Multiscale spatiotemporal variability of fecal indicator bacteria and associated particle size distributions in the sandy bottom sediments of a Pennsylvania creek.

Concentrations of the fecal indicator bacteria (FIB) Escherichia coli and enterococci are used to assess microbial impairment in irrigation and recreation water sources. Although the FIB concentrations' variability at large temporal scales, such as seasons, and large spatial scales encompassing different land use has been studied, the knowledge about smaller scale variability remains sparse. This work aimed to research the small-scale variability of E. coli and enterococci in a montane creek with sandy bottom sediments. Sediment samples were collected weekly for a year in triplicate at sampling sites in a forested headwater, an agricultural area, and a mixed urban-agricultural area. The average weekly change in concentrations was from two times at the forested site to five times at the urban-agricultural site. Mean relative deviations from averages across sampling locations increased from -25% at the forested site to 45% at the urban-agricultural site. This trend was also observed separately over the cold and warm seasons. Over a week without precipitation, E. coli concentrations decreased on average by 20% in warm period and by 45% in cold period; the enterococci concentration declined by 12% in both cold and warm periods. The sediment particle size distributions were significantly different among the three sites and between the cold and warm seasons. Rankings of sediment fine mass fractions and FIB concentrations were positively correlated at two of three sampling sites in more than 70% of observation dates. The results of this work indicate the need to evaluate the uncertainty of sediment FIB concentrations before designing sediment FIB monitoring quality.

Long short-term memory models of water quality in inland water environments.

Water quality is substantially influenced by a multitude of dynamic and interrelated variables, including climate conditions, landuse and seasonal changes. Deep learning models have demonstrated predictive power of water quality due to the superior ability to automatically learn complex patterns and relationships from variables. Long short-term memory (LSTM), one of deep learning models for water quality prediction, is a type of recurrent neural network that can account for longer-term traits of time-dependent data. It is the most widely applied network used to predict the time series of water quality variables. First, we reviewed applications of a standalone LSTM and discussed its calculation time, prediction accuracy, and good robustness with process-driven numerical models and the other machine learning. This review was expanded into the LSTM model with data pre-processing techniques, including the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise method and Synchrosqueezed Wavelet Transform. The review then focused on the coupling of LSTM with a convolutional neural network, attention network, and transfer learning. The coupled networks demonstrated their performance over the standalone LSTM model. We also emphasized the influence of the static variables in the model and used the transformation method on the dataset. Outlook and further challenges were addressed. The outlook for research and application of LSTM in hydrology concludes the review.

Temporal Stability of Salmonella enterica and Listeria monocytogenes in Surface Waters Used for Irrigation in the Mid-Atlantic United States.

Enteric bacterial pathogen levels can influence the suitability of irrigation water sources for fruits and vegetables. We hypothesize that stable spatial patterns of Salmonella enterica and Listeria monocytogenes levels may exist across surface water sources in the Mid-Atlantic U.S. Water samples were collected at four streams and two pond sites in the mid-Atlantic U.S. over 2 years, biweekly during the fruit and vegetable growing seasons, and once a month during nongrowing seasons. Two stream sites and one pond site had significantly different mean concentrations in growing and nongrowing seasons. Stable spatial patterns were determined for relative differences between the site concentrations and average concentration of both pathogens across the study area. Mean relative differences were significantly different from zero at four of the six sites for S. enterica and three of six sites for L. monocytogenes. There was a similarity between the mean relative difference distribution between sites over growing season, nongrowing season, and the entire observation period. Mean relative differences were determined for temperature, oxidation-reduction potential, specific electrical conductance, pH, dissolved oxygen, turbidity, and cumulative rainfall. A moderate-to-strong Spearman correlation (rs > 0.657) was found between spatial patterns of S. enterica and 7-day rainfall, and between relative difference patterns of L. monocytogenes and temperature (rs = 0.885) and dissolved oxygen (rs = -0.885). Persistence in ranking sampling sites by the concentrations of the two pathogens was also observed. Finding spatially stable patterns in pathogen concentrations highlights spatiotemporal dynamics of these microorganisms across the study area can facilitate the design of an effective microbial water quality monitoring program for surface irrigation water.

Hierarchical deep learning model to simulate phytoplankton at phylum/class and genus levels and zooplankton at the genus level.

Harmful algal blooms (HABs) have become a global issue, affecting public health and water industries in numerous countries. Because funds for monitoring HABs are limited, model development may be an alternative approach for understanding and managing HABs. Continuous monitoring based on grab sampling is time-consuming, costly, and labor-intensive. However, improving simulation performance remains a major challenge in modeling, and current methods are limited to simulating phytoplankton (e.g., Microcystis sp., Anabaena sp., Aulacoseira sp., Cyclotella sp., Pediastrum sp., and Eudorina sp.) and zooplankton (e.g., Cyclotella sp., Pediastrum sp., and Eudorina sp.) at the genus level. The traditional modeling approach is limited for evaluating the interactions between phytoplankton and zooplankton. Recently, deep learning (DL) models have been proposed for solving modeling problems because of their large data handling capabilities and model structure flexibilities. In this study, we evaluated the applicability of DL for simulating phytoplankton at the phylum/class and genus levels and zooplankton at the genus level. Our work was an explicit representation of the taxonomic and ecological hierarchy of the DL model structure. The prerequisite for this model design was the data collection at two taxonomic and hierarchical levels. Our model consisted of hierarchical DL with classification transformer (TF) and regression TF models. These DL models were hierarchically connected; the output of the phylum/class level model was transferred to the genus level simulation model, and the output of the genus level model was fed into the zooplankton simulation model. The classification TF model determined the phytoplankton occurrence initiation date, whereas the regression TF model quantified the cell concentration of plankton. The hierarchical DL showed potential to simulate phytoplankton at the phylum/class and genus levels by producing average R2, and root mean standard error values of 0.42 and 0.83 [log(cells mL-1)], respectively. All simulated plankton results closely matched the measured concentrations. Particularly, the simulated cyanobacteria showed good agreement with the measured cell concentration, with an R2 value of 0.72. In addition, our simulated result showed good agreement in peak concentration compared to observations. However, a limitation remained in following the temporal variation of Tintinnopsis sp. and Bosmia sp. Using an importance map from the TF model, water temperature, total phosphorus, and total nitrogen were identified as significant variables influencing phytoplankton and zooplankton blooms. Overall, our study demonstrated that DL can be used for modeling HABs at the phylum/class and genus levels.

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.

Correcting On-the-Go Field Measurement-Coordinate Mismatch by Minimizing Nearest Neighbor Difference.

Many current precision agriculture applications involve on-the-go field measurements of soil and plant properties that require accurate georeferencing. Specific equipment configuration characteristics or data transmission, reception, or logging delays may cause a mismatch between the logged data and the GPS coordinates because of time and position lags that occur during data acquisition. We propose a simple coordinate translation along the measurement tracks to correct for such positional inaccuracies, based on the local travel speed and time lag, which is estimated by minimizing the average ln-transformed absolute difference with the nearest neighbors. The correction method is evaluated using electromagnetic induction soil-sensor data for different spatial measurement layouts and densities and by comparing variograms for raw and modified coordinates. Time lags of 1 s are shown to propagate into the spatial correlation structure up to lag distances of 10 m. The correction method performs best when repeated measurements in opposite driving directions are used and worst when measurements along parallel driving tracks are only repeated at the headland turns. In the latter case, the performance of the method is further improved by limiting the search neighborhood to adjacent measurement tracks. The proposed coordinate correction method is useful for improving the positional accuracy in a wide range of soil- and plant-sensing applications, without the need to grid the data first.

Interactions of E. coli with algae and aquatic vegetation in natural waters.

Both algae and bacteria are essential inhabitants of surface waters. Their presence is of ecological significance and sometimes of public health concern triggering various control actions. Interactions of microalgae, macroalgae, submerged aquatic vegetation, and bacteria appear to be important phenomena necessitating a deeper understanding by those involved in research and management of microbial water quality. Given the long-standing reliance on Escherichia coli as an indicator of the potential presence of pathogens in natural waters, understanding its biology in aquatic systems is necessary. The major effects of algae and aquatic vegetation on E. coli growth and survival, including changes in the nutrient supply, modification of water properties and constituents, impact on sunlight radiation penetration, survival as related to substrate attachment, algal mediation of secondary habitats, and survival inhibition due to the release of toxic substances and antibiotics, are discussed in this review. An examination of horizontal gene transfer and antibiotic resistance potential, strain-specific interactions, effects on the microbial, microalgae, and grazer community structure, and hydrodynamic controls is given. Outlooks due to existing and expected consequences of climate change and advances in observation technologies via high-resolution satellite imaging, unmanned aerial vehicles (drones), and mathematical modeling are additionally covered. The multiplicity of interactions among bacteria, algae, and aquatic vegetation as well as multifaceted impacts of these interactions, create a wide spectrum of research opportunities and technology developments.

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.

Using convolutional neural network for predicting cyanobacteria concentrations in river water.

Machine learning modeling techniques have emerged as a potential means for predicting algal blooms. In this study, synthetic spatio-temporal water quality data for a river section were generated with a 3D water quality model and used to investigate the capability of a convolutional neural network (CNN) for predicting harmful cyanobacterial blooms. The CNN model displayed a reasonable capacity for short-term predictions of cyanobacteria (Microcystis) biomass. In the nowcasting of Microcystis, the CNN performance had a Nash-Sutcliffe Efficiency (NSE) of 0.87. An increase in the forecast lead time resulted in a decrease in the prediction accuracy, reducing the NSE from 0.87 to 0.58. As the spatial observation density increased from 20% to 100% of the input image grids, the CNN prediction NSE had improved from 0.70 to 0.84. Adding noise to the data resulted in accuracy deterioration, but even at the noise amplitude of 10%, the accuracy was acceptable for some applications, with NSE = 0.76. Visualization of the CNN results characterized its performance variations across the studied river reach. Overall, this study successfully demonstrated the capability of the CNN model for cyanobacterial bloom prediction using high temporal frequency images.

Data assimilation in surface water quality modeling: A review.

Data assimilation (DA) techniques are powerful means of dynamic natural system modeling that allow for the use of data as soon as it appears to improve model predictions and reduce prediction uncertainty by correcting state variables, model parameters, and boundary and initial conditions. The objectives of this review are to explore existing approaches and advances in DA applications for surface water quality modeling and to identify future research prospects. We first reviewed the DA methods used in water quality modeling as reported in literature. We then addressed observations and suggestions regarding various factors of DA performance, such as the mismatch between both lateral and vertical spatial detail of measurements and modeling, subgrid heterogeneity, presence of temporally stable spatial patterns in water quality parameters and related biases, evaluation of uncertainty in data and modeling results, mismatch between scales and schedules of data from multiple sources, selection of parameters to be updated along with state variables, update frequency and forecast skill. The review concludes with the outlook section that outlines current challenges and opportunities related to growing role of novel data sources, scale mismatch between model discretization and observation, structural uncertainty of models and conversion of measured to simulated vales, experimentation with DA prior to applications, using DA performance or model selection, the role of sensitivity analysis, and the expanding use of DA in water quality management.

Effect of the time scale on the uncertainty of geometric mean concentrations of fecal indicators in creek under baseflow conditions.

Geometric mean concentrations of fecal indicator bacteria E. coli and enterococci are commonly used to evaluate the microbial quality of irrigation, recreation, and other types of waters, as well in watershed-scale microbial water quality modeling. It is not known how the uncertainty of those geometric mean concentrations depends on the time period between sampling. We analyzed data collected under baseflow conditions from three years of weekly and several daily sampling campaigns at Conococheague Creek in Pennsylvania. Standard deviations of logarithms of geometric mean concentrations were computed over weeks, months, and seasons. The increase in standard deviations from weekly to seasonal time scale was on average about 0.1 and 0.2 for log(E. coli) and log(enterococci), respectively, and in most cases was statistically significant. This may need to be accounted for when evaluating the uncertainty of measurements for modeling purposes and in risk assessment of microbial water quality.

Assessment of a green roof practice using the coupled SWMM and HYDRUS models.

Green roof can mitigate urban stormwater and improve environmental, economic, and social conditions. Various modeling approaches have been effectively employed to implement a green roof, but previous models employed simplifications to simulate water movement in green roof systems. To address this issue, we developed a new modeling tool (SWMM-H) by coupling the stormwater management and HYDRUS-1D models to improve simulations of hydrological processes. We selected green roof systems to evaluate the coupled model. Rainfall-runoff experiments were conducted for a pilot-scale green roof and urban subbasin. Soil moisture in the green roof and runoff volume in the subbasin were simulated more accurately by using SWMM-H instead of SWMM. The scenario analysis showed that SWMM-H selected sandy loam for controlling runoff whereas SWMM recommended sand. In conclusion, SWMM-H could be a useful tool for accurately understanding hydrological processes in green roofs.

Temporal stability of E. coli and Enterococci concentrations in a Pennsylvania creek.

Microbial quality of irrigation waters is a substantial food safety factor. Escherichia coli (E. coli) and Enterococci are used as the fecal indicator bacteria (FIB) to assess microbial water quality. Analysis of temporally stable patterns of FIB can facilitate effective monitoring of microbial water quality. The objectives of this study were (1) to investigate the spatiotemporal variation of E. coli and Enterococci concentrations in a large creek traversing diverse land use areas and (2) to explore the presence of temporally stable FIB concentration patterns along the creek. Concentrations of both FIB were measured weekly at five water monitoring locations along the 20-km long creek reach in Pennsylvania at baseflow for three years. The temporal stability was assessed using mean relative deviations of logarithms of FIB concentration from the average across the reach measured at the same time. The Spearman rank correlation coefficients between logarithms of FIB concentrations on consecutive sampling times was another metric used to assess the temporal stability of FIB concentration patterns. Logarithms of FIB concentrations had sinusoidal dependence on time and significantly correlated with temperature at all locations Both FIB exhibited temporal stability of concentrations. The two most downstream locations in urbanized areas tended to have logarithms of concentrations higher than the average along the observation reach. The location in the upstream forested area had mostly lower concentrations (log E. coli 1.59, log Enterococci 1.69) than average (log E. coli 2.07, log Enterococci 2.20). concentrations in colony-forming units (CFU) (100 mL)-1. Two locations in the agricultural and sparsely urbanized area had these logarithm values close to the average. The temporal stability was more pronounced in cold seasons than in warm seasons. No significant difference was found between pattern determined for each of three observation years and for the entire three-year observation period. The Spearman rank correlations between observations on consecutive dates showed moderate to very strong relationships in most cases. Existence of the temporal stability of FIB concentrations in the creek indicates locations that inform about the average logarithm of concentrations or the geometric mean concentrations along the entire observation reach.

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.

On the Information Content of Coarse Data with Respect to the Particle Size Distribution of Complex Granular Media: Rationale Approach and Testing.

The particle size distribution (PSD) of complex granular media is seen as a mathematical measure supported in the interval of grain sizes. A physical property characterizing granular products used in the Andreasen and Andersen model of 1930 is re-interpreted in Information Entropy terms leading to a differential information equation as a conceptual approach for the PSD. Under this approach, measured data which give a coarse description of the distribution may be seen as initial conditions for the proposed equation. A solution of the equation agrees with a selfsimilar measure directly postulated as a PSD model by Martín and Taguas almost 80 years later, thus both models appear to be linked. A variant of this last model, together with detailed soil PSD data of 70 soils are used to study the information content of limited experimental data formed by triplets and its ability in the PSD reconstruction. Results indicate that the information contained in certain soil triplets is sufficient to rebuild the whole PSD: for each soil sample tested there is always at least a triplet that contains enough information to simulate the whole distribution.

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.

Development of a Nowcasting System Using Machine Learning Approaches to Predict Fecal Contamination Levels at Recreational Beaches in Korea.

Microbial contamination in beach water poses a public health threat due to waterborne diseases. To reduce the risk of exposure to fecal contamination, informing beachgoers in advance about the microbial water quality is important. Currently, determining the level of fecal contamination takes 24 h. The objective of this study is to predict the current level of fecal contamination (enterococcus [ENT] and ) using readily available environmental variables. Artificial neural network (ANN) and support vector regression (SVR) models were constructed using data from the Haeundae and Gwangalli Beaches in Busan City. The input variables included the tidal level, air and water temperature, solar radiation, wind direction and velocity, precipitation, discharge from the wastewater treatment plant, and suspended solid concentration in beach water. The dependence of fecal contamination on the input variables was statistically evaluated; precipitation, discharge from the wastewater treatment plant, and wind direction at the two beaches were positively correlated to the changes in the two bacterial concentrations ( < 0.01), whereas solar radiation was negatively correlated ( < 0.01). The performance of the ANN model for predicting ENT and at Gwangalli Beach was significantly higher than that of the SVR model with the training dataset ( < 0.05). Based on the comparison of residual values between the predicted and observed fecal indicator bacteria concentrations in two models, the ANN demonstrated better performance than SVR. This study suggests an effective prediction method to determine whether a beach is safe for recreational use.

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.