Once released, manure-borne bacteria can enter runoff via interaction with the thin mixing layer near the soil surface. The objectives of this work were to document temporal changes in profile distributions of manure-borneEscherichia coliand enterococci in the near-surface soil layers after simulated rainfalls and to examine differences in survival of the two fecal indicator bacteria. Rainfall simulations were performed in triplicate on soil-filled boxes with grass cover and solid manure application for 1 h with rainfall depths of 30, 60, and 90 mm. Soil samples were collected weekly from depth ranges of 0 to 1, 1 to 2, 2 to 5, and 5 to 10 cm for 1 month. Rainfall intensity was found to have a significant impact on the initial concentrations of fecal indicator bacteria in the soil. While total numbers of enterococci rapidly declined over time,E. colipopulations experienced initial growth with concentration increases of 4, 10, and 25 times the initial levels at rainfall treatment depths of 30, 60, and 90 mm, respectively.E. colipopulations grew to the approximately the same level in all treatments. The 0- to 1-cm layer contained more indicator bacteria than the layers beneath it, and survival of indicator bacteria was better in this layer, with decimation times between 12 and 18 days after the first week of growth. The proportion of bacteria in the 0- to 1-cm layer grew with time as the total number of bacteria in the 0- to 10-cm layer declined. The results of this work indicate the need to revisit the bacterial survival patterns that are assumed in water quality models.

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.

Evaluation of microbial water quality is commonly based on monitoring populations of fecal indicator organisms (FIO) such as E. coli (EC) and enterococci. The occurrence of elevated FIO concentrations in surface waters after storm events is well documented and has been attributed to runoff and sediment resuspension. The reasons for FIO concentration variation under baseflow conditions are less clear. The objective of this study was to quantify the variability of EC and enterococci in two small streams running through agricultural land use areas. FIO concentrations were measured at upstream and downstream locations under baseflow conditions. Concentrations were not significantly different along cross-sections of the streams. Diurnal concentration trends were observed at each of the sampling locations. Significant differences in concentrations between upstream and downstream locations were noted for both creeks during baseflow periods when no runoff or sediment resuspension occurred. A hypothetical explanation is that indicator organisms are released from sediments during baseflow conditions due to the effect of groundwater influx into streams or due to the motility of indicator organisms. If confirmed, this hypothesis may affect our understanding of the role of sediments in the microbial quality of surface waters.