Persistence of Microcystin in Three Agricultural Ponds in Georgia, USA.

Cyanobacteria and their toxins can have multiple effects on agricultural productivity and water bodies. Cyanotoxins can be transported to nearby crops and fields during irrigation and may pose a risk to animal health through water sources. Spatial and temporal variations in cyanotoxin concentrations have been reported for large freshwater sources such as lakes and reservoirs, but there are fewer studies on smaller agricultural surface water bodies.

Using machine learning models to estimate Escherichia coli concentration in an irrigation pond from water quality and drone-based RGB imagery data.

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.

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.

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.

Prediction of 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 from the concentrations of physiochemical water quality parameters. However, these relationships are often non-linear and exhibit changes above or below certain threshold values.

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.

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.

Spatial Patterns of 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.

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.