|
Showing 1 - 2 of
2 matches in All Departments
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.
|
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.