Literature DB >> 30640101

Highly time-resolved analysis of seasonal water dynamics and algal kinetics based on in-situ multi-sensor-system monitoring data in Lake Taihu, China.

Jingwei Yang1, Andreas Holbach2, Andre Wilhelms2, Yanwen Qin3, Binghui Zheng3, Hua Zou4, Boqiang Qin5, Guangwei Zhu5, Stefan Norra2.   

Abstract

Predicting algal blooms is challenging due to rapid growth rates under suitable conditions and the complex physical, chemical, and biological processes involved. Physico-chemical parameters, monitored in this study by a high-resolution in-situ multi-sensor system and derived from lab-based water sample analyses, show the seasonal variation and have different degrees of vertical gradients across the water column. Through analyzing the changes and relations between multi-factors, we reveal pictures of water quality dynamics and algal kinetics. Nitrate has regular seasonal changes different to the seasonal patterns of total dissolved Phosphorus. Positive correlations are found between Chlorophyll a fluorescence and temperature, wind-induced resuspension and mixing promote the augment of Cyanobacteria fluorescence (Phycocyanin) signal. While the resuspension can also result in the increase of turbidity and affect the light environment for hydrophytes, the algal scums are the main reason for the high turbidity on the surface, which lower the illumination radiation in the water body. Those parameters are the primary dominants responsible for the change of algae from our monitoring data, which could be used as indicators for the dynamic changes of algae in the future.
Copyright © 2019. Published by Elsevier B.V.

Entities:  

Keywords:  Algal bloom; Eutrophication; Lake dynamics; On-line monitoring; Resuspension events; Stratification

Mesh:

Year:  2019        PMID: 30640101     DOI: 10.1016/j.scitotenv.2019.01.044

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  2 in total

1.  Sensor-based detection of algal blooms for public health advisories and long-term monitoring.

Authors:  McNamara Rome; R Edward Beighley; Tom Faber
Journal:  Sci Total Environ       Date:  2021-01-28       Impact factor: 10.753

2.  Chlorophyll soft-sensor based on machine learning models for algal bloom predictions.

Authors:  Alberto Mozo; Jesús Morón-López; Stanislav Vakaruk; Ángel G Pompa-Pernía; Ángel González-Prieto; Juan Antonio Pascual Aguilar; Sandra Gómez-Canaval; Juan Manuel Ortiz
Journal:  Sci Rep       Date:  2022-08-08       Impact factor: 4.996

  2 in total

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