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Identifying the Driving Factors of Black Bloom in Lake Bay through Bayesian LASSO

1, 2,*,†, 2 and 1,*,†
1
School of Hydraulic Energy and Power Engineering, Yangzhou University, 196, Huayang Xi Road, Yangzhou 225127, China
2
School of Environmental Science and Engineering, Yangzhou University, 196, Huayang Xi Road, Yangzhou 225127, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Int. J. Environ. Res. Public Health 2019, 16(14), 2492; https://doi.org/10.3390/ijerph16142492
Received: 13 May 2019 / Revised: 5 July 2019 / Accepted: 10 July 2019 / Published: 12 July 2019
(This article belongs to the Special Issue Water Resources Systems Quality and Quantity Management)
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Abstract

Black blooms are a serious and complex problem for lake bays, with far-reaching implications for water quality and drinking safety. While Fe(II) and S(−II) have been reported as the most important triggers of this phenomenon, little effort has been devoted in investigating the relationships between Fe(II) and S(−II) and the host of potentially important aquatic factors. However, a model involving many putative predictors and their interactions will be oversaturated and ill-defined, making ordinary least squares (OLS) estimation unfeasible. In such a case, sparsity assumption is typically required to exclude the redundant predictors from the model, either through variable selection or regularization. In this study, Bayesian least absolute shrinkage and selection operator (LASSO) regression was employed to identify the major influence variables from 11 aquatic factors for Fe(II), S(−II), and suspended sediment concentration (SSC) in the Chaohu Lake (Eastern of China) bay during black bloom maintenance. Both the main effects and the interactions between these factors were studied. The method successfully screened the most important variables from many items. The determination coefficients (R2) and adjusted determination coefficients (Adjust R2) showed that all regression equations for Fe(II), S(-II), and SSC were in good agreement with the situation observed in the Chaohu Lake. The outcome of correlation and LASSO regression indicated that total phosphorus (TP) was the single most important factor for Fe(II), S(-II), and SSC in black bloom with explanation ratios (ERs) of 76.1%, 37.0%, and 12.9%, respectively. The regression results showed that the interaction items previously deemed negligible have significant effects on Fe(II), S(−II), and SSC. For the Fe(II) equation, total nitrogen (TN) × dissolved oxygen (DO) and chlorophyll a (CHLA) × oxidation reduction potential (ORP), which contributed 10.6% and 13.3% ERs, respectively, were important interaction variables. TP emerged in each key interaction item of the regression equation for S(−II). Water depth (DEP) × Fe(II) (30.7% ER) was not only the main interaction item, but DEP (5.6% ER) was also an important single factor for the SSC regression equation. It also indicated that the sediment in shallow bay is an important source for SSC in water. The uncertainty of these relationships was also estimated by the posterior distribution and coefficient of variation (CV) of these items. Overall, our results suggest that TP concentration is the most important driver of black blooms in a lake bay, whereas the other factors, such as DO, DEP, and CHLA act in concert with other aquatic factors. There results provide a basis for the further control and management policy development of black blooms. View Full-Text
Keywords: black bloom; Fe(II); S(−II); SSC; aquatic factors; Bayesian LASSO method; uncertainty analysis black bloom; Fe(II); S(−II); SSC; aquatic factors; Bayesian LASSO method; uncertainty analysis
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Wang, L.; Wang, Y.; Cheng, H.; Cheng, J. Identifying the Driving Factors of Black Bloom in Lake Bay through Bayesian LASSO. Int. J. Environ. Res. Public Health 2019, 16, 2492.

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