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Risk prediction of microcystins based on water quality surrogates: A case study in a eutrophicated urban river network.

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He, Xinchen 
Wang, Hua 
Zhuang, Wei 
Liang, Dongfang 
Ao, Yanhui 


Microcystins (MCs), the toxic by-products from harmful algal bloom (HAB), have caused world-wide concern due to their acute toxicity in freshwater ecosystems. Most studies on HAB have been conducted for shallow freshwater lakes, such as Taihu Lake in China. However, algal blooms in urban rivers located downstream of eutrophicated lakes are also a serious problem for local administrators. It is important for them to know the current and potential risk level of MCs. This environmental issue is rarely reported or discussed. Within this context, we monitored MC concentrations in the Binhu River Network (BRN) in the algal bloom season (Aug, Sep, and Oct) in 2019. To note if the MC concentrations were dangerous, we used 1.0 μg/L suggested by the World Health Organization as the standard value. The proportions of MC samples violating the standard value were 31.78% (Aug), 21.14% (Sep) and 30.77% (Oct). We also designed two statistical models to predict MC concentrations and the possibility to exceed the standard level based on 10 water quality surrogates: Artificial Neural Network (ANN) and Logistic Regression (LR) models. These two models were trained and validated by the monitoring dataset (n = 224). Both models had good performances during training and testing. Although the water quality varied diversely both in spatial and temporal scale, Cluster Analysis (CA) could detect similarities among the samples and separated them into 3 classes, with each class denoting different types of rivers based on the 10 water quality surrogates. Then the ANN and LR were applied as a function of chl-a in each class; by gradually increasing chl-a concentration, we detected chl-a thresholds in class 1, 2, 3 were 25.5, 224, and 109.5 μg/L, respectively, when MCs have a 50% possibility to exceed standard level. The threshold values provided important implications for MC management in the BRN.



Artificial neural network, Chl-a threshold, Logistic regression, Microcystin, Risk prediction, China, Ecosystem, Environmental Monitoring, Lakes, Microcystins, Water Quality

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Environ Pollut

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Elsevier BV
This work was supported by the Major Science and Technology Program for Water Pollution Control and Treatment of China (2017ZX07203002-01), Shanghai Water Bureau Research project (Assessment of the Impact of Salinity Fluctuation in the Yangtze Estuary on Water Quality of Drinking Water Sources, 2019-09), National Natural Science Foundation of China (No.51779075), A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (No. 51479064), and Qing Lan Project of Jiangsu Province (2018-12). The authors express thanks to Dr. Yuan Weihao and Dr. Liu Zhiqi for their help with data collection.