Gene Whelan

Design of a component-based integrated environmental modeling framework

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.

Estimated human health risks from recreational exposures to stormwater runoff containing animal faecal material

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.

Can E. coli or thermotolerant coliform concentrations predict pathogen presence or prevalence in irrigation waters

Abstract An increase in food-borne illnesses in the United States has been associated with fresh produce consumption. Irrigation water presents recognized risks for microbial contamination of produce. Water quality criteria rely on indicator bacteria. The objective of this review was to collate and summarize experimental data on the relationships between pathogens and thermotolerant coliform (THT) and/or generic E. coli, specifically focusing on surface fresh waters used in or potentially suitable for irrigation agriculture. We analyzed peer-reviewed publications in which concentrations of E. coli or THT coliforms in surface fresh waters were measured along with concentrations of one or more of waterborne and food-borne pathogenic organisms. The proposed relationships were significant in 35% of all instances and not significant in 65% of instances. Coliform indicators alone cannot provide conclusive, non-site-specific and non-pathogen-specific information about the presence and/or concentrations of most important pathogens in surface waters suitable for irrigation. Standards of microbial water quality for irrigation can rely not only on concentrations of indicators and/or pathogens, but must include references to crop management. Critical information on microbial composition of actual irrigation waters to support criteria of microbiological quality of irrigation waters appears to be lacking and needs to be collected.

Using the Q10 model to simulate E. coli survival in cowpats on grazing lands

Microbiological quality of surface waters can be affected by microbial load in runoff from grazing lands. This effect, with other factors, depends on the survival of microorganisms in animal waste deposited on pastures. Since temperature is a leading environmental parameter affecting survival, it indirectly impacts water microbial quality. The Q10 model is widely used to predict the effect of temperature on rates of biological processes, including survival. Objectives of this work were to (i) evaluate the applicability of the Q10 model to Escherichia coli inactivation in bovine manure deposited on grazing land (i.e., cowpats) and (ii) identify explanatory variables for the previously reported E. coli survival dynamics in cowpats. Data utilized in this study include published results on E. coli concentrations in natural and repacked cowpats from research conducted the U.S. (Virginia and Maryland), New Zealand, and the United Kingdom. Inspection of the datasets led to conceptualizing E. coli survival (in cowpats) as a two-stage process, in which the initial stage was due to growth, inactivation or stationary state of the population and the second stage was the approximately first-order inactivation. Applying the Q10 model to these datasets showed a remarkable similarity in inactivation rates, using the thermal time. The reference inactivation rate constant of 0.042 (thermal days)(-1) at 20 °C gave a good approximation (R(2)=0.88) of all inactivation stage data with Q10=1.48. The reference inactivation rate constants in individual studies were no different from the one obtained by pooling all data (P<0.05). The rate of logarithm of the E. coli concentration change during the first stage depended on temperature. Duration of the first stage, prior to the first-order inactivation stage and the initial concentration of E. coli in cowpats, could not be predicted from available data. Diet and age are probable factors affecting these two parameters however, until their environmental and management predictors are known, microbial water quality modeling must treat them as a stochastic source of uncertainty in simulation results.

Comparing temperature effects on Escherichia coli, Salmonella, and Enterococcus survival in surface waters

The objective of this study was to compare dependency of survival rates on temperature for indicator organisms Escherichia coli and Enterococcus and the pathogen Salmonella in surface waters. A database of 86 survival datasets from peer‐reviewed papers on inactivation of E. coli, Salmonella and Enterococcus in marine waters and of E. coli and Salmonella in lake waters was assembled. The Q10 model was used to express temperature effect on survival rates obtained from linear sections of semi‐logarithmic survival graphs. Available data were insufficient to establish differences in survival rates and temperature dependencies for marine waters where values of Q10 = 3 and a survival rate of 0·7 day−1 could be applied. The Q10 values in lake waters were substantially lower in marine waters, and Salmonella inactivation in lake water was, on average, twice as fast as E. coli; data on E. coli substantially outnumber data on Enterococcus and Salmonella. The relative increase in inactivation with increase in temperature is higher in marine waters than lake water, and differences in inactivation between Salmonella and E. coli at a given temperature were significant in lake water but not in marine waters.

An integrated environmental modeling framework for performing Quantitative Microbial Risk Assessments

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.

Survival of Manure-borne and Fecal Coliforms in Soil: Temperature Dependence as Affected by Site-Specific Factors.

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.

Concepts Associated with Transferring Temporal and Spatial Boundary Conditions between Modules in the Framework for Risk Analysis in Multimedia Environmental Systems (FRAMES)

This document describes concepts associated with transferring temporal and spatial boundary conditions between modules in FRAMES and how FRAMES might consider dynamic feedback.

Rainfall-induced release of microbes from manure: model development, parameter estimation, and uncertainty evaluation on small plots.

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.

Groundwater Modeling System Linkage with the Framework for Risk Analysis in Multimedia Environmental Systems

The information in this document summarizes the approach that is used to link FRAMES-2 with GMS. This linkage will provide the user with the ability to (1) send information to a specific model in GMS, thereby modifying the models input information, as allowed by the model developer, (2) run the executable of the numerical model contained in GMS, and (3) extract, from the appropriate GMS output, information required for consumption by downstream models, which are also linked with FRAMES-2.