Chlorophyll and Phycocyanin Raw Fluorescence May Inform Recreational Lake Managers on Cyanobacterial HABs and Toxins: Lake Fayetteville Case Study

Haggard, Brian E.; Grantz, Erin M.; Austin, Bradley J.; Wagner, Nicole D.; Scott, J. Thad

doi:10.1111/j.1936-704x.2022.3381.x

Cited by 4 publications

(4 citation statements)

References 23 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…cyanobacterial index). The use of fluorecence phyco/chl-a ratios to discriminate species composition has been proved in culture experiments (Rousso et al, 2022) and more cleaner environments (Haggard et al, 2023). Implementing thresholds within a turbid ambient such as lake Okeechobee is a challenge, but our results are promising.…”

Section: Discussionmentioning

confidence: 81%

Monitoring Lake Okeechobee Harmful Algal Bloom populations and dynamics with a long-duration Autonomous Surface Vehicle

Xomchuk,

Duncan,

McFarland

et al. 2023

Preprint

View full text Add to dashboard Cite

This article describes the main findings of a full year of continuous operation of a 2-meter Autonomous Sail and Solar Surface Drone, the Nav2 (Navocean Inc.), as part of a Harmful Algal Bloom (HAB) monitoring program in Lake Okeechobee. The Nav2 was equipped with a set of water quality and atmospheric sensors, that recorded high frequency measurements (\textless 1 min) and transmitted near real-time information to allow reporting through a web portal for assessment and operation responses. Major findings include detection of HABs early in the year through chlorophyll (chl-a) and phycocyanin (phyco) fluorometric measurements, as well as different spatial scales of variability in the algal patches. The 24/7 high resolution monitoring allowed detection of patch motion and discrimination between growth and motion along a transect. Furthermore, the platform can potentially fingerprint specific HAB species based on the relatively fine-scale spatial expression of the phyco to chl-a ratio, which essentially captures the bloom macrostructure (e.g. surface scums versus more uniform sub-surface waves over 0.1 - 1 km scale). Sensor outputs, when converted to concentrations based on calibrated with pure laboratory standards, did not accurately yield true chl-a or phyco values when compared to validation samples, likely due to the high turbidity of the lake. However, routine solid-state validations of fluorometric measurements proved useful for assessing consistency in optical sensors to check for sensor drift (e.g. to due biofouling), which was not significant. Overall this demonstration shows that the Nav2 can uniquely and reliably provide in situ HAB and environmental monitoring capabilities in a large, turbid, shallow lake. We envision that platform as an innovative technology for water resource managers by providing turn-key long-duration baseline environmental data (hands-off waypoint navigation), early warnings of HABs for protecting human health, and for HAB mitigation monitoring.

show abstract

Section: Discussionmentioning

confidence: 81%

Monitoring Lake Okeechobee Harmful Algal Bloom populations and dynamics with a long-duration Autonomous Surface Vehicle

Xomchuk,

Duncan,

McFarland

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The main advantage of decision trees is the clarity in their structure and ease of understanding, which can be beneficial for observing management thresholds (Haggard et al, 2023b). These models are easy to use and do not require initialization, such as predefining the water quality data distribution(s).…”

Section: Machine Learning Models and Their Featuresmentioning

confidence: 99%

“…The developed model showed improved performance compared to models without the XGBoost unique feature extraction technique and suggested the potential to make early toxicity warnings. Similarly, Haggard et al (2023b) explored the use of regression trees to identify the threshold values of raw fluorescence units (RFUs) of phycocyanin and chlorophyll-a that could influence the concentration of total microcystin in Lake Fayetteville, Arkansas. The release of microcystin in freshwater systems can be related to a certain level of chlorophylla and phycocyanin concentration, which suggests advisories for microcystins can be made based on the threshold of these easy-to-measure water quality parameters.…”

Section: Real-time and Time Series Predictionmentioning

confidence: 99%

“…Applying ML models to HAB monitoring could trigger the swift implementation of management decisions. A range of ML models have been applied for the understanding of HABs dynamics through the prediction of algal-related parameters such as chlorophyll-a, phycocyanin, algal cells, dissolved oxygen, nutrient concentrations, and toxin levels (Cao et al, 2022;Giere et al, 2020;Haggard et al, 2023b;Millie et al, 2014). These models, upon validation, could be incorporated into a real-time HAB observation and early warning systems for the early dissemination of regulatory advisories for the protection of freshwater bodies and the prevention of public health issues.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A Review of Machine Learning Models for Harmful Algal Bloom Monitoring in Freshwater Systems

Busari,

Sahoo,

Harmel

et al. 2023

Journal of Natural Resources and Agricultural Ecosystems

View full text Add to dashboard Cite

Highlights Machine Learning (ML) models are identified, reviewed, and analyzed for HAB predictions. Data preprocessing is vital for efficient ML model development. ML models for toxin production and monitoring are limited. Abstract. Harmful algal blooms (HABs) are detrimental to livestock, humans, pets, the environment, and the global economy, which calls for a robust approach to their management. While process-based models can inform practitioners about HAB enabling conditions, they have inherent limitations in accurately predicting harmful algal blooms. To address these limitations, Machine Learning (ML) models can potentially leverage large volumes of IoT data to aid in near real-time predictions. ML models have evolved as efficient tools for understanding patterns and relationships between water quality parameters and HAB expansion. This review describes ML models currently used for predicting and forecasting HABs in freshwater ecosystems and presents model structures and their application for predicting algal parameters and related toxins. The review revealed that regression trees, random forest, Artificial Neural Network (ANN), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) are the most frequently used models for HABs monitoring. This review shows ML models' prowess in identifying significant variables influencing algal growth, HAB drivers, and multistep HAB prediction. Hybrid models also improve the prediction of algal-related parameters through improved optimization techniques and variable selection algorithms. While ML models often focus on algal biomass prediction, few studies apply ML models for toxin monitoring and prediction. This limitation can be associated with a lack of high-frequency toxin datasets for model development, and exploring this domain is encouraged. This review serves as a guide for policymakers and researchers to implement ML models for HAB prediction and reveals the potential of ML models for decision support and early prediction for HAB management. Keywords: Cyanobacteria, Freshwater, Harmful algal blooms, Machine learning, Water quality.

show abstract

Monitoring Lake Okeechobee Harmful Algal Bloom populations and dynamics with a long-duration Autonomous Surface Vehicle

Xomchuk,

Duncan,

McFarland

et al. 2023

Preprint

View full text Add to dashboard Cite

This article describes the main findings of a full year of continuous oper- ation of a 2-meter Autonomous Sail and Solar Surface Drone, the Nav2 (Navocean Inc.), as part of a Harmful Algal Bloom (HAB) monitoring program in Lake Okeechobee. The Nav2 was equipped with a set of water quality and atmospheric sensors, that recorded high frequency measurements (< 1 min) and transmitted near real-time information to allow reporting through a web portal for assessment and operation responses. Major findings include detection of HABs early in the year through chlorophyll (chl-a) and phycocyanin (phyco) fluorometric measurements, as well as different spatial scales of variability in the algal patches. The 24/7 high resolution monitoring allowed detection of patch motion and discrimination between growth and motion along a transect. Furthermore, the platform can potentially fingerprint specific HAB species based on the relatively fine-scale spatial expression of the phyco to chl-a ratio, which essentially captures the bloom macrostructure (e.g. surface scums versus more uniform sub-surface waves over 0.1 - 1 km scale). Sensor outputs, when converted to concentrations based on calibrated with pure laboratory standards, did not accurately yield true chl-a or phyco values when compared to validation samples, likely due to the high turbidity of the lake. However, routine solid-state validations of fluorometric measurements proved useful for assessing consistency in optical sensors to check for sensor drift (e.g. to due biofouling), which was not significant. Overall this demonstration shows that the Nav2 can uniquely and reliably provide in situ HAB and environmental monitoring capabilities in a large, turbid, shallow lake. We envision that platform as an innovative technology for water resource managers by providing turn-key long-duration baseline environmental data (hands-off waypoint navigation), early warnings of HABs for protecting human health, and for HAB mitigation monitoring.

show abstract

Chlorophyll and Phycocyanin Raw Fluorescence May Inform Recreational Lake Managers on Cyanobacterial HABs and Toxins: Lake Fayetteville Case Study

Cited by 4 publications

References 23 publications

Monitoring Lake Okeechobee Harmful Algal Bloom populations and dynamics with a long-duration Autonomous Surface Vehicle

Monitoring Lake Okeechobee Harmful Algal Bloom populations and dynamics with a long-duration Autonomous Surface Vehicle

A Review of Machine Learning Models for Harmful Algal Bloom Monitoring in Freshwater Systems

Monitoring Lake Okeechobee Harmful Algal Bloom populations and dynamics with a long-duration Autonomous Surface Vehicle

Contact Info

Product

Resources

About