Eco-friendly management of harmful cyanobacterial blooms in eutrophic lakes through vertical flow multi-soil-layering technology.

Eutrophication has led to the widespread occurrence of cyanobacterial blooms. Toxic cyanobacterial blooms with high concentrations of microcystins (MCs) have been identified in the Lalla Takerkoust reservoir in Morocco. The objective of this study was to evaluate the efficiency of the Multi-Soil-Layering (MSL) ecotechnology in removing natural cyanobacterial blooms from the lake. Two MSL pilots were used in rectangular glass tanks (60 × 10 × 70 cm). They consisted of permeable layers (PLs) made of pozzolan and a soil mixture layer (SML) containing local soil, ferrous metal, charcoal and sawdust. The main difference between the two systems was the type of local soil used: sandy soil for MSL1 and clayey soil for MSL2. Both MSL pilots effectively reduced cyanobacterial cell concentrations in the treated water to very low levels (0.09 and 0.001 cells/mL). MSL1 showed a gradual improvement in MC removal from 52 % to 99 %, while MSL2 started higher at 90 % but dropped to 54% before reaching 86%. Both MSL systems significantly reduced organic matter levels (97.2 % for MSL1 and 95.8 % for MSL2). Both MSLs were shown to be effective in removing cyanobacteria, MCs, and organic matter with comparable performance.

Removal of bacterial indicators in on-site two-stage multi-soil-layering plant under arid climate (Morocco): prediction of total coliform content using K-nearest neighbor algorithm.

This study aims to evaluate and monitor the efficacy of a full-scale two-stage multi-soil-layering (TS-MSL) plant in removing fecal contamination from domestic wastewater. The TS-MSL plant under investigation consisted of two units in series, one with a vertical flow regime (VF-MSL) and the other with a horizontal flow regime (HF-MSL). Furthermore, this study attempts to see whether linear model (LM) and K-nearest neighbor (KNN) model can be used to predict total coliform (TC) removal in the TS-MSL system. For 24 months, the TS-MSL system was monitored, with bimonthly measurements recorded at the inlet and outlet of each compartment. Obtained results show removal of 85% of COD, 67% of TP, 27% of TN, and 3 log units of coliforms with good system stability. Thus, the effluent meets the Moroccan water quality code for reuse in the irrigation of green spaces. In addition, as compared to LM, the KNN model (R2 = 0.988) may be considered as an effective method for predicting TC removal in the TS-MSL system. Finally, sensitivity analysis has shown that TC and dissolved oxygen level in the influent were the most influential parameters for predicting TC removal in the TS-MSL system.

Neural network and cubist algorithms to predict fecal coliform content in treated wastewater by multi-soil-layering system for potential reuse.

This study aims to find the most accurate machine learning algorithms as compared to linear regression for prediction of fecal coliform (FC) concentration in the effluent of a multi-soil-layering (MSL) system and to identify the input variables affecting FC removal from domestic wastewater. The effluent quality of two different designs of the MSL system was evaluated and compared for several parameters for potential reuse in agriculture. The first system consisted of a single-stage MSL (MSL-SS), and the second system consisted of a two-stage MSL (MSL-TS). The concentration of FC in the effluent of the MSL-TS system was estimated by three machine learning algorithms: artificial neural network (ANN), Cubist, and multiple linear regression (MLR). The accuracy of the models was measured by comparing the real and predicted values. Significant (p < .001) improvements were noted for the removal of pollutants by the MSL-TS system compared with the MSL-SS system. Overall, the water quality parameters investigated complied with FAO irrigation standards. The predictive performance of the models has been compared and evaluated using several metrics. The results revealed that the ANN model yielded a superior predictive performance (R2  = .953), followed by the Cubist model (R2  = .946) and the MLR technique (R2  = .481). Based on the accurate model (ANN), the degree of influence of each predictor was investigated, and the results show that total suspended solids and pH have proved to be more useful for predicting FC concentrations.

Predicting the concentration of total coliforms in treated rural domestic wastewater by multi-soil-layering (MSL) technology using artificial neural networks.

Many indicators are involved in monitoring water quality. For instance, the fecal indicator bacteria are extremely important to detect the water quality. For this purpose, to better predict the total coliforms at the outlet of a Multi-Soil-Layering (MSL) system designed to treat domestic wastewater in rural areas, a neural network model has been developed and compared with linear regression model. The data was collected from the raw and treated wastewater of a three MSL systems during a one-year period in rural village, in Al-Haouz Province, Morocco. Fifteen physicochemical and bacteriological variables have undergone feature selection to select the best ones for predicting the total coliforms concentration in the effluent of MSL system. Furthermore, 80% of the available dataset were used to train and optimize the neural model using repeated cross validation technique. The remaining part (20%) was used to test the developed model. The neural network indicated excellent results compared to the linear regression. The optimal model was a neural network with one hidden layer and 11 neurons, where the R2 was about 97%. The importance analysis of each predictor was established, and it was found that pH and total suspended solids had the greatest influence on the total coliforms removal.