Capturing microbial sources distributed in a mixed-use watershed within an integrated environmental modeling workflow
Introduction
The United States Environmental Protection Agency (EPA) is interested in characterizing, managing, and minimizing the risks of human exposure to pathogens in water resources impacted by effluents and runoff from both agricultural activities and built infrastructure. EPA (2016a) indicates that 52.8% of the assessed river and stream miles are impaired, with pathogens being the main cause followed by sediment contamination and nutrients. The designation “pathogen” is used in the broadest sense based upon detection of fecal indicator bacteria (FIB), Escherichia coli (E. coli), and fecal coliforms. Monitoring for the presence of pathogens in manure- and sewage-contaminated waters is extremely challenging, as pathogen concentrations in water samples are often low. Such low concentrations make detection unfeasible, unless large volumes of water are analyzed. Most monitoring approaches and microbial water quality regulations are based on indicator bacteria, since they are easier to sample and quantify (EPA, 2012, EPA, 2015), although good correlations between indicators and pathogens may be suspect. For example, Haack and Duris (2013) note that “… there is a widely acknowledged variable relationship between FIB and pathogen concentrations (Field and Samadpour, 2007, Savichtcheva et al., 2007).” Therefore, states might avail themselves of water quality criteria, if they can demonstrate an equivalent level of public health protection with higher indicator concentrations.
Agriculture is one of the most likely causes of pollution, affecting almost 13% of the total river miles assessed, since applying manure for crop nutrition and production and animal shedding due to grazing are common practices. Manure applications may carry environmental contaminants such as pathogens, organic chemical residues and heavy metals (Edwards and Daniel, 1992). These contaminants adversely affect water quality mainly due to runoff-producing rainfall events. Among the various animal fecal sources, poultry are responsible for 44% of the total feces production in the United States, followed by cattle (31%) and swine (24%) (Kellog et al., 2000). In comparison, humans contribute only a small fraction (0.7%) on an equal weight basis; however, human sewage/wastewater is generally thought to constitute a much higher risk to public health due to the likelihood of viral pathogen presence (Soller et al., 2010, Schoen et al., 2011, Dufour, 1984).
Models can play a role in assessing the distribution of microbes in a mixed-use watershed and the potential risks associated with both measured and predicted indicator concentrations (i.e., degree to which concentrations indicate threats to public health under varying circumstances). Assessment of potential risks is critical in determining the appropriateness of waivers to criteria and concentration standards based on site-specific environmental settings and source conditions. Site surveys, coupled with modeling tools, are a basic way to identify sources, characterizing them, associate a level of infectivity with the source, and assess its level of impact at the point of exposure.
A Quantitative Microbial Risk Assessment (QMRA) is a source-to-receptor modeling approach that integrates disparate data – such as fate/transport, exposure, and human health effect relationships – to characterize the distribution of indicator and pathogenic microbes within a watershed, and the potential health impacts/risks from exposure to pathogenic microorganisms (Soller et al., 2010, Whelan et al., 2014a, Whelan et al., 2014b, Haas et al., 1999, Hunter et al., 2003). As Whelan et al. (2014b) note, a QMRA's conceptual design fits well within an integrated, multi-disciplinary modeling perspective which describes the problem statement; data access retrieval and processing [e.g., D4EM (EPA, 2013a, Wolfe et al., 2007)]; software frameworks for integrating models and databases [e.g., FRAMES (Whelan et al., 2014b, Johnston et al., 2011)]; infrastructures for performing sensitivity, variability, and uncertainty analyses [e.g., SuperMUSE (Babendreier and Castleton, 2005)]; and risk quantification. Coupling modeling results with epidemiology studies allows policy-related issues (EPA, 2010, EPA and USDA, 2012; for example) to be explored. An important aspect of the integrated environmental modeling (IEM) (Laniak et al., 2013) microbial workflow is its ability to define spatial and temporal microbial loadings from human and animal sources within a mixed-use watershed. Multiple software tools have been developed to estimate microbial source loadings to a watershed, such as MWASTE, COLI, SEDMOD, modifications to SWAT, SELECT, BIT, and BSLC.
Moore et al. (1989) developed MWASTE to simulate waste generation and calculate bacterial concentrations in runoff from the land-applied waste of various animals and management techniques. MWASTE only considers animal-borne bacteria and allows only one animal per execution, so multiple runs are required for the consideration of different animal species.
Walker et al. (1990) developed the COLI model to predict bacteria concentration in runoff resulting from a single storm occurring immediately after land application of manure. It uses a Monte Carlo simulation to combine a deterministic relationship with rainfall and temperature variations and calculates maximum and minimum bacteria concentration in runoff.
Fraser et al. (1996) developed a GIS-based Spatially Explicit Delivery Model (SEDMOD) that estimates spatially-distributed delivery ratios for eroded soil and associated nonpoint source pollutants. The model predicts fecal coliform loading in rivers and calculates pollutant loadings in streams by multiplying livestock fecal coliform output and a delivery ratio, estimated for each watershed cell, to predict the proportion of eroded sediment (or other non-point source pollutant) transported from the cell to the stream channel.
Parajuli (2007) manually estimated fecal bacterial loading – considering different sources such as livestock (manure application, grazing), human (septic), and wildlife – for the SWAT bacteria sub-model. Guber et al. (2016) followed this up with a limited effort that integrated infection and recovery of white-tailed deer and cattle into the watershed model SWAT. It predicted pathogen transmission between livestock and deer by considering seasonal changes in deer population, habitat, and foliage consumption; ingestion of pathogens with water, foliage, and grooming soiled hide by deer and grazing cattle; infection and recovery of deer and co-grazing cattle; pathogen shedding by infected animals; survival of pathogens in manure; and kinetic release of pathogens from applied manure and fecal material.
Teague et al. (2009) developed the Spatially Explicit Load Enrichment Calculation Tool (SELECT) to identify potential E. coli sources in Plum Creek Watershed in Texas; SELECT is a grid-based load assessment tool that considers multiple point and non-point sources (wastewater treatment plant, livestock, pets, wildlife, septic, urban). Riebschleager et al. (2012) automated SELECT within ArcGIS and added the Pollutant Connectivity Factor component which is based on potential pollutant loading, runoff potential, and travel distance. SELECT has been used to identify E. coli (Teague et al., 2009, McKee et al., 2011, Riebschleager et al., 2012, McFarland and Adams, 2014, Borel et al., 2015) and enterococci (Borel et al., 2015) sources in multiple watersheds in Texas.
The Bacterial Indicator Tool (BIT) estimates microbial loading from domestic animals, wildlife, and human activities to a mixed-use watershed (EPA, 2000). It accounts for land-application of manure and direct shedding from certain domestic animals to pasture and cropland, and from wildlife to cropland, pasture, and forest. It also estimates point source loadings from septic system failures and direct shedding to the stream from certain domestic animals. Finally, it accounts for loading in urban (built-up) areas such as residential, commercial, transportation, etc. BIT uses Microsoft Excel for calculations and considers only 10 subwatersheds when distributing loads. Land-applied loading rates are adjusted for die-off. All loadings vary monthly, except for those from wildlife, in urban areas, and from septic systems which use constant loading rates to the stream based on the fraction of septic systems that fail. Urbanized areas include categories such as commercial, mixed-urban or built-up, residential, and roadways. Loading rates to urbanized areas are supplied by the user, although default values are suggested. Stormwater runoff through drainage pipes and combined and non-combined sewer systems are not accounted for.
In a similar manner to BIT, the Bacterial Source Load Calculator (BSLC) was designed to organize and process bacterial inputs for a Total Maximum Daily Load (TMDL) bacterial impairment analysis (Zeckoski et al., 2005). BSLC calculates bacterial loads based on animal numbers and default values for manure and bacterial production rates, accounting for die-off and the fraction of domestic animal confinement. It uses externally-generated, user-supplied inputs of watershed delineations, and land-use distribution, as well as domestic animal, wildlife, and human population estimates to suggest monthly land-based and hourly stream-based bacterial loadings. Neither BIT nor BSLC offer software that supports data collection to meet model input requirements, although their documentation suggests some default values.
Prior to allocating microbial sources within a watershed, the watershed must first be delineated into subwatersheds which are the smallest modeling units. To do so, many models require users to manually and externally delineate a watershed, then manually assign environmental characteristics, animal numbers and types, farming practices, and human activities to each subwatershed. This can be a daunting task, especially if the user re-delineates the watershed. Because the delineation pattern determines size and location of subwatersheds, it has a significant impact on distribution and magnitude of microbial loading rates within them; hence, it is desirable to have an automated process to delineate a watershed; populate its subwatersheds with environmental characteristics [land-use types, waterbody network, slope, soil type, meteorological (MET) data, etc.]; and overlay sources of microbial contamination so appropriate loading rates can be easily computed on land and in stream.
The work reported here describes the expansion and modification of BIT, developing a new Microbial Source Module (MSM) (Wolfe et al., 2016, Whelan et al., 2015a). Additionally, its use and implementation was demonstrated as a component of an IEM workflow. The workflow automates the manual processes that perform QMRAs on mixed-use watersheds anywhere in the United States by determining microbial sources and estimates of microbial loadings to land and streams. The mathematical formulations of MSM and its context within an IEM workflow are described here.
Section snippets
Materials and methods
The MSM organizes, analyzes, and supplies data that calculates microbial loading rates within subwatersheds, the smallest spatial units for data that it consumes and produces. MSM correlates sources to cropland, pasture, forest, and urbanized/mixed-use land-use types for each subwatershed. Microbial sources include numbers and locations of domestic agricultural animals (dairy and beef cattle, swine, poultry, etc.) and wildlife (deer, duck, raccoon, etc.), with estimated shedding rates; manure
Input requirements
The MSM has been seamlessly linked with a suite of Comma Separated Values (CSV) files that supply user-defined microbial data. Additional data supplied by SDMPB/D4EM on watershed characteristics are also consumed by MSM. Microbial loadings data produced by MSM represent input to SDMPB which passes the information to downstream models. An example application to a real-world watershed, using the QMRA workflow, illustrates flow and microbial loadings at the pour point.
Discussion
QMRA organizes, captures, and executes microbial data to address impacts to mixed-use watersheds within a modeling workflow which involves watershed characterizations, microbial source mapping, and instantiation of the workflow in an assessment. Source-term data are critical to development of a QMRA and, thus, emphasized in this manuscript.
Limitations
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Although MSM and HSPF are not specific to the continental United States, the QMRA system is currently designed to access databases that specifically support assessments within the United States.
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MSM accounts for variations in monthly agricultural practices in a typical one-year cycle (i.e., January–December) but does not account for variations from year to year.
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The only direct input to streams from shedding is from cattle, neglecting other domestic animals, wildlife, and birds.
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Source-term data
Summary
A Quantitative Microbial Risk Assessment (QMRA) integrates databases and interdisciplinary, multiple media, exposure and effects models. Assessment of predicted indicator concentrations from modeling could be used in determining the appropriateness of waivers to criteria and standards concentration numbers on the basis of site-specific environmental settings and source conditions. Although QMRA does not preclude using source-term data and source and watershed models, it starts and is applied
Acknowledgements
The United States Environmental Protection Agency (EPA) through its Office of Research and Development partially funded and collaborated in the research described here under DW-089-92399101 to Idaho National Laboratory, DW-012-92348101 to the U.S. Department of Agriculture Agricultural Research Service, and EP-C-12-021 WA 3-52 to Eastern Research Group, Inc. Thanks are extended to Ms. Fran Rauschenberg of EPA for editing the document. Mention of trade names or commercial products does not
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Current address: Busan Development Institute, Busan, South Korea.