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Global groundwater assessments rank Iran among countries with the highest groundwater depletion rate using coarse spatial scales that hinder detection of regional imbalances between renewable groundwater supply and human withdrawals. Herein, we use in situ data from 12,230 piezometers, 14,856 observation wells, and groundwater extraction points to provide ground-based evidence about Iran’s widespread groundwater depletion and salinity problems. While the number of groundwater extraction points increased by 84.9% from 546,000 in 2002 to over a million in 2015, the annual groundwater withdrawal decreased by 18% (from 74.6 to 61.3 km³/y) primarily due to physical limits to fresh groundwater resources (i.e., depletion and/or salinization). On average, withdrawing 5.4 km³/y of nonrenewable water caused groundwater tables to decline 10 to 100 cm/y in different regions, averaging 49 cm/y across the country. This caused elevated annual average electrical conductivity (EC) of groundwater in vast arid/semiarid areas of central and eastern Iran (16 out of 30 subbasins), indicating “very high salinity hazard” for irrigation water. The annual average EC values were generally lower in the wetter northern and western regions, where groundwater EC improvements were detected in rare cases. Our results based on high-resolution groundwater measurements reveal alarming water security threats associated with declining fresh groundwater quantity and quality due to many years of unsustainable use. Our analysis offers insights into the environmental implications and limitations of water-intensive development plans that other water-scarce countries might adopt.

Genetic programming (GP) is an evolutionary regression method that has received considerable interest to model hydro-environmental phenomena recently. Considering the sparseness of hydro-meteorological stations on northern areas, this study investigates the benefits and downfalls of univariate streamflow modeling at high latitudes using GP and seasonal autoregressive integrated moving average (SARIMA). Furthermore, a new evolutionary time series model, called GP-SARIMA, is introduced to enhance streamflow forecasting accuracy at long-term horizons in a lake-river system. The paper includes testing the new model for one-step-ahead forecasts of daily mean, weekly mean, and monthly mean streamflow in the headwaters of the Oulujoki River, Finland. The results showed that a combination of correlogram and average mutual information (AMI) analysis might yield in the selection of the optimum lags that are needed to be used as the predictors of streamflow models. With Nash-Sutcliffe efficiency values of more than 99%, both GP and SARIMA models exhibited good performance for daily streamflow prediction. However, they were not able to precisely model the intramonthly snow water equivalent in the long-term forecast. The proposed ensemble model, which integrates the best GP and SARIMA models with the most efficient predictor, may eliminate one-fourth of root mean squared errors of standalone models. The GP-SARIMA also showed up to three times improvement in the accuracy of the standalone models based on the Nash-Sutcliff efficiency measure.

Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models based on historical urban flood events, using, e.g., metaheuristic optimization algorithms and wavelet analysis. The hybridized models examined in this study (Wavelet-SVR-Bat and Wavelet-SVR-GWO), designed as intelligent systems, consist of a support vector regression (SVR), integrated with a combination of wavelet transform and metaheuristic optimization algorithms, including the grey wolf optimizer (GWO), and the bat optimizer (Bat). The efficiency of the novel hybridized and standalone SVR models for spatial modeling of urban flood inundation was evaluated using different cutoff-dependent and cutoff-independent evaluation criteria, including area under the receiver operating characteristic curve (AUC), Accuracy (A), Matthews Correlation Coefficient (MCC), Misclassification Rate (MR), and F-score. The results demonstrated that both hybridized models had very high performance (Wavelet-SVR-GWO: AUC = 0.981, A = 0.92, MCC = 0.86, MR = 0.07; Wavelet-SVR-Bat: AUC = 0.972, A = 0.88, MCC = 0.76, MR = 0.11) compared with the standalone SVR (AUC = 0.917, A = 0.85, MCC = 0.7, MR = 0.15). Therefore, these hybridized models are a promising, cost-effective method for spatial modeling of urban flood susceptibility and for providing in-depth insights to guide flood preparedness and emergency response services.

Accurate climate projections help policymakers mitigate the negative effects of climatic changes and prioritize environmental issues based on scientific evidences. These projections rely heavily on the outputs of GCMs (General Circulation Models), but the large number of GCMs and their different outputs in each region confuses researchers in their selection. In this paper, we analyzed the performance of a CMIP6 (Climate Model Intercomparison Project Phase 6) multi-model ensemble for Pr (precipitation) data over NE (Northern Europe). First of all, we evaluated the overall performance of 12 CMIP6 models from GCMs in 30 years of 1985–2014. Furthermore, future projections were analyzed between 2071 and 2100 using SSP1-2.6 and SSP5-8.5 (Shared Socioeconomic Pathways). Then, simulations were statistically improved using an ensemble method to correct the systematic error of the CMIP6 models and then the capacity of postprocessed data to reproduce historical trends of climate events was investigated. Finally, the possible spatio-temporal changes of future Pr data were explored in Tana River Basin. The results of this study show that different CMIP6 models do not have the same accuracy in estimating Pr in the study area. However, the ensemble method can be effective in increasing the accuracy of the projections. The results of this study projected a change in the monthly Pr data over Tana River Basin by 2.46% and 2.06% from 2071 to 2100 compared to the historical period, based on SSP1-2.6 and SSP5-8.5, respectively.

Streamflow contemplates a fundamental criterion to evaluate the impact of human activities and climate changes on the hydrological cycle. In this study, a novel innovative deep neural network (DNN) structure by integrating a double Gated Recurrent Units (GRU) neural network model with a multiplication layer and meta-heuristic whale optimization algorithm (WOA) (i.e., hybrid 2GRU×–WOA model) is developed to improve the prediction accuracy and performance of mean monthly Chehel-Chai River’s streamflow ( CCRSF m ) in Iran. The Pearson’s correlation coefficient (PCC) and Cosine Amplitude Sensitivity (CAS) as feature (input) selection process determine the only precipitation ( P m ) as the most effective input variable among a list of on-site potential climate time series parameters recorded in the study area. Thanks to a well-proportioned layer network structural framework in the suggested hybrid 2GRU×–WOA model, it leads to an appropriate total learnable parameter ( TLP ) compared to standard individual GRU and Bi-GRU as the benchmark models developed in the comparable meta-parameters. This hybrid model under the optimal meant meta-parameters tuned i.e., coupling a state activation functions ( SAF ) of tanh-softsign , dropout rate ( P-rate ) of 0.5, numbers of hidden neurons ( NHN ) of 70, outperforms with an R 2 of 0.79, NSE of 0.76, MAE of 0.21 (m 3 /s), MBE of -0.11(m 3 /s), and RMSE of 0.36 (m 3 /s). Hybridizing the 2GRU× model with WOA algorithm causes to increase in the value of R 2 by 6.8% and reduce in the value of RMSE by 20.4%. Comparatively, standard individual GRU and Bi-GRU models result in an R 2 of 0.59 and 0.66, NSE of 0.55 and 0.6, MAE of 0.91 and 0.53 (m 3 /s), MBE of 0.047 and − 0.06 (m 3 /s), RMSE of 1.29 and 0.83 (m 3 /s), respectively.

The current research aimed to study and measure the solubility of omeprazole (used to treat and reduce stomach acid) for the first time in pure subcritical and ethanol-modified water. These tests have been performed in the 25–130 °C temperature range and constant pressure of 20 bar with the use of 0–10% ethanol weight as the cosolvent. The solubility of omeprazole in subcritical water ranged from 0.0697 × 10 –4 to 5.843 × 10 –4  mol fraction at temperatures from 25 to 130 °C, respectively. The highest solubility was obtained at 130 °C with a 10% ethanol weight as the cosolvent, which is indicative of a significant trend of improvement in omeprazole solubility with the increase in temperature and the use of ethanol cosolvent in various weight percentages. The solubility experimental data obtained were fitted using the semi-experimental and modified Apelblat linear model, and the findings revealed an excellent correlation between the experimental data and the data received from the linear and modified Apelblat model. Also, at the 25–130 °C temperature range, no degradation, phase transition, or other changes in the structure and physical state of the drug were observed.

This study investigates the performance of CMIP6 models in reproducing historical temperature and precipitation data for Iran and neighboring countries (Afghanistan, Pakistan, Turkmenistan, Azerbaijan, Armenia, Turkey, and Iraq) from 1980 to 2014. Reanalysis data from the ECMWF database (ERA5) for temperature and precipitation were utilized as a reference for the period 1980-2014. Additionally, ten Atmosphere-Ocean General Circulation Models (AOGCMs) from CMIP6 were employed to simulate temperature and precipitation data for the study region based on the IPCC Sixth Assessment Report databases. The Kling-Gupta Efficiency (KGE) index was used to evaluate the accuracy of CMIP6 models in replicating daily temperature and precipitation. The results indicate that different CMIP6 models exhibit varying degrees of accuracy in simulating historical temperatures and precipitation, depending on the month and the country. For instance, the IPSL-CM6A-LR model demonstrated the best annual performance in estimating temperature in Azerbaijan (KGE = 0.5), while the HadGEM3-GC31-LL model showed the lowest annual performance in Pakistan (KGE = -1.4). Interestingly, the models were found to be more accurate in simulating temperatures during warm months compared to cold ones. Furthermore, the accuracy of different models in estimating annual precipitation varied significantly, ranging from -0.64 (MRI-EMS2-0 model in Afghanistan) to 0.05 (CMCC-ESM2 model in Armenia). Similar to temperature, the study found that models were generally more accurate in simulating precipitation during cold months compared to warm ones.

Proton exchange membrane fuel cells (PEMFC) have received a lot of interest and use metal–organic frameworks (MOF)/polymer nanocomposite membranes. Zeolite imidazole framework-90 (ZIF-90) was employed as an addition in the sulfonated poly (1, 4-phenylene ether-ether-sulfone) (SPEES) matrix in order to investigate the proton conductivity in a novel nanocomposite membrane made of SPEES/ ZIF. The high porosity, free surface, and presence of the aldehyde group in the ZIF-90 nanostructure have a substantial impact on enhancing the mechanical, chemical, thermal, and proton conductivity capabilities of the SPEES/ZIF-90 nanocomposite membranes. The results indicate that the utilization of SPEES/ZIF-90 nanocomposite membranes with 3wt% ZIF-90 resulted in enhanced proton conductivity of up to 160 mS/cm at 90 °C and 98% relative humidity (RH). This is a significant improvement compared to the SPEES membrane which exhibited a proton conductivity of 55 mS/cm under the same conditions, indicating a 1.9-fold increase in performance. Furthermore, the SPEES/ZIF-90/3 membrane exhibited a remarkable 79% improvement in maximum power density, achieving a value of 0.52 W/cm 2 at 0.5 V and 98% RH, which is 79% higher than that of the pristine SPEES membrane.

Previous studies examining climate change and changes in the timing of seasons have used a fixed temperature threshold for season onset. In this study, the timing of seasons was determined using non-fixed threshold methods. Twelve new timing indices were defined to account for shifts in seasons and season onset day, thermal centroid day, and length. The Mann-Kendall test, Theil-Sen’s slope estimator, sequential Mann-Kendall test, and least square linear regression were used to assess trends. The timing indices were examined using data from two meteorological stations in Iran with 50 years of records. Spatio-temporal variations in each index over 30 years (1987–2016) were then determined for Khuzestan province in southwestern Iran. Trend analysis for several indices indicated that the timing of seasons had probably changed in the south and west of the study area, while mountainous regions showed non-significant trends. Based on the hottest and coldest 90-day periods (summer and winter, respectively), during the three decades studied, spring lengthened by 5–10 days/decade in the plain region of Khuzestan province and autumn shortened by about 5–8 days/decade. The centroid of winter occurred earlier, by 2–5 days/decade, in the plains area, while the thermal centroid of summer did not change significantly. Overall, the difference between the thermal centroid of winter and summer (Cwin-sum) in the plains area significantly decreased, by 6–8 days/decade, in the 30-year period.

Traditional solvent extraction (SX) procedures limit metal separation and purification, which consist of the organic and aqueous phases. Because differences in metal ion solvation lead to distinct distribution properties, non-aqueous solvent extraction (NASX) considerably expands the scope of solvent extraction by replacing the aqueous phase with alternate polar solvents. In this study, an experimental design approach used non-aqueous solvent extraction to extract cobalt from zinc plant residue. The aqueous phase comprises ethylene glycol (EG), LiCl and metal ions. In kerosene, D2EHPA, Cyanex272, Cyanex301, and Cyanex302 extractants were used as a less polar organic phase. Various factors were investigated to see how they affected extraction, including solvent type, extractant type and phase ratio, pH, Co(II) concentration, and temperature. The results revealed that at a concentration of 0.05 M, the Cyanex301 extractant could achieve the requisite extraction efficiency in kerosene. The optimal conditions were chosen as the concentration of Cyanex 301 (0.05 M), the concentration of cobalt (833 ppm), the pH (3.5), and the percent of EG (80%). As a result, during the leaching process, these systems are advised for extracting and separating a combination of various metal ions.