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It is challenging to use conventional one-variable-at-time (OVAT) batch experiments to evaluate multivariate/inter-parametric interactions between physico-chemical variables that contribute to the adsorptive removal of contaminants. Thus, chemometric prediction approaches for multivariate calibration and analysis reveal the impact of multi-parametric variation on the process of concern. Hence, we aim to develop an artificial neural network (ANN), and stepwise regression (SR) models for multivariate calibration and analysis utilizing OVAT data prepared through experimentation. After comparing the models’ performance, ANN was the superior model for this application in our work. The standard deviations (SD) between the observed and ANN-predicted values were very close. The average correlation coefficient (R2) between observed and ANN-predicted values for the training dataset was 96.9%. This confirms the ability of our developed ANN model to forecast lindane removal accurately. The testing dataset correlation coefficients (89.9% for ANN and 67.75% for SR) demonstrated a better correlation between observed and predicted ANN values. The ANN model training and testing dataset RMSE values were 1.482 and 2.402, lower than the SR values of 4.035 and 3.890. The MAPE values for the ANN model’s training and testing datasets, 0.018 and 0.031, were lower than those for the SR model. The training and testing datasets have low RSR and PBIAS values, implying model strength. The R2 and WIA values are above 0.90 for both datasets, proving the ANN model’s accuracy. Applying our developed ANN model will reduce the cost of removing inorganic and organic impurities, including lindane, and optimize chemical utilization.

The ongoing COVID-19 pandemic has caused unprecedented public health concern in Bangladesh. This study investigated the role of Non-Pharmaceutical Interventions on COVID-19 transmission and post-lockdown scenarios of 64 administrative districts and the country as a whole based on the spatiotemporal variations of effective reproduction number (Rt) of COVID-19 incidences. The daily confirmed COVID-19 data of Bangladesh and its administrative districts from March 8, 2020, to March 10, 2021, were used to estimate Rt. This study finds that the maximum value of Rt reached 4.15 (3.43, 4.97, 95% CI) in late March 2020, which remained above 1 afterwards in most of the districts. Containment measures are moderately effective in reducing transmission by 24.03%. The Rt was established below 1 from early December 2020 for overall Bangladesh and a gradual increase of Rt above 1 has been seen from early February 2021. The basic reproduction number (R0) in Bangladesh probably varied around 2.02 (1.33–3.28, 95% CI). This study finds a significant positive correlation (r = 0.75) between population density and COVID-19 incidence and explaining 56% variation in Bangladesh. The findings of this study are expected to support the policymakers to adopt appropriate measures for curbing the COVID-19 transmission effectively.

Non-motorized transport (NMT) is the use of a bicycle or walking to travel from one place to another. It is gaining popularity especially in the developed countries due to low transport externalities such as emissions and traffic congestion alongside its benefits to physical and mental health. In this paper, a comprehensive review of the existing literature related to NMT is presented focusing on the factors including built environment, geography, and weather, the health, and environmental benefits of NMT, and the motivational approach for increasing the use of NMT. The built environment, geography and weather, and socioeconomic factors significantly affect the use of NMT as a travel mode. This study reviewed some unique characteristics of NMT especially in developing countries to provide a clear understanding of the dynamics of NMT. Despite existence of vast research on NMT, a comprehensive literature review to evaluate different aspects of NMT seems essential to address the future challenges with significant automobile ownership increase in the developing nations and the associated externalities. The developing nations have to understand the factors of NMT with reference to their socio-economic conditions and perform quantitative analyses to estimate and project benefits of NMT including health benefits. The policy makers in the developing countries should consider the NMT as one element of the solution matrix to address the challenges of road transport.

Climate change threatens the security and sustainability of the global food system. In Bangladesh, the fragile food system is strongly being impacted by climate variability and extreme weather events. Estimates indicate that the agricultural sector could lose about USD7.7 billion per annum due to climate change. The average annual rice production could decline by 33% within two decades. However, a few studies investigated the impacts of fluctuating temperature and rainfall, flash floods, drought, and saline water intrusion on the food system and security in an integrated manner. This study examines climate change impacts on food system security and sustainability in Bangladesh. It reviews the country’s food system, the climatic conditions that threaten food security, and impacts climate change on the food systems and the associated vulnerabilities. The study then assesses the existing adaptation initiatives and the extent of their integration among pertinent stakeholders. It concludes that local climate change adaptation strategies and stakeholder collaboration are necessary for reducing climate change impacts on food system security.

The Red Sea, lying between Asia and Africa, is a significant eco-region and the habitat of many marine species. It is an essential route for marine transportation, and it also provides many ecosystem services. Therefore, assessing the spatial and temporal distributions of air quality parameters over the Red Sea and assess temporal trends is important. This study investigated observations of multiple satellites to collect different meteorological and tropospheric columns of aerosols, trace gas including carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3), and meteorological data. The tropospheric columns of trace gases and aerosol index exhibit seasonal and regional characteristics over the Red Sea. The correlation coefficient for air pollutants (NO2, SO2, and O3) and meteorological parameters showed a positive medium and low correlation, whereas a negative correlation was found between O3, Aerosol Index (AI), and wind speed. The back trajectory transport of air mass component using Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model showed long-range trajectory pattern. AI, trace gases concentrations, and meteorological data were investigated through Principal Component Analysis (PCA) with varimax rotation to find a discerning relationship and co-variance between air pollutants and meteorological data. This showed a relationship between meteorological data and CO and O3. Although the satellite observations have some inherent limitations compared to in-situ measurements, this study provides an overall landscape of air pollution over the Red Sea, which will benefit policymakers and researchers.

The Kingdom of Saudi Arabia has been experiencing consistent growth in industrial processes and product use (IPPU). The IPPU’s emission has been following an increasing trend. This study investigated time-series and cross-sectional analyses of the IPPU sector. Petrochemical, iron and steel, and cement production are the leading source categories in the Kingdom. In recent years, aluminum, zinc, and titanium dioxide production industries were established. During the last ten years, a significant growth was observed in steel, ethylene, direct reduce iron (DRI), and cement production. The growth of this sector depends on many factors, including domestic and international demand, socioeconomic conditions, and the availability of feedstock. The emissions from IPPU without considering energy use was 78 million tons of CO2 equivalent (CO2eq) in 2020, and the cement industry was the highest emitter (35.5%), followed by petrochemical (32.3%) and iron and steel industries (16.8%). A scenario-based projection analysis was performed to estimate the range of emissions for the years up to 2050. The results show that the total emissions could reach between 199 and 426 million tons of CO2eq in 2050. The Kingdom has started initiatives that mainly focus on climate change adaptation and economic divergence with mitigation co-benefits. In general, the focus of such initiatives is the energy sector. However, the timely accomplishment of the Saudi Vision 2030 and Saudi Green Initiative will affect mitigation scenarios significantly, including in the IPPU sector. The mitigation opportunities for this sector include (i) energy efficiency, (ii) emissions efficiency, (iii) material efficiency, (iv) the re-use of materials and recycling of products, (v) intensive and longer use of products, and (vi) demand management. The results of this study will support the Kingdom in developing an appropriate climate change mitigation roadmap.

This study proposes a machine learning-based protocol for developing a TRANSYT-7F model for an urban arterial network. The developed artificial neural network (ANN) method models the queue lengths of TRANSYT-7F using saturation flow, start-up lost time, and platoon dispersion as inputs. The queue lengths of the selected approaches of the study network can be obtained using the ANN model without running the TRANSYT-7F model. The optimum values of the selected parameters (i.e., saturation flow, start-up lost time, and platoon dispersion) were obtained using the genetic algorithm, which ensures minimum difference between the measured queue length and the ANN output (i.e., queue length). Finally, the comparison of the measured queue length and the simulated queue length with the calibrated TRANSYT-7F model revealed that the mean absolute percentage error was less than 2.5% for all approaches of the study network.

The electrical energy demand of the Kingdom of Saudi Arabia (KSA) has been observing a surging growth due to high population growth and urbanization rate. The generation mostly comes from the burning of petroleum products. The electricity sector emits more than one-third of the national greenhouse gas (GHG) of the country. Thus, it is essential to reduce the GHG from the electricity generation sector to maintain sustainability. This article mainly concentrates on the existing electricity generation and focuses on the Kingdom’s GHG emissions mitigation initiatives. Although the use of natural gas and the number of cogeneration plants have been increased in electricity generation, the country has also been focusing on other initiatives, including the adaptation of combined cycle power plants, carbon capture, utilization, and sequestration (CCUS), renewable and alternative energy sources, and various utility-scale energy efficiency measures. The ambitious and inspirational renewable energy (RE) targets of the Saudi Vision 2030 will significantly facilitate GHG emission avoidance, which will support the Kingdom to meet its Nationally Determined Contributions. This article recommends various mitigation measures, including the usage of the efficient internal combustion engine, poly-generation, combined heat and power (CHP) systems, deployment of renewable resources in the distribution and microgrid systems, bio-mass engine, and ocean thermal energy conversion plants. The decision-makers and the researchers will find the discussions and recommendations helpful in determining suitable and appropriate mitigation initiatives and technologies.

Understanding scale-up effects on the hydrodynamics of a liquid‒solid circulating fluidized bed (LSCFB) unit requires both experimental and theoretical analysis. We implement multigene genetic programming (MGGP) to investigate the solid holdup and distribution in three LSCFB systems with different heights. In addition to data obtained here, we also use a portion of data sets of LSCFB systems developed by Zheng (1999) and Liang et al. (1996) . Model predictions are in good agreement with the experimental data in both radial and axial directions and at different normalized superficial liquid and solid velocities. The radial profiles of the solid holdup are approximately identical at a fixed average cross-sectional solid holdup for the three LSCFB systems studied. Statistical performance indicators including the mean absolute percentage error (6.19%) and correlation coefficient (0.985) are within an acceptable range. The results suggest that a MGGP modeling approach is suitable for predicting the solid holdup and distribution of a scaled-up LSCFB system.

Mitigating climate change via initiatives such as reducing emissions of greenhouse gases (GHG) and renewable energy (RE) generation helps in reducing environmental pollution, increasing efficiency, and saving costs in the energy industry. Bangladesh recently formulated some policies and initiatives for reducing GHG emissions that have been increasing at an alarming rate lately, which are driven largely by economic growth, energy security, and local environmental concerns. However, little is known about the impacts of the existing policies and initiatives on curbing GHG emissions and promoting environmental sustainability, especially from the agriculture and energy sectors that contribute 44% and 39% to the country’s net emissions, respectively. This study, therefore, reviews the extent of GHG emissions in the country and analyzes the country’s key policies and initiatives for mitigating climate change and promoting environmental sustainability. The key finding is that despite the existence of a national energy policy and initiatives such as RE development, rational and efficient use of energy, Intended Nationally Determined Contributions, clean development mechanism, international and private sector collaboration, GHG emissions are projected to continue to grow at high rates due to population growth, industrialization, energy consumption growth that outpace government initiatives to reduce emission levels. The paper concludes that good governance, behavioral transformation, and public awareness about climate change are vital steps for reducing GHG emissions and promoting environmental sustainability.

The growth of population, gross domestic product (GDP), and urbanization have led to an increase in greenhouse gas (GHG) emissions in the Kingdom of Saudi Arabia (KSA). The leading GHG-emitting sectors are electricity generation, road transportation, cement, chemicals, refinery, iron, and steel. However, the KSA is working to lead the global energy sustainability campaign to reach net zero GHG emissions by 2060. In addition, the country is working to establish a framework for the circular carbon economy (CCE), in which hydrogen acts as a transversal facilitator. To cut down on greenhouse gas emissions, the Kingdom is also building several facilities, such as the NEOM green hydrogen project. The main objective of the article is to critically review the current GHG emission dynamics of the KSA, including major GHG emission driving forces and prominent emission sectors. Then, the role of hydrogen in GHG emission reduction will be explored. Finally, the researchers and decision makers will find the helpful discussions and recommendations in deciding on appropriate mitigation measures and technologies.

Over the last few years, the electric energy demand in the Kingdom of Saudi Arabia (KSA) has increased many folds due to several factors including increased population, industrialization, economic activities, and urbanization. The main source of electric power generation in KSA is the burning of petroleum products. Almost one third of greenhouse gas (GHG) emissions are contributed from the electric power generation sector, mainly, by burning diesel and natural gas. As a result, it makes it necessary to consider alternate forms of electricity generation in order to cut down emissions and to keep sustainable growth alive. The government has planned to diversify energy sources and suppliers. In recent years, energy generation from renewable sources including solar photovoltaic (PV), wind, concentrated solar power (CSP), biomass, geothermal, and tidal, has been given more importance. The ambitious Saudi Vision 2030 targets of 58.7 GW of power generation from renewable energy sources will cause a significant reduction in GHG emissions from the energy sector. This article systematically reviews the impact of renewable energy generation on GHG emissions. The detailed breakdown of GHG emission is discussed. Then, the status of renewable energy generation is investigated, focusing on the technical and economic potentials. The correlation of renewable energy generation and GHG emissions is then explained. The most distinguishing feature of this review is that it provides a comprehensive list of recommendations to reduce GHG emissions. The discussions and recommendations of this article will support decision makers, system planners, industry personnel, researchers, and academics to develop sustainable energy pathways for the Kingdom.

Experts have been searching for ways to mitigate the impacts of climate change on resources since the early 20th century. In response, the World Economic Forum introduced the concept of a “nexus”, which involves the simultaneous, systematic collaboration of multiple individuals or sectors, such as water, energy, and food, in order to create an integrated approach to reducing resource scarcity through a multi-disciplinary framework. In contrast, a circular economy (CE) involves restructuring material flows from a linear economic system and closing the loop on resource exploitation. Both the nexus and CE have been developed to address the overexploitation of resources, but they also contribute to the Sustainable Development Goals (SDGs) and decouple carbon emissions from economic growth. This study explores the potential of combining the nexus and CE to pursue the SDGs on a global scale. Our findings reveal significant research gaps and policy implementation challenges in developing countries, as well as the potential consequences of adopting integrative scenarios. Finally, we propose a system dynamics model as a way to address the difficulties of coupling policies and to better understand the interdependencies between different parts of the economy.

The road transportation sector in Saudi Arabia has been observing a surging growth of demand trends for the last couple of decades. The main objective of this article is to extract insightful information for the country’s policymakers through a comprehensive investigation of the rising energy trends. In the first phase, it employs econometric analysis to provide the causal relationship between the energy demand of the road transportation sector and different socio-economic elements, including the gross domestic product (GDP), number of registered vehicles, total population, the population in the urban agglomeration, and fuel price. Then, it estimates future energy demand for the sector using two machine-learning models, i.e., artificial neural network (ANN) and support vector regression (SVR). The core features of the future demand model include: (i) removal of the linear trend, (ii) input data projection using a double exponential smoothing technique, and (iii) energy demand prediction using the machine learning models. The findings of the study show that the GDP and urban population have a significant causal relationship with energy demand in the road transportation sector in both the short and long run. The greenhouse gas emissions from the road transportation in Saudi Arabia are directly proportional to energy consumption because the demand is solely met by fossil fuels. Therefore, appropriate policy measures should be taken to reduce energy intensity without compromising the country’s development. In addition, the SVR model outperformed the ANN model in predicting the future energy demand of the sector based on the achieved performance indices. For instance, the correlation coefficients of the SVR and the ANN models were 0.8932 and 0.9925, respectively, for the test datasets. The results show that the SVR is better for predicting energy consumption than the ANN. It is expected that the findings of the study will assist the decision-makers of the country in achieving environmental sustainability goals by initiating appropriate policies.

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.

There is a growing focus on the role of renewable energy (RE) policies such as feed-in tariffs (FITs), renewable portfolio standards (RPSs), subsidies, incentives, and research and development in the global energy policy mix and in promoting environmental sustainability. Although most developed countries have well-formulated RE policies, in developing countries, such policies face many barriers. This study analyzes the policies, drivers, and barriers to RE deployment for fostering environmental sustainability in the Gulf Cooperation Council (GCC) countries. In the GCC region, the need for economic diversification to reduce dependency on single resource, diminishing hydrocarbon reserve, loss of oil export revenue, climate change mitigation pledges, and abundant solar energy resource are the key drivers for diversifying energy sources to include RE. However, the apparent lack of consolidated policy framework for wide-scale RE utilization calls for a well-articulated policy to advance RE development in each member state. Although FIT and RPS approaches could be effective for initial deployment of small-scale RE projects, a competitive tendering and auctioning mechanisms are more suitable for large-scale projects. Whereas, developing effective energy codes could successfully promote RE deployment, the increased share of RE in energy supply would have synergistic impacts on the region. The GHG emissions avoidance expected to be achieved by the GCC countries will vary between 5 and 247 million tons of CO2 equivalent by 2030. The fulfillment of inspirational RE targets for 2030 would contribute in fulfilling climate change mitigation pledges, environmental sustainability, economic growth, and generating new jobs.

The per capita greenhouse gas (GHG) emissions of Saudi Arabia were more than three times the global average emissions in 2019. The energy sector is the most dominant GHG-emitting sector in the country; its energy consumption has increased over five times in the last four decades, from over 2000 quadrillion joules in 1981 to around 11,000 quadrillion joules in 2019, while the share of renewable energy in 2019 was only 0.1%. To reduce GHG emissions, the Saudi Arabian government has undertaken initiatives for improving energy efficiency and increasing the production of renewable energies in the country. However, there are few investigative studies into the effectiveness of these initiatives in improving energy efficiency and reducing greenhouse gas emissions. This study provides an overview of the various energy efficiency and renewable energy initiatives undertaken in Saudi Arabia. Then, it evaluates the effectiveness of energy-related policies and initiatives using an indicator-based approach. In addition, this study performs temporal and econometrics analyses to understand the trends and the causal relationships among various drivers of energy sector emissions. Energy intensity and efficiency have improved moderately in recent years. This study will support policymakers in identifying significant policy gaps in reducing the emissions from the energy sector; furthermore, this study will provide a reference for tracking the progress of their policy initiatives. In addition, the methodology used in this study could be applied in other studies to evaluate various climate change policies and their progress.

The equivalent electric circuit models reflect the electrical characteristics of the photovoltaic (PV) modules. Estimation of PV module parameters is considered as one of the challenging tasks while evaluating the performance. This article presents a new and useful approach to estimate the five-parameter PV module electrical circuit model. It translates the PV module parameter estimation process into an optimization problem using the information provided by the manufacturer on the rear side of the PV modules. It then employs an efficient metaheuristic technique, namely the backtracking search algorithm, to solve the developed optimization problem. The efficacy of the proposed approach is investigated by predicting the parameters of three PV module technologies: monocrystalline, poly-crystalline, and thin film. Finally, to check the feasibility of the proposed technique, this paper compares the approximate parameters of modeled I-V curves with experimental curves. The findings confirm the reliability of the estimated model parameters in simulating the near realistic characteristics of the PV modules.

This paper proposes an intelligent approach to detect and classify the power quality (PQ) events with the combination of machine learning and advanced signal processing techniques. It selects Stockwell transform, one of the efficient signal processing tools for feature extraction from the recorded signals. The extracted features are then fetched to one of the popular machine-learning tools, namely the artificial neural network (ANN), to develop the proposed intelligent PQ events detection and classification approach. This paper selects the hyper-parameters, e.g., number of hidden layer neurons, training algorithm, and activation functions through a systematic trial and error approach. To enhance the proposed approach performance, the weights and biases of the ANN are optimized using the grey wolf optimization (GWO) technique. Simulation results confirm the efficacy of the developed intelligent methodology in distinguishing PQ events from non-PQ events. Moreover, separates different PQ events, e.g., sag, swell, interruption, fluctuation, spike, notch, harmonics, from each other with reasonable accuracy. This research also investigates the efficacy of the proposed signal processing-based machine learning approach in the presence of measurement noises.

The potential effects of autonomous vehicles (AVs) on greenhouse gas (GHG) emissions are uncertain, although numerous studies have been conducted to evaluate the impact. This paper aims to synthesize and review all the literature regarding the topic in a systematic manner to eliminate the bias and provide an overall insight, while incorporating some statistical analysis to provide an interval estimate of these studies. This paper addressed the effect of the positive and negative impacts reported in the literature in two categories of AVs: partial automation and full automation. The positive impacts represented in AVs’ possibility to reduce GHG emission can be attributed to some factors, including eco-driving, eco traffic signal, platooning, and less hunting for parking. The increase in vehicle mile travel (VMT) due to (i) modal shift to AVs by captive passengers, including elderly and disabled people and (ii) easier travel compared to other modes will contribute to raising the GHG emissions. The result shows that eco-driving and platooning have the most significant contribution to reducing GHG emissions by 35%. On the other side, easier travel and faster travel significantly contribute to the increase of GHG emissions by 41.24%. Study findings reveal that the positive emission changes may not be realized at a lower AV penetration rate, where the maximum emission reduction might take place within 60–80% of AV penetration into the network.

The continuous growth in population, urbanization, and industrial development has been increasing the generation of solid waste (SW) in the Kingdom of Saudi Arabia. Consequently, the associated greenhouse gas (GHG) emission is also following an increasing trend. The collection and use of greenhouse gases emitted from solid waste management practices are still limited. A causality analysis examined the driving factors of the emissions from solid waste management. The methane (CH4) emissions from municipal solid waste (MSW) increased with an increase in gross domestic product (GDP) per capita and urban population, and an increase in foreign direct investment (FDI) inflows and literacy rate was likely to reduce CH4 emissions from municipal solid waste and vice versa. The CH4 emission generated from industrial solid wastes was found to be positively related to GDP per capita, urban population, and FDI inflows. However, a decrease in the unemployment rate was likely to increase CH4 emissions from industrial solid wastes. The future greenhouse gas emissions were projected under different possible socio-economic conditions. The scenario analysis based on different variations of population and GDP growth revealed that methane emission from total waste would increase at an average annual rate of 5.13% between 2020 and 2050, and is projected to reach about 4000 Gg by the end of the year 2050. Although the Kingdom has been taking some initiatives towards climate change mitigation, it has significant opportunities to adopt some of the best practices in solid waste management including reduction, recycling, composting and waste-to-energy, and carbon capture and utilization. This study also put emphasis on developing appropriate policy approaches for climate change mitigation based on the circular economy which is gaining momentum in the Kingdom.