Predicting microcystin concentration action-level exceedances resulting from cyanobacterial blooms in selected lake sites in Ohio
Cyanobacterial harmful algal blooms and the toxins they produce are a global water-quality problem. Monitoring and prediction tools are needed to quickly predict cyanotoxin action-level exceedances in recreational and drinking waters used by the public. To address this need, data were collected at eight locations in Ohio, USA, to identify factors significantly related to observed concentrations of microcystins (a freshwater cyanotoxin) that could be used in two types of site-specific regression models. Real-time models include easily- or continuously-measured factors that do not require that a sample be collected; comprehensive models use a combination of discrete sample-based measurements and real-time factors. The study sites included two recreational sites and six water treatment plant sites. Real-time models commonly included variables such as phycocyanin, pH, specific conductance, and streamflow or gage height. Many 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 were useful at some sites with lagged variables for cyanobacterial toxin genes, dissolved nutrients, and (or) nitrogen to phosphorus ratios. Because models can be used for management decisions, important measures of model performance were sensitivity, specificity, and accuracy of estimates above or below the microcystin concentration threshold standard or action level. Sensitivity is how well the predictive tool correctly predicts exceedance of a threshold, an important measure for water-resource managers. Sensitivities >90% at four Lake Erie water treatment plants indicated that models with continuous monitor data were especially promising. The planned next steps are to collect more data to build larger site-specific datasets and validate models before they can be used for management decisions.
Citation Information
| Publication Year | 2020 |
|---|---|
| Title | Predicting microcystin concentration action-level exceedances resulting from cyanobacterial blooms in selected lake sites in Ohio |
| DOI | 10.1007/s10661-020-08407-x |
| Authors | Donna S. Francy, Amie M.G. Brady, Erin A. Stelzer, Jessica R. Cicale, Courtney Paige Hackney, Harrison D Dalby, Pamela Struffolino, Daryl F. Dwyer |
| Publication Type | Article |
| Publication Subtype | Journal Article |
| Series Title | Environmental Monitoring and Assessment |
| Index ID | 70211288 |
| Record Source | USGS Publications Warehouse |
| USGS Organization | Ohio Water Science Center; Ohio-Kentucky-Indiana Water Science Center |
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Data for multiple linear regression models for predicting microcystin concentration action-level exceedances in selected lakes in Ohio
Donna Francy (Former Employee)
Hydrologist (Water Quality Specialist)
Amie M Brady
Supervisory Hydrologist
Erin A Stelzer
Microbiologist
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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 - Connect
Donna Francy (Former Employee)
Hydrologist (Water Quality Specialist)Amie M Brady
Supervisory HydrologistEmailPhoneErin A Stelzer
MicrobiologistEmailPhone