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.

Multiple linear regression models were developed using data collected in 2016 and 2017 from three recurring bloom sites in Kabetogama Lake in northern Minnesota. These models were developed to predict concentrations of cyanotoxins (anatoxin-a, microcystin, and saxitoxin) that occur within the blooms. Virtual Beach software (version 3.0.6) was used to develop four models: two cyanotoxin mixture (MIX) models and two microcystin (MC) models. Models include those using readily available environmental variables (for example, wind speed and specific conductance) and those using additional comprehensive variables (based on laboratory analyses). Many of the independent variables were averages over a certain time period prior to a sample date, whereas other independent variables were lagged between 4 and 8 days. Funding for this work was provided by the U.S Geological Survey – National Park Service Partnership and the U.S. Geological Survey Environmental Health Program (Toxic Substance Hydrology and Contaminant Biology). The resulting model equations and final datasets are included in this data release while an associated child item model archive includes all the files needed to run and develop these VB models.

Multiple linear regression models were developed using data collected in 2016 and 2017 from three recurring bloom sites in Kabetogama Lake in northern Minnesota. These models were developed to predict concentrations of cyanotoxins (anatoxin-a, microcystin, and saxitoxin) that occur within the blooms. Virtual Beach software (version 3.0.6) was used to develop four models: two cyanotoxin mixture (MIX) models and two microcystin (MC) models. Models include those using readily available environmental variables (for example, wind speed and specific conductance) and those using additional comprehensive variables (based on laboratory analyses). Many of the independent variables were averages over a certain time period prior to a sample date, whereas other independent variables were lagged between 4 and 8 days. Funding for this work was provided by the U.S Geological Survey – National Park Service Partnership and the U.S. Geological Survey Environmental Health Program (Toxic Substance Hydrology and Contaminant Biology). The resulting model equations and final datasets are included in this data release while an associated child item model archive includes all the files needed to run and develop these VB models.

Data from NLA 2012 was used to assess biovolume results for cyanobacteria (phytoplankton) in relation to both landscape and in lake factors. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.

Data from NLA 2012 was used to assess biovolume results for cyanobacteria (phytoplankton) in relation to both landscape and in lake factors. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.

With increasing concerns about freshwater cyanobacteria blooms, there is a need to identify which waterbodies are at risk for developing these blooms, especially those that produce cyanotoxins. To address this concern, we developed spatial statistical models using the US National Lakes Assessment, a survey with over 3,000 spring and summer observations of cyanobacteria abundance and microcystin concentration in lakes across the conterminous US. We combined these observations with other nationally available data to model which lake and watershed factors best explain the presence of harmful cyanobacterial blooms. We then used these models to estimate the cyanobacteria abundance and probability of microcystin detection in 124,500 lakes across the CONUS. This dataset includes the compiled data used to generate the models and the dataset used to generate prediction for a much larger population of lakes. The data package includes two tabular data files, two tabular metadata files, and one methods document.

Water sample data collected and curated by Ohio State University's Stone Laboratory and others between 2013 and 2024 in Lake Erie. The samples were collected in part of several projects funded by various state (Ohio EPA and Ohio Sea Grant) and federal agencies (US EPA, NSF, NIH, NOAA). The program column describes who or why the samples were collected. The captains program is a partnership between Stone Lab and the Lake Erie Charter Boat Association in which the captains collect water samples and Stone Lab analyzes them (https://ohioseagrant.osu.edu/products/4c0k6/charter-boat-captains-help-monitor-lake-erie-water-quality). The SL Buoy program is a high temporal resolution dataset of grab samples paired with a high temporal resolution sonde data attached to a buoy (https://doi.org/10.1007/s11356-018-2612-z). The HABs Grab were high spatial resolution samples collected on two days during peak blooms of 2018 and 2019 (https://doi.org/10.1016/j.hal.2021.102080). The flow-through program was an attempt to collect water quality data throughout the winter by pumping lake water into the research building at Stone Lab. Programs Stone Lab and UToledo were samples collected by Stone Lab and UT Lake Erie Center from research vessels at routinely monitored locations. Samples were analyzed for chlorophyll a (an indicator of algae biomass), microcystins (a group of toxins produced by cyanobacteria), total phosphorus and nitrogen (indicators of maximum biomass potential), dissolved nitrate, phosphate, and silicate (nutrients available for algae), and total suspended solids (mass of all particulates in the water).

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From 2017-2019, the Upper Midwest Environmental Sciences Center (UMESC) analyzed microcystin concentrations in samples collected from three different studies. The first study was on the movement and distribution of invasive carp (Bighead Carp, Silver Carp, Grass Carp) in the upper Mississippi River between lock and dam 16 and lock and dam 19. Samples were collected from May through October of 2017 and 2018 from backwaters, impounded areas and main channel areas in this reach of the Mississippi River. The second study was a nutrient and metal amendment study performed on natural phytoplankton communities from Lake Erie and Lake Michigan. This was a laboratory study where natural phytoplankton communities were incubated for 72 hours with amendments of ammonium, phosphate and metals (iron, zinc, molybdenum, nickel and manganese). After 72 hours, communities were sampled for microcystin concentration (among other metrics not reported here). The third study was a nutrient diffusing substrate study, where periphyton were grown on suspended substrates that leached nutrients or metals. After two weeks of deployment periphyton was collected from the substrates, diluted in purified water and then analyzed for microcystin concentration. Microcystin concentrations for all experiments were estimated using enzyme-linked immunosorbent assay (ELISA) test kits. We used a Bayesian method to calibrate the absorbance data from the kit and report here on both the microcystin concentrations of the samples analyzed, but also report the raw absorbance data from both samples and calibration standards so that others could recreate the microcystin analysis using other methods if they so choose.

From 2017-2019, the Upper Midwest Environmental Sciences Center (UMESC) analyzed microcystin concentrations in samples collected from three different studies. The first study was on the movement and distribution of invasive carp (Bighead Carp, Silver Carp, Grass Carp) in the upper Mississippi River between lock and dam 16 and lock and dam 19. Samples were collected from May through October of 2017 and 2018 from backwaters, impounded areas and main channel areas in this reach of the Mississippi River. The second study was a nutrient and metal amendment study performed on natural phytoplankton communities from Lake Erie and Lake Michigan. This was a laboratory study where natural phytoplankton communities were incubated for 72 hours with amendments of ammonium, phosphate and metals (iron, zinc, molybdenum, nickel and manganese). After 72 hours, communities were sampled for microcystin concentration (among other metrics not reported here). The third study was a nutrient diffusing substrate study, where periphyton were grown on suspended substrates that leached nutrients or metals. After two weeks of deployment periphyton was collected from the substrates, diluted in purified water and then analyzed for microcystin concentration. Microcystin concentrations for all experiments were estimated using enzyme-linked immunosorbent assay (ELISA) test kits. We used a Bayesian method to calibrate the absorbance data from the kit and report here on both the microcystin concentrations of the samples analyzed, but also report the raw absorbance data from both samples and calibration standards so that others could recreate the microcystin analysis using other methods if they so choose.