Distribution of epipelic algae and related environmental nutrients in Taihu Lake, revealed by HPLC analysis

2019 ◽  
Vol 70 (6) ◽  
pp. 849
Author(s):  
Yuzhi Song ◽  
Yan Xue ◽  
Yongxia Gao ◽  
Yan Jin
Keyword(s):  
Green Algae ◽  
Algal Biomass ◽  
Taihu Lake ◽  
Lake Taihu ◽  
Eastern China ◽  
Sampling Site ◽  
Algal Community ◽  
Pigment Analysis ◽  
Chl A ◽  
Meiliang Bay

Epipelic algal biomass and composition (determined by HPLC pigment analysis) and related environmental nutrients were investigated in a phytoplankton-dominated area (Meiliang Bay), a macrophyte-dominated area (East Taihu Lake) and a transition zones (Gonghu Bay) in a shallow eutrophic freshwater lake (Taihu Lake, eastern China). Surface sediment samples were collected at 11 sampling sites in April and August 2016. Average epipelic algal biomass for Meiliang Bay, Gonghu Bay and East Taihu Lake was 6.5, 4.9 and 4.6μg chlorophyll (Chl)-ag–1 sediment respectively in April, and 3.8, 7.4 and 2.7μg Chl-ag–1 sediment respectively in August. The maximum biomass of epipelic algae appeared at the G1 sampling site (9.6μg Chl-ag–1 sediment) in August, whereas the minimum biomass of epipelic algae appeared at the E4 sampling site (1.7μg Chl-ag–1 sediment). Pigment (fucoxanthin, zeaxanthin and Chl-b) to Chl-a ratios indicated that epipelic algae were primarily diatoms, secondarily cyanobacteria and lastly green algae in Taihu Lake. Epipelic algae were significantly correlated with total nitrogen in the water and total phosphorus in the sediment (P<0.05), except for green algae. In general, using pigment as an indicator revealed that the epiphytic algal community varied over time and space. The distribution of variations in epipelic algae was related to the heterogeneity of environmental factors.

scholarly journals Variations in the Distribution of Chl-a and Simulation Using a Multiple Regression Model

2019 ◽  
Vol 16 (22) ◽  
pp. 4553
Author(s):  
Jiancai Deng ◽  
Fang Chen ◽  
Weiping Hu ◽  
Xin Lu ◽  
Bin Xu ◽  
...  
Keyword(s):  
Regression Model ◽  
Lake Taihu ◽  
Sampling Site ◽  
Secchi Depth ◽  
Multiple Linear Regression Model ◽  
Multiple Regression Model ◽  
Interactive Effects ◽  
Important Indicator ◽  
Chl A ◽  
Meiliang Bay

Chlorophyll a (Chl-a) is an important indicator of algal biomass in aquatic ecosystems. In this study, monthly monitoring data for Chl-a concentration were collected between 2005 and 2015 at four stations in Meiliang Bay, a eutrophic bay in Lake Taihu, China. The spatiotemporal distribution of Chl-a in the bay was investigated, and a statistical model to relate the Chl-a concentration to key driving variables was also developed. The monthly Chl-a concentration in Meiliang Bay changed from 2.6 to 330.0 μg/L, and the monthly mean Chl-a concentration over 11 years was found to be higher at sampling site 1, the northernmost site near Liangxihe River, than at the three other sampling sites. The annual mean Chl-a concentration fluctuated greatly over time and exhibited an upward trend at all sites except sampling site 3 in the middle of Meiliang Bay. The Chl-a concentration was positively correlated with total phosphorus (TP; r = 0.57, p < 0.01), dissolved organic matter (DOM; r = 0.73, p < 0.01), pH (r = 0.44, p < 0.01), and water temperature (WT; r = 0.37, p < 0.01), and negatively correlated with nitrate (NO3−-N; r = −0.28, p < 0.01), dissolved oxygen (DO; r = −0.12, p < 0.01), and Secchi depth (ln(SD); r = −0.11, p < 0.05). A multiple linear regression model integrating the interactive effects of TP, DOM, WT, and pH on Chl-a concentrations was established (R = 0.80, F = 230.7, p < 0.01) and was found to adequately simulate the spatiotemporal dynamics of the Chl-a concentrations in other regions of Lake Taihu. This model provides lake managers with an alternative for the control of eutrophication and the suppression of aggregations of phytoplankton biomass at the water surface.

Pelagic and benthic algal responses in eastern Canadian Boreal Shield lakes following harvesting and wildfires

2000 ◽  
Vol 57 (S2) ◽  
pp. 136-145 ◽  
Author(s):  
Dolors Planas ◽  
Mélanie Desrosiers ◽  
S-Raphaëlle Groulx ◽  
Serge Paquet ◽  
Richard Carignan
Keyword(s):  
Repeated Measures ◽  
Algal Biomass ◽  
Forest Harvesting ◽  
Algal Community ◽  
Significant Treatment ◽  
Repeated Measures Anova ◽  
Chl A ◽  
Boreal Shield ◽  
Shield Lakes ◽  
Reference Lakes

Pelagic and benthic algal biomass and pelagic algal community structure were measured in Boreal Shield lakes impacted by forest harvesting and wildfires (Haute-Mauricie, Québec). Sixteen reference lakes in which the watershed has been unperturbed for at least 40 years, seven harvested lake watersheds (logged in 1995), and nine lake watersheds burnt in 1995 were sampled for 3 years following harvesting or wildfires. From 1996 to 1998, repeated-measures ANOVA showed significant effects between treatment and sampling years for pelagic chlorophyll a (Chl a) and biomass, but for 1997-1998 benthic Chl a, repeated-measures ANOVA showed only significant treatment effects. Chl a concentrations increased 1.4- to 3-fold in perturbed lakes as compared with reference lakes. Areal pelagic Chl a (milligrams per square metre) was lower than estimated littoral Chl a in perturbed lakes. The pelagic algal community was dominated by mixotrophic nanoflagellates in reference lakes. Watershed perturbation induced differential changes in pelagic algal communities: mixotrophic nanoflagellates increased in harvested lakes and photoautotrophic diatoms in burnt lakes. Considering only perturbed lakes, algal biomass was proportional to the fraction of the catchment area perturbed divided by the surface area of lakes in the catchment.

Estimation of Algal Succession in Lake Water Using HPLC Analysis of Pigments

1993 ◽  
Vol 50 (6) ◽  
pp. 1142-1146 ◽  
Author(s):  
Yuko Soma ◽  
Takashi Imaizumi ◽  
Kei-ichi Yagi ◽  
Sei-ichi Kasuga
Keyword(s):  
Seasonal Variation ◽  
Chlorophyll A ◽  
Green Algae ◽  
Lake Water ◽  
Algal Biomass ◽  
Hplc Analysis ◽  
Total Chlorophyll ◽  
Pigment Analysis ◽  
Algal Succession ◽  
Chlorophyll A Content

Seasonal variation in algal biomass in lake water was estimated using HPLC analysis of pigments. Carotenoids/chlorophyll a ratios were determined for cultures of Anabena, Microcystis, green algae, diatoms, and Cryptomonas. The contributions of various algal taxa to the total chlorophyll a content of lake water were calculated using the average carotenoid/chlorophyll a ratios of fingerprint pigments. The pigment analysis reflected the observed trend in the numbers of algae in lake water and proved to be a useful supplementary approach to evaluate algal biomass.

Long-term wind induced internal response mechanisms at Meiliang Bay of large, shallow Lake Taihu

2020 ◽  
Vol 56 ◽  
pp. 1
Author(s):  
Abdul Jalil ◽  
Ke Zhang ◽  
Ling Qi ◽  
Yiping Li
Keyword(s):  
Shallow Lake ◽  
Wind Field ◽  
Lake Taihu ◽  
Temporal Trends ◽  
Lake Ecosystem ◽  
Chl A ◽  
Internal Response ◽  
Large Shallow Lake ◽  
Meiliang Bay

The internal response of shallow lakes to external factors is very important to investigate for understanding their role in long-term changes of the shallow lake ecosystem. The current study investigated the impacts of long-term wind dynamics on in-lake processes of the degraded shallow lake. The long-term high-frequency wind field, water quality, and Chlorophyll-a data analysis showed that there were two groups of variables found with higher internal similarity at Meiliang bay of large, shallow Lake Taihu. The temporal trends of wind, temperature, and Chl-a found highly consistent while dissolved oxygen (DO), total nitrogen (TN), total phosphorus (TP), suspended solids (SS) and Secchi depth were not significantly correlated in long-term temporal trends analysis. The results showed that Chl-a and other shallow lake ecosystem variables (abiotic) are strongly related to long-term wind field. The changes in nutrients and lower mixing scenarios trigger the growth of Chl-a and onshore lower winds help in the formation of colonies. There was a shift in wind and internal response variables before and after 2006. Wind and internal water quality parameters were highly variable before 2006 whereas, decline in wind speeds along with stable wind directional switching caused intense blooms formation along with thermal stratification (warming) for a longer period of time (after 2006) in the shoreline areas. The current study can help to understand the internal ecosystem response mechanisms in long-term interactions with wind field to control the eutrophication and algal blooms.

Genotoxicity of surface water samples from Meiliang Bay, Taihu Lake, Eastern China

Chemosphere ◽  
2000 ◽  
Vol 41 (1-2) ◽  
pp. 129-132 ◽  
Author(s):  
L Shen ◽  
G.F Lin ◽  
J.W Tan ◽  
J.H Shen
Keyword(s):  
Surface Water ◽  
Water Samples ◽  
Taihu Lake ◽  
Eastern China ◽  
Meiliang Bay ◽  
Surface Water Samples

scholarly journals Effects of Dredging Season on Sediment Properties and Nutrient Fluxes across the Sediment–Water Interface in Meiliang Bay of Lake Taihu, China

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1606 ◽  
Author(s):  
Ji-Cheng Zhong ◽  
Ju-Hua Yu ◽  
Xiao-Lan Zheng ◽  
Shuai-Long Wen ◽  
De-Hong Liu ◽  
...  
Keyword(s):  
Taihu Lake ◽  
Lake Taihu ◽  
Soluble Reactive Phosphorus ◽  
Internal Loading ◽  
Sediment Oxygen Demand ◽  
Nutrient Fluxes ◽  
Water Interface ◽  
Sediment Properties ◽  
N Fluxes ◽  
Meiliang Bay

The influence of dredging season on sediment properties and nutrient fluxes across the sediment–water interface remains unknown. This study collected sediment cores from two sites with different pollution levels in Meiliang Bay, Taihu Lake (China). The samples were used in simulation experiments designed to elucidated the effects of dredging on internal loading in different seasons. The results showed that dredging the upper 30 cm of sediment could effectively reduce the contents of organic matter, total nitrogen, and total phosphorus in the sediments. Total biological activity in the dredged sediment was weaker (p < 0.05) than in the undredged sediment in all seasons for both the Inner Bay and Outer Bay, but the effect of 30-cm dredging on sediment oxygen demand was negligible. Dredging had a significant controlling effect on phosphorus release in both the Inner Bay and Outer Bay, and soluble reactive phosphorus (SRP) fluxes from the dredged cores were generally lower (p < 0.05) than from the undredged cores. In contrast, NH4+-N fluxes from the dredged cores were significantly higher (p < 0.05) than from the undredged cores in all seasons for both sites, this indicates short-term risk of NH4+-N release after dredging, and this risk is greatest in seasons with higher temperatures, especially for the Inner Bay. Dredging had a limited effect on NO2−-N and NO3−-N fluxes at both sites. These results suggest that dredging could be a useful approach for decreasing internal loading in Taihu Lake, and that the seasons with low temperature (non-growing season) are suitable for performing dredging projects.

scholarly journals Spatiotemporal Heterogeneities and Driving Factors of Water Quality and Trophic State of a Typical Urban Shallow Lake (Taihu, China)

2021 ◽  
Author(s):  
Yonggui Wang ◽  
Yanqi Guo ◽  
Yanxin Zhao ◽  
Lunche Wang ◽  
Yan Chen ◽  
...  
Keyword(s):  
Water Quality ◽  
Nutrient Loading ◽  
Lake Taihu ◽  
Oxygen Demand ◽  
Kendall Test ◽  
Quality Parameters ◽  
Algae Biomass ◽  
Chl A ◽  
Meiliang Bay ◽  
N Loading

Abstract Water quality deterioration and eutrophication of urban shallow lakes are global ecological problems with increasing concern and greater environmental efforts. In this study, spatiotemporal changes of water quality and eutrophication over 2015-2019 in Lake Taihu, were assessed using the monthly time series of 7 water quality parameters measured at 17 sites. The whole lake was divided into 7 sub-lakes and trophic condition was evaluated by trophic level index (TLI). Taihu had poor water quality overall which was mainly astricted by the total nitrogen (TN) and the total phosphorus (TP) and maintained a light-eutropher state, but it had improved in the last five years. It is found that all nutrient parameters reached relatively higher concentrations in the northwestern and northern Taihu with combined cluster analysis and spatial interpolation methods. Meiliang Bay was the most polluted and nutrient-rich area. Mann-Kendall test highlighted the fact that the TP and chlorophyll-a (Chl-a) concentrations increased significantly while the TN and five-day biochemical oxygen demand (BOD5) decreased. The nutrient loading input from the northwestern areas with high human activity and the geomorphological characteristic of the northern closed bays were the main contributors to the spatial heterogeneity in water quality. The main driving force of N pollution was the declining river inflow N loading. And P pollution was affected more by accumulated endogenous pollution, decline aquatic plants area, as well as closely linked with algae biomass. Further water pollution and eutrophication mitigation of Taihu should focus on the limitation of algae and those heavily polluted closed bays.

scholarly journals Recognizing Crucial Aquatic Factors Influencing Greenhouse Gas Emissions in the Eutrophication Zone of Taihu Lake, China

2019 ◽  
Vol 11 (19) ◽  
pp. 5160
Author(s):  
Yulin Wang ◽  
Liang Wang ◽  
Jilin Cheng ◽  
Chengda He ◽  
Haomiao Cheng
Keyword(s):  
Greenhouse Gas ◽  
Taihu Lake ◽  
Eastern China ◽  
Effective Means ◽  
Ghg Emissions ◽  
Regression Tree ◽  
Key Factor ◽  
Ch4 And N2o ◽  
Meiliang Bay ◽  
Positive Correlations

Greenhouse gas (GHG) emissions, which are closely related to climate change and serious ecological instability, have attracted global attention. The estimation of crucial aquatic factors for the flux of GHGs in lakes is a key step in controlling and reducing GHG emissions. The importance of 14 aquatic factors for GHG emissions was estimated in Meiliang Bay, which is an eutrophication shallow bay in Taihu Lake in eastern China. The random forest (RF) method, which is an improved version of the classified and regression tree (CART) model, was employed. No distribution assumption on variables was required in this method and it could include nonlinear actions and interactions among factors. The results show significant positive correlations among the fluxes of CO2, CH4, and N2O. The most crucial factor influencing CO2 emissions is the water temperature (WT) followed by sulfate (SO42−), alkalinity (Alk), dissolved oxygen (DO), and nitrate (NO3−–N). The important factors for CH4 emissions are WT, SO42−, DO, Alk, and NO2−–N. The outcome for N2O, in which the key factor is NO2−–N, was slightly different from those of CO2 and CH4. A comprehensive ranking index (CRI) for the fluxes of all three GHGs was also calculated and showed that WT, NO2−–N, SO42−, DO, and Alk are the most crucial aquatic factors. These results indicate that increasing DO might be the most effective means of controlling GHG emissions in eutrophication lake bays. The role of SO42− in GHG emissions, which has previously been ignored, is also worth paying attention to. This study provides a useful basis for controlling GHG emissions in eutrophication shallow lake bays.

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