An Engineered Hub & Spoke System to Seek and Destroy Toxic Photosynthetic Organisms in Water Bodies

Algal blooms have plagued waterways causing significant losses to Florida’s economy. Currently, bloom data collection is reactionary and inadequate, following regional fish kills. Algal mitigation efforts are minimal and centered around fertilizer usage. Hence, there is a need for a smart solution aimed at preemptive detection and mitigation of impending blooms. The goal is to engineer an integrated Hub & Spoke (H&S) algae mitigation system for remote algal measurements, analytics, and mitigation. The system had three Spokes and a Hub. First, the 1st Mover, includes an aerial drone surveillance system, multispectral imagery, and post-processing using software. Second, the Verifier, an engineered floatation device for real-time algal measurements with a microcontroller and six sensors (pH, DO2, TDS, Photoresistor, Color Intensity, and Temperature). Third, Destroyer, a proactive mitigation system with algal suppression agents and a camera. Lastly, a centralized Hub to observe descriptive analytics visually in a cloud dashboard. H&S-engineered prototype tests were successful. 1st Mover visuals were converted as orthomosaic images and differentiated green zones from others. The Verifier passed leak checks, sensor-microcontroller interactions were seamless, measured data, and transmitted to the Hub continuously. Destroyer submersible pump dispersed mitigation agents via a remote on/off switch from the Hub dashboard. Per lab testing, a physicochemical agent (Alum-Bentonite combo) suppressed Chlorella vulgaris algae by flocculation and sedimentation. TDS, pH, and DO2 declined dramatically signifying the suppression. A 4th-order polynomial machine learning model predicted TDS with 84% accuracy. This solution can be applied in the real world to mitigate algal blooms, saving our coastlines.

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Publication Details

Journal
Journal of Student Research
Published
2024-08-31
DOI
https://doi.org/10.47611/jsrhs.v13i3.7384
Primary Topic
Water Quality Monitoring Technologies
Type
article
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article

An Engineered Hub & Spoke System to Seek and Destroy Toxic Photosynthetic Organisms in Water Bodies

Sharanya Natarajan
Journal of Student Research
Water Quality Monitoring Technologies
article

An Engineered Hub & Spoke System to Seek and Destroy Toxic Photosynthetic Organisms in Water Bodies

Sharanya Natarajan
article en

Abstract

Algal blooms have plagued waterways causing significant losses to Florida’s economy. Currently, bloom data collection is reactionary and inadequate, following regional fish kills. Algal mitigation efforts are minimal and centered around fertilizer usage. Hence, there is a need for a smart solution aimed at preemptive detection and mitigation of impending blooms. The goal is to engineer an integrated Hub & Spoke (H&S) algae mitigation system for remote algal measurements, analytics, and mitigation. The system had three Spokes and a Hub. First, the 1st Mover, includes an aerial drone surveillance system, multispectral imagery, and post-processing using software. Second, the Verifier, an engineered floatation device for real-time algal measurements with a microcontroller and six sensors (pH, DO2, TDS, Photoresistor, Color Intensity, and Temperature). Third, Destroyer, a proactive mitigation system with algal suppression agents and a camera. Lastly, a centralized Hub to observe descriptive analytics visually in a cloud dashboard. H&S-engineered prototype tests were successful. 1st Mover visuals were converted as orthomosaic images and differentiated green zones from others. The Verifier passed leak checks, sensor-microcontroller interactions were seamless, measured data, and transmitted to the Hub continuously. Destroyer submersible pump dispersed mitigation agents via a remote on/off switch from the Hub dashboard. Per lab testing, a physicochemical agent (Alum-Bentonite combo) suppressed Chlorella vulgaris algae by flocculation and sedimentation. TDS, pH, and DO2 declined dramatically signifying the suppression. A 4th-order polynomial machine learning model predicted TDS with 84% accuracy. This solution can be applied in the real world to mitigate algal blooms, saving our coastlines.

Journal of Student ResearchVol. 13(3)
University Health Care System (US)
Openalex Percentile: Top 71%
Water Quality Monitoring Technologies
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