Urban water demand prediction based on climate change is always challenging for water utilities because of the uncertainty that results from a sudden rise in water demand due to stochastic patterns of climatic factors. For this purpose, a novel combined methodology including, firstly, data pre-processing techniques were employed to decompose the time series of water and climatic factors by using empirical mode decomposition and identifying the best model input via tolerance to avoid multi-collinearity. Second, the artificial neural network (ANN) model was optimised by an up-to-date slime mould algorithm (SMA-ANN) to predict the medium term of the stochastic signal of monthly urban water demand. Ten climatic factors over 16 years were used to simulate the stochastic signal of water demand. The results reveal that SMA outperforms a multi-verse optimiser and backtracking search algorithm based on error scale. The performance of the hybrid model SMA-ANN is better than ANN (stand-alone) based on the range of statistical criteria. Generally, this methodology yields accurate results with a coefficient of determination of 0.9 and a mean absolute relative error of 0.001. This study can assist local water managers to efficiently manage the present water system and plan extensions to accommodate the increasing water demand.

Non-steroidal anti-inflammatory drugs (NSAIDs) (concentration <µg/L) are globally acknowledged as hazardous emerging pollutants that pass via various routes in the environment and ultimately enter aquatic food chains. In this context, the article reviews the occurrence, transport, fate, and electrochemical removal of some selected NSAIDs (diclofenac (DIC), ketoprofen (KTP), ibuprofen (IBU), and naproxen (NPX)) using carbon-based anodes in the aquatic environment. However, no specific protocol has been developed to date, and various approaches have been adopted for the sampling and elimination processes of NSAIDs from wastewater samples. The mean concentration of selected NSAIDs from different countries varies considerably, ranging between 3992–27,061 µg/L (influent wastewater) and 1208–7943 µg/L (effluent wastewater). An assessment of NSAIDs removal efficiency across different treatment stages in various wastewater treatment plants (WWTPs) has been performed. Overall, NSAIDs removal efficiency in wastewater treatment plants has been reported to be around 4–89%, 8–100%, 16–100%, and 17–98% for DIC, KTP, NPX, and IBU, respectively. A microbiological reactor (MBR) has been proclaimed to be the most reliable treatment technique for NSAIDs removal (complete removal). Chlorination (81–95%) followed by conventional mechanical biological treatment (CMBT) (94–98%) treatment has been demonstrated to be the most efficient in removing NSAIDs. Further, the present review explains that the electrochemical oxidation process is an alternative process for the treatment of NSAIDs using a carbon-based anode. Different carbon-based carbon anodes have been searched for electrochemical removal of selected NSAIDs. However, boron-doped diamond and graphite have presented reliable applications for the complete removal of NSAIDs from wastewater samples or their aqueous solution.