Industrial developments in the oil and gas, petrochemical, pharmaceutical and food sector have contributed to the large production of oily wastewater worldwide. Oily wastewater pollution affects drinking water and groundwater resources, endangers aquatic life and human health, causes atmospheric pollution, and affects crop production. Several traditional and conventional methods were widely reported, and the advantages and limitations were discussed. However, with the technology innovation, new trends of coupling between techniques, use of new materials, optimization of the cleaning process, and multiphysical approach present new paths for improvement. Despite these trends of improvement and the encouraging laboratory results of modern and green methods, many challenges remain to be raised, particularly the commercialization and the global aspect of these solutions and the reliability to reduce the system’s maintenance and operational cost. In this review, the well-known oily wastewater cleaning methods and approaches are being highlighted, and the obstacles faced in the practical use of these technologies are discussed. A critical review on the technologies and future direction as the road to commercialization is also presented to persevere water resources for the benefit of mankind and all living things.

The novel coronavirus COVID‐19 is spreading all across the globe. By June 29, 2020, the World Health Organization announced that the number of cases worldwide had reached 9 994 206 and resulted in more than 499 024 deaths. The earliest case of COVID‐19 in the Kingdom of Saudi Arabia (KSA) was registered on March 2 in 2020. Since then, the number of infections as per the outcome of the tests increased gradually on a daily basis. The KSA has 182 493 cases, with 124 755 recoveries and 1551 deaths on June 29, 2020. There have been significant efforts to develop models that forecast the risks, parameters, and impacts of this epidemic. These models can aid in controlling and preventing the outbreak of these infections. In this regard, this article details the extent to which the infection cases, prevalence, and recovery rate of this pandemic are in the country and the predictions that can be made using the past and current data. The well‐known classical SIR model was applied to predict the highest number of cases that may be realized and the flattening of the curve afterward. On the other hand, the ARIMA model was used to predict the prevalence cases. Results of the SIR model indicate that the repatriation plan reduced the estimated reproduction number. The results further affirm that the containment technique used by Saudi Arabia to curb the spread of the disease was efficient. Moreover, using the results, close interaction between people, despite the current measures remains a great risk factor to the spread of the disease. This may force the government to take even more stringent measures. By validating the performance of the applied models, ARIMA proved to be a good forecasting method from current data. The past data and the forecasted data, as per the ARIMA model provided high correlation, showing that there were minimum errors.

Over the last twenty years, researchers have focused on the potential applications of electrospinning, especially its scalability and versatility. Specifically, electrospun nanofiber scaffolds are considered an emergent technology and a promising approach that can be applied to biosensing, drug delivery, soft and hard tissue repair and regeneration, and wound healing. Several parameters control the functional scaffolds, such as fiber geometrical characteristics and alignment, architecture, etc. As it is based on nanotechnology, the concept of this approach has shown a strong evolution in terms of the forms of the materials used (aerogels, microspheres, etc.), the incorporated microorganisms used to treat diseases (cells, proteins, nuclei acids, etc.), and the manufacturing process in relation to the control of adhesion, proliferation, and differentiation of the mimetic nanofibers. However, several difficulties are still considered as huge challenges for scientists to overcome in relation to scaffolds design and properties (hydrophilicity, biodegradability, and biocompatibility) but also in relation to transferring biological nanofibers products into practical industrial use by way of a highly efficient bio-solution. In this article, the authors review current progress in the materials and processes used by the electrospinning technique to develop novel fibrous scaffolds with suitable design and that more closely mimic structure. A specific interest will be given to the use of this approach as an emergent technology for the treatment of bacteria and viruses such as COVID-19.

Sterilization methods for individuals and facilities are extremely important to enable human beings to continue the basic tasks of life and to enable safe and continuous interaction of citizens in society when outbreaks of viral pandemics such as the coronavirus. Sterilization methods, their availability in gatherings, and the efficiency of their work are among the important means to contain the spread of viruses and epidemics and enable societies to practice their activities almost naturally. Despite the effective solutions given by traditional methods of surface disinfection, modern nanotechnology has proven to be an emergent innovation to protect against viruses. On this note, recent scientific breakthroughs have highlighted the ability of nanospray technology to attach to air atoms in terms of size and time-period of existence as a sterilizer for renewed air in large areas for human gatherings. Despite the ability of this method to control the outbreak of infections, the mutation of bactericidal mechanisms presents a great issue for scientists. In recent years, science has explored a more performant approach and techniques based on a surface-resistance concept. The most emergent is the self-defensive antimicrobial known as the self-disinfection surface. It consists of the creation of a bacteria cell wall to resist the adhesion of bacteria or to kill bacteria by chemical or physical changes. Besides, plasma-mediated virus inactivation was shown as a clean, effective, and human healthy solution for surface disinfection. The purpose of this article is to deepen the discussion on the threat of traditional methods of surface disinfection and to assess the state of the art and potential solutions using emergent nanotechnology.

Drought is a severe environmental disaster that results in significant social and economic damage. As such, efficient mitigation plans must rely on precise modeling and forecasting of the phenomenon. This study was designed to enhance drought forecasting through developing and evaluating the applicability of three hybrid models—the hidden Markov model–genetic algorithm (HMM–GA), the auto-regressive integrated moving average–genetic algorithm (ARIMA–GA), and a novel auto-regressive integrated moving average–genetic algorithm–ANN (ARIMA–GA–ANN)—to forecast the standard precipitation index (SPI) in the Bisha Valley, Saudi Arabia. The accuracy of the models was investigated and compared with that of classical HMM and ARIMA based on a performance evaluation and visual inspection. Furthermore, the multi-class Receiver Operating Characteristic-based Area under the Curve (ROC–AUC) was applied to evaluate the ability of the hybrid model to forecast drought events. We used data from 1968 to 2008 to train the models and data from 2009 to 2019 for validation. The performance evaluation results confirmed that the hybrid models provided superior results in forecasting the SPI one month in advance. Furthermore, the results demonstrated that the GA-induced improvement in the HMM forecasts was matched by an approximate 16.40% and 23.46% decrease in the RMSE in the training and testing results, respectively, compared to the classical HMM model. Consequently, the RMSE values of the ARIMA–GA model were reduced by an average of 10.06% and 9.36% for the training and testing processes, respectively. Finally, the ARIMA–GA–ANN, which combined the strengths of the linear stochastic model ARIMA and a non-linear ANN, achieved a greater reduction values in RMSE by an average of 32.82% and 27.47% in comparison with ARIMA in the training and testing phases, respectively. The ROC–AUC results confirmed the capability of the developed models to distinguish between events and non-events with reasonable accuracy, implying the appropriateness of these models as a tool for drought mitigation and warning systems.