Seasonal dynamics of phytoplankton population and water quality in Bidoli reservoir

Construction and Application of a Water Quality Risk Sensitive Area Identification System in the Wudongde Reservoir

Numerous water quality risks exist during the initial water storage stage in reservoirs; however, little water quality data is available for this stage. Taking the Wudongde Reservoir as an example, we proposed a water quality risk sensitive area identification system for the initial impoundment stage comprising three modules: water quality assessment, water quality similarity clustering analysis, and sensitive area identification. Temporal and spatial variation in the water quality of the whole reservoir was analyzed, combined with a comprehensive evaluation using the Canadian Council of Ministers of the Environment Water Quality Index. A water quality similar clustering module was used to form similar clusters for monitoring sections in the reservoir area. The water quality risk sensitive areas were then identified and verified through a prototype test. The reservoir water quality was primarily excellent to good, although that of the Madian and Longchuan Rivers was poor. Through cluster analysis, the Madian River and tributaries of the Longchuan River were identified as sensitive areas, and the causes of water quality risk were analyzed. Based on these findings, we suggested focus areas for water environmental protection measures, providing a basis for the protection and restoration of the reservoir water environment.

Decolorization of Reactive Black B from wastewater by electro-coagulation: optimization using multivariate RSM and ANN

The present study is aimed to model and optimize the electrocoagulation (EC) process with five important parameters for the decolorization of Reactive Black B (RBB) from simulated wastewater. A multivariate approach, response surface methodology (RSM) together with central composite design (CCD) is used to optimize process parameters such as pH (5–9), electrode gap (0.5–2.5 cm), current density (2.08–10.41 mA/cm2), process time (10–30 min), and initial dye concentration (100–500 mg/l). The predicted percentage decolorization of dye is obtained as 97.21% at optimized conditions: pH (6.8), gapping (1.3 cm), current density (8.32 mA/cm2), time (23 min), and initial dye concentration (200 mg/L), which is very close to experimental percent decolorization (98.41%). The statistical analysis of variance (ANOVA) is performed to evaluate the quadratic model (RSM), and shows good fit of experimental data with coefficient of determination R2 >0.93. An Artificial Neural Network (ANN) is also used to predict the percentage decolorization and gives overall 94.96% which shows performance accuracy between the predicted and actual value of decolorization. The additional considerations of operating cost and current efficiency are also taken care to show the efficacy of EC process with mathematical tool. The sludge characteristics are determined by FE-SEM/EDX.