Dominant genera of cyanobacteria in Lake Taihu and their relationships with environmental factors

Feng, Lijun; Liu, Shiyou; Wu, Wenxian; Ma, Jiawen; Li, Pei; Xu, Hailing; Li, Na; Feng, Yaoyu

doi:10.1007/s12275-016-6037-4

Cited by 19 publications

(7 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The variations in biomass are more dependent on changes in temperature. It is consistent with Feng et al's finding that the fluctuation of Chl‐a concentration in the northern part of Taihu Lake is more influenced by hydrometeorological factors (Feng et al, 2016). The lower nitrogen and phosphorus concentrations in the Central Area significantly promote the increase of Chl‐a concentration and turbidity, influencing the depth of light penetration and to some extent, affecting cyanobacterial growth.…”

Section: Discussionsupporting

confidence: 92%

Identification of driving factors for chlorophyll‐a in multi‐stable shallow lakes of China employing machine learning methods

Qu,

Wang,

Kumar

et al. 2023

Ecohydrology

View full text Add to dashboard Cite

Due to the rapid increase in urbanization and anthropogenic activity in the lake catchment, nutrients like phosphorus and nitrogen are abundant in urban sewage, leading to lake eutrophication and promoting algal blooms. Chlorophyll‐a (Chl‐a) is an important indicator of algal blooms; thus, predicting concentration is vital for lake restoration and management. Literature reveals that most of the previous studies of Chl‐a prediction required multiple variables and considerable computation. Thus, it is crucial to scientifically identify the key drivers of Chl‐a in Taihu Lake for the formulation of effective environmental management measures. In this study, a long‐term dataset (1991–2015) and Daniel trend test method were employed to analyse the trend of meteorological and water quality indicators in Taihu Lake in China. The redundancy of variables was eliminated by using the rank correlation method, combined with prior knowledge that provides a solid foundation for the analysis of the driving factors of Chl‐a. Four machine learning models, named Extreme Gradient Boosting (XGBoost), random forest (RF), support vector regression (SVR) and gradient boosting decision tree (GBDT), were employed to predict the concentration of Chl‐a in different regions of Taihu Lake. The result shows that the XGBoost model outperformed the other three models in terms of R2 (>0.90) and root mean square error (RMSE) (3.69–8.40). Finally, based on the predictions of the concentration of Chl‐a using the XGBoost model, the SHapley Additive exPlanation (SHAP) framework was utilized to analyse the key factors influencing Chl‐a concentration. Finding of this study suggests that the XGBoost model performed best and could be applied to the overall stretches of Taihu Lake and other lakes in China; model prediction will be helpful in providing a zoning decision‐making approach for the rational protection of the lake environment and the prevention of algal bloom hazards.

show abstract

Section: Discussionsupporting

confidence: 92%

Identification of driving factors for chlorophyll‐a in multi‐stable shallow lakes of China employing machine learning methods

Qu,

Wang,

Kumar

et al. 2023

Ecohydrology

View full text Add to dashboard Cite

show abstract

“…Meiliang Bay is the main location of cyanobacterial blooms and is located in the northern Lake Taihu ( Shen et al, 2003 ). We defined July, August and September as "bloom months" due to the higher contents of chlorophyll a and microcystins in these months as measured by Feng et al (2016) and Shen et al (2003) , respectively. Thereupon we collected water samples in March and May as "non-bloom months" to compare with the "bloom months".…”

Section: Sample Collection and Field Datamentioning

confidence: 99%

Alteration of dominant cyanobacteria in different bloom periods caused by abiotic factors and species interactions

Zhang

Fan

Peijnenburg

et al. 2021

Journal of Environmental Sciences

View full text Add to dashboard Cite

Freshwater cyanobacterial blooms have drawn public attention because they threaten the safety of water resources and human health worldwide. Heavy cyanobacterial blooms outbreak in Lake Taihu in summer annually and vanish in other months. To find out the factors impacting the cyanobacterial blooms, the present study measured the physicochemical parameters of water and investigated the composition of microbial community using the 16S rRNA gene and internal transcribed spacer amplicon sequencing in the months with or without bloom. The most interesting finding is that two major cyanobacteria, Planktothrix and Microcystis , dramatically alternated during a cyanobacterial bloom in 2016, which is less mentioned in previous studies. When the temperature of the water began increasing in July, Planktothrix appeared first and showed as a superior competitor for M. aeruginosa in NO 3 − -rich conditions. Microcystis became the dominant genus when the water temperature increased further in August. Laboratory experiments confirmed the influence of temperature and the total dissolved nitrogen (TDN) form on the growth of Planktothrix and Microcystis in a co-culture system. Besides, species interactions between cyanobacteria and non-cyanobacterial microorganisms, especially the prokaryotes, also played a key role in the alteration of Planktothrix and Microcystis . The present study exhibited the alteration of two dominant cyanobacteria in the different bloom periods caused by the temperature, TDN forms as well as the species interactions. These results helped the better understanding of cyanobacterial blooms and the factors which contribute to them.

show abstract

“…Understanding the correlation between water quality parameters and bacterial communities could help trace changes in microbiome by monitoring water quality parameters. Seasonal changes cause great variations in water quality indices (Li et al, 2014 ; Feng et al, 2016 ). However, only a few studies investigated the bacterial community structure in a water treatment plant across the four seasons (Pinto et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Microbial Communities Shaped by Treatment Processes in a Drinking Water Treatment Plant and Their Contribution and Threat to Drinking Water Safety

et al. 2017

Front. Microbiol.

View full text Add to dashboard Cite

Bacteria play an important role in water purification in drinking water treatment systems. On one hand, bacteria present in the untreated water may help in its purification through biodegradation of the contaminants. On the other hand, some bacteria may be human pathogens and pose a threat to consumers. The present study investigated bacterial communities using Illumina MiSeq sequencing of 16S rRNA genes and their functions were predicted using PICRUSt in a treatment system, including the biofilms on sand filters and biological activated carbon (BAC) filters, in 4 months. In addition, quantitative analyses of specific bacterial populations were performed by real-time quantitative polymerase chain reaction (qPCR). The bacterial community composition of post-ozonation effluent, BAC effluent and disinfected water varied with sampling time. However, the bacterial community structures at other treatment steps were relatively stable, despite great variations of source water quality, resulting in stable treatment performance. Illumina MiSeq sequencing illustrated that Proteobacteria was dominant bacterial phylum. Chlorine disinfection significantly influenced the microbial community structure, while other treatment processes were synergetic. Bacterial communities in water and biofilms were distinct, and distinctions of bacterial communities also existed between different biofilms. By contrast, the functional composition of biofilms on different filters were similar. Some functional genes related to pollutant degradation were found widely distributed throughout the treatment processes. The distributions of Mycobacterium spp. and Legionella spp. in water and biofilms were revealed by real-time quantitative polymerase chain reaction (qPCR). Most bacteria, including potential pathogens, could be effectively removed by chlorine disinfection. However, some bacteria presented great resistance to chlorine. qPCRs showed that Mycobacterium spp. could not be effectively removed by chlorine. These resistant bacteria and, especially potential pathogens should receive more attention. Redundancy analysis (RDA) showed that turbidity, ammonia nitrogen and total organic carbon (TOC) exerted significant effects on community profiles. Overall, this study provides insight into variations of microbial communities in the treatment processes and aids the optimization of drinking water treatment plant design and operation for public health.

show abstract

Dominant genera of cyanobacteria in Lake Taihu and their relationships with environmental factors

Cited by 19 publications

References 50 publications

Identification of driving factors for chlorophyll‐a in multi‐stable shallow lakes of China employing machine learning methods

Identification of driving factors for chlorophyll‐a in multi‐stable shallow lakes of China employing machine learning methods

Alteration of dominant cyanobacteria in different bloom periods caused by abiotic factors and species interactions

Microbial Communities Shaped by Treatment Processes in a Drinking Water Treatment Plant and Their Contribution and Threat to Drinking Water Safety

Contact Info

Product

Resources

About