Safe drinking water and sanitation are very important for the survival of human life. With the rapid proliferation of industries, growth in population and different forms of pollution, i.e. in water, air, soil and sediments, the living environment and the ecosystem is constantly polluted. In this context, integrating different water resources for enhanced water quality and reuse is important to solve the persisting problems and challenges in developing and the developed nations. Integrated water management offers environmental, economic and social benefits because it aims at maximizing the existing resources and prevents further depletion of the ecosystem.In this special issue, a total of 22 papers were selected for publication from an open call for papers in association with the 13th International Conference on Challenges in Environmental Science and Engineering, CESE-2020. These papers covered the following aspects as illustrated in Figure 1: (i) the performance of different bioreactor configurations for wastewater treatment, (ii) rain water harvesting, (iii) water/wastewater reuse, (iv) the application of mathematical models, neural networks and remote sensing for water quality monitoring, treatment plant performance prediction and water resource management, and (v) the implementation of the water-energy-agriculture concept in practice.Alsulaili & Refaie (2021) applied artificial neural networks (ANNs) for predicting the performance of wastewater treatment plants (WWTPs) in Kuwait using temperature, pH, conductivity, and BOD as the input parameters. According to the results, increasing the number of model inputs beyond three did not enhance the ANN performance, while the influent BOD and conductivity showed the highest effect on the effluent water quality. The results from sensitivity analysis showed that the influent COD had the highest impact on effluent BOD. The authors recommended ANN model as a reliable option for the intelligent control and monitoring of WWTPs in practice. In a study aimed at water quality monitoring for timely decision making in Namibia, Kapalanga et al. (2021) used reflectance values and field measured water quality data to develop regression analysis-based retrieval algorithms. The authors used turbidity, total suspended solids (TSS), nitrates, ammonia, total nitrogen (TN), total phosphorus (TP) and total algae counts as the major water quality parameters. It was observed that the turbidity levels exceeded the recommended limits for potable water, while the TN and TP values were within the acceptable values. According to the authors, an assessment and monitoring of water quality using the combination of remote sensing and in-situ measurements will help to provide a good estimate of the water quality. Furthermore, the authors had also used remote sensing as a framework for continuous monitoring of water quality in Olushandja Dam (Namibia). Another study also used ANNs for predicting the removal of heavy metals, i.e. Cu (II), Cd (II) and Pb (II) from synthetic wastewater in a rotating b...