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The aim of this study is to investigate the performance of solar photo-Fenton process (H2O2/Fe2+/Solar) to treat petroleum wastewater from Sohar oil Refinery, Oman. The effect of operating conditions such as pH, reaction time, Fenton ratio, and Fenton reagent concentrations are investigated. The obtained optimum conditions include H2O2 dosage (1 g L-1), Fe+2 dosage (0.08 g L-1), pH (3) and reaction time (180 min). TOC and COD removal rates are 64 % and 78 %, respectively. However, the photolytic process was less efficient in the petroleum wastewater treatment, achieving an 11.5% and 9% of COD and TOC removals, respectively. The solar photo-Fenton process has well efficient for petroleum wastewater treatment in the acidic conditions and more economic by free energy.

Carbon Capture and Storage (CCS) is an advanced technology that has the technical potential to provide dual advantages: (1) it helps countries maintain their hydrocarbon – driven economic advancement, such as Gulf Corporation Council countries (GCC) and (2) mitigating the negative effects of increasing CO2 emissions. A full system of CCS consists of CO2 separation, compression, transportation, injection into underground reservoirs and long-term monitoring. Qatar is very well aware of the adverse consequences of the rise of CO2 emissions and therefore started investing heavily in carbon capture and Storage and took major steps to mitigate the negative impact of CO2. It is believed that CCS would provide a major shift in mitigation the CO2 emissions. This paper will analyze the potential of deployment of CCS in Qatar as well as the efforts expended so far in that regard.

Herein, we describe the batch and fixed-bed column adsorption of Cu2+ and Pb2+ by raw and treated date palm leaves (DP) and orange peel (OP) waste biomass. Contact time, pH, adsorbent dose, and particle size were optimized in batch adsorption experiments, while breakthrough curves obtained in fixed-bed adsorption experiments were used to determine the effects of bed height, initial metal concentration, particle size, and flow rate. The use of treated DP and/or OP in batch adsorption mode increased the removal efficiency of metal ions by 20–30% compared to that observed for raw adsorbents. The equilibration time was estimated as 0.5 h, with rapid metal removal observed during the first 15 min at an optimum pH value of ~5. Increasing the adsorbent dose from 0.5 to 6–7 g enhanced the metal removal efficiency by ~60%, whereas a particle size increase from 50 to 300 µm decreased this value by about 30% for both Cu2+ and Pb2+ and both raw and treated DP/OP. Both breakthrough and exhaust times increased with increasing bed height of the fixed-bed column, and the effect observed for treated DP exceeded that observed for raw DP by a factor of two. Conversely, both breakthrough and exhaust times decreased with increasing initial metal concentration, particle size, and flow rate. Increasing the particle size from 100–150 to 300 µm changed the exhaust time by 8 h when treated DP was used for Pb2+ adsorption. The obtained linear regression coefficients (R2 = 0.9–0.99) suggest that both Thomas and Yoon–Nelson models are well-suited for predicting the adsorption performance of the present system.

For any river basin management, one needs tools to predict runoff at different time and spatial resolutions. Hydrological models are tools which account for the storage, flow of water and water balance in a watershed, which include exchanges of water and energy within the earth, atmosphere and oceans and utilise metrological data to generate flow. There are several sources of error in meteorological data, namely, through measurement at point level, interpolation, etc. When an erroneous input is passed to a model, one cannot expect an error free output from the prediction. Every prediction is associated with uncertainty. Quantification of these uncertainties is of prime importance in real world forecasting. In this study, an attempt has been made to study uncertainty associated with hydrological modelling, using the idea of data depth. To see the effect of uncertainty in rainfall on flow generation through a model, the input to a model was altered by adding an error and a different realisation was made. A Monte Carlo simulation generated a large number of hydrological model parameter sets drawn from the uniform distribution. The model was run using these parameters for each realisation of the rainfall. The parameters which are good for different realisations are more likely to be good parameters sets. For each parameter set, data depth was calculated and a likelihood was assigned to each parameter set based on the depth values. Based on this, the frequency distribution of the likelihood was analysed as well. The results show that uncertainty in hydrological modelling are multiplicative. The proposed methodology to assign prediction uncertainty is demonstrated using the ‘TopNet’ model for the Waipara river catchment located in the central east of the South Island, New Zealand. The results of this study will be helpful in calibration of hydrological model and in quantifying uncertainty in the prediction.