Global climate change is a phenomenon resulting from the complex interaction of human influences and natural factors. These changes lead to imbalances in climate systems as human activities such as greenhouse-gas emissions increase atmospheric gas concentrations. This situation affects the frequency and intensity of climate events worldwide, with floods being one of them. Floods manifest as water inundations due to factors such as changes in rainfall patterns, rising temperatures, erosion, and sea-level rise. These floods cause significant damage to sensitive areas such as residential areas, agricultural lands, river valleys, and coastal regions, adversely impacting people's lives, economies, and environments. Therefore, flood risk has been investigated in decision-making processes in the Diyarbakır province using the analytical hierarchy process (AHP) method and future disaggregation of global climate model data. HadGEM-ES, GFDL-ESM2M, and MPI-ESM-MR models with RCP4.5 and RCP8.5 scenarios were used. Model data were disaggregated using the equidistance quantile matching method. The study reveals flood-risk findings in the HadGEM-ES model while no flood risk was found in the GFDL-ESM2M and MPI-ESM-MR models. In the AHP method, flood risk analysis was conducted based on historical data across Diyarbakır and interpreted alongside future rainfall data.

In this study, for the first time, a venturi device was integrated into a submerged membrane bioreactor (MBR) to improve membrane surface cleaning and bioreactor oxygenation. The performances of a blower and the venturi device were compared in terms of membrane fouling and bioreactor oxygenation. Upon comparing membrane fouling, the performances were similar for a low operation flux (18 L/m².h); however, at a medium flux (32 L/m².h), the venturi system operated 3.4 times longer than the blower system, and the final transmembrane pressure was 1/3 that of the blower system. At the highest flux studied (50 L/m².h), the venturi system operated 5.4 times longer than the blower system. The most notable advantage of using a venturi device was that the dissolved oxygen (DO) concentration of the MBR was in the range of 7 to 8 mg/L at a 3 L/min aeration rate, while the DO concentration of the MBR was inadequate (a maximum of 0.29 mg/L) in the blower system. A clean water oxygenation test at a 3 L/min aeration rate indicated that the standard oxygen transfer rate for the venturi system was 9.5 times higher than that of the blower system.

Although climate models can highlight potential shifts in intensity–duration–frequency (IDF) curves, their limited geographical and temporal resolutions limit their direct use in predicting sub-daily heavy precipitation. To use global or regional model outputs to predict urban short-term precipitation, approaches that give the requisite level of spatial and temporal downscaling are required, and these processes remain one of the difficulties that have demanded intensive effort in recent years. Although no novel methods are given in this work, there are few studies in the literature that investigate the impact of climate change on the analysis and design of infrastructure-related engineering structures. Therefore, the purpose of this research is to determine the potential changes in IDF curves because of climate change. The equidistance quantile matching method was used to turn future rainfall forecast data from global climate models (HadGEM2-ES, MPI-ESM-MR, and GFDL-ESM2M) corresponding to RCP4.5 and RCP8.5 scenarios into standard duration rainfall data, and new IDF curves were generated. These IDF curves corresponded very well with those generated from observed data (R2 ≈ 1). The HadGEM2-ES model predicts up to a 25% rise in rainfall intensity, whereas the other two models expect up to a 50% drop.

Due to low sludge production and being a clean source without residuals, hydrogen-based autotrophic denitrification appears to be a promising choice for nitrate removal from agricultural drainage waters or water/wastewater with a similar composition. Although the incorporation of hydrogen-based autotrophic denitrification with membrane bioreactors (MBRs) enabled almost 100% utilization of hydrogen, the technology still needs to be improved to better utilize its advantages. This study investigated the anoxic treatment of both synthetic and real drainage waters using hydrogen gas in a recently developed membrane bioreactor configuration, a venturi-integrated submerged membrane bioreactor, for the first time. The study examined the effects of the inflow nitrate concentration, and the use of a venturi device on the removal efficiency, as well as the effects of the presence of headspace gas circulation and circulation rate on membrane fouling. The study found that using the headspace gas circulation through a venturi device did not significantly affect the treatment efficiency, and in both cases, a removal efficiency of over 90% was achieved. When the inlet $N{O}_3^{-}–N$ concentration was increased from 50 mg/L to 100 mg/L, the maximum removal efficiency decreased from 98% to 92%. It was observed that the most significant effect of the headspace gas circulation was on the membrane fouling. When the headspace gas was not circulated, the average membrane chemical washing period was 5 days. However, with headspace gas circulation, the membrane washing period increased to an average of 12 days. The study found that the headspace gas circulation method significantly affected membrane fouling. When the upper phase was circulated with a peristaltic pump instead of a venturi device, the membrane washing period decreased to one day. The study calculated the maximum hydrogen utilization efficiency to be approximately 96%.

The aim of this study is to model a hydrogenotrophic denitrification process in a venturi-integrated submerged membrane bioreactor (MBR) system. The MBR was operated in batch mode using feed concentrations of 100 and 150 mg NO₃-N/L. In contrast to most of the denitrification process models that represent the mixed culture with one composite biomass parameter, the biomass was subdivided into two main categories in this modelling study: mainly nitrate-reducing biomass and mainly nitrite-reducing biomass. The determination coefficients (r²) in the range of 0.97–0.99 indicate that the model successfully simulates the concentrations of nitrate- and nitrite-nitrogen in the bioreactor. The maximum specific growth rate of nitrite-reducing biomass (0.06 h⁻¹) was found to be higher than that of nitrate-reducing biomass (0.0002 h⁻¹). Similarly, the growth yield coefficient of nitrite-reducing biomass was higher than that of nitrate-reducing biomass (0.44 vs. 0.31 g biomass/g substrate). The kinetic and stoichiometric coefficients obtained from this modelling study suggest that the limiting step determining the overall conversion rate of hydrogenotrophic denitrification process is the conversion of nitrite to nitrogen gas. GRAPHICAL ABSTRACT