On a global scale, sewage represents the main point-source of water pollution and is also the predominant source of nitrogen contamination in urban regions. The present research is focused on the study of the main challenges that need to be addressed in order to achieve a successful inorganic nitrogen post-treatment of anaerobic effluents in the mainstream. The post-treatment is based on autotrophic nitrogen removal. The challenges are classified in terms of operational features and system configuration, namely: (i) the short-term effects of organic carbon source, the COD/N ratio and the temperature on the autotrophic nitrogen removal; the results from this study confirms that the Anammox activity is strongly influenced by temperature, in spite of the COD source and COD/N ratios applied. (ii) The long-term performance of the Anammox process under low nitrogen sludge loading rate (NSLR) and moderate to low temperatures; it demonstrates that NSLR affects nitrogen removal efficiency, granular size and biomass concentration of the bioreactor. (iii) The Anammox cultivation in a closed sponge-bed trickling filter (CSTF) and (iv) the autotrophic nitrogen removal over nitrite in a sponge-bed trickling filter (STF). Both types of Anammox sponge-bed trickling filters offer a plane technology with good nitrogen removal efficiency.

In recent years, the continued technological advances have led to the spread of low-cost sensors and devices supporting crowdsourcing as a way to obtain observations of hydrological variables in a more distributed way than the classic static physical sensors. The main advantage of using these type of sensors is that they can be used not only by technicians but also by regular citizens. However, due to their relatively low reliability and varying accuracy in time and space, crowdsourced observations have not been widely integrated in hydrological and/or hydraulic models for flood forecasting applications. Instead, they have generally been used to validate model results against observations, in post-event analyses. This research aims to investigate the benefits of assimilating the crowdsourced observations, coming from a distributed network of heterogeneous physical and social (static and dynamic) sensors, within hydrological and hydraulic models, in order to improve flood forecasting. The results of this study demonstrate that crowdsourced observations can significantly improve flood prediction if properly integrated in hydrological and hydraulic models. This study provides technological support to citizen observatories of water, in which citizens not only can play an active role in information capturing, evaluation and communication, leading to improved model forecasts and better flood management.