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

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.

Complete Genome Sequence of Shewanella sp. Strain Lzh-2, an Algicidal Bacterial Strain Isolated from Lake Taihu, People’s Republic of China

ABSTRACT Shewanella sp. strain Lzh-2 is an algicidal bacterium isolated from surface water samples collected from Meiliang Bay of Lake Taihu in China. Here, we present the complete genome sequence of Shewanella sp. Lzh-2. Some functional genes and secondary metabolite gene clusters were predicted.

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

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.

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

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.

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

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.