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Lake recovery from eutrophication: Quantitative response of trophic states to anthropogenic influences
Ecological Engineering ( IF 3.8 ) Pub Date : 2020-01-01 , DOI: 10.1016/j.ecoleng.2019.105697 Chunxue Yu , Zuoyong Li , Zhihao Xu , Zhifeng Yang
Ecological Engineering ( IF 3.8 ) Pub Date : 2020-01-01 , DOI: 10.1016/j.ecoleng.2019.105697 Chunxue Yu , Zuoyong Li , Zhihao Xu , Zhifeng Yang
Abstract Lake eutrophication is a serious environmental problem worldwide, caused by both natural processes and anthropogenic influences. For effective lake eutrophication management, a variety of models have been designed to investigate the complex interrelationship between physical, chemical and biological factors and processes in lakes. However, these models are inconvenient for predictions of lake eutrophication in practical application. To date, very few studies have focused on lake eutrophication in terms of anthropogenic influences (such as sewage emissions and agricultural practices), which are more readily regulated than natural processes. Effective lake eutrophication prediction models should base primarily on facile controlled or predictable indicators. Therefore, to meet this requirement, we designed simple predictive models to determine the interrelationship between the trophic states of lakes and nutrient inputs, which is a direct measurement to characterize anthropogenic influences. Lake Taihu (China) was used as a representative eutrophic water body to assess the accuracy of the proposed predictive models. Firstly, to comprehensively understand the role of nutrient input indicators (NIIs) during eutrophication, Pearson correlation coefficient analysis was conducted on 7 NIIs and 38 types of algae. Secondly, based on the NIIs identified with high correlation coefficients, we modified three commonly used growth models (Gompertz, logistic and Richards) to describe the lake's trophic state. A particle swarm optimization algorithm (PSOA) was used to optimize model variables. Results showed that the mean absolute percentage errors of the optimized Gompertz, logistic and the Richards models were 2.95%, 2.88% and 2.17%, respectively. Finally, these optimized models were used to predict lake eutrophication under several different nutrient input scenarios. Two adjustment scenarios revealed remarkably satisfied trophic states (including roughly oligotrophic and even ultra-oligotrophic) by the 2030s. Our results show that these established models are a simple way to support lake restoration projects by setting realistic and effective targets.
更新日期：2020-01-01
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