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Wiesner-Friedman C, Brinkman NE, Wheaton E, Nagarkar M, Hart C, Keely SP, Varughese E, Garland J, Klaver P, Turner C, Barton J, Serre M, Jahne M. Characterizing Spatial Information Loss for Wastewater Surveillance Using crAssphage: Effect of Decay, Temperature, and Population Mobility. Environ Sci Technol 2023; 57:20802-20812. [PMID: 38015885 DOI: 10.1021/acs.est.3c05587] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
Populations contribute information about their health status to wastewater. Characterizing how that information degrades in transit to wastewater sampling locations (e.g., wastewater treatment plants and pumping stations) is critical to interpret wastewater responses. In this work, we statistically estimate the loss of information about fecal contributions to wastewater from spatially distributed populations at the census block group resolution. This was accomplished with a hydrologically and hydraulically influenced spatial statistical approach applied to crAssphage (Carjivirus communis) load measured from the influent of four wastewater treatment plants in Hamilton County, Ohio. We find that we would expect to observe a 90% loss of information about fecal contributions from a given census block group over a travel time of 10.3 h. This work demonstrates that a challenge to interpreting wastewater responses (e.g., during wastewater surveillance) is distinguishing between a distal but large cluster of contributions and a near but small contribution. This work demonstrates new modeling approaches to improve measurement interpretation depending on sewer network and wastewater characteristics (e.g., geospatial layout, temperature variability, population distribution, and mobility). This modeling can be integrated into standard wastewater surveillance methods and help to optimize sewer sampling locations to ensure that different populations (e.g., vulnerable and susceptible) are appropriately represented.
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Affiliation(s)
- Corinne Wiesner-Friedman
- Oak Ridge Institute for Science and Education, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States
| | - Nichole E Brinkman
- Office of Research and Development, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States
| | - Emily Wheaton
- Office of Research and Development, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States
| | - Maitreyi Nagarkar
- Office of Research and Development, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States
| | - Chloe Hart
- Office of Research and Development, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States
| | - Scott P Keely
- Office of Research and Development, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States
| | - Eunice Varughese
- Office of Research and Development, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States
| | - Jay Garland
- Office of Research and Development, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States
| | - Peter Klaver
- LimnoTech, 501 Avis Drive, Ann Arbor, Michigan 48108, United States
| | - Carrie Turner
- LimnoTech, 501 Avis Drive, Ann Arbor, Michigan 48108, United States
| | - John Barton
- Metropolitan Sewer District of Greater Cincinnati, 1081 Woodrow Street, Cincinnati, Ohio 45204, United States
| | - Marc Serre
- Gillings School of Global Public Health, Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Michael Jahne
- Office of Research and Development, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States
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Wiesner-Friedman C, Beattie RE, Stewart JR, Hristova KR, Serre ML. Identifying sources of antibiotic resistance genes in the environment using the microbial Find, Inform, and Test framework. Front Microbiol 2023; 14:1223876. [PMID: 37731922 PMCID: PMC10508347 DOI: 10.3389/fmicb.2023.1223876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 08/07/2023] [Indexed: 09/22/2023] Open
Abstract
Introduction Antimicrobial resistance (AMR) is an increasing public health concern for humans, animals, and the environment. However, the contributions of spatially distributed sources of AMR in the environment are not well defined. Methods To identify the sources of environmental AMR, the novel microbial Find, Inform, and Test (FIT) model was applied to a panel of five antibiotic resistance-associated genes (ARGs), namely, erm(B), tet(W), qnrA, sul1, and intI1, quantified from riverbed sediment and surface water from a mixed-use region. Results A one standard deviation increase in the modeled contributions of elevated AMR from bovine sources or land-applied waste sources [land application of biosolids, sludge, and industrial wastewater (i.e., food processing) and domestic (i.e., municipal and septage)] was associated with 34-80% and 33-77% increases in the relative abundances of the ARGs in riverbed sediment and surface water, respectively. Sources influenced environmental AMR at overland distances of up to 13 km. Discussion Our study corroborates previous evidence of offsite migration of microbial pollution from bovine sources and newly suggests offsite migration from land-applied waste. With FIT, we estimated the distance-based influence range overland and downstream around sources to model the impact these sources may have on AMR at unsampled sites. This modeling supports targeted monitoring of AMR from sources for future exposure and risk mitigation efforts.
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Affiliation(s)
- Corinne Wiesner-Friedman
- Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, OH, United States
| | - Rachelle E. Beattie
- U.S. Geological Survey, Columbia Environmental Research Center, Columbia, MO, United States
- Department of Biological Sciences, Marquette University, Milwaukee, WI, United States
| | - Jill R. Stewart
- Gillings School of Global Public Health, Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | | | - Marc L. Serre
- Gillings School of Global Public Health, Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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Coffman VR, Hall DJ, Pisanic N, Wiesner-Friedman C, Rogers S, Rule A, McCormack M, Diener-West M, Davis MF, Heaney CD. Assessing Residential Exposure to Microbes from Industrial Hog Operations in Rural North Carolina: Methods and Lessons Learned. Prog Community Health Partnersh 2022; 16:61-72. [PMID: 35342111 DOI: 10.1353/cpr.2022.0006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
BACKGROUND Recent advances in molecular source tracking make answering questions from residents regarding their exposure to microbial contaminants from industrial hog operations (IHOs) possible. Associations between residential distance to IHOs and exposure can be addressed by measuring livestock-associated (Staphylococcus aureus) and pig-specific bacteria in the air, on household surfaces, and in participants' nasal and saliva swabs. OBJECTIVES Here we assess the mechanics, feasibility, capacity-building, and lessons learned during a pilot study employing this novel technology in community-based participatory research of bacterial exposure and human health. METHODS Together, our team of academics and community members designed a field- and laboratory-based pilot study. Air samples, surface and human swabs, and questionnaires from households at varying distances from IHOs were collected. Data were assessed for completeness and quality by two independent reviewers. These metrics were defined as: missingness (completeness), incorrect data type (validity), out of range (validity), and outliers (accuracy). LESSONS LEARNED While critical field equipment was obtained, and knowledge exchange occurred, leading to an increased capacity for future work, after review, 38 of 49 households were deemed eligible for inclusion in the study. Of eligible participants, 98% of required electronic survey questions were complete and 100% were valid; an improvement over prior work which employed paper surveys. While all human microbial and air samples were collected from eligible households (n = 231), (5%) of environmental swabs were reported missing. CONCLUSIONS Using community-appropriate sampling protocols, a pilot study of residential exposure to bacteria from IHOs was completed. While high-quality data was collected from those eligible, we learned the necessity of early and continual data review.
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Wiesner-Friedman C, Beattie RE, Stewart JR, Hristova KR, Serre ML. Characterizing Differences in Sources of and Contributions to Fecal Contamination of Sediment and Surface Water with the Microbial FIT Framework. Environ Sci Technol 2022; 56:4231-4240. [PMID: 35298143 DOI: 10.1021/acs.est.2c00224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Surface water monitoring and microbial source tracking (MST) are used to identify host sources of fecal pollution and protect public health. However, knowledge of the locations of spatial sources and their relative impacts on the environment is needed to effectively mitigate health risks. Additionally, sediment samples may offer time-integrated information compared to transient surface water. Thus, we implemented the newly developed microbial find, inform, and test framework to identify spatial sources and their impacts on human (HuBac) and bovine (BoBac) MST markers, quantified from both riverbed sediment and surface water in a bovine-dense region. Dairy feeding operations and low-intensity developed land-cover were associated with 99% (p-value < 0.05) and 108% (p-value < 0.05) increases, respectively, in the relative abundance of BoBac in sediment, and with 79% (p-value < 0.05) and 39% increases in surface water. Septic systems were associated with a 48% increase in the relative abundance of HuBac in sediment and a 56% increase in surface water. Stronger source signals were observed for sediment responses compared to water. By defining source locations, predicting river impacts, and estimating source influence ranges in a Great Lakes region, this work informs pollution mitigation strategies of local and global significance.
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Affiliation(s)
- Corinne Wiesner-Friedman
- Gillings School of Global Public Health, Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7400, United States
| | - Rachelle E Beattie
- Department of Biological Sciences, Marquette University, Milwaukee, Wisconsin 53233, United States
| | - Jill R Stewart
- Gillings School of Global Public Health, Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7400, United States
| | - Krassimira R Hristova
- Department of Biological Sciences, Marquette University, Milwaukee, Wisconsin 53233, United States
| | - Marc L Serre
- Gillings School of Global Public Health, Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7400, United States
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Wiesner-Friedman C, Beattie RE, Stewart JR, Hristova KR, Serre ML. Microbial Find, Inform, and Test Model for Identifying Spatially Distributed Contamination Sources: Framework Foundation and Demonstration of Ruminant Bacteroides Abundance in River Sediments. Environ Sci Technol 2021; 55:10451-10461. [PMID: 34291905 DOI: 10.1021/acs.est.1c01602] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Microbial pollution in rivers poses known ecological and health risks, yet causal and mechanistic linkages to sources remain difficult to establish. Host-associated microbial source tracking (MST) markers help to assess the microbial risks by linking hosts to contamination but do not identify the source locations. Land-use regression (LUR) models have been used to screen the source locations using spatial predictors but could be improved by characterizing transport (i.e., hauling, decay overland, and downstream). We introduce the microbial Find, Inform, and Test (FIT) framework, which expands previous LUR approaches and develops novel spatial predictor models to characterize the transported contributions. We applied FIT to characterize the sources of BoBac, a ruminant Bacteroides MST marker, quantified in riverbed sediment samples from Kewaunee County, Wisconsin. A 1 standard deviation increase in contributions from land-applied manure hauled from animal feeding operations (AFOs) was associated with a 77% (p-value <0.05) increase in the relative abundance of ruminant Bacteroides (BoBac-copies-per-16S-rRNA-copies) in the sediment. This is the first work finding an association between the upstream land-applied manure and the offsite bovine-associated fecal markers. These findings have implications for the sediment as a reservoir for microbial pollution associated with AFOs (e.g., pathogens and antibiotic-resistant bacteria). This framework and application advance statistical analysis in MST and water quality modeling more broadly.
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Affiliation(s)
- Corinne Wiesner-Friedman
- Gillings School of Global Public Health, Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7400, United States
| | - Rachelle E Beattie
- Department of Biological Sciences, Marquette University, Milwaukee, Wisconsin 53233, United States
| | - Jill R Stewart
- Gillings School of Global Public Health, Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7400, United States
| | - Krassimira R Hristova
- Department of Biological Sciences, Marquette University, Milwaukee, Wisconsin 53233, United States
| | - Marc L Serre
- Gillings School of Global Public Health, Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7400, United States
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