Artificial Intelligence Applications in Analyzing Algae's Pollutant Absorption Capacity: An Innovative Environmental Perspective / Review Article

The world faces severe environmental dangers as freshwater and aquatic life are more and more contaminated with heavy metals, organic compounds, and microplastics. They are harmful to ecosystems and human health and require effective and sustainable solutions to eradicate them. Algae are among the most ubiquitous organisms with a natural ability to remove such pollutants, but common knowledge in regards to their mechanism and efficacy has been hindered due to the complexity of environmental factors and their interactions. Further, utilization of artificial intelligence (AI) techniques for studying and predicting algal performance under different environments is still in development and faces various challenges. Article Objective: This paper seeks to address the use of algae in bioremediation of pollutants and illustrate how artificial intelligence techniques such as machine learning and artificial neural networks have enhanced our understanding of the effectiveness of algae for the removal of pollutants under different conditions. The article further tries to present case studies and real-world applications that indicate the effectiveness of such methods, along with the current issues and future prospects towards merging AI with biological indicators for pollution treatment. Article Methodology: Recent journal articles of the past couple of years (2023-2024) on the topic of algae and artificial intelligence in the environment were retrieved and studied, and research on the application of advanced techniques such as neural networks, deep learning, and big data in measuring algae efficiency was emphasized. Different types of algae and their role in heavy metal removal, organic contaminants, and microplastics were covered. Artificial intelligence models applied to predict algae biological performance were taken into account. The main challenges hindering the industrial application of these models were discussed, and promising future technologies such as interactive intelligent systems and remote sensing integration were highlighted. Studies showed that algae such as Chlorella vulgaris, Scenedesmus obliquus, and Spirulina platensis possess a high capacity to absorb and degrade various pollutants from different aquatic environments. The use of artificial intelligence enabled the development of accurate predictive models with an accuracy of 95% or more, providing faster and less expensive alternatives to traditional laboratory and field experiments. Field case studies have demonstrated the effectiveness of intelligent algorithms in dynamically identifying and optimizing optimal treatment conditions. However, higher-quality data and interdisciplinary collaboration are still needed to overcome current limitations. Future prospects point to broad potential for the use of interactive artificial intelligence and smart monitoring systems based on algae, as well as the integration of satellite data and remote sensing technologies to expand the scope of applications.