Climate change poses a challenge to the security and long-term viability of the global food supply chain. Climate unpredictability and extreme weather events have significant impacts on Saudi Arabia’s vulnerable food system, which is already under stress. The Kingdom of Saudi Arabia faces distinct challenges in comparison to other dry locations across the world. Here, the per capita water demand is high, the population is growing, the water resources are extremely limited, and there is little information on the existing groundwater supplies. Consequently, it is anticipated that there will be formidable obstacles in the future. In order to make data-driven decisions, policymakers should be aware of causal links. The complex concerns pertaining to the Saudi Arabian food system were analyzed and rationally explained in the current study. A causality analysis examined different driving factors, including temperature, greenhouse gas (GHG) emission, population, and gross domestic product (GDP) that cause vulnerabilities in the country’s food system. The results of the long-run causality test show that GDP has a positive causal relationship with the demand for food, which implies that the demand for food will increase in the long run with an increase in GDP. The result also shows that Saudi Arabia’s GDP and population growth are contributing to the increase in their total GHG emissions. Although the Kingdom has made some efforts to combat climate change, there are still plenty of opportunities for it to implement some of the greatest strategies to guarantee the nation’s food security. This study also highlights the development of appropriate policy approaches to diversify its import sources to ensure future food security.

Considering hydro-climatic diversity, integrating dynamic dimensions of water security modeling is vital for ensuring environmental sustainability and its associated full range of climate resilience. Improving climate resiliency depends on the attributing uncertainty mechanism. In this study, a conceptual resilience model is presented with the consideration of input uncertainty. The impact of input uncertainty is analyzed through a multi-model hydrological framework. A multi-model hydrological framework is attributed to a possible scenario to help apply it in a decision-making process. This study attributes water security modeling with the considerations of sustainability and climate resilience using a high-speed computer and Internet system. Then, a subsequent key point of this investigation is accounting for water security modeling to ensure food security and model development scenarios. In this context, a four-dimensional dynamic space that maps sources, resource availability, infrastructure, and vibrant economic options is essential in ensuring a climate-resilient sustainable domain. This information can be disseminated to farmers using a central decision support system to ensure sustainable food production with the application of a digital system.

Corrosion of steel reinforcement is still the main reason reinforced concrete structures fail before the end of their planned service life. Every early repair costs money, and it also adds new cement, steel, and emissions. This review examines protection methods that can extend service life while lowering environmental impact. It covers green and nano-enabled corrosion inhibitors, hydrophobic and polymer-modified surface treatments, autogenous, bacterial, and capsule-based self-healing systems, nanomaterials, and stimulus-responsive smart coatings. It also covers supplementary cementitious materials, limestone calcined clay cement, and alkali-activated binders as the first barrier against chlorides and carbon dioxide. The review then links these materials to durability-based design, probabilistic service-life models, life cycle assessment, machine learning, and sensor-based monitoring. Three findings stand out. First, most green inhibitors and smart coatings have been tested in pore solution or in short accelerated tests, and long-term data in real concrete are scarce. Second, a gain in chloride resistance only counts as a sustainability gain when it is expressed per year of service life, including maintenance. Third, data-driven models predict chloride transport well inside their training data, but few of them respect physical limits or report uncertainty. We grade each method by the evidence behind it, from pore-solution tests to long-term field use, and by its technology maturity. Most of the newer methods have not yet left the laboratory. A worked example shows that holding the chloride diffusion coefficient at its 28-day value understated the time to corrosion initiation of fly ash and slag concretes by a factor of about 4 to 7. Here, sustainability refers mainly to climate impact, expressed as global warming potential per year of service life. We propose a design loop with two decision gates. Unlike existing service-life or life cycle frameworks, it checks reliability and climate impact together, and it gives credit only to methods with measured inputs in mortar or concrete. It connects binder choice, added protection, probabilistic service-life prediction, and life cycle metrics, and it updates itself with monitoring data. The review closes with a ranked list of research gaps and practical steps for researchers, designers, and asset owners.

Indoor formaldehyde (HCHO) vapor and aqueous hexavalent chromium (Cr(VI)) are pervasive pollutants typically addressed with distinct, purpose-specific materials. In this study, a ternary Cu–Fe–Zn oxide (CuFeZnO) was synthesized via co-precipitation and evaluated as a dual-function material for both photocatalytic HCHO degradation and Cr(VI) adsorption. The catalyst exhibited a narrowed optical band gap of 2.3 eV, compared to 3.2 eV for TiO₂, consistent with enhanced visible-light absorption. Under low-intensity visible irradiation (single 18 W daylight lamp, 8.8 mW cm⁻²), 20 mg of CuFeZnO achieved 92.1% HCHO removal within 2 h, outperforming commercial TiO₂ (P25, 78.2%) and laboratory-synthesized TiO₂ (68.3%) under identical conditions, and retaining 90.8% conversion after seven reuse cycles. For Cr(VI) removal, CuFeZnO achieved 98.98% removal from a 50 mg L⁻¹ solution at pH 8 with a 0.2 g dose. Adsorption kinetics followed a pseudo-second-order model, and equilibrium data were best described by the Freundlich isotherm (R² = 0.992). A double-layer statistical physics model further captured the temperature-dependent equilibrium behavior (298–318 K) with high accuracy (R² = 0.997–0.998), yielding a near-unimolecular site occupancy (n ≈ 0.999 at 298 K) and a maximum adsorption capacity of 111.7 mg g⁻¹. The first-layer adsorption energy increased from 13.4 to 27.6 kJ mol⁻¹ with rising temperature, indicating a physisorption-dominated mechanism. Notably, CuFeZnO achieved comparable or superior HCHO conversion under substantially lower light intensity (18 W) than reported photocatalysts requiring 27–300 W sources. These results demonstrate CuFeZnO as a low-energy, reusable, and multifunctional material for simultaneous air and water remediation.

Basalt Fiber-Reinforced Concrete (BFRC) is a sustainable building material characterized by high strength, crack resistance, and durability. Because its mechanical properties involve intricate, nonlinear interactions among binder constituents, aggregates, water content, and fiber characteristics, traditional empirical mix design methods are often inefficient. Current literature largely focuses on single-property prediction using individual machine learning (ML) models, with limited integration of evolutionary optimization, Explainable AI (XAI), and practical deployment tools—leaving significant gaps in real-world applicability. To address this, this research presents a novel, integrated evolutionary ML framework for the accurate prediction and multi-objective optimization of BFRC Compressive Strength (CS) and Splitting Tensile Strength (STS). Three regression models—Random Forest, Gradient Boosting Regressor, and XGBoost—were developed and coupled with evolutionary optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE). The trade-offs between CS and STS were further evaluated through multi-objective optimization using the NSGA-II algorithm and Pareto front analysis. Additionally, SHapley Additive exPlanations (SHAP) were utilized to enhance model interpretability, revealing that cement content, water content, and fiber volume fraction are the predominant contributors to strength. The optimized models achieved high predictive accuracy, yielding $R^2$ values of 0.88 for CS and 0.95 for STS, alongside low prediction errors (Root Mean Square Error < 2.80 MPa and 0.29 MPa; Mean Absolute Error < 2.10 MPa and 0.19 MPa, respectively). Finally, a graphical user interface (GUI) was developed to enable real-time prediction and mix optimization, providing a practical decision-support tool for performance-driven and sustainable BFRC mix design.

Basalt fiber-reinforced concrete (BFRC), reinforced with chopped basalt fibers having lengths ranging from 12 to 30 mm and diameters ranging from 0.013 to 0.020 mm, is a sustainable construction material with enhanced strength and durability; however, its complex nonlinear behavior makes accurate prediction and optimal mix design challenging. This study proposes an integrated machine learning framework combining evolutionary optimization, multi-objective optimization, and explainable artificial intelligence (XAI) for BFRC strength prediction and mix design optimization. The proposed framework further incorporates a graphical user interface (GUI) deployment to enhance practical usability and support engineering decision-making. Random Forest, Gradient Boosting Regressor, and XGBoost models were optimized using Genetic Algorithms, Particle Swarm Optimization, and Differential Evolution, while NSGA-II was employed to identify optimal trade-offs between compressive strength and splitting tensile strength. SHAP analysis was applied to interpret the influence of key mix parameters on strength prediction. The optimized models achieved high prediction accuracy, with R² values of 0.88 for compressive strength and 0.95 for splitting tensile strength, demonstrating the effectiveness of the proposed framework. The developed GUI provides a practical decision-support tool for sustainable and performance-oriented BFRC mix design.