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The rapid and efficient quantification of Escherichia coli concentrations is crucial for monitoring water quality. Remote sensing techniques and machine learning algorithms have been used to detect E. coli in water and estimate its concentrations. The application of these approaches, however, is challenged by limited sample availability and unbalanced water quality datasets. In this study, we estimated the E. coli concentration in an irrigation pond in Maryland, USA, during the summer season using demosaiced natural color (red, green, and blue: RGB) imagery in the visible and infrared spectral ranges, and a set of 14 water quality parameters. We did this by deploying four machine learning models - Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), and K-nearest Neighbor (KNN) - under three data utilization scenarios: water quality parameters only, combined water quality and small unmanned aircraft system (sUAS)-based RGB data, and RGB data only. To select the training and test datasets, we applied two data-splitting methods: ordinary and quantile data splitting. These methods provided a constant splitting ratio in each decile of the E. coli concentration distribution. Quantile data splitting resulted in better model performance metrics and smaller differences between the metrics for both the training and testing datasets. When trained with quantile data splitting after hyperparameter optimization, models RF, GBM, and XGB had R2 values above 0.847 for the training dataset and above 0.689 for the test dataset. The combination of water quality and RGB imagery data resulted in a higher R2 value (>0.896) for the test dataset. Shapley additive explanations (SHAP) of the relative importance of variables revealed that the visible blue spectrum intensity and water temperature were the most influential parameters in the RF model. Demosaiced RGB imagery served as a useful predictor of E. coli concentration in the studied irrigation pond.

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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.

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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.

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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.

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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.

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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.

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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.

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