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Fecal indicator organisms (FIOs), such as Escherichia coli and enterococci, are often used as surrogates of contamination in the context of beach management; however, bacteriophages may be more reliable indicators than FIO due to their similarity to viral pathogens in terms of size and persistence in the environment. In the past, mechanistic modeling of environmental contamination has focused on FIOs, with virus and bacteriophage modeling efforts remaining limited. In this paper, we describe the development and application of a fate and transport model of somatic and F-specific coliphages for the Washington Park beach in Lake Michigan, which is affected by riverine outputs from the nearby Trail Creek. A three-dimensional model of coliphage transport and photoinactivation was tested and compared with a previously reported E. coli fate and transport model. The light-based inactivation of the phages was modeled using organism-specific action spectra. Results indicate that the coliphage models outperformed the E. coli model in terms of reliably predicting observed E. coli/coliphage concentrations at the beach. This is possibly due to the presence of additional E. coli sources that were not accounted for in the modeling. The coliphage models can be used to test hypotheses about potential sources and their behavior and for predictive modeling.

Microbial fate and transport in watersheds should include a microbial source apportionment analysis that estimates the importance of each source, relative to each other and in combination, by capturing their impacts spatially and temporally under various scenarios. A loosely configured software infrastructure was used in microbial source-to-receptor modeling by focusing on animal- and human-impacted mixed-use watersheds. Components include data collection software, a microbial source module that determines loading rates from different sources, a watershed model, an inverse model for calibrating flows and microbial densities, tabular and graphical viewers, software to convert output to different formats, and a model for calculating risk from pathogen exposure. The system automates, as much as possible, the manual process of accessing and retrieving data and completes input data files of the models. The workflow considers land-applied manure from domestic animals on undeveloped areas; direct shedding (excretion) on undeveloped lands by domestic animals and wildlife; pastureland, cropland, forest, and urban or engineered areas; sources that directly release to streams from leaking septic systems; and shedding by domestic animals directly to streams. The infrastructure also considers point sources from regulated discharges. An application is presented on a real-world watershed and helps answer questions such as: What are the major microbial sources? What practices contribute to contamination at the receptor location? What land-use types influence contamination at the receptor location? and Under what conditions do these sources manifest themselves? This research aims to improve our understanding of processes related to pathogen and indicator dynamics in mixed-use watershed systems.

Many watershed models simulate overland and instream microbial fate and transport, but few provide loading rates on land surfaces and point sources to the waterbody network. This paper describes the underlying equations for microbial loading rates associated with 1) land-applied manure on undeveloped areas from domestic animals; 2) direct shedding (excretion) on undeveloped lands by domestic animals and wildlife; 3) urban or engineered areas; and 4) point sources that directly discharge to streams from septic systems and shedding by domestic animals. A microbial source module, which houses these formulations, is part of a workflow containing multiple models and databases that form a loosely configured modeling infrastructure which supports watershed-scale microbial source-to-receptor modeling by focusing on animal- and human-impacted catchments. A hypothetical application – accessing, retrieving, and using real-world data – demonstrates how the infrastructure can automate many of the manual steps associated with a standard watershed assessment, culminating in calibrated flow and microbial densities at the watershed’s pour point.

This review outlines major directions of simpler model development in environmental modeling, metamodeling, statistical-regression- and machine-learning-based empirical models, and mechanistic mode...

Understanding pathogenic and indicator bacteria survival in soils is essential for assessing the potential of microbial contamination of water and produce. The objective of this work was to evaluate the effects of soil properties, animal source, experimental conditions, and the application method on temperature dependencies of manure-borne generic , O157:H7, and fecal coliforms survival in soils. A literature search yielded 151 survival datasets from 70 publications. Either one-stage or two-stage kinetics was observed in the survival datasets. We used duration and rate of the logarithm of concentration change as parameters of the first stage in the two-stage kinetics data. The second stage of the two-stage kinetics and the one-stage kinetics were simulated with the model to find the dependence of the inactivation rate on temperature. Classification and regression trees and linear regressions were applied to parameterize the kinetics. Presence or absence of two-stage kinetics was controlled by temperature, soil texture, soil water content, and for fine-textured soils by setting experiments in the field or in the laboratory. The duration of the first stage was predominantly affected by soil water content and temperature. In the model dependencies of inactivation rates on temperature, parameter estimates were significantly affected by the laboratory versus field conditions and by the application method, whereas inactivation rates at 20°C were significantly affected by all survival and management factors. Results of this work can provide estimates of coliform survival parameters for models of microbial water quality.

Modeling the fate and transport of Escherichia coli is of substantial interest because of how this organism serves as an indicator of fecal contamination in microbial water quality assessment. The efficacy of models used to assess the export of E. coli from agricultural fields is dependent, in part, on submodels they utilize to simulate E. coli release from land-applied manure and animal waste. Although several release submodels have been proposed, they have only been evaluated and compared with data from laboratory or small plot E. coli release experiments. Our objective was to evaluate and compare performances of three manure-borne bacteria release submodels at the field-scale. Models evaluated included the exponential release model (EM), the two-parametric Bradford and Schijven model (B-S), and the two-parametric Vadas-Kleinman-Sharpley model (VKS). Each model was independently incorporated and tested as a submodel within the export model KINEROS2/STWIR, using data of E. coli in runoff. Dairy manure was uniformly applied via surface broadcasting once a year for six consecutive years on a 0.28-ha experimental field site. Two irrigation events followed each application: the first immediately followed the initial application and the second occurred 1 week later. Manure and soil samples were collected before and after irrigation, respectively, and manure, soil, and edge-of-field runoff samples were analyzed for E. coli. Model performance was evaluated with the Akaike criterion, coefficients of determination, and standard error values. The percentage of exported manure-borne E. coli varied from 0.1 to 10% in most cases, generally reflecting the lag time between initiation of irrigation and initiation of edge-of-field runoff. The export model performed better when using the VKS submodel which was preferred in 52% of cases. The B-S and EM submodels were preferred in 24 and 6% of cases, respectively. Submodels were equally efficient in 18% of cases. Two-parametric submodels were ultimately preferred over the single parameter submodel.

Scientific evidence supporting recreational water quality benchmarks primarily stems from epidemiological studies conducted at beaches impacted by human fecal sources. Epidemiological studies conducted at locations impacted by non-human faecal sources have provided ambiguous and inconsistent estimates of risk. Quantitative Microbial Risk Assessment (QMRA) is another tool to evaluate potential human health risks from recreational exposures to non-human faecal contamination. The potential risk differential between human and selected non-human faecal sources has been characterized previously for direct deposition of animal feces to water. In this evaluation, we examine the human illness potential from a recreational exposure to freshwater impacted by rainfall-induced runoff containing agricultural animal faecal material. Risks associated with these sources would be at least an order of magnitude lower than the benchmark level of public health protection associated with current US recreational water quality criteria, which are based on contamination from human sewage sources. We examine the human illness potential from exposure to rainfall-induced runoff.The predicted risks are lower than the benchmark level of protection.This risk assessment should be helpful to inform public health decision-making.

Integrated environmental modeling (IEM) includes interdependent science-based components that comprise an appropriate software modeling system and are responsible for consuming and producing information as part of the system, but moving information from one component to another (i.e., interoperability) is the responsibility of the IEM software system. We describe and discuss the Framework for Risk Analysis in Multimedia Environmental Systems (FRAMES), a component-based IEM system, from the standpoint of software design requirements which define system functionalities. Design requirements were identified in a series of workshops, attended by IEM practitioners, and reported in the development of a number of IEM software systems. The requirements cover issues associated with standards, component connectivity, linkage protocols, system architecture and functionality, and web-based access, all of which facilitate the creation of plug & play components from stand-alone models through a series of software support tools and standards.

Standardized methods are often used to assess the likelihood of a human-health effect from exposure to a specified hazard, and inform opinions and decisions about risk management and communication. A Quantitative Microbial Risk Assessment (QMRA) is specifically adapted to detail potential human-health risks from exposure to pathogens; it can include fate and transport models for various media, including the source zone (initial fecal release), air, soil/land surface, surface water, vadose zone and aquifer. The analysis step of a QMRA can be expressed as a system of computer-based data delivery and modeling that integrates interdisciplinary, multiple media, exposure and effects models and databases. Although QMRA does not preclude using source-term and fate and transport models, it is applied most commonly where the source-term is represented by the receptor location (i.e., exposure point), so the full extent of exposure scenarios has not been rigorously modeled. An integrated environmental modeling infrastructure is, therefore, ideally suited to include fate and transport considerations and link the risk assessment paradigm between source and receptor seamlessly. A primary benefit of the source-to-outcome approach is that it allows an expanded view of relevant cause-and-effect relationships, which facilitate consideration of management options related to source terms and their fate and transport pathways. The Framework for Risk Analysis in Multimedia Environmental Systems (FRAMES) provides software technology for analysts to insert appropriate models and databases that fit the problem statement and design and construct QMRAs that are reproducible, flexible, transferable, reusable, and transparent. A sample application using different models and databases registered with FRAMES is presented. It illustrates how models are linked to assess six different manure-based contaminant sources, following three pathogens (Salmonella eterica, Cryptosporidium spp., and Escherichia coli O157:H7) to a receptor where exposures and health risk impacts are then evaluated. The modeling infrastructure demonstrates how analysts could use the system to discern which pathogens might be important and when, and which sources could contribute to their importance. IEM FRAMES is a flexible tool to support custom-designed source-to-receptor QMRAs.FRAMES captures multiple and user-defined modeling approaches.An example QMRA assesses source apportionment and pathogens of importance.Pathogen fate and transport modeling is linked to point-of-exposure risk analysis.IEM links environmental and microbial characteristics with an uncertainty analysis.

A series of simulated rainfall-runoff experiments with applications of different manure types (cattle solid pats, poultry dry litter, swine slurry) was conducted across four seasons on a field containing 36 plots (0.75 × 2 m each), resulting in 144 rainfall-runoff events. Simulating time-varying release of Escherichia coli, enterococci, and fecal coliforms from manures applied at typical agronomic rates evaluated the efficacy of the Bradford–Schijven model modified by adding terms for release efficiency and transportation loss. Two complementary, parallel approaches were used to calibrate the model and estimate microbial release parameters. The first was a four-step sequential procedure using the inverse model PEST, which provides appropriate initial parameter values. The second utilized a PEST/bootstrap procedure to estimate average parameters across plots, manure age, and microbe, and to provide parameter distributions. The experiment determined that manure age, microbe, and season had no clear relationship to the release curve. Cattle solid pats released microbes at a different, slower rate than did poultry dry litter or swine slurry, which had very similar release patterns. These findings were consistent with other published results for both bench- and field-scale, suggesting the modified Bradford–Schijven model can be applied to microbial release from manure.

A comprehensive benchmarking is being performed between three multimedia risk assessment models: RESRAD, MMSOILS, and MEPAS. Each multimedia model is comprised of a suite of modules (e.g., groundwater, air, surface water, exposure, and risk/hazard), all of which can impact the estimation of the human health risk. As a component of the comprehensive benchmarking exercise, the saturated-zone modules of each model were applied to an environmental release scenario, where uranium-234 was released from the waste site to a saturated zone. Uranium-234 time-varying emission rates exiting from the source and concentrations at three downgradient locations (0, 150, and 1500 m) are compared for each multimedia model. Time-varying concentrations for uranium-234 decay products (e.g., thorium-230, radium-226, and lead-210) at the 1500-m location are also presented. Different results reported for RESRAD, MMSOILS, and MEPAS are solely due to the assumptions and mathematical constructs inherently built into each mo...

Integrated environmental modeling (IEM) is inspired by modern environmental problems, decisions, and policies and enabled by transdisciplinary science and computer capabilities that allow the environment to be considered in a holistic way. The problems are characterized by the extent of the environmental system involved, dynamic and interdependent nature of stressors and their impacts, diversity of stakeholders, and integration of social, economic, and environmental considerations. IEM provides a science-based structure to develop and organize relevant knowledge and information and apply it to explain, explore, and predict the behavior of environmental systems in response to human and natural sources of stress. During the past several years a number of workshops were held that brought IEM practitioners together to share experiences and discuss future needs and directions. In this paper we organize and present the results of these discussions. IEM is presented as a landscape containing four interdependent elements: applications, science, technology, and community. The elements are described from the perspective of their role in the landscape, current practices, and challenges that must be addressed. Workshop participants envision a global scale IEM community that leverages modern technologies to streamline the movement of science-based knowledge from its sources in research, through its organization into databases and models, to its integration and application for problem solving purposes. Achieving this vision will require that the global community of IEM stakeholders transcend social, and organizational boundaries and pursue greater levels of collaboration. Among the highest priorities for community action are the development of standards for publishing IEM data and models in forms suitable for automated discovery, access, and integration; education of the next generation of environmental stakeholders, with a focus on transdisciplinary research, development, and decision making; and providing a web-based platform for community interactions (e.g., continuous virtual workshops). © 2012 .