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Numerical simulation of microcystin distribution in Liangxi River, downstream of Taihu Lake.

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Title: Numerical simulation of microcystin distribution in Liangxi River, downstream of Taihu Lake.
Authors: He, Xinchen1,2 (AUTHOR), Wang, Hua1,2 (AUTHOR) wanghua543543@163.com, Yan, Huaiyu1,2 (AUTHOR), Ao, Yanhui1,2 (AUTHOR)
Source: Water Environment Research (10614303). Oct2021, Vol. 93 Issue 10, p1934-1943. 10p.
Subjects: Water Environment Federation, Microcystins, Cyanobacterial toxins, Algal toxins, Computer simulation, Artificial neural networks, Algorithms
Abstract: Microcystins (MCs), the algal toxins produced by cyanobacteria, raised a worldwide concern in recent decades. Limited monitoring stations for MCs make it hard to map the MC spatial distribution in certain areas. To tackle such problems, we selected Liangxi River as our research area and developed an integrated model to get spatial continuous MC data without too many sampling sites, which integrates a hydro‐environment model and an artificial neural network algorithm (ANN). The ANN algorithm can estimate concentration MCs via environmental factors. In this paper, we selected chl‐a, TN, TP, NO2‐, NO3‐, NH3‐N, and PO43‐ as stressors. The ANN model we established showed good performances both in train (R2 = 0.8407) and test set (R2 = 0.7543). In the hydro‐environment model, by inputting river geometry and model boundary data, the spatial continuous water quality data could be simulated. The water quality data returned from the hydro‐environmental model were used as input variables of the well‐trained ANN model; the continuous MC data were derived. To evaluate this model on geo‐mapping the MC distribution in Liangxi River, we compared the performance of this model and spatial interpolation on the test set, it turns out the integrated model showed a better performance. © 2020 Water Environment Federation Practitioner points: The cost of microcystin (MC) detection is too high for routine monitoring.We integrated regression method and hydro‐environment model to predict MCs.Results derived from spatial interpolation are not robust in unmonitored area.The new integration model can minimize the drawback of spatial interpolation. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Microcystins (MCs), the algal toxins produced by cyanobacteria, raised a worldwide concern in recent decades. Limited monitoring stations for MCs make it hard to map the MC spatial distribution in certain areas. To tackle such problems, we selected Liangxi River as our research area and developed an integrated model to get spatial continuous MC data without too many sampling sites, which integrates a hydro‐environment model and an artificial neural network algorithm (ANN). The ANN algorithm can estimate concentration MCs via environmental factors. In this paper, we selected chl‐a, TN, TP, NO2‐, NO3‐, NH3‐N, and PO43‐ as stressors. The ANN model we established showed good performances both in train (R2 = 0.8407) and test set (R2 = 0.7543). In the hydro‐environment model, by inputting river geometry and model boundary data, the spatial continuous water quality data could be simulated. The water quality data returned from the hydro‐environmental model were used as input variables of the well‐trained ANN model; the continuous MC data were derived. To evaluate this model on geo‐mapping the MC distribution in Liangxi River, we compared the performance of this model and spatial interpolation on the test set, it turns out the integrated model showed a better performance. © 2020 Water Environment Federation Practitioner points: The cost of microcystin (MC) detection is too high for routine monitoring.We integrated regression method and hydro‐environment model to predict MCs.Results derived from spatial interpolation are not robust in unmonitored area.The new integration model can minimize the drawback of spatial interpolation. [ABSTRACT FROM AUTHOR]
ISSN:10614303
DOI:10.1002/wer.1484