From passive prevention to proactive intervention: An AI-assisted integrated governance system for algal bloom monitoring and control.

Journal: Environmental research
Published Date:

Abstract

Algal blooms, characterized by the excessive proliferation of microalgae in a freshwater or marine ecosystem, have evolved into a global ecological, health and economic crisis, which is often accompanied by the release of toxins and the damage of the aquatic ecosystem. It is known that the formation mechanism of algal blooms is complex and spatially-temporally heterogeneous, and mainly as a result of the interaction of climate change, natural factors, and human activities, making it extremely difficult for precise early warning, efficient treatment, and complete cure. Although the forecasting of algal bloom is effective and accurate that relies on multi-mode technology (involving remote sensing, mathematical modeling, automated monitoring, etc.), a lag and fragmentation exist when applied to guide the formulation of efficient treatment strategy. Meanwhile, as a renewable resource, microalgae recycle also plays a key role in algal bloom governance. Therefore, a systematic strategy integrated monitoring and treatment is extremely scarce and desiderate for the efficient governance of algal blooms. This review comprehensively summarizes the methods and mechanisms of the integrated governance system, and discussed the innovative applications of artificial intelligence (AI) in algal bloom governance. Predictably, this AI assistant integrated governance system will significantly enhance predictive capabilities and emergency handling efficiency for algal blooms, realizing the shift from "passive emergency response" to "active prevention and control".

Authors

  • Minjuan Lin
    School of Advanced Manufacturing, Fuzhou University, Quanzhou, 362200, China.
  • Yihao Xu
    Department of Mechanical and Industrial Engineering, Northeastern University, 360 Huntington Avenue, Boston, MA 02115, United States of America.
  • Jia Peng
    Department of computed tomography, The Affiliated Zhongshan City Hospital of Sun Yat-sen University, PR China.
  • Yaoyang Chen
    Technology Innovation Centre for Exploitation of Marine Biological Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China.
  • Yulong Zhu
    School of Earth Science and Technology, Zhengzhou University, Zhengzhou, 450000, China.
  • Xuanye Fu
    School of Advanced Manufacturing, Fuzhou University, Quanzhou, 362200, China.
  • Yi Zhu
    2State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, Guangdong China.
  • Ling Lin
    Center for Sleep Science and Medicine, Stanford University, Stanford, 94304, CA, USA.