Lake Avernus Has Turned Red: Bioindicator Monitoring Unveils the Secrets of "Gates of Hades".
Germana EspositoEvgenia GlukhovWilliam H GerwickGabriele MedioRoberta TetaMassimiliano LegaValeria CostantinoPublished in: Toxins (2023)
Lake Avernus is a volcanic lake located in southern Italy. Since ancient times, it has inspired numerous myths and legends due to the occurrence of singular phenomena, such as coloring events. Only recently has an explanation been found for them, i.e., the recurring color change over time is due to the alternation of cyanobacterial blooms that are a consequence of natural nutrient inputs as well as pollution resulting from human activities. This current report specifically describes the red coloring event that occurred on Lake Avernus in March 2022, the springtime season in this region of Italy. Our innovative multidisciplinary approach, the 'Fast Detection Strategy' (FDS), was devised to monitor cyanobacterial blooms and their toxins. It integrates remote sensing data from satellites and drones, on-site sampling, and analytical/bioinformatics analyses into a cohesive information flow. Thanks to FDS, we determined that the red color was attributable to a bloom of Planktothrix rubescens , a toxin-producing cyanobacterium. Here, we report the detection and identification of 14 anabenopeptins from this P. rubescens strain, seven of which are known and seven are newly reported herein. Moreover, we explored the mechanisms and causes behind this cyclic phenomenon, confirming cyanobacteria's role as reliable indicators of environmental changes. This investigation further validates FDS's effectiveness in detecting and characterizing cyanobacterial blooms and their associated toxins, expanding its potential applications.
Keyphrases
- water quality
- risk assessment
- endothelial cells
- escherichia coli
- loop mediated isothermal amplification
- randomized controlled trial
- heavy metals
- human health
- systematic review
- label free
- healthcare
- machine learning
- big data
- pluripotent stem cells
- drinking water
- artificial intelligence
- deep learning
- quantum dots
- air pollution
- data analysis
- life cycle