Estimation of Constituent Concentrations, Loads, and Yields in Streams of Johnson County, Northeast Kansas, Using Continuous Water-Quality Monitoring and Regression Models, October 2002 through December 2006

Rasmussen, Teresa J.; Lee, Casey J.; Ziegler, Andrew C.

doi:10.3133/sir20085014

Cited by 23 publications

(22 citation statements)

References 19 publications

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“…Again, this requires a series of concurrent samples/measurements to generate site-specific turbidity/SSC rating curves. Lastly, just as discharge has been used effectively to predict dissolved concentrations in limited cases, turbidity also has been used to predict fluvial sediment-associated constituent concentrations and bacteria levels (e.g., Rasmussen et al 2005Rasmussen et al , 2008. If this approach could be successfully applied in the case of the COA monitoring program, substantive cost savings from reduced sampling and subsequent laboratory analyses could be achieved.…”

Section: Operating Principlesmentioning

confidence: 97%

“…It would have been extremely useful, as well as cost-efficient, to be able to apply the same rating curve technique for estimating SSC and/or suspended sediment fluxes to sediment chemistry. That approach has been applied successfully elsewhere, for estimating some dissolved (e.g., discharge) and some sediment-associated (e.g., turbidity) constituent concentrations and/or fluxes, as well as for bacteria (e.g., Christensen 2001;Goolsby et al 2001;Vanni et al 2001;Rasmussen et al 2005Rasmussen et al , 2008Stelzer and Likens 2006). Based on correlation coefficients, the surrogate showing the most promise of providing usable sediment-associated chemical estimates in the COA watersheds was SSC itself.…”

Section: Annual Suspended Sediment-associated and Dissolved Chemical mentioning

confidence: 98%

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Monitoring suspended sediments and associated chemical constituents in urban environments: lessons from the city of Atlanta, Georgia, USA Water Quality Monitoring Program

Horowitz

2009

J Soils Sediments

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Background, aim, and scope The City of Atlanta, Georgia (COA) is part of the ninth largest metropolitan area in the USA and one of the fastest growing (e.g., >24% between 2000 and 2007). Since 2003, the US Geological Survey has been operating an extensive long-term water-quantity and water-quality monitoring network for the COA. The experience gained in operating this network has provided insights into the challenges as well as some solutions associated with determining urban effects on water quality, especially in terms of estimating the annual fluxes of suspended sediment, trace/major elements, and nutrients. Discussion and findings The majority (>90%) of the annual fluxes of suspended sediment and discharge (>60%) from the COA occur in conjunction with stormflow. Typically, stormflow averages ≤20% of the year. Normally, annual flux calculations employ a daily time-step; however, due to the "flashy" nature of the COA's streams, this approach can produce substantial underestimates (from 25% to 64%). Greater accuracy requires time-steps as short as every 2 to 3 h. The annual fluxes of ≥75% of trace elements (e.g., Cu, Pb, Zn), major elements (e.g., Fe, Al), and total P occur in association with suspended sediment; in turn, ≥90% of the transport of these constituents occurs in conjunction with stormflow. With the possible exception of nitrogen, baseflow sediment-associated and both baseflow and stormflow dissolved contributions represent relatively insignificant portions of the total annual load; hence, nonpoint (diffuse) sources are the dominant contributors to the fluxes of almost all of these constituents.

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Section: Operating Principlesmentioning

confidence: 97%

Section: Annual Suspended Sediment-associated and Dissolved Chemical mentioning

confidence: 98%

Monitoring suspended sediments and associated chemical constituents in urban environments: lessons from the city of Atlanta, Georgia, USA Water Quality Monitoring Program

Horowitz

2009

J Soils Sediments

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“…Multi-parameter regression equations would be acceptable (Rasmussen et al, 2008). o Surrogate/TP regression equations would not necessarily be linear in normal scale (Rasmussen et al, 2008;Helsel, Hirsch).…”

Section: Regression Analysismentioning

confidence: 99%

“…o Surrogate/TP regression equations would not necessarily be linear in normal scale (Rasmussen et al, 2008;Helsel, Hirsch).…”

Section: Regression Analysismentioning

confidence: 99%

Estimating Total Phosphorus Using a Continuous Surrogate Approach

Riddle¹,

Hammond²,

Long³

et al. 2012

proc water environ fed

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Surface water quality monitoring has traditionally relied on laboratory analysis of samples collected manually or using automated samplers. While laboratory analysis can provide data for a large variety of constituents including, but not limited to, metals, nutrients, and bacteria, this method of collection and analysis is both cumbersome and costly as well as insufficient if more frequent and spatially distributed data are needed to develop reliable estimates of pollutant loadings, fluxes, and trends. As the number of EPA 303d listings and approved TMDLs continues to grow, most notably concerning nutrients and bacteria, so does the demand for NPDES permit holders to collect and evaluate water quality data at a greater frequency than that afforded by traditional methods. Although various commercial water quality sensors have been developed over the last decade to meet detailed pollutant characterization needs, technology is not yet available for detection of many problematic pollutants.The objective of this study was to evaluate the potential for developing regression relationships between traditionally collected laboratory analysis data and water quality sensor data that would allow the approximation of continuous total phosphorus (TP) concentrations through the use of surrogate continuous water quality data. Correlations between TP concentrations and five surrogate parameters were analyzed during this study. The effect of logarithmic transformations of continuous and TP datasets on correlations was also examined. ANOVA analysis was performed on multi-parameter linear regression models developed. Finally predictive equations were developed using simple and multi-variate linear regression on surrogates identified in the correlation and ANOVA analyses. The results show that turbidity alone or paired with specific conductivity was the best surrogate continuous parameter for predicting TP.

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“…In-situ turbidity, acoustic and streamflow data can be used to compute a time series of suspended-sediment concentrations and loads at stream sites (Rasmussen et al, 2008). Continuous turbidity data provide a record of the changes in optical clarity of a waterbody over time and are a useful tool in efforts to study water-quality conditions, trends and other aspects concerning the dynamics and interactions within an aquatic ecosystem.…”

Section: Aims and Objectivesmentioning

confidence: 99%

Turbidity Dynamics during High-Flow Storm Events in the Clackamas River, Oregon 2006-2012

Doyle¹

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Estimation of Constituent Concentrations, Loads, and Yields in Streams of Johnson County, Northeast Kansas, Using Continuous Water-Quality Monitoring and Regression Models, October 2002 through December 2006

Cited by 23 publications

References 19 publications

Monitoring suspended sediments and associated chemical constituents in urban environments: lessons from the city of Atlanta, Georgia, USA Water Quality Monitoring Program

Monitoring suspended sediments and associated chemical constituents in urban environments: lessons from the city of Atlanta, Georgia, USA Water Quality Monitoring Program

Estimating Total Phosphorus Using a Continuous Surrogate Approach

Turbidity Dynamics during High-Flow Storm Events in the Clackamas River, Oregon 2006-2012

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