Organic matter, nutrients, and chemicals are some of the waste materials found in most domestic wastewaters. The purpose of this research was to evaluate the potentials of Pistia stratiotes, Salvinia molesta, and Eichhornia crassipes aquatic weed plants in the tertiary treatment of secondary domestic wastewater using hydroponic systems. Physicochemical analysis that considered turbidity, phosphate, ammoniacal nitrogen, and nitrate was conducted on the wastewater samples at different retention times of 6, 12, and 24 h for 14 days. The best performance was observed for a 24 h retention treatment system, with up to 91% (P. stratiotes), 94% (S. molesta), and 89.3% (E. crassipes) reduction for turbidity; 81.2% (P. stratiotes), 82.7% (S. molesta), and 88.5% (E. crassipes) for phosphate reduction; 88.66% (P. stratiotes), 90.47% (S. molesta), and 89.11% (E. crassipes) for ammoniacal nitrogen reduction; 83.6% (P. stratiotes), 92.1% (S. molesta) and 93% (E. crassipes) for nitrate reduction. Hence, the application of hydroponic systems in cultivation of the macrophytes proved to be effective in the phytoremediation process by removing the residual nutrients present in the treated secondary domestic wastewater. Additionally, this method can be used for the cultivation of plant-based biomass and other non-edible useful crops using the free nutrients content of the wastewater before its discharge into the natural environment.

Water resources management in Malaysia has become a crucial issue of concern due to its role in the economic and social development of the country. Kelantan river (Sungai Kelantan) basin is one of the essential catchments as it has a history of flood events. Numerous studies have been conducted in river basin modelling for the prediction of flow and mitigation of flooding events as well as water resource management. This paper presents river flow modelling based on meteorological and weather data in the Sungai Kelantan region using a cascade-forward neural network trained with particle swarm optimization algorithm (CFNNPSO). The result is compared with those trained with the Levenberg–Marquardt (LM) and Bayesian Regularization (BR) algorithm. The outcome of this study indicates that there is a strong correlation between river flow and some meteorological and weather variables (weighted rainfall, average evaporation and temperatures). The correlation scores (R) obtained between the target variable (river flow) and the predictor variables were 0.739, −0.544, and −0.662 for weighted rainfall, evaporation, and temperature, respectively. Additionally, the developed nonlinear multivariable regression model using CFNNPSO produced acceptable prediction accuracy during model testing with the regression coefficient (R²), root mean square error (RMSE), and mean of percentage error (MPE) of 0.88, 191.1 cms and 0.09%, respectively. The reliable result and predictive performance of the model is useful for decision makers during water resource planning and river management. The constructed modelling procedure can be adopted for future applications.

The objective of this research was to ascertain the best conditions for efficient applications of water lettuce, giant salvinia and water hyacinth in improving the quality of low strength domestic wastewater. Water quality assessment of the wastewater samples before (influent) and after treatment (effluent) with effect to retention times (6, 12 and 24 h) was analysed. The outcome of the study at 6 h retention showed that water lettuce (6.8–7.0 pH, 50.5% colour, 46.7% biochemical oxygen demand (BOD) and 37.8% chemical oxygen demand (COD)), giant salvinia (6.9–7.1 pH, 40.5% colour, 60% BOD and 43.2% COD) and water hyacinth (6.7–6.9 pH, 45.5% colour, 53% BOD and 35.1% COD) reduction values were achieved. At 12 h retention, water lettuce (6.6–7.0 pH, 57.2% colour, 77.1% BOD and 74.6% COD), giant salvinia (6.4–6.8 pH, 81.1% colour, 66.7% BOD and 72.2% COD) and water hyacinth (6.4–6.7 pH, 61.9% colour, 70% BOD and 61.1% COD) reduction values were achieved. Similarly, for 24 h retention, water lettuce (6.6–7.0 pH, 76.7% colour, 53.2% BOD and 70.3% COD), giant salvinia (6.6–7.0 pH, 91.4% colour, 74.7% BOD and 81.0% COD) and water hyacinth (6.4–6.9 pH, 74% colour, 58% BOD and 67.2% COD) reduction values were achieved. These findings indicated that the retention times of 12 and 24 h provided suitable conditions to break down the organic contaminants present in the shallow ponds.

Circular economy (CE) strategy is crucial in developing towards sustainable growth. It was created to promote resource utilization and the elimination of waste production. This article aimed to study the possibilities of using the CE framework in wastewater bioremediation and energy recovery using hydroponic tanks. The integration of phytoremediation with bioenergy, construction and lifespan of hydroponic tanks in phytoremediation of wastewater, selection of aquatic plants and the expected challenges in the implementation of CE in phytoremediation of wastewater were discussed. The plant-based biomass harvested and the relative growth rate (RGR) of the selected plants from the phytoremediation process was evaluated. The findings obtained indicated that the selected plants tripled in weight after 14 days cultivation period at different retention times. E. crassipes recorded the highest growth with 2.5 ± 0.03 g g⁻¹ d⁻¹, followed by S. molesta with 1.33 ± 0.05 g g⁻¹ d⁻¹ and then P. stratiotes recorded 0.92 ± 0.27 g g⁻¹ d⁻¹ at the end of the cultivation period. Therefore, the selected plants have been identified as having the potential to be used in phytoremediation as well as a source of energy production. The outcome of our review suggested the adoption of a lifecycle assessment as the CE framework for the phytoremediation of wastewater.

The objective of this study was to investigate the performance of different weight of Salvinia molesta plants in biological treatment of domestic wastewater. Three treatment systems containing 280g (GS1), 140g (GS2) and 70g (GS3) of S. molesta plants were used for the phytoremediation process. Physicochemical analysis such as pH, colour, chemical oxygen demand (COD), and biological oxygen demand (BOD5) of the influent and effluent water samples were performed according to spectrophotometric methods. The outcome of the study demonstrated that the different weight of S. molesta plants played a significant role in improving the quality of the wastewater samples, in which GS1 removed 96.8% (colour), 91% (BOD5), and 82.6% (COD). While up to 88.6% (colour), 87.1% (BOD5), and 81.1% (COD) reduction was observed for GS2 treatment systems, and GS3 was efficient in removing 85.5% (colour), 86.1% (BOD5), and 68.3% (COD). Also, a pH value of 6.29–7.19, 5.97–7.07, and 6.17–7.42 was obtained from GS1, GS2 and GS3 treatment systems, respectively. Thus, the treatment system with the highest quantity of S. molesta (GS1) demonstrated better performance compared to the other two systems (GS2 and GS3). The findings of this research can be applied in addressing the goals of sustainable development through the use of green technology to reduce the threat of water pollution in natural water bodies. Perhaps existing and future water scarcity can be resolved through the use of phytoremediation technology.

Wastewater treatment and reuse are being regarded as the most effective strategy for combating water scarcity threats. This study examined and reported the applications of the Internet of Things (IoT) and artificial intelligence in the phytoremediation of wastewater using Salvinia molesta plants. Water quality (WQ) indicators (total dissolved solids (TDS), temperature, oxidation-reduction potential (ORP), and turbidity) of the S. molesta treatment system at a retention time of 24 h were measured using an Arduino IoT device. Finally, four machine learning tools (ML) were employed in modeling and evaluating the predicted concentration of the total dissolved solids after treatment (TDSt) of the water samples. Additionally, three nonlinear error ensemble methods were used to enhance the prediction accuracy of the TDSt models. The outcome obtained from the modeling and prediction of the TDSt depicted that the best results were observed at SVM-M1 with 0.9999, 0.0139, 1.0000, and 0.1177 for R², MSE, R, and RMSE, respectively, at the training stage. While at the validation stage, the R², MSE, R, and RMSE were recorded as 0.9986, 0.0356, 0.993, and 0.1887, respectively. Furthermore, the error ensemble techniques employed significantly outperformed the single models in terms of mean square error (MSE) and root mean square error (RMSE) for both training and validation, with 0.0014 and 0.0379, respectively.