Is there any difference on cyanobacterial blooms patterns between Lake Chaohu and Lake Taihu over the last 20 years?

Guo, Honglei; Liu, Huaiqing; Lyu, Heng; Bian, Yingchun; Zhong, Suke; Li, Yangyang; Miao, Shihai; Yang, Zhijun; Xu, Jiafeng; Cao, Jin; Li, Yunmei

doi:10.1007/s11356-021-18094-x

Cited by 25 publications

(12 citation statements)

References 49 publications

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“…From March to April, Anabaena Flos-Aquae is prevalent in the formation of water blooms in Chaohu Lake; from May to September, with the increase in temperature, Microcystis predominates to form water blooms; after October, the daily average temperature drops, and the water blooms are dominated by Anabaena Flos-Aquae [30,31]. In the past 20 years, cyanobacteria blooms have occurred frequently in Chaohu Lake [32]. Due to the influence of wind and hydrodynamic factors, the lakeshore zone of Chaohu Lake has become a critical area of cyanobacteria blooms accumulation, which not only causes harm, such as black and smelly water and water eutrophication, but also seriously threatens the safety of drinking water.…”

Section: Study Areamentioning

confidence: 99%

Real-Time Identification of Cyanobacteria Blooms in Lakeshore Zone Using Camera and Semantic Segmentation: A Case Study of Lake Chaohu (Eastern China)

Wang

Qiu

et al. 2023

Sustainability

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The surface water in the lakeshore zone is the primary area where cyanobacteria bloom floats intensively. In lake water environment monitoring, it has become pressing to accurately identify the distribution and accumulation coverage area of cyanobacteria blooms in the surface water of the lakeshore zone. This study proposes a real-time and dynamic monitoring technology for cyanobacteria blooms in surface water using a shore-based camera monitoring network. The specific work is as follows: Chaohu Lake, a large eutrophic lake in China, is selected as the research object. The multithreading technology is used to dynamically obtain the hourly video images of 43 cameras around Chaohu Lake. The semantic segmentation method is used to identify the cyanobacteria blooms in the video images, calculate the coverage of cyanobacteria blooms, and draw the spatial distribution map of cyanobacteria blooms in the lakeshore zone of Chaohu Lake. To improve the accuracy of cyanobacteria blooms recognition, we use the ResNet-50 network to integrate three semantic segmentation models, namely FCN, U-net, and DeeplabV3+. By comparing the cyanobacteria blooms results identified by the three methods, it is found that the boundary of the cyanobacteria blooms results identified by DeeplabV3+(ResNet-50) is clear, which is more consistent with the real spatial information of the distribution of cyanobacteria blooms and is more suitable for monitoring the hourly dynamic changes of cyanobacteria blooms in the Chaohu Lake lakeshore zone. The results demonstrated that the time requirement of monitoring cyanobacteria blooms in real time on an hourly basis could be met by utilizing technology that uses multiple threads. The OA (Overall Accuracy), MPA (Mean Pixel Accuracy), IOU (Intersection Over Union) of cyanobacteria blooms, and the IOU of water values of the DeeplabV3+(ResNet-50) were the highest, which were 0.83, 0.82, 0.71, and 0.74, and the RMSE between the predicted and real cyanobacterial blooms coverage of 43 cameras was 6.65%. The above values show that DeeplabV3+(ResNet-50) is this technology’s most suitable semantic segmentation model. This technique can provide technical support for the scientific development of a cyanobacteria blooms management plan in the lakeshore zone of Chaohu Lake by calculating the coverage area of cyanobacteria blooms and drawing the spatial distribution map of cyanobacteria blooms in the lakeshore zone.

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Section: Study Areamentioning

confidence: 99%

Real-Time Identification of Cyanobacteria Blooms in Lakeshore Zone Using Camera and Semantic Segmentation: A Case Study of Lake Chaohu (Eastern China)

Wang

Qiu

et al. 2023

Sustainability

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“…The lake is 61.7 km long and 12.47 km wide, with an area of 769.55 km 2 (Figure 1). The average water level is ~8.37 m, and the average water depth is~2.89 m. The eutrophication in Chaohu Lake has intensified in recent years because of pesticide and the nonpoint source pollution of farmland around the lake and the discharge of domestic sewage and industrial wastewater from surrounding cities and towns (Huang et al, 2020;Guo et al, 2022). Under the comprehensive influence of environmental factors such as nitrogen and phosphorus concentrations, temperature, wind speed, and wind direction, in the water body, cyanobacterial blooms frequently appear in Chaohu Lake from the end of May to the beginning of October every year with dense blooms concentrated in the northwest area of the lake (Zhao et al, 2018).…”

Section: Study Areamentioning

confidence: 99%

Comparison of phycocyanin concentrations in Chaohu Lake, China, retrieved using MODIS and OLCI images

Wang

Cui

et al. 2022

Front. Environ. Sci.

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Phycocyanin (PC) concentration is used as an indicator to characterize cyanobacteria biomass while monitoring eutrophication in inland water. Remote sensing provides useful methods for quantifying PC concentration; however, there is a shortage of datasets for the long-term monitoring of PC concentration when only a single remote sensing data is used. Therefore, PC concentrations obtained from multisource remote sensing images should be compared before integrating them for long-term monitoring. In this study, machine learning (ML) regression algorithms are used to develop PC concentration retrieval models suitable for Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-3 Ocean and Land Colour Instrument (OLCI) images, and their accuracies are compared. The two optimal retrieval models are applied to satellite images acquired on the same days to compare the spatial consistency of the two PC concentration retrieval results. The results show that the sensitive spectral range of PC concentration is 560–680 nm. Among the ML regression algorithms, gradient boosted tree (GBT) regression exhibits the highest PC retrieval accuracy for both the MODIS images (R2 = 0.82, RMSE = 61.9 μg/L) and OLCI images (R2 = 0.86, RMSE = 45.44 μg/L). The PC concentrations retrieved from the MODIS and OLCI images acquired in bloom and no-bloom periods have a high spatial consistency in most areas of Chaohu Lake. Their correlation coefficient also exceeds 0.7, and the average relative error reaches 0.293 μg/L. However, a large difference exists in areas with high PC concentrations, which may cause by the poor applicability of atmospheric correction algorithms and PC retrieval models in these areas. The proposed PC concentration retrieval models developed using GBT regression in this paper can expend the idea for the quantitative retrieval of other inland water quality parameters in inland water, and the conclusions should enable the effective integration of MODIS and OLCI images for the time series monitoring of PC concentrations in reservoirs and lakes.

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“…The relative abundance of these gene types was found to be correlated with pH and nitrate concentrations by analyzing the various composition of mcy genes in western Lake Erie [22]. In China, there have been many studies measuring the abundance of MCs in freshwater lakes such as Chaohu, Taihu, and Dianchi using quantitative real-time PCR [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal variation of toxin-producing gene abundance in Microcystis aeruginosa from Poyang Lake

Lin

Zhang

Yuan

et al. 2023

Preprint

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Microcystis aeruginosa causes massive blooms in eutrophic freshwater and releases microcystin. Poyang Lake is the largest freshwater lake in China and has kept at a mid-nutrient level in recent years. However, there is little research on microcystins production in Poyang Lake. In this study, water and sediment samples from ten sampling sites in Poyang Lake were collected from May to December in 2020, and from January to April in 2021 respectively. Microcystis genes (mcyA,mcyB,16s rDNA) were quantified by real-time fluorescence quantitative PCR analysis,and then the spatial and temporal variation of mcy genes, physicochemical factors and bacterial population structure in the lake was analyzed. The relationship between the abundance of mcy genes and physicochemical factors in water column were also revealed. Results indicated that the microcystin-producing genes mcyA and mcyB showed significant differences in spatial and temporal levels as well, which is closely related to the physicochemical factors especially the water temperature(p < 0.05) and the nitrogen content(p < 0.05). The abundance of mcy genes in the sediment in December affected the abundance of mcy genes in the water column in the next year, while the toxic Microcystis would accumulate in the sediment. In addition to the toxic Microcystis, we also found a large number of non-toxic Microcystis in the water column and sediment, the ratio of toxic to non-toxic species can also affect the toxicity production of Microcystis aeruginosa. Overall, the results showed that Microcystis aeruginosa toxin-producing genes in Poyang Lake distributed spatially and temporally which related to the physicochemical factors of Poyang Lake.

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Is there any difference on cyanobacterial blooms patterns between Lake Chaohu and Lake Taihu over the last 20 years?

Cited by 25 publications

References 49 publications

Real-Time Identification of Cyanobacteria Blooms in Lakeshore Zone Using Camera and Semantic Segmentation: A Case Study of Lake Chaohu (Eastern China)

Real-Time Identification of Cyanobacteria Blooms in Lakeshore Zone Using Camera and Semantic Segmentation: A Case Study of Lake Chaohu (Eastern China)

Comparison of phycocyanin concentrations in Chaohu Lake, China, retrieved using MODIS and OLCI images

Spatio-temporal variation of toxin-producing gene abundance in Microcystis aeruginosa from Poyang Lake

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