Spatial reconnaissance of fluvial microcystins (MC) concentrations and select water-quality parameters, including nutrients and periphyton biomass, in 75 wadeable streams in the Piedmont region of the southeastern USA during 2014. Data set includes only those data specifically discussed in the associated journal article: Loftin, K.A., Clark, J.M., Journey, C.A., Kolpin, D.W., Van Metre, P.C., and Bradley, P.M., 2016, Spatial and temporal variation in microcystins occurrence in wadeable streams in the southeastern USA: Environmental Toxicology and Chemistry, http://dx.doi.org/10.1002/etc.3391.

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).

Water samples were collected by charter boat captains and Stone Lab scientists in order to track the water quality of Lake Erie. 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). These data are available in .xlsx format.

This U.S. Geological Survey (USGS) Data Release provides discrete water-quality data collected from four sites on the Kansas River and four of its tributaries during July 2012 through September 2016. The water-quality constituents included in this data release are the cyanotoxins microcystin and cylindrospermopsin, the taste-and-odor compounds geosmin and 2-methylisoborneol, major ions, alkalinity, nutrients, suspended sediment, indicator bacteria, and actinomycetes bacteria.

This U.S. Geological Survey (USGS) Data Release provides discrete water-quality data collected from four sites on the Kansas River and four of its tributaries during July 2012 through September 2016. The water-quality constituents included in this data release are the cyanotoxins microcystin and cylindrospermopsin, the taste-and-odor compounds geosmin and 2-methylisoborneol, major ions, alkalinity, nutrients, suspended sediment, indicator bacteria, and actinomycetes bacteria.

In 2015-2018, the U.S. Geological Survey (USGS) in cooperation with the U.S. Environmental Protection Agency Great Lakes Restoration Initiative investigated the biodegradation of microcystins in source waters and sand filters from drinking-water plants in the Western Lake Erie Basin. Four source waters and three sand filtrate samples were collected from the intakes and sand filters of Lake Erie drinking-water plants and transported to the USGS Ohio Water Microbiology Laboratory, where investigators set up microcosms to enrich for and identify indigenous bacteria capable of degrading microcystins. Quality control samples were set up in the microcosms to check analyses and included positive controls, negative controls, and replicates. Microcystin biodegradation was quantified by the disappearance of the toxin as compared to control cultures in microcosm and microplate experiments, and by the presence of a gene within microcystin-degrading bacteria that encodes for an enzyme involved in the initial steps of biodegradation. Bacteria were isolated from microcosms enriched with microcystin-LR (MC-LR) and MC-LR concentrations were measured over time by ELISA (table 1). Isolates were selected from the microcosm experiments for further growth testing in microplate experiments with various enrichment media and MC-LR over 96 hours (table 2). Biofilm formation potential for the isolates were also measured and data is shown in table 3. Isolate absorbances of ten potential microcystin degraders were incubated in a microplate with MC-LR as the sole carbon source (table 4) and concentrations of MC-LR in microplate wells were measured over time (table 5).

In 2015-2018, the U.S. Geological Survey (USGS) in cooperation with the U.S. Environmental Protection Agency Great Lakes Restoration Initiative investigated the biodegradation of microcystins in source waters and sand filters from drinking-water plants in the Western Lake Erie Basin. Four source waters and three sand filtrate samples were collected from the intakes and sand filters of Lake Erie drinking-water plants and transported to the USGS Ohio Water Microbiology Laboratory, where investigators set up microcosms to enrich for and identify indigenous bacteria capable of degrading microcystins. Quality control samples were set up in the microcosms to check analyses and included positive controls, negative controls, and replicates. Microcystin biodegradation was quantified by the disappearance of the toxin as compared to control cultures in microcosm and microplate experiments, and by the presence of a gene within microcystin-degrading bacteria that encodes for an enzyme involved in the initial steps of biodegradation. Bacteria were isolated from microcosms enriched with microcystin-LR (MC-LR) and MC-LR concentrations were measured over time by ELISA (table 1). Isolates were selected from the microcosm experiments for further growth testing in microplate experiments with various enrichment media and MC-LR over 96 hours (table 2). Biofilm formation potential for the isolates were also measured and data is shown in table 3. Isolate absorbances of ten potential microcystin degraders were incubated in a microplate with MC-LR as the sole carbon source (table 4) and concentrations of MC-LR in microplate wells were measured over time (table 5).

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.

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.

For State agencies and other water-resources managers, determining which waterbodies to allocate limited funds for protection and restoration while also maximizing cost benefit is challenging. This data release contains trophic state designations determined from secchi depth, and concentrations of chlorophyll a and microcystin at 232 lakes and reservoirs having a surface area of greater than 0.1 square kilometer in watersheds that drain to the Atlantic and eastern Gulf of Mexico coasts of the United States and in watersheds within the Tennessee River Basin. Estimates of nutrient loading (nitrogen and phosphorus, Hoos and others, 2013; Moorman and others, 2014) and flushing rates were combined with waterbody morphometry (Hollister and Milstead, 2010; Hollister and others, 2011; U.S. Environmental Protection Agency, 2018) to predict summer-season Secchi depth and concentrations of chlorophyll a and microcystin. Waterbodies were categorized by type — natural lakes, headwater reservoirs, and downstream reservoirs — and were assessed independently. Recursive partitioning and the model-based boosting routine were implemented in a R script, which is provided in this data release. The script was used to create four-node regression trees that group waterbodies into five endpoints along individual low-to-high gradients of Secchi depth, chlorophyll a concentration, and microcystin concentration, according to shared nutrient loading, flushing rate, and morphometric characteristics. Trophic state designations were assigned on the basis of the average values within each of the five endpoints. These regression trees can be used to place all waterbodies within the study area greater than or equal to 0.1 square kilometer into one of the different Secchi depth, chlorophyll a, or microcystin endpoints. Results of this study will aid water-resource managers in prioritizing lake and reservoir protection and restoration efforts based on the susceptibility of these waterbodies to eutrophication related to nutrient loading, flushing rate, and morphometric characteristics. References: Hollister J.W., and Milsted W.B., 2010, Using GIS to estimate lake volume from limited data: Reservoir Management, vol. 26, pp. 194-199. Hollister J.W., Milstead W.B., Urrutia M.A., 2011, Predicting maximum lake depth from surrounding topography: PloS ONE, vol. 6, article no. 25764, https://doi.org/10.1371/journal.pone.0025764 Hoos, A.B., Moore, R.B., Garcia, A.M., Noe, G.B., Terziotti, S.E., Johnston, C.M., and Dennis, R.L., 2013, Simulating stream transport of nutrients in the eastern United States, 2002, using a spatially-referenced regression model and 1:100,000 scale hydrography: U.S. Geological Survey Scientific Investigations Report 2013-5102, 33p. Moorman, M.C., Hoos, A.B., Bricker, S.B., Garcia, A.M., and Ator, S.W., 2014, Nutrient load summaries for major lakes and estuaries of the eastern United States, 2002: U.S. Geological Survey Data Series 820, 94p. U.S. Environmental Protection Agency, 2018, Data from the national aquatic resource surveys: U.S. Environmental Protection Agency database, accessed February 8, 2018 at https://edg.epa.gov/clipship/. [Data downloaded for AL, FL, GA, MS, NC, SC, TN, VA, CT, DE, MA, ML, ME, NY, NH, NJ, PA, and RI] U.S. Geological Survey, 2017, Forecasting toxic cyanobacterial blooms throughout the southeastern U.S., accessed February 13, 2017 at http://wilsonlab.com/bloom_network/. [U.S. Geological Survey project homepage on Wilsonlab at Auburn University website]