This study aimed to evaluate the effectiveness of a full-scale waste stabilization pond in treating domestic wastewater in a peri-urban area with arid climatic conditions. The experiment was conducted in the municipality of Saada, located 18 km west of Marrakech, Morocco. The wastewater treatment system consisted of two anaerobic ponds, four facultative ponds, and four maturation ponds arranged in series. The system treated an average inflow of 525 m3/day. In order to evaluate the efficiency of the treatment process, periodic sampling and analysis of physicochemical and bacteriological parameters were performed at the influent and effluent of each treatment stage. Phytoplankton monitoring was carried out in the three lagoons (anaerobic, facultative and maturation). The study found that maximum pollutant removal occurred during the warmer months of the year, and minimum removal occurred during the winter period. The highest removal percentages of TSS, COD, and NH4+ were 72.66 %, 79.28 %, and 72.53 %, respectively. However, the removal of total phosphorus and its assimilable form (PO43-) did not exceed 40 % and 50 %, respectively. Fecal coliform removal reached an average removal of 2.71 log units at the outlet of the maturation pond. Phytoplankton parameters, based on qualitative and quantitative aspects of microalgae, indicated a dominance of cyanobacteria (46.87 %) and chlorophyceae (28.13 %), while diatoms were moderately present (12.50 %) in all ponds. This may indicate an unsatisfactory purification performance of the plant. The quantitative study also showed a great dominance of the species Chlorella, especially in the maturation pond.

Eight years after the first launch of the Sentinel-2 satellite, its data have become an integral part of the scientific study of managed agricultural landscapes, providing insights into temporal changes and responses to factors such as climate change and overgrazing. This study uses bibliometric analysis to measure the importance of the Sentinel-2 mission in the scientific literature, focusing specifically on research in the Marrakech region from 2015 to 2023. The results reveal a dynamic research landscape, particularly a surge in publications in 2021. Notable contributors such as Abderrahman Lahrouni, Moumni Aicha, and Salah Er-raki emerge as key authors in the field of Sentinel-2 research in Marrakech. Cadi Ayyad University, in collaboration with the University of Toulouse and Mohamed 6 Polytechnic University, plays a key role in the organization of scientific research in the region. In the case study, the classification results highlight the superior performance of the Neural Networks (NN) algorithm in both object-based and pixel-based classifications. Specifically, the object-based classification outperforms the pixel-based classification, achieving an overall accuracy (OA) of 80%. This study underlines the growing importance of Sentinel-2 data, highlighting its multiple applications and growing prominence in the Marrakech region.

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.

In response to water scarcity, Morocco faces the challenge of using treated wastewater for irrigation purposes. This study evaluates the efficiency of a full-scale trickling filter (TF) system in Imintanout, Morocco. The system consists of three septic tanks, two TFs, and secondary decanters. Over a 5-year period, the system showed significant reductions in pollutants: 98 % of TSS, 94 % of BOD5, 98 % of COD, 41 % of TP, and 88 % of NH4+, with these reductions being statistically significant at the 95 % confidence level. A multiple linear regression (MLR) model successfully predicted the removal of fecal coliform (FC) by the TF, and a reduction of 2.88 log units was achieved. High cos2 values indicated the importance of hydraulic loading rate (HLR), BOD5, and FC, which were particularly affected by seasonal variations. Positive correlations between FC and TSS in certain periods highlight the seasonal variability in the composition of urban wastewater, which is effectively captured by the MLR model (R2 = 0.77). Although the treated water complied with Moroccan discharge standards, its high nitrate (140 mg L-1) and FC (4.32 log units) levels made it unsuitable for reuse in agricultural and landscape irrigation, as they exceeded safety limits. This work highlights the importance of optimizing treatment systems to produce high quality reclaimed water, which is essential to meet the challenges of water scarcity.

The goal of this research is to assess and track the efficacy of hybrid multi-soil-layering (MSL) plant in treating domestic wastewater for reuse in landscape irrigation. The investigated wastewater treatment plant is composed of a solar septic tank followed by a vertical flow MSL (VF-MSL) unit in series with a subsurface horizontal flow MSL (HSSF-MSL) unit. Wastewater samples were taken bimonthly from the plant's influent and effluent, respectively. The two-stage MSL units are composed of gravel layers alternated with soil-based layers, which are arranged in brick-like patterns, although they are operated under an HLR of 250 L m(-2) day(-1), which corresponds to 125 g COD/m(2)/d, 52 g TN/m(2)/d, and 2.70 g TP/m(2)/d. The mean removal efficiency was 75 %, 42 %, 67 %, and 47 %, respectively, for TSS, COD, NH4+, and TP for the VF-MSL system. Moreover, the VF-MSL combined with the HSSF-MSL system resulted in significant nitrogen removal as well as a significant reduction (p < 0.05) in organic matter and phosphorus with an abatement of 97 %, 79 %, 76 %, and 27 %, respectively, for TSS, COD, TP, and TN. For fecal bacteria indicators, the hybrid MSL system achieved high log removals, reaching 2.88 log units during two years of monitoring. Principal component analysis (PCA) was done on the positive correlation between COD, NH4+, TP removal, and summer seasons. Nonetheless, the winter season significantly influenced TN removal in the MSL system. The investigated hybrid multi-soil-layering plant has been shown to be effective in terms of water quality for the irrigation of green spaces in urban areas.

Excess phosphorus (P) in wastewater can produce eutrophication, posing a serious risk to the safety of water resources and ecosystems. Therefore, effective pollutant removal including P from wastewater is the key strategy to save the environment and public health. Multi-soil-layering (MSL) is a promising nature-based technology that mainly relies on a soil mixture containing iron to remove P-pollution from wastewater. Fifteen water quality parameters were monitored in the MSL influent to determine which ones have the strongest relationship with total phosphorus (TP) removal. The influence of hydraulic loading rate (HLR) and climatic variables on the removal of TP was investigated. Three data-driven methods including multiple linear regression (MLR), k-nearest neighbors (KNN), and random forest (RF) were conducted to predict TP removal at the MSL system outlet. In contrast to climatic variables, the results reveal that the HLR has a significant impact (p <0.05) on TP removal (47%-90%) in the MSL system. Furthermore, using a feature selection technique, the HLR, pH, orthophosphate, and TP were suggested as the relevant input variables affecting TP removal in the MSL system, while an examination of accuracy shows that the RF model achieves good prediction accuracy (R2 = 0.94).

Due to its unique structure and excellent purification efficiency (e.g., 98% for organic matter and between 94 and 100% for nutrients), multi-soil-layering (MSL) has emerged as an efficient eco-friendly solution for wastewater treatment and environmental protection. Through infiltration-percolation, this soil-based technology allows pollutants to move from the MSL upper layers to the outlet while maintaining direct contact with its media, which helps in their removal via a variety of physical and biochemical mechanisms. This paper attempts to comprehensively evaluate the application of MSL technology and investigate its progress and efficacy since its emergence. Thus, it will attempt via a bibliometric analysis using the Web of Science database (from 1993 to 1 June 2022) related to MSL technology, to give a clear picture of the number of publications (70 studies), the most active academics, and countries (China with 27 studies), as well as collaborations and related topics. Furthermore, through hybrid combinations, pollutant removal processes, MSL effective media, and the key efficiency parameters, this paper review will seek to provide an overview of research that has developed and examined MSL since its inception. On the other hand, the current review will evaluate the modeling approaches used to explore MSL behavior in terms of pollutant removal and simulation of its performance (R2 > 90%). However, despite the increase in MSL publications in the past years (e.g., 13 studies in 2021), many studies are still needed to fill the knowledge gaps and urging challenges regarding this emerging technology. Thus, recommendations on improving the stability and sustainability of MSLs are highlighted.

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 (R-2 = 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.

The quality of effluents from wastewater treatment plants still challenging especially in underprivileged rural areas where water resources are mostly affected by pollution, depletion and excessive exploitation. Thus, the prediction of phosphorus removal is one of the most important tasks in the management of wastewater effluent. Predictive model accuracy is crucial for safe reuse of treated water for public health and the environment. However, linear models that use a high dimensional dataset may be unable to build accurate and interpretable models. To address this complexity, the current study evaluates the effect of hydraulic retention time (HRT) on the removal of orthophosphates (PO4–P) and total phosphorus (TP) by the multi-soil-layering (MSL) eco-friendly technology. In addition, it attempts to predict this removal from domestic wastewater using a combined approach based on feature selection technique and gradient boosting machine algorithm (GBM). Sixteen physicochemical and bacterial indicators were monitored for a one-year period. The results show that the HRT impact significantly (p < 0.01) the removal of phosphorus content by the MSL system. The HRT, pH, PO4–P and TP were suggested relevant for predicting the removal of TP, while HRT and PO4–P were sufficient for predicting the removal rate of PO4–P. The analysis of accuracy using the validation dataset demonstrates that GBM models have high credibility as they achieve an R² > 0.92, while the analysis of sensitivity reveals that the HRT was the most important factor affecting phosphorus removal in the MSL system. In addition, the modeling results show that the GBM model has proven to be useful for predicting pollutant removal in the MSL technology and investigating its behavior.

The multi-soil-layering (MSL) bioreactor has been considered in the latest research as an innovative bioreactor for reducing the level of pollutants in wastewater. The efficiency of the MSL bioreactor towards nitrogen pollution is due to the mineralization of organic nitrogen in aerobic layers to ammonia, and reactivity of ammonia nitrogen with soil and gravel by its adsorption into soil layers followed by nitrification and denitrification processes when the alternating phases of oxygenated/anoxic conditions occurs in the filter. In this study, we have examined the performance of the MSL bioreactor at different hydraulic loading rates (HLRs) and predicted the removal rate of nitrogen. To improve the prediction accuracy of the models, the feature selection technique was performed before conducting the Neural Network model. The results showed a significant removal (p <0.05) efficiency for five-day biochemical oxygen demand (BOD5, 86%), ammonium (NH4+, 83%), nitrates (NO3−, 81%), total kjeldahl nitrogen (TKN, 84%), total nitrogen (TN, 84%), orthophosphates (PO43−, 91%), and total coliforms (TC, 1.62 Log units). However, no significant change was observed in the nitrite (NO2−) concentration as it's an intermediate nitrogen form. The MSL treatment efficiency demonstrated a good capacity even when HLR increased from 250 to 4000 L/m2/day, respectively (e.g., between 64% and 86% for BOD5). The HLR was selected as the most significant (p < 0.05) input variable that contribute to predict the removal rates of nitrogen. The developed models predict accurately the output variables (R2 > 0.93) and could help to investigate the MSL behavior.

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.

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.