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Site-specific multiple linear regression models were developed for one beach in Ohio (three discrete sampling sites) and one beach in Pennsylvania to estimate concentrations of Escherichia coli (E. coli) or the probability of exceeding the bathing-water standard for E. coli in recreational waters used by the public. Traditional culture-based methods are commonly used to estimate concentrations of fecal indicator bacteria, such as E. coli; however, results are obtained 18 to 24 hours post sampling and do not accurately reflect current water-quality conditions. Beach-specific mathematical models use environmental and water-quality variables that are easily and quickly measured as surrogates to estimate concentrations...
This page contains model archive summary (MAS) packages for 2 site-specific multiple linear regression models developed for one site in Ohio and one site in Pennsylvania. Sites models contain real-time factors that are used to predict Escherichia coli (E. coli) concentration action-level exceedances. Each model package contains a MAS report, the “original” data that include all explanatory variables used in model development, the “calibration” data that only contain the chosen model variables and can be used to re-create the model, the Virtual Beach (software used to the develop the model) project file for the model, and the model report that lists parameter and model details.
This dataset contains a tabular file of phytoplankton abundance and community composition analysis in samples collected from two sites in the Western Lake Erie Basin and one inland lake site in northeast Ohio. Samples were processed by the Ohio Water Microbiology Lab of the U.S. Geological Survey (USGS) and analyzed by BSA Environmental Inc. and during federal fiscal years 2016-2018. The dataset includes phytoplankton taxa (genus and species), division, tally (number of cells counted for each taxa present), density (cells per liter), and total biovolume (cubic micrometers per liter) for each sample collected. These data can be used to assess the community composition of phytoplankton at the sites, identify cyanobacteria...
Categories: Data;
Tags: Environmental Health,
USGS Science Data Catalog (SDC),
Upper Ohio,
Water Quality,
Water Resources,
This page contains model archive summary packages for 18 site-specific linear regression models developed for eight sites in Ohio. Sites models contain real-time factors (real-time model) or real-time and comprehensive factors (comp models) that are used to predict microcystin concentration action-level exceedances. Each model package contains a model archive summary report, the “original” data that include all explanatory variables used in model development, the “calibration” data that only contain the chosen model variables and can be used to re-create the model, the Virtual Beach (software used to the develop the model) project file for the model, and the model report that lists parameter and model details.
Site-specific multiple linear regression models were developed for eight sites in Ohio—six in the Western Lake Erie Basin and two in northeast Ohio on inland reservoirs--to quickly predict action-level exceedances for a cyanotoxin, microcystin, in recreational and drinking waters used by the public. Real-time models include easily- or continuously-measured factors that do not require that a sample be collected. Real-time models are presented in two categories: (1) six models with continuous monitor data, and (2) three models with on-site measurements. Real-time models commonly included variables such as phycocyanin, pH, specific conductance, and streamflow or gage height. Many of the real-time factors were averages...
The Great Lakes Restoration initiative (GLRI) template #77 (Beach Recreation Water Quality) in cooperation with 23 local and state agencies expanded the use of predictive modeling at 45 beaches throughout the Great Lakes (fig 1). Local agencies measure fecal-indicator bacteria such as Escherichia coli (E. coli.) along with easily obtained environmental variables used as surrogates to estimate concentrations of fecal-indicator bacteria through a predictive modeling approach. The predictive modeling is being developed by the use of linear regression and/or partial least-squares techniques. The models use software developed by the U.S. Environmental Protection Agency known as “Virtual Beach”. Each beach model is based...
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