Geology topics

Harrison D Dalby

Publications and source records attributed to Harrison D Dalby.

2 recordsLinked to original sources

USGS2020

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.

Ohio↗

USGS2020

Nowcasting methods for determining microbiological water quality at recreational beaches and drinking-water source waters

Nowcasts are tools used to provide timely and accurate water-quality assessments of threats to drinking-water and recreational resources from fecal contamination or cyanobacterial harmful algal blooms. They use mathematical models and techniques to provide near-real-time estimates of fecal-indicator bacteria (FIB) and cyanotoxin concentrations. Techniques include logic-based thresholds, decision trees (built with machine learning), multiple linear and binary logistic regression, artificial neural networks, and process-based deterministic models. The type of site (freshwater, marine, or river) and dependent variable (FIB or cyanotoxin) dictate which explanatory variables are used in models. Nowcast systems notify the public of associated public-health risks and can also be used to manage data for FIB models; work is ongoing to incorporate cyanotoxin models into some nowcasts. The Great Lakes NowCast in the USA has been operational since 2010 and includes 25 lake beaches and one recreational river site. Examples of other operational FIB nowcasts are described for locations in the USA and around the world. In many cases, models predicted exceedances of FIB standards with accuracies as good as or better than using the previous measured FIB concentration (persistence method). Accuracy and timeliness are vital to beach management decisions that protect public health and support the local recreation-driven economy. Nowcasts benefit the public by providing estimates of water-quality conditions in near-real-time. Managers can use nowcasts at recreational and drinking-water treatment plant sites when FIB or cyanotoxins are projected to be elevated to target sample collection, to provide near-real-time recreational advisories to the public, or to preemptively optimize drinking-water treatments or change intake options to mitigate possible adverse effects on drinking-water quality.

Journal of Microbiological Methods↗