Publications for Beach Advisory Decision Making

IAN is committed to producing practical, user-centered communications that foster a better understanding of science and enable readers to pursue new opportunities in research, education, and environmental problem-solving. Our publications synthesize scientific findings using effective science communication techniques.

Assessing bacterial levels in Charleston Harbor (Page 1)

Assessing bacterial levels in Charleston Harbor

Project status newsletter

Heath Kelsey, Emily Nastase ·
16 June 2017

This newsletter describes the collaborative project between the University of South Carolina, the University of Maryland Center for Environmental Science, and the Southeast Coastal Ocean Observing Regional Association to assess levels of bacteria in recreational waterways in Charleston Harbor. The goal of this project is to better inform the public on safety risks in various recreational waterways due to bacteria levels.

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Assessing bacterial levels in Charleston Harbor (Page 1)

Assessing bacterial levels in Charleston Harbor

Heath Kelsey, Emily Nastase ·
16 June 2017

This newsletter describes the collaborative project between the University of South Carolina, the University of Maryland Center for Environmental Science, and the Southeast Coastal Ocean Observing Regional Association to assess levels of bacteria in recreational waterways in Charleston Harbor. The goal of this project is to better inform the public on safety risks in various recreational waterways due to bacteria levels.

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Model Results and Software Comparisons in Myrtle Beach, SC Using Virtual Beach and R Regression Toolboxes (Page 1)

Model Results and Software Comparisons in Myrtle Beach, SC Using Virtual Beach and R Regression Toolboxes

Neet MJ, Kelsey RH, Porter DE, Ramage DW, and Jones AB ·
2015

Utilizing R software and a variety of data sources, daily forecasts of bacteria levels were developed and automated for beach waters in Myrtle Beach, SC. Modeled results are then shown for beach locations via a website and mobile device app. While R provides a robust set of tools for use in forecast modeling, the software has an extensive learning curve and requires skilled statistical interpretation of results.

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Model Performance Results in Myrtle Beach, SC Using Virtual Beach and R Regression Software (Page 1)

Model Performance Results in Myrtle Beach, SC Using Virtual Beach and R Regression Software

Neet MJ, Kelsey RH, Porter DE, Ramage DW, and Jones AB ·
2014

Daily forecasts of beach water bacteria levels have been developed and automated by a beach water quality forecast team. With support from the Southeast Coast Ocean Observing Regional Association (SECOORA), R software and a variety of data sources were used to model daily bacteria levels in beach swimming waters in Myrtle Beach, SC. Modeled (predicted) water quality results are then shown for beach locations via a website and mobile device app.

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