# Integrating in-situ detection technologies and developing data assimilation strategies to improve forecast accuracy and assess climate change impacts for Microcystis blooms in Lake Erie

> **NIH NIH P01** · BOWLING GREEN STATE UNIVERSITY · 2020 · $34,581

## Abstract

Abstract/Project Summary
 Cyanobacterial harmful algal blooms (cHABs) have become more frequent and intense over the past few
decades and are projected to continue to increase in severity and toxicity due to a warming climate and
anthropogenically-enhanced nutrient loading. As such, detecting and monitoring cHAB development and
toxicity are of growing importance, especially for freshwater systems such as the Laurentian Great Lakes that
supply drinking water to many municipalities. Traditional sampling and analysis methods are time-consuming,
labor intensive, and generally implemented on only a weekly or bi-weekly basis, which may fail to detect
ephemeral yet highly toxic bloom events. Fortunately, novel, fit-for-purpose detection technologies are
becoming available to address previous constraints by providing near-real time data.
 This project directly addresses four research priorities listed in the COHH3 RFA: (1) compare and
correlate current observing systems for monitoring ocean and Great Lakes properties including Harmful Algal
Blooms, (2) evaluate long-term field application potential of newly developing in situ sensors for monitoring
ocean and Great Lakes properties, (3) evaluate real-time, in-water observations of physicochemical
properties, as well as the detection of HAB species and toxins, to provide data streams for assimilation by
predictive models, (4) develop appropriate and efficient monitoring strategies for algal toxins (particularly in
drinking water) that are protective of public health. The specific aims of the proposed project are to
integrate in-situ sensing and sampling technologies with data assimilation strategies to improve
forecast accuracy, provide regional stakeholders with advanced warning of cHAB development and
toxic events, and evaluate the impacts of climate change on cHABs and internal phosphorus loading
in Lake Erie. We will accomplish these aims by integrating an autonomous, in-situ Environmental Sample
Processor, Solid Phase Adsorption Toxin Tracking devices, water quality probes, and field-portable sampling
methods, along with satellite remote sensing with the broader outcome of improving bloom forecasting models
and to develop a more timely and complete spatio-temporal picture of developing cHAB toxicity and biomass
as well as internal phosphorus loading in Lake Erie. Collectively, GLERL's long-term water quality monitoring
and NOAA's advanced cHAB forecasting model (HAB tracker), which integrates satellite data,
physicochemical, biological, molecular, and toxicity (this project) data to forecast bloom location, size and
toxicity with a 5-day lead time, will facilitate informed, timely decisions to reduce the impacts of toxic cHABs on
public health, natural resources, and local economies. Project outputs will also contribute to the Center
Program's goal of better understanding the influence of climate change on the frequency and severity of
cHABs in Lake Erie and other Great Lakes' regions, and thereby inf...

## Key facts

- **NIH application ID:** 9976544
- **Project number:** 5P01ES028939-03
- **Recipient organization:** BOWLING GREEN STATE UNIVERSITY
- **Principal Investigator:** Thomas Bridgeman
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $34,581
- **Award type:** 5
- **Project period:** 2018-09-30 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9976544

## Citation

> US National Institutes of Health, RePORTER application 9976544, Integrating in-situ detection technologies and developing data assimilation strategies to improve forecast accuracy and assess climate change impacts for Microcystis blooms in Lake Erie (5P01ES028939-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9976544. Licensed CC0.

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