Control and remediation methods for eutrophic lakes in the past 30 years

• Published in: Water science and technology Vol. 81; no. 6; pp. 1099 - 1113
• Main Authors: Zhang, Yuan, Luo, Pingping, Zhao, Shuangfeng, Kang, Shuxin, Wang, Pengbo, Zhou, Meimei, Lyu, Jiqiang
• Format: Journal Article
• Language: English
• Published: England IWA Publishing 15.03.2020
• Subjects: Algae; Aquatic ecosystems; Blooms; China; Control methods; Cyanobacteria; Ecosystem; Eutrophic environments; Eutrophic lakes; Eutrophic waters; Eutrophication; Herbicides; Lakes; Learning algorithms; Machine learning; Management; Methods; New technology; Nutrient concentrations; Nutrients; Nutritional status; Phosphorus; Pollution; Remediation; Restoration; Water Pollution; Water purification; Water quality; China
• ISSN: 0273-1223; 1996-9732
• DOI: 10.2166/wst.2020.218

Accelerated eutrophication, which is harmful and difficult to repair, is one of the most obvious and pervasive water pollution problems in the world. In the past three decades, the management of eutrophication has undergone a transformation from simple directed algal killing, reducing endogenous nutrient concentration to multiple technologies for the restoration of lake ecosystems. This article describes the development and revolution of three remediation methods in application, namely physical, chemical, and biological methods, and it outlines their possible improvements and future directions. Physical and chemical methods have obvious and quick effects to purify water in the short term and are more suitable for small-scale lakes. However, these two methods cannot fundamentally solve the eutrophic water phenomenon due to costly and incomplete removal results. Without a sound treatment system, the chemical method easily produces secondary pollution and residues and is usually used for emergency situations. The biological method is cost-effective and sustainable, but needs a long-term period. A combination of these three management techniques can be used to synthesize short-term and long-term management strategies that control current cyanobacterial blooms and restore the ecosystem. In addition, the development and application of new technologies, such as big data and machine learning, are promising approaches.