Intra-daily variation of Escherichia coli concentrations in agricultural irrigation ponds.

Microbial water quality is determined by comparing observed Escherichia coli concentrations with regulatory thresholds. Measured concentrations can be expected to change throughout the course of a day in response to diurnal variation in environmental conditions, such as solar radiation and temperature. Therefore, the time of day at which samples are taken is an important factor within microbial water quality measurements. However, little is known about the diurnal variations of E. coli concentrations in surface sources of irrigation water. The objectives of this work were to evaluate the intra-daily dynamics of E. coli in three irrigation ponds in Maryland over several years and to determine the water quality parameters to which E. coli populations are most sensitive. Water sampling was conducted across the ponds at 0900, 1200, and 1500 h on a total of 17 dates in the summers of 2019-2021. One-way ANOVA revealed significant diurnal variability in E. coli concentrations in Pond (P)1 and P2, whereas no significant effects were observed in P3. Escherichia coli die-off rates calculated between sampling time points in the same day were significantly higher in P2 than in P1 and P3, and these rates ranged from 0.005 to 0.799 h-1 across ponds. Concentrations of dissolved oxygen, pH, conductivity, and turbidity exerted the most control over E. coli populations. Results of this work demonstrate that sampling in the early-morning hours provides the most conservative assessment of the microbial quality of irrigation waters.

Prediction of E. coli Concentrations in Agricultural Pond Waters: Application and Comparison of Machine Learning Algorithms.

The microbial quality of irrigation water is an important issue as the use of contaminated waters has been linked to several foodborne outbreaks. To expedite microbial water quality determinations, many researchers estimate concentrations of the microbial contamination indicator Escherichia coli (E. coli) from the concentrations of physiochemical water quality parameters. However, these relationships are often non-linear and exhibit changes above or below certain threshold values. Machine learning (ML) algorithms have been shown to make accurate predictions in datasets with complex relationships. The purpose of this work was to evaluate several ML models for the prediction of E. coli in agricultural pond waters. Two ponds in Maryland were monitored from 2016 to 2018 during the irrigation season. E. coli concentrations along with 12 other water quality parameters were measured in water samples. The resulting datasets were used to predict E. coli using stochastic gradient boosting (SGB) machines, random forest (RF), support vector machines (SVM), and k-nearest neighbor (kNN) algorithms. The RF model provided the lowest RMSE value for predicted E. coli concentrations in both ponds in individual years and over consecutive years in almost all cases. For individual years, the RMSE of the predicted E. coli concentrations (log10 CFU 100 ml-1) ranged from 0.244 to 0.346 and 0.304 to 0.418 for Pond 1 and 2, respectively. For the 3-year datasets, these values were 0.334 and 0.381 for Pond 1 and 2, respectively. In most cases there was no significant difference (P > 0.05) between the RMSE of RF and other ML models when these RMSE were treated as statistics derived from 10-fold cross-validation performed with five repeats. Important E. coli predictors were turbidity, dissolved organic matter content, specific conductance, chlorophyll concentration, and temperature. Model predictive performance did not significantly differ when 5 predictors were used vs. 8 or 12, indicating that more tedious and costly measurements provide no substantial improvement in the predictive accuracy of the evaluated algorithms.

Modeling the photoinactivation and transport of somatic and F-specific coliphages at a Great Lakes beach.

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.

Depth-Dependent Response of Fecal Indicator Bacteria in Sediments to Changes in Water Column Nutrient Levels.

Concentrations of in bottom sediments can influence the assessment of microbial stream water quality. Runoff events bring nutrients to streams that can support the growth of in sediments. The objective of this work was to evaluate depth-dependent changes in populations after nutrients are introduced to the water column. Bovine feces were collected fresh and mixed into sediment. Studies were performed in a microcosm system with continuous flow of synthetic stream water over inoculated sediment. Dilutions of autoclaved bovine manure were added to water on Day 16 at two concentrations, and KBr tracer was introduced into the water column to evaluate ion diffusion. Concentrations of , total coliforms, and total aerobic heterotrophic bacteria, along with orthophosphate-P and ammonium N, were monitored in water and sediment for 32 d. Sediment samples were analyzed in 0- to 1-cm and 1- to 3-cm sectioned depths. Concentrations of and total coliforms in top sediments were approximately one order of magnitude greater than in bottom sediments throughout the experiment. Introduction of nutrients to the water column triggered an increase of nutrient levels in both top and bottom sediments and increased concentrations of bacteria in the water. However, the added nutrients had a limited effect on in sediment where bacterial inactivation continued. Vertical gradients of concentrations in sediments persisted during the inactivation periods both before and after nutrient addition to the water column.

Evaluating the influence of climate change on the fate and transport of fecal coliform bacteria using the modified SWAT model.

Fecal coliform bacteria (FCB) contamination of natural waters is a serious public health issue. Therefore, understanding and anticipating the fate and transport of FCB are important for reducing the risk of contracting diseases. The objective of this study was to analyze the impacts of climate change on the fate and transport of FCB. We modified both the soil and the in-stream bacteria modules in the soil and water assessment tool (SWAT) model and verified the prediction accuracy of seasonal variability of FCB loads using observations. Forty bias-correcting GCM-RCM projections were applied in the modified SWAT model to examine various future climate conditions at the end of this century (2076-2100). Lastly, we also compared the variability of FCB loads under current and future weather conditions using multi-model ensemble simulations (MMES). The modified SWAT model yielded a satisfactory performance with regard to the seasonal variability of FCB amounts in the soil and FCB loading to water bodies. The modified SWAT model presented substantial proliferation of FCB in the soil (30.1%-147.5%) due to an increase in temperature (25.1%). Also, increase in precipitation (53.3%) led to an increase in FCB loads (96.0%-115.5%) from the soil to water body. In the in-stream environment, resuspension from the stream bed was the dominant process affecting the amount of FCB in stream. Therefore, the final FCB loads increased by 71.2% because of the growing peak channel velocity and volume of water used due to an increase in precipitation. Based on the results of MMES, we concluded that the level of FCB would increase simultaneously in the soil as well as in stream by the end of this century. This study will aid in understanding the future variability of FCB loads as well as in preparing an effective management plan for FCB levels in natural waters.

Spatial Patterns of Escherichia coli Concentrations in Sediment before and after High-Flow Events in a First-Order Creek.

Understanding spatial patterns of in freshwater sediments is necessary to characterize sediments as microbial reservoirs and to evaluate the impact of sediment resuspension on microbial water quality in watersheds. Sediment particle size distributions and streambed concentrations were measured along a 500-m-long reach of a first-order creek 1 d before and on Days 1, 3, 6, and 10 after each of two artificial high-flow events, with natural high-flow events also occurring within the sampling periods. Spatial variability of was greater in sediments than in water within any given sampling; however, variation between sampling days was greater for water than for sediment. The mean relative difference analysis revealed temporally stable patterns of concentrations in sediments. rich locations along the reach corresponded to areas with higher organic matter and fine particle contents. Although low ( < 0.5 d) or negative survival rates were observed at most locations along the reach during times where no precipitation was recorded, a small number of locations showed such large concentration increase that on average the survival rate remained positive at the reach scale. The studied creek appears to have hot spots of concentration increase, where conditions for populations to increase are much more favorable than in most other locations across the reach. The effect of this increase can be seen at the reach scale but is difficult to discern without individual sampling that is dense in space and time.

Escherichia coli Export from Manured Fields Depends on the Time between the Start of Rainfall and Runoff Initiation.

After rainfall or irrigation begins, surface-applied chemicals and manure-borne microorganisms typically enter the soil with infiltration until the soil saturates, after which time the chemicals and microbes are exported from the field in the overland flow. This process is viewed as a reason for the dependence of chemical export on the time between rainfall start and runoff initiation that has been documented for agricultural chemicals. The objective of this work was to observe and quantify such dependence for released from solid farmyard dairy manure in field conditions. Experiments were performed for 6 yr and consisted of manure application followed by an immediate simulated rainfall event and a second event 1 wk later. The nonlinearity of the release seen in laboratory and plot studies did not manifest itself in the field. The number of exported cells in runoff was proportional to rainfall depth after runoff initiation in each trial. The proportionality coefficient, termed export rate, demonstrated a strong dependence on the runoff delay time that could be approximated with the exponential decrease. The export rate decreased by one order of magnitude when the rainfall depth at runoff initiation increased from 18 to 42 mm. The same dependence could approximate data from the simulated rainfall event 1 wk after the manure application, assuming that the initial content in manure after 1 wk of weathering was 10% of the initial content. Overall, accounting for the dependence of manure-borne export on the runoff delay time should improve the accuracy of export predictions related to the assessment of agricultural practices on microbial water quality.

Capturing Microbial Sources Distributed in a Mixed-use Watershed within an Integrated Environmental Modeling Workflow.

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.

Development and evaluation of the bacterial fate and transport module for the Agricultural Policy/Environmental eXtender (APEX) model.

The Agricultural Policy/Environmental eXtender (APEX) is a watershed-scale water quality model that includes detailed representation of agricultural management. The objective of this work was to develop a process-based model for simulating the fate and transport of manure-borne bacteria on land and in streams with the APEX model. The bacteria model utilizes manure erosion rates to estimate the amount of edge-of-field bacteria export. Bacteria survival in manure is simulated as a two-stage process separately for each manure application event. In-stream microbial fate and transport processes include bacteria release from streambeds due to sediment resuspension during high flow events, active release from the streambed sediment during low flow periods, bacteria settling with sediment, and survival. Default parameter values were selected from published databases and evaluated based on field observations. The APEX model with the newly developed microbial fate and transport module was applied to simulate fate and transport of the fecal indicator bacterium Escherichia coli in the Toenepi watershed, New Zealand that was monitored for seven years. The stream network of the watershed ran through grazing lands with daily bovine waste deposition. Results show that the APEX with the bacteria module reproduced well the monitored pattern of E. coli concentrations at the watershed outlet. The APEX with the microbial fate and transport module will be utilized for predicting microbial quality of water as affected by various agricultural practices, evaluating monitoring protocols, and supporting the selection of management practices based on regulations that rely on fecal indicator bacteria concentrations.

Hydrological modeling of Fecal Indicator Bacteria in a tropical mountain catchment.

The occurrence of pathogen bacteria in surface waters is a threat to public health worldwide. In particular, inadequate sanitation resulting in high contamination of surface water with pathogens of fecal origin is a serious issue in developing countries such as Lao P.D.R. Despite the health implications of the consumption of contaminated surface water, the environmental fate and transport of pathogens of fecal origin and their indicators (Fecal Indicator Bacteria or FIB) are still poorly known in tropical areas. In this study, we used measurements of flow rates, suspended sediments and of the FIB Escherichia coli (E. coli) in a 60-ha catchment in Northern Laos to explore the ability of the Soil and Water Assessment Tool (SWAT) to simulate watershed-scale FIB fate and transport. We assessed the influences of 3 in-stream processes, namely bacteria deposition and resuspension, bacterial regrowth, and hyporheic exchange (i.e. transient storage) on predicted FIB numbers. We showed that the SWAT model in its original version does not correctly simulate small E. coli numbers during the dry season. We showed that model's performance could be improved when considering the release of E. coli together with sediment resuspension. We demonstrated that the hyporheic exchange of bacteria across the Sediment-Water Interface (SWI) should be considered when simulating FIB concentration not only during wet weather, but also during the dry season, or baseflow period. In contrast, the implementation of the regrowth process did not improve the model during the dry season without inducing an overestimation during the wet season. This work thus underlines the importance of taking into account in-stream processes, such as deposition and resuspension, regrowth and hyporheic exchange, when using SWAT to simulate FIB dynamics in surface waters.

Modeling the interannual variability of microbial quality metrics of irrigation water in a Pennsylvania stream.

Knowledge of the microbial quality of irrigation waters is extremely limited. For this reason, the US FDA has promulgated the Produce Rule, mandating the testing of irrigation water sources for many farms. The rule requires the collection and analysis of at least 20 water samples over two to four years to adequately evaluate the quality of water intended for produce irrigation. The objective of this work was to evaluate the effect of interannual weather variability on surface water microbial quality. We used the Soil and Water Assessment Tool model to simulate E. coli concentrations in the Little Cove Creek; this is a perennial creek located in an agricultural watershed in south-eastern Pennsylvania. The model performance was evaluated using the US FDA regulatory microbial water quality metrics of geometric mean (GM) and the statistical threshold value (STV). Using the 90-year time series of weather observations, we simulated and randomly sampled the time series of E. coli concentrations. We found that weather conditions of a specific year may strongly affect the evaluation of microbial quality and that the long-term assessment of microbial water quality may be quite different from the evaluation based on short-term observations. The variations in microbial concentrations and water quality metrics were affected by location, wetness of the hydrological years, and seasonality, with 15.7-70.1% of samples exceeding the regulatory threshold. The results of this work demonstrate the value of using modeling to design and evaluate monitoring protocols to assess the microbial quality of water used for produce irrigation.

Modeling fate and transport of fecally-derived microorganisms at the watershed scale: State of the science and future opportunities.

Natural waters serve as habitat for a wide range of microorganisms, a proportion of which may be derived from fecal material. A number of watershed models have been developed to understand and predict the fate and transport of fecal microorganisms within complex watersheds, as well as to determine whether microbial water quality standards can be satisfied under site-specific meteorological and/or management conditions. The aim of this review is to highlight and critically evaluate developments in the modeling of microbial water quality of surface waters over the last 10 years and to discuss the future of model development and application at the watershed scale, with a particular focus on fecal indicator organisms (FIOs). In doing so, an agenda of research opportunities is identified to help deliver improvements in the modeling of microbial water quality draining through complex landscape systems. This comprehensive review therefore provides a timely steer to help strengthen future modeling capability of FIOs in surface water environments and provides a useful resource to complement the development of risk management strategies to reduce microbial impairment of freshwater sources.

Rainfall intensity effects on removal of fecal indicator bacteria from solid dairy manure applied over grass-covered soil.

The rainfall-induced release of pathogens and microbial indicators from land-applied manure and their subsequent removal with runoff and infiltration precedes the impairment of surface and groundwater resources. It has been assumed that rainfall intensity and changes in intensity during rainfall do not affect microbial removal when expressed as a function of rainfall depth. The objective of this work was to test this assumption by measuring the removal of Escherichia coli, enterococci, total coliforms, and chloride ion from dairy manure applied in soil boxes containing fescue, under 3, 6, and 9cmh(-1) of rainfall. Runoff and leachate were collected at increasing time intervals during rainfall, and post-rainfall soil samples were taken at 0, 2, 5, and 10cm depths. Three kinetic-based models were fitted to the data on manure-constituent removal with runoff. Rainfall intensity appeared to have positive effects on rainwater partitioning to runoff, and removal with this effluent type occurred in two stages. While rainfall intensity generally did not impact the parameters of runoff-removal models, it had significant, inverse effects on the numbers of bacteria remaining in soil after rainfall. As rainfall intensity and soil profile depth increased, the numbers of indicator bacteria tended to decrease. The cumulative removal of E. coli from manure exceeded that of enterococci, especially in the form of removal with infiltration. This work may be used to improve the parameterization of models for bacteria removal with runoff and to advance estimations of depths of bacteria removal with infiltration, both of which are critical to risk assessment of microbial fate and transport in the environment.

Predicting microbial water quality with models: Over-arching questions for managing risk in agricultural catchments.

The application of models to predict concentrations of faecal indicator organisms (FIOs) in environmental systems plays an important role for guiding decision-making associated with the management of microbial water quality. In recent years there has been an increasing demand by policy-makers for models to help inform FIO dynamics in order to prioritise efforts for environmental and human-health protection. However, given the limited evidence-base on which FIO models are built relative to other agricultural pollutants (e.g. nutrients) it is imperative that the end-user expectations of FIO models are appropriately managed. In response, this commentary highlights four over-arching questions associated with: (i) model purpose; (ii) modelling approach; (iii) data availability; and (iv) model application, that must be considered as part of good practice prior to the deployment of any modelling approach to predict FIO behaviour in catchment systems. A series of short and longer-term research priorities are proposed in response to these questions in order to promote better model deployment in the field of catchment microbial dynamics.

Irrigation waters and pipe-based biofilms as sources for antibiotic-resistant bacteria.

The presence of antibiotic-resistant bacteria in environmental surface waters has gained recent attention. Wastewater and drinking water distribution systems are known to disseminate antibiotic-resistant bacteria, with the biofilms that form on the inner-surfaces of the pipeline as a hot spot for proliferation and gene exchange. Pipe-based irrigation systems that utilize surface waters may contribute to the dissemination of antibiotic-resistant bacteria in a similar manner. We conducted irrigation events at a perennial stream on a weekly basis for 1 month, and the concentrations of total heterotrophic bacteria, total coliforms, and fecal coliforms, as well as the concentrations of these bacterial groups that were resistant to ampicillin and tetracycline, were monitored at the intake water. Prior to each of the latter three events, residual pipe water was sampled and 6-in. sections of pipeline (coupons) were detached from the system, and biofilm from the inner-wall was removed and analyzed for total protein content and the above bacteria. Isolates of biofilm-associated bacteria were screened for resistance to a panel of seven antibiotics, representing five antibiotic classes. All of the monitored bacteria grew substantially in the residual water between irrigation events, and the biomass of the biofilm steadily increased from week to week. The percentages of biofilm-associated isolates that were resistant to antibiotics on the panel sometimes increased between events. Multiple-drug resistance was observed for all bacterial groups, most often for fecal coliforms, and the distributions of the numbers of antibiotics that the total coliforms and fecal coliforms were resistant to were subject to change from week to week. Results from this study highlight irrigation waters as a potential source for antibiotic-resistant bacteria, which can subsequently become incorporated into and proliferate within irrigation pipe-based biofilms.

Release and Removal of Microorganisms from Land-Deposited Animal Waste and Animal Manures: A Review of Data and Models.

Microbial pathogens present a leading cause of impairment to rivers, bays, and estuaries in the United States, and agriculture is often viewed as the major contributor to such contamination. Microbial indicators and pathogens are released from land-applied animal manure during precipitation and irrigation events and are carried in overland and subsurface flow that can reach and contaminate surface waters and ground water used for human recreation and food production. Simulating the release and removal of manure-borne pathogens and indicator microorganisms is an essential component of microbial fate and transport modeling regarding food safety and water quality. Although microbial release controls the quantities of available pathogens and indicators that move toward human exposure, a literature review on this topic is lacking. This critical review on microbial release and subsequent removal from manure and animal waste application areas includes sections on microbial release processes and release-affecting factors, such as differences in the release of microbial species or groups; bacterial attachment in turbid suspensions; animal source; animal waste composition; waste aging; manure application method; manure treatment effect; rainfall intensity, duration, and energy; rainfall recurrence; dissolved salts and temperature; vegetation and soil; and spatial and temporal scale. Differences in microbial release from liquid and solid manures are illustrated, and the influential processes are discussed. Models used for simulating release and removal and current knowledge gaps are presented, and avenues for future research are suggested.

Solid Manure As a Source of Fecal Indicator Microorganisms: Release under Simulated Rainfall.

Understanding and quantifying microbial release from manure is a precondition to estimation and management of microbial water quality. The objectives of this work were to determine the effects of rainfall intensity and surface slope on the release of Escherichia coli, enterococci, total coliforms, and dissolved chloride from solid dairy manure and to assess the performance of the one-parametric exponential model and the two-parametric Bradford-Schijven model when simulating the observed release. A controlled-intensity rainfall simulator induced 1 h of release in runoff/leachate partitioning boxes at three rainfall intensities (30, 60, and 90 mm h(-1)) and two surface slopes (5% and 20%). Bacterial concentrations in initial release were more than 1 order of magnitude lower than their starting concentrations in manure. As bacteria were released, they were partitioned into runoff and leachate at similar concentrations, but in different volumes, depending on slope. Bacterial release occurred in two stages that corresponded to mechanisms associated with release of manure liquid- and solid-phases. Parameters of the two models fitted to the bacterial release dependencies on rainfall depth were not significantly affected by rainfall intensity or slope. Based on model performance tests, the Bradford-Schijven model is recommended for simulating bacterial release from solid manure.

Colloid filtration in surface dense vegetation: experimental results and theoretical predictions.

Understanding colloid and colloid-facilitated contaminant transport in overland flow through dense vegetation is important to protect water quality in the environment, especially for water bodies receiving agricultural and urban runoff. In previous studies, a single-stem efficiency theory for rigid and clean stem systems was developed to predict colloid filtration by plant stems of vegetation in laminar overland flow. Hence, in order to improve the accuracy of the single-stem efficiency theory to real dense vegetation system, we incorporated the effect of natural organic matter (NOM) on the filtration of colloids by stems. Laboratory dense vegetation flow chamber experiments and model simulations were used to determine the kinetic deposition (filtration) rate of colloids under various conditions. The results show that, in addition to flow hydrodynamics and solution chemistry, steric repulsion afforded by NOM layer on the plants stem surface also plays a significant role in controlling colloid deposition on vegetation in overland flow. For the first time, a refined single-stem efficiency theory with considerations of the NOM effect is developed that describes the experimental data with good accuracy. This theory can be used to not only help construct and refine mathematical models of colloid transport in real vegetation systems in overland flow, but also inform the development of theories of colloid deposition on NOM-coated surfaces in natural, engineered, and biomedical systems.

Rainfall-induced fecal indicator organisms transport from manured fields: model sensitivity analysis.

Microbial quality of surface waters attracts attention due to food- and waterborne disease outbreaks. Fecal indicator organisms (FIOs) are commonly used for the microbial pollution level evaluation. Models predicting the fate and transport of FIOs are required to design and evaluate best management practices that reduce the microbial pollution in ecosystems and water sources and thus help to predict the risk of food and waterborne diseases. In this study we performed a sensitivity analysis for the KINEROS/STWIR model developed to predict the FIOs transport out of manured fields to other fields and water bodies in order to identify input variables that control the transport uncertainty. The distributions of model input parameters were set to encompass values found from three-year experiments at the USDA-ARS OPE3 experimental site in Beltsville and publicly available information. Sobol' indices and complementary regression trees were used to perform the global sensitivity analysis of the model and to explore the interactions between model input parameters on the proportion of FIO removed from fields. Regression trees provided a useful visualization of the differences in sensitivity of the model output in different parts of the input variable domain. Environmental controls such as soil saturation, rainfall duration and rainfall intensity had the largest influence in the model behavior, whereas soil and manure properties ranked lower. The field length had only moderate effect on the model output sensitivity to the model inputs. Among the manure-related properties the parameter determining the shape of the FIO release kinetic curve had the largest influence on the removal of FIOs from the fields. That underscored the need to better characterize the FIO release kinetics. Since the most sensitive model inputs are available in soil and weather databases or can be obtained using soil water models, results indicate the opportunity of obtaining large-scale estimates of FIO transport from fields based on publicly available rather than site-specific information.

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.