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Data for multiple linear regression models for predicting microcystin concentration action-level exceedances in selected lakes in Ohio
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 over time periods antecedent to the time the microcystin sample was collected, including water-quality data compiled from continuous monitors. Comprehensive models use a combination of discrete sample-based measurements and real-time factors. Comprehensive models were useful at some sites with lagged variables (< 2 weeks) for cyanobacterial toxin genes, dissolved nutrients, and (or) N to P ratios. Comprehensive models are presented in three categories: (1) three models with continuous monitor data and lagged comprehensive variables, (2) five models with no continuous monitor data and lagged comprehensive variables, and (3) one model with continuous monitor data and same-day comprehensive variables. Funding for this work was provided by the Ohio Water Development Authority and the U.S. Geological Survey Cooperative Water Program.
Complete Metadata
| accessLevel | public |
|---|---|
| bureauCode |
[
"010:12"
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|
| contactPoint |
{
"fn": "Donna S Francy",
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"hasEmail": "mailto:dsfrancy@usgs.gov"
}
|
| description | 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 over time periods antecedent to the time the microcystin sample was collected, including water-quality data compiled from continuous monitors. Comprehensive models use a combination of discrete sample-based measurements and real-time factors. Comprehensive models were useful at some sites with lagged variables (< 2 weeks) for cyanobacterial toxin genes, dissolved nutrients, and (or) N to P ratios. Comprehensive models are presented in three categories: (1) three models with continuous monitor data and lagged comprehensive variables, (2) five models with no continuous monitor data and lagged comprehensive variables, and (3) one model with continuous monitor data and same-day comprehensive variables. Funding for this work was provided by the Ohio Water Development Authority and the U.S. Geological Survey Cooperative Water Program. |
| distribution |
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| identifier | http://datainventory.doi.gov/id/dataset/USGS_5d821dc7e4b0c4f70d058daa |
| keyword |
[
"City of Alliance Ohio",
"City of Oregon Ohio",
"Great Lakes",
"Maumee Bay",
"Ohio",
"Ottawa County Ohio",
"Port Clinton Ohio",
"Put-in-Bay Ohio",
"USGS:5d821dc7e4b0c4f70d058daa",
"Village of Cadiz Ohio",
"Village of Marblehead Ohio",
"Western Lake Erie",
"algal blooms",
"cyanobacteria ["blue-green algae"]",
"drinking water quality",
"farming",
"harmful algal blooms",
"hazards",
"management",
"recreational water quality",
"regression analysis",
"surface water (non-marine)",
"water resource management"
]
|
| modified | 2020-08-27T00:00:00Z |
| publisher |
{
"name": "U.S. Geological Survey",
"@type": "org:Organization"
}
|
| spatial | -84.8145, 40.1453, -80.5200, 41.7057 |
| theme |
[
"Geospatial"
]
|
| title | Data for multiple linear regression models for predicting microcystin concentration action-level exceedances in selected lakes in Ohio |
