Difference in temporal and spatial distribution pattern of cyanobacteria between the sediment and water column in Lake Chaohu

The effects of wind-wave disturbances on sediment resuspension and phosphate release in Lake Chao

As a typical shallow lake with a wind-generated flow, the resuspension state of sediment and phosphorus release under wind field disturbance plays an important role in controlling lake eutrophication in Lake Chao. In this study, we proposed a combination of experimental analysis of dynamic disturbances, wind-wave disturbance shear stress calculation, and model simulation (experimental-calculative-modeling) to quantitatively investigate the effects of wind-wave disturbances on the resuspension state of Lake Chao bottom sediment and phosphorus release and distribution. The results showed that the release rate of phosphorus from the Lake Chao bottom sediment was affected by the wind field and bottom sediment content, which varied significantly spatially and showed some difference between different seasons. Under the condition of sufficient water body disturbance, the substrate in the Western Lake area of Lake Chao mainly adsorbed phosphate from the water body, while the substrate in the Central Lake area and the Eastern Lake area adsorbed phosphate along with the release. The magnitude of the phosphorus release rate due to sediment resuspension was mainly affected by wind speed, and the distribution of phosphorus content was influenced by the circulation generated by different dominant wind directions. The wind-wave disturbances have a significant effect on the spatial and temporal distribution of phosphorus in Lake Chao, and the proposed experimental-calculative-modeling ensemble can provide relevant technical support for the study of water pollution control strategies and comprehensive remediation and management of Lake Chao.

A soft sensor for simulating algal cell density based on dynamic response to environmental changes in a eutrophic shallow lake

There is a great need for timely monitoring and rapid water quality assessment to control the algal blooms that often occur in eutrophic lakes. While algal cell density (ACD) is a critical indicator of algal growth, field monitoring is laborious and time-consuming, and rapid assessment of algal blooms based on ACD is often not possible. To address the limitations of conventional ACD detection, we proposed a soft sensor approach that uses surrogate indicators to simulate ACD in machine learning models. We conducted a case study using monitoring data from Chaohu Lake collected between 2016 and 2019. We found that ensemble learning models, especially extreme gradient boosting (XGBoost), outperformed traditional machine learning algorithms by comparing various machine learning algorithms. Also, considering the influence of input variable selection on model performance, we combined the results of different filter methods in the multi-stage variable selection process. Finally, we screened out seven key variables out of the 43 initial candidate variables, including dissolved oxygen (DO), chlorophyll-a (Chl-a), Secchi disk depth (SD), pH, permanganate index (COD_Mn), week of the year (WOY), and wind velocity (WV). Their inclusion substantially improved data accessibility and supported the development of a rapid simulation model. The final model was capable of reliable spatiotemporal generalization, with an overall R² value of 0.761. On the theoretical side, our study makes a new attempt to simulate ACD values in a eutrophic lake. For practical purposes, the soft sensor can facilitate the rapid assessment of bloom conditions, which helps the local administration with emergency prevention and control.