AR) that factors into the One Health approach to combat AR. To provide much needed data on AR in the environment, a comprehensive survey of antibiotic-resistant bacteria (ARB), antibiotic resistance genes (ARGs), and antibiotic residues was conducted in a mixed-use watershed and wastewater treatment plants (WWTPs) within the watershed to evaluate these contaminants in surface water. A culture-based approach was used to determine prevalence and diversity of ARB in surface water. Low levels of AR Salmonella (9.6%) and Escherichia coli (6.5%) were detected, while all Enterococcus were resistant to at least one tested antibiotic. Fewer than 20% of extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae (17.3%) and carbapenem-resistant Enterobacteriaceae (CRE) (7.7%) were recovered. Six ARGs were detected using qPCR, primarily the erythromycin-resistance gene, ermB. Of the 26 antibiotics measured, almost all water samples (98.7%) had detectable levels of antibiotics. Analysis of wastewater samples from three WWTPs showed that WWTPs did not completely remove AR contaminants. ARGs and antibiotics were detected in all the WWTP effluent discharges, indicating that WWTPs are the source of AR contaminants in receiving water. However, no significant difference in ARGs and antibiotics between the upstream and downstream water suggests that there are other sources of AR contamination. The widespread occurrence and abundance of medically important antibiotics, bacteria resistant to antibiotics used for human and veterinary purposes, and the genes associated with resistance to these antibiotics, may potentially pose risks to the local populations exposed to these water sources.

Enhancement of the Peroxidase Activity of g-C3N4 with Different Morphologies for Simultaneous Detection of Multiple Antibiotics

The classes and forms of antibiotics directly determine their ecotoxicity and environmental chemical behavior, and developing a sensor array for simultaneous and in situ detection of antibiotics is highly anticipated. In this study, different morphologies of g-C3N4 with different fluorescence properties and peroxidase activity were prepared by regulating the degree of interlayer stacking and planar connectivity. Subsequently, in order to enhance its enzyme activity and amplify the differences in response signals to different antibiotics, three morphologies of g-C3N4/MIL-101(Fe) were prepared by in situ growth of equivalent amounts of MIL-101(Fe) on g-C3N4, respectively. The sensor array constructed based on the cross-response signals between g-C3N4/MIL-101(Fe) and antibiotics not only realized the simultaneous detection of quinolones, furans, tetracyclines, and lincomamides but also could efficiently identify their seven different forms. In the range of 0.2-0.8 ppm, the minimum detection limit for antibiotics was 12 ppb. In addition, the recovery experiments of multicomponent-mixed antibiotics in environmental samples show that the recovery rate remained at 91.42-107.59%, confirming the reliability and practicality of the sensor array. This study not only revealed the influence of crystal morphology regulation on the optical properties and enzyme activities of nanozymes, but also provided support for tracing, ecological remediation, and in situ environmental chemical behavior research of antibiotics.

Drivers of Antibiotic Resistance Gene Abundance in an Urban River

In this study, we sought to profile the abundances and drivers of antibiotic resistance genes in an urban river impacted by combined sewage overflow (CSO) events. Water samples were collected weekly during the summer for two years; then, quantitative PCR was applied to determine the abundance of resistance genes associated with tetracycline, quinolones, and β-lactam antibiotics. In addition to sampling a CSO-impacted site near the city center, we also sampled a less urban site ~12 km upstream with no proximal sewage inputs. The tetracycline genes tetO and tetW were rarely found upstream, but were common at the CSO-impacted site, suggesting that the primary source was untreated sewage. In contrast, ampC was detected in all samples indicating a more consistent and diffuse source. The two other genes, qnrA and blaTEM, were present in only 40-50% of samples and showed more nuanced spatiotemporal patterns consistent with upstream agricultural inputs. The results of this study highlight the complex sources of ARGs in urban riverine ecosystems, and that interdisciplinary collaborations across diverse groups of stakeholders are necessary to combat the emerging threat of antibiotic resistance through anthropogenic pollution.

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

In this study, machine learning models were implemented to predict the classification of coastal waters in the region of Eastern Macedonia and Thrace (EMT) concerning Escherichia coli (E. coli) concentration and weather variables in the framework of the Directive 2006/7/EC. Six sampling stations of EMT, located on beaches of the regional units of Kavala, Xanthi, Rhodopi, Evros, Thasos and Samothraki, were selected. All 1039 samples were collected from May to September within a 14-year follow-up period (2009-2021). The weather parameters were acquired from nearby meteorological stations. The samples were analysed according to the ISO 9308-1 for the detection and the enumeration of E. coli. The vast majority of the samples fall into category 1 (Excellent), which is a mark of the high quality of the coastal waters of EMT. The experimental results disclose, additionally, that two-class classifiers, namely Decision Forest, Decision Jungle and Boosted Decision Tree, achieved high Accuracy scores over 99%. In addition, comparing our performance metrics with those of other researchers, diversity is observed in using algorithms for water quality prediction, with algorithms such as Decision Tree, Artificial Neural Networks and Bayesian Belief Networks demonstrating satisfactory results. Machine learning approaches can provide critical information about the dynamic of E. coli contamination and, concurrently, consider the meteorological parameters for coastal waters classification.

Microscopic insights into the variations of antibiotics sorption to clay minerals

Understanding the adsorption behavior of antibiotic molecules on minerals is crucial for determining the environmental fate and transport of antibiotics in soils and waters. However, the microscopic mechanisms that govern the adsorption of common antibiotics, such as the molecular orientation during the adsorption process and the conformation of sorbate species, are not well understood. To address this gap, we conducted a series of molecular dynamics (MD) simulations and thermodynamics analyses to investigate the adsorption of two typical antibiotics, tetracycline (TET) and sulfathiazole (ST), on the surface of montmorillonite. The simulation results indicated that the adsorption free energy ranged from - 23 to - 32 kJ·mol^-1, and - 9 to - 18 kJ·mol^-1 for TET and ST, respectively, which was consistent with the measured difference of sorption coefficient (K_d) for TET-montmorillonite of 11.7 L·g^-1 and ST-montmorillonite of 0.014 L·g^-1. The simulations also found that TET was adsorbed through dimethylamino groups (85% in probability) with a molecular conformation vertical to the montmorillonite's surface, while ST was adsorbed through sulfonyl amide group (95% in probability) with vertical, tilted and parallel conformations on the surface. The results confirmed that molecular spatial orientations could affect the adsorption capacity between antibiotics and minerals. Overall, the microscopic adsorption mechanisms revealed in this study provide critical insights into the complexities of antibiotics adsorption to soil and facilitate the prediction of adsorption capacity of antibiotics on minerals and their environmental transport and fate. This study contributes to our understanding of the environmental impacts of antibiotic usage and highlights the importance of considering molecular-level processes when assessing the fate and transport of antibiotics in the environment.

Antimicrobial Resistance Linked to Septic System Contamination in the Indiana Lake Michigan Watershed

Extended-spectrum β-lactamases confer resistance to a variety of β-lactam antimicrobials, and the genes for these enzymes are often found on plasmids that include additional antimicrobial resistance genes (ARG). We surveyed aquatic environments in the Indiana Lake Michigan watershed in proximity to areas with high densities of residential septic systems to determine if human fecal contamination from septic effluent correlated with the presence of antimicrobial resistance genes and phenotypically resistant bacteria. Of the 269 E. coli isolated from environmental samples and one septic source, 97 isolates were resistant to cefotaxime, a third-generation cephalosporin. A subset of those isolates showed phenotypic resistance to other β-lactams, fluoroquinolones, sulfonamides, and tetracyclines. Quantitative PCR was used to quantify human-associated Bacteroides dorei gene copies (Human Bacteroides) from water samples and to identify the presence of ARG harbored on plasmids from E. coli isolates or in environmental DNA. We found a strong correlation between the presence of ARG and human fecal concentrations, which supports our hypothesis that septic effluent is a source of ARG and resistant organisms. The observed plasmid-based resistance adds an additional level of risk, as human-associated bacteria from septic systems may expand the environmental resistome by acting as a reservoir of transmissible resistance genes.

Microbial source tracking and land use associations for antibiotic resistance genes in private wells influenced by human and livestock fecal sources

Antimicrobial resistance is a growing public health problem that requires an integrated approach among human, agricultural, and environmental sectors. However, few studies address all three components simultaneously. We investigated the occurrence of five antibiotic resistance genes (ARGs) and the class 1 integron gene (intI1) in private wells drawing water from a vulnerable aquifer influenced by residential septic systems and land-applied dairy manure. Samples (n = 138) were collected across four seasons from a randomized sample of private wells in Kewaunee County, Wisconsin. Measurements of ARGs and intI1 were related to microbial source tracking (MST) markers specific to human and bovine feces; they were also related to 54 risk factors for contamination representing land use, rainfall, hydrogeology, and well construction. ARGs and intI1 occurred in 5%-40% of samples depending on target. Detection frequencies for ARGs and intI1 were lowest in the absence of human and bovine MST markers (1%-30%), highest when co-occurring with human and bovine markers together (11%-78%), and intermediate when co-occurring with just one type of MST marker (4%-46%). Gene targets were associated with septic system density more often than agricultural land, potentially because of the variable presence of manure on the landscape. Determining ARG prevalence in a rural setting with mixed land use allowed an assessment of the relative contribution of human and bovine fecal sources. Because fecal sources co-occurred with ARGs at similar rates, interventions intended to reduce ARG occurrence may be most effective if both sources are considered.

Fecal indicators and antibiotic resistance genes exhibit diurnal trends in the Chattahoochee River: Implications for water quality monitoring

Water bodies that serve as sources of drinking or recreational water are routinely monitored for fecal indicator bacteria (FIB) by state and local agencies. Exceedances of monitoring thresholds set by those agencies signal likely elevated human health risk from exposure, but FIB give little information about the potential source of contamination. To improve our understanding of how within-day variation could impact monitoring data interpretation, we conducted a study at two sites along the Chattahoochee River that varied in their recreational usage and adjacent land-use (natural versus urban), collecting samples every 30 min over one 24-h period. We assayed for three types of microbial indicators: FIB (total coliforms and Escherichia coli); human fecal-associated microbial source tracking (MST) markers (crAssphage and HF183/BacR287); and a suite of clinically relevant antibiotic resistance genes (ARGs; blaCTX-M, blaCMY, MCR, KPC, VIM, NDM) and a gene associated with antibiotic resistance (intl1). Mean levels of FIB and clinically relevant ARGs (blaCMY and KPC) were similar across sites, while MST markers and intI1 occurred at higher mean levels at the natural site. The human-associated MST markers positively correlated with antibiotic resistant-associated genes at both sites, but no consistent associations were detected between culturable FIB and any molecular markers. For all microbial indicators, generalized additive mixed models were used to examine diurnal variability and whether this variability was associated with environmental factors (water temperature, turbidity, pH, and sunlight). We found that FIB peaked during morning and early afternoon hours and were not associated with environmental factors. With the exception of HF183/BacR287 at the urban site, molecular MST markers and intI1 exhibited diurnal variability, and water temperature, pH, and turbidity were significantly associated with this variability. For blaCMY and KPC, diurnal variability was present but was not correlated with environmental factors. These results suggest that differences in land use (natural or urban) both adjacent and upstream may impact overall levels of microbial contamination. Monitoring agencies should consider matching sample collection times with peak levels of target microbial indicators, which would be in the morning or early afternoon for the fecal associated indicators. Measuring multiple microbial indicators can lead to clearer interpretations of human health risk associated with exposure to contaminated water.