Effects of physiologic activities of plankton on CO2 flux in the Three Gorges Reservoir after rainfall during algal blooms

Li, Yixuan; Deng, Kai-Kai; Lin, Guijiao; Chen, Bin; Fang, Fang; Guo, Jinsong

doi:10.1016/j.envres.2022.114649

Cited by 13 publications

(2 citation statements)

References 45 publications

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“…However, the author believes that the occurrence of water bloom is the result of the interaction of the above two processes. According to statistics, many lakes [8][9][10][11][12][13], reservoirs [14][15][16][17][18][19] and rivers [20,21] are currently reported to have algal blooms. Therefore, the problem of water bloom is the main problem that has been or will be faced by most water bodies in China.…”

Section: Introductionmentioning

confidence: 99%

Prediction of hydrological and water quality data based on granular-ball rough set and k-nearest neighbor analysis

Dong,

Zuo,

Xiong

2024

PLoS ONE

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Hydrological and water quality datasets usually encompass a large number of characteristic variables, but not all of these significantly influence analytical outcomes. Therefore, by wisely selecting feature variables with rich information content and removing redundant features, it not only can the analysis efficiency be improved, but the model complexity can also be simplified. This paper considers introducing the granular-ball rough set algorithm for feature variable selection and combining it with the k-nearest neighbor method and back propagation network to analyze hydrological and water quality data, thus promoting overall and fused inspection. The results of hydrological water quality data analysis show that the proposed method produces better results compared to using a standalone k-nearest neighbor regressor.

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Section: Introductionmentioning

confidence: 99%

Prediction of hydrological and water quality data based on granular-ball rough set and k-nearest neighbor analysis

Dong,

Zuo,

Xiong

2024

PLoS ONE

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show abstract

“…The occurrence of algal blooms is affected by various environmental factors, mainly nutritional, meteorological, hydrological, physical, and chemical indicators of water bodies as well as biological factors [15][16][17][18]. Constructing a model encompassing all factors related to blooms is impractical, since the variables that most accurately explain their occurrence do not consistently predict it [19].…”

Section: Introductionmentioning

confidence: 99%

Algae in a Drinking Water Reservoir: Development of an Air–Water–Algal Growth Model (AWAM) for Long-Term Prediction

Zhang,

Liu,

Liu

et al. 2023

Water

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The condition of the water in drinking water source environments is critical for public health. However, in recent years, the rapid growth of algae has become a significant threat to the safety of the drinking water supply. This study investigated the temporal trends and spatial differences in algae in the Nanwan Reservoir during 2022. Regression analysis using the least-squares method demonstrated that water temperature and initial biomass concentration were critical parameters that influenced the rate of algal growth. An air–water–algal growth model (AWAM) for algal growth prediction was developed using a 30-day forecast of air temperature, an air-to-water temperature extrapolating equation, a water temperature–algal growth relationship, and only four monthly measurements of algal concentration. The results demonstrated that the model accurately predicted algal growth in the next 30 days, with an R2 of 0.738, which aligned with the monitored results. Compared to the upstream Wudaohe River inflow point, algal growth in the drinking water intake area near the downstream dam was delayed by at least 30 days. By using the upstream inflow area as a reference point, the prediction period was extended to provide a 60-day early warning. The extended prediction period and the reduced need for monitoring data make the model more convenient for guiding the prevention and control of algal blooms in drinking water reservoirs.

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Mutual feedback between algal blooming and global warming

Ma,

Yang,

Zhao

et al. 2024

J. Ocean. Limnol.

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Effects of physiologic activities of plankton on CO2 flux in the Three Gorges Reservoir after rainfall during algal blooms

Cited by 13 publications

References 45 publications

Prediction of hydrological and water quality data based on granular-ball rough set and k-nearest neighbor analysis

Prediction of hydrological and water quality data based on granular-ball rough set and k-nearest neighbor analysis

Algae in a Drinking Water Reservoir: Development of an Air–Water–Algal Growth Model (AWAM) for Long-Term Prediction

Mutual feedback between algal blooming and global warming

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