Studies on the Self-Purification of Streams

Phelps, Earle B.

doi:10.2307/4571183

Cited by 2 publications

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“…The second area of research interest is the source of streams as greenhouse gases to the atmosphere (Butman & Raymond, 2011;Raymond et al, 2013;Stanley et al, 2016), where gas exchange is needed to estimate gas flux following Equation 1. Despite that we distinguish the BOX 1 HISTORY OF GAS EXCHANGE IN RIVERS Studies of gas exchange in streams began with the concept of self-purification and the "re-aeration" of streams, that is, the ability to increase dissolved O 2 in the face of a large biological oxygen demand from organic pollution (Phelps, 1914;Streeter, 1935). Phelps (1914) recognized the role of physical processes controlling gas exchange "The rate of re-aeration of water depends not only upon the oxygen concentration existing at the time but upon the vertical distribution of that concentration."…”

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confidence: 99%

“…Despite that we distinguish the BOX 1 HISTORY OF GAS EXCHANGE IN RIVERS Studies of gas exchange in streams began with the concept of self-purification and the "re-aeration" of streams, that is, the ability to increase dissolved O 2 in the face of a large biological oxygen demand from organic pollution (Phelps, 1914;Streeter, 1935). Phelps (1914) recognized the role of physical processes controlling gas exchange "The rate of re-aeration of water depends not only upon the oxygen concentration existing at the time but upon the vertical distribution of that concentration." The reaeration of streams spawned a large body of theoretical and empirical work (Bennett & Rathbun, 1972;O'Connor & Dobbins, 1958) to address the larger problem of stream self-purification.…”

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Gas exchange in streams and rivers

Hall

Ulseth

2019

WIREs Water

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Gas exchange across the air–water boundary of streams and rivers is a globally large biogeochemical flux. Gas exchange depends on the solubility of the gas of interest, the gas concentrations of the air and water, and the gas exchange velocity (k), usually normalized to a Schmidt number of 600, referred to as k600. Gas exchange velocity is of intense research interest because it is problematic to estimate, is highly spatially variable, and has high prediction error. Theory dictates that molecular diffusivity and turbulence drives variation in k600 in flowing waters. We measure k600 via several methods from direct measures, gas tracer experiments, to modeling of diel changes in dissolved gas concentrations. Many estimates of k600 show that surface turbulence explains variation in k600 leading to predictive models based upon geomorphic and hydraulic variables. These variables include stream channel slope and stream flow velocity, the product of which, is proportional to the energy dissipation rate in streams and rivers. These empirical models provide understanding of the controls on k600, yet high residual variation in k600 show that these simple models are insufficient for predicting individual locations. The most appropriate method to estimate gas exchange depends on the scientific question along with the characteristics of the study sites. We provide a decision tree for selecting the best method to estimate k600 for individual river reaches to scaling to river networks. This article is categorized under: Water and Life > Nature of Freshwater Ecosystems Science of Water > Water Quality Water and Life > Methods

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Gas exchange in streams and rivers

Hall

Ulseth

2019

WIREs Water

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“…The authors concluded that the typhoid germ loses its vitality over time. Ruediger (1911) reported that colon bacilli and typhoid bacilli disappear from polluted river water faster in summer than in winter, when the river is covered with ice and snow, which indicates that winter seasons prolong survival of pathogens (Phelps, 1914).…”

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Modeling In- Stream Escherichia coli Concentrations

Pandey

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Predicting in-stream pathogen levels has long been known to be a challenging problem due to complex interactions between microorganisms and the natural stream environment, and the spatial heterogeneity involved in stream networks of a watershed. Here we have developed models for predicting E. coli (a pathogen indicator) in streams. In E. coli estimation, the first modeling approach uses Geographic Information Systems (GIS) based watershed indexes considering the undisturbed land cover, which encompasses the natural land cover area, wetlands, and vegetated stream corridors, and the disturbed land cover extent which includes areas receiving manure from confined animal feeding operations, tile-drained areas, and areas under cropped and urban land cover. The second approach involves developing mathematical models for calculating E. coli resuspension, deposition, in-stream routing, and growth in the streams. A hydrological model capable of predicting in-stream E. coli concentrations in the streambed sediment as well as in the water column was developed. In order to develop the hydrological model for predicting in-stream E. coli concentrations, firstly a model capable of predicting E. coli resuspension was formulated. Secondly, formulations for calculating in-stream E. coli routing, water temperature depended E. coli growth, and the streambed sediment and water column E. coli concentrations were developed. Finally, these formulations were programmed in FORTRAN language, and were integrated into the Soil and Water Assessment Tool (SWAT), a watershed scale hydrological model, written in FORTRAN. In addition to the model development, this study also involves monitoring E. coli concentrations in the streambed sediment and the water column xi extensively starting from May 2009 to December 2011 in the Squaw Creek Watershed, Iowa, USA. The observations were used to verify the model predictions, and results indicated that the models performed well. The GIS based approach developed here for estimating E. coli concentrations in streams can be potentially useful in predicting in-stream waterborne E. coli levels using watershed indexes. Approximately 95-98% of the predictions were within 1 order magnitude of the observed values, when we used hydrologically corrected watershed indexes for E. coli estimation. The model skills varied from 0.39 to 0.55. In E. coli resuspension model, approximately 81% of the predicted E. coli resuspension rates were within a factor of 2 of the inferred values (i.e., measured E. coli). All of the predicted resuspension rates were within a factor of 5 of the inferred values. The model skill value of 0.85 indicated that the model predicts E. coli resuspension rates successfully. The application of the modified SWAT model in the Squaw Creek Watershed, which was developed here, performed well. For example, approximately 62% of the predicted streambed sediment E. coli, and 82% of the predicted water column E. coli concentrations were within 1 order magnitude of the measured concentrations. The R 2 for month...

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Studies on the Self-Purification of Streams

Cited by 2 publications

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Gas exchange in streams and rivers

Gas exchange in streams and rivers

Modeling In- Stream Escherichia coli Concentrations

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