Suitability of treated wastewater for irrigation and its impact on groundwater resources in arid coastal regions: Insights for water resources sustainability

Water scarcity threatens agriculture and food security in arid regions like Saudi Arabia. The nation produces significant quantities of municipal wastewater, which, with adequate treatment, could serve as an alternative water source for irrigation, thereby reducing reliance on fossil and non-renewable groundwater. This study assessed the appropriateness of using treated wastewater (TWW) for irrigation in a dry coastal agricultural region in Eastern Saudi Arabia and its impact on groundwater resources. Field investigations were conducted in Qatif to collect water samples and field measurements. A multi-criteria approach was applied to evaluate the TWW's suitability for irrigation, including complying with Saudi Standards, the Irrigation Water Quality Index (IWQI), the National Sanitation Foundation water quality index (NSFWQI), and the individual irrigation indices. In addition, the impact of TWW on groundwater was assessed through hydrogeological and isotope approaches. The results indicate that the use of TWW in the study area complied with the Saudi reuse guidelines except for nitrate, aluminum, and molybdenum. However, irrigation water quality indices classify TWW as having limitations that necessitate the use for salt-tolerant crops on permeable and well-drained soils. Stable isotopic analysis (δ2H, δ18O) revealed that long-term irrigation with TWW affected the shallow aquifer, while deep aquifers were minimally impacted due to the presence of aquitard layer. The application of TWW irrigation has successfully maintained groundwater sustainability in the study area, as evidenced by increased groundwater levels up to 2.3 m. Although TWW contributes to crop productivity, long term agricultural sustainability could be enhanced by improving effluent quality, regulating irrigation practices, implementing buffer zones, and monitoring shallow groundwater. An integrated approach that combines advanced wastewater treatment methods, community involvement, regulatory oversight, and targeted monitoring is recommended to be implemented.

Insight into soft chemometric computational learning for modelling oily-wastewater separation efficiency and permeate flux of polypyrrole-decorated ceramic-polymeric membranes

Reliable modeling of oily wastewater emphasizes the paramount importance of sustainable and health-conscious wastewater management practices, which directly aligns with the Sustainable Development Goals (SDG) while also meeting the guidelines of the World Health Organization (WHO). This research explores the efficiency of utilizing polypyrrole-coated ceramic-polymeric membranes to model oily wastewater separation efficiency (SE) and permeate flux (PF) based on established experimental procedures. In this area, computational simulation still needs to be explored. The study developed predictive regression models, including robust linear regression (RLR), stepwise linear regression (SWR) and linear regression (LR) for the ceramic-polymeric porous membrane, aiming to interpret its complex performance across diverse conditions and, thus, develop its utility in oily wastewater treatment applications. Subsequently, a novel, simple average ensemble paradigm was explored to reduce errors and improve prediction skills. Prior to the development of the model, stability and reliability analysis of the data was conducted based on Philip Perron tests with the Bartlett kernel estimation method. The accuracy of the SE exhibited a high consistency, averaging 99.92% with minimal variability (standard deviation of 0.026%), potentially simplifying its prediction compared to PF. The modes were validated and evaluated using metrics like MAE, RMSE, Speed, and MSE, in addition to 2D graphical and cumulative distribution function graphs. The LR model emerged as the best with the lowest RMSE =0.21951, indicating superior prediction accuracy, followed closely by RLR with an RMSE = 0.22359. SWLR, while having the highest RMSE = 0.34573, marked its dominance in prediction speed with 110 observations per second. Notably, the RLR model justified a reduction in error by approximately 35.29% compared to SWLR. Moreover, the training efficiency of the LR model exceeded, demanding a mere 2.9252 s, marking a reduction of about 32.54% compared to SWLR. The improved simple ensemble learning proved merit over the three models regarding error accuracy. This study emphasizes the essential role of soft-computing learning in optimizing the design and performance of ceramic-polymeric membranes.

Photoluminescence and thermoluminescence dosimetry properties of Ag/Y co-doped ZnO nanophosphor for radiation measurements

This work explores the thermoluminescence (TL) and photoluminescence (PL) properties of Ag/Y co-doped zinc oxide (ZnO) nanophosphor. The proposed dosimeter was prepared by the coprecipitation method and sintered at temperatures from 400°C to 1000°C in an air atmosphere. Raman spectroscopy was studied to investigate the structural features of this composition. The new proposed dosimeter revealed two peaks at 150°C and 175°C with a small shoulder at high temperature (225°C). The PL spectrum showed strong green emissions between 500 to 550 nm. The Raman spectrum showed many bands related to the interaction between ZnO, silver (Ag), and yttrium oxide (Y2 O3 ). The rising sintering temperature enhanced the TL glow curve intensity. The Ag/Y co-doped ZnO nanophosphor showed an excellent linearity index within a dose from 1 to 4 Gy. The minimum detectable dose (MDD) of the Ag/Y co-doped ZnO nanopowder (pellets) equaled 0.518 mGy. The main TL properties were achieved in this work as follows: thermal fading (37% after 45 days at 1 and 4 Gy), optical fading (53% after 1 h and 68% after 6 h by exposure to sunlight), effective atomic number (27.6), and energy response (flat behavior from 0.1 to 1.3 MeV). Finally, the proposed material shows promising results nominated to be used for radiation measurements.

Development of Ag-Doped ZnO Thin Films and Thermoluminescence (TLD) Characteristics for Radiation Technology

This work examined the thermoluminescence dosimetry characteristics of Ag-doped ZnO thin films. The hydrothermal method was employed to synthesize Ag-doped ZnO thin films with variant molarity of Ag (0, 0.5, 1.0, 3.0, and 5.0 mol%). The structure, morphology, and optical characteristics were investigated using X-ray diffraction (XRD), scanning electron microscope (SEM), energy-dispersive X-ray spectroscopy (EDX), photoluminescence (PL), and UV-vis spectrophotometers. The thermoluminescence characteristics were examined by exposing the samples to X-ray radiation. It was obtained that the highest TL intensity for Ag-doped ZnO thin films appeared to correspond to 0.5 mol% of Ag, when the films were exposed to X-ray radiation. The results further showed that the glow curve has a single peak at 240-325 °C, with its maximum at 270 °C, which corresponded to the heating rate of 5 °C/s. The results of the annealing procedures showed the best TL response was found at 400 °C and 30 min. The dose-response revealed a good linear up to 4 Gy. The proposed sensitivity was 1.8 times higher than the TLD 100 chips. The thermal fading was recorded at 8% for 1 Gy and 20% for 4 Gy in the first hour. After 45 days of irradiation, the signal loss was recorded at 32% and 40% for the cases of 1 Gy and 4 Gy, respectively. The obtained optical fading results confirmed that all samples' stored signals were affected by the exposure to sunlight, which decreased up to 70% after 6 h. This new dosimeter exhibits good properties for radiation measurement, given its overgrowth (in terms of the glow curve) within 30 s (similar to the TLD 100 case), simple annealing procedure, and high sensitivity (two times that of the TLD 100).