Making waves: The NORMAN antibiotic resistant bacteria and resistance genes database (NORMAN ARB&ARG)-An invitation for collaboration to tackle antibiotic resistance.

With the global concerns on antibiotic resistance (AR) as a public health issue, it is pivotal to have data exchange platforms for studies on antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in the environment. For this purpose, the NORMAN Association is hosting the NORMAN ARB&ARG database, which was developed within the European project ANSWER. The present article provides an overview on the database functionalities, the extraction and the contribution of data to the database. In this study, AR data from three studies from China and Nepal were extracted and imported into the NORMAN ARB&ARG in addition to the existing AR data from 11 studies (mainly European studies) on the database. This feasibility study demonstrates how the scientific community can share their data on AR to generate an international evidence base to inform AR mitigation strategies. The open and FAIR data are of high potential relevance for regulatory applications, including the development of emission limit values / environmental quality standards in relation to AR. The growth in sharing of data and analytical methods will foster collaboration on risk management of AR worldwide, and facilitate the harmonization in the effort for identification and surveillance of critical hotspots of AR. The NORMAN ARB&ARG database is publicly available at: https://www.norman-network.com/nds/bacteria/.

Estimating actual SARS-CoV-2 infections from secondary data.

Eminent in pandemic management is accurate information on infection dynamics to plan for timely installation of control measures and vaccination campaigns. Despite huge efforts in diagnostic testing of individuals, the underestimation of the actual number of SARS-CoV-2 infections remains significant due to the large number of undocumented cases. In this paper we demonstrate and compare three methods to estimate the dynamics of true infections based on secondary data i.e., (a) test positivity, (b) infection fatality and (c) wastewater monitoring. The concept is tested with Austrian data on a national basis for the period of April 2020 to December 2022. Further, we use the results of prevalence studies from the same period to generate (upper and lower bounds of) credible intervals for true infections for four data points. Model parameters are subsequently estimated by applying Approximate Bayesian Computation-rejection sampling and Genetic Algorithms. The method is then validated for the case study Vienna. We find that all three methods yield fairly similar results for estimating the true number of infections, which supports the idea that all three datasets contain similar baseline information. None of them is considered superior, as their advantages and shortcomings depend on the specific case study at hand.

Prediction of hospitalisations based on wastewater-based SARS-CoV-2 epidemiology.

Wastewater-based epidemiology is widely applied in Austria since April 2020 to monitor the SARS-CoV-2 pandemic. With a steadily increasing number of monitored wastewater facilities, 123 plants covering roughly 70 % of the 9 million population were monitored as of August 2022. In this study, the SARS-CoV-2 viral concentrations in raw sewage were analysed to infer short-term hospitalisation occupancy. The temporal lead of wastewater-based epidemiological time series over hospitalisation occupancy levels facilitates the construction of forecast models. Data pre-processing techniques are presented, including the approach of comparing multiple decentralised wastewater signals with aggregated and centralised clinical data. Time­lead quantification was performed using cross-correlation analysis and coefficient of determination optimisation approaches. Multivariate regression models were successfully applied to infer hospitalisation bed occupancy. The results show a predictive potential of viral loads in sewage towards Covid-19 hospitalisation occupancy, with an average lead time towards ICU and non-ICU bed occupancy between 14.8-17.7 days and 8.6-11.6 days, respectively. The presented procedure provides access to the trend and tipping point behaviour of pandemic dynamics and allows the prediction of short-term demand for public health services. The results showed an increase in forecast accuracy with an increase in the number of monitored wastewater treatment plants. Trained models are sensitive to changing variant types and require recalibration of model parameters, likely caused by immunity by vaccination and/or infection. The utilised approach displays a practical and rapidly implementable application of wastewater-based epidemiology to infer hospitalisation occupancy.

Expanding the Pathogen Panel in Wastewater Epidemiology to Influenza and Norovirus.

Since the start of the 2019 pandemic, wastewater-based epidemiology (WBE) has proven to be a valuable tool for monitoring the prevalence of SARS-CoV-2. With methods and infrastructure being settled, it is time to expand the potential of this tool to a wider range of pathogens. We used over 500 archived RNA extracts from a WBE program for SARS-CoV-2 surveillance to monitor wastewater from 11 treatment plants for the presence of influenza and norovirus twice a week during the winter season of 2021/2022. Extracts were analyzed via digital PCR for influenza A, influenza B, norovirus GI, and norovirus GII. Resulting viral loads were normalized on the basis of NH4-N. Our results show a good applicability of ammonia-normalization to compare different wastewater treatment plants. Extracts originally prepared for SARS-CoV-2 surveillance contained sufficient genomic material to monitor influenza A, norovirus GI, and GII. Viral loads of influenza A and norovirus GII in wastewater correlated with numbers from infected inpatients. Further, SARS-CoV-2 related non-pharmaceutical interventions affected subsequent changes in viral loads of both pathogens. In conclusion, the expansion of existing WBE surveillance programs to include additional pathogens besides SARS-CoV-2 offers a valuable and cost-efficient possibility to gain public health information.

Data modelling recipes for SARS-CoV-2 wastewater-based epidemiology.

Wastewater based epidemiology is recognized as one of the monitoring pillars, providing essential information for pandemic management. Central in the methodology are data modelling concepts for both communicating the monitoring results but also for analysis of the signal. It is due to the fast development of the field that a range of modelling concepts are used but without a coherent framework. This paper provides for such a framework, focusing on robust and simple concepts readily applicable, rather than applying latest findings from e.g., machine learning. It is demonstrated that data preprocessing, most important normalization by means of biomarkers and equal temporal spacing of the scattered data, is crucial. In terms of the latter, downsampling to a weekly spaced series is sufficient. Also, data smoothing turned out to be essential, not only for communication of the signal dynamics but likewise for regressions, nowcasting and forecasting. Correlation of the signal with epidemic indicators requires multivariate regression as the signal alone cannot explain the dynamics but - for this case study - multiple linear regression proofed to be a suitable tool when the focus is on understanding and interpretation. It was also demonstrated that short term prediction (7 days) is accurate with simple models (exponential smoothing or autoregressive models) but forecast accuracy deteriorates fast for longer periods.

Viral variant-resolved wastewater surveillance of SARS-CoV-2 at national scale.

SARS-CoV-2 surveillance by wastewater-based epidemiology is poised to provide a complementary approach to sequencing individual cases. However, robust quantification of variants and de novo detection of emerging variants remains challenging for existing strategies. We deep sequenced 3,413 wastewater samples representing 94 municipal catchments, covering >59% of the population of Austria, from December 2020 to February 2022. Our system of variant quantification in sewage pipeline designed for robustness (termed VaQuERo) enabled us to deduce the spatiotemporal abundance of predefined variants from complex wastewater samples. These results were validated against epidemiological records of >311,000 individual cases. Furthermore, we describe elevated viral genetic diversity during the Delta variant period, provide a framework to predict emerging variants and measure the reproductive advantage of variants of concern by calculating variant-specific reproduction numbers from wastewater. Together, this study demonstrates the power of national-scale WBE to support public health and promises particular value for countries without extensive individual monitoring.

Emergence of SARS-CoV-2 Alpha lineage and its correlation with quantitative wastewater-based epidemiology data.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) gave rise to an international public health emergency in 3 months after its emergence in Wuhan, China. Typically for an RNA virus, random mutations occur constantly leading to new lineages, incidental with a higher transmissibility. The highly infective alpha lineage, firstly discovered in the UK, led to elevated mortality and morbidity rates as a consequence of Covid-19, worldwide. Wastewater surveillance proved to be a powerful tool for early detection and subsequent monitoring of the dynamics of SARS-CoV-2 and its variants in a defined catchment. Using a combination of sequencing and RT-qPCR approaches, we investigated the total SARS-CoV-2 concentration and the emergence of the alpha lineage in wastewater samples in Vienna, Austria linking it to clinical data. Based on a non-linear regression model and occurrence of signature mutations, we conclude that the alpha variant was present in Vienna sewage samples already in December 2020, even one month before the first clinical case was officially confirmed and reported by the health authorities. This provides evidence that a well-designed wastewater monitoring approach can provide a fast snapshot and may detect the circulating lineages in wastewater weeks before they are detectable in the clinical samples. Furthermore, declining 14 days prevalence data with simultaneously increasing SARS-CoV-2 total concentration in wastewater indicate a different shedding behavior for the alpha variant. Overall, our results support wastewater surveillance to be a suitable approach to spot early circulating SARS-CoV-2 lineages based on whole genome sequencing and signature mutations analysis.

Assessment of Graphical Methods for Determination of the Limiting Current Density in Complex Electrodialysis-Feed Solutions.

Electrodialysis (ED) is a promising technology suitable for nutrient recovery from a wide variety of liquid waste streams. For optimal operating conditions, the limiting current density (LCD) has to be determined separately for each treated feed and ED equipment. LCD is most frequently assessed in the NaCl solutions. In this paper, five graphical methods available in literature were reviewed for LCD determination in a series of five feed solutions with different levels of complexity in ion and matrix composition. Wastewater from microbial fermentation was included among the feed solutions, containing charged and uncharged particles. The experiments, running in the batch ED with an online conductivity, temperature, and pH monitoring, were conducted to obtain data for the comparison of various LCD determination methods. The results revealed complements and divergences between the applied LCD methods with increasing feed concentrations and composition complexity. The Cowan and Brown method had the most consistent results for all of the feed solutions. Online conductivity monitoring was linearly correlated with the decreasing ion concentration in the feed solution and corresponding LCD. Therefore, the results obtained in this study can be applied as a base for the automatized dynamic control of the operating current density-voltage in the batch ED. Conductivity alone should not be used for the ED control since LCD depends on the ion exchange membranes, feed flow, temperature and concentration, ionic species, their concentration ratios, and uncharged particles of the feed solution.

Effect of ozonation on the biodegradability of urban wastewater treatment plant effluent.

The present work aimed to study the effect of ozonation on the organic sum parameters linked to enhanced biodegradability. Laboratory experiments were conducted with the effluent of four Austrian urban wastewater treatment plants with low food to microorganism ratios and different matrix characteristics. Biochemical oxygen demand over 5 days (BOD5) was measured before ozonation and after application of different specific ozone doses (Dspec) (0.4, 0.6 and 0.8 g O3/g DOC). Other investigated organic parameters comprised chemical oxygen demand (COD), dissolved organic carbon (DOC), UV absorption at 254 nm (UV254), which are parameters that are applied in routine wastewater analysis. Carbamazepine and benzotriazole were measured as reference micropollutants. The results showed a dose-dependent increase in biological activity after ozonation; this increase was linked to the enhanced biodegradability of substances that are recalcitrant to biodegradation in conventional activated sludge treatment. The highest relative change was determined for BOD5, which already occurred between 0 and 0.4 g O3/g DOC for all samples. Increasing the Dspec to 0.6 and 0.8 g O3/g DOC resulted in a less pronounced increase. DOC was not substantially decreased after ozonation, which was consistent with a low reported degree of mineralization, while partial oxidation led to a quantifiable decrease in COD (7 to 17%). Delta UV254 and the decline in specific UV absorption after ozonation clearly correlated with Dspec. In contrast, for COD and biodegradable DOC (BDOC), a clear dose-response pattern was identified only after exposure to BOD5 measurement. Indications for improved biodegradability were further supported by the rise in the BOD5/COD ratio. The results indicated that subsequent biological processes have a higher degradation potential after ozonation. The further reduction in biodegradable organic carbon emission by the combination of ozonation and biological post treatment represents another step towards sustainable water resource management in addition to micropollutant abatement.

Quest for Optimal Regression Models in SARS-CoV-2 Wastewater Based Epidemiology.

Wastewater-based epidemiology is a recognised source of information for pandemic management. In this study, we investigated the correlation between a SARS-CoV-2 signal derived from wastewater sampling and COVID-19 incidence values monitored by means of individual testing programs. The dataset used in the study is composed of timelines (duration approx. five months) of both signals at four wastewater treatment plants across Austria, two of which drain large communities and the other two drain smaller communities. Eight regression models were investigated to predict the viral incidence under varying data inputs and pre-processing methods. It was found that population-based normalisation and smoothing as a pre-processing of the viral load data significantly influence the fitness of the regression models. Moreover, the time latency lag between the wastewater data and the incidence derived from the testing program was found to vary between 2 and 7 days depending on the time period and site. It was found to be necessary to take such a time lag into account by means of multivariate modelling to boost the performance of the regression. Comparing the models, no outstanding one could be identified as all investigated models are revealing a sufficient correlation for the task. The pre-processing of data and a multivariate model formulation is more important than the model structure.

Data filtering methods for SARS-CoV-2 wastewater surveillance.

In the case of SARS-CoV-2 pandemic management, wastewater-based epidemiology aims to derive information on the infection dynamics by monitoring virus concentrations in the wastewater. However, due to the intrinsic random fluctuations of the viral signal in wastewater caused by several influencing factors that cannot be determined in detail (e.g. dilutions; number of people discharging; variations in virus excretion; water consumption per day; transport and fate processes in sewer system), the subsequent prevalence analysis may result in misleading conclusions. It is thus helpful to apply data filtering techniques to reduce the noise in the signal. In this paper we investigate 13 smoothing algorithms applied to the virus signals monitored in four wastewater treatment plants in Austria. The parameters of the algorithms have been defined by an optimization procedure aiming for performance metrics. The results are further investigated by means of a cluster analysis. While all algorithms are in principle applicable, SPLINE, Generalized Additive Model and Friedman's Super Smoother are recognized as superior methods in this context (with the latter two having a tendency to over-smoothing). A first analysis of the resulting datasets indicates the positive effect of filtering to the correlation of the viral signal to monitored incidence values.

Making Waves: Collaboration in the time of SARS-CoV-2 - rapid development of an international co-operation and wastewater surveillance database to support public health decision-making.

The presence of SARS-CoV-2 RNA in wastewater was first reported in March 2020. Over the subsequent months, the potential for wastewater surveillance to contribute to COVID-19 mitigation programmes has been the focus of intense national and international research activities, gaining the attention of policy makers and the public. As a new application of an established methodology, focused collaboration between public health practitioners and wastewater researchers is essential to developing a common understanding on how, when and where the outputs of this non-invasive community-level approach can deliver actionable outcomes for public health authorities. Within this context, the NORMAN SCORE "SARS-CoV-2 in sewage" database provides a platform for rapid, open access data sharing, validated by the uploading of 276 data sets from nine countries to-date. Through offering direct access to underpinning meta-data sets (and describing its use in data interpretation), the NORMAN SCORE database is a resource for the development of recommendations on minimum data requirements for wastewater pathogen surveillance. It is also a tool to engage public health practitioners in discussions on use of the approach, providing an opportunity to build mutual understanding of the demand and supply for data and facilitate the translation of this promising research application into public health practice.

Resilience of agricultural soils to antibiotic resistance genes introduced by agricultural management practices.

Antimicrobial resistance (AR) represents a global threat in human and veterinary medicine. In that regard, AR proliferation and dissemination in agricultural soils after manure application raises concerns on the enrichment of endogenous soil bacterial population with allochthonous antibiotic resistance genes (ARGs). Natural resilience of agricultural soils and background concentrations of ARGs play key roles in the mitigation of AR propagation in natural environments. In the present study, we carried out a longitudinal sampling campaign for two crop vegetation periods to monitor spatial and temporal changes in the abundance of seven clinically relevant ARGs (sul1, ermB, vanA, aph(3')-IIa, aph(3')-IIIa, blaTEM-1 and tet(W)) and ribosomal 16S RNA. The absolute and relative abundances of the selected ARGs were quantified in total community DNA extracted from agricultural (manured and non-manured) and forest soils, fresh pig faeces and manure slurry. We observed that ARG concentrations return to background levels after manure-induced exposure within a crop growing season, highlighting the resilience capacity of soil. Naturally occurring high background concentrations of ARGs can be found in forest soil in due distance under low anthropogenic influences. It was observed that pesticide application increases the concentrations of three out of seven ARGs tested (ermB, aph(3')-IIIa and tet(W)). Moreover, we noticed that the absolute abundances of sul1, vanA, ermB and blaTEM-1 resistance genes show an increase by 100- to 10,000- fold, from maturation of fresh pig faeces to manure. Outcomes of our study suggest that agricultural soil environments show a strong capacity to alleviate externally induced disturbances in endogenous ARG concentrations. Naturally occurring high concentrations of ARGs are present also in low human impacted environments represented by the indigenous resistome.

Genotoxic activities of wastewater after ozonation and activated carbon filtration: Different effects in liver-derived cells and bacterial indicators.

Aim of this study was to investigate the impact of advanced wastewater treatment techniques (combining ozonation with activated carbon filtration) on acute and genotoxic activities of tertiary treated wastewater. Concentrated samples were tested in Salmonella/microsome assays. Furthermore, induction of DNA damage was measured in liver-derived cells (human hepatoma and primary rat hepatocytes) in single cell gel electrophoresis experiments, which are based on the measurement of DNA migration in an electric field. These cell types possess phase I and phase II enzymes, which catalyze the activation/detoxification of mutagens. Acute toxicity was determined with the trypan blue exclusion technique. We found no evidence for mutagenic effects of non-ozonated samples in several bacterial tester strains (TA98, TA100, YG7108, YG7104, YG7112 and YG7113) but clear induction of His+ mutants after O3 treatment in two strains with defective genes encoding for DNA repair, which are highly sensitive towards alkylating agents (YG7108 and YG7104). These effects were reduced after activated carbon filtration. Furthermore, we detected a slight increase of mutagenic activity in strain YG1024 with increased acetyltransferase activity, which is sensitive towards aromatic amines and nitro compounds in untreated water, which was not reduced by O3 treatment. A completely different pattern of mutagenic activity was seen in liver-derived cells; non ozonated samples caused in both cell types pronounced DNA damage, which was reduced (by ca. 25%) after ozonation. Activated carbon treatment did not cause a substantial further reduction of DNA damage. Additional experiments with liver homogenate indicate that the compounds which cause the effects in the human cells are promutagens which require enzymatic activation. None of the waters caused acute toxicity in the liver-derived cells and in the bacterial indicators. Assuming that hepatic mammalian cells reflect the genotoxic properties of the waters in vertebrates (including humans) more adequately as genetically modified bacterial indicators, we conclude that ozonation has beneficial effects in regard to the reduction of genotoxic properties of treated wastewaters.

A global multinational survey of cefotaxime-resistant coliforms in urban wastewater treatment plants.

The World Health Organization Global Action Plan recommends integrated surveillance programs as crucial strategies for monitoring antibiotic resistance. Although several national surveillance programs are in place for clinical and veterinary settings, no such schemes exist for monitoring antibiotic-resistant bacteria in the environment. In this transnational study, we developed, validated, and tested a low-cost surveillance and easy to implement approach to evaluate antibiotic resistance in wastewater treatment plants (WWTPs) by targeting cefotaxime-resistant (CTX-R) coliforms as indicators. The rationale for this approach was: i) coliform quantification methods are internationally accepted as indicators of fecal contamination in recreational waters and are therefore routinely applied in analytical labs; ii) CTX-R coliforms are clinically relevant, associated with extended-spectrum ß-lactamases (ESBLs), and are rare in pristine environments. We analyzed 57 WWTPs in 22 countries across Europe, Asia, Africa, Australia, and North America. CTX-R coliforms were ubiquitous in raw sewage and their relative abundance varied significantly (<0.1% to 38.3%), being positively correlated (p < 0.001) with regional atmospheric temperatures. Although most WWTPs removed large proportions of CTX-R coliforms, loads over 103 colony-forming units per mL were occasionally observed in final effluents. We demonstrate that CTX-R coliform monitoring is a feasible and affordable approach to assess wastewater antibiotic resistance status.

Removal of extracellular free DNA and antibiotic resistance genes from water and wastewater by membranes ranging from microfiltration to reverse osmosis.

Free DNA in the effluent from wastewater treatment plants has recently been observed to contain antibiotic resistance genes (ARGs), which may contribute to the spread of antibiotic resistance via horizontal gene transfer in the receiving environment. Technical membrane systems applied in wastewater and drinking water treatment are situated at central nodes between the environmental and human related aspects of the "One Health" approach and are considered as effective barriers for antibiotic resistant bacteria. However, they are not evaluated for their permeability for ARGs encoded in free DNA, which may result, for example, from the release of free DNA after bacterial die-off during particular treatment processes. This study examined the potential and principle mechanisms for the removal of free DNA containing ARGs by technical membrane filtration. Ten different membranes, varied by the charge (neutral and negative) and the molecular weight cut off (in a range from microfiltration to reverse osmosis), were tested for the removal of free DNA (pure supercoiled and linearized plasmids encoding for ARGs and free linear chromosomal DNA with a broader fragment size spectrum) in different water matrices (distilled water and wastewater treatment plant effluent). Our results showed that membranes with a molecular weight cut off smaller than 5000 Da (ultrafiltration, nanofiltration and reverse osmosis) could retain ≥99.80% of free DNA, both pure plasmid and linear fragments of different sizes, whereas microfiltration commonly applied in wastewater treatment showed no retention. Size exclusion was identified as the main retention mechanism. Additionally, surface charging of the membrane and adsorption of free DNA on the membrane surface played a key role in prevention of free DNA permeation. Currently, majority of the applied membranes is negatively charged to prevent adsorption of natural organic matter. In our study, negatively charged membranes showed lower retention of free DNA compared to neutral ones due to repulsion of free DNA molecules, reduced adsorption and decreased blockage of the membrane surface. Therefore, the applied membrane may not be as an effective barrier for ARGs encoded in free DNA, as it would be predicted based only on the molecular weight cut off. Thus, careful considerations of membrane's specifications (molecular weight cut-off and charge) are required during design of a filtration system for retention of free DNA.

Antibiotic resistance genes in treated wastewater and in the receiving water bodies: A pan-European survey of urban settings.

There is increasing public concern regarding the fate of antibiotic resistance genes (ARGs) during wastewater treatment, their persistence during the treatment process and their potential impacts on the receiving water bodies. In this study, we used quantitative PCR (qPCR) to determine the abundance of nine ARGs and a class 1 integron associated integrase gene in 16 wastewater treatment plant (WWTP) effluents from ten different European countries. In order to assess the impact on the receiving water bodies, gene abundances in the latter were also analysed. Six out of the nine ARGs analysed were detected in all effluent and river water samples. Among the quantified genes, intI1 and sul1 were the most abundant. Our results demonstrate that European WWTP contribute to the enrichment of the resistome in the receiving water bodies with the particular impact being dependent on the effluent load and local hydrological conditions. The ARGs concentrations in WWTP effluents were found to be inversely correlated to the number of implemented biological treatment steps, indicating a possible option for WWTP management. Furthermore, this study has identified blaOXA-58 as a possible resistance gene for future studies investigating the impact of WWTPs on their receiving water.

Primary productivity and climate change in Austrian lowland rivers.

There is increasing evidence of water temperature being a key controlling factor of stream ecosystem metabolism. Although the focus of research currently lies on carbon emissions from fluvial networks and their potential role as positive climate feedback, it is also important to estimate the risk of eutrophication streams will be exposed to in the future. In this work, a methodological approach is developed to create a scientific basis for such assessment and is applied to two Austrian lowland rivers with significantly different characteristics. Gross primary productivity (GPP) is determined through the open diel oxygen method and its temperature dependence is quantified based on the metabolic theory of ecology. This relationship is combined with the outcomes of a climate change scenario obtained through a novel integrated modelling framework. Results indicate that in both rivers, a 1.5°C warming would provoke an increase of GPP of 7-9% and that such an increase would not be limited by nutrient availability. The results further suggest that the situation for the relatively shallow river might be more critical, given that its GPP values in summer are five times higher than in the deeper murky river.

High Throughput Analysis of Integron Gene Cassettes in Wastewater Environments.

Integrons are extensively targeted as a proxy for anthropogenic impact in the environment. We developed a novel high-throughput amplicon sequencing pipeline that enables characterization of thousands of integron gene cassette-associated reads, and applied it to acquire a comprehensive overview of gene cassette composition in effluents from wastewater treatment facilities across Europe. Between 38 100 and 172 995 reads per-sample were generated and functionally characterized by screening against nr, SEED, ARDB and ß-lactamase databases. Over 75% of the reads were characterized as hypothetical, but thousands were associated with toxin-antitoxin systems, DNA repair, cell membrane function, detoxification and aminoglycoside and ß-lactam resistance. Among the reads characterized as ß-lactamases, the carbapenemase blaOXA was dominant in most of the effluents, except for Cyprus and Israel where blaGES was also abundant. Quantitative PCR assessment of blaOXA and blaGES genes in the European effluents revealed similar trends to those displayed in the integron amplicon sequencing pipeline described above, corroborating the robustness of this method and suggesting that these integron-associated genes may be excellent targets for source tracking of effluents in downstream environments. Further application of the above analyses revealed several order-of-magnitude reductions in effluent-associated ß-lactamase genes in effluent-saturated soils, suggesting marginal persistence in the soil microbiome.