Spatial patterns of water quality and remote sensing indices from UAV-based multispectral imagery across an irrigation pond.

Water quality of irrigation water is an essential factor for public safety and farm sustainability. Imaging surface water sources from unmanned aerial vehicles (UAVs) has become an important source of water quality information. Water quality variables (WQVs) in irrigation ponds have been shown to have persistent spatial patterns.

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.

Fine-scale spatiotemporal variations in bacterial community diversity in agricultural pond water.

Microbial communities in surface waters are affected by environmental conditions and can influence changes in water quality. To explore the hypothesis that the microbiome in agricultural waters associates with spatiotemporal variations in overall water quality and, in turn, has implications for resource monitoring and management, we characterized the relationships between the microbiota and physicochemical properties in a model irrigation pond as a factor of sampling time (i.e.

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.

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.

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.

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.

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.

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.

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.

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.

Fate and transport in environmental quality.

Changes in pollutant concentrations in environmental media occur both from pollutant transport in water or air and from local processes, such as adsorption, degradation, precipitation, straining, and so on. The terms "fate and transport" and "transport and fate" reflect the coupling of moving with the carrier media and biogeochemical processes describing local transformations or interactions. The Journal of Environmental Quality (JEQ) was one of the first to publish papers on fate and transport (F&T).

Modeling the kinetics of manure-borne fecal indicator removal in runoff.

Several manure-borne microorganism removal models have been developed to provide accurate estimations of the number of microorganisms removed from manure or manured soils undergoing rainfall. It has been commonly assumed that these models perform equally well when used to simulate microbe removal in runoff from manures of different consistency and levels of weathering. The objectives of this work were (a) to observe kinetics of the removal of Escherichia coli and enterococci with runoff for two different manure consistencies and three manure weathering durations, and (b) to compare performance of the log-linear, Vadas-Kleinman-Sharpley, and Bradford-Shijven models in simulation of the observed kinetics.

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.

Intraseasonal variation of phycocyanin concentrations and environmental covariates in two agricultural irrigation ponds in Maryland, USA.

Recently, cyanobacteria blooms have become a concern for agricultural irrigation water quality. Numerous studies have shown that cyanotoxins from these harmful algal blooms (HABs) can be transported to and assimilated into crops when present in irrigation waters. Phycocyanin is a pigment known only to occur in cyanobacteria and is often used to indicate cyanobacteria presence in waters.

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.

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.

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.

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.

Drone-based imaging to assess the microbial water quality in an irrigation pond: A pilot study.

Microbial water quality datasets are essential in irrigated agricultural practices to detect and inform measures to prevent the contamination of produce. Escherichia coli (E. coli) concentrations are commonly used to evaluate microbial water quality.

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.

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.