Detection and modeling techniques for the monitoring of harmful algal blooms: a systematic literature review

Harmful algal blooms (HABs) are caused by naturally occurring species of phytoplankton that generate toxins that can harm humans and aquatic animals. Although these HABs have been present in our waters, recent anthropogenic activities have made their occurrence more frequent and erratic. Due to the increasing number of poisonous and blooming species in water bodies worldwide, there is a need for more effective systems to mitigate their effect. A systematic review was conducted to evaluate and understand the various detection and modeling techniques used for HABs in the marine environment, largely focusing on the aquaculture and fisheries sectors. Our investigation identified 2338 papers published from January 2000 to April 2024 from reputable databases. After screening for eligibility using predetermined criteria, 74 eligible studies were found and categorized into pigment-based technology, morphological-based technology, and nucleic acid technology. Additionally, only seven studies applied modeling approaches to forecast harmful blooms by employing pigment-based technology and offline learning models. Early warning systems are crucial for managing HABs, since it is the first step toward implementing mitigation strategies. However, there is a greater need for real-time in situ analysis geared toward identifying microalgae species that can proactively manage this threat and protect aquaculture and fisheries resources.