Next Article in Journal
Image Analysis as a Non-Destructive Approach in Selective Characterization of Promising Indian Chickpea Cultivars
Previous Article in Journal
The Contribution of Impact Damage to the Quality Changes of Stored Banana Fruit
 
 
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Abstract

IA-GES-BLOOM-CM: Towards a Comprehensive Warning and Management System for Cyanobacterial Blooms †

1
Departamento de Arquitectura de Computadores y Automática, Universidad Complutense de Madrid, 28040 Madrid, Spain
2
Departamento de Fisiología Vegetal, Universidad Autónoma de Madrid, 28049 Madrid, Spain
*
Author to whom correspondence should be addressed.
Presented at the 7th Iberian Congress on Cyanotoxins/3rd Iberoamerican Congress on Cyanotoxins, Ponta Delgada, Portugal, 18–20 June 2022.
Presenting author (oral communication).
Biol. Life Sci. Forum 2022, 14(1), 48; https://doi.org/10.3390/blsf2022014048
Published: 6 September 2022

Abstract

:
Cyanobacterial Blooms (CBs) are an ecological and public health problem since they may be followed by the production of secondary metabolites, which are toxic for humans and other animals. This threatens the life of multiple species and prevents the use of water resources for recreational and consumption purposes. Therefore, their proper management is essential to minimize the exposure of the population and ecosystems to the harmful effects of CBs. The ability to predict the formation of CBs in a specific water body is limited by the difficulty of acquiring enough data to determine their state with the appropriate temporal and spatial granularity. Moreover, as CBs are complex phenomena that are influenced by many factors, the conclusions derived for a certain water body are hard to extrapolate to others. IA-GES-BLOOM-CM is a synergy project funded by the Community of Madrid, Spain, for boosting the collaboration of researchers from different fields (including biology, automation, and information and communication technologies) to develop disruptive solutions for CB prediction and management. Its aim is to develop a comprehensive and reliable system to automatically and efficiently characterize continental water bodies, predict where and when the CBs are expected to occur, determine their potential risks, and provide the authorities with early warnings of CB breakouts. To this end, we are conceiving a system, supported by Autonomous Surface Vehicles (ASVs, a kind of robotized boats), Modeling and Simulation (M&S) tools, and the Internet of Things (IoT). More specifically, on one hand, the ASVs, which are equipped with probes, will be (1) responsible for capturing information related to the CBs from any point of the water column and surface and will be (2) intelligently guided to the points of interest to make relevant observations in order to optimize the monitoring efforts. On the other hand, M&S tools, including dynamical models and machine learning, will be in charge of predicting the CB temporal and spatial evolution in order to guide the ASVs (whose data, in turn, will be used to fine-tune the models) and warn the authorities about relevant CBs. Finally, an IoT infrastructure will support the communications and deployment of the system, closing the gap between the authorities in charge of the water bodies and the information provided by the different elements of the system. In this paper, we will provide an overview of the main ideas of the project and of its initial developments.

Author Contributions

IoT and software, J.C.; ASV and methodology, J.A.L.-O.; models, E.B.-P.; validation E.P. and S.C.; supervision, A.Q. and E.B.-P. All authors have read and agreed to the published version of the manuscript.

Funding

This work is supported by the Madrid Regional Government Project IA-GES-BLOOM-CM titled “Towards a comprehensive system for the alert and management of cyanobacteria blooms in inland waters”, Ref. Y2020/TCS-6420.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

López-Orozco, J.A.; Chacón, J.; Perona, E.; Cirés, S.; Quesada, A.; Besada-Portas, E. IA-GES-BLOOM-CM: Towards a Comprehensive Warning and Management System for Cyanobacterial Blooms. Biol. Life Sci. Forum 2022, 14, 48. https://doi.org/10.3390/blsf2022014048

AMA Style

López-Orozco JA, Chacón J, Perona E, Cirés S, Quesada A, Besada-Portas E. IA-GES-BLOOM-CM: Towards a Comprehensive Warning and Management System for Cyanobacterial Blooms. Biology and Life Sciences Forum. 2022; 14(1):48. https://doi.org/10.3390/blsf2022014048

Chicago/Turabian Style

López-Orozco, José Antonio, Jesús Chacón, Elvira Perona, Samuel Cirés, Antonio Quesada, and Eva Besada-Portas. 2022. "IA-GES-BLOOM-CM: Towards a Comprehensive Warning and Management System for Cyanobacterial Blooms" Biology and Life Sciences Forum 14, no. 1: 48. https://doi.org/10.3390/blsf2022014048

Article Metrics

Back to TopTop