Efficiency evaluation of titanium oxide nanocomposite membrane in adsorption of chromium from oil effluents.

Reverse osmosis and nanofiltration (NF) are the essential physical separation technologies used to remove contaminants from liquid streams. A hybrid of nanofiltration and forward osmosis (FO) was used to increase the removal efficiency of heavy metals in synthesized oil effluents. Thin-film nanocomposite (TFN) membranes were synthesized by applying surface polymerization on a polysulfone substrate to use in the forward osmosis process. The impact of different membrane fabrication conditions such as time, temperature, and pressure on effluent flux, the effect of different concentrations of the heavy metal solution on adsorption rate and sedimentation rate, the impact of TiO2 nanoparticles on the performance and structure of forward osmosis membranes were investigated. The morphology, composition, and properties of TiO2 nanocomposites made by the infrared spectrometer and X-ray diffraction (XRD) were studied. Kinetic modeling and Langmuir, Freundlich, and Tamkin relationships were used to draw adsorption isotherms and evaluate adsorption equilibrium data. The results indicated that pressure and temperature directly affect water outlet flux, and time affects it indirectly. Evaluating the isothermal relationships revealed that chromium adsorption from the TFN 0.05 ppm membrane and thin-film composite (TFC) membrane follows the Langmuir model with correlation coefficients of 0.996 and 0.995, respectively. The significant removal of heavy metals and the acceptable amount of water flux demonstrated the appropriate potential of the titanium oxide nanocomposite membrane, which can be used as an effective adsorbent to remove chromium from aqueous solutions.

Optimization of geographical space of ecosystem service areas and land-use planning, Iran.

This study provides a framework for optimizing ecosystem service zones based on landscape patterns. Ecosystem service zoning is a tool for making more informed land-use decisions and integrating ecosystem services into land development strategies. Accordingly, a meta-heuristic algorithm (simulated annealing-genetic hybrid) was applied to optimize zones of three ecosystem services, including aesthetic value, recreation value, and noise pollution reduction service. The goal was to select ecosystem service zones in terms of area, percentage of landscape, shape, and number of patches in two land-use types, including the built-up and green cover. The results indicated that optimum zones based on green infrastructure comprised more extent and average suitability than human infrastructure. The western and southern parts of the target area showed the highest values of ecosystem services for the two land-use classes. The convergence curve of the objective functions indicated the good performance of the employed algorithm in optimizing ecosystem service zones. Overall, the results indicated that optimizing ecosystem service zones based on landscape patterns improved the land-use management framework in the study area. Moreover, the used algorithm allowed for integrating different parameters into ecosystem services zone configurations.